The study characterized the status and trend of land cover transformation in Kirisia forest ecosystem between 1973 and 2015 using remote sensing and GIS. The dominant land cover types consisted of indigenous forest fo...The study characterized the status and trend of land cover transformation in Kirisia forest ecosystem between 1973 and 2015 using remote sensing and GIS. The dominant land cover types consisted of indigenous forest followed by shrub land and bush land. The findings showed a major increase in the built environment by 55.4% and an overall reduction in forest cover by 21.3%. Up to 83.9 km2 of the original indigenous forest was lost between 1973 and 1986 due to severe fires. Thereafter, 23.7 km2 of the remaining indigenous forest was lost between 1986 and 2000 mainly through charcoal burning, illegal timber logging and livestock forage harvesting. A slight recovery occurred between 2000 and 2015 with a 5% increase in indigenous forest cover mostly through natural succession by shrub land and bush land in the burnt forest areas especially following the 1998 El Nino period. The land cover change in the forest ecosystem was not exceptional in Kenya but mirrors similar changes that have been documented in other valued dry land watershed ecosystems in the country including the national water towers. The continued loss of forest cover is likely to affect the water recharge capacity in the watershed thereby creating severe water scarcity for the people in Mararal town as well as nearly 142,954 other individuals in the Kirisia region. Appropriate interventions are therefore needed to mitigate the negative land cover change in Kirisia forest and restore its hydrological functions and water recharge capacity.展开更多
Massive investments in climate change mitigation and adaptation are projected during coming decades.Many of these investments will seek to modify how land is managed.The return on both types of investments can be incr...Massive investments in climate change mitigation and adaptation are projected during coming decades.Many of these investments will seek to modify how land is managed.The return on both types of investments can be increased through an understanding of land potential:the potential of the land to support primary production and ecosystem services,and its resilience.A Land-Potential Knowledge System(LandPKS)is being developed and implemented to provide individual users with point-based estimates of land potential based on the integration of simple,geo-tagged user inputs with cloud-based information and knowledge.This system will rely on mobile phones for knowledge and information exchange,and use cloud computing to integrate,interpret,and access relevant knowledge and information,including local knowledge about land with similar potential.The system will initially provide management options based on long-term land potential,which depends on climate,to-pography,and relatively static soil properties,such as soil texture,depth,and mineralogy.Future mod-ules will provide more specific management information based on the status of relatively dynamic soil properties such as organic matter and nutrient content,and of weather.The paper includes a discus-sion of how this system can be used to help distinguish between meteorological and edaphic drought.展开更多
文摘The study characterized the status and trend of land cover transformation in Kirisia forest ecosystem between 1973 and 2015 using remote sensing and GIS. The dominant land cover types consisted of indigenous forest followed by shrub land and bush land. The findings showed a major increase in the built environment by 55.4% and an overall reduction in forest cover by 21.3%. Up to 83.9 km2 of the original indigenous forest was lost between 1973 and 1986 due to severe fires. Thereafter, 23.7 km2 of the remaining indigenous forest was lost between 1986 and 2000 mainly through charcoal burning, illegal timber logging and livestock forage harvesting. A slight recovery occurred between 2000 and 2015 with a 5% increase in indigenous forest cover mostly through natural succession by shrub land and bush land in the burnt forest areas especially following the 1998 El Nino period. The land cover change in the forest ecosystem was not exceptional in Kenya but mirrors similar changes that have been documented in other valued dry land watershed ecosystems in the country including the national water towers. The continued loss of forest cover is likely to affect the water recharge capacity in the watershed thereby creating severe water scarcity for the people in Mararal town as well as nearly 142,954 other individuals in the Kirisia region. Appropriate interventions are therefore needed to mitigate the negative land cover change in Kirisia forest and restore its hydrological functions and water recharge capacity.
文摘Massive investments in climate change mitigation and adaptation are projected during coming decades.Many of these investments will seek to modify how land is managed.The return on both types of investments can be increased through an understanding of land potential:the potential of the land to support primary production and ecosystem services,and its resilience.A Land-Potential Knowledge System(LandPKS)is being developed and implemented to provide individual users with point-based estimates of land potential based on the integration of simple,geo-tagged user inputs with cloud-based information and knowledge.This system will rely on mobile phones for knowledge and information exchange,and use cloud computing to integrate,interpret,and access relevant knowledge and information,including local knowledge about land with similar potential.The system will initially provide management options based on long-term land potential,which depends on climate,to-pography,and relatively static soil properties,such as soil texture,depth,and mineralogy.Future mod-ules will provide more specific management information based on the status of relatively dynamic soil properties such as organic matter and nutrient content,and of weather.The paper includes a discus-sion of how this system can be used to help distinguish between meteorological and edaphic drought.
