Human Settlement Analysis Based on Multi-temporal Remote Sensing Data： A Case Study of Xuzhou City, China

To evaluate urban human settlement, we propose a human settlement environment development index(HSEDI) model by choosing vegetation coverage, land surface temperature, impervious surfaces, slope, wetness, and water condition as the evaluation factors. We applied the proposed model to Xuzhou City, Jiangsu Province, China. Landsat-5 Thematic Mapper(TM) images from 1998 to 2010 and digital elevation model(DEM) data with a 30-m resolution were used to calculate the values of the six evaluation factors. The HSEDI value in Xuzhou City was found to be between 2.24 and 8.10 from 1998 to 2010, and it was further divided into five levels, uninhabitable, moderately uninhabitable, generally inhabitable, moderately inhabitable, and inhabitable. The best HSEDI value was in 2007. The generally inhabitable region was about 100.98 km^2, covering 30.87% of the total area in 2007; the moderately inhabitable region was about 170.58 km2 covering 52.15% of the total area; the inhabitable region was about 32.03 km^2, covering 9.79% of the total area; the percentage of the uninhabitable region was zero; and that of the moderately uninhabitable region was very small, less than 1.00%. Moreover, we analyzed the habitability in the respect of spatial patterns and change detection. Results show that the degraded regions of habitability quality are mainly located in the urban fringe and the improved regions are mainly located in the main urban and rural areas. Reason for the degraded habitability quality is the rapid progress of urbanization. However, the increase in urban green spaces and the construction of the main urban area promoted the improved habitability quality. Besides, we further analyzed socio-economic and socio-demographic data to confirm the results of the habitability analysis. The results indicate that the human settlement in Xuzhou City is in a satisfactory condition, but some efforts should be made to control the possible uninhabitable and moderately uninhabitable regions, and to improve the quality of the generally inhabitable regions.

Assessing the impact of urbanization on net primary productivity using multi-scale remote sensing data： a case study of Xuzhou, China

An improved Carnegie Ames Stanford Approach （CASA） model based on two kinds of remote sensing （RS） data, Landsat Enhanced Thematic Mapper Plus （ETM ＋） and Moderate Resolution Imaging Spectro- radiometer （MODIS）, and climate variables were applied to estimate the Net Primary Productivity （NPP） of Xuzhou in June of each year from 2001 to 2010. The NPP of the study area decreased as the spatial scale increased. The average NPP of terrestrial vegetation in Xuzhou showed a decreasing trend in recent years, likely due to changes in climate and environment. The study area was divided into four sub-regions, designated as highest, moderately high, moderately low, and lowest in NPR The area designated as the lowest sub-region in NPP increased with expanding scale, indicating that the NPP distribution varied with different spatial scales. The NPP of different vegetation types was also significantly influenced by scale. In particular, the NPP of urban woodland produced lower estimates because of mixed pixels. Similar trends in NPP were observed with different RS data. In addition, expansion of residential areas and reduction of vegetated areas were the major reasons for NPP change. Land cover changes in urban areas reduced NPP, which could chiefly be attributed to human-induced disturbance.

Land surface temperature retrieval from Landsat 8 data and validation with geosensor network

A method for the retrieval of land surface temperature （LST） from the two thermal bands of Landsat 8 data is proposed in this paper. The emissivities of vegetation, bare land, buildings, and water are estimated using different features of the wavelength ranges and spectral response functions. Based on the Planck function of the Thermal Infrared Sensor （TIRS） band 10 and band 11, the radiative transfer equation is rebuilt and the LST is obtained using the modified emissivity parameters. A sensitivity analysis for the LST retrieval is also conducted. The LST was retrieved from Landsat 8 data for the city of Zoucheng, Shandong Province, China, using the proposed algorithm, and the LST reference data were obtained at the same time from a geosensor network （GSN）. A comparative analysis was conducted between the retrieved LST and the reference data from the GSN. The results showed that water had a higher LST error than the other land-cover types, of less than 1.2℃, and the LST errors for buildings and vegetation were less than 0.75℃. The difference between the retrieved LST and reference data was about 1℃ on a clear day. These results confirm that the proposed algorithm is effective for the retrieval of LST from the Landsat 8 thermal bands, and a GSN is an effective way to validate and improve the performance of LST retrieval.