Poverty has always been a global concern that has restricted human development.The first goal(SDG 1)of the United Nations Sustainable Development Goals(SDGs)is to eliminate all forms of poverty all over the world.The ...Poverty has always been a global concern that has restricted human development.The first goal(SDG 1)of the United Nations Sustainable Development Goals(SDGs)is to eliminate all forms of poverty all over the world.The establishment of a scientific and effective localized SDG 1 evaluation and monitoring method is the key to achieving SDG 1.This paper proposes SDG 1 China district and county-level localization evaluation method based on multi-source remote sensing data for the United Nations Sustainable Development Framework.The temporal and spatial distribution characteristics of China’s poverty areas and their SDG 1 evaluation values in 2012,2014,2016,and 2018 have been analyzed.Based on the SDGs global indicator framework,this paper first constructed SDG 1 China’s district and county localization indicator system and then extracted multidimensional feature factors from nighttime light images,land cover data,and digital elevation model data.Secondly,we establish SDG 1 China’s localized partial least squares estimation model and SDG 1 China’s localized machine learning estimation model.Finally,we analyze and verify the spatiotemporal distribution characteristics of China’s poverty areas and counties and their SDG 1 evaluation values.The results show that SDG 1 China’s district and county localization indicator system proposed in this study and SDG 1 China’s localized partial least squares estimation model can better reflect the poverty level of China’s districts and counties.The estimated model R^(2) is 0.65,which can identify 72.77%of China’s national poverty counties.From 2012 to 2018,the spatial distribution pattern of SDG evaluation values in China’s districts and counties is that the SDG evaluation values gradually increase from western China to eastern China.In addition,the average SDG 1 evaluation value of China’s districts and counties increased by 23%from 2012 to 2018.This paper is oriented to the United Nations SDGs framework,explores the SDG 1 localized evaluation method of China’s districts and counties based on multisource remote sensing data,and provides a scientific and rapid regional poverty monitoring and evaluation program for the implementation of the 2030 agenda poverty alleviation goals.展开更多
Accurately and timely monitoring the spatial distribution and composition of mangrove species is critical for assessing mangroves’health,dynamics,and biodiversity,as well as mangroves’degradation and restoration.Rec...Accurately and timely monitoring the spatial distribution and composition of mangrove species is critical for assessing mangroves’health,dynamics,and biodiversity,as well as mangroves’degradation and restoration.Recent advances in machine learning algorithms,coupled with spaceborne remote sensing technique,offer an unprecedented opportunity to map mangroves at species level with high resolution over large extents.However,a single data source or data type is insufficient to capture the complex features of mangrove species and cannot satisfy the need for fine species classification.Moreover,identifying and selecting effective features derived from integrated multisource data are essential for integrating high-dimensional features for mangrove species discrimination.In this study,we developed a novel framework for mangrove species classification using spectral,texture,and polarization information derived from 3-source spaceborne imagery:WorldView-2(WV-2),OrbitaHyperSpectral(OHS),and Advanced Land Observing Satellite-2(ALOS-2).A total of 151 remote sensing features were first extracted,and 18 schemes were designed.Then,a wrapper method by combining extreme gradient boosting with recursive feature elimination(XGBoost-RFE)was conducted to select the sensitive variables and determine the optical subset size of all features.Finally,an ensemble learning algorithm of XGBoost was applied to classify 6 mangrove species in the Zhanjiang Mangrove National Nature Reserve,China.Our results showed that combining multispectral,hyperspectral,and L-band synthetic aperture radar features yielded the best mangrove species classification results,with an overall accuracy of 94.02%,a quantity disagreement of 4.44%,and an allocation disagreement of 1.54%.In addition,this study demonstrated important application potential of the XGBoost classifier.The proposed framework could provide fine-scale data and conduce to mangroves’conservation and restoration.展开更多
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
基金supported by the National Natural Science Foundation of China[grant numbers 41971423 and 31972951]the Natural Science Foundation of Hunan Province[grant numbers 2020JJ3020 and 2020JJ5164]+1 种基金the Science and Technology Planning Project of Hunan Province[grant numbers 2019RS2043 and 2019GK2132]the Postgraduate Scientific Research Innovation Project of Hunan Province[grant number CX20210991].
文摘Poverty has always been a global concern that has restricted human development.The first goal(SDG 1)of the United Nations Sustainable Development Goals(SDGs)is to eliminate all forms of poverty all over the world.The establishment of a scientific and effective localized SDG 1 evaluation and monitoring method is the key to achieving SDG 1.This paper proposes SDG 1 China district and county-level localization evaluation method based on multi-source remote sensing data for the United Nations Sustainable Development Framework.The temporal and spatial distribution characteristics of China’s poverty areas and their SDG 1 evaluation values in 2012,2014,2016,and 2018 have been analyzed.Based on the SDGs global indicator framework,this paper first constructed SDG 1 China’s district and county localization indicator system and then extracted multidimensional feature factors from nighttime light images,land cover data,and digital elevation model data.Secondly,we establish SDG 1 China’s localized partial least squares estimation model and SDG 1 China’s localized machine learning estimation model.Finally,we analyze and verify the spatiotemporal distribution characteristics of China’s poverty areas and counties and their SDG 1 evaluation values.The results show that SDG 1 China’s district and county localization indicator system proposed in this study and SDG 1 China’s localized partial least squares estimation model can better reflect the poverty level of China’s districts and counties.The estimated model R^(2) is 0.65,which can identify 72.77%of China’s national poverty counties.From 2012 to 2018,the spatial distribution pattern of SDG evaluation values in China’s districts and counties is that the SDG evaluation values gradually increase from western China to eastern China.In addition,the average SDG 1 evaluation value of China’s districts and counties increased by 23%from 2012 to 2018.This paper is oriented to the United Nations SDGs framework,explores the SDG 1 localized evaluation method of China’s districts and counties based on multisource remote sensing data,and provides a scientific and rapid regional poverty monitoring and evaluation program for the implementation of the 2030 agenda poverty alleviation goals.
基金National Natural Science Foundation of China(42171379,42222103,42101379,and 42171372)Science and Technology Development Program of Jilin Province,China(20210101396JC)+2 种基金Youth Innovation Promotion Association of the Chinese Academy of Sciences(2017277 and 2021227)Young Scientist Group Project of Northeast Institute of Geography and Agroecology,Chinese Academy of Sciences(2022QNXZ03)Shenzhen Science and Technology Program(JCYJ20210324093210029).
文摘Accurately and timely monitoring the spatial distribution and composition of mangrove species is critical for assessing mangroves’health,dynamics,and biodiversity,as well as mangroves’degradation and restoration.Recent advances in machine learning algorithms,coupled with spaceborne remote sensing technique,offer an unprecedented opportunity to map mangroves at species level with high resolution over large extents.However,a single data source or data type is insufficient to capture the complex features of mangrove species and cannot satisfy the need for fine species classification.Moreover,identifying and selecting effective features derived from integrated multisource data are essential for integrating high-dimensional features for mangrove species discrimination.In this study,we developed a novel framework for mangrove species classification using spectral,texture,and polarization information derived from 3-source spaceborne imagery:WorldView-2(WV-2),OrbitaHyperSpectral(OHS),and Advanced Land Observing Satellite-2(ALOS-2).A total of 151 remote sensing features were first extracted,and 18 schemes were designed.Then,a wrapper method by combining extreme gradient boosting with recursive feature elimination(XGBoost-RFE)was conducted to select the sensitive variables and determine the optical subset size of all features.Finally,an ensemble learning algorithm of XGBoost was applied to classify 6 mangrove species in the Zhanjiang Mangrove National Nature Reserve,China.Our results showed that combining multispectral,hyperspectral,and L-band synthetic aperture radar features yielded the best mangrove species classification results,with an overall accuracy of 94.02%,a quantity disagreement of 4.44%,and an allocation disagreement of 1.54%.In addition,this study demonstrated important application potential of the XGBoost classifier.The proposed framework could provide fine-scale data and conduce to mangroves’conservation and restoration.
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.
