Land Cover Mapping and Change Analysis in Tropical Humid-highlands: Case of Ndakaini Water Reservoir in Central Kenya

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 30th 202

Farmers’ Perceptions of Climate Change and Its Effects on Tree Cover in the Drylands of South Eastern Kenya: The Case of Matungulu Sub-County, Machakos County

Matungulu Sub-County is a semi-arid region in Kenya that is prone to frequent droughts, water shortages and food insecurity. Rapid population growth, coupled with climate change, has led to not only an increasing demand for more land, but also to over-exploitation and degradation of local natural resources, leading to a myriad of socio-economic and environmental challenges in the area. The objective of this study was to assess farmers’ perceptions of climate change and its effects on tree cover in Matungulu Sub-County. A structured questionnaire, FDGs, key informants and direct observations were used to collect household socio-economic data from 412 randomly selected households in two locations, namely, Sengani and Koma. Temperature and rainfall data from the Kenya Metrological Department was used to study changes in climatic trends over the three-decade period between 1987 and 2017 in the study area for comparison. Descriptive and inferential statistics were used in the data analysis. Farmer-perceived climatic patterns were collaborated by data from the Kenya Meteorological Department.  84% of respondent farmers perceived climate change to have affected species composition, density and distribution of tree cover. The farmers unanimously indicated climate change was real in the region. Keywords: farmers, climate change, effects, perceptions, livelihoods, indigenous knowledg