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

页数：4

共 50 条

[21] Support vector regression based on data shifting
Orchel, Marcin
NEUROCOMPUTING, 2012, 96 : 2 - 11
[22] LAND COVER CLASSIFICATION FROM FULL-WAVEFORM LIDAR DATA BASED ON SUPPORT VECTOR MACHINES
Zhou, M.
Li, C. R.
Ma, L.
Guan, H. C.
XXIII ISPRS CONGRESS, COMMISSION III, 2016, 41 (B3): : 447 - 452
[23] Estimation of solar radiation using support vector regression
Bhola, Parveen
Bhardwaj, Saurabh
JOURNAL OF INFORMATION & OPTIMIZATION SCIENCES, 2019, 40 (02): : 339 - 350
[24] Software Defect Estimation using Support Vector Regression
Fagundes, Roberta A. A.
de Souza, Renata M. C. R.
22ND INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING & KNOWLEDGE ENGINEERING (SEKE 2010), 2010, : 265 - 268
[25] CORRELATION OF RANGELANDS BRUSH CANOPY COVER WITH LANDSAT MSS DATA
BOYD, WE
JOURNAL OF RANGE MANAGEMENT, 1986, 39 (03): : 268 - 271
[26] Integrating profiling LIDAR with Landsat data for regional boreal forest canopy attribute estimation and change characterization
Wulder, Michael A.
Han, Tian
White, Joanne C.
Sweda, Tatsuo
Tsuzuki, Hayato
REMOTE SENSING OF ENVIRONMENT, 2007, 110 (01) : 123 - 137
[27] Advanced signal processing based on support vector regression for LIDAR applications
Gelfusa, M.
Murari, A.
Malizia, A.
Lungaroni, M.
Peluso, E.
Parracino, S.
Talebzadeh, S.
Vega, J.
Gaudio, P.
IMAGE AND SIGNAL PROCESSING FOR REMOTE SENSING XXI, 2015, 9643
[28] The accuracy of large-area forest canopy cover estimation using Landsat in boreal region
Hadi
Korhonen, Lauri
Hovi, Aarne
Ronnholm, Petri
Rautiainen, Miina
INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION, 2016, 53 : 118 - 127
[29] Simultaneous data reconciliation and joint bias and leak estimation based on support vector regression
Miao, Yu
Su, Hongye
Wang, Wei
Chu, Jian
COMPUTERS & CHEMICAL ENGINEERING, 2011, 35 (10) : 2141 - 2151
[30] Estimation of Soil Moisture with SAR Data in Large Area Based on Support Vector Regression
Meng, Dexin
Ma, Jianwei
Xin, Jingfeng
Sun, Yayong
Huang, Shifeng
Zhang, Furong
THIRTEENTH INTERNATIONAL CONFERENCE ON DIGITAL IMAGE PROCESSING (ICDIP 2021), 2021, 11878

← 1 2 3 4 5 →