With the development of urbanization, urban heat island effect issue is becoming more and more severe. What's more, the vegetation eco-environmental quality (VEEQ) is severely damaged, resulting in the decline of urban ecosystem function. Therefore, it is of great significance to use remote sensing technique to analyze the response of urban heat island to VEEQ quantitatively. As is known to all, vegetation is the main body in the vegetation ecological environment system. Water and heat conditions are the important driving forces for its formation and evolution. Good soil condition is the basis for vegetation survival. Besides, the terrain is conducive to the judgment of the vegetation distribution. Accordingly, several indexes involving vegetation index, heat index, soil moisture index, soil brightness index, elevation factor and slope factor were selected and extracted from Landsat8 OLI images to establish the evaluation index system of VEEQ. Based on Landsat8 TIRS images, this paper applied the radiative transfer equation method to retrieve land surface temperature (LST) and the urban island grade was divided based on the mean and standard deviation values of LST. The principal component analysis method was utilized to determine the weigh value of each index and then a comprehensive evaluation model of VEEQ was established. Furthermore, the quantitative relationship between LST and VEEQ was analyzed. The results showed that, there existed obvious heat island effects in Haidian District of Beijing city and its surrounding areas. The poor quality areas and the high quality areas of vegetation ecological environment had strengthening and weakening thermal environment effects respectively. There was a strong negative relationship between LST and VEEQ.
Hollow village is a special phenomenon in the process of urbanization in China, which causes the waste of land resources. Therefore, it's imminent to carry out the hollow village recognition and renovation. However, there are few researches on the remote sensing identification of hollow village. In this context, in order to recognize the abandoned homesteads by remote sensing technique, the experiment was carried out as follows. Firstly, Gram-Schmidt transform method was utilized to complete the image fusion between multi-spectral images and panchromatic image of WorldView-2. Then the fusion images were made edge enhanced by high pass filtering. The multi-resolution segmentation and spectral difference segmentation were carried out to obtain the image objects. Secondly, spectral characteristic parameters were calculated, such as the normalized difference vegetation index (NDVI), the normalized difference water index (NDWI), the normalized difference Soil index (NDSI) etc. The shape feature parameters were extracted, such as Area, Length/Width Ratio and Rectangular Fit etc.. Thirdly, the SEaTH algorithm was used to determine the thresholds and optimize the feature space. Furthermore, the threshold classification method and the random forest classifier were combined, and the appropriate amount of samples were selected to train the classifier in order to determine the important feature parameters and the best classifier parameters involved in classification. Finally, the classification results was verified by computing the confusion matrix. The classification results were continuous and the phenomenon of salt and pepper using pixel classification was avoided effectively. In addition, the results showed that the extracted Abandoned Homesteads were in complete shapes, which could be distinguished from those confusing classes such as Homestead in Use and Roads.