Remote Sensing Letters · 2011 · 24 citations · 27 references
Natural ResourcesEarth SciencesForestryForest InventoryForest ManagementRemote SensingLidar Remote SensingLidar-based Forest MeasurementRemote Sensing For Forest InventoryVegetation Change DetectionVegetation Cover AssessmentVegetation MonitoringAirborne Laser ScanningForest Resource MappingSatellite Data IntegrationSatellite-based Forest InventorySatellite-based Forest MappingSatellite-based Vegetation MonitoringVegetation Cover Change DetectionVegetation Cover Mapping
The aim of this study was to investigate to which degree the accuracy of vegetation classification could be improved by combining optical satellite data and airborne laser scanner (ALS) data, compared with using satellite data only. A Satellite Pour l'Observation de la Terre (SPOT) 5 scene and Leica ALS 50-II data from 2009, covering a test area in the mid-Sweden (latitude 60° 43′ N, longitude 16° 43′ E), were used in maximum likelihood and decision tree classifications. Training and validation data were obtained from the interpretation of digital aerial photo stereo models. Combination of SPOT and ALS data gave classification accuracies up to 72%, compared with 56% using only SPOT data. This indicates that integrating features from large area laser scanning may lead to significant improvements in satellite data-based vegetation classifications.
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Photogrammetric Engineering and Remote Sensing
John Nicol, B. King · UCL Discovery (University College London) · 2007 · 9.4K citations
Global land cover mapping from MODIS: algorithms and early results
M. A. Friedl, D.K. McIver, J.C.F. Hodges et al. · Remote Sensing of Environment · 2002 · 2.8K citations