GIS and Artificial Intelligence Application in Smart Forest Ecosystem Sustainability Evaluation of Olokemeji Forest Reserve, Ogun State, Nigeria
DOI:
https://doi.org/10.54536/ajgt.v3i1.2621Keywords:
Geographic Information Systems (GIS), Artificial Intelligence (AI), Convolution Neural Network (CNN), Normalized Difference Vegetation Index (NDVI), Satellite ImageryAbstract
The increase in human population over the years has accelerated growth in anthropogenic activities, which have led to the conversion of forest reserves to other land uses. In the sequel, it has become imperative for researchers to focus on the mapping of forest reserves through the use of GIS and Artificial Intelligence (AI) with time-efficient, automated, and low-cost methods to preserve the existing forest reserve and its sustainability evaluation implementation. This research aimed at utilizing GIS and artificial intelligence applications in smart forest ecosystem sustainability evaluation of Olokemeji forest reserve, Ogun State, with the following objectives: (i.) assessment of the current state of the forest ecosystem.(ii.) identify potential threats and risks to the study area and (iii.) develop sustainable management strategies for its conservation and preservation. In pursuance of this, GIS and AI were deployed in this study to assess the spatial characteristics of the forest ecosystem in Olokemeji forest reserve. Landsat imagery, ground coordinates, and a research questionnaire were the major data used. Object-based classification, Normalized Difference Vegetation Index (NDVI), and Land Use Land Cover in ArcGIS 10.2 software was deployed in data generation and analysis. The results showed that in 2013, about 1657.115 ha of the study area was occupied by dense forest cover while in 2023, it decreased to 1188.060 ha, with a difference of about 469.055 ha. By implementing smart forest monitoring and evaluation systems that use artificial intelligence, the government and commercial groups should set regulations focused on reducing the escalating risks to forest reserves.
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