Tunicate Swarm Algorithm with Deep Learning Based Land Use and Cover Change Detection in Nallamalla Forest India

Every biological system on the planet is severely impacted by environmental change, and its primary driver is deforestation. Meanwhile, quantitative analysis of changes in Land Use and Land Cover (LULC) is one of the prominent ways to manage and understand land transformation; thus, it is essential...

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Bibliographic Details
Published in:Applied sciences 2023-01, Vol.13 (2), p.1173
Main Authors: Lavanya, K., Mahendran, Anand, Selvanambi, Ramani, Mazzara, Manuel, Hemanth, Jude D
Format: Article
Language:English
Subjects:
Algorithms
Belief networks
Change detection
Classification
Climate change
Deep learning
Deforestation
Drought
Environmental changes
Land cover
land cover classification
Land use
Landsat
Mapping
Nallamalla forest
Neural networks
Neurons
parameter optimization
Satellite imagery
Semantics
Soil erosion
Support vector machines
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Summary:Every biological system on the planet is severely impacted by environmental change, and its primary driver is deforestation. Meanwhile, quantitative analysis of changes in Land Use and Land Cover (LULC) is one of the prominent ways to manage and understand land transformation; thus, it is essential to inspect the performance of various techniques for LULC mapping to recognize the better classifier to more applications of earth observation. This article develops a Tunicate Swarm Algorithm with Deep Learning Enabled Land Use and Land Cover Change Detection (TSADL-LULCCD) technique in Nallamalla Forest, India. The presented TSADL-LULCCD technique mainly focuses on the identification and classification of land use in the Nallamalla forest using LANDSAT images. To accomplish this, the presented TSADL-LULCCD technique employs a dense EfficientNet model for feature extraction. In addition, the Adam optimizer is applied for the optimal hyper parameter tuning of the dense EfficientNet approach. For land cover classification, the TSADL-LULCCD technique exploits the Deep Belief Network (DBN) approach. To tune the hyper parameters related to the DBN system, the TSA is used. The experimental validation of the TSADL-LULCCD algorithm is tested on LANDSAT-7-based Nallamalla region images. The experimental results stated that the TSADL-LULCCD technique exhibits better performance over other existing models in terms of different evaluation measures.
ISSN:2076-3417
2076-3417
DOI:10.3390/app13021173