Global land cover products have been created for global environmental studies by several institutions and organizations. The Global Mapping Project coordinated by the International Steering Committee for Global Mapping (ISCGM) has been periodically producing global land cover datasets as one of the eight basic global datasets. It has produced a new fifteen-second (approximately 500 m resolution at the equator) global land cover dataset -GLCNMO2013 (or GLCNMO version 3). This paper describes the method of producing GLCNMO2013. GLCNMO2013 has 20 land cover classes, and they were mapped by improved methods from GLCNMO version 2. In GLCNMO2013, five classes, which are urban, mangrove, wetland, snow/ice, and water were independently classified. The remaining 15 classes were divided into 4 groups and mapped individually by supervised classification. 2006 polygons of training data collected for GLCNMO2008 were used for supervised classification. In addition, about 3000 polygons of new training data were collected globally using Google Earth, MODIS Normalized Difference Vegetation Index (NDVI) seasonal change patterns, existing regional land cover maps, and existing four global land cover products. The primary data of this product were Moderate Resolution Imaging Spectroradiometer (MODIS) data of 2013. GLCNMO2013 was validated at 1006 sampled points. The overall accuracy of GLCNMO2013 was 74.8%, and the overall accuracy for eight aggregated classes was 90.2%. The accuracy of the GLCNMO2013 was not improved compared with the GLCNMO2008 at heterogeneous land covers. It is necessary to prepare the training data for mosaic classes and heterogeneous land covers for improving the accuracy.

Differential Synthetic Aperture Radar Interferometry (DInSAR) is a remote sensing technique that is capable of detecting land surface deformation with centimeter accuracy. In this research, this technique was applied to two pairs of Advanced Land Observing Satellite (ALOS) Phased Array L-band SAR (PALSAR) data to detect land subsidence in the Kathmandu valley from 2007 to 2010. The result revealed several subsidence areas towards the center of the valley ranging from a maximum of 9.9 km 2 to a minimum of 1 km 2 coverage with a maximum velocity of 4.8 cm/year, and a minimum velocity of 1.1 cm/year, respectively. The majority of the subsidence was observed in old settlement areas with mixed use development. The subsidence depth was found to gradually increase from the periphery towards the center in almost all detected subsidence areas. The subsidence depth was found to be in a range of 1 cm to 17 cm. It was found that the concentration of deep water extraction wells was higher in areas with higher subsidence rates. It was also found that the detected subsidence area was situated over geological formations mainly consisting of unconsolidated fine-grained sediments (silica, sand, silt, clay and silty sandy gravel), which is the major factor affecting the occurrence of land subsidence due to groundwater extraction.

Land subsidence is identified as a global problem and intensive studies are being conducted worldwide to detect and monitor risk of this problem. Risk assessment of land subsidence is simply an evaluation of the probability and frequency of occurrence of land subsidence, exposure of people and property to the subsidence and consequence of that exposure. Remote sensing technology was used to extract information of land subsidence in Kathmandu Valley, Nepal. Also, Disaster Risk Index method and Analytic Hierarchy Process (AHP) along with Geographic Information System (GIS) tools were used to assess risk of land subsidence in Kathmandu Valley, Nepal. Subsidence volume for locations Central Kathmandu, Chauni, Lalitpur, Imadol, Thimi, Madhyaour Thimi, New Baneshwor, Koteshwor and Gothatar was calculated using a simple mathematical formula. The subsidence depth for these locations was found to be in a range of 1 cm to 17 cm and the maximum subsidence velocity was found to be 4.8 cm/yr. This study revealed that the location where maximum subsidence was observed (i.e. Central Kathmandu and Lalitpur) was found to be at high risk of experiencing land subsidence induced damage. Other location where subsidence was observed was found to be at medium risk and the rest of the Kathmandu valley was found to be at low risk with current data situation. This study can be considered as the first step towards other comprehensive study relating to land subsidence risk assessment. The outcome of this research provides a basic understanding of the current situation that can further assist in developing prevention and risk management techniques.