Availability of accurate and up-to-date land use land cover information is central to many resource management, planning and monitoring programmes. For example, the land cover is a desired input parameter for a number of agricultural, hydrological and ecological models. Traditional methods of land cover mapping have been limited to field surveys that are time-consuming and uneconomical with data collected over long time intervals. The methods are particularly inefficient and impractical for real-time global and regional land cover mapping (Defries and Townshend, 1994).
Satellite sensor remote sensing images due to their synoptic view, map like format and repetitive coverage are a viable source of gathering effective land cover information. Figure 1 shows an IRS 1C image from PAN sensor. Typically, the pixels of the remote sensing image are grouped into meaningful and homogeneous land cover classes using digital image classification. Though remote sensing has long been championed for the provision of actual land cover information, there are a range of factors that restrict the ability of remote sensing to accurately extract the information at various scales (Arora and Foody, 1997). Failure to understand these factors may result into inappropriate land cover classifications. This paper addresses some of the issues related to land cover mapping from remote sensing data.
Fig. 1: IRS 1C PAN image acquired on 3.4.2000
(A portion of area in Jalpaiguri district, West Bengal)
Digital image classification
Digital image classification is the process of assigning a pixel (or groups of pixels) of remote sensing image to a land cover class. The objective is to classify each pixel into only one class (crisp or hard classification) or to associate the pixel with many classes (fuzzy or soft classification). The classification techniques may be categorized either on the basis of training process (supervised and unsupervised) or on the basis of theoretical model (parametric and non-parametric). Several classification algorithms (classifiers) have been developed under this categorization. For instance, Maximum Likelihood Classifier (MLC) is a supervised parametric algorithm whereas k-means clustering is an unsupervised parametric algorithm.
Generally, supervised classification is performed that has three distinct stages namely training, allocation and testing. Training is the identification of a sample of pixels of known class membership gathered from reference data such as ground truth, existing maps and aerial photographs. These training pixels are used to derive various statistics (e.g. mean and standard deviation) for each land cover class and are input to the second stage of the classification. In this stage, the pixels of the image are allocated to the class with which they show the greatest similarity based on the statistics. After classification in the allocation stage, the accuracy of classification is determined in the final stage. A sample or group of testing pixels is selected and their class identities are compared on both the classified image and the reference data. The pixels of agreement and disagreement for each testing sample are represented in the form of an error matrix, which can then be used to evaluate the classification
accuracy.
Fig. 2: Crisp Classification of IRC 1C PAN image using MLC
Issue related to land cover classification from Remote Sensing
The digital image classification as such seems to be a simple process but in reality there are complications that limit the accuracy of land cover classification. These may arise partly due to the characteristics of the remote sensing images and the assumptions underlying the techniques employed in the classification process (Mather, 1990). Some thoughts on these issues are now presented.
