Journal
EUROPEAN JOURNAL OF REMOTE SENSING
Volume 50, Issue 1, Pages 626-635Publisher
TAYLOR & FRANCIS LTD
DOI: 10.1080/22797254.2017.1387505
Keywords
Change detection; land cover database; image segmentation; visual interpretation; accuracy assessment; cartographic updating
Categories
Funding
- Consejo Nacional de Ciencia y Tecnologia [CONACYT, Fondo Mixto Conacyt-Gobierno del Estado de Michoaca, SEP-CONACYT] [178816]
- Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior
- Direccion General de Asuntos del Personal Academico, Universidad Nacional Autonoma de Mexico
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This article presents a hybrid classification method combining image segmentation, GIS analysis, and visual interpretation, and its application to elaborate a multi-date cartographic database with 23 land use/cover (LUC) classes using SPOT 5 imagery for the Mexican state of Michoacan (similar to 60,000 km(2)). First, the resolution of an existing 1: 100,000 LUC map produced through visual interpretation of 2007 SPOT images was improved. 2007 SPOT images were segmented, and each segment received the majority LUC category from the 1:100,000 map. Segments were characterized from the images (spectral indices) and the map (LUC class). A multivariate trimming was applied to detect uncertain segments presenting discrepancy between their spectral response and the LUC class assigned from the map. For these uncertain segments, a category was determined by digital classification, but a definitive category was assigned through visual interpretation. Finally, accuracy of the resulting LUC map was assessed. The same procedure was applied to downgrade (2004) and to update (2014) the map. The implemented method enabled us to improve the scale of an existing 2007 LUC map and to detect land use/cover changes in previous (downgrading) and later (updating) dates with an overall accuracy of 83.3% +/- 3.1%.
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