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Session Chair: Barbara Ryan, GEO Secretariat, Switzerland
Satellites for Syria: New methods for assessing agricultural production in conflict areas to support productivity assessments, rehabilitation efforts, and (post conflict) assistance
Annemarie Klaasse1, Eva Haas2, Remco Dost1, Michael Riffler2, Bekzod Shamsiev3, Keith Garrett3
1eLEAF, The Netherlands; 2GeoVille, Austria; 3World Bank, USA
Despite the need to understand the consequences of armed conflicts on a countries’ economy and population, agricultural statistics in conflict-affected countries are often not available, or of questionable accuracy. However, timely and reliable information on agricultural production is needed to plan preventive interventions by building resilience prior to the conflict, target humanitarian aid during the conflict, and focus rehabilitation actions after the conflict ends.
Satellite Earth Observation (EO) is a powerful and cost-effective technique to assess agricultural production in areas with no or limited access. It provides historical and near-real time operational data to rapidly identify changes in a consistent and repeatable manner.
The example of Syria demonstrates that satellite Earth Observation is an excellent tool to assess agricultural production in areas under conflict, not only to monitor the impact of conflict on the agricultural sector, but also to map its dynamics, resilience and coping and adaptive mechanisms over time.
Assessment of Land Use and Land Cover Change Using GIS and Remote Sensing Techniques: A Case Study of Abobo District, Gambella Region, Ethiopia
Ludwig Maximilian University of Munich, Germany
With the expansion of large scale land acquisition and population growth results significant and rapid changes in land use and land cover change (LUCC) in the area. In recent times, it’s observed that in Gambella region, Ethiopia. There is an increasing demand for agricultural investment and high population growth. This demand results LUCC and as well increase various environmental impacts. The aim of the study is to quantify change and analyze the LUCC from 1987 to 2017 (30 years). The satellite images used in this study collected from Landsat Thematic Mapper at resolution of 30 m of 1987 and sentinel2A image at resolution of 10m of 2017. This satellite images are used for quantification of spatial and temporal dynamics of LUCC. Supervised classification is used to classify Land use/Land cover. Finally post-classification approach is used for detecting and assessing LUCC of Gambella region a case study of Abobo District.
Monitoring Agricultural Investments In Ethiopia: A Remote Sensing Based Approach
Matthias Hack1, Fabian Loew2, Guido Lemoine3, Oliver Schoenweger1, Mulugeta Tadesse1, Felix Rembold3, Dimo Dimov2
1Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ) GmbH, Germany; 2MapTailor Geospatial Consulting GbR; 3Joint Research Center (JRC) European Commission
Between 2002 and 2012, the Ethiopian Government leased about 2.4 million hectares of land for commercial agricultural investments to private domestic and foreign investors. In order to steer these large scale agriculture investments towards the envisaged benefits, it is crucial to monitor the investments’ implementation progress frequently. But the investment sites are dispersed across wide geographic areas and due to capacity constraints monitoring is limited to field visits of selected single investment sites only. To overcome this bottleneck, the Ethiopian Horticulture and Agricultural Investment Authority in cooperation with the Support to Responsible Agricultural Investment Project (S2RAI) (financed by the European Union and Germany and implemented by GIZ, technically supported by the Joint Research Center of the European Commission) is currently developing a monitoring tool, based on satellite remote sensing data, which will facilitate the regular assessment of the implementation of agricultural investment projects.
The Role of Geospatial Water Resource Management for Sustainable Land Use in Africa
Christian Tottrup1, Norman Kiesslich2, Niels Wielaard3, Remco Dost4, Peter Bauer-Gottwein5, Suhyb Salama6, Benjamin Koetz7
1DHI GRAS, Denmark; 2GeoVille, Austria; 3Satelligence, Netherlands; 4eLEAF, Netherlands; 5DTU Environment, Denmark; 6ITC, University of Twente, Netherlands; 7European Space Agency, Italy
Water plays an essential role in the sustainable management of land use – particularly in the context of agriculture as 70% of freshwater is used for irrigation, but also for erosion, natural hazards and resilience to climate change. The successful implementation and monitoring of Integrated Water Resource Management (IWRM) initiatives is one major contribution to sustainable land use but requires access to reliable data and information. There is a growing awareness that Earth Observation (EO) data has the potential to serve geospatial data needs especially in the context of International Financing Institutions (IFIs) and Official Developing Assistance (ODA) normally operating in regions where policies and management decisions are often based on sparse and unreliable information. We will provide examples on how Earth Observation is supporting World Bank funded development projects on the African continent in order to promote sustainable land and water management practices and the Sustainable Development Goals (SDGs).
The Data is Not Enough: Some Hurdles We Must Overcome in the Democratization of Remote Sensing and GIS Technology
Hexagon Geospatial, United States of America
For many years the data collection for the census in South Africa was a manual process. Field workers used to receive paper maps to orientate themselves to their enumeration areas. This has been a tedious and complicated way of collecting data which required extra knowledge of map interpretation.
With the improvement and democratization of technology, Statistics South Africa, the largest and arguably the most advanced national statistical office in Africa, benefits from the HxGN Smart Census solution.
The HxGN Smart Census solution enables the use of imagery base maps in a web-based smart GIS application with predefined workflows that control and limit each user (including fieldworkers) to their allocated geographical areas and tasks. A mobile application, intelligent caching, data storage and backups make it possible for users, after only a limited amount of training, to have all the functionality required to do data capturing in the field without internet access.