Lake Balkhash is the third largest inland lake in Central Asia after the Caspian Sea and the Aral Sea.The Ili River-Balkash Lake Basin resides in the southeastern part of the Republic of Kazakhstan and the western par...Lake Balkhash is the third largest inland lake in Central Asia after the Caspian Sea and the Aral Sea.The Ili River-Balkash Lake Basin resides in the southeastern part of the Republic of Kazakhstan and the western part of China's Ili Prefecture,which belongs to the arid and semi-arid region.In the middle to late 20^(th)century,the Ili River-Balkash Lake Basin was affected by climate change and human activities,and the problems of water ecology and water resources became increasingly prominent,which became the focus of attention for China and Kazakhstan.In this study,the water level derived from radar altimeter data,the water surface area extracted from Landsat data,and the temperature and precipitation data in the basin were comprehensively utilised.Data analysis of the time course and correlation of hydrological,meteorological elements in the lake basin,water dynamic changes,and influencing factors of Lake Balkhash was studied.The results show that the cyclical change of regional climate is the main factor affecting the change of lake water,and human activities in the short term can regulate the change of water volume in Lake Balkhash.The research results in this paper can provide a scientific basis for the solution of water disputes in cross-border rivers between China and Kazakhstan.展开更多
Vegetation coverage recovery after the Wenchuan earthquake has important implications for preventing post-seismic geohazards and soil erosion.However,spatiotemporal changes in vegetation coverage recovery and its driv...Vegetation coverage recovery after the Wenchuan earthquake has important implications for preventing post-seismic geohazards and soil erosion.However,spatiotemporal changes in vegetation coverage recovery and its driving factors have not been sufficiently studied in the quake-hit areas.This paper aims to analyze vegetation coverage recovery and its driving factors in the quake-hit areas using monadic linear regression,coefficient of variation,and geographical detector.First,we used Moderate-resolution Imaging Spectroradiometer(MODIS)data to calculate the vegetation coverage from 2008 to 2018 in the quake-hit areas.Second,we assessed the trend and stability of vegetation recovery in the quake-hit areas based on vegetation coverage.Finally,combined with topography,climate,soil type,vegetation type,and human activities in the quake-hit areas,the driving factors affecting vegetation coverage recovery were analyzed.The results showed that the vegetation coverage level in the quake-hit areas recovered about 90%of that before the earthquake.Vegetation coverage recovery was mainly improved in a stepwise manner:increasing and then stabilizing,then increasing and stabilizing again.Elevation,soil type,and road density were the main factors affecting vegetation coverage recovery,and the interaction among all factors positively strengthened their impacts on vegetation coverage recovery.In addition,the results also revealed the categories that were conducive to vegetation coverage recovery among the same environmental factors and can provide a scientific reference for vegetation coverage recovery in the quake-hit areas.展开更多
Most existing classification studies use spectral information and those were adequate for cities or plains. This paper explores classification method suitable for the ALOS （Advanced Land Observing Satellite） in moun...Most existing classification studies use spectral information and those were adequate for cities or plains. This paper explores classification method suitable for the ALOS （Advanced Land Observing Satellite） in mountainous terrain. Mountainous terrain mapping using ALOS image faces numerous challenges. These include spectral confusion with other land cover features, topographic effects on spectral signatures （such as shadow）. At first, topographic radiometric correction was carried out to remove the illumination effects of topography. In addition to spectral features, texture features were used to assist classification in this paper. And texture features extracted based on GLCM （Gray Level Co- occurrence Matrix） were not only used for segmentation, but also used for building rules. The performance of the method was evaluated and compared with Maximum Likelihood Classification （MLC）. Results showed that the object-oriented method integrating spectral and texture features has achieved overall accuracy of 85.73% with a kappa coefficient of 0.824, which is 13.48% and o.145 respectively higher than that got by MLC method. It indicated that texture features can significantly improve overall accuracy, kappa coefficient, and the classification precision of existing spectrum confusion features. Object-oriented method Integrating spectral and texture features is suitable for land use extraction of ALOS image in mountainous terrain.展开更多
基金jointly funded by the China Geological Survey Project “Cooperative Mapping of Hydrology and Environmental Geology in Five Countries of Central Asia” (DD20160106)National Key Research and Development Program “National Water Resources Stereo Monitoring System and Remote Sensing Technology Application” (2017YFC0405802)the key supporting research project of National Natural Science Foundation of China “High-resolution Model Development and Simulation Studies of Lake Processes in the Tibetan Plateau and Their Interaction with the Atmosphere” (91637209)
文摘Lake Balkhash is the third largest inland lake in Central Asia after the Caspian Sea and the Aral Sea.The Ili River-Balkash Lake Basin resides in the southeastern part of the Republic of Kazakhstan and the western part of China's Ili Prefecture,which belongs to the arid and semi-arid region.In the middle to late 20^(th)century,the Ili River-Balkash Lake Basin was affected by climate change and human activities,and the problems of water ecology and water resources became increasingly prominent,which became the focus of attention for China and Kazakhstan.In this study,the water level derived from radar altimeter data,the water surface area extracted from Landsat data,and the temperature and precipitation data in the basin were comprehensively utilised.Data analysis of the time course and correlation of hydrological,meteorological elements in the lake basin,water dynamic changes,and influencing factors of Lake Balkhash was studied.The results show that the cyclical change of regional climate is the main factor affecting the change of lake water,and human activities in the short term can regulate the change of water volume in Lake Balkhash.The research results in this paper can provide a scientific basis for the solution of water disputes in cross-border rivers between China and Kazakhstan.
基金This study is supported and funded by the National Natural Science Foundation of China(Grant No.42074021)Department of Science and Technology of Sichuan Province(Grant No.20ZDYF1142+3 种基金Grant No.2020JDTD0003)China Scholarship Council(CSC No.202007000081)Science and Technology Bureau of Nanchong City(Grant Nos.20YFZJ0029 and 19SXHZ0039)Linguo Yuan is funded by the National Program for Support of Top-notch Young Professionals.
文摘Vegetation coverage recovery after the Wenchuan earthquake has important implications for preventing post-seismic geohazards and soil erosion.However,spatiotemporal changes in vegetation coverage recovery and its driving factors have not been sufficiently studied in the quake-hit areas.This paper aims to analyze vegetation coverage recovery and its driving factors in the quake-hit areas using monadic linear regression,coefficient of variation,and geographical detector.First,we used Moderate-resolution Imaging Spectroradiometer(MODIS)data to calculate the vegetation coverage from 2008 to 2018 in the quake-hit areas.Second,we assessed the trend and stability of vegetation recovery in the quake-hit areas based on vegetation coverage.Finally,combined with topography,climate,soil type,vegetation type,and human activities in the quake-hit areas,the driving factors affecting vegetation coverage recovery were analyzed.The results showed that the vegetation coverage level in the quake-hit areas recovered about 90%of that before the earthquake.Vegetation coverage recovery was mainly improved in a stepwise manner:increasing and then stabilizing,then increasing and stabilizing again.Elevation,soil type,and road density were the main factors affecting vegetation coverage recovery,and the interaction among all factors positively strengthened their impacts on vegetation coverage recovery.In addition,the results also revealed the categories that were conducive to vegetation coverage recovery among the same environmental factors and can provide a scientific reference for vegetation coverage recovery in the quake-hit areas.
基金supported jointly by Key Laboratory of Geo-special Information Technology, Ministry of Land and Resources (Grant No. KLGSIT2013-12)Knowledge Innovation Program (Grant No. KSCX1-YW-09-01) of Chinese Academy of Sciences
文摘Most existing classification studies use spectral information and those were adequate for cities or plains. This paper explores classification method suitable for the ALOS （Advanced Land Observing Satellite） in mountainous terrain. Mountainous terrain mapping using ALOS image faces numerous challenges. These include spectral confusion with other land cover features, topographic effects on spectral signatures （such as shadow）. At first, topographic radiometric correction was carried out to remove the illumination effects of topography. In addition to spectral features, texture features were used to assist classification in this paper. And texture features extracted based on GLCM （Gray Level Co- occurrence Matrix） were not only used for segmentation, but also used for building rules. The performance of the method was evaluated and compared with Maximum Likelihood Classification （MLC）. Results showed that the object-oriented method integrating spectral and texture features has achieved overall accuracy of 85.73% with a kappa coefficient of 0.824, which is 13.48% and o.145 respectively higher than that got by MLC method. It indicated that texture features can significantly improve overall accuracy, kappa coefficient, and the classification precision of existing spectrum confusion features. Object-oriented method Integrating spectral and texture features is suitable for land use extraction of ALOS image in mountainous terrain.
