Accurate estimates of forest aboveground biomass(AGB)are essential for global carbon cycle studies and have widely relied on approaches using spectral and structural information of forest canopies extracted from vario...Accurate estimates of forest aboveground biomass(AGB)are essential for global carbon cycle studies and have widely relied on approaches using spectral and structural information of forest canopies extracted from various remote sensing datasets.However,combining the advantages of active and passive data sources to improve estimation accuracy remains challenging.Here,we proposed a new approach for forest AGB modeling based on allometric relationships and using the form of power-law to integrate structural and spectral information.Over 60 km^(2) of drone light detection and ranging(LiDAR)data and 1,370 field plot measurements,covering the four major forest types of China(coniferous forest,sub-tropical broadleaf forest,coniferous and broadleaf-leaved mixed forest,and tropical broadleaf forest),were collected together with Sentinel-2 images to evaluate the proposed approach.The results show that the most universally useful structural and spectral metrics are the average values of canopy height and spectral index rather than their maximum values.Compared with structural attributes used alone,combining structural and spectral information can improve the estimation accuracy of AGB,increasing R^(2) by about 10%and reducing the root mean square error by about 22%;the accuracy of the proposed approach can yield a R^(2) of 0.7 in different forests types.The proposed approach performs the best in coniferous forest,followed by sub-tropical broadleaf forest,coniferous and broadleaf-leaved mixed forest,and then tropical broadleaf forest.Furthermore,the simple linear regression used in the proposed method is less sensitive to sample size and outperforms statistically multivariate machine learning-based regression models such as stepwise multiple regression,artificial neural networks,and Random Forest.The proposed approach may provide an alternative solution to map large-scale forest biomass using space-borne LiDAR and optical images with high accuracy.展开更多
Forests account for 80%of the total carbon exchange between the atmosphere and terrestrial ecosystems.Thus,to better manage our responses to global warming,it is important to monitor and assess forest aboveground carb...Forests account for 80%of the total carbon exchange between the atmosphere and terrestrial ecosystems.Thus,to better manage our responses to global warming,it is important to monitor and assess forest aboveground carbon and forest aboveground biomass(FAGB).Different levels of detail are needed to estimate FAGB at local,regional and national scales.Multi-scale remote sensing analysis from high,medium and coarse spatial resolution data,along with field sampling,is one approach often used.However,the methods developed are still time consuming,expensive,and inconvenient for systematic monitoring,especially for developing countries,as they require vast numbers of field samples for upscaling.Here,we recommend a convenient two-scale approach to estimate FAGB that was tested in our study sites.The study was conducted in the Chitwan district of Nepal using GeoEye-1(0.5 m),Landsat(30 m)and Google Earth very high resolution(GEVHR)Quickbird(0.65 m)images.For the local scale(Kayerkhola watershed),tree crowns of the area were delineated by the object-based image analysis technique on GeoEye images.An overall accuracy of 83%was obtained in the delineation of tree canopy cover(TCC)per plot.A TCC vs.FAGB model was developed based on the TCC estimations from GeoEye and FAGB measurements from field sample plots.A coefficient of determination(R2)of 0.76 was obtained in the modelling,and a value of 0.83 was obtained in the validation of the model.To upscale FAGB to the entire district,open source GEVHR images were used as virtual field plots.We delineated their TCC values and then calculated FAGB based on a TCC versus FAGB model.Using the multivariate adaptive regression splines machine learning algorithm,we developed a model from the relationship between the FAGB of GEVHR virtual plots with predictor parameters from Landsat 8 bands and vegetation indices.The model was then used to extrapolate FAGB to the entire district.This approach considerably reduced the need for field data and commercial very high resolution imagery while achieving two-scale forest information and FAGB estimates at high resolution(30 m)and accuracy(R2=0.76 and 0.7)with minimal error(RMSE=64 and 38 tons ha-1)at local and regional scales.This methodology is a promising technique for cost-effective FAGB and carbon estimations and can be replicated with limited resources and time.The method is especially applicable for developing countries that have low budgets for carbon estimations,and it is also applicable to the Reducing Emissions from Deforestation and Forest Degradation(REDD?)monitoring reporting and verification processes.展开更多
Background:Aboveground biomass(AGB)is a fundamental indicator of forest ecosystem productivity and health and hence plays an essential role in evaluating forest carbon reserves and supporting the development of target...Background:Aboveground biomass(AGB)is a fundamental indicator of forest ecosystem productivity and health and hence plays an essential role in evaluating forest carbon reserves and supporting the development of targeted forest management plans.Methods:Here,we proposed a random forest/co-kriging framework that integrates the strengths of machine learning and geostatistical approaches to improve the mapping accuracies of AGB in northern Guangdong Province of China.We used Landsat time-series observations,Advanced Land Observing Satellite(ALOS)Phased Array L-band Synthetic Aperture Radar(PALSAR)data,and National Forest Inventory(NFI)plot measurements,to generate the forest AGB maps at three time points(1992,2002 and 2010)showing the spatio-temporal dynamics of AGB in the subtropical forests in Guangdong,China.Results:The proposed model was capable of mapping forest AGB using spectral,textural,topographical variables and the radar backscatter coefficients in an effective and reliable manner.The root mean square error of the plotlevel AGB validation was between 15.62 and 53.78 t∙ha^(−1),the mean absolute error ranged from 6.54 to 32.32 t∙ha^(−1),the bias ranged from−2.14 to 1.07 t∙ha^(−1),and the relative improvement over the random forest algorithm was between 3.8%and 17.7%.The largest coefficient of determination(0.81)and the smallest mean absolute error(6.54 t∙ha^(−1)were observed in the 1992 AGB map.The spectral saturation effect was minimized by adding the PALSAR data to the modeling variable set in 2010.By adding elevation as a covariable,the co-kriging outperformed the ordinary kriging method for the prediction of the AGB residuals,because co-kriging resulted in better interpolation results in the valleys and plains of the study area.Conclusions:Validation of the three AGB maps with an independent dataset indicated that the random forest/cokriging performed best for AGB prediction,followed by random forest coupled with ordinary kriging(random forest/ordinary kriging),and the random forest model.The proposed random forest/co-kriging framework provides an accurate and reliable method for AGB mapping in subtropical forest regions with complex topography.The resulting AGB maps are suitable for the targeted development of forest management actions to promote carbon sequestration and sustainable forest management in the context of climate change.展开更多
The ecosystem in northeastern China and the Russian Far East is a hotspot of scientific research into the global carbon balance.Forest aboveground biomass(AGB) is an important component in the land surface carbon cycl...The ecosystem in northeastern China and the Russian Far East is a hotspot of scientific research into the global carbon balance.Forest aboveground biomass(AGB) is an important component in the land surface carbon cycle.In this study,using forest inventory data and forest distribution data,the AGB was estimated for forest in Daxinganlin in northeastern China by combining charge-coupled device(CCD) data from the Small Satellite for Disaster and Environment Monitoring and Forecast(HJ-1) and Geoscience Laser Altimeter System(GLAS) waveform data from the Ice,Cloud and land Elevation Satellite(ICESat).The forest AGB prediction models were separately developed for different forest types in the research area at GLAS footprint level from GLAS waveform parameters and field survey plot biomass in the Changqing(CQ) Forest Center,which was calculated from forest inventory data.The resulted statistical regression models have a R2=0.68 for conifer and R2=0.71 for broadleaf forests.These models were used to estimate biomass for all GLAS footprints of forest located in the study area.All GLAS footprint biomass coupled with various spectral reflectivity parameters and vegetation indices derived from HJ-1 satellite CCD data were used in multiple regression analyses to establish biomass prediction models(R2=0.55 and R2=0.52 for needle and broadleaf respectively).Then the models were used to produce a forest AGB map for the whole study area using the HJ-1 data.Biomass data obtained from forest inventory data of the Zhuanglin(ZL) Forest Center were used as independent field measurements to validate the AGB estimated from HJ-1 CCD data(R2=0.71).About 80% of biomass samples had an error less than 20 t ha-1,and the mean error of all validation samples is 5.74 t ha-1.The pixel-level biomass map was then stratified into different biomass levels to illustrate the AGB spatial distribution pattern in this area.It was found that HJ-1 wide-swath data and GLAS waveform data can be combined to estimate forest biomass with good precision,and the biomass data can be used as input data for future carbon budget analysis.展开更多
Biomass in karst terrain has rarely been measured because the steep mountainous limestone terrain has limited the ability to sample woody plants.Satellite observation, especially at high spatial resolution, is an impo...Biomass in karst terrain has rarely been measured because the steep mountainous limestone terrain has limited the ability to sample woody plants.Satellite observation, especially at high spatial resolution, is an important surrogate for the quantification of the biomass of karst forests and shrublands. In this study, an artificial neural network(ANN) model was built using Pléiades satellite imagery and field biomass measurements to estimate the aboveground biomass(AGB) in the Houzhai River Watershed, which is a typical plateau karst basin in Central Guizhou Province, Southwestern China. A back-propagation ANN model was also developed.Seven vegetation indices, two spectral bands of Pléiades imagery, one geomorphological parameter,and land use/land cover were selected as model inputs. AGB was chosen as an output. The AGB estimated by the allometric functions in 78 quadrats was utilized as training data(54 quadrats, 70%),validation data(12 quadrats, 15%), and testing data(12 quadrats, 15%). Data-model comparison showed that the ANN model performed well with an absolute root mean square error of 11.85 t/ha, which was 9.88%of the average AGB. Based on the newly developed ANN model, an AGB map of the Houzhai River Watershed was produced. The average predicted AGB of the secondary evergreen and deciduous broadleaved mixed forest, which is the dominant forest type in the watershed, was 120.57 t/ha. The average AGBs of the large distributed shrubland,tussock, and farmland were 38.27, 9.76, and 11.69 t/ha, respectively. The spatial distribution pattern ofthe AGB estimated by the new ANN model in the karst basin was consistent with that of the field investigation. The model can be used to estimate the regional AGB of karst landscapes that are distributed widely over the Yun-Gui Plateau.展开更多
Global and regional environmental changes such as land use and climate change have significantly integrated and interactive effects on forest. These integrated effects will undoubtedly alter the distribution, function...Global and regional environmental changes such as land use and climate change have significantly integrated and interactive effects on forest. These integrated effects will undoubtedly alter the distribution, function and succession processes of forest ecosystems. In order to adapt to these changes, it is necessary to understand their individual and integrated effects. In this study, we proposed a framework by using coupling models to gain a better understanding of the complex ecological processes. We combined an agent-based model for land use and land cover change（ABM/LUCC）, an ecosystem process model（PnET-Ⅱ）, and a forest dynamic landscape model（LANDIS-Ⅱ） to simulate the change of forest aboveground biomass（AGB） which was driven by land use and climate change factors for the period of 2010–2050 in Taihe County of southern China, where subtropical coniferous plantations dominate. We conducted a series of land use and climate change scenarios to compare the differences in forest AGB. The results show that：（1） land use, including town expansion, deforestation and forest conversion and climate change are likely to influence forest AGB in the near future in Taihe County.（2） Though climate change will make a good contribution to an increase in forest AGB, land use change can result in a rapid decrease in the forest AGB and play a vital role in the integrated simulation. The forest AGB under the integrated scenario decreased by 53.7%（RCP2.6 ＋ land use）, 57.2%（RCP4.5 ＋ land use）, and 56.9%（RCP8.5 ＋ land use） by 2050, which is in comparison to the results under separate RCPs without land use disturbance.（3） The framework can offer a coupled method to better understand the complex and interactive ecological processes, which may provide some supports for adapting to land use and climate change, improving and optimizing plantation structure and function,and developing measures for sustainable forest management.展开更多
基金supported by the Strategic Priority Research Program of Chinese Academy of Sciences(Grant No.XDA19050401)the National Natural Science Foundation of China(41871332,31971575,41901358).
文摘Accurate estimates of forest aboveground biomass(AGB)are essential for global carbon cycle studies and have widely relied on approaches using spectral and structural information of forest canopies extracted from various remote sensing datasets.However,combining the advantages of active and passive data sources to improve estimation accuracy remains challenging.Here,we proposed a new approach for forest AGB modeling based on allometric relationships and using the form of power-law to integrate structural and spectral information.Over 60 km^(2) of drone light detection and ranging(LiDAR)data and 1,370 field plot measurements,covering the four major forest types of China(coniferous forest,sub-tropical broadleaf forest,coniferous and broadleaf-leaved mixed forest,and tropical broadleaf forest),were collected together with Sentinel-2 images to evaluate the proposed approach.The results show that the most universally useful structural and spectral metrics are the average values of canopy height and spectral index rather than their maximum values.Compared with structural attributes used alone,combining structural and spectral information can improve the estimation accuracy of AGB,increasing R^(2) by about 10%and reducing the root mean square error by about 22%;the accuracy of the proposed approach can yield a R^(2) of 0.7 in different forests types.The proposed approach performs the best in coniferous forest,followed by sub-tropical broadleaf forest,coniferous and broadleaf-leaved mixed forest,and then tropical broadleaf forest.Furthermore,the simple linear regression used in the proposed method is less sensitive to sample size and outperforms statistically multivariate machine learning-based regression models such as stepwise multiple regression,artificial neural networks,and Random Forest.The proposed approach may provide an alternative solution to map large-scale forest biomass using space-borne LiDAR and optical images with high accuracy.
基金supported by the CAS Strategic Priority Research Program(No.XDA19030402)the National Key Research and Development Program of China(No.2016YFD0300101)+2 种基金the Natural Science Foundation of China(Nos.31571565,31671585)the Key Basic Research Project of the Shandong Natural Science Foundation of China(No.ZR2017ZB0422)Research Funding of Qingdao University(No.41117010153)
文摘Forests account for 80%of the total carbon exchange between the atmosphere and terrestrial ecosystems.Thus,to better manage our responses to global warming,it is important to monitor and assess forest aboveground carbon and forest aboveground biomass(FAGB).Different levels of detail are needed to estimate FAGB at local,regional and national scales.Multi-scale remote sensing analysis from high,medium and coarse spatial resolution data,along with field sampling,is one approach often used.However,the methods developed are still time consuming,expensive,and inconvenient for systematic monitoring,especially for developing countries,as they require vast numbers of field samples for upscaling.Here,we recommend a convenient two-scale approach to estimate FAGB that was tested in our study sites.The study was conducted in the Chitwan district of Nepal using GeoEye-1(0.5 m),Landsat(30 m)and Google Earth very high resolution(GEVHR)Quickbird(0.65 m)images.For the local scale(Kayerkhola watershed),tree crowns of the area were delineated by the object-based image analysis technique on GeoEye images.An overall accuracy of 83%was obtained in the delineation of tree canopy cover(TCC)per plot.A TCC vs.FAGB model was developed based on the TCC estimations from GeoEye and FAGB measurements from field sample plots.A coefficient of determination(R2)of 0.76 was obtained in the modelling,and a value of 0.83 was obtained in the validation of the model.To upscale FAGB to the entire district,open source GEVHR images were used as virtual field plots.We delineated their TCC values and then calculated FAGB based on a TCC versus FAGB model.Using the multivariate adaptive regression splines machine learning algorithm,we developed a model from the relationship between the FAGB of GEVHR virtual plots with predictor parameters from Landsat 8 bands and vegetation indices.The model was then used to extrapolate FAGB to the entire district.This approach considerably reduced the need for field data and commercial very high resolution imagery while achieving two-scale forest information and FAGB estimates at high resolution(30 m)and accuracy(R2=0.76 and 0.7)with minimal error(RMSE=64 and 38 tons ha-1)at local and regional scales.This methodology is a promising technique for cost-effective FAGB and carbon estimations and can be replicated with limited resources and time.The method is especially applicable for developing countries that have low budgets for carbon estimations,and it is also applicable to the Reducing Emissions from Deforestation and Forest Degradation(REDD?)monitoring reporting and verification processes.
基金the Natural Science Foundation of China(Nos.31670552,31971577)China Postdoctoral Science Foundation(No.2019 M651842)the Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD).
文摘Background:Aboveground biomass(AGB)is a fundamental indicator of forest ecosystem productivity and health and hence plays an essential role in evaluating forest carbon reserves and supporting the development of targeted forest management plans.Methods:Here,we proposed a random forest/co-kriging framework that integrates the strengths of machine learning and geostatistical approaches to improve the mapping accuracies of AGB in northern Guangdong Province of China.We used Landsat time-series observations,Advanced Land Observing Satellite(ALOS)Phased Array L-band Synthetic Aperture Radar(PALSAR)data,and National Forest Inventory(NFI)plot measurements,to generate the forest AGB maps at three time points(1992,2002 and 2010)showing the spatio-temporal dynamics of AGB in the subtropical forests in Guangdong,China.Results:The proposed model was capable of mapping forest AGB using spectral,textural,topographical variables and the radar backscatter coefficients in an effective and reliable manner.The root mean square error of the plotlevel AGB validation was between 15.62 and 53.78 t∙ha^(−1),the mean absolute error ranged from 6.54 to 32.32 t∙ha^(−1),the bias ranged from−2.14 to 1.07 t∙ha^(−1),and the relative improvement over the random forest algorithm was between 3.8%and 17.7%.The largest coefficient of determination(0.81)and the smallest mean absolute error(6.54 t∙ha^(−1)were observed in the 1992 AGB map.The spectral saturation effect was minimized by adding the PALSAR data to the modeling variable set in 2010.By adding elevation as a covariable,the co-kriging outperformed the ordinary kriging method for the prediction of the AGB residuals,because co-kriging resulted in better interpolation results in the valleys and plains of the study area.Conclusions:Validation of the three AGB maps with an independent dataset indicated that the random forest/cokriging performed best for AGB prediction,followed by random forest coupled with ordinary kriging(random forest/ordinary kriging),and the random forest model.The proposed random forest/co-kriging framework provides an accurate and reliable method for AGB mapping in subtropical forest regions with complex topography.The resulting AGB maps are suitable for the targeted development of forest management actions to promote carbon sequestration and sustainable forest management in the context of climate change.
基金supported by National Basic Research Program of China (Grant No.2007CB714404)National Natural Science Foundation of China (Grant Nos.40701124,40930530)
文摘The ecosystem in northeastern China and the Russian Far East is a hotspot of scientific research into the global carbon balance.Forest aboveground biomass(AGB) is an important component in the land surface carbon cycle.In this study,using forest inventory data and forest distribution data,the AGB was estimated for forest in Daxinganlin in northeastern China by combining charge-coupled device(CCD) data from the Small Satellite for Disaster and Environment Monitoring and Forecast(HJ-1) and Geoscience Laser Altimeter System(GLAS) waveform data from the Ice,Cloud and land Elevation Satellite(ICESat).The forest AGB prediction models were separately developed for different forest types in the research area at GLAS footprint level from GLAS waveform parameters and field survey plot biomass in the Changqing(CQ) Forest Center,which was calculated from forest inventory data.The resulted statistical regression models have a R2=0.68 for conifer and R2=0.71 for broadleaf forests.These models were used to estimate biomass for all GLAS footprints of forest located in the study area.All GLAS footprint biomass coupled with various spectral reflectivity parameters and vegetation indices derived from HJ-1 satellite CCD data were used in multiple regression analyses to establish biomass prediction models(R2=0.55 and R2=0.52 for needle and broadleaf respectively).Then the models were used to produce a forest AGB map for the whole study area using the HJ-1 data.Biomass data obtained from forest inventory data of the Zhuanglin(ZL) Forest Center were used as independent field measurements to validate the AGB estimated from HJ-1 CCD data(R2=0.71).About 80% of biomass samples had an error less than 20 t ha-1,and the mean error of all validation samples is 5.74 t ha-1.The pixel-level biomass map was then stratified into different biomass levels to illustrate the AGB spatial distribution pattern in this area.It was found that HJ-1 wide-swath data and GLAS waveform data can be combined to estimate forest biomass with good precision,and the biomass data can be used as input data for future carbon budget analysis.
基金supported by the National Key R and D Program of China(2016YFC0502101)the National Basic Research Program of China(2013CB956704)the Opening Fund of the State Key Laboratory of Environmental Geochemistry(SKLEG2017911)
文摘Biomass in karst terrain has rarely been measured because the steep mountainous limestone terrain has limited the ability to sample woody plants.Satellite observation, especially at high spatial resolution, is an important surrogate for the quantification of the biomass of karst forests and shrublands. In this study, an artificial neural network(ANN) model was built using Pléiades satellite imagery and field biomass measurements to estimate the aboveground biomass(AGB) in the Houzhai River Watershed, which is a typical plateau karst basin in Central Guizhou Province, Southwestern China. A back-propagation ANN model was also developed.Seven vegetation indices, two spectral bands of Pléiades imagery, one geomorphological parameter,and land use/land cover were selected as model inputs. AGB was chosen as an output. The AGB estimated by the allometric functions in 78 quadrats was utilized as training data(54 quadrats, 70%),validation data(12 quadrats, 15%), and testing data(12 quadrats, 15%). Data-model comparison showed that the ANN model performed well with an absolute root mean square error of 11.85 t/ha, which was 9.88%of the average AGB. Based on the newly developed ANN model, an AGB map of the Houzhai River Watershed was produced. The average predicted AGB of the secondary evergreen and deciduous broadleaved mixed forest, which is the dominant forest type in the watershed, was 120.57 t/ha. The average AGBs of the large distributed shrubland,tussock, and farmland were 38.27, 9.76, and 11.69 t/ha, respectively. The spatial distribution pattern ofthe AGB estimated by the new ANN model in the karst basin was consistent with that of the field investigation. The model can be used to estimate the regional AGB of karst landscapes that are distributed widely over the Yun-Gui Plateau.
基金National Basic Research Program of China（973 Program）,No.2015CB452702 National Natural Science Foundation of China,No.41571098,No.41371196,No.41530749 National Key Technology R＆D Program,No.2013BAC03B04
文摘Global and regional environmental changes such as land use and climate change have significantly integrated and interactive effects on forest. These integrated effects will undoubtedly alter the distribution, function and succession processes of forest ecosystems. In order to adapt to these changes, it is necessary to understand their individual and integrated effects. In this study, we proposed a framework by using coupling models to gain a better understanding of the complex ecological processes. We combined an agent-based model for land use and land cover change（ABM/LUCC）, an ecosystem process model（PnET-Ⅱ）, and a forest dynamic landscape model（LANDIS-Ⅱ） to simulate the change of forest aboveground biomass（AGB） which was driven by land use and climate change factors for the period of 2010–2050 in Taihe County of southern China, where subtropical coniferous plantations dominate. We conducted a series of land use and climate change scenarios to compare the differences in forest AGB. The results show that：（1） land use, including town expansion, deforestation and forest conversion and climate change are likely to influence forest AGB in the near future in Taihe County.（2） Though climate change will make a good contribution to an increase in forest AGB, land use change can result in a rapid decrease in the forest AGB and play a vital role in the integrated simulation. The forest AGB under the integrated scenario decreased by 53.7%（RCP2.6 ＋ land use）, 57.2%（RCP4.5 ＋ land use）, and 56.9%（RCP8.5 ＋ land use） by 2050, which is in comparison to the results under separate RCPs without land use disturbance.（3） The framework can offer a coupled method to better understand the complex and interactive ecological processes, which may provide some supports for adapting to land use and climate change, improving and optimizing plantation structure and function,and developing measures for sustainable forest management.
