Methodologies Used in Identifying Land Cover Types
This paper reviews background, methodology, and accuracy, regarding remote sensing and identifying land cover types. Rational is discussed regarding the use of remote sensing apparatus, multi spectral imaging, supervised and unsupervised techniques. The origins of GIS are discussed. Key methods, (supervised and unsupervised), are defined. Comparisons between
digital and visual classifications are made. Spectral mixture analysis and supervised classification geometric correction are discussed. The superiority of visual classification is stated.
A landscape is composed of ever changing elements. Their spatial and temporal patterns distinguish a landscape to an observer; at the same time they inform us of the complexity of dynamic processes at various scales.
For certain remote sensing applications, such as monitoring environmental change, it is essential to be able to compare classes from image to image. In spite of successes in classifying some individual multi spectral images using conventional supervised or unsupervised techniques, it has been difficult to obtain consistent classes from images taken at different times, owing to variability in illumination, atmospheric effects and instrumental response. As a result, with a few exceptions (e.g., Hall et al., 1991, and Lucas et al., 1993), monitoring of changes in land cover by remote sensing typically has been restricted to measurements of changes in spatial patterns, and less attention has been given to
Satellite image classification has proven a valuable source of data for land use inventory: Visual interpretation or digital classification techniques can provide remote sensing data for thematic maps. Digital analysis is considered much less subjective. However, land use classes vary spectrally, especially where land covers present high spatial complexity, these classes often lack unique signature and di...