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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?

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Title: 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?


1
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)

2
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

3
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
    2007. 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 2005.
  • 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.

4
  • 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

5
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
6
What are the challenges?
This presentation uses 4 projects to review
variety of challenges encountered
  • 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 1973-2005 and a survey of recent
    initiatives to restore forest functions (David
    Lowery 2006)

7
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)

8
Challenge 1 Project Planning vs. Data
Requirements
Project name Objective 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 1970-1991 1963-2000 at monthly (minus July, August, Sept October and Nov.) Monthly RS data dating back to 1970. 12 month rainfall per year obtained from many stations.
2. Changes in Forest cover of the Mau forest in Kenya between 1973-2005 To quantify changes in forest cover of Mau forest between 1973-2006 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 1973-2004 (if any!) Demographic data from 1973-2005, 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 14-20 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 1940-2006 at 5 years interval, and stream flow 1960-1992, Historical fieldwork based on questionnaire Monthly high resolution RS data dating back to 1960. Demographic profile, Water requirement information
9
Challenge 2 Data Requirements vs. Data Available
Project name Ideal Data requirements RS Data available Remarks
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 1970. 12 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 1973-2005 and a survey of recent initiatives to restore forest functions Two seasonal RS data per year. Policy statements for 1973-2004 (if any!) Demographic data from 1973-2006, 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 1960. 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
10
Challenge 3 Project Output vs. Methods and Tools
Project name Output RS 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 1973-1986-2000 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 1973-2005 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 values, Basic pre-processing, Visual Image interpretation, mixed unsupervised classification Classification tools in Erdas Imagine, ESRI software for visual data interpretation
11
1.Evaluating Land use changes effects on river
flow using USGS steam flow model in Mara river
basin (Mutie SM et al (2006)
12
2. Changes in Forest cover of the Mau forest in
Kenya between 1973-2005 and a survey of recent
initiatives to restore forest functions (Lowery
2006)
13
3.Post Conflict Environmental Assessment for
Sudan (ICRAF-UNEP, 2006)
14
4. Rainwater Harvesting innovations in response
to water scarcity, The Lare Division experience
15
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.

16
Conclusion
  • Are we capturing the right indicators of change?
  • Discuss!
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