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2.6.2. Difference between RUSLE and USLE
The USLE Wishmeier and Smith, 1978 is the most widely used model in predicting soil erosion. It is used in education and research as a starting point in
developing understanding of erosion hazard prediction because of its simplicity and clarity Hagos, 1998. Many scientists have proposed changes, but all are
woven around the same concept of rainfall erosivity, soil erodibility, slope length, slope class, land cover and land management factors are taken as directly
proportional to the rate of annual soil erosion Sohan and Lal, 2001. RUSLE is a revised version of USLE, intended to provide more accurate
estimates of erosion Renard et al., 1994. It contains the same factors as USLE, but all equations used to obtain factor values have been revised. It updates the
content and incorporates new material that has been available informally or from scattered research reports and professional journals. The major revisions occur in
the C, P, and LS factors. The cover factor C and management factor P in RUSLE consider not only agricultural land but also multifunctional land use type
and management. The slope length and aspect gradient factor combine to become slope length and steepness factor LS in RUSLE.
2.7. High Conservation Value Forest HCVF
One of the Forest Stewardship Council FSC principles is the management of High Conservation Value Forest HCVF. This is relatively a new
principle, which has been developed to replace the previously used concept of old growth or virgin forest. Through this principle, FSC requires unique approach in
managing forest ecosystem and conserving the biodiversity value FSC 2001. In this case, forest management unit should maintain or enhance such value, not
21 preserve it. The key of HCVF principle is the concept of conservation values.
HCVF have nine principles. The use of remote sensing and spatial information to support identification
of HCVF is certainly potential. Some of the HCVF elements could be assessed through the remote sensing and GIS analysis resulting the location of forest area
containing some High Conservation Values HCV. One HCV element, which is potentially assessed by the support of remote sensing and GIS is HCVF principle
four. The principle four HCVF 4 mention that “Forest areas which provide basic services of nature in critical situations e.g. watershed protection, erosion
control”. That principle including 3 sub – factors, i.e. • Functions as unique source of drinking water for local communities HCV 4.1
• Part of critical major catchments HCV 4.2 • Has critical erosion risk HCV 4.3
Ancillary data that can be used are topographic information and its derived products DEM, slope map and other terrain features to support prediction of
potential soil erosion risk after Rainforest Alliance and ProForest 2003.
2.8. Accuracy of Modeling in GIS Environment
Accuracy is the degree of likelihood that the information provided is correct. This definition focuses on two components of accuracy. The first and
more familiar aspect of accuracy is that it predicts the proportion of information that is expected to be correct or the magnitude of error to be expected. The second
and often ignored aspect of accuracy is that it involves a probability. When a map or other data set is asserted to be 80 accurate it means that when the data set is
used, it can be expected that on average 80 of the information will be correct.
22 The measure of this probability of having a higher or lower accuracy than
expected is termed the level of confidence. So, when a map is rated 80 accurate with a 90 level of confidence it means that if a large number of accuracy tests
were done on the map, then 80 or more of the test points would be correct in 9 out of every 10 tests. The level of accuracy depends on the information to be
provided and the level of detail required. An acceptable level of accuracy is that level where the costs of making the wrong decision are equal to the costs of
acquiring more accurate information. In the GIS environment, map accuracy depends on many factors. At the
micro level, there are components such as positional accuracy, attribute accuracy, logical consistency, and resolution. At the macro level, there are components such
as completeness, time, and lineage. Finally usage components are accessibility and direct or indirect costs. There are also different sources of errors associated
with all geographic information. Some of the more common errors are related to data collection, data input, data storage, data manipulation, data output, and the
way of using and understanding results. Paper data such as different maps and associated geographic attributes and
data are used as one of the sources of input data to the GIS environment. In this process the paper data are converted to digital data. The level of accuracy of the
digital data will be the same as paper data if they are correctly converted to the digital form with a suitable package in an acceptable resolution. Once the data are
converted, the accuracy of the output data resulting from different manipulations depends on the resolution power of the software done with the skill of the
operator.
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2.9. Validation of soil erosion models
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