Abstract
We successfully used optical remote sensing approach to test the skills of post-classification change detection technique as well as techniques of circumventing the challenges of cloud/cloud-shadow contamination and of working in a data-scarce environment in tropical humid highlands. The aim was to generate an accurate estimate of current land cover distribution map and analyze land-cover change around Ndakaini area in Kenya. Landsat imageries (TM and ETM + ) acquired between 1985 and 2011 and corresponding to the study area was selected. Employing bands 3 and 4 of respective Landsat images, thresholding techniques, Boolean and masking operations were implemented in detecting cloud/cloud-shadows and subsequent removal and filling of gaps. In absence of other historical ancillary data about land cover types, a total of 278 points across the study area were captured from Google Earth and used to evaluate the accuracy of each of the generated land cover maps. From the results, cloud/cloud-shadow gaps were reduced immensely (e.g. 90% for the 1985 image and 82% for the 2011 image). With regard to quality of classification outputs, the respective land cover/land-use maps of 2000, 2005 and 2010 anniversaries had fairly high level of overall accuracy (64%, 79% and 68% respectively) and Kappa statistic (0.47, 0.69 and 0.53 respectively) while classification outputs of 1985 and 1995 yielded slightly lower overall accuracy (60%) and Kappa statistic (0.42). Post-classification change involving three land cover classes, tea plantation, forest/woodlot and annual crop fields denoted as others were successfully determined and conclusions based on trend analysis drawn. The satisfactory results of this study imply the usefulness of post-classification change detection method in generating information about land cover dynamics in tropical humid highlands especially when coupled with robust techniques that adequately circumvent the cloud and cloud-shadow problem and scarcity of ancillary data often common in these areas. Keywords : Post-classification change detection, thresholding and Boolean techniques, landcover change, tropical humid-highlands DOI: 10.7176/JNSR/10-8-03 Publication date: April 30 th 2020
Highlights
Land cover change information at different spatial and time-scales is critical in evaluating ecosystem conditions and environmental trends (Alphan et al, 2009)
The information is critical in resource economics as land cover change is attributed to dynamics of proximal livelihood options as well as externalities related to economic activities and resources
The analysis focused on establishing the change in extent of surface areas of key land cover types and determining where there was occurrence of land cover change and between which land cover categories
Summary
Land cover change information at different spatial and time-scales is critical in evaluating ecosystem conditions and environmental trends (Alphan et al, 2009). The information is critical in resource economics as land cover change is attributed to dynamics of proximal livelihood options as well as externalities related to economic activities and resources. In Ndakaini for instance, an area adjacent to Aberdare forest in Kenya, the land cover change have been recognized to bear heavy impact on the water resources especially the quantity and quality of water in Ndakaini dam, being associated with observed dam-siltation coupled with growing uncertainty in water supply in the future as well as ever rising treatment cost borne by Nairobi Water and Sanitation Company. The reservoir was constructed between 1989 and 1994, replacing previous agricultural related land uses and has since affected the land use and land cover in the area, which in return affects the water flow and quality
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