@conference{Prasetyo2019,
title = {Canopy cover of mangrove estimation based on airborne LIDAR & Landsat 8 OLI},
author = {Lilik B Prasetyo and Wim I Nursal and Yudi Setiawan and Yoga Rudianto and Ketut Wikantika and Bambang Irawan},
url = {https://iopscience.iop.org/article/10.1088/1755-1315/335/1/012029},
doi = {10.1088/1755-1315/335/1/012029},
year = {2019},
date = {2019-10-28},
volume = {335},
publisher = {IOP Conf. Ser.: Earth Environ. Sci},
abstract = {Mangroves are very important ecosystems, because of their economic value and environmental services, including as a habitat for various wildlife species, storing carbon, and protecting land from sea abrasion. Indonesia is known to have large mangrove area and diversity. It is estimated that the area of mangroves in Indonesia in 2015 reached about 3 million hectares, with 15 families, 18 genera, 41 true mangrove species and 116 species of mangrove associations. Unfortunately, the area to continue to decline due to degradation and conversion to other land uses, especially ponds and built up areas. Usually, mangrove degradation assessment is carried out by field survey and relying on Normalized Difference Vegetation Index (NDVI) clustering derived from satellite image data. Field surveys require a large amount of time and cost, meanwhile NDVI clustering is either inaccurate or too rough. Therefore, exploration of another methods are needed. Our result showed that pixel value of Band 5, Band 6, NDVI and PC1 can be used to estimate canopy cover. Regression using quadratic equation is better than linear equations. However, we noticed limitations of optical Landsat 8 OLI data for canopy cover mapping, namely pixel saturation on high canopy cover and high pixel value of bush/shrubs/regrowth that was not always representing high canopy cover.},
keywords = {canopy cover, Landsat, LiDAR, mangrove},
pubstate = {published},
tppubtype = {conference}
}
