URBAN SPATIAL AND TEMPORAL CHANGES ANALYSIS BASED ON SPECTRAL, POLARIMETRIC, TEMPORAL, SPATIAL DIMENSIONS AND DECISION LEVEL FUSION

In order to monitor the pattern, distribution and trend of urban land use/ land cover change, it is necessary to integrate polarization, spatial, spectral and multi-temporal remotely sensed data to assess the spatial pattern and dynamics changes of urban areas in both the spatial and the temporal dimensions. In this paper, multi-temporal Landsat TM/ETM+ optical data, and dualpolarized, horizontal-horizontal (HH) and vertical-vertical (VV) PALSAR data are integrated. Specifically, derive spatial information is included by means of eight textures extracted from PALSAR HH and HV data, and from optical bands using Grey-level Co-occurrence Matrix (GLCM), including the mean, variance, homogeneity, contrast, dissimilarity, entropy, second moment, and correlation. They are used to generate multi-temporal land cover maps by image classification and decision level fusion. Finally, more than ten quantitative landscape indices, including Number of Patches (NP), Patch Density (PD), Largest Patch Index (LPI), Mean of patch Area Distribution (AREA_MN), Area-weight-mean of Shape index (SHAPE_AM), Area-weight-mean of Fractal dimension index (FRAC_AM), Area-weight-mean of Euclidean Nearest-Neighbor Distance (ENN_AM), Contagion Index (CONTAG), Interspersion and Juxtaposition Index (IJI) and Shannon’s Diversity Index (SHDI), are selected to analyse and evaluate the spatial-temporal changes at the patch level, class level and landscape level. At the same time, land use and land cover transfer matrixes are used to assess the dynamic change trends for different land cover types. The results demonstrate the significance of combining multi-temporal optical data, SAR data and spatial information by means of texture variables for landscape pattern monitoring. Analysis based on Land Use and Land Cover (LULC) and landscape indexes at the annual and seasonal levels show LULC changes between 2001 and 2011 and allow detection exactly in what season they happened.