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Meso-scale digital terrain models (DTMs) and canopy-height estimates, or digital canopy models (DCMs), are two lidar products that have immense potential for research in tropical rain forest (TRF) ecology and management. In this study, we used a small-footprint lidar sensor (airborne laser scanner, ALS) to estimate sub-canopy elevation and canopy height in(More)
BACKGROUND Monitoring land change at multiple spatial scales is essential for identifying hotspots of change, and for developing and implementing policies for conserving biodiversity and habitats. In the high diversity country of Colombia, these types of analyses are difficult because there is no consistent wall-to-wall, multi-temporal dataset for land-use(More)
Understanding the spatial pattern of ecosystem services is important for effective environmental policy and decision-making. In this study, we use a geospatial decision-support tool (Marxan) to identify conservation priorities for habitat and a suite of ecosystem services (storage carbon, soil retention and water yield) in the Upper Paraná Atlantic Forest(More)
This study explores a method to classify seven tropical rainforest tree species from full-range (400–2,500 nm) hyperspectral data acquired at tissue (leaf and bark), pixel and crown scales using laboratory and airborne sensors. Metrics that respond to vegetation chemistry and structure were derived using narrowband indices, derivative-and absorption-based(More)
Web-based applications that integrate geospatial information, or the geoweb, offer exciting opportunities for remote sensing science. One such application is a Web-based system for automating the collection of reference data for producing and verifying the accuracy of land-use/land-cover (LULC) maps derived from satellite imagery. Here we describe the(More)
Forest transitions (FT) have been observed in many developed countries and more recently in the developing world. However, our knowledge of FT from tropical regions is mostly derived from case studies from within a particular country, making it difficult to generalize findings across larger regions. Here we overcome these difficulties by conducting a recent(More)
Land change in the Greater Antilles differs markedly among countries because of varying socioeconomic histories and global influences. We assessed land change between 2001 and 2010 in municipalities (second administrative units) of Cuba, Dominican Republic, Haiti, Jamaica, and Puerto Rico. Our analysis used annual land-use/land-cover maps derived from MODIS(More)
In this study, a 1-D Convolutional Neural Network (CNN) architecture was developed, trained and utilized to classify single (summer) and three seasons (spring, summer, fall) of hyperspectral imagery over the San Francisco Bay Area, California for the year 2015. For comparison, the Random Forests (RF) and Support Vector Machine (SVM) classifiers were trained(More)
OPINION STATEMENT The stroke system of care is undergoing significant evolution. There are promising data to suggest that with new technologies and approaches, primary prevention and community education will become easier and more accessible, and will allow people to have greater participation in their own healthcare. The evidence-based primary and(More)
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