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Based on the result of the first land resources survey in Tibet Autonomous Region (TAR) carried out in the late 1980s, land-use map of Lhasa area in 1990 is produced using aerial photographs obtained in April, May, and October 1991 for the main agricultural area in the river valleys and Landsat TM images acquired in the late 1980s and 1991 for the rest of the area. Using these remotely sensed data, land-use status of Lhasa area in 1991, 1992, 1993, 1995, 1999, and 2000 is mapped through updating annual changes of cultivated land, artificial forest and grass planting, grassland restoration, built-up area expansion, and so on. Land-use map for the Lhasa area in 2007 is made using ALOS AVNIR-2 composite images acquired on October 24 and December 26, 2007, through updating changes of main land-use types. Of which, based on the land-use status in 1990, 1995, 2000, and 2007, the spatial and temporal land-use change and dynamics in the Lhasa area located at central Tibetan Plateau from 1990 to 2007 are analyzed using GIS spatial analysis techniques in this chapter.

A new land-use status classification system is developed based on the first land-use status survey of Tibet Autonomous Region (TAR) to be applicable for mapping land-use status in Tibet with different spatial scales of remote sensing data from aerial photograph to orbit satellite. It is a hierarchically based three-level classification system and contains 8 level I, 34 level II, and 12 level III. Land-use status map for the Lhasa area located at central Tibetan Plateau is made based on the color infrared aerial photographs and Landsat Thematic Mapper (TM) using proposed classification system. Results show that in the Lhasa area the grassland covers the largest area extent with 71.48% of total land area, followed by unused land (16.88%) and water body (5.25%). Other land-use types account for less than 4%. Among these, cultivated land is 70021.72 ha., covering 2.37% of total land area, and is distributed in all counties except Damshung County in the north.

Land-cover classification is an important application area of satellite remote sensing. However, deriving thematic map from satellite imagery through classification approaches is not a straightforward task, especially from high-resolution satellite imagery. In this study, Terra/Moderate Resolution Imaging Spectroradiometer (MODIS) multispectral composite image is successfully used to land-cover classification for the Lhasa area located at central Tibetan Plateau (TP) using maximum likelihood classifier. Accuracy assessment for final results is also made using quantitative approaches. Study shows that there is a good agreement between classification results and reference data for defined land-cover classes in central TP. The overall classification accuracy is 87.68%. Reference and ancillary data are increasingly available and are very useful for refining accuracy of classification results during postclassification process. The integration of digital elevation model (DEM) into land-cover classification is particularly important in mountain region since land-cover distribution in mountain region is spatially topography-dependent. Study also suggests that with increase of spatial resolution, how to effectively use the spatial information inherent in satellite remote sensing images to extract thematic maps for various applications remains a challenge and is an important task to be fulfilled in the future.

Fractional vegetation cover (FVC) is an important parameter in the study of ecosystem balance, soil erosion, and climate change and is often used to evaluate and monitor vegetation degradation and desertification. Remote sensing provides the only feasible way to estimate FVC at regional and global scales. In the present study, an empirical model of FVC estimation is developed for central Tibetan Plateau (TP) based on the relationships between vegetation indices from Terra/Moderate Resolution Imaging Spectroradiometer (MODIS) and corresponding field measurements derived from digital camera, which is followed by in-depth analysis on the spatial distribution of vegetation coverage using proposed method. Study shows that a linear relationship exists between vegetation coverage from the field observation and MODIS NDVI with coefficient of determination of R2 = 0.90, which is slightly better than MODIS SAVI performance with R2 = 0.89 and is an optimal regression model for FVC estimation. Vegetation coverage ranges 20–90% in the most part of central TP, presenting moderate to high as a whole, and generally decreases from east to west with strong regional differences due to discrepancies in land-cover types, plant species, topography and water resources availability, and so on.

Land use is one of the most important ways that humans use land resources to meet their material, social, and cultural needs, and its spatial distribution pattern largely features topographic dependence in mountain regions. Study on spatial distribution of different land-use types in the Lhasa area at central Tibetan Plateau shows that the altitude difference between the highest and the lowest points is 3612 m with an average elevation of 4616 meters above sea level (masl), and most of land ranges from 3550 to 5500 masl with 92.27% of total land area. As a mountainous region, in the study area the flat land without slope gradient is limited in area and covers 23.70% of total land area. Most of cultivated land is in elevation range from 3550 to 4000 masl with covering 74.32% of total cultivated land, while forest distribution is spatially much broader and natural shrub is dominant cover type. Over 50% of settlement and built-up land is in elevation range from 3650 to 3750 masl. Grassland is the largest land-use type with 93.78% lying in elevation range from 4000 to 6000 masl, while lake covers the largest area in water body and is composed of eastern part of Namtso Lake within Lhasa area.

Biomass is an important component of grassland ecosystems and plays a critical role in the sustainable use of grassland resources and the global carbon cycle. Satellite remote sensing provides an important approach for estimating aboveground biomass (AGB) at large spatial scales while biomass harvesting offers reliable and site-specific biomass magnitude and is only way to give indispensable ground truth for satellite remote sensing. In this study, estimate models for grassland AGB for the Lhasa area located at the central Tibetan Plateau (TP) are developed based on the relationships between the field measurements and Terra/Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation indices (NDVI, EVI), and the models are validated against independent field measurements. The result shows that exponential relationships exist between AGB and MODIS vegetation indices. MODIS NDVI is more effective to estimate grassland AGB in the study area with R2 = 0.63 than EVI with R2 = 0.50 and is an optimal regression model for AGB estimation. For green AGB estimation, the performance of NDVI (R2 = 0.69) is also better than EVI (R2 = 0.59). In the study area, AGB spatially presents decreases from east to west, with great regional differences due to inhomogeneous grassland types and impact of various environmental and climatic factors. AGB is above 100 g/m2 in some eastern regions whereas it is lower than 20 g/m2 in the west.

In this chapter, land-cover change based on the Normalized Difference Vegetation Index (NDVI) derived from the NOAA AVHRR Global Vegetation Index (GVI) for the Lhasa area at the central Tibetan Plateau from 1985 to 1999 is presented, and its sensitivity to climate conditions is discussed, followed by analysis on vegetation phenologies and dynamics using the discrete Fourier transform (DFT). The time series of NDVI demonstrate a positive trend from 1985 to 1999, which means that general vegetation biomass on land surface presents increasing, and this trend is strongly associated with increased rainfall and temperature from the mid-1980s to 1990s. The correlation analysis shows that the NDVI is more sensitive to precipitation (r = 0.75, P < 0.01) than temperature (r = 0.63, P < 0.01) in this semiarid climate zone. The study also indicated that DFT is a very useful tool to understand vegetation phenologies and dynamic change through decomposition of temporal data to frequency domain.