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Abstract:A 30-year series of global monthly Normalized Difference Vegetation Index (NDVI) imagery derived from the Global Inventory Modeling and Mapping Studies (GIMMS) NDVI3g archive was analyzed for the presence of trends in changing seasonality. Using the Seasonal Trend Analysis (STA) procedure, over half (56.30%) of land surfaces were found to exhibit significant trends. Almost half (46.10%) of the significant trends belonged to three classes of seasonal trends (or changes). Class 1 consisted of areas that experienced a uniform increase in NDVI throughout the year, and was primarily associated with forested areas, particularly broadleaf forests. Class 2 consisted of areas experiencing an increase in the amplitude of the annual seasonal signal whereby increases in NDVI in the green season were balanced by decreases in the brown season. These areas were found primarily in grassland and shrubland regions. Class 3 was found primarily in the Taiga and Tundra biomes and exhibited increases in the annual summer peak in NDVI. While no single attribution of cause could be determined for each OPEN ACCESSRemote Sens. 2013, 5 4800 of these classes, it was evident that they are primarily found in natural areas (as opposed to anthropogenic land cover conversions) and that they are consistent with climate-related ameliorations of growing conditions during the study period.

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SignificanceWhile infrastructure expansion has been broadly investigated as a driver of deforestation, the impacts of extractive industry and its interactions with infrastructure investment on forest cover are less well studied. These challenges are urgent given growing pressure for infrastructure investment and resource extraction. We use geospatial and qualitative data from Amazonia, Indonesia, and Mesoamerica to explain how infrastructure and extractive industry lead directly and indirectly to deforestation, forest degradation, and increasingly precarious rights for forest peoples. By engaging in explicit analyses of community rights, the politics of development policy, and institutions for transparency, anticorruption, and the defense of human rights, Sustainability Science could be more effective in examining deforestation and related climate-change impacts and in contributing to policy innovation.

Land-cover characterization of large heterogeneous landscapes is challenging because of the confusion caused by high intra-class variability and heterogeneous landscape artefacts. Neighbourhood context can be used to supplement spectral information, and a novel way of incorporating spatial dependence in a heterogeneous region is tested here using an ensemble learning technique called random forests and a measure of local spatial dependence called the Getis statistic. The overall Kappa accuracy of the random forest classifier that used a combination of spectral and local spatial (Getis) variables at three different neighbourhood sizes (3 Â 3, 7 Â 7, and 11 Â 11) ranged from 0.85 to 0.92. This accuracy was higher than that of a non-spatial random forest classifier having an overall Kappa accuracy of 0.78, which was run using the spectral variables only. This study demonstrated that the use of the Getis statistic with different neighbourhood sizes leads to substantial increase in per class classification accuracy of heterogeneous land-cover categories.