Sensing (DRSRS) Application of Geo-Spatial Information for - - PowerPoint PPT Presentation

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Department of Resource Surveys and Remote Sensing (DRSRS)

Application of Geo-Spatial Information for Sustainable Development Functions and Operations

P.O. Box 47146, 00100; Tel: 254 (02) 609013/27; Fax: 254 (02) 609705, Nairobi, Kenya

Mwangi J. Kinyanjui (Ph.D) KENYATTA UNIVERSITY GIS DAY – 18th Nov. 2014

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DRSRS - KU GIS DAY Presentation – 18th Nov. 2014

BACKGROUND Over time the scope of the unit expanded. 1982 - Land use/cover mapping was initiated in high potential areas using SPOT satellite. 1984 - Crop forecasting programme started 1987

• Installed

Geographical Information System. 1988 - It became a full-fledged Department under the Ministry of Planning and National Development but has moved across ministries since them without changing mandate

DRSRS is situated along Popo Rd, off Mombasa Rd and opposite Belle-Vue Cinema in South ‘C’.

The Department of Resource Surveys and Remote Sensing (DRSRS) formerly known as Kenya Rangeland Ecological Monitoring Unit (KREMU) was established in 1976. Main aim Monitor rangelands of Kenya through livestock, wildlife and vegetation surveys using remote sensing, aerial surveys and ground sampling techniques.

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DRSRS - KU GIS DAY Presentation – 18th Nov. 2014

DEPARTMENT OF RESOURCE SURVEYS AND REMOTE SENSING (DRSRS)

MISSION To promote sustainable development

• f Geo-spatial Information Databases

while up-holding efficiency in its dissemination for purpose

• f

alleviating poverty and supporting sustainable development.

MANDATE Collection, storage, analysis, updating and dissemination

• f geo-spatial information on

natural resources to facilitate informed decision-making for sustainable management of these resources so as to alleviate poverty and enhance environmental management. Data and information from DRSRS is used in formulation

• f policies and decision -

making in various government ministries and agencies.

VISION To become a national focal centre of excellence in matters related to development of national Geo-spatial Databases on most renewable and non-renewable natural resources and environment for rapid decision- making and policy formulation.

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DRSRS - KU GIS DAY Presentation – 18th Nov. 2014

ACTIVITIES To Generate data for

• Sustainable management of livestock/wildlife and associated

environment/ecological attributes in the Kenya Rangelands;

• Conservation of forests, water towers, wetlands, fragile

ecosystems;

• Crop forecasting for food security management
• Seasonal, spatial and annual biomass monitoring;

Also

• Maintain archives of Environmental Information database e.g.

wildlife data since 1976

• To coordinate projects using remote sensing technology in

government. DEPARTMENT OF RESOURCE SURVEYS AND REMOTE SENSING (DRSRS)

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DRSRS - KU GIS DAY Presentation – 18th Nov. 2014

Why remote sensing?



Ground plots are expensive



Some ground points cant be accessed



We need time series information



We need information about large areas



We need to analyse interplays and effects

• f overlays

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DRSRS - KU GIS DAY Presentation – 18th Nov. 2014

DRSRS Methods of Data Acquisition

OUTPUTS

• Maps
• Statistics
• Models
• Reports

Database integration, Analysis and Modeling in GIS/RS Platforms

Multi-Stage Sampling Concept Stage 1: Remote Sensing Approach

 Orbiting Space Satellite (3,000 - 35,000 km) Advantages: - Cheap, faster, synoptic, covers wide area and easily comparable

Stage 2: Aerial Surveys

 Low-High Flight Aircraft

• Aerial Photography (100-3,000m)
• Animal Census (100-200m)

Costs Implication: Dependent on size of

area, sampling resolution and efforts

Stage 3: Ground Surveys/Measurement

 Attribute identification, scale accuracy and socio-economic surveys Cost Implication: Often expensive and time consuming

Scale Scale

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DRSRS - KU GIS DAY Presentation – 18th Nov. 2014

Multi-stage Data Gathering Concept:

   

Ground Sampling Method

 

Satellite data, Aerial photography and Ground sampling/checks.

r n g l s

• t t

i l a e u e r i i l

Preliminary Vegetation Maps

Final Vegetation Map

Satellite Image

C o v e r T y p e s A g r i c u l t u r a l L a n d B u r n t F o r e s t C o m m e r c i a l R a n c h D e g r a d e d F o r e s t D e n s e G r a s s y S h r u b l a n d D w a r f s h r u b G r a s s l a n d F o r e s t P l a n

Aircraft Satellite

C o T y p e s A g r i c l t u r a l L a n d B u r n t F o r e s t C o m m e r c i a l R a n c h D e g r a d e d F o r e s t D e n s e r a s s y S h r u b l a n d D w a r f h r u b G r a s s l a n d F o r e s t P l a n

Herbaceous cover sampling

• Line transect
• Quadrant method
• Use of GPS
• Checklist

Socio-economic aspects

• Biodiversity assessment
• Questionnaire surveys

Woody cover sampling

• Point Center Quarter (PCQ)
• Line transect

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DRSRS - KU GIS DAY Presentation – 18th Nov. 2014

Forest cover types Area (m 2 ) Tall Dense Forest 79,089,679 Tall Dense Forest 14,201,207 Tall Dense Forest 9,823,538 Tall Dense Forest 13,851,548 Degraded Forest 1,377,926 Tall Dense Shrubland 7,825,291 Tall Medium Forest 2,641,590 Tall Medium Forest 1,438,911 Tall Medium Forest 1,741,192 Degraded Forest 1,038,913 Degraded Forest 388,952 Degraded Forest 5,668,493 Vegetation cover Statistics of Rumuruti Forest

C o v e r T y p e s A g r i c u l t u r a l L a n d B u r n t F

• r e

s t C

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m e r c i a l R a n c h D e g r a d e d F

• r e

s t D e n s e G r a s s y S h r u b l a n d D w a r f s h r u b G r a s s l a n d F

• r e

s t P l a n

OUTPUTS/PRODUCTS

r n n g l a s h

• t n

t i l a e e d S u e n r u s s l n d R i r i V t o n S e n t h e S l l a l a S T a D e r t T a D e r l a T a i u o s t T a i u h b l d

Include vegetation cover maps, land use statistics, species checklists, technical reports, journal articles etc.

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DRSRS - KU GIS DAY Presentation – 18th Nov. 2014

Vegetation Cover Types Dense Grassed Shrubland Open Grassed Shrubland Open Wooded Shrubland Swampy Grassland Dense Shrubbed Grassland Open Shrubbed Grassland Sparsed Shrubbed Grassland Dense Riverine Woodland Water 5 5 Kilom eters

N

Vegetation Cover Types of Mara National Reserve

1°4 0' 1°40' 1°3 0' 1°30' 1°2 0' 1°20' 34° 50' 34° 50' 35° 00' 35° 00' 35° 10' 35° 10' 35° 20' 35° 20' 35 35

Legend

Map prepared by: Department of Resource Surveys and Remote Sensing (DRSRS) - 2008 Loc ation of Study Area Area % of Vegetation Cover Types 58.3% 11.9% 7.2% 6.4% 2.7% 6.0% 0.3% 6.3% 1.0% Dense Grassed Shrubland Dense Riverine Woodland Dense Shrubbed Grassland Open Grassed Shrubland Open Shrubbed Grassland Open Wooded Shrubland River Sparsed Shrubbed Grassland Swampy Grassland

Woodla nd (Forest) 6% Shrubla nd 24% Grassla nd 70%

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DRSRS - KU GIS DAY Presentation – 18th Nov. 2014

Aerial Sampling Techniques

5 Km 120 m (400ft)

Animal Census

• Aerial Surveys: Systematic

reconnaissance flights methodology (Norton-Griffiths, 1978)

• Analysis: Jolly (1969) for

statistics; Geographic Information System (GIS) for spatial mapping of population distribution, statistical packages (SPSS, Systat)

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DRSRS - KU GIS DAY Presentation – 18th Nov. 2014

OUTPUTS/PRODUCTS

These include technical reports, spatial distribution maps, population estimate statistical summaries, and trend graphs.

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Wildlife (2005)

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10 10 Kilometers

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y = 239.38x - 446663 R2 = 0.1328 y = -654.45x + 1E+06 R2 = 0.3726 10,000 20,000 30,000 40,000 50,000 60,000 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 Year Population Estimate Plain's zebra Wildlife minus Plain's Zebra Linear (Plain's zebra) Linear (Wildlife minus Plain's Zebra)

Species 1997 1999 2001 2003 2005 2008 2,655 2,717 1,666 1,953 955 3,026 Elephant 1,847 2,645 1,747 2,947 4,592 3,792 Eland 3,667 2,933 2,417 1,562 1,265 1,709 Impala 8,436 5,714 4,391 4,389 5,131 7,441 Giraffe 1,856 1,209 1,720 1,395 1,601 1,931 Warthog 825 469 715 363 770 1,077 Oryx 1,385 1,128 461 1,390 1,115 1,486 Waterbuck 621 279 389 37 416 294 Grant's gazelle 6,997 5,254 9,072 4,956 4,653 4,949 Thomson's gazelle 5,150 4,035 4,038 2,529 3,468 4,735 Ostrich 284 523 525 391 380 587 Gerenuk 319 144 217 325 301 151 Kongoni (’s hartebeest) 2,131 1,724 1,186 865 619 641 Burchell’s zebra 35,859 32,725 26,095 36,372 32,309 29,852 Grey's zebra 870 1,002 787 948 3,326 2,554 Total Wildlife 72,902 62,501 55,498 60,422 60,902 64,226 Total wildlife minus Burchell's zebra 37,043 29,776 29,403 24,050 28,593 34,374

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DRSRS - KU GIS DAY Presentation – 18th Nov. 2014

Wildlife

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# # 100 100 200 Kilom eters High Potential Areas Parks and National Reserves Elephant # 1 - 5 # 6 - 11 # 12 - 20

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35 - 57 N Distribution of Elephant in the Kenya R angelands Legend

167000 35462 21573 13139 15801 16800 17702 y = 105690x-1.154 R² = 0.8066 20000 40000 60000 80000 100000 120000 140000 160000 180000 1973 1977-80 1981-85 1986-88 1989-91 1992-94 2000-04

• Pop. Estimate

Year

Trend: Elephant population declined by 90% from 1973 (167,000) to 2004 (18,000) Possible cause: Land use change, poaching, drought and competition

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DRSRS - KU GIS DAY Presentation – 18th Nov. 2014

Wildlife

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# # ## # # # # # # # # # # # # # # # # # # # # # # # # 100 100 200 Kilom eters High Potential Areas Parks and National Reserves Zebra G revy # 1 - 2 # 3 - 5 # 6 - 8 # 9 - 16

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17 - 22 N Distribution of Zebra Grevy in the Kenya R angelands Legend

District 1979 PE SE PE SE PE SE PE SE PE SE PE SE Garissa 904 411 484 176 371 145 NS NS NS NS NS NS Isiolo 2,969 1,555 NS NS 610 310 1,021 628 985 424 351 211 Laikipia 794 766 17 17 298 272 691 285 181 125 2,265 1,289 Marsabit 4,922 1,607 2,838 654 2,055 804 2,187 542 1969 531 NS NS Samburu 2,619 875 1,880 962 638 308 760 985 995 712 2,296 1,080 Tana River 136 135 1,174 496 221 159 539 215 34 34 NS NS Wajir 645 463

• 18

18 69 53 NS NS

• Total

12,989 2,570 8,500* 6,393 1,277 4,211 979 5,267 987 4,164 992 4,912 1,695 2001-04 1977 1980-83 1987-88 1989-92 1993-4 y = -1215.3x + 11495 R2 = 0.681

• 2,000

4,000 6,000 8,000 10,000 12,000 14,000 1977 1979 1980-83 1987-88 1989-92 1993-94 2001-04 Year Population Estimate

Trend: G. Zebra population declined by 62% from 13,000 in 1977 to 4,912 in 2004 Possible cause: Land use changes, poaching, drought and competition

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DRSRS - KU GIS DAY Presentation – 18th Nov. 2014

LAND USE CHANGE IN MAU FOREST COMPLEX

• Areas of forest loss in the Mau forest complex

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DRSRS - KU GIS DAY Presentation – 18th Nov. 2014

FOREST LOST TO CROPLAND AND GRASSLAND 1990 -2014

Block Loss to cropland Loss to grassland Total (ha) Eastern Mau 32,413 2,811 35,223 South West Mau 18,788 2,697 21,485 Maasai Mau 6,752 1,838 8,590 Mount Londiani 2,826 3,362 6,189 Northern Tinderet 2,761 2,252 5,013 Tinderet 562 1,701 2,263 Ol Posimoru 145 1,752 1,897 Western Mau 1,059 764 1,824 Maji Mazuri 475 1,113 1,589 Eburru 1,013 158 1,171 Timboroa 555 387 942 Grand Total 67,350 18,834 86,184

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DRSRS - KU GIS DAY Presentation – 18th Nov. 2014

Variations in Vegetation health – Rain fed Agriculture in Gucha District 2001 and 2009

0.55 0.6 0.65 0.7 0.75 0.8 10-Jan 10-Feb 10-Mar 10-Apr 10-May 10-Jun 10-Jul 10-Aug 10-Sep 10-Oct 10-Nov 10-Dec Normalised Difference Vegetation Index

Months of the year Year 2001 Year 2009 AVG 1998-2008

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DRSRS - KU GIS DAY Presentation – 18th Nov. 2014

January 10th 2009 February 10th 2009 March 10th 2009

LAND USE LAND COVER CHANGES

Dekadal (10 day interval) data on vegetation health and density in Kenya (NDVI)

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DRSRS - KU GIS DAY Presentation – 18th Nov. 2014

December 10th 2008 January 10th 2009 November 10th 2009

Image differencing to show hotspots of vegetation change compared to previous 10 days

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DRSRS - KU GIS DAY Presentation – 18th Nov. 2014

REMOTE SENSING APPLICATIONS

Outputs/Products These include technical reports, land use/cover maps and statistics

Land use in Kisumu municipality Land use change in Narok District Forest cover change detection

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DRSRS - KU GIS DAY Presentation – 18th Nov. 2014

Application of Remote Sensing Data: Mapping indicators of land degradation and food security

Early Warning Systems for Drought Monitoring: Impacts of environmental stress

• n natural resources

1 – 10 Mar 1997 (high rainfall) 1 – 10 Mar 1996 (drought) 1 – 10 Mar 1995 (normal) 1 – 10 Mar 1998 (El-Nino)

NDVI variation within same period in Isiolo District (1995 – 1998)

Estimating primary biomass production for assessment of carrying capacity (livestock) and grazing pressure. Good management tool for pastoralists livestock and wildlife management in drought mitigation.

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DRSRS - KU GIS DAY Presentation – 18th Nov. 2014

APPLICATION OF DRSRS DATA

• Livestock production, range infrastructure planning and

development The data on numbers/distribution are used  Locating range infrastructure e.g. watering points  Proper range management practices (stocking levels)

• Planning, conservation and management of wildlife

 Planning and management protected areas (reserves/parks), migration corridors etc; (KWS);  Conservation and management of endangered species of wildlife e.g elephant, Grevy’s zebra, Hirola (Hunter’s hartebeest, etc.)  Design of tourist circuits and lodges  Human-wildlife conflict resolution  Allocation of cropping/culling quotas  Setting up anti-poaching mechanism  Wildlife research

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DRSRS - KU GIS DAY Presentation – 18th Nov. 2014

APPLICATION OF DATA ….Cont’

Application of RSD Data/information

• 1. Forest cover mapping for conservation and management
• 2. Biomass mapping for Green House Gas inventory and

National communication to UNFCCC 2. Crop forecast used for national food security planning and management 3. Landuse and cover studies useful for land use planning and land policy development, land evaluations, landuse plans, and for general environmental planning and management 4. Urban landuse mapping useful for physical planning and urban environmental planning (City & Urban councils, MLH) and general environmental planning and management (NEMA); and 5. Early warning system data is useful predicting effects of drought and range management

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DRSRS - KU GIS DAY Presentation – 18th Nov. 2014

Other uses of RS in Kenya

• San Marco Centre in Malindi deals with Telescopy and

Astronomy –observation of the sky

• Department of Defence has introduced Drones – un

manned Aircraft which take continuous photographs. KWS is exploring possibilities of use in managing poaching

• Use of LiDAR in measuring tree heights by KFS- Uses

manned aircraft which records pulses which indicate heights of objects

• Use
• f

RADAR in forest stock assessment – Backscattering in RADAR sensor records volume of the

• bject
• Mineral exploration
• Exploration of underground water e.g. in Turkana
• Geo located Data Recording and submisssion e.g KPLC

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DRSRS - KU GIS DAY Presentation – 18th Nov. 2014

Radar interactions with forest structure

(H,V) (H,V)

฀  

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DRSRS - KU GIS DAY Presentation – 18th Nov. 2014

Multiple Return

Discreet return LIDAR

multi3

1st return 2nd return 3rd return

time energy detected

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DRSRS - KU GIS DAY Presentation – 18th Nov. 2014

Some Recent and on-going collaborations



Kenya’s Atlas of our changing environment – Funded by UNEP



Mapping of wildlife corridors – A RRI project funded by government in 2012-103. involved KWS, ILRI, NMK, UoN,



Establishing land use statistics in Kenya based on IPCC guidelines. A project of KFS funded by Japan in 2012. Involved KFS, DRSRS, RCMRD, SoK



Biomass mapping in Mau forest Ecosystem - A project of KFS funded by Japan in 2012. Involved KFS, DRSRS, and KEFRI



Developing a wetland map for Kenya



The System for Land based emission Estimation for Kenya (SLEEK) – funded by the Australian Government through Clinton foundation. Is an integrated programme involving many government departments, agencies and universities



Mapping of Water towers of Kenya – involved KWTA and DRSRS



Several on-going County projects to map resources for sustainable use

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DRSRS - KU GIS DAY Presentation – 18th Nov. 2014

Opportunities for students

1.

Internships – students are exposed to the variety of state

• f art GIS and RS techniques currently used in DRSRS

2.

Data provision – students interested in data e.g. for wildlife, satellite imagery etc. can access them. NB Some satellite imagery are not available for sharing

3.

Mentorships – students willing to do specific projects can consult and learn what are the possible or best practices

4.

Joint research – Researchers are encouraged to develop joint researches with staff from DRSRS to allow use of our state of the art equipment

5.

Supervision – students may incorporate supervisors from DRSRS to benefit from some of the existing knowledge/equipment

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DRSRS - KU GIS DAY Presentation – 18th Nov. 2014

The COVE Tool

 D:\GFOI FAO\kenya

data\Kenya_Report_May2014_v1.pdf

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DRSRS - KU GIS DAY Presentation – 18th Nov. 2014

Than anks ks for for Your ur Att tten enti tion As Asan ante e San ana