Multi-temporal Satellite Images and Combination of Multi-Classification Approaches for Evaluation of Changes in Forest Cover: A case study

Abstract

The primary objective of this study is to develop a method to monitor the changes in forest cover using three different techniques, namely Spatial Data Mining (SDM), supervised and unsupervised classification, and Geographical Information System (GIS) overlay processing methods. The secondary objective was to monitor the land use changes using multi-temporal satellite images (Landsat 8 images of 2016 and 2020). The study area is located in Nuwara Eliya, Sri Lanka. The first and second approaches consisted of supervised and unsupervised classification approaches. The use of a combination of two classification mapping approaches provides better results compared with the use of a single classification, which is a novelty of the present study. The GIS overly-processing technique combined the two maps obtained from supervised and unsupervised classifications to develop an improved map for land use changes. The improved map was reclassified and converted into ASCII (American Standard Code for Information Interchange) format, which was then pre-processed to convert the data set into the suitable format for the SDM modeling. The converted format was used to implement SDM modeling with the support of the Clustering-outline detection algorithm. The overall accuracy of the Landsat 8 images was 94.6. Results revealed that the forest cover extent was diminished by 5.28% in the study area between 2016 and 2020. The ground measurement was done with the help of the forest department to verify the results. The study revealed that the forest area decreased, and farming lands increased due to intensive agriculture. Future studies would be necessary to determine the validity and suitability of the developed model for other climatic zones in the country.