Based on four Landsat ETM+ images covering Wuhan City, Normalized Difference Vegetation Index (NDVI) and Normalized Difference Built-up Index (NDBI) in spring, summer, autumn and winter were calculated. The land surface temperature (LST) of the city in four seasons was estimated using the radiative transfer model. The spatial and temporal pattern and characteristics of NDVI, NDBI and LST and their relationship were analyzed in detail. Results show that the regions with higher NDBI and LST values were mainly in the municipal district of Wuhan City, including Hankou and Wuchang. The maximum NDBI of the district in four seasons was 0.669, 0.685, 0.659 and 0.703 respectively, while the maximum LST was 43.529°C, 59.849°C, 47.143°C and 23.875°C. Higher NDVI values were mainly distributed in the regions such as along the Han River, the lake beach and the wetlands area in the southwest and central hilly areas, and the maximum NDVI in different seasons was 0.432, 0.564, 0.442 and 0.158 respectively. There existed a strong positive correlation between NDBI and LST in all seasons. The Pearson's correlation coefficient in four seasons was 0.639, 0.717, 0.807 and 0.762 respectively. In autumn, NDBI and LST were most significantly correlated. The relationship between NDVI and LST changed with season, but without obvious regularity. The correlation between NDVI and LST was weaker in four seasons than that between NDBI and LST, with the Pearson coefficients of -0.515, -0.577, -0.246 and -0.038 respectively. NDVI values had negatively linkage with NDBI. The Pearson's correlation coefficients in four seasons was -0.568, -0.582, -0.536 and -0.242, which indicated that NDVI could be used to characterize the evolution of urban construction land. The throughout study suggests that NDBI not only can be used as an important indicator to analyze LST and urban heat island effects, but also will provide a reliable basis for urban construction and planning.

Northeast China is located at high northern latitudes and is a typical region of relatively high sensitivity to global climate change. Studies of the land surface phenology in Northeast China and its response to climate change are important for understanding global climate change. In this study, the land surface phenology parameters were calculated using the third generation dataset from the Global Inventory Modeling and Mapping Studies (GIMMS 3g) that was collected from 1982 to 2013 were estimated to analyze the variations of the land surface phenology in Northeast China at different scales and to discuss the internal relationships between phenology and climate change. We examined the phonological changes of all ecoregions. The average start of the growing season (SOS) did not exhibit a significant trend throughout the study area; however, the end of the growing season (EOS) was significantly delayed by 4.1 days or 0.13 days/year (p < 0.05) over the past 32 years. The SOS for the Hulunbuir Plain, Greater Khingan Mountains and Lesser Khingan Mountains was earlier, and the SOS for the Sanjing, Songnen and Liaohe Plains was later. In addition, the EOS of the Greater Khingan Mountains, Lesser Khingan Mountains and Changbai Mountains was later than the EOS of the Liaohe Plain. The spring temperature had the greatest impact on the SOS. Precipitation had an insignificant impact on forest SOS and a relatively large impact on grassland SOS. The EOS was affected by both temperature and precipitation. Furthermore, although temperature had a lag effect on the EOS, no significant lag effect was observed for the SOS.

Spatio-temporal patterns of rain-use efficiency (RUE) can explicitly present the steady-state of ecosystem water use and thus ecosystem functioning. The west of Songliao Plain, located along the east fringe of the agro-pasture transitional zone in northern China, is highly sensitive to global change. In this study, satellite-based RUE was calculated using time series SPOT VEGETATION (SPOT-VGT) Normalized Difference Vegetation Index (NDVI) images and precipitation data for the study area from 1999 to 2011. Based on regression model by fitting simple linear regression through the pixel-based time series of RUE in the growing season and calculating the slopes, the change trend of RUE was determined. The grey relational analysis (GRA) method was extended to the spatial scale, and used to select sensitive climate and socio-economic factors that affected RUE variations. The result demonstrated that vegetation RUE increased slightly with an undulating trend, implying the ecosystem function tended to improve between 1999 and 2011. In total, 4.23% of the total area had experienced a significant increase in RUE, whereas 1.29% of the total area presented a significant decrease. Areas showing increased RUE trends mostly coincided with areas of land cover conversions from grassland to forest, shrub to forest and cropland to forest, which suggested a positive linkage with ecological protection policy and projects at national and local levels. Based on the obtained spatial Grey Relation Grade (GRG) values, the pattern of the impact factors clearly showed a spatial heterogeneity. Spatially, sunshine duration, temperature and population density were most closely related to RUE in the west of Songliao Plain between 1999 and 2011.

Land degradation is a widespread environmental issue and an important factor in limiting sustainability. In this study, we aimed to improve the accuracy of monitoring human-induced land degradation by using phenological signal detection and residual trend analysis (RESTREND). We proposed an improved model for assessing land degradation named phenology-based RESTREND (P-RESTREND). This method quantifies the influence of precipitation on normalized difference vegetation index (NDVI) variation by using the bivariate linear regression between NDVI and precipitation in pre-growing season and growing season. The performances of RESTREND and P-RESTREND for discriminating land degradation caused by climate and human activities were compared based on vegetation-precipitation relationship. The test area is in Western Songnen Plain, Northeast China. It is a typical region with a large area of degraded drylands. The MODIS 8-day composite reflectance product and daily precipitation data during 2000–2015 were used. Our results showed that P-RESTREND was more effective in distinguishing different drivers of land degradation than the RESTREND. Degraded areas in the Songnen grasslands can be effectively detected by P-RESTREND. Therefore, this modified model can be regarded as a practical method for assessing human-induced land degradation.

Rapidly and accurately extracting water body information from remote sensing satellite images is critical for the investigation of water resources, planning watershed, dynamically monitoring, water environment and water conservation, etc. With an image of Landsat-7 ETM+ in Lushan Mountain region, the spectral characteristics of water body and other main surface features were analyzed in the paper. A method of modified normalized difference water index (MNDWI) combined with K-T transformation was proposed to distinguish water body from mountain shadow. First, MNDWI of original image was calculated and the threshold values were set across the interval (0.25, 0.8). Then the original image was processed by K-T transformation method, and the pixels were extracted using the third component (wetness) threshold value of less than -80. Finally, the two images were added together after mask processing, thus water information was identified. The results showed that this method of combing MNDWI with K-T transformation could not only remove the influence of mountain shadow of MNDWI method alone, but also avoid the confusion of water and buildings existing in image after K-T transformation. The extraction accuracy of water body was up to 93%, which suggests that this approach has better performance than some previous methods such as single-band threshold technique and the spectrum-photometric method.