Deforestation and fragmentation are major components of global change; both are contributing to the rapid loss of tropical forest area with important implications for ecosystem functioning and biodiversity conservation. The forests of South Ecuador are a biological ‘hotspot’ due to their high diversity and endemism levels. We examined the deforestation and fragmentation patterns in this area of high conservation value using aerial photographs and Aster satellite scenes. The registered annual deforestation rates of 0.75% (1976–1989) and 2.86% (1989–2008) for two consecutive survey periods, the decreasing mean patch size and the increasing isolation of the forest fragments show that the area is under severe threat. Approximately 46% of South Ecuador’s original forest cover had been converted by 2008 into pastures and other anthropogenic land cover types. We found that deforestation is more intense at lower elevations (premontane evergreen forest and shrubland) and that the deforestation front currently moves in upslope direction. Improved awareness of the spatial extent, dynamics and patterns of deforestation and forest fragmentation is urgently needed in biologically diverse areas like South Ecuador.
The increased variety of satellite remote sensing platforms creates opportunities for estimating tropical forest diversity needed for environmental decision-making. As little as 10% of the original seasonally dry tropical forest (SDTF) remains for Ecuador, Peru, and Colombia. Remnant forests show high rates of species endemism, but experience degradation from climate change, wood-cutting, and livestock-grazing. Forest census data provide a vital resource for examining remote sensing methods to estimate diversity levels. We used spatially referenced trees ≥5 cm in diameter and simulated 0.10 ha plots measured from a 9 ha SDTF in southwestern Ecuador to compare machine learning (ML) models for six α-diversity indices. We developed 1 m tree canopy height and elevation models from stem mapped trees, at a scale conventionally derived from light detection and ranging (LiDAR). We then used an ensemble ML approach comparing single- and combined-sensor models from RapidEye, Sentinel-2 and interpolated canopy height and topography surfaces. Validation data showed that combined models often outperformed single-sensor approaches. Combined sensor and model ensembles for tree species richness, Shannon’s H, inverse Simpson’s, unbiased Simpson’s, and Fisher’s alpha indices typically showed lower root mean squared error (RMSE) and increased goodness of fit (R2). Piélou’s J, a measure of evenness, was poorly predicted. Mapped tree species richness (R2 = 0.54, F = 27.3, p = <0.001) and Shannon’s H′ (R2 = 0.54, F = 26.9, p = <0.001) showed the most favorable agreement with field validation observations (n = 25). Small-scale model experiments revealed essential relationships between dry forest tree diversity and data from multiple satellite sensors with repeated global coverage that can help guide larger-scale biodiversity mapping efforts.
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