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.

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.

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.

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.

The water resources of the Juba and Shabelle rivers in southern Somalia are important for irrigation and food production, but are influenced by seasonal floods. Prior to the outbreak of civil war in 1991, the Somali Ministry of Agriculture successfully operated a hydrometric network covering the Juba and the Shabelle, data from which provided input to a flow forecasting model. The war resulted in the neglect and abandonment of monitoring stations and an enforced cessation of data collection and management. In 2001 and 2002, part of the pre-war hydrometric network was reinstated and water levels were again recorded at some stations. This paper examines the implications of the 11-year hiatus in data collection, and the now much reduced monitoring network, for assessing and managing the surface water resources. The problems faced have relevance to other basins, within Africa and elsewhere, where there has been a similar decline in data collection.