assessment of soil erosion using RUSLE modelling by RS & GIS

1. Hello!
I am kaushal Gadariya
You can find me at kaushalgadariya@gmail.com

2. Assessment of soil erosion by
RUSLE model using remote
sensing and GIS

3. CONTENTS
o What title says?
o What is erosion?
o Types of erosion
o Causes of erosion
o Global scenario
o Indian scenario
o Erosion modelling
o Types of models
o Models comparison
o What is remote sensing?
o What is GIS?
o Need of RS & GIS in RUSLE
o RUSLE equation
o RUSLE factors description
o Sensors data for RUSLE modelling
o Framework of RUSLE model in GIS
o Spatial data availability
o Conclusion
o References

4. RUSLE
Soil
Erosion
RS & GIS
What title says?

5. “Soil Erosion can be defined as a method
of detachment, transportation of surface
soil particles from its origin and deposition
at some other area.

6. “Soil loss refers to that material actually
removed from the particular hill slope or
slope segment which may be less than
erosion due to onsite deposition in micro-
topographic depressions (Toy & Renard
1998).

7. Types of Soil Erosion
Geological
Erosion
Accelerated
Erosion
Wind Erosion
Water Erosion
Splash
Sheet
Rill
Gully
Stream bank

8. Why it happened?
due to natural
process
increased by
human
actions
poor land
use
Practices such as
deforestation,
overgrazing,
mining,
unmanaged
construction
activities and
road-building etc.

9. Effects of soil erosion globally
It is estimated that one-sixth of the world’s soils area affected by
water erosion.
(Walling and Fang, 2003).

10. 1094 M ha
Globally human impact on land around
of which 43% suffer from deforestation and the removal of natural
vegetation, 29% from overgrazing, 24% from improper management of
the agricultural land and 4% from over-exploitation of natural
vegetation.
(Walling and Fang, 2003).

11. Soil erosion in India
It is estimated that in India, out of the total geographic area of 328 million hectares,
about 187 million hectares is subjected to varying degree of water erosion problems
(anonymous, 1996).
Heavy sedimentation of reservoirs and dams reduce their live storage capacity and
also reduce their life span thereby leading to an alarming situation of the serious
overpowering flow in the country.
It has been predicted that about 5334 million tons of soil is lost yearly due to runoff.

12. IIRS Report
2015 ▪ According to a 2015 report of the
Indian Institute of Remote Sensing
(IIRS), the estimated amount of soil
erosion that occurred in India was
147 million hectares.
▪ 29 percent of the soil that is eroded
is lost in the sea while 61 percent is
just relocated.
9 M ha from
wind erosion
14 M ha form
flooding
16 M ha from
acidification
94 M ha from
water erosion

13. Soil erosion modelling
Soil erosion modelling is able to deal with many of the complex interactions
that control erosion rates by simulating erosion in the watershed.
Specialists have created many prescient models that estimate soil loss and
discover areas where conservation measures will have the best drive on
reducing soil loss for soil erosion as-assessments.

14. Models
Conceptual (semi
empirical)
Statistical (empirical)
USLE MUSLE RUSLE
Physical
(deterministic)

15. ▪ The Universal Soil Loss Equation (Wischmeier and
Smith 1978) is an experimental model based on
comprehensive information from the USA.
▪ This equation is basically designed and commonly used
to predict average annual soil loss.
USLE

16. ▪ In 1977, Williams and Berndt built up the Modified
Universal Soil Loss Equation (MUSLE), it is a changed
version of USLE.
▪ While USLE derives sediment yield based on
precipitation, MUSLE conclude it by utilizing runoff
factor, which performs the antecedent soil water
content.
▪ This alteration permits the utilization of USLE for
predicting sediment loss on a storm event premise.
MUSLE

17. ▪ RUSLE is an equation that estimates average annual
soil loss BY sheet and rill erosion on those portions
of landscape profiles where erosion, but not
deposition, is occurring.
▪ It is the upgraded equation of USLE.
RUSLE

18. COMPARISON
• It estimate the average annual soil loss from an area.
• It uses rainfall erosivity factor generated from annual rainfall data.
USLE
• It replaces the rainfall erosivity factor with Runoff factor.
• It estimate event wised soil loss.
MUSLE
• It similar as USLE, but the factors were upgraded with higher accuracy.
RUSLE

19. Upgrades in RUSLE
 A greatly expanded erosivity map.
 Minor changes in R factors.
 Expanded information on soil
erodibility.
 A slope length factor that varies with
soil susceptibility to rill erosion.
 A nearly linear slope steepness
relationship that reduces computed
soil loss values for very steep slopes.
 A sub factor method for computing
values for the cover-management
factor.
 Improved factor values for the
effects of contouring, terracing, strip
cropping, and management practices
for rangeland.

20. What is Remote
Sensing?

21. What is Remote Sensing?
 Remote sensing means
obtaining information
About an object, area or
phenomenon without
coming in direct contact
with it.

22. “ According to UN, 95th planetary
meeting held on 3rd October 1986,
“Remote sensing means sensing of
surface from space by making use of
the properties of electromagnetic wave
emitted, reflected or diffracted by the
sensed object”.

23.  Infrared image in which
vegetation seen in red
colour.
Satellite image

24. What is GIS?

25. A geographic information system (GIS) is a framework for gathering, managing, and
analysing data. Rooted in the science of geography, GIS integrates many types of data. It
analyses spatial location and organizes layers of information into visualizations using
maps and 3D scenes. With this unique capability, GIS reveals deeper insights into data,
such as patterns, relationships, and situations—helping users make smarter decisions.
Environmental Systems Research Institute (ESRI), International supplier of GIS software
A geographic information system (GIS) is a computerized system designed to
capture, store, manipulate, analyse, manage, and present spatial or geographic
data.
Wikipedia

26. Need of RS & GIS in RUSLE
modelling?
One of the major drawbacks in the application of erosion models is the low
availability of input data.
The conventional methods proved to be too costly and time consuming for
generating this input data.
With the advent of remote sensing technology, deriving the spatial information on
input parameters has become more handy and cost-effective.
Multi-temporal satellite images provide valuable information related to seasonal land
use dynamics, erosional features, such as gullies, rainfall interception by vegetation,
and vegetation cover factor.

27. RUSLE EQN.
𝑨 = 𝑹 ∗ 𝑲 ∗ 𝑳𝑺 ∗ 𝑪 ∗ 𝑷
A= spatial average soil loss (t /ha/Year)
R= rainfall erosivity factor (MJ.mm/ha/h/year)
K= soil erodibility factor (t.h/MJ/mm)
LS= slope length and steepness factor
C= cover – administrative factor (0 to 1)
P= support practice factor (0 to 1)

28. RFactor
definition
 It quantifies the effect of rainfall impact and also reflects the amount and rate of
runoff likely to be associated with precipitation events. (Xu, Shao, Kong, Peng, &
Cai, 2008)
Unit  MJ.mm/h/ha/year
Data source  Rainfall data from Indian Meteorological Department (IMD).
Approach
 Mean annual precipitation/ Modified Fourier index equation (Arnoldus, 1977,
1980)
Equation
𝑀𝐹𝐼 =
σ𝑖=1
12
𝑝𝑖
2
σ𝑖=1
12
𝑝
Where, MFI is the modified Fourier Index (mm), Pi is the monthly precipitation (mm),
and P is the mean annual precipitation (mm).
𝑅 = ෍
𝑖=1
12
1.735 ∗ 10(1.5∗𝑙𝑜𝑔𝑀𝐹𝐼−0.8188)

29. KFactor
definition
 It refers to the inherent susceptibility of soil to erosion by rainwater and runoff.
(Thomas, Joseph, & Thrivikramji, 2017).
 It depends on physical properties such as particle size distribution, organic
matter, soil structure, permeability.
Unit  t.h/MJ/mm
Data source
 soil map data from NBSS & LUP (The National Bureau of Soil Survey and Land Use
Planning, India).
Approach
 nomograph and its equation is used to determine K factor for a soil based on its
texture; % silt plus very fine sand, % sand, % organic matter, soil structure, and
permeability (Wischmeier and Smith, 1978).
Equation
𝐾 =
2.1 ∗ 10−4
12 − 𝑂𝑀 𝑀1.14
+ 3.25 𝑆 − 2 + 2.5(𝑃 − 3)
7.59
∗ 100
Where,
OM is soil organic matter content, M is product of the primary particle size fractions,
(%Silt + %Very fine sand) X (100 - % clay), S is soil structure code, P is permeability
class.

30. KFactor

31. LSFactor
definition
 It is the ratio of soil loss per unit area from a field slopes to that from a 22.33m
length of uniform 9% slope under otherwise identical conditions (Wischmeier &
Smith, 1978).
Unit  dimensionless
Data source  Digital Elevation Model (CARTO DEM- 30m Resolution). www.bhuvan.nrsc.gov.in
Approach
 Several equations for deriving the slope length (L) and steepness factors (S)
collectively called LS-factor.
 One of the equation used in GIS operation was given by (Moore and Burch, 1986).
Equation
𝐿𝑆 = 𝑓𝑙𝑜𝑤 𝐴𝑐𝑐𝑢𝑚𝑢𝑙𝑎𝑡𝑖𝑜𝑛 ∗
𝐶𝑒𝑙𝑙 𝑠𝑖𝑧𝑒
22.13
0.4
∗
sin 𝑆𝑙𝑜𝑝𝑒
0.0896
1.3
Where,
Flow accumulation represent the contribution of an area accumulated upslope for a
given cell, Cell Size is the size of the grid cell, and Sin Slope is the slope degree value
in sin.

32. LS Factor
open GIS
interface
Add DEM
layer
fill
•Arc tool box
•spatial analyst tool
•hydrology
•fill
flow direction
•hydrology
•flow direction
flow
accumulation
•hydrology
•flow accumu.
slope
•hydrology
•slope
LS factor
•spatial analyst tool
•map algebra
•raster calculator
Equation: LS= Power(“fac”*90/22.1,0.4)* Power(Sin(“slope”*0.01745)/0.09,1.4)*1.4
formula: LS= (“LS1”/100)

33. CFactor
definition
 The C factor is one important erosion factor that can most easily be influenced by
humans to reduce erosion (McCool, Foster, Renard, & Weesies, 1995).
 Defined as the ratio of soil loss under specific cropping conditions to soil loss
occurring in bare soil (Alkharabsheh et al., 2013; Wischmeier & Smith, 1978).
 the C-factor reflects the effect of cropping and other management practices on
erosion rates (Uddin, Murthy, Wahid, & Matin, 2016).
Unit  Range (0-1). zero for a well-protected land cover to 1 for barren areas.
Data source
 Satellite images of LULC such as IRS LISS- 3, Sentinel-2, eMODIS NDVI,
LANDSAT.
Approach
 Realistic c- values can be obtained with NDVI time series equation for monitoring
c factor (Durgion et al., 2014) especially for tropical regions.
Equation 𝐶𝑟 =
−𝑁𝐷𝑉𝐼 + 1
2
Where, Cr is the dominated rescaled C-factor.

34. PFactor
definition
 It is the ratio of soil loss with a specific support practice to the corresponding loss
with up slope and down slope cultivation (Wischmeier & Smith, 1978).
 The map generated is used for understanding the conservation practices being
taken up in the study area.
Unit
 Range (0-1). the value approaching to 0 indicates good conservation practice and
the value approaching to 1 indicates poor conservation practice.
Data source  DEM and LANDSAT images for slope and land use map.
Approach  empirical Equation that uses steepness factor given by (Wener, 1981)
Equation
𝑃 = 0.2 + 0.03 ∗ 𝑆
Where, S is the slope steepness (%)., this equation gives better result in tropical
regions.

35. Sensors used in RUSLE Erosion modelling

36. Conceptual
framework of soil
loss analysis by
RUSLE model

37. SPATIAL DATA AVAILABILITY

38. https://earthexplorer.usgs.gov/

39. https://bhuvan-app1.nrsc.gov.in/thematic/thematic/index.php

44. CONCLUSION
▪ Use of GIS techniques to measure the soil loss can be more
authenticate and reliable with high-resolution spatial data. The
powerful spatial processing capabilities of GIS and its capability with
remote sensing data have made the soil erosion modelling
approaches more comprehensive and robust.

45. REFERENCES
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Planetary Change, 39: 111-126.
▪ Toy, T.J. and Renard, K.G. 1998. Guidelines for the use of the Revised Universal Soil Loss Equation (RUSLE) version 1.06
on Mined Lands, Construction Sites and Reclaimed Lands. Office of Surface Mining, Denver.
▪ http://www.yourarticlelibrary.com/soil/soil-erosion-paragraphs-on-soil-erosion-in-india/13895
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46. REFERENCES
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48. Thanks!
!
Any questions?
You can find me at kaushalgadariya@gmail.com