For the sustainable management of natural resources
and to understand how the climate affects the landscape, accurate land use and
land cover (LULC) classification is essential. Robust classification techniques
and high-quality datasets are necessary for precise and effective LULC
classification. The effectiveness of various combinations of satellite data and
classification techniques must be carefully evaluated to help choose the
optimal strategy for LULC classification, given the growing availability of satellite
data, geospatial analysis tools, and classification techniques. This study
focuses on the LULC classification of Jalandhar, Punjab, India, using machine
learning (ML) algorithms and visual image interpretation. Sentinel-2 satellite
data, with its high spatial and spectral resolution, has been utilized for
feature extraction and classification. Python was employed for implementing
various ML algorithms, including Random Forest (RF), Support Vector Machine
(SVM), K-Nearest Neighbor (KNN), Gradient Boosting (GB), Multi-Layer Perceptron
(MLP), and Decision Tree (DT), while ArcGIS was used to classify LULC using
visual image interpretation and for maps preparation. Agriculture was the
dominant class across all methods, with GB estimating 1774.26 sq.km, followed
by plantation (268.13 sq.km) and built-up areas (171.76 sq.km). Waterbodies
were mapped with high precision due to their distinct spectral features, with
estimates ranging from 18.34 sq.km (GB) to 26.05 sq.km (Visual interpretation).
Among all models, GB outperformed others with the highest overall accuracy
(95.0%) and a kappa value of 0.94, followed by RF (94.2%), and SVM (93.8%).
Visual interpretation achieved a comparative accuracy of 90.1%, though it
showed limitations in distinguishing spectrally mixed classes like plantation
and built-up. This study concludes that while Visual interpretation remains a
useful and accessible method, especially for real-time interpretation, ML-based
approaches, particularly GB and RF, offer superior accuracy and reliability.
The study highlights the importance of visual interpretation for a better
accurate LULC at a regional level; meanwhile, leveraging advancements in ML
algorithms in a hybrid approach will enhance the accuracy in many-fold.
