Individual Tree Detection-and-Counting(ITDC)is among the important tasks in town areas,and numerous methods are proposed in this direction.Despite their many advantages,still,the proposed methods are inadequate to pro...Individual Tree Detection-and-Counting(ITDC)is among the important tasks in town areas,and numerous methods are proposed in this direction.Despite their many advantages,still,the proposed methods are inadequate to provide robust results because they mostly rely on the direct field investigations.This paper presents a novel approach involving high-resolution imagery and the Canopy-Height-Model(CHM)data to solve the ITDC problem.The new approach is studied in six urban scenes:farmland,woodland,park,industrial land,road and residential areas.First,it identifies tree canopy regions using a deep learning network from high-resolution imagery.It then deploys the CHM-data to detect treetops of the canopy regions using a local maximum algorithm and individual tree canopies using the region growing.Finally,it calculates and describes the number of individual trees and tree canopies.The proposed approach is experimented with the data from Shanghai,China.Our results show that the individual tree detection method had an average overall accuracy of 0.953,with a precision of 0.987 for woodland scene.Meanwhile,the R^(2) value for canopy segmentation in different urban scenes is greater than 0.780 and 0.779 for canopy area and diameter size,respectively.These results confirm that the proposed method is robust enough for urban tree planning and management.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
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
Background Monitoring forage in livestock operations is critical to sustainable rangeland management of soil and ecological processes that provide both livestock and wildlife habitat.Traditional ground-based sampling ...Background Monitoring forage in livestock operations is critical to sustainable rangeland management of soil and ecological processes that provide both livestock and wildlife habitat.Traditional ground-based sampling methods have been widely used and provide valuable information;however,they are time-consuming,labor-intensive,and limited in their ability to capture larger extents of the spatial and temporal dynamics of rangeland ecosystems.Drones provide a solution to collect data to larger extents than field-based methods and with higher-resolution than traditional remote sensing platforms.Our objectives were to(1)assess the accuracy of vegetation cover height in grasses using drones,(2)quantify the spatial distribution of vegetation cover height in grazed and non-grazed pastures during the dormant(fall-winter)and growing seasons(spring-summer),and(3)evaluate the spatial distribution of vegetation cover height as a proxy for northern bobwhite(Colinusvirginianus)habitat in South Texas.We achieved this by very fine scale drone-derived imagery and using class level landscape metrics to assess vegetation cover height configuration.Results Estimated heights from drone imagery had a significant relationship with the field height measurements in September(r2=0.83;growing season)and February(r^(2)=0.77;dormant season).Growing season pasture maintained residual landscape habitat configuration adequate for bobwhites throughout the fall and winter of 2022-2023 following grazing.Dormant season pasture had an increase in bare ground cover,and a shift from many large patches of tall herbaceous cover(40-120 cm)to few large patches of low herbaceous cover(5-30 cm)(p<0.05).Conclusions Drones provided high-resolution imagery that allowed us to assess the spatial and temporal changes of vertical herbaceous vegetation structure in a semi-arid rangeland subject to grazing.This study shows how drone imagery can be beneficial for wildlife conservation and management by providing insights into changes in fine-scale vegetation spatial and temporal heterogeneity from livestock grazing.展开更多
基金supported by the project funded by International Research Center of Big Data for Sustainable 740 Development Goals[Grant Number CBAS2022GSP07]Fundamental Research Funds for the Central Universities,Chongqing Natural Science Foundation[Grant Number CSTB2022NSCQMSX 2069]Ministry of Education of China[Grant Number 19JZD023].
文摘Individual Tree Detection-and-Counting(ITDC)is among the important tasks in town areas,and numerous methods are proposed in this direction.Despite their many advantages,still,the proposed methods are inadequate to provide robust results because they mostly rely on the direct field investigations.This paper presents a novel approach involving high-resolution imagery and the Canopy-Height-Model(CHM)data to solve the ITDC problem.The new approach is studied in six urban scenes:farmland,woodland,park,industrial land,road and residential areas.First,it identifies tree canopy regions using a deep learning network from high-resolution imagery.It then deploys the CHM-data to detect treetops of the canopy regions using a local maximum algorithm and individual tree canopies using the region growing.Finally,it calculates and describes the number of individual trees and tree canopies.The proposed approach is experimented with the data from Shanghai,China.Our results show that the individual tree detection method had an average overall accuracy of 0.953,with a precision of 0.987 for woodland scene.Meanwhile,the R^(2) value for canopy segmentation in different urban scenes is greater than 0.780 and 0.779 for canopy area and diameter size,respectively.These results confirm that the proposed method is robust enough for urban tree planning and management.
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supported by the Hildebrand Foundation,Ken Leonard Fund for Livestock Interactions Research,Harvey Weil Foundation,Comision Nacional de Ciencia y Tecnologia(CONACYT),Houston Safari Club,South Texas Quail Coalition Chapter,Hill Country Quail Coalition,and R.Stacy from Houston,TX.
文摘Background Monitoring forage in livestock operations is critical to sustainable rangeland management of soil and ecological processes that provide both livestock and wildlife habitat.Traditional ground-based sampling methods have been widely used and provide valuable information;however,they are time-consuming,labor-intensive,and limited in their ability to capture larger extents of the spatial and temporal dynamics of rangeland ecosystems.Drones provide a solution to collect data to larger extents than field-based methods and with higher-resolution than traditional remote sensing platforms.Our objectives were to(1)assess the accuracy of vegetation cover height in grasses using drones,(2)quantify the spatial distribution of vegetation cover height in grazed and non-grazed pastures during the dormant(fall-winter)and growing seasons(spring-summer),and(3)evaluate the spatial distribution of vegetation cover height as a proxy for northern bobwhite(Colinusvirginianus)habitat in South Texas.We achieved this by very fine scale drone-derived imagery and using class level landscape metrics to assess vegetation cover height configuration.Results Estimated heights from drone imagery had a significant relationship with the field height measurements in September(r2=0.83;growing season)and February(r^(2)=0.77;dormant season).Growing season pasture maintained residual landscape habitat configuration adequate for bobwhites throughout the fall and winter of 2022-2023 following grazing.Dormant season pasture had an increase in bare ground cover,and a shift from many large patches of tall herbaceous cover(40-120 cm)to few large patches of low herbaceous cover(5-30 cm)(p<0.05).Conclusions Drones provided high-resolution imagery that allowed us to assess the spatial and temporal changes of vertical herbaceous vegetation structure in a semi-arid rangeland subject to grazing.This study shows how drone imagery can be beneficial for wildlife conservation and management by providing insights into changes in fine-scale vegetation spatial and temporal heterogeneity from livestock grazing.
