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Snail intermediate host monitoring and control are essential for interrupting the parasitic disease schistosomiasis. Identifying large-scale high-risk areas of snail spread after floods has been greatly facilitated by remote sensing imagery. However, previous studies have usually assumed that all inundation areas carry snails and may have overestimated snail spread areas. Furthermore, these studies only used a single environmental factor to estimate the snail survival risk probability, failing to analyze multiple variables, to accurately distinguish the snail survival risk in the snail spread areas. This paper proposes a systematic framework for early monitoring of snail diffusion to accurately map snail spread areas from remote sensing imagery and enhance snail survival risk probability estimation based on the snail spread map. In particular, the flooded areas are extracted using the Sentinel-1 Dual-Polarized Water Index based on synthetic aperture radar images to map all-weather flooding areas. These flood maps are used to extract snail spread areas, based on the assumption that only inundation areas that spatially interacted with (i.e., are close to) the previous snail distribution regions before flooding are identified as snail spread areas, in order to reduce the misclassification in snail spread area identification. A multiple logistic regression model is built to analyze how various types of snail-related environmental factors, including the normalized difference vegetation index (NDVI), wetness, river and channel density, and landscape fractal dimension impact snail survival, and estimate its risk probabilities in snail spread area. An experiment was conducted in Jianghan Plain, China, where snails are predominantly linearly distributed along the tributaries and water channels of the middle and lower reaches of the Yangtze River. The proposed method could accurately map floods under clouds, and a total area of 231.5 km2 was identified as the snail spread area. The snail survival risk probabilities were thus estimated. The proposed method showed a more refined snail spread area and a more reliable degree of snail survival risk compared with those of previous studies. Thus, it is an efficient way to accurately map all-weather snail spread and survival risk probabilities, which is helpful for schistosomiasis interruption.

China is the largest developing country worldwide, with rapid economic growth and the highest population. Light pollution is an environmental factor that significantly influences the quality and health of wildlife, as well as the people of any country. The objective of this study is to model the light pollution spatial pattern, and monitor changes in trends of spatial distribution from 1992 to 2012 in China using nighttime light imagery from the Defense Meteorological Satellite Program Operational Linescan System. Based on the intercalibration of nighttime light imageries of the study area from 1992 to 2012, this study obtained the change trends map. This result shows an increase in light pollution of the study area; light pollution in the spatial scale increased from 2.08% in the period from 1992–1996 to 2000–2004, to 5.64% in the period from 2000–2004 to 2008–2012. However, light pollution change trends presented varying styles in different regions and times. In the 1990s, the increasing trend in light pollution regions mostly occurred in larger urban cities, which are mainly located in eastern and coastal areas, whereas the decreasing trend areas were chiefly industrial and mining cities rich in mineral resources, in addition to the central parts of large cities. Similarly, the increasing trend regions dominated urban cities of the study area, and the expanded direction changed from larger cities to small and middle-sized cities and towns in the 2000s. The percentages of regions where light pollution transformed to severe and slight were 5.64% and 0.39%, respectively. The results can inform and help identify how local economic and environmental decisions influence our global nighttime environment, and assist government agencies in creating environmental protection measures.

Mapping the magnitude and spatial distribution of forest aboveground biomass (AGB, in Mg·ha−1) is crucial to improve our understanding of the terrestrial carbon cycle. Landsat/TM (Thematic Mapper) and ICESat/GLAS (Ice, Cloud, and land Elevation Satellite, Geoscience Laser Altimeter System) data were integrated to estimate the AGB in the Changbai Mountain area. Firstly, four forest types were delineated according to TM data classification. Secondly, different models for prediction of the AGB at the GLAS footprint level were developed from GLAS waveform metrics and the AGB was derived from field observations using multiple stepwise regression. Lastly, GLAS-derived AGB, in combination with vegetation indices, leaf area index (LAI), canopy closure, and digital elevation model (DEM), were used to drive a data fusion model based on the random forest approach for extrapolating the GLAS footprint AGB to a continuous AGB map. The classification result showed that the Changbai Mountain region was characterized as forest-rich in altitudinal vegetation zones. The contribution of remote sensing variables in modeling the AGB was evaluated. Vegetation index metrics account for large amount of contribution in AGB ranges