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Details van artikel 5 van 9 gevonden artikelen
| Titel: |
Mapping the Land Cover of Mexico Using AVHRR Time-Series Data Sets |
| Auteur: |
Ortega-Huerta, Miguel A. Martinez-Meyer, Enrique Egbert, Stephen L. Price, Kevin P. Peterson, A. Townsend |
| Verschenen in: |
Geocarto international |
| Paginering: | Jaargang 15 (2000) nr. 3 pagina's 7-20 |
| Jaar: |
2000-09 |
| Inhoud: |
An important methodological and analytical requirement for analyzing spatial relationships between regional habitats and species distributions in Mexico is the development of standard methods for mapping the country's land cover/land use formations. This necessarily involves the use of global data such as that produced by the Advanced Very High Resolution Radiometer (AVHRR). We created a nine-band time-series composite image from AVHRR Normalized Difference Vegetation Index (NDVI) bi-weekly data. Each band represented the maximum NDVI for a particular month of either 1992 or 1993. We carried out a supervised classification approach, using the latest comprehensive land cover/vegetation map created by the Mexican National Institute of Geography (INEGI) as reference data. Training areas for 26 land cover/vegetation types were selected and digitized on the computer's screen by overlaying the INEGI vector coverage on the NDVI image. To obtain specific spectral responses for each vegetation type, as determined by its characteristic phenology and geographic location, the statistics of the spectral signatures were subjected to a cluster analysis. A total of 104 classes distributed among the 26 land cover types were used to perform the classification. Elevation data were used to direct classification output for pine-oak and coastal vegetation types. The overall correspondence value of the classification proposed in this paper was 54%; however, for main vegetation formations correspondence values were higher (60-80%). In order to obtain refinements in the proposed classification we recommend further analysis of the signature statistics and adding topographic data into the classification algorithm. |
| Uitgever: |
Taylor & Francis |
| Bronbestand: |
Elektronische Wetenschappelijke Tijdschriften |
Details van artikel 5 van 9 gevonden artikelen
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