Remote sensing dynamic monitoring of ecosystem service value of soil conservation with time series data
文献类型: 外文期刊
作者: Gu, Xiaohe 1 ; Guo, Wei 1 ; Wang, Yancang 1 ; Wang, Yancang 1 ;
作者机构: 1.Beijing Res Ctr Informat Technol Agr, Beijing, Peoples R China
2.Shandong Univ Sci & Technol, Coll Geometr, Qingdao, Peoples R China
关键词: soil conservation;ecosystem service value;opportunity cost;dynamic chang
期刊名称:2014 THIRD INTERNATIONAL WORKSHOP ON EARTH OBSERVATION AND REMOTE SENSING APPLICATIONS (EORSA 2014)
ISSN: 2380-8039
年卷期: 2014 年
页码:
收录情况: SCI
摘要: Terrestrial ecosystem has important ecological function and ecological services value. Soil conservation is one of the important ecological service functions of ecosystem, which is benefit for improve ecological environment and realize economy sustainable development. The soil conservation functions of ecosystem mainly include maintaining soil fertility, reducing waterway silt-up and protecting topsoil. Beijing area was chosen as study area, of which the ecosystem changed rapidly. Time series LandSat images were used to analyze the dynamic change of spatial pattern of ecosystem in Beijing area in recent thirty years. By using the method of opportunity cost, the study evaluated the ecosystem service value of soil conservation and analyzed its dynamic change of spatial pattern. Results showed that the order of ecosystem service value in Beijing area was 1978 > 1992 > 2000 > 2010. Because of the close relation with soil erosion, the ecosystem service value fluctuated with yearly precipitation. The main contributors of ecosystem service value of soil conservation were forest, of which the proportion could reach above 80%. The government was committed to promoting the percentage of forest cover, so the forest was the stable contributor for soil conservation in Beijing area.
- 相关文献
作者其他论文 更多>>
-
Recognition of maize seedling under weed disturbance using improved YOLOv5 algorithm
作者:Tang, Boyi;Zhao, Chunjiang;Tang, Boyi;Zhou, Jingping;Pan, Yuchun;Qu, Xuzhou;Cui, Yanglin;Liu, Chang;Li, Xuguang;Zhao, Chunjiang;Gu, Xiaohe;Li, Xuguang
关键词:Object detection; Maize seedlings; UAV RGB images; YOLOv5; Attention mechanism
-
A Novel Approach for Maize Straw Type Recognition Based on UAV Imagery Integrating Height, Shape, and Spectral Information
作者:Liu, Xin;Gong, Huili;Guo, Lin;Zhou, Jingping;Gong, Huili;Guo, Lin;Gong, Huili;Guo, Lin;Gong, Huili;Guo, Lin;Gong, Huili;Guo, Lin;Gu, Xiaohe;Zhou, Jingping
关键词:maize straw type; multispectral imagery; SESI; object-oriented classification; UAV
-
Monitoring the interannual dynamic changes of soil organic matter using long-term Landsat images
作者:Liu, Chang;Liu, Chang;Zhang, Chi;Chen, Wentao;Qu, Xuzhou;Tang, Boyi;Ma, Kai;Gu, Xiaohe;Sun, Qian
关键词:Soil organic matter; Remote sensing; Machine learning; Transfer learning; Spatial-temporal change
-
Winter wheat harvest detection via Sentinel-2 MSI images
作者:Yue, Jibo;Yao, Yihan;Shen, Jianing;Li, Ting;Wei, Yihao;Xu, Xin;Guo, Wei;Fu, Yuanyuan;Qiao, Hongbo;Ma, Xinming;Wang, Jian;Xu, Nianxu;Feng, Haikuan;Feng, Haikuan;Lin, Yinghao;Lin, Yinghao
关键词:Wheat; maturity; harvest; monitoring; vegetation index
-
Using UAV-based multispectral images and CGS-YOLO algorithm to distinguish maize seeding from weed
作者:Tang, Boyi;Zhou, Jingping;Zhao, Chunjiang;Pan, Yuchun;Lu, Yao;Liu, Chang;Ma, Kai;Sun, Xuguang;Gu, Xiaohe;Tang, Boyi;Zhou, Jingping;Zhang, Ruifang
关键词:Object detection; Maize seedlings; Weed disturbance; YOLO; UAV multispectral images
-
Multi-variety monitoring of potato late blight severity using UAV data with improved SMOTE-CS for small sample modeling and deep feature learning
作者:Sun, Heguang;Mai, Huanming;Deng, Xiaoling;Feng, Ziheng;Feng, Haikuan;Yang, Guijun;Song, XiaoYu;Mao, Yanzhi;Li, Qingquan;Guo, Mei;Guo, Wei
关键词:Potato late blight; Remote sensing; SMOTE-CS; Deep learning; Transfer learning
-
Remote Sensing Dissolved Organic Matter in Freshwater Aquaculture Ponds by the Integration of UAV and Satellite Multispectral Images
作者:Chen, Guangxin;Chen, Tianen;Chen, Guangxin;Wang, Yancang;Gu, Xiaohe
关键词:Aquaculture; Autonomous aerial vehicles; Water quality; Remote sensing; Monitoring; Satellites; Satellite images; Accuracy; Estimation; Reflectivity; Dissolved organic matter; uncrewed aerial vehicle (UAV); multi-source remote sensing; freshwater aquaculture; machine learning



