Spatio-Temporal Analysis of Forest Cover Change and Its Implications for Environmental Sustainability

Abstract

The rapid loss of forest cover and the onward transformation of land use has become a major environmental issue because of the negative effects on conservation of biodiversity, sequestration of carbon, maintainability of the ecological balance and control of the climate. The successful assessment of the forest ecosystems in terms of space and time is thus regarded as the key to the successful environmental management and climate resistance planning. This paper explores a spatio-temporal analysis model of looking at the change in forest cover and the suggested implications to environmental sustainability through Remote Sensing, Geographic Information Systems (GIS), and a machine learning classifier (Random Forest). Satellite records of Landsat and Sentinel systems under multi-temporal conditions were employed to study the changes in forest cover over the years. The suggested approach consists of satellite image preprocessing, vegetation feature detection, land-use/land-cover classification, and analysis of the temporal change detection. The reason why the Random Forest classifier was used is because it is the most powerful in terms of classification, resistant to overfitting, and it works well with complicated environmental data. Accuracy, Precision, Recall, F1-score and Kappa coefficient metrics were used to classify the performance to enable reliable performance on the environment prediction and consistency in the classification. The experiment showed that there were marked variations in forest cover with climatic variability and anthropogenic activities such as deforestation and degradation trends. The analysis with the help of NDVI also revealed the shifts of the vegetation density and ecosystem well-being in the study area. Random Forest model has high classification accuracy and was good at distinguishing between forest and non-forest areas. The paper also brings to the fore, the environmental sustainability effect of a reduced forest such as the effects on biodiversity, soil conservation, ecological balance and long term climate resilience. The findings indicate that Remote Sensing, GIS and the use of the Random Forest-based intelligent environmental monitoring are an effective and scalable system to analyse forest change to aid in sustainable forest management, ecological restoration planning, and evidence-based environmental governance.