Maximum likelihood classifier (MLC) and support vector machines (SVMs) are commonly used supervised classification methods in remote sensing applications. MLC is a parametric method, whereas SVM is a nonparametric method. In an environmental application, a hybrid scheme is designed to identify forest encroachment (FE) pockets by classifying medium-resolution remote sensing images with SVM, incorporating knowledge-base and GPS readings in the geographical information system. The classification scheme has enabled us to identify small scattered noncontiguous FE pockets supported by ground truthing. On Baratang Island, the detected FE area from the classified thematic map for the year 2003 was ∼202 ha, and for the year 2013, the encroachment was ∼206 ha. While some of the older FE pockets were vacated, new FE pockets appeared in the area. Furthermore, comparisons of different classification results in terms of Z-statistics indicate that linear SVM is superior to MLC, whereas linear and nonlinear SVM are not significantly different. Accuracy assessment shows that SVM-based classification results have higher accuracy than MLC-based results. Statistical accuracy in terms of kappa values achieved for the linear SVM-classified thematic maps for the years 2003 and 2013 is 0.98 and 1.0, respectively.
Land use/land cover is an important watershed surface characteristic that affects surface runoff and erosion. Many of the available hydrological models divide the watershed into Hydrological Response Units (HRU), which are spatial units with expected similar hydrological behaviours. The division into HRU's requires good-quality spatial data on land use/land cover. This paper presents different approaches to attain an optimal land use/land cover map based on remote sensing imagery for a Himalayan watershed in northern India. First digital classifications using maximum likelihood classifier (MLC) and a decision tree classifier were applied. The results obtained from the decision tree were better and even improved after post classification sorting. But the obtained land use/land cover map was not sufficient for the delineation of HRUs, since the agricultural land use/land cover class did not discriminate between the two major crops in the area i.e. paddy and maize. Subsequently the digital classification on fused data (ASAR and ASTER) were attempted to map land use/land cover classes with emphasis to delineate the paddy and maize crops but the supervised classification over fused datasets did not provide the desired accuracy and proper delineation of paddy and maize crops. Eventually, we adopted a visual classification approach on fused data. This second step with detailed classification system resulted into better classification accuracy within the 'agricultural land' class which will be further combined with topography and soil type to derive HRU's for physically-based hydrological modeling.
Land use/cover is an important watershed surface characteristic that affects surface runoff and erosion. Many of the
available hydrological models divide the watershed into Hydrological Response Units (HRU), which are spatial units
with expected similar hydrological behaviours. The division into HRU's requires good-quality spatial data on land
use/cover. This paper presents different approaches to attain an optimal land use/cover map based on remote sensing
imagery for a Himalayan watershed in northern India. First digital classifications using maximum likelihood classifier
(MLC) and a decision tree classifier were applied. The results obtained from the decision tree were better and even
improved after post classification sorting. But the obtained land use/cover map was not sufficient for the delineation of
HRUs, since the agricultural land use/cover class did not discriminate between the two major crops in the area i.e. paddy
and maize. Therefore we adopted a visual classification approach using optical data alone and also fused with ENVISAT
ASAR data. This second step with detailed classification system resulted into better classification accuracy within the
'agricultural land' class which will be further combined with topography and soil type to derive HRU's for physically-based
hydrological modelling.
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