1. Analysis of the Drivers of Landcover
and Landuse Change in Western
Kenya over the Last 30 years
Mike Norton-Griffiths & Harvey Herr (Jnr)

3. Research Objectives
• LBDA /ICRAF
– Develop statistically robust and useful measures of landuse and
landcover change
– Identify data on the potential drivers of change and their
dynamics
• ICRAF
– Use of Quickbird imagery for inventory and monitoring of agro-
forestry components in the smallholder landuse matrix
– Procedures for efficient selection of Sentinel Landscapes and
Sites
– Quantify system dynamics for future monitoring

5. LAKE BASIN
DEVELOPMENT
AUTHORITY 1983 SURVEY
GRID WITH ICRAF
SENTINEL SITES, WESTERN
KENYA

14. FEWS NET/USGS RECENT CLIMATE VARIATION DATA (http://earlywarning.usgs.gov/fews/reports.php)

15. FEWS NET/USGS RECENT CLIMATE VARIATION DATA (http://earlywarning.usgs.gov/fews/reports.php)

16. FEWS NET/USGS RECENT CLIMATE VARIATION DATA (http://earlywarning.usgs.gov/fews/reports.php)

17. FEWS NET/USGS RECENT CLIMATE VARIATION DATA (http://earlywarning.usgs.gov/fews/reports.php)

18. LAKE BASIN
DEVELOPMENT
AUTHORITY 1983 SURVEY
GRID WITH ICRAF
SENTINEL SITES, WESTERN
KENYA

19. 19

20. 20

21. 21

22. LAKE BASIN
DEVELOPMENT
AUTHORITY 1983 SURVEY
GRID WITH ICRAF
SENTINEL SITES, WESTERN
KENYA

24. GEOREFERENCING THE 1983 AERIAL PHOTOS
UTM UTM ICRAF Film
OID Record # CODE XX YY Name Direction Scanned Control Georef
Easting Northing BLOCK Photo
28 949 2628 26 28 722500 9972500 4 Katuk Odeyo 1491-0061 S Y
28 949 2628 26 28 722500 9972500 4 Katuk Odeyo 1491-0062 S Y
28 949 2628 26 28 722500 9972500 4 Katuk Odeyo 1491-0063 S Y
27 948 2627 26 27 722500 9967500 4 Katuk Odeyo 1491-0064 S N 3
27 948 2627 26 27 722500 9967500 4 Katuk Odeyo 1491-0065 S N 3
27 948 2627 26 27 722500 9967500 4 Katuk Odeyo 1491-0066 S Y 3
27 948 2627 26 27 722500 9967500 4 Katuk Odeyo 1491-0067 S Y 3
27 948 2627 26 27 722500 9967500 4 Katuk Odeyo 1502-0001 N Y 3
27 948 2627 26 27 722500 9967500 4 Katuk Odeyo 1502-0002 N Y 1
27 948 2627 26 27 722500 9967500 4 Katuk Odeyo 1502-0003 N Y Y 1
27 948 2627 26 27 722500 9967500 4 Katuk Odeyo 1502-0004 N Y 3
28 949 2628 26 28 722500 9972500 4 Katuk Odeyo 1502-0005 N Y
28 949 2628 26 28 722500 9972500 4 Katuk Odeyo 1502-0006 N Y
28 949 2628 26 28 722500 9972500 4 Katuk Odeyo 1502-0007 N Y
28 949 2628 26 28 722500 9972500 4 Katuk Odeyo 1502-0008 N Y Y
30 1000 2727 27 27 727500 9967500 4 Katuk Odeyo 1511-0001 N Y 1
30 1000 2727 27 27 727500 9967500 4 Katuk Odeyo 1511-0002 N Y 1
31 1001 2728 27 28 727500 9972500 4 Katuk Odeyo 1511-0003 N Y
KATUK ODEYO Flight Lines and Photos
Strong features for control points
XX -->
26 27 28
1491S 1502N 1511N 1521S
LOCATION KNOWN WITHIN ~ 5km2 8 6
61 7 5 71
28
62 6 4 72
63 5 3 73
64 4 2 74
65 3 1 1 3
66 2 2 35 4
27
67 1 66 3 34 5
1 65 4 33 6
36 2 64 5 32 7
35 3 63 6 31 8
26
34 4 62 7 30 9
5 29 10
2492N 2501S 2511N 2521S 2531N 2541S

25. KATUK ODEYO 1983, AERIAL PHOTO ID: 1521_0074S
KATUK ODEYO Flight Lines and Photos
Strong features for control points
XX -->
26 27 28
1491S 1502N 1511N 1521S
8 6
61 7 5 71
28
62 6 4 72
63 5 3 73
64 4 2 74
65 3 1 1 3
66 2 2 35 4
27
67 1 66 3 34 5
1 65 4 33 6
36 2 64 5 32 7
35 3 63 6 31 8
26
34 4 62 7 30 9
5 29 10
2492N 2501S 2511N 2521S 2531N 2541S

26. 10x10 km 01/27/10 QUICKBIRD SATELLITE IMAGE, “LOWER NYANDO/KATUK ODEYO”

27. 10x10 km 01/27/10 QUICKBIRD SATELLITE IMAGE, “LOWER NYANDO/KATUK ODEYO”

30. KATUK ODEYO 1983, AERIAL PHOTO ID: 2492_0035N
KATUK ODEYO Flight Lines and Photos
Strong features for control points
XX -->
26 27 28
1491S 1502N 1511N 1521S
8 6
61 7 5 71
28
62 6 4 72
63 5 3 73
64 4 2 74
65 3 1 1 3
66 2 2 35 4
27
67 1 66 3 34 5
1 65 4 33 6
36 2 64 5 32 7
35 3 63 6 31 8
26
34 4 62 7 30 9
5 29 10
2492N 2501S 2511N 2521S 2531N 2541S

31. 10x10 km 01/27/10 QUICKBIRD SATELLITE IMAGE, “LOWER NYANDO/KATUK ODEYO”

35. SOME DIFFICULT EXAMPLES:

39. ESTIMATING THE LOCATION OF OTHER (UN-GEOREFRENCED) SAMPLES
~ 17 sample sites, LOCATIONS???
KATUK ODEYO Flight Lines and Photos
Strong features for control points
XX -->
26 27 28
1491S 1502N 1511N 1521S
8 6
61 7 5 71
28
62 6 4 72
63 5 3 73
64 4 2 74
65 3 1 1 3
66 2 2 35 4
27
67 1 66 3 34 5
1 65 4 33 6
36 2 64 5 32 7
26 35 3 63 6 31 8
34 4 62 7 30 9
5 29 10
2492N 2501S 2511N 2521S 2531N 2541S

40. FLIGHT LINES CREATED FROM GEOREFERENCED SITES & ASSUMED FLIGHT PATH
KATUK ODEYO Flight Lines and Photos
Strong features for control points
XX -->
26 27 28
1491S 1502N 1511N 1521S
8 6
61 7 5 71
28
62 6 4 72
63 5 3 73
64 4 2 74
65 3 1 1 3
66 2 2 35 4
27
67 1 66 3 34 5
1 65 4 33 6
36 2 64 5 32 7
26 35 3 63 6 31 8
34 4 62 7 30 9
5 29 10
2492N 2501S 2511N 2521S 2531N 2541S

41. ESTIMATED SAMPLE SITESon the same flightON LOCATION RELATIVE TOeach other,
Estimated sample sites CREATED BASED path are equidistant from KNOWN SITES &
GRID distance determined by the length of the flight line within the grid square or
the
from the grid to the edge of a block. Estimated sites that lie between a geo-
referenced site and grid or block edge are spaced evenly along the distance of the
flight line.
10x10 km 01/27/10 QUICKBIRD SATELLITE IMAGE, “LOWER NYANDO/KATUK ODEYO”

42. Success Rate and Implications:
Of a total of 142 sample sites, 52 (47%) were exactly georeferenced, 90 were
estimated (63%).
We have discovered automated georeferencing software which should speed
things up.

47. Fields with crops, harvested, bare fields, "patches"
Active Cultivation Woody Crops: tea, coffee, orchards
Ag. Overhead: tracks, pathways, field dividers
Grass fallow
Fallow
Bush fallow
Managed Herbaceous Cover Managed pastures, grassy areas around compounds and fields
Plantations, woodlots
Managed Woody Cover Scattered trees in crops, roads, homesteads, hedgerows
Hedgerows: natural, planted, windrows, tree hedges
Indigenous forest, riparian strips
Natural Woody Vegetation Scattered tress in bushland and grassland
Bush cover (non-tree woody vegetation)
Natural Herbaceous Cover Grass dominated areas (not managed pastures or fallow)
Number of compounds
Number of traditional roofs
Infrastructure Number of modern roofs
Compound area
Roads, tracks and pathways (NOT in crops)
Miscellaneous Bare areas, rock outcrops, rivers, lakes
Landscape Managed (1), Mixed (2), Natural (3)
Land Management Good (1), Medium (2), Poor (3)
Infrastructure quality Good (1), Medium (2), Poor (3)

48. Extensification of Agriculture

49. Changes to Active Cultivation and
Fallow Land

50. Changes to Natural Vegetation

51. Other Indices of Change
1983 2010
Field dividers
& Hedgerows 38.6 50.1
(km/ km-2)
Ratio Modern
: Traditional 1:8.5 1:0.3
Roofs

52. Trees on Farms

53. Land Tenure as a Driver of Change

54. Comparison Between Remote Sensing
Platforms
• Objectives:
– What is the most efficient remote sensing
platform for the Phase 2 activities
– Can Quickbird be used for the inventory and
monitoring of agro-forestry components within
the ICRAF sentinel sites

55. Which Platforms and Technology?
– Compare quickbird visually generated data against
high resolution digital photography
– Visual compared with computer classification of
tree density, and “trees on farms”
– Visual compared with computer classification of
landcover and landuse

58. 2010 Land-use/Landcover mapping Western Kenya Integrated Ecosystem
Management Project (WKIEMP)

59. Reporting and Databases
• Report on Phase 1 will cover methodology,
preliminary results and the resource
requirements for the Phase 2 study of the
entire LBDA area
• Regional data are already on-line, more layers
are being prepared
• All bock, grid and point data will be available
in Xcel format, with complete geo-referencing
for use by LBDA and ICRAF researchers

60. LBDA / ICRAF Phase II
• Yes, robust and useful descriptors of land cover and land
use change can be derived from the 1983 and the 2010
Quickbird imagery.
• Yes, regional data on potential drivers are readily
quantifiable in both time and space
• However, higher resolution imagery (digital aerial
photography) will produce significantly better results than
Quickbird imagery and will offer a much more flexible
approach to re-sampling
• Yes, it is possible to design a re-sampling programme (using
either Quickbird or digital aerial photography) to analyse
the drivers of landcover and landuse change over the last
30 years.

61. Implications for ICRAF
• While Quickbird imagery is appropriate for the survey and
monitoring of certain agro-forestry components within the
complexity of the African smallholder production systems,
the finer details of the agro-forestry components cannot be
consistently identified.
• Visual analysis of Quickbird sample sites is an essential
component to the development and testing of computer
models to monitor landcover and landuse
• Appropriate methods are available for the efficient
selection of sentinel landscapes and sites
• To monitor internal (project) or external (climate) impacts,
the dynamics and trajectory of the system must be
understood