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A national lidar dataset refers to a high-resolution lidar dataset comprising most—and ideally all—of a nation's terrain. Datasets of this type typically meet specified quality standards and are publicly available for free (or at nominal cost) in one or more uniform formats from government or academic sources.
Currently, the best source for nationwide LiDAR availability from public sources is the United States Interagency Elevation Inventory (USIEI). [1] The USIEI is a collaborative effort of NOAA and the U.S. Geological Survey, with contributions from the Federal Emergency Management Agency, the Natural Resources Conservation Service, the US Army Corps of Engineers, and the National Park Service.
Global Ecosystem Dynamics Investigation (GEDI, pronounced / ˈ dʒ ɛ d aɪ /) is a NASA mission to measure how deforestation has contributed to atmospheric CO 2 concentrations. [1] [2] A full-waveform LIDAR was attached to the International Space Station to provide the first global, high-resolution observations of forest vertical structure.
The National Agriculture Imagery Program (NAIP) acquires aerial imagery during the agricultural growing seasons in the continental United States. It is administered by the USDA 's Farm Service Agency (FSA) through the Aerial Photography Field Office (APFO) in Salt Lake City .
Interferometric synthetic aperture radar, abbreviated InSAR (or deprecated IfSAR), is a radar technique used in geodesy and remote sensing.This geodetic method uses two or more synthetic aperture radar (SAR) images to generate maps of surface deformation or digital elevation, using differences in the phase of the waves returning to the satellite [1] [2] [3] or aircraft.
Lidar (/ ˈ l aɪ d ɑːr /, also LIDAR, LiDAR or LADAR, an acronym of "light detection and ranging" [1] or "laser imaging, detection, and ranging" [2]) is a method for determining ranges by targeting an object or a surface with a laser and measuring the time for the reflected light to return to the receiver.
According to the Journal Citation Reports, the journal has a 2019 impact factor of 1.650, ranking it 57th out of 93 journals in the category "Biology," 35th out of 59 journals in the category "Mathematical & Computational Biology" and 39th out of 124 journals in the category "Statistics & Probability". [10]
A supervised classification is a system of classification in which the user builds a series of randomly generated training datasets or spectral signatures representing different land-use and land-cover (LULC) classes and applies these datasets in machine learning models to predict and spatially classify LULC patterns and evaluate classification accuracies.