Department of Resource Surveys and Remote Sensing (DRSRS)€¦ · DRSRS - KU GIS DAY Presentation...

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

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.


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.


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)


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


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


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




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


Forest cover types Area (m 2 )

Tall Dense Forest 79,089,679




Degraded Forest 1,377,926

Tall Dense Shrubland 7,825,291

Tall Medium Forest 2,641,590




Degraded Forest 388,952


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




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.


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%


58.3%

11.9%

7.2%6.4%2.7%

6.0%

0.3%

6.3%

1.0%


Woodland (Forest)

6% Shrubland

24%

Grassland 70%


58.3%

11.9%

7.2%6.4%2.7%

6.0%

0.3%

6.3%

1.0%


Woodland (Forest)

6% Shrubland

24%

Grassland 70%

Prepared by P. W. Wargute, H. P. Roimen and Lucy W. Njino


58.3%

11.9%

7.2%6.4%2.7%

6.0%

0.3%

6.3%

1.0%


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


58.3%

11.9%

7.2%6.4%2.7%

6.0%

0.3%

6.3%

1.0%


Woodland

(Forest) 6% Shrubla

nd 24%

Grassland

70%


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)


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


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


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


LAND USE CHANGE IN MAU FOREST COMPLEX

•Areas of forest loss in the Mau forest complex


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


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


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)


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

Image differencing to show hotspots of

vegetation change compared to previous 10 days


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


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.


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


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


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

•


Radar interactions with forest structure

(H,V)

(H,V)


Multiple Return Discreet return LIDAR multi3

1st return

2nd return

3rd return

time

energy detected


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


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


The COVE Tool

D:\GFOI FAO\kenya

data\Kenya_Report_May2014_v1.pdf

D:/GFOI FAO/kenya data/Kenya_Report_May2014_v1.pdf










Thanks for Your Attention

Asante Sana

Department of Resource Surveys and Remote Sensing (DRSRS)€¦ · DRSRS - KU GIS DAY Presentation...

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Transcript of Department of Resource Surveys and Remote Sensing (DRSRS)€¦ · DRSRS - KU GIS DAY Presentation...