The Study Of Urban Green Space Surveying Based On High Resolution RS Images

The green space system plays the important ecological and social roles in unban area. With the development of economy and society, more and more attantion are payed to urban green space planning, construction, evaluation, monitoring and management. It becomes a basic, efficient and precies method to obtain the urban green space information by using remote sensing (RS) technology. There are several high spatial resolution RS images available now for urban green space surveying, including aerial and satellite images with less than 1m of the spacial resolution. In this paper, image correction, data fusion, classification related with urban green space system were studied. Based on those studies, functions of main types of green spaces were evaluated.The first research objective is to probe the area and position precisions of different urban high resolution images corrected by using 2D 2sd polynomial in urban region. Different precision (acquired from GPS field surveying and 1:10000 topography maps) and number (25 and 6) of ground control points (GCPs) were used to correct the SPOT5 image; six GPS GCPS were used to correct the IKONOS image; six GPS GCPS were used to correct the QUICKBIRD image. Some other GPS points were used as check points (CPs) to check the position and area precisions of the corrected images. The results showed that: (1) For urban SPOT5 image, through 2D 2sd polynomial function by using 6 high precision GPS GCPs, better position precision was obtained in flat urban area. (2) Satisfied position precision could be reached by using 1∶10000 topography maps if GCPs are enough and their locations were well distributed. (3) Satisfied area precision was reached on the images corrected by GPS GCPs and map GCPs for urban green space system surveying. (4) The relationship between position and area errors is complex on the corrected images, so the area errors cannot be deduced by point errors according to theory of errors.(5)In the flat urban region, better position and precision were obtained through 2D 2sd polynomial function using 6 high precision GPS GCPs for IKONOS and QUICKBIRD images.The second research objective is to probe the data fusion for urban vegetation mapping based on image correction. The effect is not consistent when different fusion methods were used for different purposes. In this study, SPOT5 images were fused using four fusion methods, including PCA, HIS, PCA based on wavelet transformation, and HIS based on wavelet transformation. The conclusions were that the image fused by PCA based on wavelet transformation reached better spectral quality, but the spatial construction is not well. Because the location characteristics of urban vegetation is fragmental, so this method is not suitable for vegetation information obtaining. Better image spatial construction and spectral quality were obtained by PCA fusion. Because texture of trivial objectives is distinct, this method is best for vegetation mapping among these four methods.Tasseled Cap (KT) transformation is useful to distinguish different types of vegetation. The bands after transformation are correlated with nature landscape such as vegetation, soil. The IKONOS data was fused with KT and PCA methods. The fusion results were analyzed and compared within full image and vegetation subset. The result showed that the KT fusion method is better than PCA in full image and vegetation subset in the study area. The fusion results are differential with different KT transformation matrix, and the method of fusion should be chosen according to study area, data, and classification methods.The third research objective is to probe the methods of urban green space classification. The area of urban green space is the sum of six types of green space, including public green space, residential green space, nursries, recreation forest, green area for environmental protection, roadside green spaces. Green coverage is the area of all growing planting projection, including tree, shrub and grass. The cultivated lands in the urban region are not belongs to the green space.The green space area has social attribute, so it is difficult to get the each type classification information by spectral characteristics. In this paper, based on the image analysis technology of oriented-objective, spectral characteristics combined with ration of length and width, distance characteristics was used to get the information of roadside green space by computer automatic classification on 20cm resolution aerial image. The results showed that classification precision is better than 76. 56%.In order to obtain the green coverage information, the spectral characteristics of main classification objectives including various type vegetation, water body, bare soil, cultivated land, road area, and building of SPOT5 multi-spectral image were analyzed. Based on spectral analysis, two index models, (G ? R)/(G+R) and (G + SWIR)/80 were calculated. Decision tree algorithm classification method combining cultivated land distributions was used to get the green coverage by using two index models and the fourth band of multi-spectral image. Contrast to the supervised classification on fused image, the classification precision is obviously higher.The fourth research objective is to probe the relationship between the green space classification and scale. The uncertainty between RS image information acquiring and the size of scale is two highly related conceptions. It is necessary to study the scale impact to information acquire precision caused by image information uncertainty. In urban area, the structure of vegetation is complicated and the location of green space is fragmental. In the same time much green space is located in the shade of high buildings. Fragmental green space can not be fully reflected on lower resolution image, but there are more shadows and the shade of high buildings with higher resolution images. In this paper the green space information of same region was obtained by visual interpretation from SPOT5, IKONOS, and QUICKBIRD images and the classification precision were compared with. The results showed that: (1) For big size green space, higher resolution images have the better classification precision than lower. (2) Higher resolution images have obviously fine precision for fragmental green space classification. (3) In the urban region that most buildings less than 7 levels, the precision differences caused by the green space information lose for the shadow and shade of building is not obvious. So QUICKBIRD image is most suitable for urban green space surveying among three high resolution images.The fifth research objective is to probe the synthetical methods of the function evaluation of main green space types. In order to evaluate the function of public green space, four factors were chosen according to public green space function characteristics and evaluation requirement, which are percentage of greenery coverage, size of area, ratio of residential area within service radius, ratio of road area inside service radius of each public green space. Tai'an, Shandong, was chosen as the study case. Evalation factors data were obtained by RS decision tree algorithm classification based on vegetation indexs and ArcGIS spatial analysis methods from SPOT5 image. Fuzzy multi-level synthetic evaluation was used to evaluation the public green space of entire urban region. The result of evaluation is that the synthetical grade of public green space ecological and social function of Tai'an is 77.449, which indicates fine quality according to evaluation standard.In order to evaluate the ecological function of roadside green space in study region, there are three steps to get urban road system map and road green coverage map. (1) Urban road system map was obtained through buffer analysis by using middle lines of urban roads drawn by manual methods on SPOT5 2.5m resolution false color image. (2) Urban green coverage map was obtained by decision tree algorithm classification based on vegetation index on SPOT5 multi-spectral image. (3) Road green space coverage map was obtained by spatial overlap analysis by using the road system map and urban green space coverage map. Based on these two maps, percentage of road greenery coverage and road green space connectivity was calculated to evaluate the quality of the road green space system.