Accepted Article
Received Date : 10-Aug-2013 Revised Date : 19-Feb-2014 Accepted Date : 27-Feb-2014 Article type
: Primary Research Articles
Changes in forest biomass and linkage to climate and forest disturbances over northeastern China Yuzhen Zhang1, Shunlin Liang1,2 1. State Key Laboratory of Remote Sensing Science,
College of Global Change and Earth System Science, Beijing Normal University, Beijing, China 2. Department of Geographical Science, University of Maryland, College Park, USA
Corresponding author: Yuzhen Zhang, College of Global Change and Earth System Science, Beijing Normal University, Beijing 100875, China. Email: [email protected] Keywords: forest biomass dynamics; forest disturbances; northeastern China Type of Paper: Primary research articles
This article has been accepted for publication and undergone full peer review but has not been through the copyediting, typesetting, pagination and proofreading process, which may lead to differences between this version and the Version of Record. Please cite this article as doi: 10.1111/gcb.12588 This article is protected by copyright. All rights reserved.
Accepted Article
Abstract The forests of northeastern China store nearly half of the country’s total biomass carbon stocks. In this study, we investigated the changes in forest biomass by using satellite observations and found that a significant increase in forest biomass took place between 2001 and 2010. To determine the possible reasons for this change, several statistical methods were used to analyze the correlations between forest biomass dynamics and forest disturbances (i.e., fires, insect damage, logging, and afforestation and reforestation), climatic factors, and forest development. Results showed that forest development was the most important contributor to the increasing trend of forest biomass from 2001 to 2010, and climate controls were the secondary important factor. Among the four types of forest disturbance considered in this study, forest recovery from fires, and afforestation and reforestation during the past few decades played an important role in short-term biomass dynamics. This study provided observational evidence and valuable information for the relationships between forest biomass and climate as well as forest disturbances.
Introduction Forests are an important component of the global carbon cycle. They contain ~80% of all aboveground carbon and 40% of all below-ground terrestrial carbon (Dixon et al., 1994). Changes in forest biomass carbon stocks and the reasons for such changes have emerged as important research topics. Information on these issues can enable us to infer current and near-future changes in net carbon sources and sinks (Houghton, 2005).
This article is protected by copyright. All rights reserved.
Accepted Article
The history of land use and forest management is a major factor that determines the transition of forest carbon sinks and sources. From 1850 to 2000, land-use change released an estimated 108– 188 Pg C into the atmosphere, which amounts to ~28–40% of the total anthropogenic emissions of carbon (Houghton, 2010). During the period from 2000 to 2006, terrestrial ecosystems released an estimated 1.5 Pg C yr–1 into the atmosphere due to land-use change, mainly because
of tropical deforestation (Houghton et al., 2009). The gross tropical deforestation emission from 1990 to 2007 was estimated to be 2.9 ± 0.5 Pg C yr–1, partially compensated by a carbon sink in tropical forest regrowth of 1.6 ± 0.5 Pg C yr–1 (Pan et al., 2011). Recent studies have emphasized the impact of climate change on forest productivity and carbon sequestration (Boisvenue & Running, 2006; Bonan, 2008). Some studies reported positive effects, including increased growth rates and carbon sequestration due to warmer temperatures, a longer growing season, and carbon dioxide fertilization, while some have reported negative effects, such as reduced growth due to climate-induced severe weather events (Allen et al., 2010;
Kirilenko & Sedjo, 2007; McMahon et al., 2010; Miles et al., 2010). There have also been analyses that comprehensively expressed the impact of variable climatic factors on forest growth in different parts of the world (Nemani et al., 2003; Running et al., 2004). Among these studies to evaluate the climatic impacts on forests, most are based on model simulations. However, because of the complicated mechanisms behind these ecophysiological interactions, the response functions used in forest simulation models are hypotheses rather than established facts, and the accuracy of model simulations of forest responses has yet to be determined (Kirilenko & Sedjo, 2007; Loehle & LeBlanc, 1996). In addition to land use change and climate change, forest disturbances have been identified as critical factors in the regulation of carbon dynamics. They may occur naturally (e.g., forest
This article is protected by copyright. All rights reserved.
Accepted Article
insects and diseases, fires, and hurricanes) or be human-induced (e.g., afforestation and harvesting), and may cause changes in forest composition, structure, and functional processes (Alexander et al., 2012; Frolking et al., 2009; Goward et al., 2008; Johnstone et al., 2010; Liu et al., 2011; Mack et al., 2008). Each type of disturbance affects forests in different ways. The severity of the disturbance determines the magnitude and, in some cases, even the direction of subsequent carbon cycle changes (Goetz et al., 2012). Possible changes in vegetation structure (e.g., shifting from forest to meadow or shrubs) after forest disturbances potentially affects carbon stocks in the system (Ryan et al., 2010). Furthermore, climate change threatens to increase the frequency of disturbances (Dale et al., 2001), which further complicates the situation
(Miles et al., 2010). Given that land use, climate change, and natural disturbances have a great impact on forests, we have attempted to evaluate their impacts on forest biomass over northeastern China. We aim to address the following questions: First, has forest biomass changed over the past decade? If forest biomass has changed, what is the rate and spatial distribution of forest biomass change? Second, what are possible causes of the change? Does natural development alone contribute to forest biomass dynamics? Are there any correlations between the changes in forest biomass and human activities (e.g., large-scale tree planting, logging, and manmade fires) or natural disturbances (e.g., insects, diseases, and wildfires)? What is the contribution of climatic factors to forest biomass dynamics?
This article is protected by copyright. All rights reserved.
Accepted Article
Materials and methods Study area Northeastern China was selected as our study area (Fig. 1a). It consists of the Heilongjiang, Jilin, and Liaoning provinces, as well as the eastern part of the Inner Mongolia Autonomous Region. The study area spans approximately fifteen degrees in latitude and twenty degrees in longitude, and accounts for approximately 30% of forest land in China. It includes all the major forest types in temperate East Asia. Forests in this area are mainly distributed over three forest regions, which are the Da Hingan Ling, Xiao Hingan Ling, and Changbai Mountains. These forests store nearly half of the country’s total biomass carbon stocks. They represent a transition zone between boreal and temperate vegetation, and are likely to respond more strongly to climatic changes (Vetter et al., 2005; Zhao et al., 2012).
Northeastern China is also a pilot region for a few forestry programs in China. Yu et al. (2011) summarized a chronology of major events and the evolution of forest policy in northeastern China. Excessive logging and neglected cultivation resulted in substantial decreases in the natural forest area and forest quality prior to 1980 (He et al., 2011). Since 1998, forest policy has been redirected toward afforestation and reforestation, and logging was restricted to control harvesting levels. According to forestry yearbook statistical data, 3.79 million hectares of land underwent reforestation and afforestation from 2001 to 2010. Forests in northeastern China are important to the nation as key bases for timber supplies, potential carbon sequestration regions, and broad ecological shelters. Therefore, understanding the effects of human-induced activities on forests in this region has important implications.
This article is protected by copyright. All rights reserved.
Accepted Article
Forest biomass maps during 2000–2010 Forest biomass can be effectively mapped using remote sensing data (Liang et al., 2012). We mapped forest biomass at 500-m resolution during 2000–2010 using the Random Forests (RF) model trained with field data, Geoscience Laser Altimeter System (GLAS) data, and Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance data, and then aggregated into the 1km resolution to match the following forest disturbance maps. Further details of forest biomass mapping can be found in one of our previous publications (Zhang et al., 2014). Forest disturbances Four types of human activities and natural disturbances were considered, forest fires, insects and diseases, afforestation and reforestation, and large-scale logging. Two kinds of datasets were used to describe forest disturbances. One was the forestry statistical yearbook dataset published by the State Forestry Administration (SFA, 1988-2010), which contained information on annual fires burned forest areas, forest areas affected by insects and diseases, and areas undergoing afforestation and reforestation. The other dataset was MODIS data-derived forest disturbances.
Many algorithms have been proposed to detect forest disturbances based on use of remote sensing data (Hansen et al., 2010; Masek et al., 2011; Mildrexler et al., 2009; Verbesselt et al.,
2010). In this study, we used the MODIS global disturbance index (MGDI) algorithm to detect the locations of forest disturbances. It was based on annual maximum MODIS Land Surface Temperature (LST) data and annual maximum MODIS Enhanced Vegetation Index (EVI) data (Mildrexler et al., 2007; Mildrexler et al., 2009). The underlying principle of the MGDI algorithm was that LST decreased with an increase in vegetation density through latent heat
This article is protected by copyright. All rights reserved.
Accepted Article
transfer. MGDIs from 2001 to 2010 were calculated using maximum composite LSTs and EVIs from 2000 to 2010. Since different kinds of forest disturbances influence forest ecosystems in different ways, we need to distinguish them. MODIS fire products were used to separate fire disturbances and nonfire disturbances. Both the MODIS active fire product and the MODIS burned area product were used. The former detected active fires and other thermal anomalies, and the latter gave the extent of burn scars over a specified time period (Justice et al., 2002). Fire pixels, which were categorized as high confidence or nominal confidence in the active fire product and labeled as burned in the MODIS burned area product, were used to locate fire disturbances in this study. Non-fire disturbances were separated using MODIS Vegetation Continuous Field (VCF) data (Hansen et al., 2003). VCF data contain a percentage of vegetation types for each pixel. Compared to traditional discrete classification data, this data set is more appropriate for describing changes in forest cover. In contrast to large-scale logging, insect infestations represent a type of non-instantaneous disturbance event that does not cause an immediate reduction in forest cover (Mildrexler et al., 2009). Based on these properties, pixels with a large decrease in forest cover were used to identify where deforestation or large-scale logging occurred, and pixels with MGDI values larger than a certain threshold were used to identify areas affected by insects and diseases. The key to separating disturbances caused by insects and diseases from those caused by large-scale logging was to determine the threshold value for the MGDI data and the threshold value for changes in MODIS VCF data respectively. The kappa coefficient was a good way to quantify the level of agreement, and thus used to determine the thresholds in this study (Congalton, 1991; Kennedy et al., 2007). Unlike logging and insects, afforestation and reforestation can contribute to an increase in forest cover. After masking out pixels that were
This article is protected by copyright. All rights reserved.
Accepted Article
recovered from other disturbances, pixels that exhibited a sharp increase in forest cover were considered to indicate afforestation and reforestation areas. Although remote sensing techniques can monitor and map forest disturbances on an annual basis, the accuracy of the information is only acceptable when a forest disturbance causes an acute alteration to the spectroradiometric or textural properties of the landscape (Coppin et al., 2004;
Rogan & Miller, 2007). To evaluate the quality of the forest disturbances maps, we used forestry statistical data as references. However, planted trees, forest insects, and diseases may not be reflected from remote sensing data in the same year, so forest disturbances recorded by statistical yearbook and satellite data based disturbances differed in definition. Lag correlation was used to determine the lag effects of MODIS-derived forest disturbances before the comparison. Climate data Climate data were obtained from the China Meteorological Administration (CMA). Monthly precipitation and mean, minimum, and maximum temperatures for all the stations located in northeastern China were used. Spatial climate data were required at the pixel scale for our analyses. However, spatial resolutions of the current products were almost too coarse, and high-
resolution data are only available for limited parts of the world or for limited periods of time, e.g., Parameter elevation Regression on Independent Slopes Model (PRISM) products for the
United States (Daly et al., 2002) and WorldClim data from 1950 to 2000 (Hijmans et al., 2005). Therefore, monthly weather station records were interpolated into gridded climate data with a spatial resolution of 1 km through a thin-plate smoothing spline algorithm that was implemented using the Australian National University Splines (ANUSPLIN) package (Hutchinson, 2004). Longitude and latitude served as independent variables during the interpolation. Elevation data
This article is protected by copyright. All rights reserved.
Accepted Article
from the Shuttle Radar Topography Mission (SRTM) were used as independent or covariance variables (Jarvis et al., 2008). Forest development Forest development or aging is an important mechanism known to affect forest biomass dynamics and should be included as a factor in the analysis. Observed tree age and the time since disturbance are two common methods used to estimate forest age. In this study, we used time since disturbance to quantify forest age for those disturbed areas, and stand age to quantify forest age for undisturbed areas. Because the lag effect of MODIS data derived forest disturbances, years of lagged data were added to the time since disturbances for disturbed areas. The Monod function was used to describe the relation between forest biomass and stand age (McMahon et al., 2010). Since we did not know the pixel-wise stand age, a further derivation was made on the basis of Monod function. The function was (Eq. 1): (1)
where AGBi was forest biomass at the ith year since 2000, and a, b, and c were coefficients
relative to the maximum forest biomass that a stand can achieve, the half-saturation of the function, and the stand age in 2000, respectively. Actual stand age minus the stand age in 2000 was Agei. Using ten years of forest biomass data and transformed stand age data, coefficients of
Eq. 1 were fitted using Levenberg-Marquardt method. Analysis methods To account for pixel-wise changes in forest biomass during 2001–2010, Theil-Sen estimators were used to calculate the rate of changes in forest biomass at the pixel scale due to their
This article is protected by copyright. All rights reserved.
Accepted Article
insensitivity to outliers and effectiveness with small sample sizes (Sen, 1968; Theil, 1950). Kendall’s tau statistics were used to test significance. The coefficient of variation (CV) was calculated to describe the spatial distribution of changes in forest biomass. We used generalized linear models (GLMs) to quantify the magnitude of impacts of potential factors (e.g., climate, human-induced activities, and natural forest disturbances) on forest biomass dynamics. Before the regression, the multicollinearity problem was detected using the Variance Inflation Factor (VIF), and the correlations between variables were reduced by “centering” the variables. We developed GLMs for undisturbed pixels and disturbed pixels separately. The Akaike information criterion (AIC) was used to select the best model. For undisturbed areas, independent variables included forest age, annual mean temperature, and annual precipitation. Sequential (Type I) sums of squares were used for analysis of variance. The advantage of this method was that the effect of a variable can be examined once we have accounted for the effects of other variables (Wang et al., 2009). Forest age was first entered into the model before climatic
factors. In this study we used the fitted biomass values with Eq. 1 as a surrogate for stand age, assuming that fitted biomass dynamics revealed the natural growth under ideal conditions. It was entered into the model in the form of changes in biomass (i.e., the Theil-Sen slope of fitted forest biomass from 2001 to 2010). For disturbed areas, three variables were created for each type of forest disturbance to describe: 1) whether fire, insect damage, logging, and afforestation and reforestation occurred (labeled 0 if undisturbed and 1 if disturbed); 2) the severity of these disturbances; and 3) forest recovery after disturbance (i.e., time since disturbances). The severities of large-scale logging and afforestation and reforestation were described by the corresponding changes in forest cover (VCF). For insect
This article is protected by copyright. All rights reserved.
Accepted Article
damage, the severity was described by the corresponding MGDI values; while for fire events, severity was described by burned areas between 2001 and 2010. Because of the multicollinearity detected between these variables, only three variables were used to describe forest disturbances when we developed GLMs. The variable forest disturbance type labeled four different types of forest disturbances, and the other two variables included were time since disturbances and the intensity of disturbances. Annual mean temperature and annual precipitation were also included into GLMs. We also developed GLMs for each type of forest disturbances separately, based on the corresponding forest disturbance variables and climate variables, in order to find out how these forest disturbances influence forest biomass dynamics. To further evaluate the impacts of different types of forest disturbances on forest biomass, we used the partial least-squares (PLS) regression combined with variable importance in the projection scores to describe the relative importance of these variables in the forest biomass dynamics (Wold et al., 2001). Twelve forest disturbance variables (three variables for each type of forest disturbances, four types of forest disturbance) and two climate variables were all included in the model. Finally, we conducted an overall analysis in areas that experienced a significant increase or decrease in forest biomass, whether they were disturbed or not, to quantify the relative contributions of driving factors to forest biomass dynamics with PLS method, and to address the reasons for the changes in forest biomass over northeastern China.
This article is protected by copyright. All rights reserved.
Accepted Article
Results Forest biomass dynamics across northeastern China Average values of forest biomass from 2001 to 2010 indicated that high forest biomass density was mainly present in three mountainous regions (Fig. 1b). The biomass of forests in the Changbai Mountain region was the largest, followed by that in the Xiao Hingan Ling and Da Hingan Ling regions. The coefficients of variation (Fig. 1c) suggested that inter-annual variations in forest biomass around the Da Hingan Ling and Xiao Hingan Ling mountains were larger than those in other regions. The regions in which forest biomass were larger, such as the Xiao Hingan Ling and Changbai mountains, tended to exhibit a greater magnitude of increase. We extracted those pixels with significant increasing or decreasing trends for further analyses (Fig. 1d). During the last decade, approximately 5.14 million hectares of forests experienced significant increases in forest biomass and 1.04 million hectares of forests experienced significant decreases (P < 0.05). We aggregated the annual forest biomass of all forest pixels in the region and obtained the overall forest biomass from 2001 to 2010. Inter-annual variations in overall forest biomass were small. At decadal time scale, the biomass change was approximately 4% because of cumulative differences between annual gains and losses. When biomass estimates were converted to carbon by multiplying by a standard factor of 0.5 (Brown & Gaston, 2001; Myneni et al., 2001), the forest biomass carbon stock was estimated to be 2.571 ± 0.075 Pg C. Forests of northeastern China accumulated 0.212 Pg C, with a total increase of 8.25% and an annual rate of 0.024 Pg C during 2001–2010.
This article is protected by copyright. All rights reserved.
Accepted Article
Potential driving factors of forest biomass dynamics Forest disturbances According to MODIS fire products, 1.26 million hectares of forests suffered fire damage during 2001–2010 (Fig. 2a). The total fire-burned areas increased from 2001 to 2003, but gradually decreased during 2003–2010. Compared with statistical data from the forestry yearbook for areas of forests burned, MODIS fire products did not efficiently capture the peak value in 2006. Except for this period of time, the extracted MODIS fire disturbances were in agreement with statistical data from the forestry yearbook. Overall, the trend of MODIS data on burned areas was consistent with records in the forestry yearbook. The correlation coefficient between the MODIS data and records from the forestry yearbook was 0.92. Insect infestation may not be reflected instantaneously in imagery. For this study region, we found a two-year lag of MODIS data derived insect damage (Fig. 2b). Trends in the area of MODIS data-derived insect damage were similar to trends in the area from the forestry yearbook, but with smaller magnitudes. Areas of forests disturbed by logging and areas of afforestation and reforestation were 0.16 million hectares and 2.89 million hectares, respectively (Fig. 2c and Fig. 2d). For afforestation and reforestation, results of the lag correlation showed that the effect can be revealed after 11 years (Fig. 2d). Climate To investigate the climate change over northeastern China, we analyzed trends in the average temperature and precipitation of all stations from 1960 to 2010 in this region. During this period, mean annual temperature increased significantly, while no significant decreases in precipitation were observed. However, during 2001–2010, we did not observe any trends at a 0.05 confidence
This article is protected by copyright. All rights reserved.
Accepted Article
level for the annual mean temperature, maximum temperature, or minimum temperature. However, the overall annual precipitation increased significantly (Fig. 3). Impacts of major driving factors on forest biomass dynamics In regions where large-scale disturbances were not detected between 2001 and 2010, stand age explained 45.1% of the variance of forest biomass dynamics, temperature and precipitation explained 3.5%, and the interaction between stand age and two climatic factors explained 2.4%. In regions where we detected a large-scale disturbance, time since disturbance only explained 1.0% of the variation of forest biomass dynamics when we considered all types of disturbances. This suggested the relatively limited impacts of short-term forest recovery. The variable forest disturbance type explained 4.3% of the variation of forest biomass dynamics. Results were different when we considered these forest disturbances separately. The factors of disturbances were dominant factors in logging disturbed areas. Forest biomass decreased linearly with increased logging intensity and increased with time since logging. Logging intensity explained 13.3% of the variation of forest biomass change, and time since logging explained 41.2% of the variation. In contrast to areas which were affected by logging, the impacts of climate factors and disturbance factors contributed almost the same to forest biomass dynamics in afforestation and reforestation areas. For insect damage, temperature was the primary influence on the changes in forest biomass, and explained 27.3% of the variation. Time since insect damage was the second chief influence and explained 13.6% of the variation. In fire burned areas, climate was also the dominant factor in changes in forest biomass. Temperature and precipitation explained a variation of 20.5% and 9.1%, respectively. The climatic influence outweighed the importance of fire disturbance.
This article is protected by copyright. All rights reserved.
Accepted Article
When combining the factors of forest disturbance and climate in disturbed areas, we found that forest recovery from fires was the most important contributor to the increase in forest biomass observed during the past decade, followed by the intensity of afforestation and reforestation during 2001–2010 (Fig. 4 and Fig. 5). Insects had marginal effects on forest biomass dynamics, and logging had minimum effects on changes in biomass. Although there were uncertainties in
the detection of forest disturbances (Fig. 2), the relative contributions of forest disturbances and climate to forest biomass dynamics did not vary much either with MODIS data-derived disturbances or with disturbances data from the forestry yearbook (Fig. 5), which indicated the robustness of the evaluation. Forest development or stand age tended to contribute most to the significant increase in forest biomass among all the factors considered in this study probably because the majority of pixels were not disturbed during the past decade (Fig. 5). Compared with forest disturbances, mean temperature and annual precipitation also had great impact on forest growth rates (Fig. 4 and Fig. 5).
Discussions Uncertainties in detection of forest disturbances It is not feasible to periodically detect forest disturbances over large areas through field investigations, so most studies used remote sensing data to monitor forest disturbances. In this study, MODIS data were the main data source, and a simple algorithm based on annual maximum EVI and LST data was selected to detect forest disturbances. Since some subtle changes cannot be reflected in MODIS data, omission errors were inevitable in the detection. This problem may be partly resolved when we use high-resolution data to detect forest
This article is protected by copyright. All rights reserved.
Accepted Article
disturbance, but satisfactory results still cannot be fully achieved. For example, some authors found that forest thinning and selective logging could not be reliably detected using Landsat data despite having access to detailed field data on timber extraction practices (Olsson, 1995; Rogan & Miller, 2007; Souza & Barreto, 2000). In addition, change detection approaches used to classify the cause of disturbance can be problematic since disturbance types are not mutually exclusive or exhaustive (Coppin &
Bauer, 1996). Although a relatively large discrepancy
existed between the forestry statistical records and the forest disturbance detected using MODIS data, it seemed that the uncertainties in detection of forest disturbances did not substantially affect our evaluation results. Results of the relative importance of major driving factors on biomass dynamics were basically consistent (Fig. 5). The impacts of driving factors on forest biomass Previous studies have suggested that increases in forest growth, net primary productivity, and carbon stocks in northeastern China may be due to the combined effects of changes in climate, resources, and forest management, but these studies did not establish the causes of these increases (Fang et al., 2003; Tan et al., 2007). Some studies modeled the impact of climate and forest disturbances on the forests. For example, Li et al. (2013) evaluated the effects of climate, fire, and timber harvesting on the forest composition and spatial pattern of tree species using a forest landscape model. They found that climate warming would significantly increase the abundance of most trees, but had less impact on the abundance of conifers. Timber harvesting and burning may significantly alter forest ecosystem dynamics by increasing forest fragmentation and decreasing forest diversity. Cheng et al. (2009) simulated the effects of climate change on forest composition and forest biomass of forests in northeastern China and found that a warmer climate tended to be detrimental to major forest types. Peng et al. (2009)
This article is protected by copyright. All rights reserved.
Accepted Article
provided a picture of the potential impact of future climate change and increasing CO2 concentrations on forest carbon budgets in northeastern China. Simulated effects of climate change and CO2 fertilization on the increase in forest productivity were estimated to be 10–12% for the 2030s and 28–37% for the 2090s. In our study, we provided observational evidence that mean temperature and annual precipitation had an effect on biomass change. Results suggested the large contribution of climatic factors to short-term biomass change. The long-term influence of climate should be further investigated. Among the factors of forest disturbances, forest recovery from fires contributed most to the observed increase in forest biomass in disturbed areas. This is probably because since 1987, the government and local forest bureaus have made fire prevention and suppression a priority. However, it should be noted that changes in fire policy may not be a sustainable method to accumulate carbon over the long-term because carbon can return to the atmosphere during future fire events (Canadell et al., 2007). Overall, fires may be neutral towards long-term biomass dynamics. For forest insect damage, only some severe insect infestations were detected in this study. We did not observe a significant impact of insect-caused damage on forest biomass, but outbreaks of insect infestations and diseases in forests resulted in a substantial decrease in forest biomass even though the decreases may be less visible than fires and logging in our study. The impact of insects and diseases on forest carbon cycling can be different with respect to the type of insect and the possibility of tree mortality caused by such disturbances. Forest productivity may be reduced through defoliation, tree mortality, or other growth reductions (Hicke et al., 2012). Simulations by Medvigy et al. (2012) showed that forest biomass decreases with increases in defoliation intensity. Many defoliation events killed trees only after several years of severe
This article is protected by copyright. All rights reserved.
Accepted Article
attack, and thus they have less immediate impact than fires (Fleming et al., 2002). That is probably why we observed a smaller impact of insect damage. But, since insects and diseases remain serious threats to forests in China and are expected to increase with global warming, we should not ignore this problem (Dale et al., 2001; Li, 2004). China’s forest policy has changed significantly since 1978. Several articles reviewed the policy reforms and key forestry programs during the past few decades (Song & Zhang, 2010; Wang et al., 2007; Wang et al., 2004; Zhang et al., 2000). Scientists are now raising questions regarding how effective these projects will be. Some studies reported that the ambitious afforestation and reforestation activities increased forest cover dramatically. In this study, we have evaluated the impacts of afforestation and reforestation in terms of forest biomass dynamics. Results suggest that the afforestation and reforestation activities in previous years had the second largest effects on biomass change among the four types of forest disturbances. The impact of deforestation or logging on forest biomass was not obvious. This may be due to a 10-year ban on logging in the largest forest reserve areas in the northeast, which limited the amount of forest area that has been affected. However, some impacts of disturbances, such as thinning and harvesting, are not reflected completely in this study. In northeastern China, selective cutting accounted for 30.25% and released thinning for 30.97% of the total harvest volume in 2006 (Zhang et al., 2011). According to some studies, selective cutting with a harvest intensity of 13% to 60% is an effective measure to improve forest growth (Zheng & Zhou, 2008). Timber harvest can be a positive factor in increasing forest regrowth if the harvest is combined with effective tending and thinning under a carefully guided management plan in northeastern China (Zhang et al., 2011). Thinning practices resulted in a 14% increase in the total ecosystem carbon sink at the thinned sites via an increase in forest biomass and a decrease in respiration (Wang et al., 2013).
This article is protected by copyright. All rights reserved.
Accepted Article
We used stand age to describe forest development in undisturbed areas and time since disturbances to describe forest development in disturbed areas respectively. Bradford et al. (2008) studied the relationship between stand age or time since disturbance and forest carbon pools and fluxes, and found that, compared with predictions of forest carbon pools and fluxes based on stand age, predictions based on time since disturbance had higher accuracy but explain a smaller proportion of variance in the response variable. Results indicated that stand age provided the best insight into individual plot conditions, while time since disturbance represented a more integrated stand-level measure of disturbance history and successional status. Our study compared their relative contribution to forest biomass dynamics, results revealed the larger impacts of forest development (or stand age). However, if forests in some areas were disturbed before 2000, the forest aging we studied would contain some information on forest recovery from previous disturbances. Other factors may influence forest biomass dynamics Other forest disturbances, such as windthrow, droughts, snow, and ice storms, have always influenced forest ecosystems. We only used the self-calibrating Palmer Drought Severity Index (sc-PDSI) to assess drought severity (Wells et al., 2004). The majority of regions were generally normal, showing neither severe wet spells nor severe droughts. A few stations showed mild droughts and incipient droughts during the last 51 years. However, all ground meteorological stations with significant (P < 0.05) trends revealed that sc-PDSI is increasing. Seasonal sc-PDSIs were also calculated, and the results indicated that no spring or growth season droughts occurred during the past decade in northeastern China. In the future study, the impacts of these factors on biogeochemical processes should be evaluated.
This article is protected by copyright. All rights reserved.
Accepted Article
In this study, we did not explore the indirect impacts such as the interactions between climate and forest disturbances and disturbance interactions. Some studies have examined the interactions between climate change and forest disturbances (Dale et al., 2000; Dale et al., 2001;
Flannigan et al., 2000). According to those studies, the frequency of fires has increased
significantly with warming temperatures, earlier snowmelt, lengthening fire seasons, and drought (Brown et al., 2008; Schoennagel et al., 2007; Westerling et al., 2006). Changes in growing
season temperatures, moisture availability, and drought from warming summer temperatures and decreased precipitation have also facilitated insect outbreaks (Berg et al., 2006; Weed et al., 2013). The effects of climate change on forest diseases were not studied as extensively as those on insect outbreaks. The interactions between fire and forest insects are of particular concern, but they are not always clear (Jenkins et al., 2008; Parker et al., 2006). There may be an increase (Lundquist, 2007; Metz et al., 2010), a decrease (Kulakowski et al., 2003), or nearly no change (Bebi et al., 2003) in fire hazard and burn severity in insect-affected stands. Fires can, in turn, increase the susceptibility of forests to insects and diseases, reduce herbivory (Knight & Holt, 2005), or increase the risk of insect attacks (McCullough et al., 1998). Understanding the interactions among these disturbances would contribute further to assessing their relative importance in ecosystem properties. We investigated changes in above-ground biomass and the mechanisms driving the change in this study. However, if such analyses were extended to above-ground and below-ground biomass, the situation would become more complicated as the effects of these driving mechanisms on the turnover rate and carbon accumulation from the atmosphere may be different. Since time is an integral component of forest biomass dynamics and forest carbon cycles, the
This article is protected by copyright. All rights reserved.
Accepted Article
results of this study may not hold true for longer time scales. With the availability of historical data, future studies can further assess the recovery from various disturbances or forest growth resulting from afforestation and reforestation. Acknowledgements We are very grateful for anonymous reviewers for their constructive comments and suggestions. This study is funded by grant 2013AA122800. References
Alexander HD, Mack MC, Goetz S, Beck PSA, Belshe EF (2012) Implications of increased deciduous cover on stand structure and aboveground carbon pools of Alaskan boreal forests. Ecosphere, 3, art45. Allen CD, Macalady AK, Chenchouni H et al. (2010) A global overview of drought and heat-induced tree mortality reveals emerging climate change risks for forests. Forest Ecology and Management, 259, 660-684. Bebi P, Kulakowski D, Veblen TT (2003) Interactions between fire and spruce beetles in a subalpine rocky mountain forest landscape. Ecology, 84, 362-371. Berg EE, David Henry J, Fastie CL, De Volder AD, Matsuoka SM (2006) Spruce beetle outbreaks on the Kenai Peninsula, Alaska, and Kluane National Park and Reserve, Yukon Territory: Relationship to summer temperatures and regional differences in disturbance regimes. Forest Ecology and Management, 227, 219-232. Boisvenue C, Running SW (2006) Impacts of climate change on natural forest productivity – evidence since the middle of the 20th century. Global Change Biology, 12, 862-882. Bonan GB (2008) Forests and climate change: forcings, feedbacks, and the climate benefits of forests. Science, 320, 1444-1449. Bradford JB, Birdsey RA, Joyce LA, Ryan MG (2008) Tree age, disturbance history, and carbon stocks and fluxes in subalpine Rocky Mountain forests. Global Change Biology, 14, 2882-2897. Brown PM, Heyerdahl EK, Kitchen SG, Weber MH (2008) Climate effects on historical fires (1630–1900) in Utah. International Journal of Wildland Fire, 17, 28-39. Brown S, Gaston G (2001) Tropical Africa: Land Use, Biomass, and Carbon Estimates for 1980: NDP-055. Carbon Dioxide Information Center, Oak Ridge National Laboratory, Oak Rigde, Tennessee, revised for the web. Canadell JG, Pataki DE, Pitelka LF (2007) Terrestrial ecosystems in a changing world, Springer. Cheng X, Yan X (2009) Effects of climate change on typical forests in northeastern China. Frontiers of Forestry in China, 4, 409-415. Congalton RG (1991) A review of assessing the accuracy of classifications of remotely sensed data. Remote Sensing of Environment, 37, 35-46. Coppin P, Jonckheere I, Nackaerts K, Muys B, Lambin E (2004) Digital change detection methods in ecosystem monitoring: a review. International Journal of Remote Sensing, 25, 1565-1596. Coppin PR, Bauer ME (1996) Digital change detection in forest ecosystems with remote sensing imagery. Remote Sensing Reviews, 13, 207-234.
This article is protected by copyright. All rights reserved.
Accepted Article
Dale VH, Joyce LA, Mcnulty S, Neilson RP (2000) The interplay between climate change, forests, and disturbances. Science of The Total Environment, 262, 201-204. Dale VH, Joyce LA, Mcnulty S et al. (2001) Climate change and forest disturbances. BioScience, 51, 723734. Daly C, Gibson WP, Taylor GH, Johnson GL, Pasteris P (2002) A knowledge-based approach to the statistical mapping of climate. Climate Research, 22, 99-113. Dixon RK, Brown S, Houghton RA, Solomon AM, Trexler MC, Wisniewski J (1994) Carbon pools and flux of global forest ecosystems. Science, 263, 185-190. Fang J, Piao S, Field CB et al. (2003) Increasing net primary production in China from 1982 to 1999. Frontiers in Ecology and the Environment, 1, 293-297. Flannigan MD, Stocks BJ, Wotton BM (2000) Climate change and forest fires. Science of The Total Environment, 262, 221-229. Fleming R, Candau J-N, Mcalpine R (2002) Landscape-scale analysis of interactions between insect defoliation and forest fire in Central Canada. Climatic Change, 55, 251-272. Frolking S, Palace MW, Clark DB, Chambers JQ, Shugart HH, Hurtt GC (2009) Forest disturbance and recovery: A general review in the context of spaceborne remote sensing of impacts on aboveground biomass and canopy structure. Journal of Geophysical Research: Biogeosciences, 114, G00E02. Goetz SJ, Bond-Lamberty B, Law BE et al. (2012) Observations and assessment of forest carbon dynamics following disturbance in North America. Journal of Geophysical Research: Biogeosciences, 117, G02022. Goward SN, Masek JG, Cohen W et al. (2008) Forest disturbance and North American carbon flux. Eos, Transactions American Geophysical Union, 89, 105-106. Hansen MC, Defries RS, Townshend JRG, Carroll M, Dimiceli C, Sohlberg RA (2003) Global Percent Tree Cover at a Spatial Resolution of 500 Meters: First Results of the MODIS Vegetation Continuous Fields Algorithm. Earth Interactions, 7, 1-15. Hansen MC, Stehman SV, Potapov PV (2010) Quantification of global gross forest cover loss. Proceedings of the National Academy of Sciences, 107, 8650-8655. He H, Shifley S, Thompson F (2011) Overview of Contemporary Issues of Forest Research and Management in China. Environmental management, 48, 1061-1065. Hicke JA, Allen CD, Desai AR et al. (2012) Effects of biotic disturbances on forest carbon cycling in the United States and Canada. Global Change Biology, 18, 7-34. Hijmans RJ, Cameron SE, Parra JL, Jones PG, Jarvis A (2005) Very high resolution interpolated climate surfaces for global land areas. International Journal of Climatology, 25, 1965-1978. Houghton RA (2005) Aboveground Forest Biomass and the Global Carbon Balance. Global Change Biology, 11, 945-958. Houghton RA (2010) How well do we know the flux of CO2 from land-use change? Tellus B, 62, 337-351. Houghton RA, Hall F, Goetz SJ (2009) Importance of biomass in the global carbon cycle. Journal of Geophysical Research: Biogeosciences, 114, G00E03. Hutchinson M (2004) ANUSPLIN Version 4.3. User Guide, The Australian National University, Centre for Resources and Environmental Studies, Canberra. Jarvis A, Reuter H, Nelson A, Guevara E (2008) Hole-filled SRTM for the globe Version 4. available from the CGIAR-CSI SRTM 90m Database (http://srtm. csi. cgiar. org). Jenkins MJ, Hebertson E, Page W, Jorgensen CA (2008) Bark beetles, fuels, fires and implications for forest management in the Intermountain West. Forest Ecology and Management, 254, 16-34. Johnstone JF, Chapin FS, Hollingsworth TN, Mack MC, Romanovsky V, Turetsky M (2010) Fire, climate change, and forest resilience in interior AlaskaThis article is one of a selection of papers from
This article is protected by copyright. All rights reserved.
Accepted Article
The Dynamics of Change in Alaska's Boreal Forests: Resilience and Vulnerability in Response to Climate Warming. Canadian Journal of Forest Research, 40, 1302-1312. Justice CO, Giglio L, Korontzi S et al. (2002) The MODIS fire products. Remote Sensing of Environment, 83, 244-262. Kennedy RE, Cohen WB, Schroeder TA (2007) Trajectory-based change detection for automated characterization of forest disturbance dynamics. Remote Sensing of Environment, 110, 370-386. Kirilenko AP, Sedjo RA (2007) Climate change impacts on forestry. Proceedings of the National Academy of Sciences, 104, 19697-19702. Knight TM, Holt RD (2005) Fire generates spatial gradients in herbivory: an example from a Florida sandhill ecosystem. Ecology, 86, 587-593. Kulakowski D, Veblen TT, Bebi P (2003) Effects of fire and spruce beetle outbreak legacies on the disturbance regime of a subalpine forest in Colorado. Journal of Biogeography, 30, 1445-1456. Li W (2004) Degradation and restoration of forest ecosystems in China. Forest Ecology and Management, 201, 33-41. Li X, He HS, Wu Z, Liang Y, Schneiderman JE (2013) Comparing Effects of Climate Warming, Fire, and Timber Harvesting on a Boreal Forest Landscape in Northeastern China. PLoS ONE, 8, e59747. Liang S, Li X, Wang J (2012) Advanced remote sensing: terrestrial information extraction and applications, Beijing, Academic Press. Liu S, Bond-Lamberty B, Hicke JA et al. (2011) Simulating the impacts of disturbances on forest carbon cycling in North America: Processes, data, models, and challenges. Journal of Geophysical Research: Biogeosciences, 116, G00K08. Loehle C, Leblanc D (1996) Model-based assessments of climate change effects on forests: a critical review. Ecological Modelling, 90, 1-31. Lundquist J (2007) The relative influence of diseases and other small-scale disturbances on fuel loading in the Black Hills. Plant Disease, 91, 147-152. Mack M, Treseder K, Manies K et al. (2008) Recovery of Aboveground Plant Biomass and Productivity After Fire in Mesic and Dry Black Spruce Forests of Interior Alaska. Ecosystems, 11, 209-225. Masek JG, Cohen WB, Leckie D et al. (2011) Recent rates of forest harvest and conversion in North America. Journal of Geophysical Research: Biogeosciences, 116, G00K03. Mccullough DG, Werner RA, Neumann D (1998) Fire and Insects in Northern and Boreal Forest Ecosystems of North America. Annual Review of Entomology, 43, 107-127. Mcmahon SM, Parker GG, Miller DR (2010) Evidence for a recent increase in forest growth. Proceedings of the National Academy of Sciences, 107, 3611-3615. Medvigy D, Clark KL, Skowronski NS, Schäfer KVR (2012) Simulated impacts of insect defoliation on forest carbon dynamics. Environmental Research Letters, 7, 045703. Metz MR, Frangioso KM, Meentemeyer RK, Rizzo DM (2010) Interacting disturbances: wildfire severity affected by stage of forest disease invasion. Ecological Applications, 21, 313-320. Mildrexler DJ, Zhao M, Heinsch FA, Running SW (2007) A new satellite-based methodology for continential-scale disturbance detection. Ecological Applications, 17, 235-250. Mildrexler DJ, Zhao M, Running SW (2009) Testing a MODIS Global Disturbance Index across North America. Remote Sensing of Environment, 113, 2103-2117. Miles L, Dunning E, Doswald N, Osti M (2010) A safer bet for REDD+: Review of the evidence on the relationship between biodiversity and the resilience of forest carbon stocks. Working Paper v2. Myneni RB, Dong J, Tucker CJ et al. (2001) A large carbon sink in the woody biomass of Northern forests. Proceedings of the National Academy of Sciences, 98, 14784-14789. Nemani RR, Keeling CD, Hashimoto H et al. (2003) Climate-Driven Increases in Global Terrestrial Net Primary Production from 1982 to 1999. Science, 300, 1560-1563.
This article is protected by copyright. All rights reserved.
Accepted Article
Olsson H (1995) Reflectance calibration of thematic mapper data for forest change detection. International Journal of Remote Sensing, 16, 81-96. Pan Y, Birdsey RA, Fang J et al. (2011) A Large and Persistent Carbon Sink in the World’s Forests. Science, 333, 988-993. Parker TJ, Clancy KM, Mathiasen RL (2006) Interactions among fire, insects and pathogens in coniferous forests of the interior western United States and Canada. Agricultural and Forest Entomology, 8, 167-189. Peng C, Zhou X, Zhao S, Wang X, Zhu B, Piao S, Fang J (2009) Quantifying the response of forest carbon balance to future climate change in Northeastern China: Model validation and prediction. Global and Planetary Change, 66, 179-194. Rogan J, Miller J (2007) Integrating GIS and remotely sensed data for mapping forest disturbance and change. In: Understanding Forest Disturbance and Spatial Pattern: Remote Sensing and GIS Approaches, MA Wulder and SE Franklin (Eds). (eds Wulder MA, Franklin SE) pp 133-171. Boca Raton, FL, USA, CRC Press (Taylor and Francis). Running SW, Nemani RR, Heinsch FA, Zhao M, Reeves M, Hashimoto H (2004) A Continuous SatelliteDerived Measure of Global Terrestrial Primary Production. BioScience, 54. Ryan MG, Harmon ME, Birdsey RA et al. (2010) A synthesis of the science on forests and carbon for U.S. forests. Ecological Society of America: Issues In Ecology, 13, 1-16. Schoennagel T, Veblen TT, Kulakowski D, Holz A (2007) Multidecadal climate variability and climate interactions affect subalpine fire occurrence, western Colorado (USA). Ecology, 88, 2891-2902. Sen PK (1968) Estimates of the Regression Coefficient Based on Kendall's Tau. Journal of the American Statistical Association, 63, 1379-1389. Sfa (1988-2010) China Forestry Statistical Yearbook, Beijing, China Forestry Press. Song C, Zhang Y (2010) Forest cover in China from 1949 to 2006. In: Reforesting Landscapes: Linking Pattern and Process. (eds Nagendra H, Southworth J) pp 341-356. Berlin, Springer. Souza C, Barreto P (2000) An alternative approach for detecting and monitoring selectively logged forests in the Amazon. International Journal of Remote Sensing, 21, 173-179. Tan K, Piao S, Peng C, Fang J (2007) Satellite-based estimation of biomass carbon stocks for northeast China's forests between 1982 and 1999. Forest Ecology and Management, 240, 114-121. Theil H (1950) A rank-invariant method of linear and polynomial regression analysis III In: Proceedings KNAW. Verbesselt J, Hyndman R, Newnham G, Culvenor D (2010) Detecting trend and seasonal changes in satellite image time series. Remote Sensing of Environment, 114, 106-115. Vetter M, Wirth C, Böttcher H, Churkina G, Schulze E-D, Wutzler T, Weber G (2005) Partitioning direct and indirect human-induced effects on carbon sequestration of managed coniferous forests using model simulations and forest inventories. Global Change Biology, 11, 810-827. Wang G, Innes JL, Lei J, Dai S, Wu SW (2007) China's Forestry Reforms. Science, 318, 1556-1557. Wang H, Liu W, Wang W, Zu Y (2013) Influence of Long-Term Thinning on the Biomass Carbon and Soil Respiration in a Larch (Larix gmelinii) Forest in Northeastern China. The Scientific World Journal, 2013, 1-9. Wang S, Cornelis Van Kooten G, Wilson B (2004) Mosaic of reform: forest policy in post-1978 China. Forest Policy and Economics, 6, 71-83. Wang X, Fang J, Sanders NJ, White PS, Tang Z (2009) Relative importance of climate vs local factors in shaping the regional patterns of forest plant richness across northeast China. Ecography, 32, 133-142. Weed AS, Ayres MP, Hicke J (2013) Consequences of climate change for biotic disturbances in North American forests. Ecological Monographs, 83, 441-470.
This article is protected by copyright. All rights reserved.
Accepted Article
Wells N, Goddard S, Hayes MJ (2004) A Self-Calibrating Palmer Drought Severity Index. Journal of Climate, 17, 2335-2351. Westerling AL, Hidalgo HG, Cayan DR, Swetnam TW (2006) Warming and Earlier Spring Increase Western U.S. Forest Wildfire Activity. Science, 313, 940-943. Wold S, Sjöström M, Eriksson L (2001) PLS-regression: a basic tool of chemometrics. Chemometrics and Intelligent Laboratory Systems, 58, 109-130. Yu D, Zhou L, Zhou W et al. (2011) Forest Management in Northeast China: History, Problems, and Challenges. Environmental management, 48, 1122-1135. Zhang K, Hori Y, Zhou S-Z, Michinaka T, Hirano Y, Tachibana S (2011) Impact of Natural Forest Protection Program policies on forests in northeastern China. Forestry Studies in China, 13, 231-238. Zhang P, Shao G, Zhao G, Le Master DC, Parker GR, Dunning JB, Li Q (2000) China's Forest Policy for the 21st Century. Science, 288, 2135-2136. Zhang Y, Liang S, Sun G (2014) Forest Biomass Mapping of Northeastern China Using GLAS and MODIS Data. Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of, 7, 140152. Zhao J, Yan X, Guo J, Jia G (2012) Evaluating Spatial-Temporal Dynamics of Net Primary Productivity of Different Forest Types in Northeastern China Based on Improved FORCCHN. PLoS ONE, 7, e48131. Zheng L, Zhou X (2008) Dynamics Effects of Selective Cutting Intensity on the Species Composition and Diversity of Natual Forest. Journal of Mountain Science, 26, 699-706.
This article is protected by copyright. All rights reserved.
Accepted Article This article is protected by copyright. All rights reserved.
Accepted Article This article is protected by copyright. All rights reserved.
Accepted Article This article is protected by copyright. All rights reserved.
Accepted Article This article is protected by copyright. All rights reserved.
Received Date : 10-Aug-2013 Revised Date : 19-Feb-2014 Accepted Date : 27-Feb-2014 Article type
: Primary Research Articles
Changes in forest biomass and linkage to climate and forest disturbances over northeastern China Yuzhen Zhang1, Shunlin Liang1,2 1. State Key Laboratory of Remote Sensing Science,
College of Global Change and Earth System Science, Beijing Normal University, Beijing, China 2. Department of Geographical Science, University of Maryland, College Park, USA
Corresponding author: Yuzhen Zhang, College of Global Change and Earth System Science, Beijing Normal University, Beijing 100875, China. Email: [email protected] Keywords: forest biomass dynamics; forest disturbances; northeastern China Type of Paper: Primary research articles
This article has been accepted for publication and undergone full peer review but has not been through the copyediting, typesetting, pagination and proofreading process, which may lead to differences between this version and the Version of Record. Please cite this article as doi: 10.1111/gcb.12588 This article is protected by copyright. All rights reserved.
Accepted Article
Abstract The forests of northeastern China store nearly half of the country’s total biomass carbon stocks. In this study, we investigated the changes in forest biomass by using satellite observations and found that a significant increase in forest biomass took place between 2001 and 2010. To determine the possible reasons for this change, several statistical methods were used to analyze the correlations between forest biomass dynamics and forest disturbances (i.e., fires, insect damage, logging, and afforestation and reforestation), climatic factors, and forest development. Results showed that forest development was the most important contributor to the increasing trend of forest biomass from 2001 to 2010, and climate controls were the secondary important factor. Among the four types of forest disturbance considered in this study, forest recovery from fires, and afforestation and reforestation during the past few decades played an important role in short-term biomass dynamics. This study provided observational evidence and valuable information for the relationships between forest biomass and climate as well as forest disturbances.
Introduction Forests are an important component of the global carbon cycle. They contain ~80% of all aboveground carbon and 40% of all below-ground terrestrial carbon (Dixon et al., 1994). Changes in forest biomass carbon stocks and the reasons for such changes have emerged as important research topics. Information on these issues can enable us to infer current and near-future changes in net carbon sources and sinks (Houghton, 2005).
This article is protected by copyright. All rights reserved.
Accepted Article
The history of land use and forest management is a major factor that determines the transition of forest carbon sinks and sources. From 1850 to 2000, land-use change released an estimated 108– 188 Pg C into the atmosphere, which amounts to ~28–40% of the total anthropogenic emissions of carbon (Houghton, 2010). During the period from 2000 to 2006, terrestrial ecosystems released an estimated 1.5 Pg C yr–1 into the atmosphere due to land-use change, mainly because
of tropical deforestation (Houghton et al., 2009). The gross tropical deforestation emission from 1990 to 2007 was estimated to be 2.9 ± 0.5 Pg C yr–1, partially compensated by a carbon sink in tropical forest regrowth of 1.6 ± 0.5 Pg C yr–1 (Pan et al., 2011). Recent studies have emphasized the impact of climate change on forest productivity and carbon sequestration (Boisvenue & Running, 2006; Bonan, 2008). Some studies reported positive effects, including increased growth rates and carbon sequestration due to warmer temperatures, a longer growing season, and carbon dioxide fertilization, while some have reported negative effects, such as reduced growth due to climate-induced severe weather events (Allen et al., 2010;
Kirilenko & Sedjo, 2007; McMahon et al., 2010; Miles et al., 2010). There have also been analyses that comprehensively expressed the impact of variable climatic factors on forest growth in different parts of the world (Nemani et al., 2003; Running et al., 2004). Among these studies to evaluate the climatic impacts on forests, most are based on model simulations. However, because of the complicated mechanisms behind these ecophysiological interactions, the response functions used in forest simulation models are hypotheses rather than established facts, and the accuracy of model simulations of forest responses has yet to be determined (Kirilenko & Sedjo, 2007; Loehle & LeBlanc, 1996). In addition to land use change and climate change, forest disturbances have been identified as critical factors in the regulation of carbon dynamics. They may occur naturally (e.g., forest
This article is protected by copyright. All rights reserved.
Accepted Article
insects and diseases, fires, and hurricanes) or be human-induced (e.g., afforestation and harvesting), and may cause changes in forest composition, structure, and functional processes (Alexander et al., 2012; Frolking et al., 2009; Goward et al., 2008; Johnstone et al., 2010; Liu et al., 2011; Mack et al., 2008). Each type of disturbance affects forests in different ways. The severity of the disturbance determines the magnitude and, in some cases, even the direction of subsequent carbon cycle changes (Goetz et al., 2012). Possible changes in vegetation structure (e.g., shifting from forest to meadow or shrubs) after forest disturbances potentially affects carbon stocks in the system (Ryan et al., 2010). Furthermore, climate change threatens to increase the frequency of disturbances (Dale et al., 2001), which further complicates the situation
(Miles et al., 2010). Given that land use, climate change, and natural disturbances have a great impact on forests, we have attempted to evaluate their impacts on forest biomass over northeastern China. We aim to address the following questions: First, has forest biomass changed over the past decade? If forest biomass has changed, what is the rate and spatial distribution of forest biomass change? Second, what are possible causes of the change? Does natural development alone contribute to forest biomass dynamics? Are there any correlations between the changes in forest biomass and human activities (e.g., large-scale tree planting, logging, and manmade fires) or natural disturbances (e.g., insects, diseases, and wildfires)? What is the contribution of climatic factors to forest biomass dynamics?
This article is protected by copyright. All rights reserved.
Accepted Article
Materials and methods Study area Northeastern China was selected as our study area (Fig. 1a). It consists of the Heilongjiang, Jilin, and Liaoning provinces, as well as the eastern part of the Inner Mongolia Autonomous Region. The study area spans approximately fifteen degrees in latitude and twenty degrees in longitude, and accounts for approximately 30% of forest land in China. It includes all the major forest types in temperate East Asia. Forests in this area are mainly distributed over three forest regions, which are the Da Hingan Ling, Xiao Hingan Ling, and Changbai Mountains. These forests store nearly half of the country’s total biomass carbon stocks. They represent a transition zone between boreal and temperate vegetation, and are likely to respond more strongly to climatic changes (Vetter et al., 2005; Zhao et al., 2012).
Northeastern China is also a pilot region for a few forestry programs in China. Yu et al. (2011) summarized a chronology of major events and the evolution of forest policy in northeastern China. Excessive logging and neglected cultivation resulted in substantial decreases in the natural forest area and forest quality prior to 1980 (He et al., 2011). Since 1998, forest policy has been redirected toward afforestation and reforestation, and logging was restricted to control harvesting levels. According to forestry yearbook statistical data, 3.79 million hectares of land underwent reforestation and afforestation from 2001 to 2010. Forests in northeastern China are important to the nation as key bases for timber supplies, potential carbon sequestration regions, and broad ecological shelters. Therefore, understanding the effects of human-induced activities on forests in this region has important implications.
This article is protected by copyright. All rights reserved.
Accepted Article
Forest biomass maps during 2000–2010 Forest biomass can be effectively mapped using remote sensing data (Liang et al., 2012). We mapped forest biomass at 500-m resolution during 2000–2010 using the Random Forests (RF) model trained with field data, Geoscience Laser Altimeter System (GLAS) data, and Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance data, and then aggregated into the 1km resolution to match the following forest disturbance maps. Further details of forest biomass mapping can be found in one of our previous publications (Zhang et al., 2014). Forest disturbances Four types of human activities and natural disturbances were considered, forest fires, insects and diseases, afforestation and reforestation, and large-scale logging. Two kinds of datasets were used to describe forest disturbances. One was the forestry statistical yearbook dataset published by the State Forestry Administration (SFA, 1988-2010), which contained information on annual fires burned forest areas, forest areas affected by insects and diseases, and areas undergoing afforestation and reforestation. The other dataset was MODIS data-derived forest disturbances.
Many algorithms have been proposed to detect forest disturbances based on use of remote sensing data (Hansen et al., 2010; Masek et al., 2011; Mildrexler et al., 2009; Verbesselt et al.,
2010). In this study, we used the MODIS global disturbance index (MGDI) algorithm to detect the locations of forest disturbances. It was based on annual maximum MODIS Land Surface Temperature (LST) data and annual maximum MODIS Enhanced Vegetation Index (EVI) data (Mildrexler et al., 2007; Mildrexler et al., 2009). The underlying principle of the MGDI algorithm was that LST decreased with an increase in vegetation density through latent heat
This article is protected by copyright. All rights reserved.
Accepted Article
transfer. MGDIs from 2001 to 2010 were calculated using maximum composite LSTs and EVIs from 2000 to 2010. Since different kinds of forest disturbances influence forest ecosystems in different ways, we need to distinguish them. MODIS fire products were used to separate fire disturbances and nonfire disturbances. Both the MODIS active fire product and the MODIS burned area product were used. The former detected active fires and other thermal anomalies, and the latter gave the extent of burn scars over a specified time period (Justice et al., 2002). Fire pixels, which were categorized as high confidence or nominal confidence in the active fire product and labeled as burned in the MODIS burned area product, were used to locate fire disturbances in this study. Non-fire disturbances were separated using MODIS Vegetation Continuous Field (VCF) data (Hansen et al., 2003). VCF data contain a percentage of vegetation types for each pixel. Compared to traditional discrete classification data, this data set is more appropriate for describing changes in forest cover. In contrast to large-scale logging, insect infestations represent a type of non-instantaneous disturbance event that does not cause an immediate reduction in forest cover (Mildrexler et al., 2009). Based on these properties, pixels with a large decrease in forest cover were used to identify where deforestation or large-scale logging occurred, and pixels with MGDI values larger than a certain threshold were used to identify areas affected by insects and diseases. The key to separating disturbances caused by insects and diseases from those caused by large-scale logging was to determine the threshold value for the MGDI data and the threshold value for changes in MODIS VCF data respectively. The kappa coefficient was a good way to quantify the level of agreement, and thus used to determine the thresholds in this study (Congalton, 1991; Kennedy et al., 2007). Unlike logging and insects, afforestation and reforestation can contribute to an increase in forest cover. After masking out pixels that were
This article is protected by copyright. All rights reserved.
Accepted Article
recovered from other disturbances, pixels that exhibited a sharp increase in forest cover were considered to indicate afforestation and reforestation areas. Although remote sensing techniques can monitor and map forest disturbances on an annual basis, the accuracy of the information is only acceptable when a forest disturbance causes an acute alteration to the spectroradiometric or textural properties of the landscape (Coppin et al., 2004;
Rogan & Miller, 2007). To evaluate the quality of the forest disturbances maps, we used forestry statistical data as references. However, planted trees, forest insects, and diseases may not be reflected from remote sensing data in the same year, so forest disturbances recorded by statistical yearbook and satellite data based disturbances differed in definition. Lag correlation was used to determine the lag effects of MODIS-derived forest disturbances before the comparison. Climate data Climate data were obtained from the China Meteorological Administration (CMA). Monthly precipitation and mean, minimum, and maximum temperatures for all the stations located in northeastern China were used. Spatial climate data were required at the pixel scale for our analyses. However, spatial resolutions of the current products were almost too coarse, and high-
resolution data are only available for limited parts of the world or for limited periods of time, e.g., Parameter elevation Regression on Independent Slopes Model (PRISM) products for the
United States (Daly et al., 2002) and WorldClim data from 1950 to 2000 (Hijmans et al., 2005). Therefore, monthly weather station records were interpolated into gridded climate data with a spatial resolution of 1 km through a thin-plate smoothing spline algorithm that was implemented using the Australian National University Splines (ANUSPLIN) package (Hutchinson, 2004). Longitude and latitude served as independent variables during the interpolation. Elevation data
This article is protected by copyright. All rights reserved.
Accepted Article
from the Shuttle Radar Topography Mission (SRTM) were used as independent or covariance variables (Jarvis et al., 2008). Forest development Forest development or aging is an important mechanism known to affect forest biomass dynamics and should be included as a factor in the analysis. Observed tree age and the time since disturbance are two common methods used to estimate forest age. In this study, we used time since disturbance to quantify forest age for those disturbed areas, and stand age to quantify forest age for undisturbed areas. Because the lag effect of MODIS data derived forest disturbances, years of lagged data were added to the time since disturbances for disturbed areas. The Monod function was used to describe the relation between forest biomass and stand age (McMahon et al., 2010). Since we did not know the pixel-wise stand age, a further derivation was made on the basis of Monod function. The function was (Eq. 1): (1)
where AGBi was forest biomass at the ith year since 2000, and a, b, and c were coefficients
relative to the maximum forest biomass that a stand can achieve, the half-saturation of the function, and the stand age in 2000, respectively. Actual stand age minus the stand age in 2000 was Agei. Using ten years of forest biomass data and transformed stand age data, coefficients of
Eq. 1 were fitted using Levenberg-Marquardt method. Analysis methods To account for pixel-wise changes in forest biomass during 2001–2010, Theil-Sen estimators were used to calculate the rate of changes in forest biomass at the pixel scale due to their
This article is protected by copyright. All rights reserved.
Accepted Article
insensitivity to outliers and effectiveness with small sample sizes (Sen, 1968; Theil, 1950). Kendall’s tau statistics were used to test significance. The coefficient of variation (CV) was calculated to describe the spatial distribution of changes in forest biomass. We used generalized linear models (GLMs) to quantify the magnitude of impacts of potential factors (e.g., climate, human-induced activities, and natural forest disturbances) on forest biomass dynamics. Before the regression, the multicollinearity problem was detected using the Variance Inflation Factor (VIF), and the correlations between variables were reduced by “centering” the variables. We developed GLMs for undisturbed pixels and disturbed pixels separately. The Akaike information criterion (AIC) was used to select the best model. For undisturbed areas, independent variables included forest age, annual mean temperature, and annual precipitation. Sequential (Type I) sums of squares were used for analysis of variance. The advantage of this method was that the effect of a variable can be examined once we have accounted for the effects of other variables (Wang et al., 2009). Forest age was first entered into the model before climatic
factors. In this study we used the fitted biomass values with Eq. 1 as a surrogate for stand age, assuming that fitted biomass dynamics revealed the natural growth under ideal conditions. It was entered into the model in the form of changes in biomass (i.e., the Theil-Sen slope of fitted forest biomass from 2001 to 2010). For disturbed areas, three variables were created for each type of forest disturbance to describe: 1) whether fire, insect damage, logging, and afforestation and reforestation occurred (labeled 0 if undisturbed and 1 if disturbed); 2) the severity of these disturbances; and 3) forest recovery after disturbance (i.e., time since disturbances). The severities of large-scale logging and afforestation and reforestation were described by the corresponding changes in forest cover (VCF). For insect
This article is protected by copyright. All rights reserved.
Accepted Article
damage, the severity was described by the corresponding MGDI values; while for fire events, severity was described by burned areas between 2001 and 2010. Because of the multicollinearity detected between these variables, only three variables were used to describe forest disturbances when we developed GLMs. The variable forest disturbance type labeled four different types of forest disturbances, and the other two variables included were time since disturbances and the intensity of disturbances. Annual mean temperature and annual precipitation were also included into GLMs. We also developed GLMs for each type of forest disturbances separately, based on the corresponding forest disturbance variables and climate variables, in order to find out how these forest disturbances influence forest biomass dynamics. To further evaluate the impacts of different types of forest disturbances on forest biomass, we used the partial least-squares (PLS) regression combined with variable importance in the projection scores to describe the relative importance of these variables in the forest biomass dynamics (Wold et al., 2001). Twelve forest disturbance variables (three variables for each type of forest disturbances, four types of forest disturbance) and two climate variables were all included in the model. Finally, we conducted an overall analysis in areas that experienced a significant increase or decrease in forest biomass, whether they were disturbed or not, to quantify the relative contributions of driving factors to forest biomass dynamics with PLS method, and to address the reasons for the changes in forest biomass over northeastern China.
This article is protected by copyright. All rights reserved.
Accepted Article
Results Forest biomass dynamics across northeastern China Average values of forest biomass from 2001 to 2010 indicated that high forest biomass density was mainly present in three mountainous regions (Fig. 1b). The biomass of forests in the Changbai Mountain region was the largest, followed by that in the Xiao Hingan Ling and Da Hingan Ling regions. The coefficients of variation (Fig. 1c) suggested that inter-annual variations in forest biomass around the Da Hingan Ling and Xiao Hingan Ling mountains were larger than those in other regions. The regions in which forest biomass were larger, such as the Xiao Hingan Ling and Changbai mountains, tended to exhibit a greater magnitude of increase. We extracted those pixels with significant increasing or decreasing trends for further analyses (Fig. 1d). During the last decade, approximately 5.14 million hectares of forests experienced significant increases in forest biomass and 1.04 million hectares of forests experienced significant decreases (P < 0.05). We aggregated the annual forest biomass of all forest pixels in the region and obtained the overall forest biomass from 2001 to 2010. Inter-annual variations in overall forest biomass were small. At decadal time scale, the biomass change was approximately 4% because of cumulative differences between annual gains and losses. When biomass estimates were converted to carbon by multiplying by a standard factor of 0.5 (Brown & Gaston, 2001; Myneni et al., 2001), the forest biomass carbon stock was estimated to be 2.571 ± 0.075 Pg C. Forests of northeastern China accumulated 0.212 Pg C, with a total increase of 8.25% and an annual rate of 0.024 Pg C during 2001–2010.
This article is protected by copyright. All rights reserved.
Accepted Article
Potential driving factors of forest biomass dynamics Forest disturbances According to MODIS fire products, 1.26 million hectares of forests suffered fire damage during 2001–2010 (Fig. 2a). The total fire-burned areas increased from 2001 to 2003, but gradually decreased during 2003–2010. Compared with statistical data from the forestry yearbook for areas of forests burned, MODIS fire products did not efficiently capture the peak value in 2006. Except for this period of time, the extracted MODIS fire disturbances were in agreement with statistical data from the forestry yearbook. Overall, the trend of MODIS data on burned areas was consistent with records in the forestry yearbook. The correlation coefficient between the MODIS data and records from the forestry yearbook was 0.92. Insect infestation may not be reflected instantaneously in imagery. For this study region, we found a two-year lag of MODIS data derived insect damage (Fig. 2b). Trends in the area of MODIS data-derived insect damage were similar to trends in the area from the forestry yearbook, but with smaller magnitudes. Areas of forests disturbed by logging and areas of afforestation and reforestation were 0.16 million hectares and 2.89 million hectares, respectively (Fig. 2c and Fig. 2d). For afforestation and reforestation, results of the lag correlation showed that the effect can be revealed after 11 years (Fig. 2d). Climate To investigate the climate change over northeastern China, we analyzed trends in the average temperature and precipitation of all stations from 1960 to 2010 in this region. During this period, mean annual temperature increased significantly, while no significant decreases in precipitation were observed. However, during 2001–2010, we did not observe any trends at a 0.05 confidence
This article is protected by copyright. All rights reserved.
Accepted Article
level for the annual mean temperature, maximum temperature, or minimum temperature. However, the overall annual precipitation increased significantly (Fig. 3). Impacts of major driving factors on forest biomass dynamics In regions where large-scale disturbances were not detected between 2001 and 2010, stand age explained 45.1% of the variance of forest biomass dynamics, temperature and precipitation explained 3.5%, and the interaction between stand age and two climatic factors explained 2.4%. In regions where we detected a large-scale disturbance, time since disturbance only explained 1.0% of the variation of forest biomass dynamics when we considered all types of disturbances. This suggested the relatively limited impacts of short-term forest recovery. The variable forest disturbance type explained 4.3% of the variation of forest biomass dynamics. Results were different when we considered these forest disturbances separately. The factors of disturbances were dominant factors in logging disturbed areas. Forest biomass decreased linearly with increased logging intensity and increased with time since logging. Logging intensity explained 13.3% of the variation of forest biomass change, and time since logging explained 41.2% of the variation. In contrast to areas which were affected by logging, the impacts of climate factors and disturbance factors contributed almost the same to forest biomass dynamics in afforestation and reforestation areas. For insect damage, temperature was the primary influence on the changes in forest biomass, and explained 27.3% of the variation. Time since insect damage was the second chief influence and explained 13.6% of the variation. In fire burned areas, climate was also the dominant factor in changes in forest biomass. Temperature and precipitation explained a variation of 20.5% and 9.1%, respectively. The climatic influence outweighed the importance of fire disturbance.
This article is protected by copyright. All rights reserved.
Accepted Article
When combining the factors of forest disturbance and climate in disturbed areas, we found that forest recovery from fires was the most important contributor to the increase in forest biomass observed during the past decade, followed by the intensity of afforestation and reforestation during 2001–2010 (Fig. 4 and Fig. 5). Insects had marginal effects on forest biomass dynamics, and logging had minimum effects on changes in biomass. Although there were uncertainties in
the detection of forest disturbances (Fig. 2), the relative contributions of forest disturbances and climate to forest biomass dynamics did not vary much either with MODIS data-derived disturbances or with disturbances data from the forestry yearbook (Fig. 5), which indicated the robustness of the evaluation. Forest development or stand age tended to contribute most to the significant increase in forest biomass among all the factors considered in this study probably because the majority of pixels were not disturbed during the past decade (Fig. 5). Compared with forest disturbances, mean temperature and annual precipitation also had great impact on forest growth rates (Fig. 4 and Fig. 5).
Discussions Uncertainties in detection of forest disturbances It is not feasible to periodically detect forest disturbances over large areas through field investigations, so most studies used remote sensing data to monitor forest disturbances. In this study, MODIS data were the main data source, and a simple algorithm based on annual maximum EVI and LST data was selected to detect forest disturbances. Since some subtle changes cannot be reflected in MODIS data, omission errors were inevitable in the detection. This problem may be partly resolved when we use high-resolution data to detect forest
This article is protected by copyright. All rights reserved.
Accepted Article
disturbance, but satisfactory results still cannot be fully achieved. For example, some authors found that forest thinning and selective logging could not be reliably detected using Landsat data despite having access to detailed field data on timber extraction practices (Olsson, 1995; Rogan & Miller, 2007; Souza & Barreto, 2000). In addition, change detection approaches used to classify the cause of disturbance can be problematic since disturbance types are not mutually exclusive or exhaustive (Coppin &
Bauer, 1996). Although a relatively large discrepancy
existed between the forestry statistical records and the forest disturbance detected using MODIS data, it seemed that the uncertainties in detection of forest disturbances did not substantially affect our evaluation results. Results of the relative importance of major driving factors on biomass dynamics were basically consistent (Fig. 5). The impacts of driving factors on forest biomass Previous studies have suggested that increases in forest growth, net primary productivity, and carbon stocks in northeastern China may be due to the combined effects of changes in climate, resources, and forest management, but these studies did not establish the causes of these increases (Fang et al., 2003; Tan et al., 2007). Some studies modeled the impact of climate and forest disturbances on the forests. For example, Li et al. (2013) evaluated the effects of climate, fire, and timber harvesting on the forest composition and spatial pattern of tree species using a forest landscape model. They found that climate warming would significantly increase the abundance of most trees, but had less impact on the abundance of conifers. Timber harvesting and burning may significantly alter forest ecosystem dynamics by increasing forest fragmentation and decreasing forest diversity. Cheng et al. (2009) simulated the effects of climate change on forest composition and forest biomass of forests in northeastern China and found that a warmer climate tended to be detrimental to major forest types. Peng et al. (2009)
This article is protected by copyright. All rights reserved.
Accepted Article
provided a picture of the potential impact of future climate change and increasing CO2 concentrations on forest carbon budgets in northeastern China. Simulated effects of climate change and CO2 fertilization on the increase in forest productivity were estimated to be 10–12% for the 2030s and 28–37% for the 2090s. In our study, we provided observational evidence that mean temperature and annual precipitation had an effect on biomass change. Results suggested the large contribution of climatic factors to short-term biomass change. The long-term influence of climate should be further investigated. Among the factors of forest disturbances, forest recovery from fires contributed most to the observed increase in forest biomass in disturbed areas. This is probably because since 1987, the government and local forest bureaus have made fire prevention and suppression a priority. However, it should be noted that changes in fire policy may not be a sustainable method to accumulate carbon over the long-term because carbon can return to the atmosphere during future fire events (Canadell et al., 2007). Overall, fires may be neutral towards long-term biomass dynamics. For forest insect damage, only some severe insect infestations were detected in this study. We did not observe a significant impact of insect-caused damage on forest biomass, but outbreaks of insect infestations and diseases in forests resulted in a substantial decrease in forest biomass even though the decreases may be less visible than fires and logging in our study. The impact of insects and diseases on forest carbon cycling can be different with respect to the type of insect and the possibility of tree mortality caused by such disturbances. Forest productivity may be reduced through defoliation, tree mortality, or other growth reductions (Hicke et al., 2012). Simulations by Medvigy et al. (2012) showed that forest biomass decreases with increases in defoliation intensity. Many defoliation events killed trees only after several years of severe
This article is protected by copyright. All rights reserved.
Accepted Article
attack, and thus they have less immediate impact than fires (Fleming et al., 2002). That is probably why we observed a smaller impact of insect damage. But, since insects and diseases remain serious threats to forests in China and are expected to increase with global warming, we should not ignore this problem (Dale et al., 2001; Li, 2004). China’s forest policy has changed significantly since 1978. Several articles reviewed the policy reforms and key forestry programs during the past few decades (Song & Zhang, 2010; Wang et al., 2007; Wang et al., 2004; Zhang et al., 2000). Scientists are now raising questions regarding how effective these projects will be. Some studies reported that the ambitious afforestation and reforestation activities increased forest cover dramatically. In this study, we have evaluated the impacts of afforestation and reforestation in terms of forest biomass dynamics. Results suggest that the afforestation and reforestation activities in previous years had the second largest effects on biomass change among the four types of forest disturbances. The impact of deforestation or logging on forest biomass was not obvious. This may be due to a 10-year ban on logging in the largest forest reserve areas in the northeast, which limited the amount of forest area that has been affected. However, some impacts of disturbances, such as thinning and harvesting, are not reflected completely in this study. In northeastern China, selective cutting accounted for 30.25% and released thinning for 30.97% of the total harvest volume in 2006 (Zhang et al., 2011). According to some studies, selective cutting with a harvest intensity of 13% to 60% is an effective measure to improve forest growth (Zheng & Zhou, 2008). Timber harvest can be a positive factor in increasing forest regrowth if the harvest is combined with effective tending and thinning under a carefully guided management plan in northeastern China (Zhang et al., 2011). Thinning practices resulted in a 14% increase in the total ecosystem carbon sink at the thinned sites via an increase in forest biomass and a decrease in respiration (Wang et al., 2013).
This article is protected by copyright. All rights reserved.
Accepted Article
We used stand age to describe forest development in undisturbed areas and time since disturbances to describe forest development in disturbed areas respectively. Bradford et al. (2008) studied the relationship between stand age or time since disturbance and forest carbon pools and fluxes, and found that, compared with predictions of forest carbon pools and fluxes based on stand age, predictions based on time since disturbance had higher accuracy but explain a smaller proportion of variance in the response variable. Results indicated that stand age provided the best insight into individual plot conditions, while time since disturbance represented a more integrated stand-level measure of disturbance history and successional status. Our study compared their relative contribution to forest biomass dynamics, results revealed the larger impacts of forest development (or stand age). However, if forests in some areas were disturbed before 2000, the forest aging we studied would contain some information on forest recovery from previous disturbances. Other factors may influence forest biomass dynamics Other forest disturbances, such as windthrow, droughts, snow, and ice storms, have always influenced forest ecosystems. We only used the self-calibrating Palmer Drought Severity Index (sc-PDSI) to assess drought severity (Wells et al., 2004). The majority of regions were generally normal, showing neither severe wet spells nor severe droughts. A few stations showed mild droughts and incipient droughts during the last 51 years. However, all ground meteorological stations with significant (P < 0.05) trends revealed that sc-PDSI is increasing. Seasonal sc-PDSIs were also calculated, and the results indicated that no spring or growth season droughts occurred during the past decade in northeastern China. In the future study, the impacts of these factors on biogeochemical processes should be evaluated.
This article is protected by copyright. All rights reserved.
Accepted Article
In this study, we did not explore the indirect impacts such as the interactions between climate and forest disturbances and disturbance interactions. Some studies have examined the interactions between climate change and forest disturbances (Dale et al., 2000; Dale et al., 2001;
Flannigan et al., 2000). According to those studies, the frequency of fires has increased
significantly with warming temperatures, earlier snowmelt, lengthening fire seasons, and drought (Brown et al., 2008; Schoennagel et al., 2007; Westerling et al., 2006). Changes in growing
season temperatures, moisture availability, and drought from warming summer temperatures and decreased precipitation have also facilitated insect outbreaks (Berg et al., 2006; Weed et al., 2013). The effects of climate change on forest diseases were not studied as extensively as those on insect outbreaks. The interactions between fire and forest insects are of particular concern, but they are not always clear (Jenkins et al., 2008; Parker et al., 2006). There may be an increase (Lundquist, 2007; Metz et al., 2010), a decrease (Kulakowski et al., 2003), or nearly no change (Bebi et al., 2003) in fire hazard and burn severity in insect-affected stands. Fires can, in turn, increase the susceptibility of forests to insects and diseases, reduce herbivory (Knight & Holt, 2005), or increase the risk of insect attacks (McCullough et al., 1998). Understanding the interactions among these disturbances would contribute further to assessing their relative importance in ecosystem properties. We investigated changes in above-ground biomass and the mechanisms driving the change in this study. However, if such analyses were extended to above-ground and below-ground biomass, the situation would become more complicated as the effects of these driving mechanisms on the turnover rate and carbon accumulation from the atmosphere may be different. Since time is an integral component of forest biomass dynamics and forest carbon cycles, the
This article is protected by copyright. All rights reserved.
Accepted Article
results of this study may not hold true for longer time scales. With the availability of historical data, future studies can further assess the recovery from various disturbances or forest growth resulting from afforestation and reforestation. Acknowledgements We are very grateful for anonymous reviewers for their constructive comments and suggestions. This study is funded by grant 2013AA122800. References
Alexander HD, Mack MC, Goetz S, Beck PSA, Belshe EF (2012) Implications of increased deciduous cover on stand structure and aboveground carbon pools of Alaskan boreal forests. Ecosphere, 3, art45. Allen CD, Macalady AK, Chenchouni H et al. (2010) A global overview of drought and heat-induced tree mortality reveals emerging climate change risks for forests. Forest Ecology and Management, 259, 660-684. Bebi P, Kulakowski D, Veblen TT (2003) Interactions between fire and spruce beetles in a subalpine rocky mountain forest landscape. Ecology, 84, 362-371. Berg EE, David Henry J, Fastie CL, De Volder AD, Matsuoka SM (2006) Spruce beetle outbreaks on the Kenai Peninsula, Alaska, and Kluane National Park and Reserve, Yukon Territory: Relationship to summer temperatures and regional differences in disturbance regimes. Forest Ecology and Management, 227, 219-232. Boisvenue C, Running SW (2006) Impacts of climate change on natural forest productivity – evidence since the middle of the 20th century. Global Change Biology, 12, 862-882. Bonan GB (2008) Forests and climate change: forcings, feedbacks, and the climate benefits of forests. Science, 320, 1444-1449. Bradford JB, Birdsey RA, Joyce LA, Ryan MG (2008) Tree age, disturbance history, and carbon stocks and fluxes in subalpine Rocky Mountain forests. Global Change Biology, 14, 2882-2897. Brown PM, Heyerdahl EK, Kitchen SG, Weber MH (2008) Climate effects on historical fires (1630–1900) in Utah. International Journal of Wildland Fire, 17, 28-39. Brown S, Gaston G (2001) Tropical Africa: Land Use, Biomass, and Carbon Estimates for 1980: NDP-055. Carbon Dioxide Information Center, Oak Ridge National Laboratory, Oak Rigde, Tennessee, revised for the web. Canadell JG, Pataki DE, Pitelka LF (2007) Terrestrial ecosystems in a changing world, Springer. Cheng X, Yan X (2009) Effects of climate change on typical forests in northeastern China. Frontiers of Forestry in China, 4, 409-415. Congalton RG (1991) A review of assessing the accuracy of classifications of remotely sensed data. Remote Sensing of Environment, 37, 35-46. Coppin P, Jonckheere I, Nackaerts K, Muys B, Lambin E (2004) Digital change detection methods in ecosystem monitoring: a review. International Journal of Remote Sensing, 25, 1565-1596. Coppin PR, Bauer ME (1996) Digital change detection in forest ecosystems with remote sensing imagery. Remote Sensing Reviews, 13, 207-234.
This article is protected by copyright. All rights reserved.
Accepted Article
Dale VH, Joyce LA, Mcnulty S, Neilson RP (2000) The interplay between climate change, forests, and disturbances. Science of The Total Environment, 262, 201-204. Dale VH, Joyce LA, Mcnulty S et al. (2001) Climate change and forest disturbances. BioScience, 51, 723734. Daly C, Gibson WP, Taylor GH, Johnson GL, Pasteris P (2002) A knowledge-based approach to the statistical mapping of climate. Climate Research, 22, 99-113. Dixon RK, Brown S, Houghton RA, Solomon AM, Trexler MC, Wisniewski J (1994) Carbon pools and flux of global forest ecosystems. Science, 263, 185-190. Fang J, Piao S, Field CB et al. (2003) Increasing net primary production in China from 1982 to 1999. Frontiers in Ecology and the Environment, 1, 293-297. Flannigan MD, Stocks BJ, Wotton BM (2000) Climate change and forest fires. Science of The Total Environment, 262, 221-229. Fleming R, Candau J-N, Mcalpine R (2002) Landscape-scale analysis of interactions between insect defoliation and forest fire in Central Canada. Climatic Change, 55, 251-272. Frolking S, Palace MW, Clark DB, Chambers JQ, Shugart HH, Hurtt GC (2009) Forest disturbance and recovery: A general review in the context of spaceborne remote sensing of impacts on aboveground biomass and canopy structure. Journal of Geophysical Research: Biogeosciences, 114, G00E02. Goetz SJ, Bond-Lamberty B, Law BE et al. (2012) Observations and assessment of forest carbon dynamics following disturbance in North America. Journal of Geophysical Research: Biogeosciences, 117, G02022. Goward SN, Masek JG, Cohen W et al. (2008) Forest disturbance and North American carbon flux. Eos, Transactions American Geophysical Union, 89, 105-106. Hansen MC, Defries RS, Townshend JRG, Carroll M, Dimiceli C, Sohlberg RA (2003) Global Percent Tree Cover at a Spatial Resolution of 500 Meters: First Results of the MODIS Vegetation Continuous Fields Algorithm. Earth Interactions, 7, 1-15. Hansen MC, Stehman SV, Potapov PV (2010) Quantification of global gross forest cover loss. Proceedings of the National Academy of Sciences, 107, 8650-8655. He H, Shifley S, Thompson F (2011) Overview of Contemporary Issues of Forest Research and Management in China. Environmental management, 48, 1061-1065. Hicke JA, Allen CD, Desai AR et al. (2012) Effects of biotic disturbances on forest carbon cycling in the United States and Canada. Global Change Biology, 18, 7-34. Hijmans RJ, Cameron SE, Parra JL, Jones PG, Jarvis A (2005) Very high resolution interpolated climate surfaces for global land areas. International Journal of Climatology, 25, 1965-1978. Houghton RA (2005) Aboveground Forest Biomass and the Global Carbon Balance. Global Change Biology, 11, 945-958. Houghton RA (2010) How well do we know the flux of CO2 from land-use change? Tellus B, 62, 337-351. Houghton RA, Hall F, Goetz SJ (2009) Importance of biomass in the global carbon cycle. Journal of Geophysical Research: Biogeosciences, 114, G00E03. Hutchinson M (2004) ANUSPLIN Version 4.3. User Guide, The Australian National University, Centre for Resources and Environmental Studies, Canberra. Jarvis A, Reuter H, Nelson A, Guevara E (2008) Hole-filled SRTM for the globe Version 4. available from the CGIAR-CSI SRTM 90m Database (http://srtm. csi. cgiar. org). Jenkins MJ, Hebertson E, Page W, Jorgensen CA (2008) Bark beetles, fuels, fires and implications for forest management in the Intermountain West. Forest Ecology and Management, 254, 16-34. Johnstone JF, Chapin FS, Hollingsworth TN, Mack MC, Romanovsky V, Turetsky M (2010) Fire, climate change, and forest resilience in interior AlaskaThis article is one of a selection of papers from
This article is protected by copyright. All rights reserved.
Accepted Article
The Dynamics of Change in Alaska's Boreal Forests: Resilience and Vulnerability in Response to Climate Warming. Canadian Journal of Forest Research, 40, 1302-1312. Justice CO, Giglio L, Korontzi S et al. (2002) The MODIS fire products. Remote Sensing of Environment, 83, 244-262. Kennedy RE, Cohen WB, Schroeder TA (2007) Trajectory-based change detection for automated characterization of forest disturbance dynamics. Remote Sensing of Environment, 110, 370-386. Kirilenko AP, Sedjo RA (2007) Climate change impacts on forestry. Proceedings of the National Academy of Sciences, 104, 19697-19702. Knight TM, Holt RD (2005) Fire generates spatial gradients in herbivory: an example from a Florida sandhill ecosystem. Ecology, 86, 587-593. Kulakowski D, Veblen TT, Bebi P (2003) Effects of fire and spruce beetle outbreak legacies on the disturbance regime of a subalpine forest in Colorado. Journal of Biogeography, 30, 1445-1456. Li W (2004) Degradation and restoration of forest ecosystems in China. Forest Ecology and Management, 201, 33-41. Li X, He HS, Wu Z, Liang Y, Schneiderman JE (2013) Comparing Effects of Climate Warming, Fire, and Timber Harvesting on a Boreal Forest Landscape in Northeastern China. PLoS ONE, 8, e59747. Liang S, Li X, Wang J (2012) Advanced remote sensing: terrestrial information extraction and applications, Beijing, Academic Press. Liu S, Bond-Lamberty B, Hicke JA et al. (2011) Simulating the impacts of disturbances on forest carbon cycling in North America: Processes, data, models, and challenges. Journal of Geophysical Research: Biogeosciences, 116, G00K08. Loehle C, Leblanc D (1996) Model-based assessments of climate change effects on forests: a critical review. Ecological Modelling, 90, 1-31. Lundquist J (2007) The relative influence of diseases and other small-scale disturbances on fuel loading in the Black Hills. Plant Disease, 91, 147-152. Mack M, Treseder K, Manies K et al. (2008) Recovery of Aboveground Plant Biomass and Productivity After Fire in Mesic and Dry Black Spruce Forests of Interior Alaska. Ecosystems, 11, 209-225. Masek JG, Cohen WB, Leckie D et al. (2011) Recent rates of forest harvest and conversion in North America. Journal of Geophysical Research: Biogeosciences, 116, G00K03. Mccullough DG, Werner RA, Neumann D (1998) Fire and Insects in Northern and Boreal Forest Ecosystems of North America. Annual Review of Entomology, 43, 107-127. Mcmahon SM, Parker GG, Miller DR (2010) Evidence for a recent increase in forest growth. Proceedings of the National Academy of Sciences, 107, 3611-3615. Medvigy D, Clark KL, Skowronski NS, Schäfer KVR (2012) Simulated impacts of insect defoliation on forest carbon dynamics. Environmental Research Letters, 7, 045703. Metz MR, Frangioso KM, Meentemeyer RK, Rizzo DM (2010) Interacting disturbances: wildfire severity affected by stage of forest disease invasion. Ecological Applications, 21, 313-320. Mildrexler DJ, Zhao M, Heinsch FA, Running SW (2007) A new satellite-based methodology for continential-scale disturbance detection. Ecological Applications, 17, 235-250. Mildrexler DJ, Zhao M, Running SW (2009) Testing a MODIS Global Disturbance Index across North America. Remote Sensing of Environment, 113, 2103-2117. Miles L, Dunning E, Doswald N, Osti M (2010) A safer bet for REDD+: Review of the evidence on the relationship between biodiversity and the resilience of forest carbon stocks. Working Paper v2. Myneni RB, Dong J, Tucker CJ et al. (2001) A large carbon sink in the woody biomass of Northern forests. Proceedings of the National Academy of Sciences, 98, 14784-14789. Nemani RR, Keeling CD, Hashimoto H et al. (2003) Climate-Driven Increases in Global Terrestrial Net Primary Production from 1982 to 1999. Science, 300, 1560-1563.
This article is protected by copyright. All rights reserved.
Accepted Article
Olsson H (1995) Reflectance calibration of thematic mapper data for forest change detection. International Journal of Remote Sensing, 16, 81-96. Pan Y, Birdsey RA, Fang J et al. (2011) A Large and Persistent Carbon Sink in the World’s Forests. Science, 333, 988-993. Parker TJ, Clancy KM, Mathiasen RL (2006) Interactions among fire, insects and pathogens in coniferous forests of the interior western United States and Canada. Agricultural and Forest Entomology, 8, 167-189. Peng C, Zhou X, Zhao S, Wang X, Zhu B, Piao S, Fang J (2009) Quantifying the response of forest carbon balance to future climate change in Northeastern China: Model validation and prediction. Global and Planetary Change, 66, 179-194. Rogan J, Miller J (2007) Integrating GIS and remotely sensed data for mapping forest disturbance and change. In: Understanding Forest Disturbance and Spatial Pattern: Remote Sensing and GIS Approaches, MA Wulder and SE Franklin (Eds). (eds Wulder MA, Franklin SE) pp 133-171. Boca Raton, FL, USA, CRC Press (Taylor and Francis). Running SW, Nemani RR, Heinsch FA, Zhao M, Reeves M, Hashimoto H (2004) A Continuous SatelliteDerived Measure of Global Terrestrial Primary Production. BioScience, 54. Ryan MG, Harmon ME, Birdsey RA et al. (2010) A synthesis of the science on forests and carbon for U.S. forests. Ecological Society of America: Issues In Ecology, 13, 1-16. Schoennagel T, Veblen TT, Kulakowski D, Holz A (2007) Multidecadal climate variability and climate interactions affect subalpine fire occurrence, western Colorado (USA). Ecology, 88, 2891-2902. Sen PK (1968) Estimates of the Regression Coefficient Based on Kendall's Tau. Journal of the American Statistical Association, 63, 1379-1389. Sfa (1988-2010) China Forestry Statistical Yearbook, Beijing, China Forestry Press. Song C, Zhang Y (2010) Forest cover in China from 1949 to 2006. In: Reforesting Landscapes: Linking Pattern and Process. (eds Nagendra H, Southworth J) pp 341-356. Berlin, Springer. Souza C, Barreto P (2000) An alternative approach for detecting and monitoring selectively logged forests in the Amazon. International Journal of Remote Sensing, 21, 173-179. Tan K, Piao S, Peng C, Fang J (2007) Satellite-based estimation of biomass carbon stocks for northeast China's forests between 1982 and 1999. Forest Ecology and Management, 240, 114-121. Theil H (1950) A rank-invariant method of linear and polynomial regression analysis III In: Proceedings KNAW. Verbesselt J, Hyndman R, Newnham G, Culvenor D (2010) Detecting trend and seasonal changes in satellite image time series. Remote Sensing of Environment, 114, 106-115. Vetter M, Wirth C, Böttcher H, Churkina G, Schulze E-D, Wutzler T, Weber G (2005) Partitioning direct and indirect human-induced effects on carbon sequestration of managed coniferous forests using model simulations and forest inventories. Global Change Biology, 11, 810-827. Wang G, Innes JL, Lei J, Dai S, Wu SW (2007) China's Forestry Reforms. Science, 318, 1556-1557. Wang H, Liu W, Wang W, Zu Y (2013) Influence of Long-Term Thinning on the Biomass Carbon and Soil Respiration in a Larch (Larix gmelinii) Forest in Northeastern China. The Scientific World Journal, 2013, 1-9. Wang S, Cornelis Van Kooten G, Wilson B (2004) Mosaic of reform: forest policy in post-1978 China. Forest Policy and Economics, 6, 71-83. Wang X, Fang J, Sanders NJ, White PS, Tang Z (2009) Relative importance of climate vs local factors in shaping the regional patterns of forest plant richness across northeast China. Ecography, 32, 133-142. Weed AS, Ayres MP, Hicke J (2013) Consequences of climate change for biotic disturbances in North American forests. Ecological Monographs, 83, 441-470.
This article is protected by copyright. All rights reserved.
Accepted Article
Wells N, Goddard S, Hayes MJ (2004) A Self-Calibrating Palmer Drought Severity Index. Journal of Climate, 17, 2335-2351. Westerling AL, Hidalgo HG, Cayan DR, Swetnam TW (2006) Warming and Earlier Spring Increase Western U.S. Forest Wildfire Activity. Science, 313, 940-943. Wold S, Sjöström M, Eriksson L (2001) PLS-regression: a basic tool of chemometrics. Chemometrics and Intelligent Laboratory Systems, 58, 109-130. Yu D, Zhou L, Zhou W et al. (2011) Forest Management in Northeast China: History, Problems, and Challenges. Environmental management, 48, 1122-1135. Zhang K, Hori Y, Zhou S-Z, Michinaka T, Hirano Y, Tachibana S (2011) Impact of Natural Forest Protection Program policies on forests in northeastern China. Forestry Studies in China, 13, 231-238. Zhang P, Shao G, Zhao G, Le Master DC, Parker GR, Dunning JB, Li Q (2000) China's Forest Policy for the 21st Century. Science, 288, 2135-2136. Zhang Y, Liang S, Sun G (2014) Forest Biomass Mapping of Northeastern China Using GLAS and MODIS Data. Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of, 7, 140152. Zhao J, Yan X, Guo J, Jia G (2012) Evaluating Spatial-Temporal Dynamics of Net Primary Productivity of Different Forest Types in Northeastern China Based on Improved FORCCHN. PLoS ONE, 7, e48131. Zheng L, Zhou X (2008) Dynamics Effects of Selective Cutting Intensity on the Species Composition and Diversity of Natual Forest. Journal of Mountain Science, 26, 699-706.
This article is protected by copyright. All rights reserved.
Accepted Article This article is protected by copyright. All rights reserved.
Accepted Article This article is protected by copyright. All rights reserved.
Accepted Article This article is protected by copyright. All rights reserved.
Accepted Article This article is protected by copyright. All rights reserved.
