Phenology and classification of abandoned agricultural land based on ALOS-1 and 2 PALSAR multi-temporal measurements

作     者：Noryusdiana Mohamad Yusoff Farrah Melissa Muharam Wataru Takeuchi Soni Darmawan Muhamad Hafiz Abd Razak 

作者机构：Department of Agriculture TechnologyFaculty of AgricultureUniversiti Putra MalaysiaSerdangMalaysia Malaysian Remote Sensing AgencyKuala LumpurMalaysia Institute of Plantation StudiesUniversiti Putra MalaysiaSelangorMalaysia Institute of Industrial ScienceUniversity of TokyoMeguro-kuJapan Institut Teknologi Nasional(ITENAS)West JavaIndonesia FELCRA BerhadKuala LumpurMalaysia 

出 版 物：《International Journal of Digital Earth》 (国际数字地球学报（英文）)

年 卷 期：2017年第10卷第2期

页      面：155-174页

核心收录：

学科分类：08[工学] 0708[理学-地球物理学] 0835[工学-软件工程] 0704[理学-天文学] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported by the Fakulti Pertanian Universiti Putra Malaysia[Grant GP-IPM/2014/9434000] 

主　　题：Classification land abandonment agriculture ALOS PALSAR synthetic aperture radar 

摘      要：Agricultural crop abandonment negatively impacts local economy and environment since land,as a resource for agriculture,is not optimally *** take necessary actions to rehabilitate abandoned agricultural lands,the identification of the spatial distribution of these lands must be *** optical images had previously illustrated potentials in the identification of agricultural land abandonment,tropical areas often suffer cloud coverage problem that limits the availability of the ***,this study was conducted to investigate the potential of ALOS-1 and 2(Advanced Land Observing Satellite-1 and 2)PALSAR(Phased Array L-band Synthetic Aperture Radar)images for the identification and classification of abandoned agricultural crop areas,namely paddy,rubber and oil palm *** crop phenology for paddy and rubber was identified from ALOS-1 PALSAR;nonetheless,oil palm did not demonstrate any useful phenology for discriminating between the abandoned *** accuracy obtained for these abandoned lands of paddy,rubber and oil palm was 93.33%±0.06%,78%±2.32%and 63.33%±1.88%,*** study confirmed that the understanding of crop phenology in relation to image date selection is essential to obtain high accuracy for classifying abandoned and non-abandoned agricultural *** finding also portrayed that PALSAR offers a huge advantage for application of vegetation in tropical areas.