What are the impacts of different classification algorithms, spectral indices, and satellite sensor resolutions on the accuracy of LULC change detection using remote sensing and GIS, and how can machine learning or AI-based approaches improve the reliability of LULC mapping for sustainable land management?
The accuracy of LULC classification depends on the choice of algorithm, use of spectral indices, and sensor resolution.
he accuracy of Land Use/Land Cover (LULC) classification using Remote Sensing (RS) and Geographic Information Systems (GIS) is significantly influenced by the choice of classification algorithm, spectral indices, and satellite sensor resolution.
Different classification algorithms—such as Maximum Likelihood, Random Forest, and Support Vector Machines—yield varying accuracies depending on the complexity of the landscape and the amount of training data. Machine learning algorithms, especially ensemble models like Random Forest, often outperform traditional statistical methods due to their ability to handle nonlinear relationships and high-dimensional data.
Spectral indices like NDVI, NDBI, and SAVI enhance specific land cover features (e.g., vegetation, built-up areas), improving class separability and helping reduce misclassification. The selection of appropriate indices tailored to the study area is crucial for better accuracy.
Satellite sensor resolution—spatial, spectral, temporal, and radiometric—plays a foundational role. Higher spatial resolution (e.g., WorldView or Sentinel-2) provides finer detail, enabling precise mapping in heterogeneous areas, while coarser resolutions (e.g., MODIS) suit regional-scale analysis but may generalize local variations.
In sum, the synergy between advanced classification methods, carefully chosen spectral indices, and suitable sensor resolution determines the quality and reliability of LULC outputs in RS and GIS applications.
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