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

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.

Complementarity of Two Rice Mapping Approaches: Characterizing Strata Mapped by Hypertemporal MODIS and Rice Paddy Identification Using Multitemporal SAR

“…Linear stretching was applied to generate the digital NDVI numbers (DN), where −1, the minimum NDVI value, was assigned to 0 and the maximum NDVI value of 1 to 255. The upper envelope of the NDVI time series was derived using the adaptive Savitzky-Golay filter in TIMESAT [53][54][55] (Figure S2) and the resulting time series was classified using the ISODATA [56] clustering algorithm through unsupervised classification [9,[57][58][59].…”

From Land to Sea, a Review of Hypertemporal Remote Sensing Advances to Support Ocean Surface Science

“…This limits the spatial and temporal coverage of data needed to maximise hypertemporal data exploitation. Hypertemporal datasets are also very data rich [57]. Due to the number of bands in the feature space, visual pattern recognition of training groupings is hardly feasible, further limiting our ability to undertake supervised classification studies.…”

“…First proposed for hypertemporal research by de Bie et al [13], subsequent years have seen refinements and tests of the algorithm's use and modes of deployment on a number of different landcover [10,13,56,81] and species modelling applications [20,23]. A primary concern regarding unsupervised classification is the lack of knowledge beforehand of how many clusters best represent and capture the variability in the hypertemporal dataset [57]. The more clusters the dataset is divided into, the better the fit to the data.…”

Distinguishing between Hazardous Flooding and Non-Hazardous Agronomic Inundation in Irrigated Rice Fields: A Case Study from West Java

“…Low positive or even negative EVI values also signify free-standing water (e.g., rivers, lakes, floods). From the dataset, the time-series EVI from 2000 (DOY 49) to 2014 (DOY 153) was analyzed using the Iterative Self-Organizing DATA (ISODATA) unsupervised classification to generate rice field areas [41,42]. The unsupervised classification is selected because of the lack of information about the spatial distribution of land uses in the study area.…”