![]() ![]() INTR EARTHNET DRIVERSI find that drivers differ across varying initial forest cover, with this factor being the largest predictor of deforestation severity. To determine drivers of deforestation severity in coastal Tanzania, I use generalised additive models (GAMs) to describe the non-linear relationship between deforestation severity and a set of geographic, biogeographic, and socioeconomic data. Here I examine 3 sources of remotely sensed data (supplemented Landsat data, MOD12Q1 data, and MOD44B data) and their suitability for use in deforestation models. It can also be used to analyse protected area effectiveness. Model development can be used to elucidate the drivers of deforestation, predict the location of future deforestation, produce scenarios of future deforestation rates, inform the design of government policy, and provide a baseline against which to test for additionality in programmes such as REDD+. These forest remnants are threatened by further fragmentation, degradation, and deforestation. Closer examination has revealed a unique and diverse ecosystem, home to exceptional levels of endemism across many major taxa, distributed heterogeneously across several hundred forest patches. On a hâte de lire tous les articles qui vont émerger.The coastal forests of Tanzania have typically been overlooked in favour of the more spectacular Eastern Arc Mountains. N'oubliez pas d'enregistrer votre Geste pour le climat dans le portail Reality Hub. Toutes les information sont sur le site de la LNC !
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