The principles of remotely estimating grassland cover density in an alpine meadow soil from space lie in the synchronous collection of in situ samples with the satellite pass and statistically linking these cover dens...The principles of remotely estimating grassland cover density in an alpine meadow soil from space lie in the synchronous collection of in situ samples with the satellite pass and statistically linking these cover densities to their image properties according to their geographic coordinates. The principles and procedures for quantifying grassland cover density from satellite image data were presented with an example from Qinghai Lake, China demonstrating how quantification could be made more accurate through the integrated use of remote sensing and global positioning systems (GPS). An empirical model was applied to an entire satellite image to convert pixel values into ground cover density. Satellite data based on 68 field samples was used to produce a map of ten cover densities. After calibration a strong linear regression relationship (r2 = 0.745) between pixel values on the satellite image and in situ measured grassland cover density was established with an 89% accuracy level. However, to minimize positional uncertainty of field samples, integrated use of hyperspatial satellite data and GPS could be utilized. This integration could reduce disparity in ground and space sampling intervals, and improve future quantification accuracy even more.展开更多
There has been a great deal of Interests in the estimation of grassland biophysical parameters such as percentage of vegetation cover （PVC）, aboveground biomass, and leaf-area index with remote sensing data at the c...There has been a great deal of Interests in the estimation of grassland biophysical parameters such as percentage of vegetation cover （PVC）, aboveground biomass, and leaf-area index with remote sensing data at the canopy scale. In this paper, the percentage of vegetation cover was estimated from vegetation indices using Moderate Resolution Imaging Spectroradiometer （MODIS） data and red-edge parameters through the first derivative spectrum from in situ hypserspectral reflectance data. Hyperspectral reflectance measurements were made on grasslands in Inner Mongolia, China, using an Analytical Spectral Devices spectroradiometer. Vegetation indices such as the difference, simple ratio, normalized difference, renormalized difference, soil-adjusted and modified soil-adjusted vegetation indices （DVI, RVI, NDVI, RDVI, SAVI L=0.5 end MSAVI2） were calculated from the hyperspectral reflectance of various vegetation covers. The percentage of vegetation cover was estimated using an unsupervised spectral-contextual classifier automatically. Relationships between percentage of vegetation cover and various vegetation indices and red-edge parameters were compared using a linear and second-order polynomial regression. Our analysis indicated that MSAVI2 and RVI yielded more accurate estimations for a wide range of vegetation cover than other vegetation indices and red-edge parameters for the linear and second-order polynomial regression, respectively.展开更多
Soil carbon pools could become a CO_2 source or sink, depending on the directions of land use/cover changes. A slight change of soil carbon will inevitably affect the atmospheric CO_2 concentration and consequently th...Soil carbon pools could become a CO_2 source or sink, depending on the directions of land use/cover changes. A slight change of soil carbon will inevitably affect the atmospheric CO_2 concentration and consequently the climate. Based on the data from 127 soil sample sites, 48 vegetation survey plots, and Landsat TM images, we analyzed the land use/cover changes, estimated soil organic carbon（SOC） storage and vegetation carbon storage of grassland, and discussed the impact of grassland changes on carbon storage during 2000 to 2013 in the Ili River Valley of Northwest China. The results indicate that the areal extents of forestland, shrubland, moderate-coverage grassland（MCG）, and the waterbody（including glaciers） decreased while the areal extents of high-coverage grassland（HCG）,low-coverage grassland（LCG）, residential and industrial land, and cultivated land increased. The grassland SOC density in 0–100 cm depth varied with the coverage in a descending order of HCG〉MCG〉LCG.The regional grassland SOC storage in the depth of 0–100 cm in 2013 increased by 0.25×1011 kg compared with that in 2000. The regional vegetation carbon storage（S_（rvc）） of grassland was 5.27×10~9 kg in2013 and decreased by 15.7% compared to that in 2000. The vegetation carbon reserves of the under-ground parts of vegetation（S_（ruvb）） in 2013 was 0.68×10~9 kg and increased by approximately 19.01%compared to that in 2000. This research can improve our understanding about the impact of land use/cover changes on the carbon storage in arid areas of Northwest China.展开更多
基金supported by the National Basic Research Program of China (No. 2006CB400505) and the National NaturalSciences Foundation of China (Nos. 49971056 and 40171007)
文摘The principles of remotely estimating grassland cover density in an alpine meadow soil from space lie in the synchronous collection of in situ samples with the satellite pass and statistically linking these cover densities to their image properties according to their geographic coordinates. The principles and procedures for quantifying grassland cover density from satellite image data were presented with an example from Qinghai Lake, China demonstrating how quantification could be made more accurate through the integrated use of remote sensing and global positioning systems (GPS). An empirical model was applied to an entire satellite image to convert pixel values into ground cover density. Satellite data based on 68 field samples was used to produce a map of ten cover densities. After calibration a strong linear regression relationship (r2 = 0.745) between pixel values on the satellite image and in situ measured grassland cover density was established with an 89% accuracy level. However, to minimize positional uncertainty of field samples, integrated use of hyperspatial satellite data and GPS could be utilized. This integration could reduce disparity in ground and space sampling intervals, and improve future quantification accuracy even more.
基金Supported by the National Natural Science Foundation of China （30371018） and the State Key Basic Research and Development Plan of Chfna （2003DEA2C010-13）.
文摘There has been a great deal of Interests in the estimation of grassland biophysical parameters such as percentage of vegetation cover （PVC）, aboveground biomass, and leaf-area index with remote sensing data at the canopy scale. In this paper, the percentage of vegetation cover was estimated from vegetation indices using Moderate Resolution Imaging Spectroradiometer （MODIS） data and red-edge parameters through the first derivative spectrum from in situ hypserspectral reflectance data. Hyperspectral reflectance measurements were made on grasslands in Inner Mongolia, China, using an Analytical Spectral Devices spectroradiometer. Vegetation indices such as the difference, simple ratio, normalized difference, renormalized difference, soil-adjusted and modified soil-adjusted vegetation indices （DVI, RVI, NDVI, RDVI, SAVI L=0.5 end MSAVI2） were calculated from the hyperspectral reflectance of various vegetation covers. The percentage of vegetation cover was estimated using an unsupervised spectral-contextual classifier automatically. Relationships between percentage of vegetation cover and various vegetation indices and red-edge parameters were compared using a linear and second-order polynomial regression. Our analysis indicated that MSAVI2 and RVI yielded more accurate estimations for a wide range of vegetation cover than other vegetation indices and red-edge parameters for the linear and second-order polynomial regression, respectively.
基金financially supported by the National Science and Technology Support Plan (2014BAC15B03)the National Natural Science Foundation of China (41371503, 41371128)the West Light Foundation of the Chinese Academy of Sciences (YB201302)
文摘Soil carbon pools could become a CO_2 source or sink, depending on the directions of land use/cover changes. A slight change of soil carbon will inevitably affect the atmospheric CO_2 concentration and consequently the climate. Based on the data from 127 soil sample sites, 48 vegetation survey plots, and Landsat TM images, we analyzed the land use/cover changes, estimated soil organic carbon（SOC） storage and vegetation carbon storage of grassland, and discussed the impact of grassland changes on carbon storage during 2000 to 2013 in the Ili River Valley of Northwest China. The results indicate that the areal extents of forestland, shrubland, moderate-coverage grassland（MCG）, and the waterbody（including glaciers） decreased while the areal extents of high-coverage grassland（HCG）,low-coverage grassland（LCG）, residential and industrial land, and cultivated land increased. The grassland SOC density in 0–100 cm depth varied with the coverage in a descending order of HCG〉MCG〉LCG.The regional grassland SOC storage in the depth of 0–100 cm in 2013 increased by 0.25×1011 kg compared with that in 2000. The regional vegetation carbon storage（S_（rvc）） of grassland was 5.27×10~9 kg in2013 and decreased by 15.7% compared to that in 2000. The vegetation carbon reserves of the under-ground parts of vegetation（S_（ruvb）） in 2013 was 0.68×10~9 kg and increased by approximately 19.01%compared to that in 2000. This research can improve our understanding about the impact of land use/cover changes on the carbon storage in arid areas of Northwest China.
