Mapping land cover gradients through analysis of hyper-temporal NDVI imagery |
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| Institution: | 1. Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, P.O. Box 217, 7500 AE Enschede, the Netherlands;2. Pakistan Space and Upper Atmosphere Research Commission (SUPARCO), Sector 28, Gulzar-e-Hijri, Karachi 75270, Pakistan;3. Coastal and Marine Research Centre, Environmental Research Institute, University College Cork, County Cork, Ireland;4. Natural History Museum of Crete, University of Crete, P.O. Box 2208, GR-71409 Irakleio, Crete, Greece;1. Universidade Federal do Oeste do Pará, Instituto de Ciências e Tecnologia das Águas, Laboratório de Ecologia de Ictioplâncton e Invertebrados aquáticos, Anexo ao Campus Amazônia Boulevard, Av. Mendonça Furtado, 2946, Fátima, CEP 68040-470, Santarém, PA, Brazil;2. Instituto Nacional de Pesquisas da Amazônia, Coordenação de Biodiversidade, Manaus, AM, Brazil;3. Governo do Estado do Rio Grande do Sul, Secretaria Estadual de Saúde, Porto Alegre, RS, Brazil;4. Universidade Federal de São Carlos, Centro de Ciências Biológicas e da Saúde, São Carlos, SP, Brazil;1. Centre for Spinal studies and Surgery, D Floor, West Block, Queens Medical Centre, Derby Rd, Nottingham NG7 2UH, UK;2. Barts Health, Royal London Hospital, Whitechapel Rd, London E1 1BB, UK;3. Percivall Pott Rotation, Royal London Hospital, Whitechapel Rd, London E1 1BB, UK;1. Mining Institute of the Ural Branch Russian Academy of sciences, 78-a, Str. Sibirskaya, Perm, 614007, Russia;2. Perm National Research Politechnic University, 29 Komsomolsky prospect, Perm, 614990, Russia;1. Water Resources Engineering Institute, Aleksandras Stulginskis University, Universiteto 10, LT-53361 Kaunas, Lithuania;2. Bioforsk - Norwegian Institute for Agricultural and Environmental Research, Frederik A. Dahls vei 20, 1432 ?s, Norway;1. Linze Inland River Basin Research Station, Key Laboratory of Ecohydrology of Inland River Basin, Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences, Lanzhou, 730000, China;2. Pratacultural College, Gansu Agricultural University, Lanzhou, 730000, China |
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| Abstract: | The green cover of the earth exhibits various spatial gradients that represent gradual changes in space of vegetation density and/or in species composition. To date, land cover mapping methods differentiate at best, mapping units with different cover densities and/or species compositions, but typically fail to express such differences as gradients. Present interpretation techniques still make insufficient use of freely available spatial-temporal Earth Observation (EO) data that allow detection of existing land cover gradients. This study explores the use of hyper-temporal NDVI imagery to detect and delineate land cover gradients analyzing the temporal behavior of NDVI values. MODIS-Terra MVC-images (250 m, 16-day) of Crete, Greece, from February 2000 to July 2009 are used. The analysis approach uses an ISODATA unsupervised classification in combination with a Hierarchical Clustering Analysis (HCA). Clustering of class-specific temporal NDVI profiles through HCA resulted in the identification of gradients in landcover vegetation growth patterns. The detected gradients were arranged in a relational diagram, and mapped. Three groups of NDVI-classes were evaluated by correlating their class-specific annual average NDVI values with the field data (tree, shrub, grass, bare soil, stone, litter fraction covers). Multiple regression analysis showed that within each NDVI group, the fraction cover data were linearly related with the NDVI data, while NDVI groups were significantly different with respect to tree cover (adj. R2 = 0.96), shrub cover (adj. R2 = 0.83), grass cover (adj. R2 = 0.71), bare soil (adj. R2 = 0.88), stone cover (adj. R2 = 0.83) and litter cover (adj. R2 = 0.69) fractions. Similarly, the mean Sorenson dissimilarity values were found high and significant at confidence interval of 95% in all pairs of three NDVI groups. The study demonstrates that hyper-temporal NDVI imagery can successfully detect and map land cover gradients. The results may improve land cover assessment and aid in agricultural and ecological studies. |
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| Keywords: | Land cover Gradient Hyper-temporal NDVI MODIS Mapping |
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