Uncovering the challenges of domestic energy access in the context of weather and climate extremes in Somalia
In Somalia, challenges related to energy access is influenced by both weather and climate extremes and associated conflict. The objective of this article is to gain an improved understanding of these risks and challenges, which are faced by the most vulnerable populations in the country. In particular, cooking energy-related challenges faced by households affected by weather and climate extremes and conflicts include protection risks, malnutrition, health risks, environmental degradation and heightened tension and conflict between social groups. Interventions to address these issues should focus on both fuel supply and fuel demand as well as on improving the livelihoods of affected populations. In the aftermath of an extreme weather event it is recommended that assessments of the energy needs of all affected populations, including both hosts and Internally Displaced People (IDPs), be conducted. Post-disaster support should include the promotion of energy-efficient technologies for cooking as well as alternative sources of fuel where available, including non-wood based renewable energy. The implementation of a field inventory to assess the status of natural resources in areas vulnerable to climate impacts could help to determine woody biomass trends and enable the development of ecosystem restoration plans. These could include provisions for the establishment of woodlots and agro-forestry, thus building resilience to environmental degradation while maintaining woody biomass resources in and around displacement camps. Interventions should also be designed jointly with partners, and activities should be conflict-sensitive to ensure an enhanced state of resiliency and preparedness among vulnerable populations.Towards a spatial Data Infrastructure for Somalia using open source standards
SDI is a well-known concept in Africa, many countries are on the way to having a formal SDI strategy Certain countries, such as Somalia, are starting the process of nation building after years of war. These countries stand to leapfrog other African countries by implementing current SDI best practices. The FAO‐SWALIM project is in the unique position to be able to assist Somali authorities in providing some of the building blocks for SDI development, even though SWALIM does not have the legal mandate to do so. This paper highlights what SWALIM can currently contribute and what significant work (and resources) are still required for a Somalia National SDI.Assessment of Charcoal Driven Deforestation Rates in a Fragile Rangeland Environment in North Eastern Somalia Using Very High Resolution Imagery
Multi-temporal very high-resolution satellite images and field work have been used for quantifying the tree cutting rate over a 5 years period in a very arid tiger bush area of North Eastern Somalia with intensive charcoal production activities. By applying both a classical area frame sampling approach with visual interpretation of the samples and a semi-automatic tree detection algorithm, it was possible to create baseline tree density layers for the 2 years of observation and to quantify the tree cutting rates for the period from 2001 to 2006. An average annual tree loss of −2.8%, coupled with the total absence of regrowth during the 5 years study period, confirm the tremendous ecological impacts of charcoal driven tree cutting on tiger bush vegetation. Analysis of the results evidences spatial and temporal patterns in the cutting locations and poses the basis for a better understanding of the ecological and human dimensions of deforestation in the fragile rangeland environment of Northern Somalia.Mapping Prosopis spp. with Landsat 8 Data in Arid Environments: Evaluating Effectiveness of Different Methods and Temporal Imagery selection for Hargeisa, Somaliland
Prosopis spp is a fast and aggressive invader threatening many arid and semi-arid areas globally. The species is native to the American dry zones and was introduced in Somaliland for dune stabilization and fuel wood production in the 1970’s and 1980’s. Its deep rooting system is capable of tapping into the ground water table thereby reducing its reliance on infrequent rainfalls and near-surface water. The competitive advantage of Prosopis is further fuelled by the hybridization of the many introduced sub species that made the plant capable of adapting to the new environment and replacing endemic species. This study aimed to test the mapping accuracy achievable with Landsat 8 data acquired during the wet and the dry seasons within a Random Forest (RF) classifier, using both pixel- and object-based approaches. Maps are produced for the Hargeisa area (Somaliland), where reference data was collected during the dry season of 2015. Results were assessed through a 10-fold cross-validation procedure. In our study, the highest overall accuracy (74%) was achieved when applying a pixel-based classification using a combination of the wet and dry season Earth observation data. Object-based mapping were less reliable due to the limitations in spatial resolution of the Landsat data (15–30 m) and problems in finding an appropriate segmentation scale.Mapping Prosopis Juliflora in West Somaliland with Landsat-8 Satellite Imagery and Ground Information
Prosopis juliflora is a drought-tolerant fast-growing tree species originating from South and Central America with a high invasion potential in arid and semi-arid areas in Africa. It was introduced in Somaliland in the 1980s and is reported to have spread vigorously since. Despite being recognized as a serious issue in the country, the actual scale of the problem is unknown. In this study, we mapped the species in a study area that includes the capital, Hargeisa, using Landsat 8 satellite imagery. During a field campaign in 2015, we collected canopy-level spectral signatures of P. juliflora and native trees to analyse the potential use of spectral data in discriminating the invasive species. P. Juliflora was found to be generally distinguishable because of its greater vigour during the dry season. We tested the accuracy of the random forest classifier and different classification set-ups, varying the spatial resolution (original 30m vs pan-sharpened 15m) and image acquisition dates (during the wet season, the dry season and a combination of the two). Best overall accuracy (84%) was achieved by using pan-sharpened data from the two seasons. About 30 years since its introduction, the invasive species was detected in 9% of the total investigated area with highest occurrence in the proximity of human settlements and along seasonal watercourses. © 2016 The Authors. Land Degradation and Development published by John Wiley & Sons, Ltd.