New Fusion Algorithm for Improving Secondary Forest Cover Mapping

Research Article- DOI: 10.23953/cloud.ijarsg.456

Abstract

Secondary forests play a central role in recovering earlier lost carbon and biodiversity via deforestation and degradation, yet little data is applicable to the magnitude of numerous succession phases. Such information is considered a priority in tropical regions with elevated past and current disturbance rates; however, regrowth in the area is rapid. Focusing on Kuala Krai district, Kelantan state, Malaysia, this paper offer a new fusion algorithm by using the clustering method (fuzzy k-means (FKM)) and Vector Supporting Machines (SVM) procedures. The methodology scheme applied was split into two phases, a clustering map firstly was acquired using FKM from the Sentinel-2A MSI (10 m) image; at the same time, the initial image used to extract Green Normalized Vegetation Index (GNDVI) layer. Using SVM classifier, the classification map was created. Second, SVM and FKM fusion as a hybrid classifier were tested, verified and compared to MLC-parametric and SVM-nonparametric classification algorithms. The study results reveal the effectiveness of the GNDVI layer and FKM segmentation map to enhance SVM classification through applying the Sentinel-2A MS image by approximately 8 % and 14 %, respectively, as opposed to SVM and MLC. Thus this study is inspiring as it is extremely difficult to generate a reliably map land cover in heterogeneous areas, especially in tropical areas, and yet this job is crucial for conservation projects, climate change mitigation strategies, and expansion plans and regional development policies. 

doi: https://doi.org/10.23953/cloud.ijarsg.456