FAO SWALIM

Kenya

FAO SWALIM

Kenya
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Omuto C.T.,University of Nairobi | Balint Z.,FAO SWALIM | Alim M.S.,FAO SWALIM
Land Degradation and Development | Year: 2014

Land degradation is a gradual, negative environmental process that is accelerated by human activities. Its gradual nature allows degradation to proceed unnoticed, thus reducing the likelihood of appropriate and timely control action. Presently, there are few practical frameworks to help countries design national strategies and policies for its control. The study presented here developed a framework for the national assessment of land degradation. This framework is envisaged to support governments in formulating policies on land degradation. It uses time-series remote sensing data to identify the rate and extent of land degradation, local experts to identify prevalent degradation types and drivers of the degradation and field observations to validate the overall assessment. Its simplicity, use of freely downloadable input data and self-triangulation of the assessment methods make it suitable for rapid assessment of land degradation on a national scale. It was tested in Somalia, where it exhibited accuracy greater than 60 per cent when assessing land degradation. This framework is relevant for designing national strategies and policies that address land degradation and provides an opportunity for accurate identification of areas to target with comprehensive local assessment. Testing of the framework in Somalia showed that about one-third of the country was degraded because of loss of vegetation cover, topsoil loss and to the decline of soil moisture. Overgrazing, excessive tree cutting and poor agronomic practices in agricultural areas were identified as the primary drivers of the country's land degradation. These drivers are encouraged by the prevailing communal land tenure practices, poor governance and civil war. Copyright © 2011 John Wiley & Sons, Ltd.


Omuto C.T.,University of Nairobi | Vargas R.R.,FAO SWALIM | Alim M.S.,FAO SWALIM | Paron P.,FAO SWALIM
Journal of Arid Environments | Year: 2010

Many researchers have used time-series analysis of remotely sensed images to gain understanding of the dynamics of loss of vegetation cover in drylands. However, complex interactions between vegetation and climate still mask the potential of remote sensing signals to detect human-induced loss of vegetation cover. This paper presents mixed-effect modelling method for time-series NDVI-rainfall relationship to account for the complex interaction between vegetation and climate. Mixed-effects method is a form of statistical modelling that can simultaneously model environmental relationships for a population and for different groups within the population. In this study, it was used to model the NDVI-rainfall relationship in Somalia and for different vegetation types in the country. Its time-series application removed the interaction between vegetation and rainfall and identified areas experiencing human-induced loss of vegetation cover in the country. On average, it gave an accurate relationship between rainfall and NDVI (r2>60%) and detected areas with human-induced loss of vegetation cover (kappa=75%). Although the potential of mixed-effects was shown using vegetation types, other factors such as soil types and land use can also be included in the method to improve accuracy of time-series NDVI images in detecting human-induced loss of vegetation cover in the drylands. © 2010 Elsevier Ltd.


Balint Z.,FAO SWALIM | Mutua F.,University of Nairobi | Muchiri P.,FAO SWALIM | Omuto C.T.,University of Nairobi
Developments in Earth Surface Processes | Year: 2013

A first step in any drought management system is to monitor the state and the evolution of the drought. This study addresses the problem of nonexistent operational drought monitoring systems and presents a new methodology for monitoring the evolution and severity of drought with the new, Combined Drought Index (CDI). It is based on the fact that drought is a natural phenomenon created by a combination of several factors, such as deficiency in rainfall amount, persistence of below average rainfall, temperature excess and soil moisture characteristics. By combining the factors in the preceding text, the CDI compares present conditions with multiyear average (normal) conditions for the same time period. The methodology was applied at selected locations of different climate zones in Kenya. The results were compared with available official records of drought events (impacts), showing a very good positive relationship between the two. An attempt to detect the long-term trends of drought events using the CDI indicates that there is an increasing trend of drought events in the country, while the drought severity is not necessarily getting worse in all stations. The CDI method also revealed the possibility of drought early warning and drought-related climate change analysis in Kenya. © 2013 Elsevier B.V..

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