A B S T R A C TAgriculture is the backbone of Kenya's economy. Agriculture in Kenya is characterized by low productivity due to low external inputs, lack of good farming practices, soil erosion, and other losses. In most farming regions of the country, agriculture depends entirely on rainfall which sometimes is scarce. The problem of selecting the correct land for the cultivation of certain crops is a long-standing and mainly empirical issue. The objective of this study is to extrapolate and generate a crop suitability map showing areas suitable for agricultural activities in Taita Hills in Kenya. It utilizes the information on environmental condition, altitude, rainfall and other relevant parameters of the case study where the variability of rainfall and recurrent droughts have a great impact on the lives of people whose livelihood is mainly dependent on subsistence agriculture. The methods used include development of elevation models, watershed mapping, climate variability mapping, soil erosion mapping that incorporates the revised universal soil loss empirical (RUSSLE) model and multi-criteria evaluation analysis. The analysis was done using the sum weighted overlay analysis of soil erodibility, slopes, vegetation index and rainfall availability in the modeling. Four categories were achieved and mapped out: most suitable, more suitable, less suitable and least suitable. The research implies that there can be both suitable areas and unsuitable areas for crops in Taita Hills. The study helps farmers to be aware of the environmental conditions of their agricultural land and the impacts that may arise due to varying climate conditions on their cropping patterns.
Remote sensing and GIS applications are being widely used for various projects relating to natural resource management. Forests are very important national assets for economic, environmental protection, social and cultural values and should be conserved in order to realize all these benefits. Kenya's forests are rapidly declining due to pressure from increased population, technological innovation, urbanization human development and other land uses. Mau forest is one of the major forests in Kenya that is a catchment area for many Great Rift Valley lakes within the country and faces a lot of destruction. Continued destruction of the Mau forest will cause catastrophic environmental damage, resulting in massive food crises and compromising the livelihoods of millions of Kenyans, and the possible collapse of the tourism industry. The purpose of this research was to investigate the relationship between the increasing rate of deforestation and the reduction of the volumes of water in the neighboring lakes between the years 1989 to 2010. Satellite images from Landsat-5 Thematic Mapper (TM) and Landsat-7 Enhanced Thematic Mapper (ETM+) were used for the detection of changes in the Mau forest and the dynamics of the neighboring water bodies that included lakes: Naivasha, Baringo, Nakuru, Elementaita and Bogoria. The research showed that from a period of 1989 to 2010 Mau forest has been decreasing due to deforestation and the water bodies have irregular dynamics in that, from 1989 to 2000, there was rise in the volume of water, this is attributed to the El Nino rains experienced in the country during the year 1997 and 1998. But between 2000 and 2010 the volume decreased as the forest is also decreasing. It is recommended that the government creates awareness to sensitize the public on the importance of such forests as catchment areas in Kenya.
The Ecotourism is considered as the most attractive subset of tourism industry which can contribute to natural resource conservation and local development under proper management. The most stable solution in developing countries for developing ecotourism is through proper assessment, identification and prioritization of different areas with the capacity to support tourism within the counties and country at large, and then creating enabling environment through infrastructure creation. This paper presents an identification of the potential areas for ecotourism in enhancing the socioeconomic status of the indigenous communities of the Kwale County, using Analytic Hierarchy Process and Geographic Information System. The research used satellite data and weighted overlays of auxiliary data from analytical hierarchy process which were then integrated with other GIS datasets to evaluate and assess the ecotourism potential in Kwale County. The analysis indicates that the highly suitable areas are mainly located in Matuga constituency and LungaLunga. Matuga is characteristically endowed green forests and abundances of wildlife, since Shimba Hills Game Reserve and Mwaluganje elephant sanctuary are located here. LungaLunga also has a variety of forests and mangrove.The moderately suitable areas are mostly located in Kinango and Matuga constituencies, since most of these are largely free from urban settlements with a unique and outstanding natural beauty, diverse attractions and great tourism potential. The marginally suitable areas were located in Kinango and parts of LungaLunga constituencies. These areas have low levels of visibility and a presence to settlements. The unsuitable areas for ecotourism were mainly located in parts of Kinango, Matuga and LungaLunga. These areas are generally rugged, have settlements or are not visible thus lack scenic beauty.
Bilharzia is vector-borne disease carried by a parasite that is hosted by fresh water snails. The distribution of the disease is concurrent with the existence of the freshwater snails and is dependent on certain suitable environmental conditions. It is difficult to identify the specific habitats of the snails as they are often inaccessible on the ground, the snails also migrate by means of flowing water, making it difficult to keep a track of the freshwater snails' habitat. This paper aimed at using GIS, Remote Sensing and Species Distribution Modelling techniques to model the suitable habitats for the freshwater snails and to prove that the snails migrate when there are sudden changes in water levels whilst showing the population at risk of bilharzia. The SDM used is the Maximum Entropy (MAXENT) for its ability to make right predictions even with small presence sites. The AUC value of the model was 0.951. The research results showed that the environmental variables; brightness Index, elevation, temperatures were negatively correlated with the snails' presence while the wetness index, MSAVI, greenness index and soil pH were positively correlated. The snails are observed to favor clay soils of the montmorillonite type and the crop-lands land cover. Areas consistently submerged by water especially after flooding are shown to be the most suitable areas where snails migrate by means of river or canal water. The research proves that Mwea is not the source habitat of the freshwater snails. The neighboring sub-counties within Kirinyaga County should be investigated using such models as a likely source-habitat of the freshwater snails. Destroying the source habitats will lead to complete eradication of the freshwater snails within Mwea.
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