We present a new glacier inventory for high-mountain Asia named "Glacier Area Mapping for Discharge from the Asian Mountains" (GAMDAM). Glacier outlines were delineated manually using 356 Landsat ETM+ scenes in 226 path-row sets from the period 1999–2003, in conjunction with a digital elevation model (DEM) and high-resolution Google Earth^TM imagery. Geolocations are largely consistent between the Landsat imagery and DEM due to systematic radiometric and geometric corrections made by the United States Geological Survey. We performed repeated delineation tests and peer review of glacier outlines in order to maintain the consistency and quality of the inventory. Our GAMDAM glacier inventory (GGI) includes 87 084 glaciers covering a total area of 91 263 ± 13 689 km² throughout high-mountain Asia. In the Hindu Kush–Himalaya range, the total glacier area in our inventory is 93% that of the ICIMOD (International Centre for Integrated Mountain Development) inventory. Discrepancies between the two regional data sets are due mainly to the effects of glacier shading. In contrast, our inventory represents significantly less surface area (−24%) than the recent global Randolph Glacier Inventory, version 4.0 (RGI), which includes 119 863 ± 9201 km² for the entirety of high Asian mountains. Likely causes of this disparity include headwall definition, effects of exclusion of shaded glacier areas, glacier recession since the 1970s, and inclusion of seasonal snow cover in the source data of the RGI, although it is difficult to evaluate such effects quantitatively. Further rigorous peer review of GGI will both improve the quality of glacier inventory in high-mountain Asia and provide new opportunities to study Asian glaciers.

Among meteorological elements, precipitation has a large spatial variability and less observation, particularly in high-mountain Asia, although precipitation in mountains is an important parameter for hydrological circulation. We estimated precipitation contributing to glacier mass at the median elevation of glaciers, which is presumed to be at equilibrium-line altitude (ELA) such that mass balance is zero at that elevation, by tuning adjustment parameters of precipitation. We also made comparisons between the median elevation of glaciers, including the effect of drifting snow and avalanche, and eliminated those local effects. Then, we could obtain the median elevation of glaciers depending only on climate to estimate glacier surface precipitation. The calculated precipitation contributing to glacier mass can elucidate that glaciers in arid high-mountain Asia receive less precipitation, while much precipitation makes a greater contribution to glacier mass in the Hindu Kush, the Himalayas, and the Hengduan Shan due to not only direct precipitation amount but also avalanche nourishment. We classified glaciers in high-mountain Asia into summer-accumulation type and winter-accumulation type using the summer-accumulation ratio and confirmed that summer-accumulation-type glaciers have a higher sensitivity than winter-accumulation-type glaciers.

To understand the formation conditions of debris-covered glaciers, we examined the dimension and shape of debris-covered areas and potential debris-supply (PDS) slopes of 213 glaciers in the Bhutan Himalaya. This was undertaken using satellite images with 2.5 m spatial resolution for manual delineation of debris-covered areas and PDS slopes. The most significant correlation exists between surface area of southwest-facing PDS slopes and debris-covered area. This result suggests that the southwest-facing PDS slopes supply the largest quantity of debris mantle. The shape of debris-covered areas is also an important variable, quantitatively defined using a geometric index. Elongate or stripe-like debris-covered areas on north-flowing glaciers are common throughout the Bhutan Himalaya. In contrast, south-flowing glaciers have large ablation zones, entirely covered by debris. Our findings suggest that this difference is caused by effective diurnal freeze–thaw cycles rather than seasonal freeze–thaw cycles, permafrost degradation, or snow avalanches. In terms of geographic setting, local topography also contributes to glacier debris supply and the proportion of debris cover on the studied glaciers is suppressed by the arid Tibetan climate, whereas the north-to-south asymmetric topography of the Bhutan Himalaya has less influence on the proportion of debris cover.

A comprehensive glacier–glacial lake inventory was developed for the Bhutan Himalayas based on satellite observations between 1987–1990 and 2006–2011. In total, 733 lakes (covering 82.6 km²) were delineated between 4000 and 6000 m a.s.l. and their relationships to associated glaciers were documented. Using this new inventory, the scale and potential for glacial lake outburst flooding (GLOF) based on multiple criteria was examined. This included a history of connectivity characteristics of glacial lakes to mother glaciers, potential flood volumes, and debris-cover of mother glaciers in addition to the conventional criteria of expansion rate and lake size. The majority of the lakes with high expansion rates (more than double in size) and large areas (>0.1 km²) met the conditions of being continuously in contact with a mother debris-covered glacier for nearly 20 years. Based on these multiple criteria, two lakes were identified as having potential for large-scale GLOF. Potentially dangerous glacial lakes listed in the International Centre for Integrated Mountain Development (ICIMOD) study were re-visited, and some overlaps with the glacier–glacial lake inventory were found.

Digital glacier inventories are invaluable data sets for revealing the characteristics of glacier distribution and for upscaling measurements from selected locations to entire mountain ranges. Here, we present a new inventory of Advanced Land Observing Satellite (ALOS) imagery and compare it with existing inventories for the Bhutan Himalaya. The new inventory contains 1583 glaciers (1487 ± 235 km²), thereof 219 debris-covered glaciers (951 ± 193 km²) and 1364 debris-free glaciers (536 ± 42 km²). Moreover, we propose an index for quantifying consistency between two glacier outlines. Comparison of the overlap ratio demonstrates that the ALOS-derived glacier inventory contains delineation uncertainties of 10–20 % which depend on glacier size, that the shapes and geographical locations of glacier outlines derived from the fourth version of the Randolph Glacier Inventory have been improved in the fifth version, and that the latter is consistent with other inventories. In terms of whole glacier distribution, each data set is dominated by glaciers of 1.0–5.0 km² area (31–34 % of the total area), situated at approximately 5400 m elevation (nearly 10 % in 100 m bin) with either north or south aspects (22 and 15 %). However, individual glacier outlines and their area exhibit clear differences among inventories. Furthermore, consistent separation of glaciers with inconspicuous termini remains difficult, which, in some cases, results in different values for glacier number. High-resolution imagery from Google Earth can be used to improve the interpretation of glacier outlines, particularly for debris-covered areas and steep adjacent slopes.

In this paper, evaluation results are presented for multi-temporal interferometric coherence analysis using a Synthetic Aperture Radar (SAR) for damage assessment in an urban area. The latest space-borne SARs potentially have a high enough spatial resolution to assess individual buildings. However, interferometric coherence analysis has not been evaluated for its limitation in sensitivity and size of damaged buildings. In particular, the correlation between the coherence analysis and the damage level referred to by architectural assessments has been an open question. In this paper, analytical results using ALOS-2 PALSAR-2 datasets are presented from the 2016 Kumamoto earthquakes in Japan. For reference, building damage was assessed throughout the central urban area and specifically at a catastrophically damaged district. The results show that the buildings should be larger than a window size of the coherence for damage detection, and the damage level should be larger than Level-2 of 5, classified with the European Macroseismic Scale 1998 (EMS-98).