Estimating feature extraction changes of Berkelah Forest, Malaysia from multisensor remote sensing data using an object-based technique

Rozali, Syaza and Abd Latif, Zulkiflee and Adnan, Nor Aizam and Hussin, Yousif and Blackburn, Alan and Pradhan, Biswajeet (2020) Estimating feature extraction changes of Berkelah Forest, Malaysia from multisensor remote sensing data using an object-based technique. Geocarto International. ISSN 1010-6049

Text (FinalGeocartoManuscript_Zul)
FinalGeocartoManuscript_Zul.pdf - Accepted Version
Available under License Creative Commons Attribution-NonCommercial.
Download (261kB)

Abstract

The study involves an object-based segmentation method to extract feature changes in tropical rainforest cover using Landsat image and airborne LiDAR (ALS). Disturbance event that are represents the changes are examined by the classification of multisensor data; that is a highly accurate ALS with different resolutions of multispectral Landsat image. Disturbance Index (DI) derived from Tasseled Cap Transformation, Normalized Difference Vegetation Index (NDVI), and the ALS height are the variables for object-based segmentation process. The classification is categorized into two classes; disturbed and non-disturbed forest cover using Nearest Neighbor (NN), Random Forest (RF) and Support Vector Machine (SVM). The overall accuracy ranging from 88% to 96% and kappa ranging from 0.79 to 0.91. Mcnemar’s test p-value (<0.05) is applied to check the classification for each method used which is RF 0.03 and SVM 0.01. The accuracy increases when the integration of ALS in Landsat image (SpectralLandsat; and SpectralLandsat + HeightALS).