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
The Aral Sea Basin in Central Asia is an important geographical environment unit in the center of Eurasia.It is of great significance to the ecological protection and sustainable development of Central Asia to carry o...The Aral Sea Basin in Central Asia is an important geographical environment unit in the center of Eurasia.It is of great significance to the ecological protection and sustainable development of Central Asia to carry out dynamic monitoring and effective evaluation of the eco-environmental quality of the Aral Sea Basin.In this study,the arid remote sensing ecological index(ARSEI)for large-scale arid areas was developed,which coupled the information of the greenness index,the salinity index,the humidity index,the heat index,and the land degradation index of arid areas.The ARSEI was used to monitor and evaluate the eco-environmental quality of the Aral Sea Basin from 2000 to 2019.The results show that the greenness index,the humidity index and the land degradation index had a positive impact on the quality of the ecological environment in the Aral Sea Basin,while the salinity index and the heat index exerted a negative impact on the quality of the ecological environment.The eco-environmental quality of the Aral Sea Basin demonstrated a trend of initial improvement,followed by deterioration,and finally further improvement.The spatial variation of these changes was significant.From 2000 to 2019,grassland and wasteland(saline alkali land and sandy land)in the central and western parts of the basin had the worst ecological environment quality.The areas with poor ecological environment quality are mainly distributed in rivers,wetlands,and cultivated land around lakes.During the period from 2000 to 2019,except for the surrounding areas of the Aral Sea,the ecological environment quality in other areas of the Aral Sea Basin has been improved in general.The correlation coefficients between the change in the eco-environmental quality and the heat index and between the change in the eco-environmental quality and the humidity index were–0.593 and 0.524,respectively.Climate conditions and human activities have led to different combinations of heat and humidity changes in the eco-environmental quality of the Aral Sea Basin.However,human activities had a greater impact.The ARSEI can quantitatively and intuitively reflect the scale and causes of large-scale and long-time period changes of the eco-environmental quality in arid areas;it is very suitable for the study of the eco-environmental quality in arid areas.展开更多
A region surrounding Qinghai Lake was chosen as the study area and nine ecological landscape types that were recognized based on Landsat TM image classification of land cover/use types along with the ancillary data, a...A region surrounding Qinghai Lake was chosen as the study area and nine ecological landscape types that were recognized based on Landsat TM image classification of land cover/use types along with the ancillary data, and their ecological features and the measures dealing with the eco environmental problems are presented in this paper. The study has shown that using this approach the ecological landscape types in a region like the study area can be readily recognized and their ecological features can be rather accurately derived. Moreover, the deterioration in near all landscape ecological types in the area is quite serious. Therefore, effective and proper measures have to be taken in order to realize a sustainable development of the region.展开更多
With the support of RS and GIS technology,the ecological environment of Linze was evaluated and the changes of ecological environment were analyzed from the spatial scope of ecosystem with the remote sensing images in...With the support of RS and GIS technology,the ecological environment of Linze was evaluated and the changes of ecological environment were analyzed from the spatial scope of ecosystem with the remote sensing images in 2000 and 2017 as the information source.The results showed that the areas with excellent,relatively good and relatively poor ecological environment in Linze showed a decreasing trend from 2000 to 2017,which decreased by 262.50,7,156.25 and 12,256.30 hm^2,respectively.From the landscape patch pattern,the patch density in areas with relatively good ecological environment increased the fastest from 2000 to 2017,which was 13.05 pieces/hm^2.The largest plaque index in areas with relatively good ecological environment decreased by 1.314%,and the dominance of large plaque in the region decreased.The assessment results of ecological environment based on remote sensing demonstrated that the ecological environment in Linze was generally healthy from 2000 to 2017,and the deterioration was improved.Among them,the area transformed from relatively poor ecological environment to other types was the largest,which was 17,512.92 hm^2,and the proportion of the area transformed into general ecological environment was the highest,accounting for 80.7%of the transformed area of the region.展开更多
The Qinghai Lake is the largest inland lake in China.The significant difference of dielectric properties between water and ice suggests that a simple method of monitoring the Qinghai lake freeze-up and break-up dates ...The Qinghai Lake is the largest inland lake in China.The significant difference of dielectric properties between water and ice suggests that a simple method of monitoring the Qinghai lake freeze-up and break-up dates using satellite passive microwave remote sensing data could be used.The freeze-up and break-up dates from the Qinghai Lake hydrological station and the MODIS L1B reflectance data were used to validate the passive microwave remote sensing results.The validation shows that passive microwave remote sensing data can accurately monitor the lake ice.Some uncertainty comes mainly from the revisit frequency of satellite overpass.The data from 1978 to 2006 show that lake ice duration is reduced by about 14―15 days.The freeze-up dates are about 4 days later and break-up dates about 10 days earlier.The regression analyses show that,at the 0.05 significance level,the correlations are 0.83,0.66 and 0.89 between monthly mean air temperature(MMAT) and lake ice duration days,freeze-up dates,break-up dates,respectively.Therefore,inter-annual variations of the Qinghai Lake ice duration days can significantly reflect the regional climate variation.展开更多
In this paper,RS,GIS and GPS technologies are used to interpret the remote sensing images of the north shore of Qinghai Lake from 1987 to 2014 according to the inversion results of vegetation coverage(FVC),albedo,land...In this paper,RS,GIS and GPS technologies are used to interpret the remote sensing images of the north shore of Qinghai Lake from 1987 to 2014 according to the inversion results of vegetation coverage(FVC),albedo,land surface temperature(LST),soil moisture(WET)and other major parameters after image preprocessing,such as radiometric correction,geometric correction and atmospheric correction.On this basis,the decision tree classification method based on landsat8 remote sensing image is used to classify the desertification land in this area,and the development and change of desertification in this period are analyzed.The results show that the fluctuation of desertification land area in this area increased during the study period,but from 2003 to 2014,the land area of mild desertification,moderate desertification and severe desertification landwere respectively decreased 0.92,145.89 and 29.39 km2,while the area of serious desertification land still has a slow increasing trend.Whether the driving force of desertification change trend in this area is caused by human factors or global change needs to be further studied.展开更多
Land desertification is a widely concerned ecological environment problem.Studying the evolution trend of desertification types is of great significance to prevent and control land desertification.In this study,we app...Land desertification is a widely concerned ecological environment problem.Studying the evolution trend of desertification types is of great significance to prevent and control land desertification.In this study,we applied the decision tree classification method,to study the land area and temporal and spatial change law of different types of desertification in the North Bank of Qinghai Lake area from 1987 to 2014,based on the current land use situation and TM remote sensing image data of Haiyan County,Qinghai Province,The results show that the area of mild desertification land and moderate desertification land in the study area has decreased,while the area of severe desertification land and extreme desertification land has increased significantly in the past 30 years.The area of desertification land decreased by 4.02 km2,of which the area of mild and moderate desertification land decreased by 39.73 km2 and 36.8 km2 respectively,and the area of severe and extreme desertification land increased by 32.78 km2 and 39.73 km2 respectively.As for the mutual transformation relationship,the transformation from severe desertification land to extreme desertification land is the main,and the junction of severe desertification land and extreme desertification land is the sensitive area of transformation.In the north shore of Qinghai Lake,the sandy land tends to expand eastward.The research provides reference basis for local land desertification monitoring,and has a great guidance for local effective land desertification and soil and water conservation.展开更多
Landscape ecology emphasizes large areas and ecological effects of the spatial patterning of ecosystem. Recent developments in landscape ecology have emphasized the important relationship between spatial patterns and ...Landscape ecology emphasizes large areas and ecological effects of the spatial patterning of ecosystem. Recent developments in landscape ecology have emphasized the important relationship between spatial patterns and many ecological processes. Quantitative methods in landscape ecology link spatial patterns and ecological processes at broad spatial and temporal scales. In turn the increased attention on temporal change of ecosystem has highlighted the need for quantitative methods that can analyze patterns. This research applies quantitative methods——change detection to assess the ecosystem temporal change in the arid and semiarid area. Remote sensing offers the temporal change of ecosystem on landscape characteristics.展开更多
Soil moisture(SM), which plays a crucial role in studies of the climate, ecology, agriculture and the environment, can be estimated and mapped by remote sensing technology over a wide region. However, remotely sensed ...Soil moisture(SM), which plays a crucial role in studies of the climate, ecology, agriculture and the environment, can be estimated and mapped by remote sensing technology over a wide region. However, remotely sensed SM is constrained by its estimation accuracy, which mainly stems from the influence of vegetation cover on soil spectra information in mixed pixels. To overcome the low-accuracy defects of existing surface albedo method for estimating SM, in this paper, Qinghai Lake Basin, an important animal husbandry production area in Qinghai Province, China, was chosen as an empirical research area. Using the surface albedo computed from moderate resolution imaging spectroradiometer(MODIS) reflectance products and the actual measured SM data, an albedo/vegetation coverage trapezoid feature space was constructed. Bare soil albedo was extracted from the surface albedo mainly containing information of soil, vegetation, and both albedo models for estimating SM were constructed separately. The accuracy of the bare soil albedo model(root mean square error=4.20, mean absolute percent error=22.75%, and theil inequality coefficient=0.67) was higher than that of the existing surface albedo model(root mean square error=4.66, mean absolute percent error=25.46% and theil inequality coefficient=0.74). This result indicated that the bare soil albedo greatly improved the accuracy of SM estimation and mapping. As this method eliminated the effect of vegetation cover and restored the inherent soil spectra, it not only quantitatively estimates and maps SM at regional scales with high accuracy, but also provides a new way of improving the accuracy of soil organic matter estimation and mapping.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This work was funded by the National Natural Science Foundation of China(U1603242)the Major Science and Technology Projects in Inner Mongolia,China(ZDZX2018054).
文摘The Aral Sea Basin in Central Asia is an important geographical environment unit in the center of Eurasia.It is of great significance to the ecological protection and sustainable development of Central Asia to carry out dynamic monitoring and effective evaluation of the eco-environmental quality of the Aral Sea Basin.In this study,the arid remote sensing ecological index(ARSEI)for large-scale arid areas was developed,which coupled the information of the greenness index,the salinity index,the humidity index,the heat index,and the land degradation index of arid areas.The ARSEI was used to monitor and evaluate the eco-environmental quality of the Aral Sea Basin from 2000 to 2019.The results show that the greenness index,the humidity index and the land degradation index had a positive impact on the quality of the ecological environment in the Aral Sea Basin,while the salinity index and the heat index exerted a negative impact on the quality of the ecological environment.The eco-environmental quality of the Aral Sea Basin demonstrated a trend of initial improvement,followed by deterioration,and finally further improvement.The spatial variation of these changes was significant.From 2000 to 2019,grassland and wasteland(saline alkali land and sandy land)in the central and western parts of the basin had the worst ecological environment quality.The areas with poor ecological environment quality are mainly distributed in rivers,wetlands,and cultivated land around lakes.During the period from 2000 to 2019,except for the surrounding areas of the Aral Sea,the ecological environment quality in other areas of the Aral Sea Basin has been improved in general.The correlation coefficients between the change in the eco-environmental quality and the heat index and between the change in the eco-environmental quality and the humidity index were–0.593 and 0.524,respectively.Climate conditions and human activities have led to different combinations of heat and humidity changes in the eco-environmental quality of the Aral Sea Basin.However,human activities had a greater impact.The ARSEI can quantitatively and intuitively reflect the scale and causes of large-scale and long-time period changes of the eco-environmental quality in arid areas;it is very suitable for the study of the eco-environmental quality in arid areas.
文摘A region surrounding Qinghai Lake was chosen as the study area and nine ecological landscape types that were recognized based on Landsat TM image classification of land cover/use types along with the ancillary data, and their ecological features and the measures dealing with the eco environmental problems are presented in this paper. The study has shown that using this approach the ecological landscape types in a region like the study area can be readily recognized and their ecological features can be rather accurately derived. Moreover, the deterioration in near all landscape ecological types in the area is quite serious. Therefore, effective and proper measures have to be taken in order to realize a sustainable development of the region.
文摘With the support of RS and GIS technology,the ecological environment of Linze was evaluated and the changes of ecological environment were analyzed from the spatial scope of ecosystem with the remote sensing images in 2000 and 2017 as the information source.The results showed that the areas with excellent,relatively good and relatively poor ecological environment in Linze showed a decreasing trend from 2000 to 2017,which decreased by 262.50,7,156.25 and 12,256.30 hm^2,respectively.From the landscape patch pattern,the patch density in areas with relatively good ecological environment increased the fastest from 2000 to 2017,which was 13.05 pieces/hm^2.The largest plaque index in areas with relatively good ecological environment decreased by 1.314%,and the dominance of large plaque in the region decreased.The assessment results of ecological environment based on remote sensing demonstrated that the ecological environment in Linze was generally healthy from 2000 to 2017,and the deterioration was improved.Among them,the area transformed from relatively poor ecological environment to other types was the largest,which was 17,512.92 hm^2,and the proportion of the area transformed into general ecological environment was the highest,accounting for 80.7%of the transformed area of the region.
基金Supported by the National Basic Research Program of China (Grand No.2007CB411506)National Natural Science Foundation of China (Grand Nos.40601065 and 40701030)
文摘The Qinghai Lake is the largest inland lake in China.The significant difference of dielectric properties between water and ice suggests that a simple method of monitoring the Qinghai lake freeze-up and break-up dates using satellite passive microwave remote sensing data could be used.The freeze-up and break-up dates from the Qinghai Lake hydrological station and the MODIS L1B reflectance data were used to validate the passive microwave remote sensing results.The validation shows that passive microwave remote sensing data can accurately monitor the lake ice.Some uncertainty comes mainly from the revisit frequency of satellite overpass.The data from 1978 to 2006 show that lake ice duration is reduced by about 14―15 days.The freeze-up dates are about 4 days later and break-up dates about 10 days earlier.The regression analyses show that,at the 0.05 significance level,the correlations are 0.83,0.66 and 0.89 between monthly mean air temperature(MMAT) and lake ice duration days,freeze-up dates,break-up dates,respectively.Therefore,inter-annual variations of the Qinghai Lake ice duration days can significantly reflect the regional climate variation.
基金supported by the National Natural Science Foundation of China“Study on the dynamic mechanism of grassland ecosystem response to climate change in Qinghai Plateau”under grant number U20A2098the Second Tibet Plateau Scientific Expedition and Research Program(STEP)under grant number 2019QZKK0804.
文摘In this paper,RS,GIS and GPS technologies are used to interpret the remote sensing images of the north shore of Qinghai Lake from 1987 to 2014 according to the inversion results of vegetation coverage(FVC),albedo,land surface temperature(LST),soil moisture(WET)and other major parameters after image preprocessing,such as radiometric correction,geometric correction and atmospheric correction.On this basis,the decision tree classification method based on landsat8 remote sensing image is used to classify the desertification land in this area,and the development and change of desertification in this period are analyzed.The results show that the fluctuation of desertification land area in this area increased during the study period,but from 2003 to 2014,the land area of mild desertification,moderate desertification and severe desertification landwere respectively decreased 0.92,145.89 and 29.39 km2,while the area of serious desertification land still has a slow increasing trend.Whether the driving force of desertification change trend in this area is caused by human factors or global change needs to be further studied.
基金This research was supported by the National Nature&Science Foundation of China“Study on the dynamic mechanism of grassland ecosystem response to climate change in Qinghai Plateau”(No.U20A2098)the second Tibetan plateau scientific expedition and research program(STEP,No.2019QZKK0804)China Huaneng Group Co.Science and Technology Program“The research development and implement on the evaluation technology of wind resource”(No.HNKJ18-H31).
文摘Land desertification is a widely concerned ecological environment problem.Studying the evolution trend of desertification types is of great significance to prevent and control land desertification.In this study,we applied the decision tree classification method,to study the land area and temporal and spatial change law of different types of desertification in the North Bank of Qinghai Lake area from 1987 to 2014,based on the current land use situation and TM remote sensing image data of Haiyan County,Qinghai Province,The results show that the area of mild desertification land and moderate desertification land in the study area has decreased,while the area of severe desertification land and extreme desertification land has increased significantly in the past 30 years.The area of desertification land decreased by 4.02 km2,of which the area of mild and moderate desertification land decreased by 39.73 km2 and 36.8 km2 respectively,and the area of severe and extreme desertification land increased by 32.78 km2 and 39.73 km2 respectively.As for the mutual transformation relationship,the transformation from severe desertification land to extreme desertification land is the main,and the junction of severe desertification land and extreme desertification land is the sensitive area of transformation.In the north shore of Qinghai Lake,the sandy land tends to expand eastward.The research provides reference basis for local land desertification monitoring,and has a great guidance for local effective land desertification and soil and water conservation.
文摘Landscape ecology emphasizes large areas and ecological effects of the spatial patterning of ecosystem. Recent developments in landscape ecology have emphasized the important relationship between spatial patterns and many ecological processes. Quantitative methods in landscape ecology link spatial patterns and ecological processes at broad spatial and temporal scales. In turn the increased attention on temporal change of ecosystem has highlighted the need for quantitative methods that can analyze patterns. This research applies quantitative methods——change detection to assess the ecosystem temporal change in the arid and semiarid area. Remote sensing offers the temporal change of ecosystem on landscape characteristics.
基金supported by the National Philosophy and Social Science Foundation of China (14XMZ072)the Natural Science Foundation of Qinghai Province, China (2017-ZJ-901 and 2014-ZJ-723)+1 种基金the National Natural Science Foundation of China (40861022 and 41661023)the Cooperative Scientific Research Project of "Chunhui Plan", Ministry of Education of China (Z2012092 and S2016026)
文摘Soil moisture(SM), which plays a crucial role in studies of the climate, ecology, agriculture and the environment, can be estimated and mapped by remote sensing technology over a wide region. However, remotely sensed SM is constrained by its estimation accuracy, which mainly stems from the influence of vegetation cover on soil spectra information in mixed pixels. To overcome the low-accuracy defects of existing surface albedo method for estimating SM, in this paper, Qinghai Lake Basin, an important animal husbandry production area in Qinghai Province, China, was chosen as an empirical research area. Using the surface albedo computed from moderate resolution imaging spectroradiometer(MODIS) reflectance products and the actual measured SM data, an albedo/vegetation coverage trapezoid feature space was constructed. Bare soil albedo was extracted from the surface albedo mainly containing information of soil, vegetation, and both albedo models for estimating SM were constructed separately. The accuracy of the bare soil albedo model(root mean square error=4.20, mean absolute percent error=22.75%, and theil inequality coefficient=0.67) was higher than that of the existing surface albedo model(root mean square error=4.66, mean absolute percent error=25.46% and theil inequality coefficient=0.74). This result indicated that the bare soil albedo greatly improved the accuracy of SM estimation and mapping. As this method eliminated the effect of vegetation cover and restored the inherent soil spectra, it not only quantitatively estimates and maps SM at regional scales with high accuracy, but also provides a new way of improving the accuracy of soil organic matter estimation and mapping.
