department of resource surveys and remote sensing (drsrs)€¦ · drsrs - ku gis day 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 of Geo-spatial Information Databases while up-holding efficiency in its dissemination for purpose of alleviating poverty and supporting sustainable development.
MANDATE Collection, storage, analysis, updating and dissemination of 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 of 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
of 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 o 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 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
OUTPUTS/PRODUCTS
r
n n g l a s h o 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
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 WoodlandDense Shrubbed Grassland Open Grassed ShrublandOpen Shrubbed Grassland Open Wooded ShrublandRiver Sparsed Shrubbed GrasslandSwampy Grassland
Woodland (Forest)
6% Shrubland
24%
Grassland 70%
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 WoodlandDense Shrubbed Grassland Open Grassed ShrublandOpen Shrubbed Grassland Open Wooded ShrublandRiver Sparsed Shrubbed GrasslandSwampy Grassland
Woodland (Forest)
6% Shrubland
24%
Grassland 70%
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 WoodlandDense Shrubbed Grassland Open Grassed ShrublandOpen Shrubbed Grassland Open Wooded ShrublandRiver Sparsed Shrubbed GrasslandSwampy Grassland
Woodland (Forest)
6% Shrubland
24%
Grassland 70%
Prepared by P. W. Wargute, H. P. Roimen and Lucy W. Njino
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 WoodlandDense Shrubbed Grassland Open Grassed ShrublandOpen Shrubbed Grassland Open Wooded ShrublandRiver Sparsed Shrubbed GrasslandSwampy Grassland
Woodland (Forest)
6% Shrubland
24%
Grassland 70%
Vegetation Cove r Types
Dense Grassed Shrub land
Open Grassed Sh rubland
Open Wooded Shrubland
Swampy Grassland
Dense Shrubbed Grassland
Open Shrubbed Grassland
Sparsed Sh rubbed Grassland
Dense Riverine Woodland
Water
5 0 5 Kilom eters
N
Vegetation Cover Types of Mara National Reserve
1°4 0' 1°4 0'
1°3 0' 1°3 0'
1°2 0' 1°2 0'
34° 50'
34° 50'
35° 00'
35° 00'
35°10'
35°10'
35°20'
35°20'
35
35
Legend
Map prepared by: Department of ResourceSurveys 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 WoodlandDense Shrubbed Grassland Open Grassed ShrublandOpen Shrubbed Grassland Open Wooded ShrublandRiver Sparsed Shrubbed GrasslandSwampy Grassland
Woodland
(Forest) 6% Shrubla
nd 24%
Grassland
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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43493
Wildlife (2005)
# 1 - 15
# 16 - 36
# 37 - 73
# 74 - 13510 0 10 Kilometers
S
N
EW
240 00 0
240 00 0
300 00 0
300 00 0
0
0
600
00
600
00
y = 239.38x - 446663
R2 = 0.1328
y = -654.45x + 1E+06
R2 = 0.3726
0
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
Po
pu
lati
on
Esti
mate
.
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 0 100 200 Kilom eters
High Potentia l Areas
Parks and National Reserves
Elephant
# 1 - 5
# 6 - 11
# 12 - 20
# 21 - 34
# 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
0
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 0 100 200 Kilom eters
High Potentia l Areas
Parks and National Reserves
Zebra G revy
# 1 - 2
# 3 - 5
# 6 - 8
# 9 - 16
# 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
2001-041977 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
Po
pu
lati
on
Esti
mate
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
No
rma
lis
ed
Dif
fere
nce
Ve
geta
tio
n In
de
x
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
on 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 of RADAR in forest stock assessment –
Backscattering in RADAR sensor records volume of the
object
• 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
of 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
Thanks for Your Attention
Asante Sana