MAPPING and CHANGE ANALYSIS in MANGROVE FOREST by USING LANDSAT IMAGERY

T. T. Dan, C. F. Chen, S. H. Chiang, S. Ogawa

Research output: Contribution to journalConference articlepeer-review

18 Scopus citations

Abstract

Mangrove is located in the tropical and subtropical regions and brings good services for native people. Mangrove in the world has been lost with a rapid rate. Therefore, monitoring a spatiotemporal distribution of mangrove is thus critical for natural resource management. This research objectives were: (i) to map the current extent of mangrove in the West and Central Africa and in the Sundarbans delta, and (ii) to identify change of mangrove using Landsat data. The data were processed through four main steps: (1) data pre-processing including atmospheric correction and image normalization, (2) image classification using supervised classification approach, (3) accuracy assessment for the classification results, and (4) change detection analysis. Validation was made by comparing the classification results with the ground reference data, which yielded satisfactory agreement with overall accuracy 84.1% and Kappa coefficient of 0.74 in the West and Central Africa and 83.0% and 0.73 in the Sundarbans, respectively. The result shows that mangrove areas have changed significantly. In the West and Central Africa, mangrove loss from 1988 to 2014 was approximately 16.9%, and only 2.5% was recovered or newly planted at the same time, while the overall change of mangrove in the Sundarbans increased approximately by 900 km2 of total mangrove area. Mangrove declined due to deforestation, natural catastrophes deforestation and mangrove rehabilitation programs. The overall efforts in this study demonstrated the effectiveness of the proposed method used for investigating spatiotemporal changes of mangrove and the results could provide planners with invaluable quantitative information for sustainable management of mangrove ecosystems in these regions.

Original languageEnglish
Pages (from-to)109-116
Number of pages8
JournalISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Volume3
DOIs
StatePublished - 7 Jun 2016
Event23rd International Society for Photogrammetry and Remote Sensing Congress, ISPRS 2016 - Prague, Czech Republic
Duration: 12 Jul 201619 Jul 2016

Keywords

  • Change detection
  • Deforestation
  • Image classification
  • Landsat data
  • Mangrove forest

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