By means of ground survey and ＂3S＂ technology,taking Maduo County in three river sources areas as example,the remote sensing model between biomass and normalized difference vegetation index（NDVI） in sampling point...By means of ground survey and ＂3S＂ technology,taking Maduo County in three river sources areas as example,the remote sensing model between biomass and normalized difference vegetation index（NDVI） in sampling points was established after calculating the NDVI distribution of TM image in Maduo County,and the grade distribution map of grassland productivity in Maduo County was obtained according to the grade division of grassland productivity in Qinghai Province,so as to monitor grassland productivity step by step.The results showed that grassland coverage area in Maduo County in 2009 was about 2.22 million hm2,and NDVI was mainly from 0 to 0.5,accounting for 88.64% of total grassland area in Maduo County;there was a significant correlation between biomass and NDVI in sampling point,with the correlation coefficient of above 0.7,and their model could be quantitatively expressed as follows,namely Biomass = 552.632 × NDVI1.137;grassland productivity in Maduo County was 750-3 000 kg/hm2 which occupied 72.1% of total grassland area;the highest grassland productivity in Maduo County was 4 500-6 500 kg/hm2,but it accounted for below 1% of total grassland area.展开更多
This study selected vegetation cover as the main evaluation index, calculated the grassland degradation index （GDI） and established the remote sensing monitoring and evaluation system for grassland degradation in No...This study selected vegetation cover as the main evaluation index, calculated the grassland degradation index （GDI） and established the remote sensing monitoring and evaluation system for grassland degradation in Northern Tibet, according to the National Standard （GB19377-2003）, based on the remote sensing data such as NDVI data derived from NOAA/AVHRR with a spatial resolution of 8 km of 1981-2000, from SPOT/VGT with a spatial resolution of 1 km of 2001 and from MODIS with a spatial resolution of 0.25 km of 2002-2004 respectively in this area, in combination with the actual condition of grassland degradation. The grassland degradation processes and their responses to climate change during 1981-2004 were discussed and analyzed in this paper. The result indicated that grassland degradation in Northern Tibet is very serious, and the mean value of GDI in recent 20 years is 2.54 which belongs to the serious degradation grade. From 1981 to 2004, the GDI fluctuated distinctly with great interannual variations in the proportion of degradation degree and GDI but the general tendency turned to severe-grade during this period with the grassland degradation grade changed from light degraded to serious degraded in Northern Tibet. The extremely serious degraded and serious degraded grassland occupied 1.7% and 8.0% of the study area, the moderate and light degraded grassland accounted for 13.2% and 27.9% respectively, and un-degraded grassland occupied 49.2% of the total grassland area in 2004. The grassland degradation was serious, especially in the conjunctive area of Naqu, Biru and Jiali counties, the headstream of the Yangtze River lying in the Galadandong snow mountain and glaciers, the area along the Qinghai-Tibet highway and railway, and areas around the Tanggula and Nianqingtanggula snow mountains and glaciers. So the snow mountains and glaciers as well as their adjacent areas in Northern Tibet were sensitive to climate change and the areas along the vital communication line with frequent human activities experienced relatively serious grassland degradation.展开更多
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
In recent years, grassland degradation has become one of the most important ecological problems in China under the interwoven influence of environmental and human factors. Based on the analysis of the spatial-temporal...In recent years, grassland degradation has become one of the most important ecological problems in China under the interwoven influence of environmental and human factors. Based on the analysis of the spatial-temporal characteristics and driving factors of grassland degradation and in order to deeply understand the research status of grassland degradation monitoring methods and evaluation index system, this paper mainly investigates the research progress of grassland degradation remote sensing monitoring methods and evaluation indicators. Furthermore, this paper summarizes the more commonly used remote sensing monitoring methods and evaluation methods, analyzes the problems existing in the evaluation indicators of grassland degradation, and points out the research direction of the evaluation indicators in the future. Finally, a comprehensive remote sensing monitoring and evaluation system are established in this paper. Research findings: because of the variety of grassland degradation types and the emergence of remote sensing monitoring and evaluation methods, establishing a comprehensive remote sensing monitoring and evaluation system to classify and summarize the research methods of different grassland degradation can lay a foundation for the development of grassland degradation evaluation and monitoring in the future and provide research ideas. It is the trend of grassland degradation remote sensing research in the future.展开更多
基金Supported by National High Technology Research and Development Program of China(863Program)(2008AA10Z223)~~
文摘By means of ground survey and ＂3S＂ technology,taking Maduo County in three river sources areas as example,the remote sensing model between biomass and normalized difference vegetation index（NDVI） in sampling points was established after calculating the NDVI distribution of TM image in Maduo County,and the grade distribution map of grassland productivity in Maduo County was obtained according to the grade division of grassland productivity in Qinghai Province,so as to monitor grassland productivity step by step.The results showed that grassland coverage area in Maduo County in 2009 was about 2.22 million hm2,and NDVI was mainly from 0 to 0.5,accounting for 88.64% of total grassland area in Maduo County;there was a significant correlation between biomass and NDVI in sampling point,with the correlation coefficient of above 0.7,and their model could be quantitatively expressed as follows,namely Biomass = 552.632 × NDVI1.137;grassland productivity in Maduo County was 750-3 000 kg/hm2 which occupied 72.1% of total grassland area;the highest grassland productivity in Maduo County was 4 500-6 500 kg/hm2,but it accounted for below 1% of total grassland area.
基金The National Basic Research Program of China, No.2002CB412508 Cooperation project with Naqu Bureau of Agriculture and Husbandry Management Department and Institute of Agricultural Environment and Sustainable Development
文摘This study selected vegetation cover as the main evaluation index, calculated the grassland degradation index （GDI） and established the remote sensing monitoring and evaluation system for grassland degradation in Northern Tibet, according to the National Standard （GB19377-2003）, based on the remote sensing data such as NDVI data derived from NOAA/AVHRR with a spatial resolution of 8 km of 1981-2000, from SPOT/VGT with a spatial resolution of 1 km of 2001 and from MODIS with a spatial resolution of 0.25 km of 2002-2004 respectively in this area, in combination with the actual condition of grassland degradation. The grassland degradation processes and their responses to climate change during 1981-2004 were discussed and analyzed in this paper. The result indicated that grassland degradation in Northern Tibet is very serious, and the mean value of GDI in recent 20 years is 2.54 which belongs to the serious degradation grade. From 1981 to 2004, the GDI fluctuated distinctly with great interannual variations in the proportion of degradation degree and GDI but the general tendency turned to severe-grade during this period with the grassland degradation grade changed from light degraded to serious degraded in Northern Tibet. The extremely serious degraded and serious degraded grassland occupied 1.7% and 8.0% of the study area, the moderate and light degraded grassland accounted for 13.2% and 27.9% respectively, and un-degraded grassland occupied 49.2% of the total grassland area in 2004. The grassland degradation was serious, especially in the conjunctive area of Naqu, Biru and Jiali counties, the headstream of the Yangtze River lying in the Galadandong snow mountain and glaciers, the area along the Qinghai-Tibet highway and railway, and areas around the Tanggula and Nianqingtanggula snow mountains and glaciers. So the snow mountains and glaciers as well as their adjacent areas in Northern Tibet were sensitive to climate change and the areas along the vital communication line with frequent human activities experienced relatively serious grassland degradation.
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.
文摘In recent years, grassland degradation has become one of the most important ecological problems in China under the interwoven influence of environmental and human factors. Based on the analysis of the spatial-temporal characteristics and driving factors of grassland degradation and in order to deeply understand the research status of grassland degradation monitoring methods and evaluation index system, this paper mainly investigates the research progress of grassland degradation remote sensing monitoring methods and evaluation indicators. Furthermore, this paper summarizes the more commonly used remote sensing monitoring methods and evaluation methods, analyzes the problems existing in the evaluation indicators of grassland degradation, and points out the research direction of the evaluation indicators in the future. Finally, a comprehensive remote sensing monitoring and evaluation system are established in this paper. Research findings: because of the variety of grassland degradation types and the emergence of remote sensing monitoring and evaluation methods, establishing a comprehensive remote sensing monitoring and evaluation system to classify and summarize the research methods of different grassland degradation can lay a foundation for the development of grassland degradation evaluation and monitoring in the future and provide research ideas. It is the trend of grassland degradation remote sensing research in the future.
