A Comparison of LUCC Detection Algorithms in a Mesoamerican Lowland Tropical Forest

Abstract

Land use and land cover changes occur throughout the world, but none is more concerning than tropical deforestation, much of it for agricultural purposes. Rural-rural frontier migrant farmers such as those colonizing the Sierra del Lacandón National Park in Petén, Guatemala act as a primary direct agent in this land cover conversion. This paper seeks to compare three different algorithms for monitoring changes in forested land cover, making use of freely available remotely sensed Landsat images from two years, 1991 and 2000. In the intervening 9 years, some forested land was converted to cropped, pasture, or fallow land, while other areas experienced no change. This paper contains a detailed description of the methods employed for three different change detection techniques, producing a total of five land change maps: multidate principal components analysis (PCA), normalized difference vegetation index (NDVI ) image differencing, and brightness greenness wetness (BGW) image differencing. Of the five land change maps produced, the Greenness component of the BGW transformation had the highest overall accuracy, at 86%, and is conservative in detecting change. The amount of change detected by this algorithm represents approximately 300 km2 of forest loss, or 11.9% of the area examined.

Keywords

• lucc
• landsat
• pca
• ndvi
• bgw