In this study, we have used four methods to investigate the start of the growing season (SGS) on the Tibetan Plateau (TP) from 1982 to 2012, using Normalized Difference Vegetation Index (NDVI) data obtained from Global Inventory Modeling and Mapping Studies (GIMSS, 1982–2006) and SPOT VEGETATION (SPOT-VGT, 1999–2012). SGS values estimated using the four methods show similar spatial patterns along latitudinal or altitudinal gradients, but with significant variations in the SGS dates. The largest discrepancies are mainly found in the regions with the highest or the lowest vegetation coverage. Between 1982 and 1998, the SGS values derived from the four methods all display an advancing trend, however, according to the more recent SPOT VGT data (1999–2012), there is no continuously advancing trend of SGS on the TP. Analysis of the correlation between the SGS values derived from GIMMS and SPOT between 1999 and 2006 demonstrates consistency in the tendency with regard both to the data sources and to the four analysis methods used. Compared with other methods, the greatest consistency between the in situ data and the SGS values retrieved is obtained with Method 3 (Threshold of NDVI ratio). To avoid error, in a vast region with diverse vegetation types and physical environments, it is critical to know the seasonal change characteristics of the different vegetation types, particularly in areas with sparse grassland or evergreen forest.

In recent decades, human activities have significantly transformed land use and land cover (LULC) and the environment of the Central Himalayas region. LULC is a major component of environmental and climatic research. The aim of this study was to determine the changes in cropland status and its drivers in the Koshi River Basin (KRB) of the Central Himalayas region of Nepal between 1978 and 2010. The cropland status in 1978 was obtained from the Land Resources Mapping Project (LRMP) datasets. The cropland status in 1992 and 2010 was determined on the basis of satellite imagery, with an object-oriented classification method, together with field investigations. Advanced geographical tools were used for data processing and binary logistic regression models were used for the statistical analysis of potential driving factors of cropland change. A noticeable overall change in cropland area was found, with rapid increases from 1978 onward at differing rates and to different extents. The cropland area covered 7165 km² in 1978. It peaked at 7867.49 km² in 1992, and had reduced slightly (by 90 km²) to 7776.66 km² by 2010. The change in cropland area was mainly related to four potential driving factors: topography (elevation, slope, and soil types), socioeconomics (population and foreign labor migration), climate (annual mean temperature and precipitation), and neighborhood factors (roads, rivers, and settlements). However, the effects of the different variables have occurred over various stages and at different rates. An understanding of long-term changes in cropland status in the KRB would be useful, and this could be extended to spatial reconstructions with the help of historical data, including cropland and climatic archives.