Arif Kurnia Wijayanto, S.TP, M.Sc

@conference{Rudianto2019,

title = {Canopy cover estimation of agroforestry based on airborne LiDAR and Landsat 8 OLI},

author = {Yoga Rudianto and Lilik B Prasetyo and Yudi Setiawan and Sahid A Hudjimartsu},

url = {https://www.spiedigitallibrary.org/conference-proceedings-of-spie/11372/2541549/Canopy-cover-estimation-of-agroforestry-based-on-airborne-LiDAR-and/10.1117/12.2541549.short},

doi = {10.1117/12.2541549},

year  = {2019},

date = {2019-12-28},

volume = {11372},

publisher = {SPIE},

abstract = {Agroforestry/mixed gardens is a land management system that combines agricultural, livestock production with tree to obtain various products in a sustainable manner so as to increase social, economic and environmental benefits This system can be a form of mitigation and adaptation to global climate change, especially in areas with high population densities, but with less agricultural labor, such as in urban fringe area. Based on the formal definition of forests from the Indonesian Ministry of Environment and Forestry of Indonesia based on canopy cover, agroforestry might be considered as forest, whereas the canopy cover >30%. The research aim to estimate canopy cover base on integration of Lidar and Landsat 8 OLI of agroforestry in the Cidanau watershed. The most suitable equation model is an exponential equation (FRCI = 22.928e (-80.439 * 'RED')), however, some underestimation in high canopy cover ( >70%) and underestimation in low canopy cover (< 60%) should be anticipated. The result showed that agroforestry in some location have canopy cover greater than 30% and therefore it can be considered as a forest.},

keywords = {agroforestry, canopy cover, Landsat, LiDAR},

pubstate = {published},

tppubtype = {conference}

}

@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}

}