Canopy Cover Estimation Based on LiDAR and Landsat 8 Data using Support Vector Regression

被引：0
作者：
Tampinongkol, Felliks Feiters [1 ]
Setiawan, Yudi [2 ]
Nursalam, Wim Iqbal [3 ]
Hudjimartsu, Sahid [4 ]
Prasetyo, Lilik Budi [2 ]
机构：
[1] Univ Bunda Mulia, Dept Informat, Jl Ancol Barat IV, Jakarta 14430, Indonesia
[2] IPB Univ, Dept Forest Resource Conservat, Jl Raya Dramaga, Bogor 16680, Indonesia
[3] IPB Univ, Environm Anal & Spatial Modeling Lab, Forests2020 Programme, Jl Raya Dramaga, Bogor 16680, Indonesia
[4] Ibn Khaldun Univ, Geoinformat Informat Engn Dept, Jl KH Soleh Iskandar KM 2, Bogor, Indonesia
来源：
PROCEEDINGS OF 2021 INTERNATIONAL CONFERENCE ON DATA AND SOFTWARE ENGINEERING (ICODSE): DATA AND SOFTWARE ENGINEERING FOR SUPPORTING SUSTAINABLE DEVELOPMENT GOALS | 2021年
关键词：
Canopy cover; Landsat; 8; OLI; LiDAR; Machine learning; Support vector; SVR; AIRBORNE LIDAR;
D O I：
10.1109/ICoDSE53690.2021.9648453
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Indonesia has large areas of forest that spread in almost every island of Indonesia. Forest in Indonesia also have various types of ecosystem, therefore they have an important role to protect each element contained within the ecosystem. The forest monitoring system in Indonesia still using the traditional approach for monitoring forests areas. This paper aims to generate a prediction model using remote sensing data and support vector regression for the model to estimate forest cover, especially in Indonesia. Landsat 8 OLI reflectance value from each band was used to estimate forest canopy cover with the integration of LiDAR data. The prediction model of forest canopy cover was observed at R-2 = 0.6921 and RMSE = 0.1658 of canopy cover. In this case R-2 means the correlation between LiDAR point cloud with Landsat bands. The SVR kernel used in this study was radial basis function with parameter (Cost: 10, Gamma: 1 and Epsilon: 0.1).
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页数：4
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