Mapping tree height in agroforestry system using Landsat 8 data

Yudi Setiawan; Lilik B. Prasetyo; Sahid Hudjimartsu; Wim Ikbal; Desi Suyamto

doi:10.1117/12.2326014

6 August 2018 Mapping tree height in agroforestry system using Landsat 8 data

Yudi Setiawan, Lilik B. Prasetyo, Sahid Hudjimartsu, Wim Ikbal, Desi Suyamto

Proceedings Volume 10773, Sixth International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2018); 107730P (2018) https://doi.org/10.1117/12.2326014
Event: Sixth International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2018), 2018, Paphos, Cyprus

Abstract

Agroforestry is a land use management-system represents unique vegetation characteristics among tree vegetation types. Tree height is a vegetation variable used to characterize vertical structure, including mixed vegetation structure in agroforestry. Estimation of tree heights with multispectral imagery is a relatively new application and is dependent on integrating synoptic coverage optical data with samples of height data, often from LiDAR-derived reference data. In this study, multispectral Landsat 8 data, Unmanned Aerial Vehicle (UAV)-based LiDAR height data and a log-linear regression model were used to estimate tree height for agroforestry land use in western part of Java Island, Indonesia. We generated a Canopy Height Model (CHM) directly from height-normalized LiDAR points and used as reference data in modeling the key height variable in the multispectral bands of Landsat 8. The analysis showed that red band was the best band to estimate tree height in agroforestry land use, followed by swir band. The log-linear regression algorithm of red band accurately reproduced the LiDAR-derived height training data using Landsat 8 data with overestimate 1.46 m in estimating tree height < 5 m and underestimate 7.79 m for tree height > 20 m.

Citation Download Citation

Yudi Setiawan, Lilik B. Prasetyo, Sahid Hudjimartsu, Wim Ikbal, and Desi Suyamto "Mapping tree height in agroforestry system using Landsat 8 data", Proc. SPIE 10773, Sixth International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2018), 107730P (6 August 2018); https://doi.org/10.1117/12.2326014

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KEYWORDS

Earth observing sensors

Landsat

LIDAR

Data modeling

RGB color model

Vegetation

Reflectivity

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