Classification and change detection of Sabah mangrove forest using decision-tree learning technique

Kamaruddin, N.A. and Shigeo, F. (2018) Classification and change detection of Sabah mangrove forest using decision-tree learning technique. In: 9th IGRSM International Conference and Exhibition on Geospatial and Remote Sensing: Geospatial Enablement, 24-25 Apr 2018, Kuala Lumpur, Malaysia.

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Abstract

The objective of this study is to determine the potential of decision tree-learning technique to classify and detects the changes of the Sabah mangrove forest area. The study area was conducted in the Mengkabong mangrove forest which is located on the west coast of Sabah. The multi-temporal of Landsat series (TM, ETM+, and OLI-TIRS) with five years interval data from 1990 and 2013 were used in this study. The results show that the use of decision-tree learning technique integrated with multi-temporal Landsat series and GIS data can be effective in delineating spatial and temporal change of the Sabah mangrove forest. The selection of suitable attributes from spectral features of Landsat data, topographic data and GIS database has promoted the high accuracy of the mangrove classification result with 90.8%. 40 hectares of Mengkabong mangrove were reduced from 1990 to 2013 and the fragmentation was obvious. In conclusion, the decision-tree learning technique was successfully classified and detects the changes of mangrove forest in the Mengkabong area.

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