Background: In recent decades the future of global forests has been a matter of increasing concern, particularly in relation to the threat of forest ecosystem responses under potential climate change. To the future pr...Background: In recent decades the future of global forests has been a matter of increasing concern, particularly in relation to the threat of forest ecosystem responses under potential climate change. To the future predictions of these responses, the current forest biomass carbon storage(FCS) should first be clarified as much as possible,especially at national scales. However, few studies have introduced how to verify an FCS estimate by delimiting the reasonable ranges. This paper addresses an estimation of national FCS and its verification using two-step process to narrow the uncertainty. Our study focuses on a methodology for reducing the uncertainty resulted by converting from growing stock volume to above-and below-ground biomass(AB biomass), so as to eliminate the significant bias in national scale estimations.Methods: We recommend splitting the estimation into two parts, one part for stem and the other part for AB biomass to preclude possible significant bias. Our method estimates the stem biomass from volume and wood density(WD), and converts the AB biomass from stem biomass by using allometric relationships.Results: Based on the presented two-step process, the estimation of China’s FCS is performed as an example to explicate how to infer the ranges of national FCS. The experimental results demonstrate a national FCS estimation within the reasonable ranges(relative errors: + 4.46% and-4.44%), e.g., 5.6–6.1 PgC for China’s forest ecosystem at the beginning of the 2010 s. These ranges are less than 0.52 PgC for confirming each FCS estimate of different periods during the last 40 years. In addition, our results suggest the upper-limits by specifying a highly impractical value of WD(0.7 t·m-3) on the national scale. As a control reference, this value decides what estimate is impossible to achieve for the FCS estimates.Conclusions: Presented methodological analysis highlights the possibility to determine a range that the true value could be located in. The two-step process will help to verify national FCS and also to reduce uncertainty in related studies. While the true value of national FCS is immeasurable, our work should motivate future studies that explore new estimations to approach the true value by narrowing the uncertainty in FCS estimations on national and global scales.展开更多
Background: The stem curve of standing trees is an essential parameter for accurate estimation of stem volume.This study aims to directly quantify the occlusions within the single-scan terrestrial laser scanning(TLS) ...Background: The stem curve of standing trees is an essential parameter for accurate estimation of stem volume.This study aims to directly quantify the occlusions within the single-scan terrestrial laser scanning(TLS) data,evaluate its correlation with the accuracy of the retrieved stem curves, and subsequently, to assess the capacity of single-scan TLS to estimate stem curves.Methods: We proposed an index, occlusion rate, to quantify the occlusion level in TLS data. We then analyzed three influencing factors for the occlusion rate: the percentage of basal area near the scanning center, the scanning distance and the source of occlusions. Finally, we evaluated the effects of occlusions on stem curve estimates from single-scan TLS data.Results: The results showed that the correlations between the occlusion rate and the stem curve estimation accuracies were strong(r = 0.60–0.83), so was the correlations between the occlusion rate and its influencing factors(r = 0.84–0.99). It also showed that the occlusions from tree stems were the main factor of the low detection rate of stems, while the non-stem components mainly influenced the completeness of the retrieved stem curves.Conclusions: Our study demonstrates that the occlusions significantly affect the accuracy of stem curve retrieval from the single-scan TLS data in a typical-size(32 m × 32 m) forest plot. However, the single-scan mode has the capacity to accurately estimate the stem curve in a small forest plot(< 10 m × 10 m) or a plot with a lower occlusion rate, such as less than 35% in our tested datasets. The findings from this study are useful for guiding the practice of retrieving forest parameters using single-scan TLS data.展开更多
Background:The universal occurrence of randomly distributed dark holes(i.e.,data pits appearing within the tree crown)in LiDAR-derived canopy height models(CHMs)negatively affects the accuracy of extracted forest inve...Background:The universal occurrence of randomly distributed dark holes(i.e.,data pits appearing within the tree crown)in LiDAR-derived canopy height models(CHMs)negatively affects the accuracy of extracted forest inventory parameters.Methods:We develop an algorithm based on cloth simulation for constructing a pit-free CHM.Results:The proposed algorithm effectively fills data pits of various sizes whilst preserving canopy details.Our pitfree CHMs derived from point clouds at different proportions of data pits are remarkably better than those constructed using other algorithms,as evidenced by the lowest average root mean square error(0.4981 m)between the reference CHMs and the constructed pit-free CHMs.Moreover,our pit-free CHMs show the best performance overall in terms of maximum tree height estimation(average bias=0.9674 m).Conclusion:The proposed algorithm can be adopted when working with different quality LiDAR data and shows high potential in forestry applications.展开更多
Sudden oak death(SOD)is one of the most rapid and destructive forest pathogens,which has caused the death of many host plants in Europe and America.There are currently no cases in China where there are more host plant...Sudden oak death(SOD)is one of the most rapid and destructive forest pathogens,which has caused the death of many host plants in Europe and America.There are currently no cases in China where there are more host plants and a more suitable climate for this pathogen to survive.Therefore,it is vital to discern the potential suitable habitat,quantify the risk levels,and monitor the potential high-risk areas.In this study,we modelled the potential invasion range and risk level of this pathogen at present and in future scenarios in China,using the least correlated components of all the environmental factors based on the Genetic Algorithm for Ruleset Production niche model and GIS analysis.The results indicate that most areas in China are free from a potential SOD risk,and the majority of potential occurrence areas are concentrated in Southern China(Yunnan,Sichuan,Guizhou,Chongqing,Hunan,Fujian).The area of high and extremely high risk in 2050(RCP26,RCP45,RCP60,and RCP85)is larger than that at present.The most susceptible area is Yunnan province with 80%of the area prone to SOD at extremely high risk in present and future scenarios.The results will be important for monitoring potential high-risk areas in the currently uninfected parts of China.展开更多
This paper deals with a new type of multi-angle remotely sensed data--CHRIS(the Compact High Resolution Imaging Spec-trometer),by using rational function models(RFM)and rigorous sensor models(RSM).For ortho-rectifying...This paper deals with a new type of multi-angle remotely sensed data--CHRIS(the Compact High Resolution Imaging Spec-trometer),by using rational function models(RFM)and rigorous sensor models(RSM).For ortho-rectifying an image set,a rigorous sen-sor model-Toutin's model was employed and a set of reported parameters including across track angle,along track angle,IFOV,altitude,period,eccentricity and orbit inclination were input,then,the orbit calculation was started and the track information was given to the raw data.The images were ortho-rectified with geocoded ASTER images and digital elevation(DEM)data.Results showed that with 16 ground control points(GCPs),the correction accuracy decreased with view zenith angle,and the RMSE value increased to be over one pixel at 36 degree off-nadir.When the GCPs were approximately chosen as in Toutin's model,a RFM with three coefficients produced the same accuracy trend versus view zenith angle while the RMSEs for all angles were improved and within about one pixel.展开更多
Three typical polluted dust particles （i.e., single coated dust, two-sphere/spheroid system, and coated dust with ag- gregate） including internal and semi-external mixtures are modeled, and their scattering properti...Three typical polluted dust particles （i.e., single coated dust, two-sphere/spheroid system, and coated dust with ag- gregate） including internal and semi-external mixtures are modeled, and their scattering properties at 1.6-μm wavelength are calculated by using the generalized multi-sphere Mie-solution （GMM） method. We investigate the influences of par- ticle size, morphology, and chemical composition on the scattering parameters of polluted dust particles. The analysis results demonstrate that the single scattering albedo of coated dust is much smaller than that of pure dust, especially for the spheroidal black carbon （BC） coated dust. When a dust particle semi-mixes with another aerosol particle to form a two-sphere/spheroid system, its scattering properties are much more sensitive to the size and species of monomers than the monomer shape. If an aggregated BC attaches to the coated dust, the scattering properties of whole particle mainly depend on the host particle （coated dust）.展开更多
Background:Accurate mapping of forest canopy heights at a fine spatial resolution over large geographical areas is challenging.It is essential for the estimation of forest aboveground biomass and the evaluation of for...Background:Accurate mapping of forest canopy heights at a fine spatial resolution over large geographical areas is challenging.It is essential for the estimation of forest aboveground biomass and the evaluation of forest ecosystems.Yet current regional to national scale forest height maps were mainly produced at coarse-scale.Such maps lack spatial details for decision-making at local scales.Recent advances in remote sensing provide great opportunities to fill this gap.Method:In this study,we evaluated the utility of multi-source satellite data for mapping forest heights over Hunan Province in China.A total of 523 plot data collected from 2017 to 2018 were utilized for calibration and validation of forest height models.Specifically,the relationships between three types of in-situ measured tree heights(maximum-,averaged-,and basal area-weighted-tree heights)and plot-level remote sensing metrics(multispectral,radar,and topo variables from Landsat,Sentinel-1/PALSAR-2,and SRTM)were analyzed.Three types of models(multilinear regression,random forest,and support vector regression)were evaluated.Feature variables were selected by two types of variable selection approaches(stepwise regression and random forest).Model parameters and model performances for different models were tuned and evaluated via a 10-fold cross-validation approach.Then,tuned models were applied to generate wall-to-wall forest height maps for Hunan Province.Results:The best estimation of plot-level tree heights(R2 ranged from 0.47 to 0.52,RMSE ranged from 3.8 to 5.3 m,and rRMSE ranged from 28%to 31%)was achieved using the random forest model.A comparison with existing forest height maps showed similar estimates of mean height,however,the ranges varied under different definitions of forest and types of tree height.Conclusions:Primary results indicate that there are small biases in estimated heights at the province scale.This study provides a framework toward establishing regional to national scale maps of vertical forest structure.展开更多
Background:Determining the spatial distribution of tree heights at the regional area scale is significant when performing forest above-ground biomass estimates in forest resource management research.The geometric-opti...Background:Determining the spatial distribution of tree heights at the regional area scale is significant when performing forest above-ground biomass estimates in forest resource management research.The geometric-optical mutual shadowing(GOMS)model can be used to invert the forest canopy structural parameters at the regional scale.However,this method can obtain only the ratios among the horizontal canopy diameter(CD),tree height,clear height,and vertical CD.In this paper,we used a semi-variance model to calculate the CD using high spatial resolution images and expanded this method to the regional scale.We then combined the CD results with the forest canopy structural parameter inversion results from the GOMS model to calculate tree heights at the regional scale.Results:The semi-variance model can be used to calculate the CD at the regional scale that closely matches(mainly with in a range from-1 to 1 m)the CD derived from the canopy height model(CHM)data.The difference between tree heights calculated by the GOMS model and the tree heights derived from the CHM data was small,with a root mean square error(RMSE)of 1.96 for a 500-m area with high fractional vegetation cover(FVC)(i.e.,forest area coverage index values greater than 0.8).Both the inaccuracy of the tree height derived from the CHM data and the unmatched spatial resolution of different datasets will influence the accuracy of the inverted tree height.And the error caused by the unmatched spatial resolution is small in dense forest.Conclusions:The semi-variance model can be used to calculate the CD at the regional scale,together with the canopy structure parameters inverted by the GOMS model,the mean tree height at the regional scale can be obtained.Our study provides a new approach for calculating tree height and provides further directions for the application of the GOMS model.展开更多
基金supported by the National Key Research and Development Program of China(Grant Nos:2017YFA0604401,2016YFC0501101)the Open Fund of State Key Laboratory of Remote Sensing Science(No.OFSLRSS201704)+1 种基金the Meteorology Scientific Research Fund in the Public Welfare of China(No.GYHY201506010)partly supported by the National Basic Research Program in China(No.2013CB956602)
文摘Background: In recent decades the future of global forests has been a matter of increasing concern, particularly in relation to the threat of forest ecosystem responses under potential climate change. To the future predictions of these responses, the current forest biomass carbon storage(FCS) should first be clarified as much as possible,especially at national scales. However, few studies have introduced how to verify an FCS estimate by delimiting the reasonable ranges. This paper addresses an estimation of national FCS and its verification using two-step process to narrow the uncertainty. Our study focuses on a methodology for reducing the uncertainty resulted by converting from growing stock volume to above-and below-ground biomass(AB biomass), so as to eliminate the significant bias in national scale estimations.Methods: We recommend splitting the estimation into two parts, one part for stem and the other part for AB biomass to preclude possible significant bias. Our method estimates the stem biomass from volume and wood density(WD), and converts the AB biomass from stem biomass by using allometric relationships.Results: Based on the presented two-step process, the estimation of China’s FCS is performed as an example to explicate how to infer the ranges of national FCS. The experimental results demonstrate a national FCS estimation within the reasonable ranges(relative errors: + 4.46% and-4.44%), e.g., 5.6–6.1 PgC for China’s forest ecosystem at the beginning of the 2010 s. These ranges are less than 0.52 PgC for confirming each FCS estimate of different periods during the last 40 years. In addition, our results suggest the upper-limits by specifying a highly impractical value of WD(0.7 t·m-3) on the national scale. As a control reference, this value decides what estimate is impossible to achieve for the FCS estimates.Conclusions: Presented methodological analysis highlights the possibility to determine a range that the true value could be located in. The two-step process will help to verify national FCS and also to reduce uncertainty in related studies. While the true value of national FCS is immeasurable, our work should motivate future studies that explore new estimations to approach the true value by narrowing the uncertainty in FCS estimations on national and global scales.
基金supported by the National Natural Science Foundation of China(Grant Nos.41671414,41971380,41331171 and 41171265)the National Key Research and Development Program of China(No.2016YFB0501404)
文摘Background: The stem curve of standing trees is an essential parameter for accurate estimation of stem volume.This study aims to directly quantify the occlusions within the single-scan terrestrial laser scanning(TLS) data,evaluate its correlation with the accuracy of the retrieved stem curves, and subsequently, to assess the capacity of single-scan TLS to estimate stem curves.Methods: We proposed an index, occlusion rate, to quantify the occlusion level in TLS data. We then analyzed three influencing factors for the occlusion rate: the percentage of basal area near the scanning center, the scanning distance and the source of occlusions. Finally, we evaluated the effects of occlusions on stem curve estimates from single-scan TLS data.Results: The results showed that the correlations between the occlusion rate and the stem curve estimation accuracies were strong(r = 0.60–0.83), so was the correlations between the occlusion rate and its influencing factors(r = 0.84–0.99). It also showed that the occlusions from tree stems were the main factor of the low detection rate of stems, while the non-stem components mainly influenced the completeness of the retrieved stem curves.Conclusions: Our study demonstrates that the occlusions significantly affect the accuracy of stem curve retrieval from the single-scan TLS data in a typical-size(32 m × 32 m) forest plot. However, the single-scan mode has the capacity to accurately estimate the stem curve in a small forest plot(< 10 m × 10 m) or a plot with a lower occlusion rate, such as less than 35% in our tested datasets. The findings from this study are useful for guiding the practice of retrieving forest parameters using single-scan TLS data.
基金the National Natural Science Foundation of China(Grant Nos.41671414,41971380 and 41171265)the National Key Research and Development Program of China(No.2016YFB0501404).
文摘Background:The universal occurrence of randomly distributed dark holes(i.e.,data pits appearing within the tree crown)in LiDAR-derived canopy height models(CHMs)negatively affects the accuracy of extracted forest inventory parameters.Methods:We develop an algorithm based on cloth simulation for constructing a pit-free CHM.Results:The proposed algorithm effectively fills data pits of various sizes whilst preserving canopy details.Our pitfree CHMs derived from point clouds at different proportions of data pits are remarkably better than those constructed using other algorithms,as evidenced by the lowest average root mean square error(0.4981 m)between the reference CHMs and the constructed pit-free CHMs.Moreover,our pit-free CHMs show the best performance overall in terms of maximum tree height estimation(average bias=0.9674 m).Conclusion:The proposed algorithm can be adopted when working with different quality LiDAR data and shows high potential in forestry applications.
基金supported by the Natural Science Foundation of China(No.41601368)the National Key Research and Development Program of China(No.2016YFB0501505)the Instrument Development Project of the State Key Laboratory of Remote Sensing Science(No.Y7Y01100KZ)
文摘Sudden oak death(SOD)is one of the most rapid and destructive forest pathogens,which has caused the death of many host plants in Europe and America.There are currently no cases in China where there are more host plants and a more suitable climate for this pathogen to survive.Therefore,it is vital to discern the potential suitable habitat,quantify the risk levels,and monitor the potential high-risk areas.In this study,we modelled the potential invasion range and risk level of this pathogen at present and in future scenarios in China,using the least correlated components of all the environmental factors based on the Genetic Algorithm for Ruleset Production niche model and GIS analysis.The results indicate that most areas in China are free from a potential SOD risk,and the majority of potential occurrence areas are concentrated in Southern China(Yunnan,Sichuan,Guizhou,Chongqing,Hunan,Fujian).The area of high and extremely high risk in 2050(RCP26,RCP45,RCP60,and RCP85)is larger than that at present.The most susceptible area is Yunnan province with 80%of the area prone to SOD at extremely high risk in present and future scenarios.The results will be important for monitoring potential high-risk areas in the currently uninfected parts of China.
基金Chinese Program for High Technology Research and Development(2006AA12Z114)National Natural Science Foundation of China(40601070)
文摘This paper deals with a new type of multi-angle remotely sensed data--CHRIS(the Compact High Resolution Imaging Spec-trometer),by using rational function models(RFM)and rigorous sensor models(RSM).For ortho-rectifying an image set,a rigorous sen-sor model-Toutin's model was employed and a set of reported parameters including across track angle,along track angle,IFOV,altitude,period,eccentricity and orbit inclination were input,then,the orbit calculation was started and the track information was given to the raw data.The images were ortho-rectified with geocoded ASTER images and digital elevation(DEM)data.Results showed that with 16 ground control points(GCPs),the correction accuracy decreased with view zenith angle,and the RMSE value increased to be over one pixel at 36 degree off-nadir.When the GCPs were approximately chosen as in Toutin's model,a RFM with three coefficients produced the same accuracy trend versus view zenith angle while the RMSEs for all angles were improved and within about one pixel.
基金Project supported by the Key Program of the National Natural Science Foundation of China(Grant No.41130528)the National Basic Research Program of China(Grant No.2010CB950801)
文摘Three typical polluted dust particles （i.e., single coated dust, two-sphere/spheroid system, and coated dust with ag- gregate） including internal and semi-external mixtures are modeled, and their scattering properties at 1.6-μm wavelength are calculated by using the generalized multi-sphere Mie-solution （GMM） method. We investigate the influences of par- ticle size, morphology, and chemical composition on the scattering parameters of polluted dust particles. The analysis results demonstrate that the single scattering albedo of coated dust is much smaller than that of pure dust, especially for the spheroidal black carbon （BC） coated dust. When a dust particle semi-mixes with another aerosol particle to form a two-sphere/spheroid system, its scattering properties are much more sensitive to the size and species of monomers than the monomer shape. If an aggregated BC attaches to the coated dust, the scattering properties of whole particle mainly depend on the host particle （coated dust）.
基金This work was funded by the Open Fund of State Key Laboratory of Remote Sensing Science(OFSLRSS201904)National Natural Science Foundation of China(41901351)+1 种基金Start-up Program of Wuhan University(2019-2021)Natural Science Foundation of Ningxia Province(2021AAC03017).
文摘Background:Accurate mapping of forest canopy heights at a fine spatial resolution over large geographical areas is challenging.It is essential for the estimation of forest aboveground biomass and the evaluation of forest ecosystems.Yet current regional to national scale forest height maps were mainly produced at coarse-scale.Such maps lack spatial details for decision-making at local scales.Recent advances in remote sensing provide great opportunities to fill this gap.Method:In this study,we evaluated the utility of multi-source satellite data for mapping forest heights over Hunan Province in China.A total of 523 plot data collected from 2017 to 2018 were utilized for calibration and validation of forest height models.Specifically,the relationships between three types of in-situ measured tree heights(maximum-,averaged-,and basal area-weighted-tree heights)and plot-level remote sensing metrics(multispectral,radar,and topo variables from Landsat,Sentinel-1/PALSAR-2,and SRTM)were analyzed.Three types of models(multilinear regression,random forest,and support vector regression)were evaluated.Feature variables were selected by two types of variable selection approaches(stepwise regression and random forest).Model parameters and model performances for different models were tuned and evaluated via a 10-fold cross-validation approach.Then,tuned models were applied to generate wall-to-wall forest height maps for Hunan Province.Results:The best estimation of plot-level tree heights(R2 ranged from 0.47 to 0.52,RMSE ranged from 3.8 to 5.3 m,and rRMSE ranged from 28%to 31%)was achieved using the random forest model.A comparison with existing forest height maps showed similar estimates of mean height,however,the ranges varied under different definitions of forest and types of tree height.Conclusions:Primary results indicate that there are small biases in estimated heights at the province scale.This study provides a framework toward establishing regional to national scale maps of vertical forest structure.
基金partially supported by the National Natural Science Foundation of China(No.41871231)partially supported by the National Key Research and Development Program of China(No.2016YFB0501502)the Special Funds for Major State Basic Research Project(No.2013CB733403)。
文摘Background:Determining the spatial distribution of tree heights at the regional area scale is significant when performing forest above-ground biomass estimates in forest resource management research.The geometric-optical mutual shadowing(GOMS)model can be used to invert the forest canopy structural parameters at the regional scale.However,this method can obtain only the ratios among the horizontal canopy diameter(CD),tree height,clear height,and vertical CD.In this paper,we used a semi-variance model to calculate the CD using high spatial resolution images and expanded this method to the regional scale.We then combined the CD results with the forest canopy structural parameter inversion results from the GOMS model to calculate tree heights at the regional scale.Results:The semi-variance model can be used to calculate the CD at the regional scale that closely matches(mainly with in a range from-1 to 1 m)the CD derived from the canopy height model(CHM)data.The difference between tree heights calculated by the GOMS model and the tree heights derived from the CHM data was small,with a root mean square error(RMSE)of 1.96 for a 500-m area with high fractional vegetation cover(FVC)(i.e.,forest area coverage index values greater than 0.8).Both the inaccuracy of the tree height derived from the CHM data and the unmatched spatial resolution of different datasets will influence the accuracy of the inverted tree height.And the error caused by the unmatched spatial resolution is small in dense forest.Conclusions:The semi-variance model can be used to calculate the CD at the regional scale,together with the canopy structure parameters inverted by the GOMS model,the mean tree height at the regional scale can be obtained.Our study provides a new approach for calculating tree height and provides further directions for the application of the GOMS model.
