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.An Assessment of the Surface Water Resources of the Juba-Shabelle Basin in Southern Somalia
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.Mapping Forest Degradation Caused by the Recent Increase of Charcoal Production in Southern Somalia
Following more than 20 years of civil unrest, environmental information for Southern Somalia is scarce while there is clear evidence that the war economy fueled by the conflict is rapidly depleting the country’s natural resources and especially the woody biomass. Wood charcoal production is one of the most relevant businesses supporting war regimes such as the extreme Islamist group Al Shabaab, which has ruled in Southern Somalia from 2006 to 2012 and is still occupying large areas. In this study we first used Very High Resolution (VHR) satellite imagery of February 2013 for developing a semi-automatic mapping method of charcoal production sites as a proxy of tree loss over a 754 km2 woody area along the Juba river in Southern Somalia. The accuracy of semiautomatic charcoal production site detection varied between 80 and 95% as compared to visual interpretation and reduced significantly the subjectivity and the required time. The analysis was then applied to previous years (2011-2012) for a 52.6 km2 subset of the study area, and led to a tree loss estimation of 8.63%, corresponding to 15,434 trees over the 3 years period. The results are crucial for better understanding the dimension and impact of charcoal production in Southern Somalia and are a first step towards the development of a charcoal production monitoring system.