LaHMa: a landscape heterogeneity mapping method using hyper-temporal datasets

C. A. J. M. de Bie, Thi Thu Ha Nguyen, Amjad Ali, R. Scarrott, A. K. Skidmore

Research output: Contribution to journalArticlepeer-review

19 Citations (Scopus)

Abstract

A new quantitative method extracts a landscape heterogeneity map (LaHMa) from hyper-temporal remote-sensing data. The feature extraction method is data-driven, unbiased, and builds on the commonly used data reduction technique of Iterative Self-Organizing Data Analysis (ISODATA) clustering with the support of divergence separability indices. First, the relevant spatial-temporal variation in normalized difference vegetation index (NDVI) is classified through ISODATA clustering. Second, a series of prepared cluster maps are overlaid to examine and detect the frequency with which boundaries between clusters occur at the same location. This step identifies the boundary strength between clusters and detects spatial heterogeneity within them. Results of the method are explored for the typical agriculture-defined landscape of the Mekong delta, Vietnam, using NDVI-imagery time-series from SPOT-Vegetation and MODIS-Terra. The method extracts useful landscape heterogeneity features and can support land-cover mapping requiring information on fragmentation and land-cover gradients.

Original languageEnglish
Pages (from-to)2177-2192
Number of pages16
JournalInternational Journal of Geographical Information Science
Volume26
Issue number11
DOIs
Publication statusPublished - Nov 2012
Externally publishedYes

Keywords

  • heterogeneity
  • gradient
  • landscape
  • NDVI
  • hyper-temporal

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