Flipping through field notebooks…
This package is part of the Watershed Function SFA project and contains a remote sensing dataset acquired at the East River, Colorado. The remote sensing dataset is composed of vegetation maps computed from hyperspectral and LiDAR airborne data acquired by the NEON team in June 2018. The maps show the spatial distribution of plant species among trees, shrubs, and meadows at 1-meter resolution, covering four main catchments located in the Upper Colorado River Basin: the East River (67.5 km2), Washington Gulch (93.0 km2), Oh-be-Joyful Creek-Slate River (86.9 km2), and Coal Creek (53.2 km2). The maps were obtained through a supervised classification approach based on the support vector machine learning algorithm. The data input to the algorithm is the hyperspectral and LiDAR dataset. As pre-processing, an NDVI-based threshold was applied to mask bare soil, man-made structures, water, and shadows. The classification algorithm was applied following a hierarchical strategy. In the first step, the tree species were estimated. The algorithm was then applied to the remaining areas for the identification of shrubs and meadow plants. The various estimations were then merged to provide the final vegetation map. Some of the files are geotiffs, which require GIS software to visualize. jpeg files are extracted geotiffs.
Knowledge graph centered on Plant species distribution within the Upper Colora with 9 nodes and 20 connections. Top connected: East River, Washington Gulch, Coal Creek, Slate River, Forest inventory plot sampling.
Items connected by shared entities, co-authorship, citations, or semantic similarity.