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Owing to the influence of global change, land cover and land use have changed significantly over the last decade in the cold and arid regions of China, such as Madoi County which is located in the source area of the Yellow River. In this paper, land-use/cover change and landscape dynamics are investigated using satellite remote sensing (RS) and a geographical information system (GIS). The objectives of this paper are to determine land-use/cover transition rates between different cover types in the Madoi County over 10 years e.g., from 1990 to 2000. Second, the changes of landscape metrics using various indices and models are quantified. The impact factors of LUCC (Land-Use land cover Change) are systematically identified by integrating remote sensing as well as statistical data, including climate, frozen soil, hydrological data and the socio-economic data. Using 30 m×30 m spatial resolution Landsat (Enhanced) Thematic Mapper (TM/ETM+) data in our study area, nine land cover classes can be discriminated. Our results show that Grassland, Marshes and Water Bodies decrease notably, while oppositely, Sands - Gobi and Barren land increase significantly. The number of lakes with an acreage larger than six hectares decreased from 405 in 1990 to 261 in 2000. Numerous small lakes dried out. The area of grassland with a high cover fraction decreased as well, while the surface area of grassland with a medium level of cover fraction increased. The medium cover fraction grassland mainly originates from high cover fraction grassland. The desertification of land is a serious issue. (ii) The inter-transformations between Grasslands, Barren Land, Sands, Gobi, Water Bodies and Marshes are remarkable. The Shannon-Weaver Diversity Index (SWDI), the Evenness Index (EI) and the extent of Landscape Heterogeneity (LH) has improved. Marshes have become more fragmented hence, with less connected patches. (iii) In the recent 30 years, average annual temperature, the power of evaporation and the index of dryness did increase significantly. Moreover, soil moisture content (SMC) decreased and the drought trend accelerated. The degradation of frozen soil has impacted on the decrease of surface water area and induced a drop in groundwater levels. Monitoring LUCC in sensitive regions would not only benefit from a study of vulnerable ecosystems in cold and high altitude regions, but would provide scientifically based decision-making tools for local governments as well. [ABSTRACT FROM AUTHOR]

The agricultural impacts of tropical cyclones remain the primary threat to livelihoods in Southeast Asia and Latin America. The impacts take two forms, one is windthrow, i.e. uprooted or snapped trees, caused by strong wind, and the other is hydrogeological effects from heavy precipitation. The empirical effects are different for the two forms. However, little previous research has been devoted to distinguishing the two effects to estimate agricultural losses, and even fewer have used moderate-resolution (10 m resolution) images. This study presents a methodological progression to address this deficiency. First, an object-based image analytical method distinguishes the two effects using Satellite Pour l’Observation de la Terre – 5 satellite images. Various object-based features are compared to acquire their spectral, textural, and geometric characteristics for the interpretation. Second, a public participation geographical information system (PPGIS) approach is developed that combines community empowerment and collaborative field survey to rebuild and represent ground truth during disasters for image validation. The method is applied to a case study of typhoon Bopha that struck Compostela Valley, eastern Mindanao, the Philippines in December 2012. Our assessment indicates that the producer accuracy reaches 88.9% for debris and mud flows and 83.3% for windthrow, and user accuracy reaches 94% and 81%, respectively. The result indicates that the proposed methods have great potential for distinguishing the two effects. It also highlights the efficacy of integrating PPGIS with remote sensing, for image validation purposes and in practice to enhance local residents’ environmental consciousness for enhancing adaptive capacity in resource limited regions. [ABSTRACT FROM PUBLISHER]

For the past three decades, ecologists and biogeographers have increasingly incorporated remote sensing and geographical information systems (GISs) to inventory and analyse spatially organized data. Although there are many studies exploring the effects of fine resolution on remote sensing and GIS mapping, there is still a gap on how to identify the most appropriate spatial resolutions for studying landscapes and their structures and dynamics. This study investigated the effects of landscape changes over a 64-year study period at different spatial resolutions using four resampling schemes. The study was conducted on a national wildlife refuge of five land-use and land-cover (LULC) categories using aerial photos recorded in three distant years (1938, 1971, and 2001). This refuge has undergone major landscape changes in the last 64 years. Among the five LULC categories studied, the one that lost the most surface is agriculture; the most gain was made in forest and water. In terms of net change and swapping, agriculture and forest were the most dynamic categories in the National Wildlife Refuge. Our findings showed considerable spatial variability in landscape dynamics at different scales. We specifically observed that hard-classified maps with spatial resolutions of 30 m or finer provided better analysis of landscape dynamics, whereas with soft-classified maps it could go up to 90 m or finer. This implies that there is a range of optimum resolution that could allow for the use of medium-resolution data, such as Landsat, for reliable land change analysis. These findings offer further insight on landscape change analysis at different spatial resolutions and advance our understanding and knowledge on the effect of scale on landscape ecology. [ABSTRACT FROM AUTHOR]