Background: Forest canopy height is a key forest structure parameter. Precisely estimating forest canopy height is vital to improve forest management and ecological modelling. Compared with discrete-return LiDAR(Light...Background: Forest canopy height is a key forest structure parameter. Precisely estimating forest canopy height is vital to improve forest management and ecological modelling. Compared with discrete-return LiDAR(Light Detection and Ranging), small-footprint full-waveform airborne LiDAR(FWL) techniques have the capability to acquire precise forest structural information. This research mainly focused on the influence of voxel size on forest canopy height estimates.Methods: A range of voxel sizes(from 10.0 m to 40.0 m interval of 2 m) were tested to obtain estimation accuracies of forest canopy height with different voxel sizes. In this study, all the waveforms within a voxel size were aggregated into a voxel-based LiDAR waveform, and a range of waveform metrics were calculated using the voxelbased LiDAR waveforms. Then, we established estimation model of forest canopy height using the voxel-based waveform metrics through Random Forest(RF) regression method.Results and conclusions: The results showed the voxel-based method could reliably estimate forest canopy height using FWL data. In addition, the voxel sizes had an important influence on the estimation accuracies(R2 ranged from 0.625 to 0.832) of forest canopy height. However, the R2 values did not monotonically increase or decrease with the increase of voxel size in this study. The best estimation accuracy produced when the voxel size was 18 m(R2= 0.832, RMSE = 2.57 m, RMSE% = 20.6%). Compared with the lowest estimation accuracy, the R2 value had a significant improvement(33.1%) when using the optimal voxel size. Finally, through the optimal voxel size, we produced the forest canopy height distribution map for this study area using RF regression model. Our findings demonstrate that the optimal voxel size need to be determined for improving estimation accuracy of forest parameter using small-footprint FWL data.展开更多
Forest canopy height is one of the important forest parameters for accurately assessing forest biomass or carbon sequestration. ICESat-2 ATLAS provides the potential for retrieval of forest canopy height at global or ...Forest canopy height is one of the important forest parameters for accurately assessing forest biomass or carbon sequestration. ICESat-2 ATLAS provides the potential for retrieval of forest canopy height at global or regional scale, but the current canopy height product (ATL08) has coarse resolution and high uncertainty compared to airborne LiDAR-derived canopy height (hereafter ALCH) in mountainous regions, and is not ready for such applications as biomass modeling at finer scale. The objective of this research was to explore the approach to accurately retrieve canopy height from ATLAS data by incorporating an airborne-derived digital terrain model(DTM) and a data-filtering strategy. By linking ATLAS ATL03 with ATL08 products, the geospatial locations,types, and (absolute) heights of photons were obtained, and canopy heights at different lengths (from 20 to 200 m at 20-m intervals) of segments along a track were computed with the aid of airborne LiDAR DTM. Based on the relationship between the numbers of canopy photons within the segments and accuracy of ATLAS mean canopy height compared to ALCH, a filtering method for excluding a certain portion of unreliable segments was proposed.This method was further applied to different ATLAS ground tracks for retrieval of canopy heights and the results were evaluated using corresponding ALCH. The results show that the incorporation of high-precision DTM and ATLAS products can considerably improve the retrieval accuracy of forest canopy height in mountainous regions.Using the proposed filtering approach, the correlation coefficients (r) between ATLAS canopy height and corresponding ALCH were 0.61–0.91, 0.65–0.92, 0.68–0.94 for segment lengths of 20, 60, and 100 m, respectively;RMSE were 1.90–4.35, 1.55–3.63, and 1.34–3.23 m for the same segment lengths. The results indicate the necessity of using high-precision DTM and using the proposed filtering method to retrieve accurate canopy height from ICESat-2 ATLAS in mountainous regions with dense forest cover and complex terrain conditions.展开更多
The Geoscience Laser Altimeter System(GLAS)accurately detects the vertical structural information of a target within its laser spot and is a promising system for the inversion of structural features and other biophysi...The Geoscience Laser Altimeter System(GLAS)accurately detects the vertical structural information of a target within its laser spot and is a promising system for the inversion of structural features and other biophysical parameters of forest ecosystems.Since the GLAS footprints are discontinuously distributed with a relativity low density,continuous vegetation height distributions cannot be mapped with a high accuracy using GLAS data alone.The MODIS BRDF product provides more forest structural information than other optical remote sensing data.This study aimed to map forest canopy heights over China from the GLAS and MODIS BRDF data.Firstly,the waveform characteristic parameters were extracted from the GLAS data by the method of wavelet analysis,and the terrain index was calculated using the ASTER GDEM data.Secondly,the model reducing the topographic influence was constructed from the waveform characteristic parameters and terrain index.Thirdly,the final canopy height estimation model was constructed from the neural network combining the canopy height estimated with the GLAS point and the MODIS BRDF data,and applied to get the continuous canopy height map over China.Finally,the map was validated by the measured data and the airborne Li DAR data,and the validation results indicated that forest canopy heights can be estimated with high accuracy from combined GLAS and MODIS data.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
Leaf traits can directly reflect the adaptation strategies of plants to the environment.However,there is limited knowledge on the adaptation strategies of heteromorphic leaves of male and female Populus euphratica Oli...Leaf traits can directly reflect the adaptation strategies of plants to the environment.However,there is limited knowledge on the adaptation strategies of heteromorphic leaves of male and female Populus euphratica Oliv.in response to individual developmental stages(i.e.,diameter class)and canopy height changes.In this study,morphological and physiological properties of heteromorphic leaves of male and female P.euphratica were investigated.Results showed that both male and female P.euphratica exhibited increased leaf area(LA),leaf dry weight(LDW),leaf thickness(LT),net photosynthetic rate(P_(n)),transpiration rate(T_(r)),stomatal conductance(g_(s)),proline(Pro),and malondialdehyde(MDA)concentration,decreased leaf shape index(LI)and specific leaf area(SLA)with increasing diameter and canopy height.Leaf water potential(LWP)increased with increasing diameter,LWP decreased significantly with increasing canopy height in both sexes,and carbon isotope fraction(δ^(13)C)increased significantly with canopy height in both sexes,all of which showed obvious resistance characteristics.However,males showed greater LA,LT,P_(n),T_(r),and Pro than females at the same canopy height,and males showed significantly higher LA,SLA,LT,P_(n),T_(r),g_(s),and MDA,but lower LWP and δ_(1)3C than females at the same canopy height,suggesting that male P.euphratica have stronger photosynthetic and osmoregulatory abilities,and are sensitive to water deficiency.Moreover,difference between male and female P.euphratica is closely related to the increase in individual diameter class and canopy height.In summary,male plants showed higher stress tolerance than female plants,and differences in P_(n),g_(s),T_(r),Pro,MDA,δ_(13)C,and LWP between females and males were related to changes in leaf morphology,diameter class,and canopy height.The results of this study provide a theory for the differences in growth adaptation strategies during individual development of P.euphratica.展开更多
Leaf Area of Index(LAI)refers to half of the total leaf area of all crops per unit area.It is an important index to represent the photosynthetic capacity and biomass of crops.To obtain LAI conditions of summer maize i...Leaf Area of Index(LAI)refers to half of the total leaf area of all crops per unit area.It is an important index to represent the photosynthetic capacity and biomass of crops.To obtain LAI conditions of summer maize in different growth stages quickly and accurately,further guiding field fertilization and irrigation.The Unmanned aerial vehicles(UAV)multispectral data,growing degree days,and canopy height model of 2020-2021 summer maize were used to carry out LAI inversion.The vegetation index was constructed by the ground hyperspectral data and multispectral data of the same range of bands.The correlation analysis was conducted to verify the accuracy of the multispectral data.To include many bands as possible,four vegetation indices which included R,G,B,and NIR bands were selected in this study to test the spectral accuracy.There were nine vegetation indices calculated with UAV multispectral data which were based on the red band and the near-infrared band.Through correlation analysis of LAI and the vegetation index,vegetation indices with a higher correlation to LAI were selected to construct the LAI inversion model.In addition,the Canopy Height Model(CHM)and Growing degree days(GDD)of summer maize were also calculated to build the LAI inversion model.The LAI inversion of summer maize was carried out based on multi-growth stages by using the general linear regression model(GLR),Multivariate nonlinear regression model(MNR),and the partial least squares regression(PLSR)models.R²and RMSE were used to assess the accuracy of the model.The results show that the correlation between UAV multispectral data and hyperspectral data was greater than 0.64,which was significant.The Wide Dynamic Range Vegetation Index(WDRVI),Normalized Difference Vegetation Index(NDVI),Ratio Vegetation Index(RVI),Plant Biochemical Index(PBI),Optimized Soil-Adjusted Vegetation Index(OSAVI),CHM and GDD have a higher correlation with LAI.By comparing the models constructed by the three methods,it was found that the PLSR has the best inversion effect.It was based on OSAVI,GDD,RVI,PBI,CHM,NDVI,and WDRVI,with the training model’s R²being 0.8663,the testing model’s R²being 0.7102,RMSE was 1.1755.This study showed that the LAI inversion model based on UAV multispectral vegetation index,GDD,and CHM improves the accuracy of LAI inversion effectively.That means the growing degree days and crop population structure change have influenced the change of maize LAI certainly,and this method can provide decision support for maize growth monitoring and field fertilization.展开更多
Current researches based on areal or spaceborne stereo images with very high resolutions(<1 m)have demonstrated that it is possible to derive vegetation height from stereo images.The second version of the Advanced ...Current researches based on areal or spaceborne stereo images with very high resolutions(<1 m)have demonstrated that it is possible to derive vegetation height from stereo images.The second version of the Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model(ASTER GDEM)is the state-of-the-art global elevation data-set developed by stereo images.However,the resolution of ASTER stereo images(15 m)is much coarser than areal stereo images,and the ASTER GDEM is compiled products from stereo images acquired over 10 years.The forest disturbances as well as forest growth are inevitable in 10 years time span.In this study,the features of ASTER GDEM over vegetated areas under both flat and mountainous conditions were investigated by comparisons with lidar data.The factors possibly affecting the extraction of vegetation canopy height considered include(1)co-registration of DEMs;(2)spatial resolution of digital elevation models(DEMs);(3)spatial vegetation structure;and(4)terrain slope.The results show that the accurate coregistration between ASTER GDEM and national elevation dataset(NED)is necessary over mountainous areas.The correlation between ASTER GDEM minus NED and vegetation canopy height is improved from 0.328 to 0.43 by degrading resolutions from 1 arc-second to 5 arc-second and further improved to 0.6 if only homogenous vegetated areas were considered.展开更多
Leaf longevity is an important adaptive strategy that allows plants to maximize photosynthetic carbon gain.Due to the difficulty of identifying overwintering bud scars and distinguishing the age sequence of twigs,leaf...Leaf longevity is an important adaptive strategy that allows plants to maximize photosynthetic carbon gain.Due to the difficulty of identifying overwintering bud scars and distinguishing the age sequence of twigs,leaf longevity is rarely studied in Cupressaceae species,which further limits our understanding of the leaf economic spectrum (LES) for these populations.Here,we investigated the leaf longevity,as well as mass-based leaf nitrogen concentration (N;),of Juniperus saltuaria at different canopy heights for both subalpine and alpine timberline forests in the Sergymla Mountains,southeastern Tibet.We found that the mean leaf longevity was 4.2±1.2 years,and overall it did not differ significantly between different elevations.Along the vertical profiles of juniper canopies,the leaf longevity did not reflect a linear trend.With increasing leaf longevity,N;showed declining trends.We further analyzed the relationship between leaf longevity and the corresponding length of green twigs,and found that the length of green twigs could only explain 1%-3%of the variation in leaf longevity,indicating that the length of green twigs is a poor predictor for the variation in leaf longevity.In summary,for the J.saltuaria species in timberline or nearby subalpine forests,the effects of elevation and canopy depths on leaf longevity are minor,and the leaf trait analysis is in accordance with the prediction of LES.展开更多
基金funded by the Guangxi Natural Science Fund for Innovation Research Team (No. 2019JJF50001)the Natural Science Foundation of Fujian Province,China (No. 2019 J01396)+1 种基金the Special Fund for Guangxi Innovation and Driving Development (Major science and technology projects)(No. 2018AA13005)the Youth Innovation Promotion Association CAS (2019130)。
文摘Background: Forest canopy height is a key forest structure parameter. Precisely estimating forest canopy height is vital to improve forest management and ecological modelling. Compared with discrete-return LiDAR(Light Detection and Ranging), small-footprint full-waveform airborne LiDAR(FWL) techniques have the capability to acquire precise forest structural information. This research mainly focused on the influence of voxel size on forest canopy height estimates.Methods: A range of voxel sizes(from 10.0 m to 40.0 m interval of 2 m) were tested to obtain estimation accuracies of forest canopy height with different voxel sizes. In this study, all the waveforms within a voxel size were aggregated into a voxel-based LiDAR waveform, and a range of waveform metrics were calculated using the voxelbased LiDAR waveforms. Then, we established estimation model of forest canopy height using the voxel-based waveform metrics through Random Forest(RF) regression method.Results and conclusions: The results showed the voxel-based method could reliably estimate forest canopy height using FWL data. In addition, the voxel sizes had an important influence on the estimation accuracies(R2 ranged from 0.625 to 0.832) of forest canopy height. However, the R2 values did not monotonically increase or decrease with the increase of voxel size in this study. The best estimation accuracy produced when the voxel size was 18 m(R2= 0.832, RMSE = 2.57 m, RMSE% = 20.6%). Compared with the lowest estimation accuracy, the R2 value had a significant improvement(33.1%) when using the optimal voxel size. Finally, through the optimal voxel size, we produced the forest canopy height distribution map for this study area using RF regression model. Our findings demonstrate that the optimal voxel size need to be determined for improving estimation accuracy of forest parameter using small-footprint FWL data.
基金financially supported by the National Natural Science Foundation of China (No. 32171787)
文摘Forest canopy height is one of the important forest parameters for accurately assessing forest biomass or carbon sequestration. ICESat-2 ATLAS provides the potential for retrieval of forest canopy height at global or regional scale, but the current canopy height product (ATL08) has coarse resolution and high uncertainty compared to airborne LiDAR-derived canopy height (hereafter ALCH) in mountainous regions, and is not ready for such applications as biomass modeling at finer scale. The objective of this research was to explore the approach to accurately retrieve canopy height from ATLAS data by incorporating an airborne-derived digital terrain model(DTM) and a data-filtering strategy. By linking ATLAS ATL03 with ATL08 products, the geospatial locations,types, and (absolute) heights of photons were obtained, and canopy heights at different lengths (from 20 to 200 m at 20-m intervals) of segments along a track were computed with the aid of airborne LiDAR DTM. Based on the relationship between the numbers of canopy photons within the segments and accuracy of ATLAS mean canopy height compared to ALCH, a filtering method for excluding a certain portion of unreliable segments was proposed.This method was further applied to different ATLAS ground tracks for retrieval of canopy heights and the results were evaluated using corresponding ALCH. The results show that the incorporation of high-precision DTM and ATLAS products can considerably improve the retrieval accuracy of forest canopy height in mountainous regions.Using the proposed filtering approach, the correlation coefficients (r) between ATLAS canopy height and corresponding ALCH were 0.61–0.91, 0.65–0.92, 0.68–0.94 for segment lengths of 20, 60, and 100 m, respectively;RMSE were 1.90–4.35, 1.55–3.63, and 1.34–3.23 m for the same segment lengths. The results indicate the necessity of using high-precision DTM and using the proposed filtering method to retrieve accurate canopy height from ICESat-2 ATLAS in mountainous regions with dense forest cover and complex terrain conditions.
基金supported by the Major International Cooperation and Exchange Project of National Natural Science Foundation of China(Grant No.41120114001)the National Basic Research Program of China(Grant NO.2013CB733405)+1 种基金the National Natural Science Foundation of China(Grant Nos.41371350,41171279)the 100 Talents Program of the Chinese Academy of Sciences and Beijing Natural Science Foundation(Grant No.4144074)
文摘The Geoscience Laser Altimeter System(GLAS)accurately detects the vertical structural information of a target within its laser spot and is a promising system for the inversion of structural features and other biophysical parameters of forest ecosystems.Since the GLAS footprints are discontinuously distributed with a relativity low density,continuous vegetation height distributions cannot be mapped with a high accuracy using GLAS data alone.The MODIS BRDF product provides more forest structural information than other optical remote sensing data.This study aimed to map forest canopy heights over China from the GLAS and MODIS BRDF data.Firstly,the waveform characteristic parameters were extracted from the GLAS data by the method of wavelet analysis,and the terrain index was calculated using the ASTER GDEM data.Secondly,the model reducing the topographic influence was constructed from the waveform characteristic parameters and terrain index.Thirdly,the final canopy height estimation model was constructed from the neural network combining the canopy height estimated with the GLAS point and the MODIS BRDF data,and applied to get the continuous canopy height map over China.Finally,the map was validated by the measured data and the airborne Li DAR data,and the validation results indicated that forest canopy heights can be estimated with high accuracy from combined GLAS and MODIS data.
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funded by the National Natural Science Foundation of China(U1803231,31860198,31060026)the Innovative Team Building Plan for Key Areas of Xinjiang Production and Construction Corps(2018CB003).
文摘Leaf traits can directly reflect the adaptation strategies of plants to the environment.However,there is limited knowledge on the adaptation strategies of heteromorphic leaves of male and female Populus euphratica Oliv.in response to individual developmental stages(i.e.,diameter class)and canopy height changes.In this study,morphological and physiological properties of heteromorphic leaves of male and female P.euphratica were investigated.Results showed that both male and female P.euphratica exhibited increased leaf area(LA),leaf dry weight(LDW),leaf thickness(LT),net photosynthetic rate(P_(n)),transpiration rate(T_(r)),stomatal conductance(g_(s)),proline(Pro),and malondialdehyde(MDA)concentration,decreased leaf shape index(LI)and specific leaf area(SLA)with increasing diameter and canopy height.Leaf water potential(LWP)increased with increasing diameter,LWP decreased significantly with increasing canopy height in both sexes,and carbon isotope fraction(δ^(13)C)increased significantly with canopy height in both sexes,all of which showed obvious resistance characteristics.However,males showed greater LA,LT,P_(n),T_(r),and Pro than females at the same canopy height,and males showed significantly higher LA,SLA,LT,P_(n),T_(r),g_(s),and MDA,but lower LWP and δ_(1)3C than females at the same canopy height,suggesting that male P.euphratica have stronger photosynthetic and osmoregulatory abilities,and are sensitive to water deficiency.Moreover,difference between male and female P.euphratica is closely related to the increase in individual diameter class and canopy height.In summary,male plants showed higher stress tolerance than female plants,and differences in P_(n),g_(s),T_(r),Pro,MDA,δ_(13)C,and LWP between females and males were related to changes in leaf morphology,diameter class,and canopy height.The results of this study provide a theory for the differences in growth adaptation strategies during individual development of P.euphratica.
基金financially supported by Top Talents Program for One Case One Discussion of Shandong Province,Natural Science Foundation of Shandong Province(Grant No.ZR2021 MD091)China Agriculture Research System(CARS-15-22)Academy of Ecological Unmanned Farm(Grant No.2019 ZBXC200).
文摘Leaf Area of Index(LAI)refers to half of the total leaf area of all crops per unit area.It is an important index to represent the photosynthetic capacity and biomass of crops.To obtain LAI conditions of summer maize in different growth stages quickly and accurately,further guiding field fertilization and irrigation.The Unmanned aerial vehicles(UAV)multispectral data,growing degree days,and canopy height model of 2020-2021 summer maize were used to carry out LAI inversion.The vegetation index was constructed by the ground hyperspectral data and multispectral data of the same range of bands.The correlation analysis was conducted to verify the accuracy of the multispectral data.To include many bands as possible,four vegetation indices which included R,G,B,and NIR bands were selected in this study to test the spectral accuracy.There were nine vegetation indices calculated with UAV multispectral data which were based on the red band and the near-infrared band.Through correlation analysis of LAI and the vegetation index,vegetation indices with a higher correlation to LAI were selected to construct the LAI inversion model.In addition,the Canopy Height Model(CHM)and Growing degree days(GDD)of summer maize were also calculated to build the LAI inversion model.The LAI inversion of summer maize was carried out based on multi-growth stages by using the general linear regression model(GLR),Multivariate nonlinear regression model(MNR),and the partial least squares regression(PLSR)models.R²and RMSE were used to assess the accuracy of the model.The results show that the correlation between UAV multispectral data and hyperspectral data was greater than 0.64,which was significant.The Wide Dynamic Range Vegetation Index(WDRVI),Normalized Difference Vegetation Index(NDVI),Ratio Vegetation Index(RVI),Plant Biochemical Index(PBI),Optimized Soil-Adjusted Vegetation Index(OSAVI),CHM and GDD have a higher correlation with LAI.By comparing the models constructed by the three methods,it was found that the PLSR has the best inversion effect.It was based on OSAVI,GDD,RVI,PBI,CHM,NDVI,and WDRVI,with the training model’s R²being 0.8663,the testing model’s R²being 0.7102,RMSE was 1.1755.This study showed that the LAI inversion model based on UAV multispectral vegetation index,GDD,and CHM improves the accuracy of LAI inversion effectively.That means the growing degree days and crop population structure change have influenced the change of maize LAI certainly,and this method can provide decision support for maize growth monitoring and field fertilization.
基金This work was partially supported by the National Basic Research Program of China(Grant no.2013CB733404)the National Natural Science Foundation of China(Grant nos.41001208 and 91125003)support for the study was also provided by the NASA Terrestrial Ecology Program(NNX09AG66G).
文摘Current researches based on areal or spaceborne stereo images with very high resolutions(<1 m)have demonstrated that it is possible to derive vegetation height from stereo images.The second version of the Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model(ASTER GDEM)is the state-of-the-art global elevation data-set developed by stereo images.However,the resolution of ASTER stereo images(15 m)is much coarser than areal stereo images,and the ASTER GDEM is compiled products from stereo images acquired over 10 years.The forest disturbances as well as forest growth are inevitable in 10 years time span.In this study,the features of ASTER GDEM over vegetated areas under both flat and mountainous conditions were investigated by comparisons with lidar data.The factors possibly affecting the extraction of vegetation canopy height considered include(1)co-registration of DEMs;(2)spatial resolution of digital elevation models(DEMs);(3)spatial vegetation structure;and(4)terrain slope.The results show that the accurate coregistration between ASTER GDEM and national elevation dataset(NED)is necessary over mountainous areas.The correlation between ASTER GDEM minus NED and vegetation canopy height is improved from 0.328 to 0.43 by degrading resolutions from 1 arc-second to 5 arc-second and further improved to 0.6 if only homogenous vegetated areas were considered.
基金The Key Scientific and Technological Research Projects in Tibet Autonomous Region (XZ202101ZY0005G)The Second Tibetan Plateau Scientific Expedition and Research (STEP) Program (2019QZKK0301-1)。
文摘Leaf longevity is an important adaptive strategy that allows plants to maximize photosynthetic carbon gain.Due to the difficulty of identifying overwintering bud scars and distinguishing the age sequence of twigs,leaf longevity is rarely studied in Cupressaceae species,which further limits our understanding of the leaf economic spectrum (LES) for these populations.Here,we investigated the leaf longevity,as well as mass-based leaf nitrogen concentration (N;),of Juniperus saltuaria at different canopy heights for both subalpine and alpine timberline forests in the Sergymla Mountains,southeastern Tibet.We found that the mean leaf longevity was 4.2±1.2 years,and overall it did not differ significantly between different elevations.Along the vertical profiles of juniper canopies,the leaf longevity did not reflect a linear trend.With increasing leaf longevity,N;showed declining trends.We further analyzed the relationship between leaf longevity and the corresponding length of green twigs,and found that the length of green twigs could only explain 1%-3%of the variation in leaf longevity,indicating that the length of green twigs is a poor predictor for the variation in leaf longevity.In summary,for the J.saltuaria species in timberline or nearby subalpine forests,the effects of elevation and canopy depths on leaf longevity are minor,and the leaf trait analysis is in accordance with the prediction of LES.
