Analysis of the Drivers of Landcover and Landuse Change in Western Kenya over the Last 30 years

Analysis of the Drivers of Landcover and Landuse Change in Western

Kenya over the Last 30 years

Mike Norton-Griffiths & Harvey Herr (Jnr)

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

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

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

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

KATUK ODEYO Flight Lines and Photos Strong features for control points

XX -->26 27 28

1491S 1502N 1511N 1521S

28

8 6 61 7 5 71 62 6 4 72 63 5 3 73 64 4 2 74

27

65 3 1 1 366 2 2 35 467 1 66 3 34 5 1 65 4 33 6

26

36 2 64 5 32 735 3 63 6 31 834 4 62 7 30 9 5 29 10

2492N 2501S 2511N 2521S 2531N 2541S

OID Record # CODE XX YYUTM

EastingUTM

NorthingICRAF BLOCK

NameFilm

PhotoDirection Scanned Control Georef

28 949 2628 26 28 722500 9972500 4 Katuk Odeyo 1491-0061 S Y28 949 2628 26 28 722500 9972500 4 Katuk Odeyo 1491-0062 S Y28 949 2628 26 28 722500 9972500 4 Katuk Odeyo 1491-0063 S Y27 948 2627 26 27 722500 9967500 4 Katuk Odeyo 1491-0064 S N 327 948 2627 26 27 722500 9967500 4 Katuk Odeyo 1491-0065 S N 327 948 2627 26 27 722500 9967500 4 Katuk Odeyo 1491-0066 S Y 327 948 2627 26 27 722500 9967500 4 Katuk Odeyo 1491-0067 S Y 327 948 2627 26 27 722500 9967500 4 Katuk Odeyo 1502-0001 N Y 327 948 2627 26 27 722500 9967500 4 Katuk Odeyo 1502-0002 N Y 127 948 2627 26 27 722500 9967500 4 Katuk Odeyo 1502-0003 N Y Y 127 948 2627 26 27 722500 9967500 4 Katuk Odeyo 1502-0004 N Y 328 949 2628 26 28 722500 9972500 4 Katuk Odeyo 1502-0005 N Y28 949 2628 26 28 722500 9972500 4 Katuk Odeyo 1502-0006 N Y28 949 2628 26 28 722500 9972500 4 Katuk Odeyo 1502-0007 N Y28 949 2628 26 28 722500 9972500 4 Katuk Odeyo 1502-0008 N Y Y30 1000 2727 27 27 727500 9967500 4 Katuk Odeyo 1511-0001 N Y 130 1000 2727 27 27 727500 9967500 4 Katuk Odeyo 1511-0002 N Y 131 1001 2728 27 28 727500 9972500 4 Katuk Odeyo 1511-0003 N Y

GEOREFERENCING THE 1983 AERIAL PHOTOS

LOCATION KNOWN WITHIN ~ 5km2

KATUK ODEYO 1983, AERIAL PHOTO ID: 1521_0074S


XX -->26 27 28

1491S 1502N 1511N 1521S

28

8 6 61 7 5 71 62 6 4 72 63 5 3 73 64 4 2 74

27

65 3 1 1 366 2 2 35 467 1 66 3 34 5 1 65 4 33 6

26

36 2 64 5 32 735 3 63 6 31 834 4 62 7 30 9 5 29 10

2492N 2501S 2511N 2521S 2531N 2541S

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

KATUK ODEYO 1983, AERIAL PHOTO ID: 2492_0035N


XX -->26 27 28

1491S 1502N 1511N 1521S

28

8 6 61 7 5 71 62 6 4 72 63 5 3 73 64 4 2 74

27

65 3 1 1 366 2 2 35 467 1 66 3 34 5 1 65 4 33 6

26

36 2 64 5 32 735 3 63 6 31 834 4 62 7 30 9 5 29 10

2492N 2501S 2511N 2521S 2531N 2541S

SOME DIFFICULT EXAMPLES:

ESTIMATING THE LOCATION OF OTHER (UN-GEOREFRENCED) SAMPLES~ 17 sample sites, LOCATIONS???


XX -->26 27 28

1491S 1502N 1511N 1521S

28

8 6 61 7 5 71 62 6 4 72 63 5 3 73

64 4 2 74

27

65 3 1 1 366 2 2 35 4

67 1 66 3 34 5

1 65 4 33 6

26

36 2 64 5 32 735 3 63 6 31 834 4 62 7 30 9 5 29 10

2492N 2501S 2511N 2521S 2531N 2541S

FLIGHT LINES CREATED FROM GEOREFERENCED SITES & ASSUMED FLIGHT PATH


XX -->26 27 28

1491S 1502N 1511N 1521S

28

8 6 61 7 5 71 62 6 4 72 63 5 3 73

64 4 2 74

27

65 3 1 1 366 2 2 35 4

67 1 66 3 34 5

1 65 4 33 6

26

36 2 64 5 32 735 3 63 6 31 834 4 62 7 30 9 5 29 10

2492N 2501S 2511N 2521S 2531N 2541S

Estimated sample sites on the same flight path are equidistant from each other, the distance determined by the length of the flight line within the grid square or 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.

ESTIMATED SAMPLE SITES CREATED BASED ON LOCATION RELATIVE TO KNOWN SITES & GRID


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.

Active CultivationFields with crops, harvested, bare fields, "patches"Woody Crops: tea, coffee, orchardsAg. Overhead: tracks, pathways, field dividers

Fallow Grass fallowBush fallow

Managed Herbaceous Cover Managed pastures, grassy areas around compounds and fields

Managed Woody CoverPlantations, woodlotsScattered trees in crops, roads, homesteads, hedgerowsHedgerows: natural, planted, windrows, tree hedges

Natural Woody VegetationIndigenous forest, riparian stripsScattered tress in bushland and grasslandBush cover (non-tree woody vegetation)

Natural Herbaceous Cover Grass dominated areas (not managed pastures or fallow)

Infrastructure

Number of compoundsNumber of traditional roofsNumber of modern roofsCompound areaRoads, 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)

Extensification of Agriculture

Changes to Active Cultivation and Fallow Land

Changes to Natural Vegetation

Other Indices of Change

1983 2010

Field dividers & Hedgerows (km/ km-2)

38.6 50.1

Ratio Modern : Traditional Roofs

1:8.5 1:0.3

Trees on Farms

Land Tenure as a Driver of Change

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

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

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

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

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.

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

Analysis of the Drivers of Landcover and Landuse Change in Western Kenya over the Last 30 years

Technology

Transcript of Analysis of the Drivers of Landcover and Landuse Change in Western Kenya over the Last 30 years