Classification for non infected and infected ganoderma boninense of oil palm trees using ALOS PALSAR-2 backscattering coefficient

9th IGRSM International Conference and Exhibition on Geospatial & Remote Sensing

地球科学;计算机科学

Hashim, I.C.^1 ; Shariff, A.R.M.^2 ; Bejo, S.K.^2 ; Muharam, F.M.^3 ; Ahmad, K.^4

Geospatial Information Research Centre (GISRC), Level 6 Tower Block, Faculty of Engineering, Universiti Putra Malaysia (UPM), Serdang, Selangor

43400, Malaysia^1

Department of Biological and Agriculture, Level 3, Faculty of Engineering, Universiti Putra Malaysia (UPM), Serdang, Selangor

43400, Malaysia^2

Department of Agriculture Technology, Faculty of Agriculture, Universiti Putra Malaysia, UPM Serdang, Selangor Darul Ehsan

43400, Malaysia^3

Department of Plant Protection, Faculty of Agriculture, Universiti Putra Malaysia, UPM Serdang, Selangor Darul Ehsan

43400, Malaysia^4

关键词: Backscattering coefficients;    Decision tree classification;    Decision tree classifiers;    Dual-polarizations;    Image preprocessing;    Oil palm plantations;    Overall accuracies;    Radiometric calibrations;   

Others  :  https://iopscience.iop.org/article/10.1088/1755-1315/169/1/012066/pdf
DOI  :  10.1088/1755-1315/169/1/012066

学科分类：计算机科学（综合）

来源: IOP

【 摘 要 】

Malaysia's monthly export of oil palm product in 2015 was 25,370,294 tonnes valued at about RM60 million. Consequently, Malaysia is now one of the leading manufacturers and exporters of palm oil and its derivatives in the world. However, oil palm plantations in Malaysia are now facing the threat of a Basal Stem Rot (BSR) disease that is caused by fungus called the Ganoderma boninense. This disease reduces oil palm production as an infected mature oil palm dies after 2-3 years of being infected. A decision tree classification approach is proposed in this study for discriminating between non infected and infected of G. boninense in oil palm tree using backscatter values of ALOS PALSAR 2 in FELCRA Seberang Perak 10, Kampung Gajah, Perak. The methodology involves (1) collection of ALOS PALSAR 2 image which include dual polarization HH (Horizontal - transmit and Horizontal - receive) and HV (Horizontal - transmit and Vertical - receive); (2) infection status of the oil palm trees in the study area that comprise 92 trees; and (3) image pre-processing that includes radiometric calibration, speckle filtering and linear conversion to dB. The final stage is the backscatter classification of G. boninense health status using the Decision Tree classifier. The overall accuracy for HH and HV backscatter classification were 45.65% and 56.52% respectively. Further investigations may need to be carried out to improve existing accuracy.