Temporal difference and density-based learning method applied for deforestation detection using ALOS-2/PALSAR-2

Irene Erlyn Wina Rachmawan, Takeo Tadono, Masato Hayashi, Yasushi Kiyoki

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Remote sensing has established as key technology for monitoring of environmental degradation such as forest clearing. One of the state-of-the-art microwave EO systems for forest monitoring is Japan's L-band ALOS-2/PALSAR-2 which provides outstanding means for observing tropical forests due its cloud and canopy penetration capability. However, the complexity of the physical backscattering properties of forests and the associated spatial and temporal variabilities, render straightforward change detection methods based on simple thresholding rather inaccurate with high false alarm rates. In this paper, we develop a framework to alleviate problems caused by forest backscatter variability. We define three essential elements, namely "structures of density", "speed of change", and "expansion patterns" which are obtained by differential computing between two repeat-pass PALSAR-2 images. To improve both the detection and assessing of deforestation, a "deforestation behavior pattern" is sought through temporal machine learning mechanism of the three proposed elements. Our results indicate that the use of "structure of density" can introduce a more robust performance for detecting deforestation. Meanwhile, "speed of change" and "expansion pattern" are capable to provide additional information with respect to the drivers of deforestation and the land-use change.

Original languageEnglish
Title of host publication2018 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages4905-4908
Number of pages4
ISBN (Electronic)9781538671504
DOIs
Publication statusPublished - 2018 Oct 31
Event38th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Valencia, Spain
Duration: 2018 Jul 222018 Jul 27

Publication series

NameInternational Geoscience and Remote Sensing Symposium (IGARSS)
Volume2018-July

Conference

Conference38th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018
CountrySpain
CityValencia
Period18/7/2218/7/27

Keywords

  • Density-based
  • Synthetic Aperture Radar (SAR)
  • Temporal difference learning

ASJC Scopus subject areas

  • Computer Science Applications
  • Earth and Planetary Sciences(all)

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    Rachmawan, I. E. W., Tadono, T., Hayashi, M., & Kiyoki, Y. (2018). Temporal difference and density-based learning method applied for deforestation detection using ALOS-2/PALSAR-2. In 2018 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Proceedings (pp. 4905-4908). [8518412] (International Geoscience and Remote Sensing Symposium (IGARSS); Vol. 2018-July). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IGARSS.2018.8518412