Geocarto International · 2013 · 12 citations · 32 references
Natural SciencesEarth SciencesGeoinformaticsCoastal GeographyGeographic Information SystemsRemote SensingLand Cover Change DetectionLand Cover ClassificationLand Cover MappingLand UseLand Use Change DetectionRemote Sensing Change DetectionClassification Accuracy AssessmentCoastal Change DetectionCoastal Land Cover Change DetectionHigh-resolution Satellite Image AnalysisLand Cover Classification SchemesLand Use Change MappingSpatial Change QuantificationUnsupervised Classification
This paper discusses the development and implementation of a method that can be used with multi-decadal Landsat data for computing general coastal US land use and land cover (LULC) maps consisting of seven classes. With Mobile Bay, Alabama as the study region, the method that was applied to derive LULC products for nine dates across a 34-year time span. Classifications were computed and refined using decision rules in conjunction with unsupervised classification of Landsat data and Coastal Change and Analysis Program value-added products. Each classification’s overall accuracy was assessed by comparing stratified random locations to available high spatial resolution satellite and aerial imagery, field survey data and raw Landsat RGBs. Overall classification accuracies ranged from 83 to 91% with overall κ statistics ranging from 0.78 to 0.89. Accurate classifications were computed for all nine dates, yielding effective results regardless of season and Landsat sensor. This classification method provided useful map inputs for computing LULC change products.
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Dengsheng Lu, Paul W. Mausel, Eduardo S. Brondízio et al. · International Journal of Remote Sensing · 2004 · 3.2K citations