1 swalim workshop 12-13 june, nairobi monitoring land cover dynamics in sub-saharan africa h.d. eva,...

1SWALIM Workshop 12-13 June, Nairobi

Monitoring Land Cover Dynamics in sub-Saharan Africa

H.D. Eva, A. Brink and D. Simonetti


Outline

• Introduction

• Method

• Results

• Discussion

• The future


Monitoring Natural Ressources for Development Cooperation (MONDE)

• Land– Land cover change, land availability, pressure, degradation,

phenology, fires• Forest

– Deforestation, logging, sustainable management• Biodiversity

– Protected areas, specific ecosystems• Fresh water

– Inland water quality, surface water availability in dry areas• Coastal areas and marine resources

– Fish resources, sensitive ecosystems, coastal pressure• Urban areas

– Environmental degradation around cities, urban growth

Introduction


Key Questions - Thematic

• What have been the main land cover changes in sub-Saharan Africa over the last 25 years?

• What are the main drivers and pressures that have affected sub-Saharan Africa?

• What impacts do such land cover changes have?

Introduction


Key Questions - Method

• Test the suitability of Remote Sensing data from different sources (i.e. MSS and TM) for detecting both spatial and thematic changes

• Test the sampling scheme

Introduction


Methodology

• Sample of high (30-80m) resolution satellite data from the 1970’s (MSS), the 1980’s(TM) and 2000 (TM)

• Sample stratification by using the White eco-regions

• 57 samples subdivided into 511 sub-samples, resulting in 1% coverage of the area

• Land cover change assessment of 4 key classes:

– Agriculture– Forest– Natural non forest vegetation– Barren– (water)

• Extrapolation of the results by direct expansion to eco-region and full sub-Saharan level

Method


Stratification and Sampling

Method


Sampling and Classification

• From each sample site 9 (20 by 20 km) boxes are extracted and independently classified using unsupervised clustering

• Classes regrouped into

– Forest

– Non Forest Natural Vegetation

– Agriculture

– Barren

– (Water)

• Class identification carried out by expert knowledge with aid from existing maps,Africover (FAO), other ancillary data and Google Earth

Method


Forest Cover Change in Madagascar

Method

1975 2000

10

SWALIM Workshop 12-13 June, Nairobi

Agriculture expansion on Somalia

Method

1975 2000

11


Irrigated Agriculture in Sudan

Method

20001975

12


Land Abandonment in Angola

Method

20001975

13


Results

• Sub-Saharan Africa’s land cover in the year 2000

• Land cover changes for Sub-Saharan Africa – 1975 to 2000

• Potential impacts

Results

14


Sub-Saharan Africa’s land cover in the year 2000

17.3 18.8

60.8

2.5

0.0

10.0

20.0

30.0

40.0

50.0

60.0

70.0

Agriculture Forest NF vegetation Barren

land cover class

%

Results

15


Distribution of land cover (%) by ecoregion for the year 2000

-

10.0

20.0

30.0

40.0

50.0

60.0

70.0

eco-region

%

Agriculture

Forest

NF veg.

Barren

Results

16


Proportion of land cover (%) within each ecoregion for the year 2000

-

10.0

20.0

30.0

40.0

50.0

60.0

70.0

80.0

90.0

100.0

eco-region

%

Agriculture

Forest

NF veg.Barren

Results

17


Land cover changes for Sub-Saharan Africa – 1975 to 2000

123,413

(71,325)

(58,894)

6,565

(100,000)

(50,000)

-

50,000

100,000

150,000

Agriculture Forest NF veg. Barren

land cover class

tota

l ch

an

ge (

000 h

a)

T(%)C: +57

AAC: 4,937

AA(%)C: 2.3

T(%)C: -16

AAC: -2,853

AA(%)C: -0.7

T(%)C: -5

AAC: -2,356

AA(%)C: -0.2

T(%)C: +15

AAC: 263

AA(%)C: 0.6

Results

18


Loss of Natural Vegetation

-70.0

-60.0

-50.0

-40.0

-30.0

-20.0

-10.0

0.0

10.0

20.0

GUIN

EO-CO

NGOLI

AN

ZAMBEZIA

N

SUDANIAN

SOM

ALIA-M

ASAI

KAROO-N

AMIB

AFROM

ONTANE

GUIN

EA-CONG

OLIA/Z

AMBEZIA

GUIN

EA-CONG

OLIA/S

UDANIA

ZANZIBAR-T

ONGO-C

OAST

KALAHARI-H

IGHVELD

SAHEL

MADAGASCAR

eco-region

% o

f th

e 19

75 la

nd

co

ver

Forest

NF Veg.

Results

19


Available Land after Agro-Ecological Zoning

0

20

40

60

80

100

120

140

160

eco-region

%

% suitable area

% suitable land used

Results

20


Methodological Problems

• The images

– MSS – TM use different path/row system(WRS1/WRS2). This often requires mosaicing of MSS images

– Geo-referencing problems between MSS and TM

– The capacity to extract changes between MSS and TM is severely compromised by noise, spatial resolution, spectral resolution and seasonal differences

Discussion

21



• MSS – TM - Difference in Spatial resolution

TM resolution vs. TM degraded to MSS resolution

R2 = 0.9888

0

5000

10000

15000

20000

25000

30000

35000

40000

45000

0 5000 10000 15000 20000 25000 30000 35000 40000 45000

ha

ha Series1

Linear (Series1)

Discussion

22



• The Sampling

– Stratification by eco-regions not ideal

– 1% sample is probably too low

Discussion

23



• Classification and Visual Interpretation

– Unsupervised classification and manual class identification is time consuming and needs expert knowledge

Discussion

24


Discussion

25


2007 and Future

The future

26


2007 and Future

• Senegal – Sampling• 68 Samples

The future

27


2007 and Future

• Tanzania – Sampling• 310 Samples

The future

1 swalim workshop 12-13 june, nairobi monitoring land cover dynamics in sub-saharan africa h.d. eva,...

Documents

swalim workshop

nairobi land abandonment

nairobi sampling

nairobi forest cover

nairobi stratification

future slide

simonetti slide

land availability