Due to the strong penetrability,long-wavelength synthetic aperture radar(SAR)can provide an opportunity to reconstruct the three-dimensional structure of the penetrable media.SAR tomography(TomoSAR)technology can resy...Due to the strong penetrability,long-wavelength synthetic aperture radar(SAR)can provide an opportunity to reconstruct the three-dimensional structure of the penetrable media.SAR tomography(TomoSAR)technology can resynthesize aperture perpendicular to the slant-range direction and then obtain the tomographic profile consisting of power distribution of different heights,providing a powerful technical tool for reconstructing the three-dimensional structure of the penetrable ground objects.As an emerging technology,it is different from the traditional interferometric SAR(InSAR)technology and has advantages in reconstructing the three-dimensional structure of the illuminated media.Over the past two decades,many TomoSAR methods have been proposed to improve the vertical resolution,aiming to distinguish the locations of different scatters in the unit pixel.In order to cope with the forest mission of European Space Agency(ESA)that is designed to provide P-band SAR measurements to determine the amount of biomass and carbon stored in forests,it is necessary to systematically evaluate the performance of forest height and underlying topography inversion using TomoSAR technology.In this paper,we adopt three typical algorithms,namely,Capon,Multiple Signal Classification(MUSIC),and Compressed Sensing(CS),to evaluate the performance in forest height and underlying topography inversion.The P-band airborne full-polarization(FP)SAR data of LopèNational Park in the AfriSAR campaign implemented by ESA in 2016 is adopted to verify the experiment.Furthermore,we explore the effects of different baseline designs and filter methods on the reconstruction of the tomographic profile.The results show that a better tomographic profile can be obtained by using Hamming window filter and Capon algorithm in uniform baseline distribution and a certain number of acquisitions.Compared with LiDAR results,the root-mean-square error(RMSE)of forest height and underlying topography obtained by Capon algorithm is 2.17 m and 1.58 m,which performs the best among the three algorithms.展开更多
Background:The assessment of change in forest ecosystems,especially the change of canopy heights,is essential for improving global carbon estimates and understanding effects of climate change.Spaceborne lidar systems ...Background:The assessment of change in forest ecosystems,especially the change of canopy heights,is essential for improving global carbon estimates and understanding effects of climate change.Spaceborne lidar systems provide a unique opportunity to monitor changes in the vertical structure of forests.NASA’s Ice,Cloud and Land Elevation Satellites,ICESat-1 for the period 2003 to 2009,and ICESat-2(available since 2018),have collected elevation data over the Earth’s surface with a time interval of 10 years.In this study,we tried to discover forest canopy changes by utilizing the global forest canopy height map of 2005(complete global coverage with 1 km resolution)derived from ICESat-1 data and the ATL08 land and vegetation products of 2019(sampling footprints with 17 m diameter)from ICESat-2.Results:Our study revealed a significant increase in forest canopy heights of China’s Beijing-Tianjin-Hebei region.Evaluations of unchanging areas for data consistency of two products show that the bias values decreased significantly from line-transect-level(−8.0 to 6.2 m)to site-level(^(−1).5 to 1.1 m),while RMSE values are still relatively high(6.1 to 15.2 m,10.2 to 12.0 m).Additionally,58%of ATL08 data are located in‘0m’pixels with an average height of 7.9 m,which are likely to reflect the ambitious tree planting programs in China.Conclusions:Our study shows that it is possible,with proper calibrations,to use ICESat-1 and-2 products to detect forest canopy height changes in a regional context.We expect that the approach presented in this study is potentially suitable to derive a fine-scale map of global forest change.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
Accurate estimation of forest terrain and canopy height is crucial for timely understanding of forest growth.Gao Fen-7(GF-7)Satellite is China’s first sub-meter-level three-dimensional(3D)mapping satellite for civili...Accurate estimation of forest terrain and canopy height is crucial for timely understanding of forest growth.Gao Fen-7(GF-7)Satellite is China’s first sub-meter-level three-dimensional(3D)mapping satellite for civilian use,which was equipped with a two-line-array stereo mapping camera and a laser altimeter system that can provide stereo images and full waveform LiDAR data simultaneously.Most of the existing studies have concentrated on evaluating the accuracy of GF-7 for topographic survey in bare land,but few have in-depth studied its ability to measure forest terrain elevation and canopy height.The purpose of this study is to evaluate the potential of GF-7 LiDAR and stereo image for forest terrain and height measurement.The Airborne Laser Scanning(ALS)data were utilized to generate reference terrain and forest vertical information.The validation test was conducted in Pu’er City,Yunnan Province of China,and encouraging results have obtained.The GF-7 LiDAR data obtained the accuracy of forest terrain elevation with RMSE of 8.01 m when 21 available laser footprints were used for results verification;meanwhile,when it was used to calculate the forest height,R^(2)of 0.84 and RMSE of 3.2 m were obtained although only seven effective footprints were used for result verification.The canopy height values obtained from GF-7 stereo images have also been proven to have high accuracy with the resolution of 20 m×20 m compared with ALS data(R2=0.88,RMSE=2.98 m).When the results were verified at the forest sub-compartment scale that taking into account the forest types,further higher accuracy(R^(2)=0.96,RMSE=1.23 m)was obtained.These results show that GF-7 has considerable application potential in forest resources monitoring.展开更多
With the advent of very high-resolution(VHR)imaging satellites,it is possible to measure the heights of forest stands or even individual trees more accurately.However,the accurate geometric processing of VHR images de...With the advent of very high-resolution(VHR)imaging satellites,it is possible to measure the heights of forest stands or even individual trees more accurately.However,the accurate geometric processing of VHR images depends on ground control points(GCPs).Collecting GCPs through fieldwork is time-consuming and labor-intensive,which presents great challenges for regional applications in remote or mountainous regions,particularly for international applications.This study proposes a promising approach that leverages GF-7 VHR stereoscopic images and Google Earth’s multi-temporal historical imagery to accurately extract forest heights without the need for fieldworks.Firstly,an algorithm is proposed to collect GCPs using Multi-temporal Averaging of historical imagery provided by Google Earth(GE),known as MAGE.Digital surface model(DSM)is then derived using GF-7 stereoscopic imagery and MAGE GCPs in Switzerland.Forest heights are finally extracted by subtracting ground surface elevations from GF-7 DSM.Results show that absolute coordinate errors of MAGE GCPs are less than 2.0 m.The root mean square error(RMSE)of forest heights extracted from GF-7 DSM,derived using the original geolocation model,is 12.3 m,and the determination coefficient(R^(2))of linear estimation model is 0.72.When the geolocation model is optimized using MAGE GCPs,the RMSE is reduced to 1.5 m and the R^(2)increases to 0.95.These results not only demonstrate the effectiveness of MAGE GCPs but,more importantly,also reveal the significance of precise geometric processing of VHR stereoscopic imagery in forest height estimations.展开更多
Although numerous studies have proposed explanations for the specific and relative effects of stand structure,plant diversity,and environmental conditions on carbon(C)storage in forest ecosystems,understanding how the...Although numerous studies have proposed explanations for the specific and relative effects of stand structure,plant diversity,and environmental conditions on carbon(C)storage in forest ecosystems,understanding how these factors collectively affect C storage in different community layers(trees,shrubs,and herbs)and forest types(mixed,broad-leaved(E),broad-leaved(M),and coniferous forest)continues to pose challenges.To address this,we used structural equation models to quantify the influence of biotic factors(mean DBH,mean height,maximum height,stem density,and basal area)and abiotic factors(elevation and canopy openness),as well as metrics of species diversity(Shannon–Wiener index,Simpson index,and Pielou’s evenness)in various forest types.Our analysis revealed the critical roles of forest types and elevation in explaining a substantial portion of variability in C storage in the overstory layer,with a moderate influence of stand factors(mean DBH and basal area)and a slightly negative impact of tree species diversity(Shannon–Wiener index).Notably,forest height emerged as the primary predictor of C storage in the herb layer.Regression relationships further highlighted the significant contribution of tree species diversity to mean height,understory C storage,and branch biomass within the forest ecosystem.Our insights into tree species diversity,derived from structural equation modeling of C storage in the overstory,suggest that the effects of tree species diversity may be influenced by stem biomass in statistical reasoning within temperate forests.Further research should also integrate tree species diversity with tree components biomass,forest mean height,understory C,and canopy openness to understand complex relationships and maintain healthy and sustainable ecosystems in the face of global climate challenges.展开更多
Forest height is a major factor shaping geographic biomass patterns,and there is a growing dependence on forest height derived from satellite light detecting and ranging(LiDAR)to monitor large-scale biomass patterns.H...Forest height is a major factor shaping geographic biomass patterns,and there is a growing dependence on forest height derived from satellite light detecting and ranging(LiDAR)to monitor large-scale biomass patterns.However,how the relationship between forest biomass and height is modulated by climate and biotic factors has seldom been quantified at broad scales and across various forest biomes,which may be crucial for improving broad-scale biomass estimations based on satellite LiDAR.Methods We used 1263 plots,from boreal to tropical forest biomes across China,to examine the effects of climatic(energy and water avail-ability)and biotic factors(forest biome,leaf form and leaf phenol-ogy)on biomass-height relationship,and to develop the models to estimate biomass from forest height in China.Important Findings(i)Forest height alone explained 62%of variation in forest biomass across China and was far more powerful than climate and other biotic factors.(ii)However,the relationship between biomass and forest height were significantly affected by climate,forest biome,leaf phenology(evergreen vs.deciduous)and leaf form(needleleaf vs.broadleaf).among which,the effect of climate was stronger than other factors.The intercept of biomass-height relationship was more affected by precipitation while the slope more affected by energy availability.(iii)When the effects of climate and biotic factors were considered in the models,geographic biomass patterns could be well predicted from forest height with an r2 between 0.63 and 0.78(for each forest biome and for all biomes together).For most biomes,forest biomass could be well predicted with simple models includ-ing only forest height and climate.(iv)We provided the first broad-scale models to estimate biomass from forest height across China,which can be utilized by future LiDAR studies.(v)our results suggest that the effect of climate and biotic factors should be carefully considered in models estimating broad-scale forest biomass patterns with satellite LiDAR.展开更多
The underlying topography and forest height play an indispensable role in many fields,including geomorphology,civil engineering construction,forest investigation,and the modeling of natural disasters.As a new microwav...The underlying topography and forest height play an indispensable role in many fields,including geomorphology,civil engineering construction,forest investigation,and the modeling of natural disasters.As a new microwave remote sensing technology with three-dimensional imaging capability,synthetic aperture radar(SAR)tomography(TomoSAR)has already been proven to be an important tool for underlying topography and forest height estimation.Many spectrum estimation methods have now been proposed for TomoSAR.However,most of the commonly used methods are susceptible to noise and inevitably produce sidelobes,resulting in a reduced accuracy for the inversion of forest structural parameters.In this paper,to solve this problem,a nonparametric spectrum estimation method with low sidelobes-the G-Pisarenko method-is introduced.This method performs a logarithmic operation on the covariance matrix to obtain the main scattering characteristics of the objects of interest while suppressing the noise as much as possible.The effectiveness of the proposed method is demonstrated by the use of both simulated data and P-band airborne SAR data from a tropical forest region in Gabon,Africa.The results show that the proposed method can reduce the sidelobes and improve the estimation accuracy for the underlying topography and forest height.展开更多
For inversion of forest canopy height in large scale,it is of great significance to integrate space-borne Lidar and optical remote sensing data effectively.The homemade satellite will provide a plentiful datum for for...For inversion of forest canopy height in large scale,it is of great significance to integrate space-borne Lidar and optical remote sensing data effectively.The homemade satellite will provide a plentiful datum for forest ecological researches.In this paper,the processing of GLAS waveform data and the algorithm of forest canopy height in different terrain were implemented.The GLAS+MERSI joint inversion model of canopy height of different forest types in regional scale was established and used to map the forest canopy height of Jiangxi province.Overall,high accuracy was observed for the canopy height estimated by GLAS+MERSI joint inversion model with R^(2)=0.733 for the needle-leaf forest,following by the broadleaf forest(R^(2)=0.610).The results showed that the established model was workable.It was found that the GLAS+MERSI joint inversion model which considers the optical remote sensing of biophysical parameters can provide good estimates of forest canopy height at regional scale.The space distribution characteristic was found consistent with the data of land cover.展开更多
This study was designed to use LiDAR data to research tree heights in montane forest blocks of Kenya. It uses a completely randomised block design to asses if differences exist in forest heights: 1) among montane fore...This study was designed to use LiDAR data to research tree heights in montane forest blocks of Kenya. It uses a completely randomised block design to asses if differences exist in forest heights: 1) among montane forest blocks, 2) among Agro ecological zones (AEZ) within each forest block and 3) between similar AEZ in different forest blocks. Forest height data from the Geoscience Laser Altimeter System (GLAS) on the Ice Cloud and Land Elevation Satellite (ICE-SAT) for the period 2003-2009 was used for 2146 circular plots, of 0.2 - 0.25 ha in size. Results indicate that, tree height is largely influenced by Agro ecological conditions and the wetter zones have taller trees in the upper, middle and lower highlands. In the upper highland zones of limited human activity, tree heights did not vary among forest blocks. Variations in height among forest blocks and within forest blocks were exaggerated in regions of active human intervention.展开更多
Height–diameter relationships are essential elements of forest assessment and modeling efforts.In this work,two linear and eighteen nonlinear height–diameter equations were evaluated to find a local model for Orient...Height–diameter relationships are essential elements of forest assessment and modeling efforts.In this work,two linear and eighteen nonlinear height–diameter equations were evaluated to find a local model for Oriental beech（Fagus orientalis Lipsky） in the Hyrcanian Forest in Iran.The predictive performance of these models was first assessed by different evaluation criteria： adjusted R^2（R^2_（adj））,root mean square error（RMSE）,relative RMSE（%RMSE）,bias,and relative bias（%bias） criteria.The best model was selected for use as the base mixed-effects model.Random parameters for test plots were estimated with different tree selection options.Results show that the Chapman–Richards model had better predictive ability in terms of adj R^2（0.81）,RMSE（3.7 m）,%RMSE（12.9）,bias（0.8）,%Bias（2.79） than the other models.Furthermore,the calibration response,based on a selection of four trees from the sample plots,resulted in a reduction percentage for bias and RMSE of about 1.6–2.7%.Our results indicate that the calibrated model produced the most accurate results.展开更多
基金supported by ESA-MOST Dragon Programme 5[grant number 59332].
文摘Due to the strong penetrability,long-wavelength synthetic aperture radar(SAR)can provide an opportunity to reconstruct the three-dimensional structure of the penetrable media.SAR tomography(TomoSAR)technology can resynthesize aperture perpendicular to the slant-range direction and then obtain the tomographic profile consisting of power distribution of different heights,providing a powerful technical tool for reconstructing the three-dimensional structure of the penetrable ground objects.As an emerging technology,it is different from the traditional interferometric SAR(InSAR)technology and has advantages in reconstructing the three-dimensional structure of the illuminated media.Over the past two decades,many TomoSAR methods have been proposed to improve the vertical resolution,aiming to distinguish the locations of different scatters in the unit pixel.In order to cope with the forest mission of European Space Agency(ESA)that is designed to provide P-band SAR measurements to determine the amount of biomass and carbon stored in forests,it is necessary to systematically evaluate the performance of forest height and underlying topography inversion using TomoSAR technology.In this paper,we adopt three typical algorithms,namely,Capon,Multiple Signal Classification(MUSIC),and Compressed Sensing(CS),to evaluate the performance in forest height and underlying topography inversion.The P-band airborne full-polarization(FP)SAR data of LopèNational Park in the AfriSAR campaign implemented by ESA in 2016 is adopted to verify the experiment.Furthermore,we explore the effects of different baseline designs and filter methods on the reconstruction of the tomographic profile.The results show that a better tomographic profile can be obtained by using Hamming window filter and Capon algorithm in uniform baseline distribution and a certain number of acquisitions.Compared with LiDAR results,the root-mean-square error(RMSE)of forest height and underlying topography obtained by Capon algorithm is 2.17 m and 1.58 m,which performs the best among the three algorithms.
基金National Natural Science Foundation of China:41971289.
文摘Background:The assessment of change in forest ecosystems,especially the change of canopy heights,is essential for improving global carbon estimates and understanding effects of climate change.Spaceborne lidar systems provide a unique opportunity to monitor changes in the vertical structure of forests.NASA’s Ice,Cloud and Land Elevation Satellites,ICESat-1 for the period 2003 to 2009,and ICESat-2(available since 2018),have collected elevation data over the Earth’s surface with a time interval of 10 years.In this study,we tried to discover forest canopy changes by utilizing the global forest canopy height map of 2005(complete global coverage with 1 km resolution)derived from ICESat-1 data and the ATL08 land and vegetation products of 2019(sampling footprints with 17 m diameter)from ICESat-2.Results:Our study revealed a significant increase in forest canopy heights of China’s Beijing-Tianjin-Hebei region.Evaluations of unchanging areas for data consistency of two products show that the bias values decreased significantly from line-transect-level(−8.0 to 6.2 m)to site-level(^(−1).5 to 1.1 m),while RMSE values are still relatively high(6.1 to 15.2 m,10.2 to 12.0 m).Additionally,58%of ATL08 data are located in‘0m’pixels with an average height of 7.9 m,which are likely to reflect the ambitious tree planting programs in China.Conclusions:Our study shows that it is possible,with proper calibrations,to use ICESat-1 and-2 products to detect forest canopy height changes in a regional context.We expect that the approach presented in this study is potentially suitable to derive a fine-scale map of global forest change.
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supported by the National Key Research and Development Program of China[grant numbers 2021YFE0117700 and 2022YFF1302100]the ESA-MOST China Dragon 5 Cooperation[grant number 59313]National Science and Technology Major Project of China's High Resolution Earth Observation System[grant numbers 30-Y30A02-9001-20/22-7 and 21-Y20B01-9001-19/22].
文摘Accurate estimation of forest terrain and canopy height is crucial for timely understanding of forest growth.Gao Fen-7(GF-7)Satellite is China’s first sub-meter-level three-dimensional(3D)mapping satellite for civilian use,which was equipped with a two-line-array stereo mapping camera and a laser altimeter system that can provide stereo images and full waveform LiDAR data simultaneously.Most of the existing studies have concentrated on evaluating the accuracy of GF-7 for topographic survey in bare land,but few have in-depth studied its ability to measure forest terrain elevation and canopy height.The purpose of this study is to evaluate the potential of GF-7 LiDAR and stereo image for forest terrain and height measurement.The Airborne Laser Scanning(ALS)data were utilized to generate reference terrain and forest vertical information.The validation test was conducted in Pu’er City,Yunnan Province of China,and encouraging results have obtained.The GF-7 LiDAR data obtained the accuracy of forest terrain elevation with RMSE of 8.01 m when 21 available laser footprints were used for results verification;meanwhile,when it was used to calculate the forest height,R^(2)of 0.84 and RMSE of 3.2 m were obtained although only seven effective footprints were used for result verification.The canopy height values obtained from GF-7 stereo images have also been proven to have high accuracy with the resolution of 20 m×20 m compared with ALS data(R2=0.88,RMSE=2.98 m).When the results were verified at the forest sub-compartment scale that taking into account the forest types,further higher accuracy(R^(2)=0.96,RMSE=1.23 m)was obtained.These results show that GF-7 has considerable application potential in forest resources monitoring.
基金sponsored by the National Natural Science Foundation of China(Grant Nos.42022009 and 42090013)the National Key Research and Development Program of China(2020YFE0200800).
文摘With the advent of very high-resolution(VHR)imaging satellites,it is possible to measure the heights of forest stands or even individual trees more accurately.However,the accurate geometric processing of VHR images depends on ground control points(GCPs).Collecting GCPs through fieldwork is time-consuming and labor-intensive,which presents great challenges for regional applications in remote or mountainous regions,particularly for international applications.This study proposes a promising approach that leverages GF-7 VHR stereoscopic images and Google Earth’s multi-temporal historical imagery to accurately extract forest heights without the need for fieldworks.Firstly,an algorithm is proposed to collect GCPs using Multi-temporal Averaging of historical imagery provided by Google Earth(GE),known as MAGE.Digital surface model(DSM)is then derived using GF-7 stereoscopic imagery and MAGE GCPs in Switzerland.Forest heights are finally extracted by subtracting ground surface elevations from GF-7 DSM.Results show that absolute coordinate errors of MAGE GCPs are less than 2.0 m.The root mean square error(RMSE)of forest heights extracted from GF-7 DSM,derived using the original geolocation model,is 12.3 m,and the determination coefficient(R^(2))of linear estimation model is 0.72.When the geolocation model is optimized using MAGE GCPs,the RMSE is reduced to 1.5 m and the R^(2)increases to 0.95.These results not only demonstrate the effectiveness of MAGE GCPs but,more importantly,also reveal the significance of precise geometric processing of VHR stereoscopic imagery in forest height estimations.
基金supported by the Fundamental Research Funds for the Central Universities(2021ZY89)the National Natural Science Foundation of China(32201258 and 32271652)+4 种基金Research Service Project on the Effects of Extreme Climate on Biodiversity and Conservation Strategies in Mentougou District(2024HXFWBH-XJL-02)the Fang Jingyun Ecological Study Studio of Yunnan Province(China)the State Scholarship Fund of China(2011811457)support to the Xingdian Scholar Fund of Yunnan Provincethe Double Top University Fund of Yunnan University.
文摘Although numerous studies have proposed explanations for the specific and relative effects of stand structure,plant diversity,and environmental conditions on carbon(C)storage in forest ecosystems,understanding how these factors collectively affect C storage in different community layers(trees,shrubs,and herbs)and forest types(mixed,broad-leaved(E),broad-leaved(M),and coniferous forest)continues to pose challenges.To address this,we used structural equation models to quantify the influence of biotic factors(mean DBH,mean height,maximum height,stem density,and basal area)and abiotic factors(elevation and canopy openness),as well as metrics of species diversity(Shannon–Wiener index,Simpson index,and Pielou’s evenness)in various forest types.Our analysis revealed the critical roles of forest types and elevation in explaining a substantial portion of variability in C storage in the overstory layer,with a moderate influence of stand factors(mean DBH and basal area)and a slightly negative impact of tree species diversity(Shannon–Wiener index).Notably,forest height emerged as the primary predictor of C storage in the herb layer.Regression relationships further highlighted the significant contribution of tree species diversity to mean height,understory C storage,and branch biomass within the forest ecosystem.Our insights into tree species diversity,derived from structural equation modeling of C storage in the overstory,suggest that the effects of tree species diversity may be influenced by stem biomass in statistical reasoning within temperate forests.Further research should also integrate tree species diversity with tree components biomass,forest mean height,understory C,and canopy openness to understand complex relationships and maintain healthy and sustainable ecosystems in the face of global climate challenges.
文摘Forest height is a major factor shaping geographic biomass patterns,and there is a growing dependence on forest height derived from satellite light detecting and ranging(LiDAR)to monitor large-scale biomass patterns.However,how the relationship between forest biomass and height is modulated by climate and biotic factors has seldom been quantified at broad scales and across various forest biomes,which may be crucial for improving broad-scale biomass estimations based on satellite LiDAR.Methods We used 1263 plots,from boreal to tropical forest biomes across China,to examine the effects of climatic(energy and water avail-ability)and biotic factors(forest biome,leaf form and leaf phenol-ogy)on biomass-height relationship,and to develop the models to estimate biomass from forest height in China.Important Findings(i)Forest height alone explained 62%of variation in forest biomass across China and was far more powerful than climate and other biotic factors.(ii)However,the relationship between biomass and forest height were significantly affected by climate,forest biome,leaf phenology(evergreen vs.deciduous)and leaf form(needleleaf vs.broadleaf).among which,the effect of climate was stronger than other factors.The intercept of biomass-height relationship was more affected by precipitation while the slope more affected by energy availability.(iii)When the effects of climate and biotic factors were considered in the models,geographic biomass patterns could be well predicted from forest height with an r2 between 0.63 and 0.78(for each forest biome and for all biomes together).For most biomes,forest biomass could be well predicted with simple models includ-ing only forest height and climate.(iv)We provided the first broad-scale models to estimate biomass from forest height across China,which can be utilized by future LiDAR studies.(v)our results suggest that the effect of climate and biotic factors should be carefully considered in models estimating broad-scale forest biomass patterns with satellite LiDAR.
基金supported in part by the National Natural Science Foundation of China[grant number 42101400],[grant number 42171387]in part by the Strategic Priority Research Program of the Chinese Academy of Sciences[grant number XDA19070202].
文摘The underlying topography and forest height play an indispensable role in many fields,including geomorphology,civil engineering construction,forest investigation,and the modeling of natural disasters.As a new microwave remote sensing technology with three-dimensional imaging capability,synthetic aperture radar(SAR)tomography(TomoSAR)has already been proven to be an important tool for underlying topography and forest height estimation.Many spectrum estimation methods have now been proposed for TomoSAR.However,most of the commonly used methods are susceptible to noise and inevitably produce sidelobes,resulting in a reduced accuracy for the inversion of forest structural parameters.In this paper,to solve this problem,a nonparametric spectrum estimation method with low sidelobes-the G-Pisarenko method-is introduced.This method performs a logarithmic operation on the covariance matrix to obtain the main scattering characteristics of the objects of interest while suppressing the noise as much as possible.The effectiveness of the proposed method is demonstrated by the use of both simulated data and P-band airborne SAR data from a tropical forest region in Gabon,Africa.The results show that the proposed method can reduce the sidelobes and improve the estimation accuracy for the underlying topography and forest height.
文摘For inversion of forest canopy height in large scale,it is of great significance to integrate space-borne Lidar and optical remote sensing data effectively.The homemade satellite will provide a plentiful datum for forest ecological researches.In this paper,the processing of GLAS waveform data and the algorithm of forest canopy height in different terrain were implemented.The GLAS+MERSI joint inversion model of canopy height of different forest types in regional scale was established and used to map the forest canopy height of Jiangxi province.Overall,high accuracy was observed for the canopy height estimated by GLAS+MERSI joint inversion model with R^(2)=0.733 for the needle-leaf forest,following by the broadleaf forest(R^(2)=0.610).The results showed that the established model was workable.It was found that the GLAS+MERSI joint inversion model which considers the optical remote sensing of biophysical parameters can provide good estimates of forest canopy height at regional scale.The space distribution characteristic was found consistent with the data of land cover.
文摘This study was designed to use LiDAR data to research tree heights in montane forest blocks of Kenya. It uses a completely randomised block design to asses if differences exist in forest heights: 1) among montane forest blocks, 2) among Agro ecological zones (AEZ) within each forest block and 3) between similar AEZ in different forest blocks. Forest height data from the Geoscience Laser Altimeter System (GLAS) on the Ice Cloud and Land Elevation Satellite (ICE-SAT) for the period 2003-2009 was used for 2146 circular plots, of 0.2 - 0.25 ha in size. Results indicate that, tree height is largely influenced by Agro ecological conditions and the wetter zones have taller trees in the upper, middle and lower highlands. In the upper highland zones of limited human activity, tree heights did not vary among forest blocks. Variations in height among forest blocks and within forest blocks were exaggerated in regions of active human intervention.
基金This research received no specific grant from any funding agency in the public,commercial,or not-for-profit sectors
文摘Height–diameter relationships are essential elements of forest assessment and modeling efforts.In this work,two linear and eighteen nonlinear height–diameter equations were evaluated to find a local model for Oriental beech（Fagus orientalis Lipsky） in the Hyrcanian Forest in Iran.The predictive performance of these models was first assessed by different evaluation criteria： adjusted R^2（R^2_（adj））,root mean square error（RMSE）,relative RMSE（%RMSE）,bias,and relative bias（%bias） criteria.The best model was selected for use as the base mixed-effects model.Random parameters for test plots were estimated with different tree selection options.Results show that the Chapman–Richards model had better predictive ability in terms of adj R^2（0.81）,RMSE（3.7 m）,%RMSE（12.9）,bias（0.8）,%Bias（2.79） than the other models.Furthermore,the calibration response,based on a selection of four trees from the sample plots,resulted in a reduction percentage for bias and RMSE of about 1.6–2.7%.Our results indicate that the calibrated model produced the most accurate results.
