Mangrove Forest Cover Extraction of the Coastal Areas of Negros Occidental, Western Visayas, Philippines Using Lidar Data

Abstract

ABSTRACT:Mangroves have a lot of economic and ecological advantages which include coastal protection, habitat for wildlife, fisheries and forestry products. Determination of the extent of mangrove patches in the coastal areas of the Philippines is therefore important especially in resource conservation, protection and management. This starts with a well-defined and accurate map. LiDARwas used in the mangrove extraction in the different coastal areas of Negros Occidental in Western Visayas, Philippines. Total coastal study area is 1,082.55 km² for the 14 municipalities/ cities processed. Derivatives that were used in the extraction include, DSM, DTM, Hillshade, Intensity, Number of Returns and PCA. The RGB bands of the Orthographic photographs taken at the same time with the LiDAR data were also used as one of the layers during the processing. NDVI, GRVI and Hillshade using Canny Edge Layer were derived as well to produce an enhanced segmentation. Training and Validation points were collected through field validation and visual inspection using Stratified Random Sampling. The points were then used to feed the Support Vector Machine (SVM) based on tall structures. Only four classes were used, namely, Built-up, Mangroves, Other Trees and Sugarcane. Buffering and contextual editing were incorporated to reclassify the extracted mangroves. Overall accuracy assessment is at 98.73% (KIA of 98.24%) while overall accuracy assessment for Mangroves only is at 98.00%. Using this workflow, mangroves can already be extracted in a largescale level with acceptable overall accuracy assessments.

Approaches, potential, and challenges in the use of remote sensing to study mangrove and other tropical wetland forests

“…The launching of new technologies has enabled the use of more detailed sources of information (Pada et al 2016, Tassi & Vizzari 2020, Pham et al 2023. Along with the progress in image processing and classification techniques with new analytical algorithms, we have increased our capacity to assess temporal changes in wetland forests accurately (Heenkenda et al 2014, De Luca et al 2022, Pan et al 2022, Shafi et al 2023.…”