Land use and land cover were mapped between 3500 and 5000 meters above sea level m.a.s.l. in the Río Chambo basin in south-central Ecuador from Landsat MSS, TM, ETM and OLI imagery acquired between 1979 and 2014. The area mapped has been dominated by páramo and a variety of agricultural land uses since 1979. The main land-use transitions have been from páramo to agriculture, native forest to páramo and agriculture, and agriculture back to páramo. Significant areas of páramo have remained unchanged over the 35-year period analyzed, while the area of native forest has declined and that of bare soil increased. Plantations of non-native timber species increased from 1979 to 1999, but their area has now declined. Most land-use transformations have occurred at lower elevations in the 3500-5000 m.a.s.l. range. This is particularly the case for the loss of native forest and the degradation of páramo and agriculture to areas of bare (eroded) soils. A drivers-based approach revealed that these land-use transformations were related to import substitution and afforestation policies geared toward internal markets, exports and environmental conservation.
Abstract-Digital classification technique using Object-Based Image Analysis (OBIA) of SPOT-6 imagery could improve classification accuracy and provide detail type of land cover. This method is better applied for data with higher spatial resolution imagery which has high heterogeneity where the pixel size is smaller the actual size of the objects. The sequence steps were image preprocessing and pansharpening, identify the potential of landcover types using Jeffries-Matusita (JM) feature separability, determine a stratification boundary, and developing the Object-Based Orientation classification using Erdas Imagine Objective. Detail process of OBIA was image pixel segmentation with the parameters were consists of determining the minimum pixel segmentation ratio which was 1000 and applying the single feature probability and NDVI, and determine cue weight. The second segmentation was object vector classification which segmented the vector object using the empirical distribution analysis. The segmentation is based on region growing of the Multi-Bayesian network. The classification result is then assessed by comparing the classification result of Maximum Likelihood (MLC) using confusion matrix. The result shows that object-based classification technique could improve the classification by 83% compared to the MLC with only 67%. The method of segmentation used the stratification zone in order to make an optimum cue weight in detecting the object through texture, size, and shape while also applying the spectral-based method.
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