We developed a forest type classification technology for the Daxing＇an Mountains of northeast China using multisource remote sensing data.A SPOT-5 image and two temporal images of RADARSAT-2 full-polarization SAR wer...We developed a forest type classification technology for the Daxing＇an Mountains of northeast China using multisource remote sensing data.A SPOT-5 image and two temporal images of RADARSAT-2 full-polarization SAR were used to identify forest types in the Pangu Forest Farm of the Daxing＇an Mountains.Forest types were identified using random forest（RF） classification with the following data combination types： SPOT-5 alone,SPOT-5 and SAR images in August or November,and SPOT-5 and two temporal SAR images.We identified many forest types using a combination of multitemporal SAR and SPOT-5 images,including Betula platyphylla,Larix gmelinii,Pinus sylvestris and Picea koraiensis forests.The accuracy of classification exceeded 88% and improved by 12% when compared to the classification results obtained using SPOT data alone.RF classification using a combination of multisource remote sensing data improved classification accuracy compared to that achieved using single-source remote sensing data.展开更多
Agricultural land use(ALU)critically influences food production and water resource allocation.This study examines the dynamics of ALU in the North China Plain(NCP),a region characterized by intensive agri-culture and ...Agricultural land use(ALU)critically influences food production and water resource allocation.This study examines the dynamics of ALU in the North China Plain(NCP),a region characterized by intensive agri-culture and severe groundwater over-exploitation,focusing on the multidimensional drivers and their implications for water resource management.By employing an elaborate classification scheme based on satellite imagery and extensive first-hand field data,we identified significant shifts in crop patterns.From 2013 to 2017,there was a notable transition from double crops(primarily wheat-maize)to single crops(primarily maize),covering 4600 km^(2)and accounting for 42%of single crops in 2013.From 2017 to 2022,there was a shift from single crops to economic forests,encompassing 3600 km^(2)and 22%of eco-nomic forests in 2017,including orchards,timber trees,and shelter forest belts.These shifts resulted in an 11%decrease in grain acreage(6800 km^(2))but an 11%increase in crop water consumption(6.3 km^(3))during 2013-2022.Notably,water consumption by economic forests increased by 126%(9.4 km^(3))during this period.This study highlights the critical need to balance competing demands for food and water security,providing valuable insights applicable to other agriculturally intensive regions worldwide.展开更多
基金supported by the National Natural Science Foundation of China(Nos.31500518,31500519,and 31470640)
文摘We developed a forest type classification technology for the Daxing＇an Mountains of northeast China using multisource remote sensing data.A SPOT-5 image and two temporal images of RADARSAT-2 full-polarization SAR were used to identify forest types in the Pangu Forest Farm of the Daxing＇an Mountains.Forest types were identified using random forest（RF） classification with the following data combination types： SPOT-5 alone,SPOT-5 and SAR images in August or November,and SPOT-5 and two temporal SAR images.We identified many forest types using a combination of multitemporal SAR and SPOT-5 images,including Betula platyphylla,Larix gmelinii,Pinus sylvestris and Picea koraiensis forests.The accuracy of classification exceeded 88% and improved by 12% when compared to the classification results obtained using SPOT data alone.RF classification using a combination of multisource remote sensing data improved classification accuracy compared to that achieved using single-source remote sensing data.
基金supported by the National Key Research and Development Program of China(2021YFB3900604)National Natural Science Foundation of China(52079065,52325901,and 42471399)World Bank China Programmatic Research on Water.
文摘Agricultural land use(ALU)critically influences food production and water resource allocation.This study examines the dynamics of ALU in the North China Plain(NCP),a region characterized by intensive agri-culture and severe groundwater over-exploitation,focusing on the multidimensional drivers and their implications for water resource management.By employing an elaborate classification scheme based on satellite imagery and extensive first-hand field data,we identified significant shifts in crop patterns.From 2013 to 2017,there was a notable transition from double crops(primarily wheat-maize)to single crops(primarily maize),covering 4600 km^(2)and accounting for 42%of single crops in 2013.From 2017 to 2022,there was a shift from single crops to economic forests,encompassing 3600 km^(2)and 22%of eco-nomic forests in 2017,including orchards,timber trees,and shelter forest belts.These shifts resulted in an 11%decrease in grain acreage(6800 km^(2))but an 11%increase in crop water consumption(6.3 km^(3))during 2013-2022.Notably,water consumption by economic forests increased by 126%(9.4 km^(3))during this period.This study highlights the critical need to balance competing demands for food and water security,providing valuable insights applicable to other agriculturally intensive regions worldwide.
