Paper
24 February 2004 Landscape-scale characterization of vegetation phenology using AVHRR-NDVI and Landsat-TM data
Tiziana Simoniello, M. Teresa Carone, Maria Lanfredi, Maria Macchiato, Vincenzo Cuomo
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Abstract
The strict link between intra-annual vegetation dynamics (phenology) and Earth's climate makes phenological information fundamental to improve understanding and models of inter-annual variability in terrestrial carbon exchange and climate-biosphere interactions. In order to monitor phenology in a landscape characterized by heterogeneous features rapidly changing over the territory, we performed multitemporal classifications of NDVI-AVHRR data and interfaced them with Landsat-TM data and orography. The sample area is the Vulture basin (Southern Italy), where cultivated and densely vegetated areas coexist with urban and recently built industrial areas. These land cover patterns rapidly change over the territory at very small spatial scales; it is a complex zone very interesting for studying the use of remote sensing techniques in the integrated monitoring context. Clusters having homogeneous NDVI time behaviors were identified. In spite of its spatial resolution, AVHRR NDVI effectively picks up the characteristic phenology for different covers and altitudes. Moreover, some pixels having particular microclimate were clustered and their characterization was only possible by using orography and TM classification information. The comparison of two intra-annual classifications (1996 and 1998) showed that the proposed approach can be very useful for studying change in pattern of vegetation dynamics.
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Tiziana Simoniello, M. Teresa Carone, Maria Lanfredi, Maria Macchiato, and Vincenzo Cuomo "Landscape-scale characterization of vegetation phenology using AVHRR-NDVI and Landsat-TM data", Proc. SPIE 5232, Remote Sensing for Agriculture, Ecosystems, and Hydrology V, (24 February 2004); https://doi.org/10.1117/12.511335
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Cited by 5 scholarly publications.
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KEYWORDS
Vegetation

Data modeling

Earth observing sensors

Landsat

Data acquisition

Image classification

Agriculture

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