Different GIS and remote sensing techniques for detection of changes in vegetation cover : a MFS study in the Nam Ngum and Nam Lik catchment areas in the Lao PDR

University essay from Lunds universitet/Institutionen för naturgeografi och ekosystemvetenskap

Abstract: The Lao People’s Democratic Republic (Lao PDR) is a small landlocked country in South-East Asia, where virgin forest still covers almost 47 % of the country’s total area (1991). Logging offers the potential to improve the economy of the Lao PDR. Besides legal logging activities, illegal logging to provide the population with agricultural land, pasture and firewood is taking place. Since the Lao PDR is mainly comprised of mountainous terrain, and has a poor infrastructure, accessibility to remote areas in the country is limited. To be able to survey the extent of the changes in vegetation that is taking place in these inaccessible areas, remote sensing is an alternative. This study has evaluated some different methods and courses of action to approach the problems of detecting vegetation changes within the Remote Sensing and GIS (Geographical Information Systems) disciplines. Four different satellite image data sets, with differing spatial resolution, have been analysed in the study; the NOAA/NASA Pathfinder AVHRR Land Project 8 km data set, the 1 km AVHRR Global Land Data Set and Landsat MSS and TM images. Depending on the purpose of study, each of the methods tested have their benefits. The results indicate that the 8 kilometre data set provides an extensive temporal coverage, which will never be achieved by the finer resolution data. It is also possible to detect and locate changes in the vegetation cover with the 8 km resolution, assuming that the changes cover a large areal extent (minimum of 32 km2). The finer resolution images (the 1 kilometre data set and the Landsat MSS/TM scenes) benefit however from the gain in spatial precision which facilitates the task of locating smaller areas as well as improves the visual interpretation, although short time-series and higher costs may be a limiting factor.

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