Abstract
ABSTRACTThis 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 (~60,000 km2). 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%.
Highlights
The importance of updated and consistent land use/ cover (LUC) cartography is widely recognized
The 2007 1:50,000 scale LUC map was obtained with a minimum mapping area of 1 ha by improving the resolution of the 2007 1:100,000 scale map (Figure 3)
The accuracy assessment of the 2007 LUC map indicated an overall accuracy of 83.3% with a confidence interval of 3.1%
Summary
The importance of updated and consistent land use/ cover (LUC) cartography is widely recognized. Quality data about LUC and LUCC are crucial to understand current and future dynamics of deforestation and its causes – such as wildfires, urbanization, cropping, loss of biodiversity, and the influence of LUCC on climate change (Herold et al, 2009; Radoux et al, 2014). Remote Sensing data have been widely used in the last decades to elaborate LUC maps (Manakos & Braun, 2014; Millington & Alexander, 2000; Thenkabail, 2015). The increasing availability of remote sensing data and the constant improvement in change detection techniques makes possible to assess dynamics such as forest extent and deforestation (Lu, Mausel, Brondizio, & Moran, 2004). In spite of the improvement of remotely sensed images in the last decades, updating LUC databases is not an easy task (Radoux & Defourny, 2010)
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