Transcript Integration of GIS, Remote Sensing and USLE to Estimate

Ernest W. Tollner
Herbert Ssegane
Tommy Jordan
The process…
Define the relationship
Assemble the data sources
Process the data and assemble a
GIS layer
Compute the composite layer
Remap based on an index
Nzoia
Basin
A  RKSLCP
Where
A = Average Annual Soil loss in (Mg ha-1)
R = Rainfall –Runoff erosive index factor in (MJ mm ha-1hr-1yr-1)
K = Soil erodibility factor (ton ha hr ha-1MJ-1mm-1)
S = Slope steepness factor (dimensionless)
L = Slope Length factor (dimensionless)
C = Crop-Management factor (dimensionless)
P = Conservation Practice factor (dimensionless)
The annual rainfall data for Kenya was obtained
from flood early warning system (FEWS) dataset,
a program developed jointly by the USGS and
USAID. The data was accessed at USGS (2007).
The soils data used was obtained in a binary
format from the north oceanic and atmospheric
administration (NOAA) at a spatial resolution of
0.0833 arc degrees. The data was accessed at
national geophysical data center (NGDC, 2007).
The Digital Elevation Model (DEM) data was
obtained from the Global land covers facility
(GLCF) under the shuttle radar and thematic
mapper (SRTM) data category. Resolution was 90
m.
The US Geological Survey (USGS) provides global
land use and land cover data on continental
scale with a spatial resolution of one kilometer.

0.0483P1.610 ,
P  850mm 
R

2
587.8  1.249 P  0.004105P , P  850mm 
WhereR = rainfall erosivity (MJ mm ha-1 hr-1 yr-1), and
P = annual precipitation (mm)

K  0.02930.65  Dg  0.24Dg
2
2




OM
OM

2
  4.02 f clay  1.72 f clay 
exp 0.0021
 0.00037

f clay
f clay 






Dg  3.5 f clay  2.0 f silt  0.5 f sand
Where K = soil erodibility (ton ha hr ha-1MJ-1mm-1)
OM = percent organic matter
fsand = sand fraction
fsilt = silt fraction
fclay = clay fraction
Dg = the geometric mean of particle size
Sand percent
Clay percent
Silt percent
Organic matter percent
  FlowAccumulation * CellSize
     CellSize
 
 1.4
L
  1.4
CellSize * 22.130.4
0.43  0.30s  0.043s 2 
S
6.613
Where
s = the percent slope
Crop management
factor, C
Management practice
Factor, P
Urban and built-up land
0.01
1
Dryland cropland and pasture
0.013
0.1
Irrigated cropland and pasture
0.013
0.1
Cropland / grassland mosaic
0.3
0.12
Cropland / woodland mosaic
0.3
0.12
Grassland
0.04
0.12
Shrubland
0.036
0.12
Savanna
0.039
0.12
Deciduous broadleaf forest
0.006
0.8
Evergreen broadleaf forest
0.006
0.8
Herbaceous wetland
0
1
Wooden wetland
0
1
0.4
1
Land classes
Barren and sparsely vegetated
Soil
Soil Erosion
Erosion
class
(tons ha-1yr1)
0-5
Slight
5 - 10
Moderate
10 - 20
High
20 - 80
Very High
> 80
Severe
Erosion
Map
Index
1
2
3
4
5
Renard and Freimund (1994)
Since most of Nzoia River basin (84.2 %) is
under slight agricultural erosion hazard, other
erosion sources such as unpaved and poorly
graded roads plus point sources should be
investigated.
Results from the erosion estimation showed on
average, potential erosion highest in the cropland
and woodland mosaic (180 Mg ha-1 yr-1) and
lower in deciduous broadleaf forest (23 Mg ha-1
yr-1) and shrubland (22.5 Mg ha-1 yr-1).
Conclusions
Data resolution is critical, and this problem is
being addressed as more data is collected or one
pays more.
GIS and remote sensing platforms provide fast
and robust tools for spatial modeling and are
useful for strategic land use management and
environmental monitoring.
GIS related tools offer tremendous educational
opportunities for engaging students.
Questions