The Chocó-Darien Global Ecoregion (CGE) in South America is one of 25 global biodiversity hotspots prioritized for conservation. I performed the first land-use and land-cover (LULC) change analysis for the entire CGE in this dissertation. There were three main objectives: 1) Select the best available imagery to build annual land-use and land-cover maps from 2001 to 2015 across the CGE. 2) Model LULC across the CGE to assess forest change trends from 2002 to 2015 and identify the effect of proximate causes of deforestation and reforestation. 3) Estimate the effects of underlying drivers on deforestation and reforestation across the CGE between 2002 and 2015. I developed annual LULC maps across the CGE from 2002 to 2015 using MODIS (Moderate Resolution Imaging Spectro radiometer) vegetation index products and random forest classification. The LULC maps resulted in high accuracies (Kappa = 0.87; SD = 0.008). We detected a gradual replacement of forested areas with agriculture and secondary vegetation (agriculture reverting to early regeneration of natural vegetation) across the CGE. Forest loss was higher between 2010-2015 when compared to 2002-2010. LULC change trends, proximate causes, and reforestation transitions varied according to administrative authority (countries: PanamanianCGE, Colombian CGE, and Ecuadorian CGE). Population growth and road density were underlying drivers of deforestation. Armed conflicts, Gross Domestic Product, and average annual rain were proximate causes and underlying drivers related reforestation.

Coarse resolution satellite observations of the Earth provide critical data in support of land cover and land use monitoring at regional to global scales. This dissertation focuses on methodology and dataset development that exploit multi-temporal data from the Moderate Resolution Imaging Spectroradiometer (MODIS) to improve current information related to regional forest cover change and urban extent. In the first element of this dissertation, I develop a novel distance metric-based change detection method to map annual forest cover change at 500m spatial resolution. Evaluations based on a global network of test sites and two regional case studies in Brazil and the United States demonstrate the efficiency and effectiveness of this methodology, where estimated changes in forest cover are comparable to reference data derived from higher spatial resolution data sources. In the second element of this dissertation, I develop methods to estimate fractional urban cover for temperate and tropical regions of China at 250m spatial resolution by fusing MODIS data with nighttime lights using the Random Forest regression algorithm. Assessment of results for 9 cities in Eastern, Central, and Southern China show good agreement between the estimated urban percentages from MODIS and reference urban percentages derived from higher resolution Landsat data. In the final element of this dissertation, I assess the capability of a new nighttime lights dataset from the Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) for urban mapping applications. This dataset provides higher spatial resolution and improved radiometric quality in nighttime lights observations relative to previous datasets. Analyses for a study area in the Yangtze River Delta in China show that this new source of data significantly improves representation of urban areas, and that fractional urban estimation based on DNB can be further improved by fusion with MODIS data. Overall, the research in this dissertation contributes new methods and understanding for remote sensing-based change detection methodologies. The results suggest that land cover change products from coarse spatial resolution sensors such as MODIS and VIIRS can benefit from regional optimization, and that urban extent mapping from nighttime lights should exploit complementary information from conventional visible and near infrared observations.

La degradación del bosque tropical causada por la agricultura indigena tradicional y la explotación forestal comercial fue evaluada, en el sector central de la Reserva Forestal Imataca en la Guayana venezolana.El enfoque conceptual de paisajes permitió combinar dos niveles de análisis, local y paisajes, con el objeto de detectar las relaciones espaciales que forman patrones, gradientes y estructuras, útiles para el seguimiento y control de los usos del bosque tropical y su estado de conservación. La evaluación integró varios métodos orientados a describir y analizar la composición, estructura y distribución de las comunidades arbóreas, tomando en cuenta su condición: no perturbado o perturbado.Se levantó información social relativa a los usos de la tierra considerados, a través de visitas y mediciones en las áreas de producción de las comunidades Kariñas, al igual que en concesiones forestales tomadas como muestra. Un inventario forestal en parcelas muestra, permitió colectar datos sobre algunos parámetros estructurales del bosque, los cuales sirvieron de base para calcular índices tales como diversidad alfa y beta, complejidad estructural y otros. La detección y cartografía de los bosques no perturbados y perturbados segun el uso considerado, se hizo sobre imagenes de satélite Landsat, a las cuales se aplicó técnicas de percepción remota tales como analisis textural, indices de vegetación y cobertura fraccional.Se estimaron áreas de superposición entre territorios tradicionales Kariña y concesiones forestales comerciales, las cuales probablemente serán objeto de nuevos conflictos que pueden ser graves, considerando el proceso de demarcación de territorios indígenas tradicionales en curso, y la explotación minera próxima a comenzar.El interés de estudiar la compleja situación de la Reserva Forestal Imataca se debe a la necesidad de promover un modelo sustentable de manejo y conservación de bosques para la Guayana venezolana.