Detection and Analysis of Land-Cover Change: A Case of two Mindanao Provinces with History of Forest Resource Utilization

Meriam M. Makinano

Thesis (M.S. Remote Sensing)--University of the Philippines Diliman-2010

Abstract

This study presents an integrated approach involving Remote Sensing (RS), Geographic Information System (GIS) and statistical analysis to detect and analyze 25-year land use/land-cover change (LULCC) in the provinces of Agusan del Norte and Agusan del Sur in Northeastern Mindanao, Philippines with history of forest resource utilization in the context of limited land-cover information due to cloud contamination of RS images. Using cloud and shadow masking algorithm and state-of-the-art RS image analysis techniques provided by the Support Vector Machine classifier, highly accurate land-cover maps were obtained from Landsat Multi-Spectral Scanner (MSS) and Enhanced Thermatic Mapper + (ETM+) images and used to detect land-cover transitions in the study area from 1976-2001. The differences in deforestation and other land-cover change types in the two provinces were then characterized and compared using GIS-based spatial analysis techniques. The significance and magnitude of the relationship between the detected deforestation and various georeferenced socio-economic and bio-physical factors were determined through logistic regression analysis. Major results showed that the detected changes in land-cover were found to be different in the Agusan provinces. Forest to rangeland is the major land-cover change in Agusan del Norte form 1976 to 2001 in Agusan del Sur, the two most prominent land-cover change types are the conversions of rangeland to forest and of forest to plant trees. The results of GIS-based characterization of deforestation and logistic regression analysis based on combined bio-physical and socio-economic factors provided significant results as to what factors were associated with deforestation in the Agusan provinces. For Agusan del Norte, the bio-physical factors DISTRIV (distance to rivers) and ELEV (elevation) were found to be the most positively and negatively related to deforestation, respectively. For Agusan del Sur, DISTNEWBUILT (distance to new built-up-areas) and ELEV are found to be the most positively and negatively related to deforestation, respectively. With the identification of the factors associated with deforestation, this study has provided a first step in controlling forest loss which is very useful in comprehensive forest management planning and in formulation of appropriate forest policy. This study is a significant contribution to LULCC research by providing a series of techniques to understand deforestation and relate it to bio-physical and socio-economic factors using an un-ideal dataset. An important finding of this study is that it is possible to analyzed deforestation using cloud contaminated RS images. Local agencies in the Agusan provinces may use the land-cover maps and statistics obtained in this study to further evaluate the process of deforestation in these provinces in order to create and evaluate strategies that attempt to mitigate its negative effects.