Accuracy Assessment of Land Use/Land Cover Classification Using Remote Sensing and GIS
By S. Rwanga, J. Ndambuki. 2017
837 citations recorded by the source corpus.
Remote sensing is one of the tool which is very important for the production of Land use and land cover maps through a process called image classification. For the image classification process to be successfully, several factors should be considered including availability of quality Landsat imagery and secondary data, a precise classification process and user’s experiences and expertise of the procedures. The objective of this research was to classify and map land-use/land-cover of the study area using remote sensing and Geospatial Information System (GIS) techniques. This research includes two sections (1) Landuse/Landcover (LULC) classification and (2) accuracy assessment. In this study supervised classification was performed using Non Parametric Rule. The major LULC classified were agriculture (65.0%), water body (4.0%), and built up areas (18.3%), mixed forest (5.2%), shrubs (7.0%), and Barren/bare land (0.5%). The study had an overall classification accuracy of 81.7% and kappa coefficient (K) of 0.722. The kappa coefficient is rated as substantial and hence the classified image found to be fit for further research. This study present essential source of information whereby planners and decision makers can use to sustainably plan the environment.
Subfields: Ecology, Global and Planetary Change, Management, Monitoring, Policy and Law
View Paper: https://www.semanticscholar.org/paper/a26bf7c728f8c6c097bf6b0a583dc4bdf1636696
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In Ecology
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Citations in the corpus
- Prediction of Land Use and Land Cover Changes for North Sumatra, Indonesia, Using an Artificial-Neural-Network-Based Cellular Automaton (2019, methodology): “The Kappa coefficient is widely used in LULC assessments for accuracy [37] to measure the true agreement between the observed agreement and chance agreement [38].”
- Urban Expansion in Ethiopia from 1987 to 2017: Characteristics, Spatial Patterns, and Driving Forces (2019, background): “The results displayed that the overall accuracy of all cities was greater than 85 percent (Table 2), which can achieve the accuracy requirements for the land cover change valuation [56,57].”