[Evaluation of eco-environmental quality based on artificial neural network and remote sensing techniques].
In the present study, vegetation, soil brightness, and moisture indices were extracted from Landsat ETM remote sensing image, heat indices were extracted from MODIS land surface temperature product, and climate index and other auxiliary geographical information were selected as the input of neural network. The remote sensing eco-environmental background value of standard interest region evaluated in situ was selected as the output of neural network, and the back propagation (BP) neural network prediction model containing three layers was designed. The network was trained, and the remote sensing eco-environmental background value of Fuzhou in China was predicted by using software MATLAB. The class mapping of remote sensing eco-environmental background values based on evaluation standard showed that the total classification accuracy was 87. 8%. The method with a scheme of prediction first and classification then could provide acceptable results in accord with the regional eco-environment types.
Li
Hongyi
H
Insitutue of Agricultural Remote Sensing and Information Technology, Zhejiang University, Hangzhou 310029, China. lihongyi1981@sohu.com
Shi
Zhou
Z
Sha
Jinming
J
Cheng
Jieliang
J
D002980:Climate; D004463:Ecology / Q000379:methods; D017753:Ecosystem; D004777:Environment; D005069:Evaluation Studies as Topic; D016571:Neural Networks, Computer; D011786:Quality Control; D017215:Satellite Communications; D012987:Soil / Q000032:analysis
D012987:Soil