Monitoring Land Use/Land Cover Change Using GIS and Remote Sensing: A Case Study of Chania Catchment, Kenya
DOI:
https://doi.org/10.2200/aerj.v2i2.167Keywords:
Change Detection, Land Use Land Cover, Remote Sensing, Supervised Classification, Unsupervised ClassificationAbstract
Sustainable Development Goal (SDG) number 15 focuses on life on land. It requires that we protect, restore and promote sustainable use of terrestrial ecosystems, sustainably manage forests, combat desertification, and halt and reverse land degradation and halt biodiversity loss. The challenge however is lack of sufficient historical land use and land cover information that will inform the policy makers the extent of land degradation. Land use and Land Cover (LULC) maps of a watershed gives an opportunity to visualise the areas covered by each class of LULC in order to quantify the ecological production and related ecosystem services generated in a river system. This study was thus carried out in Chania river system to demonstrate and validate the use of GIS and Remote sensing techniques as a means of providing LULC information. In order to carry out the LULC classification, Landsat 8 imageries with 30m resolution for February and March 2016 were downloaded from United States Geological Survey (USGS) site. For change detection, Landsat 7 imagery for February 2005 was used. Using the Environment for Visualizing Images (ENVI) software the imageries metadata were converted into reflectance by carrying out radiometric calibration. Maximum likelihood and Parallelepiped methods of classification were eventually used to carry out the classification. Maximum likelihood assumes that the pixel for each class in each band is normally distributed and calculates the probability that a given pixel belongs to a specific class. Parallelepiped classification uses a simple decision rule to classify multispectral data. If a pixel value lies above the low threshold and below the high threshold for all bands being classified, it is assigned to that class. Results between the two methods were compared against each other and the best result adopted. Maximum likelihood classification yielded a higher accuracy level of 97.99% and a Kappa Coefficient of 0.97. Eleven LULC classes were classified. The study revealed that GIS and remote sensing techniques provide sufficient means of detecting change in a catchment. In the Chania context the results revealed a substantive decline of forest cover by 7.78% in 11 years with a steep increase in built up areas, areas under tea, coffee and maize. The decline in forest cover and the increase in agricultural activity and settlements is an indicator that there are negative gains in SDG goal 15 and there is need for further efforts to sustainably manage forests.
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