The Key Challenges in Application of GIS and Remote Sensing in Environmental Variables Change Analysis in the Horn of Africa Region. Are we capturing the right indicators of change? Meshack Nyabenge GIS Analyst World Agroforestry Centre (ICRAF)

Content  Introduction  Challenges in the context of:-  Project planning vs. Ideal data requirements  Ideal data requirements vs. RS data available  Project output vs. Data interpretation methods and tools  Summary of key problems  Discussion

Introduction Despite the enormous global investments in GIS and remote sensing, detailed and diversified methods and tools developed for different applications in the last three decades, developing countries still faced with frequent environmental related risks and calamities. – The total global market expenditures for remote sensing products were more than $7 billion in 2006 and should reach almost $7.3 billion in At a compounded annual growth rate (CAGR) of 6.3%, the market will reach more than $9.9 billion by 2012 (Electronics Industry Market Research and Knowledge Network 2007) – GPS production value globally is expected to grow to $21.5 billion in 2008, up from $13 billion in 2003, according to the Industrial Economics and Knowledge Center (IEK) of the Industrial Technology Research Institute (ITRI) – The confluence of those three technologies - GIS, remote sensing and GPS - led many analysts to predict that a powerful new industry was about to be born. Some boldly predicted it would surpass $30 billion in sales by – This translates to availability of these resources to civilian use as opposed to initial development dates. – GIS for examples has realized entry open sources software and tools not only targeting specific applications, but also accessible various users communities. – Remote sensing equally has gained a lot entry of new products like RADARSAT, ASTER, Quickbird, IKONOS, etc, This variety has brought high resolution satellite products, cheap and affordable imagery for different applications.

Common environmental problems – Frequent floods within major river basins – Continuous drying up of lakes within major catchment – Unpredicted drought resulting in livestock and human deaths – Frequent forest fires affecting forest related biodiversity

What are major causes of these problems? Climate change High rapid growth of human population linked poverty Poor formulated policies in natural resource utilization. etc What are the response from institutions within horn of Africa? Emergence of many Research and Development projects addressing environment and sustainable natural resource management Formation of many NGOs, CBOs and NARs handling specific components of environmental issues. Reviews of new policies and legislative acts directly touching key environmental components. etc GIS and Remote Sensing are continuously used to supplement and complement these programs both at national to local level

What are the challenges? 1.Evaluating Landuse changes effects on river flow using USGS steam flow model in Mara river basin (Mutie SM et al (2006) 2. Changes in Forest cover of the Mau forest in Kenya between and a survey of recent initiatives to restore forest functions (David Lowery 2006) This presentation uses 4 projects to review variety of challenges encountered

What are the challenges? This presentation uses 4 projects to review variety of challenges encountered 3. Post Conflict Environmental Assessment for Sudan (ICRAF- UNEP (2006) 4. Rainwater Harvesting innovations in response to water scarcity, The Lare Division experience (RELMA- ICRAF(2005)

Challenge 1: Project Planning vs. Data Requirements Project nameObjective Length and frequency of key data used or investigated IDEAL RS and other Data requirements 1.Evaluating Landuse changes effects on river flow using USGS steam flow model in Mara river basin To evaluate the effects of land use changes on the hydrology of river Mara Rainfall from & at monthly (minus July, August, Sept October and Nov.) Monthly RS data dating back to month rainfall per year obtained from many stations. 2. Changes in Forest cover of the Mau forest in Kenya between To quantify changes in forest cover of Mau forest between using satellite images and ground surveys Landsat Jan 1973 (mss), Jan 1986,Jan 1995, Feb 2000, Feb 2003 & Mar 2005, 14-2 years interval Two seasonal RS data per year. Policy statements for (if any!) Demographic data from , policy statements 3.Post Conflict Environmental Assessment for Sudan Provide neutral and objective information on the most critical environmental problems faced by the country and on the potential risks to human health, livelihoods and ecosystem services Landsat Feb 1973,Jan 1986 and ASTER Jun 2006 Historical questionnaire years interval Two seasonal RS data per year for 1976, 1986, 1996,2006. However 5 years data interval would be ideal. Conflict trends and demographic profiles 4. Rainwater Harvesting innovations in response to water scarcity, The Lare Division experience Identify the impact of land cover change on the local hydrologic regime and its contribution to successful adoption to new rain harvesting innovations Rainfall from at 5 years interval, and stream flow , Historical fieldwork based on questionnaire Monthly high resolution RS data dating back to Demographic profile, Water requirement information

Challenge 2: Data Requirements vs. Data Available Project nameIdeal Data requirements RS Data availableRemarks 1.Evaluating Landuse changes effects on river flow using USGS steam flow model in Mara river basin Two seasonal RS data per year dating back to month rainfall per year obtained from many stations. MSS Jan 1973, & Jul 1975, TM Jan 1986 & Oct 1986 ETM Jan 2000 & Jul 2000 Spatial and temporal difference in data within a single year and across years. Limited data to match rainfall information 2. Changes in Forest cover of the Mau forest in Kenya between and a survey of recent initiatives to restore forest functions Two seasonal RS data per year. Policy statements for (if any!) Demographic data from , policy statements MSS Landsat Jan 1973 TM Jan 1986,Jan 1995, EMT Feb 2000, Feb 2003 & Mar 2005, Simple fieldwork data based on survey Single season data and therefore does not capture seasonal changes. Lack of detailed and historical auxiliary data to support data processing. 3.Post Conflict Environmental Assessment for Sudan Two seasonal RS data per year for 1976, 1986, 1996,2006. However 5 years would be good. Conflict trends and demographic profiles MSS Feb 1973 TM Jan 1996 ASTER Jun 2006 Limited Fieldwork data Single season data and therefore does not capture seasonal changes. Limited time series data Different spatial, temporal and platforms- 4. Rainwater Harvesting innovations in response to water scarcity, The Lare Division experience Monthly high resolution RS data dating back to Demographic profile, Water requirement information MSS Jan1973 TM Feb 1986 ETM Mar 2003 QuickBird Nov 2004 Limited data to capture these hydrological changes. Very coarse RS data to depict local hydrological characteristics

Challenge 3: Project Output vs. Methods and Tools Project nameOutputRS interpretation methods used Tools available 1.Evaluating Landuse changes effects on river flow using USGS steam flow model in Mara river basin Land use changes maps and values between ; Stream flow profiles Basic pre-processing, supervised classification, digital change detection approach using raw and classified data. Image classification in IDRISI Kilimanjaro and ESRI software for data integration and output 2. Changes in Forest cover of the Mau forest in Kenya between and a survey of recent initiatives to restore forest functions Time series forest cover maps and values Basic pre-processing, without atmospheric correction, supervised classification using classified data to determine changes over time Image classification tools in Erdas Imagine plus ERSI software for data integration. 3.Post Conflict Environmental Assessment for Sudan Land use maps and values and changes Basic pre-processing, Visual Image interpretation, mixed unsupervised classification Classification tools in Erdas Imagine, ESRI software for visual data interpretation 4. Rainwater Harvesting innovations in response to water scarcity, The Lare Division experience Land use maps and valuesLand use maps and values, Basic pre-processing, Visual Image interpretation, mixed unsupervised classification Classification tools in Erdas Imagine, ESRI software for visual data interpretation

1.Evaluating Land use changes effects on river flow using USGS steam flow model in Mara river basin (Mutie SM et al (2006)

2. Changes in Forest cover of the Mau forest in Kenya between and a survey of recent initiatives to restore forest functions (Lowery 2006). Changes in Forest cover of the Mau forest in Kenya between and a survey of recent initiatives to restore forest functions (Lowery 2006)

3.Post Conflict Environmental Assessment for Sudan (ICRAF-UNEP, 2006)

4. Rainwater Harvesting innovations in response to water scarcity, The Lare Division experience

Summary of Challenges Project planning does not cater for idea RS data requirements against keys researchable environmental variable in terms of temporal, spectral and spatial (Little money is put RS data). Little attempt is made supplement Landsat, SPOT or ASTER or Quickbird with low resolution images (MODIS, NOAA) to support and stratify broad scale landscape change. Limited use of better pre-processing and post-processing tools (atmospheric correction, etc) to refine RS data quality to depict the researched environmental variables changes Minimum use of diversified tools within new software like Ecognition, TNT mips, ENVI,other open source software like DIVA, SPRING, among to complement data interpretation is evident.

Conclusion Are we capturing the right indicators of change? Discuss!