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).
引用
收藏
页数：4
相关论文
共 50 条
  • [41] Ensemble of Illumination Estimation Methods using Support Vector Regression
    Chang, Youngha
    Iiyama, Takuya
    Mukai, Nobuhiko
    INTERNATIONAL WORKSHOP ON ADVANCED IMAGING TECHNOLOGY (IWAIT) 2021, 2021, 11766
  • [42] Regression forecast and abnormal data detection based on support vector regression
    Wang, Lei
    Zhang, Rui-Qing
    Sheng, Wei
    Xu, Zhi-Gao
    Zhongguo Dianji Gongcheng Xuebao/Proceedings of the Chinese Society of Electrical Engineering, 2009, 29 (08): : 92 - 96
  • [43] Parameter estimation for Weibull distribution using support vector regression
    Ling, Dan
    Huang, Hong-Zhong
    Miao, Qiang
    Yang, Bo
    DETC2007: PROCEEDINGS OF THE ASME INTERNATIONAL DESIGN ENGINEERING TECHNOLOGY CONFERENCE AND COMPUTERS AND INFORMATION IN ENGINEERING CONFERENCE, VOL 4, 2008, : 445 - 449
  • [44] Using Support Vector Regression for Web Development Effort Estimation
    Corazza, Anna
    Di Martino, Sergio
    Ferrucci, Filomena
    Gravino, Carmine
    Mendes, Emilia
    SOFTWARE PROCESS AND PRODUCT MEASUREMENT, PROCEEDINGS, 2009, 5891 : 255 - +
  • [45] Estimation of walking energy expenditure by using support vector regression
    Su, S. W.
    Wang, L.
    Celler, B. G.
    Ambikairajah, E.
    Savkin, A. V.
    2005 27TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY, VOLS 1-7, 2005, : 3526 - 3529
  • [46] VERTICAL CANOPY COVER RETRIEVAL FOR GREATER KHINGAN FOREST BASED ON A GEOMETRIC-OPTICAL MODEL USING LANDSAT DATA
    Gu, Chengyan
    Tian, Xin
    Li, Zengyuan
    2016 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS), 2016, : 4434 - 4437
  • [47] Parameter Estimation of Scattering Components Using Matrix Pencil Based Support Vector Regression
    Chahine, Khaled
    Arnaout, Mohamad
    Ghazal, Bilal
    2015 THIRD INTERNATIONAL CONFERENCE ON TECHNOLOGICAL ADVANCES IN ELECTRICAL, ELECTRONICS AND COMPUTER ENGINEERING (TAEECE), 2015, : 295 - 298
  • [48] Nonlinear estimation and modeling of fMRI data using spatio-temporal support vector regression
    Wang, YM
    Schulz, RT
    Constable, RT
    Staib, LH
    INFORMATION PROCESSING IN MEDICAL IMAGING, PROCEEDINGS, 2003, 2732 : 647 - 659
  • [49] Extending Airborne Lidar-Derived Estimates of Forest Canopy Cover and Height Over Large Areas Using kNN With Landsat Time Series Data
    Ahmed, Oumer S.
    Franklin, Steven E.
    Wulder, Michael A.
    White, Joanne C.
    IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, 2016, 9 (08) : 3489 - 3496
  • [50] Estimation of Forest Height, Biomass and Volume Using Support Vector Regression and Segmentation From Lidar Transects and Quickbird Imagery
    Chen, Gang
    Hay, Geoffrey J.
    Zhou, Yanlian
    2010 18TH INTERNATIONAL CONFERENCE ON GEOINFORMATICS, 2010,
12345