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 NyabengeGIS 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 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.

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

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 1973-2005 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 RequirementsProject 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 2006Historical questionnaire14-20 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

Challenge 2: Data Requirements vs. Data AvailableProject name Ideal Data

requirementsRS 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 1986ETM 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 1973TM 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 1973TM Jan 1996ASTER Jun 2006Limited Fieldwork data

Single season data and therefore does not capture seasonal changes.Limited time series dataDifferent 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 Jan1973TM Feb 1986ETM Mar 2003QuickBird 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 ToolsProject name Output RS interpretation

methods usedTools 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

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 1973-2005 and a survey of recent initiatives to restore forest functions (Lowery 2006)

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

CFL(NAT)

CFOR CFOR

B&SHRB

WGLOFOR

BUILTUP0.0

10.0

20.0

30.0

40.0

50.0

60.0

70.0

80.0

1970 1980 2000

CFL(NAT) OFOR CFOR B&SHRB WGL BURNT BUILTUP L/WAT

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!