Monitoring of oil palm plantations and growth variations with a dense vegetation model

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

  • Khar Chun Teng
  • Jun Yi Koay
  • Seng Heng Tey
  • Hong Tat Ewe
  • Hean Teik Chuah

The development of microwave remote sensing models for the monitoring of vegetation has received wide attention in recent years. For vegetation in the tropics, it is necessary to consider a dense medium model for the theoretical modelling of the microwave interaction with the vegetation medium. In this paper, a multilayer model based on the radiative transfer theory for a dense vegetation medium is developed where the coherence effects and near field interaction effects of closely spaced leaves and branches are considered by incorporating the Dense Medium Phase and Amplitude Correction Theory (DM-PACT) and Fresnel Phase Corrections. The iterative solutions of the radiative transfer model are computed with input based on ground truth measurements of physical parameters of oil palm plantations in the state of Perak, Malaysia, and compared with the SAR images obtained from RADARSAT2. Preliminary results are analyzed for dominant scattering mechanisms as well as monitoring of growth variation of oil palm trees for further development of operation models for long term monitoring of oil palm plantations.

Original languageEnglish
Title of host publicationProceedings of the 2014 International Geoscience and Remote Sensing Symposium (IGARSS)
Number of pages3
PublisherIEEE
Publication date2014
Pages298-300
Article number6946416
ISBN (Electronic)978-1-4799-5775-0
DOIs
Publication statusPublished - 2014
Event2014 IEEE International Geoscience and Remote Sensing Symposium - Québec City, Canada
Duration: 13 Jul 201418 Jul 2014

Conference

Conference2014 IEEE International Geoscience and Remote Sensing Symposium
LandCanada
ByQuébec City
Periode13/07/201418/07/2014

    Research areas

  • electromagnetic modeling, radar applications, Radar remote sensing, remote monitoring, vegetation

ID: 169106060