Approach for estimation of ecosystem services value using multitemporal remote sensing images

2021 ◽  
Vol 16 (01) ◽  
Author(s):  
Liyan Wang ◽  
Chao Chen ◽  
Zili Zhang ◽  
Wei Gan ◽  
Jie Yu ◽  
...  
2014 ◽  
Author(s):  
L. Gómez-Chova ◽  
J. Amorós-López ◽  
J. Muñoz-Marí ◽  
G. Camps-Valls

2020 ◽  
Vol 198 ◽  
pp. 04026
Author(s):  
Liyan Wang ◽  
Chao Chen ◽  
Kai Wang

It is an effective method to study the value change of ecological services based on land use and cover change information. This paper analyzed the land use and cover change information in the research area, which is based on the remote sensing images and social statistics data of 2005, 2010, and 2015, and then, quantitative estimation of the ecosystem service value was performed. Yangtze-Huaihe river basin, China is a fragile ecological area, which is selected as the research area. During 2005-2015, the area of cultivated land and construction land was the main land use types in the study area, the land use and cover change in the study area were obvious, which was characterized by the increasing of construction land area and the decreasing of cultivated land area, and the total ecosystem services value in the research area has been decreasing continuously, the value from 34.376 billion yuan in 2005 to 26.161 billion yuan in 2015.


2020 ◽  
Vol 2020 ◽  
pp. 1-9 ◽  
Author(s):  
Liang Huang ◽  
Qiuzhi Peng ◽  
Xueqin Yu

In order to improve the change detection accuracy of multitemporal high spatial resolution remote-sensing (HSRRS) images, a change detection method of multitemporal remote-sensing images based on saliency detection and spatial intuitionistic fuzzy C-means (SIFCM) clustering is proposed. Firstly, the cluster-based saliency cue method is used to obtain the saliency maps of two temporal remote-sensing images; then, the saliency difference is obtained by subtracting the saliency maps of two temporal remote-sensing images; finally, the SIFCM clustering algorithm is used to classify the saliency difference image to obtain the change regions and unchange regions. Two data sets of multitemporal high spatial resolution remote-sensing images are selected as the experimental data. The detection accuracy of the proposed method is 96.17% and 97.89%. The results show that the proposed method is a feasible and better performance multitemporal remote-sensing image change detection method.


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