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         Geoplanning 
Vol 4, No. 2, 2017, 131-142                                                                                                                                                        Journal of Geomatics and Planning 

                                                                                                 E-ISSN: 2355-6544 
http://ejournal.undip.ac.id/index.php/geoplanning 

doi: 10.14710/geoplanning.4.2.131-142 

REMOTE SENSING AND GIS APPROACHES TO A QUALITATIVE ASSESSMENT OF SOIL 
EROSION RISK IN SERANG WATERSHED, KULONPROGO, INDONESIA  

N. Arif a,b, P. Danoedoro b, H. Hartono b 

a Faculty of Science and Technology, Universitas Muhammadiyah Gorontalo, Indonesia 
b Faculty of Geography, Universitas Gadjah Mada, Yogyakarta, Indonesia 

Abstract: This research aims to determine the risk of soil erosion qualitatively by 
integrating remote sensing with the geographic information system. Factors that 
contributed to the occurrence of erosion in the area of study were analyzed using the 
method of the variation of combined input data of the factors controlling erosion (soil, 
climate, topography, vegetation, and humans). The input data were quantitative data 
changed into qualitative data that were obtained from field data and extracted from 
remote sensing imagery, i.e. SPOT 5. A number of parameters were calculated using 
the RUSLE model equation. The model was validated by observing the qualitative 
erosion indicators in the field (pedestal, tree root exposure, armor layers, rill erosion, 
and gully erosion) by observing slope steepness in each sample area. The area of study 
was Serang watershed located in Kulon Progo Regency, Yogyakarta. It is one of the 
critically potential watersheds viewed from the landform and land use. The results of 
various combinations generated the highest of accuracy by 90.57 % with extremely 
erosion dominating the area of study. The factors with the highest contribution to 
erosion in Serang Watershed were slope length and steepness (LS) and erodibility (K).  
 

 Copyright © 2017 GJGP-UNDIP  
This open access article is distributed under a  

Creative Commons Attribution (CC-BY-NC-SA) 4.0 International license. 
 
 

How to cite (APA 6th Style): 
Arif, N., Danoedoro, P., & Hartono, H. (2017). Remote Sensing and GIS Approaches to a Qualitative Assessment of Soil Erosion Risk in Serang 
Watershed, Kulon Progo, Indonesia. Geoplanning: Journal of Geomatics and Planning, 4(2), 131-142. doi:10.14710/geoplanning.4.2.131-142 

 

1. INTRODUCTION  

Soil erosion is one of the indicators of land quality due to the destructive effect it has on land and the 
effect of reduced productivity of land (Morgan, 2009; Parveen & Kumar, 2012). Erosion affects the 
sustainability of agricultural production on a global scale (Bouaziz, Leidig, & Gloaguen, 2011). An 
assessment of erosion in an area is vital in order to evaluate land management and to provide a basis for 
land users and decision makers with regard to land conservation efforts and environmental monitoring. 
Numerous researches have been conducted, especially in the field of applied environment, including the 
research in erosion and landslides. These researches integrated remote sensing with the geographic 
information system which managed to generate more accurate and effective predictions (Asis & Omasa, 
2007; Liao et al., 2012; Pradhan, Lee, & Buchroithner, 2010; Pradhan & Saro, 2007).  

Remote sensing and GIS can be used to generate information about the variables associated with the 
erosion calculation formula. There are many factors that contribute to erosion, namely rainfall, vegetation, 
topography, soil, and land use, all of which were used as the basis for assessing the erosion risk. This 
research relied on remote sensing data to obtain landscape information such as vegetation and land use 
while GIS was implemented to process, simulate scenarios, and visualize modeling results. The SPOT 5 
imagery was used in this study because it offers a higher resolution of 2.5 to 5 meters in panchromatic 
mode and 10 meters in multispectral mode providing potential solutions in the study of natural resources. 
This is due to its capacity in covering vast areas such as area of study, as well as having channels that can 
decrease vegetation information through index C as one of the model inputs. 

Article Info: 
Received: 17 February 2017 
in revised form: 06 May 2017 
Accepted: 7 July 2017 
Available Online:  30 October 2017 
 

Keywords:  
Remote sensing, Serang Watershed, 
soil erosion 
 

Corresponding Author: 
Nursida Arif 
Faculty of Science and Technology, 
Universitas Muhammadiyah 
Gorontalo, Indonesia 
Email: nursida.arif@gmail.com  

OPEN ACCESS 

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Most researches on soil erosion in the area of study were conducted using quantitative approaches to 
determine the amount of soil eroded in tons per hectare (Dibyosaputro et al., 2012; Santoso & Senawi, 
2012; Widarsih & Senawi, 2012) and it is not common to assess erosion qualitatively. Basically, the 
qualitative approach employed in these researches was a combination of the quantitative approach. 
Factors controlling erosion were calculated using the RUSLE model equation and qualitatively divided into 
several classes. The output of the model was in the form of a qualitative map of the erosion risk without 
information about the amount of soil loss. A model is considered quantitative when the values are 
mathematically combined to provide an index at a certain scale (la Rosa & Van Diepen, 2002). The 
numerical value of a variable may change at a certain period, unlike a qualitative assessment which tends 
to be more constant and unchanged (Bredeweg et al., 2009). 

In this research, the field validation was qualitatively conducted by developing the formula for the 
assessment of the qualitative indicators of erosion. A high rate of erosion can be seen from the erosion 
indicators such as pedestal, armor layers, tree root exposure, rill erosion, and gully erosion (Stocking & 
Murnaghan, 2000). To develop a quantitative model that can accurately represent the real condition in the 
field, it is necessary to conduct validation through detailed measurements for a long period of time 
requiring higher costs. In fact, the use of the erosion plot was rarely calibrated with the local condition and 
many used less realistic assumptions resulting in less reliable measurement results (Bergsma, 2008). This 
short coming makes qualitative methods reliable as a quick solution to predict erosion (Bouaziz, Leidig, & 
Gloaguen, 2011; Desmet & Govers, 1995). Ypsilantis (2011) stated that qualitative models of area method 
are effective and more affordable. They can be implemented in a larger area within a relatively short period 
of time, unlike quantitative methods that require intensive and more detailed monitoring of particular land 
conditions. This is in accordance with the conditions in Indonesia where the technical facilities and history 
of actual erosion measurement are minimal as shown in the study area. So that method is needed which 
can be the solution of the limitation with low cost and efficient but more accurate that is through 
qualitative based modeling by utilizing remote sensing image and geographic information system. It is 
expected that the method of fast assessment will be able to quickly locate which erosion-prone areas 
whose conservation should get priority. 

2. DATA AND METHODS 

2.1. Study Area 
The research was undertaken in Serang Watershed which is situated between Progo Watershed and 

Bogowonto Watershed in Kulon Progo Regency, the Province of Yogyakarta Special Region. Geographically, 
it is located at 7°43’40” S - 7°55’30” S and 110°03’49” E - 110°13’50” E. Administratively, it is located in 
Kulon Progo Regency, which includes several subdistricts, namely Wates, Sentolo, Temon Pengasih, Kokap, 
Girimulyo, and some area of Panjatan and Nanggulan subdistricts. 

Based on the monthly rainfall data from 2004 to 2014 in 11 rainfall stations around Serang Watershed, 
the wet season occurred from November to April while the dry season occurred from May to October. 
Most stations in Serang Watershed fell into Category D, i.e. in a temperate climate. Most land in the area of 
study is utilized as mixed farms. In regard to the landform, the area of study is considered as an erosion-
prone area comprised of denudation-generated hills, which were formerly a volcano, and structural hills 
(Figure 1).  

2.2. Methods 
The variables employed to construct the model were the extraction of factors affecting erosion, 

namely climate, soil, vegetation, and humans. Field observation of the qualitative indicators of erosion was 
undertaken instead of quantitative calculations of the actual erosion for validation of the model. This 
research employed the same approach of qualitative methods conducted by Bouaziz, Leidig, & Gloaguen 
(2011), namely trials on several combinations of factors controlling erosion to determine the most 
influential factor in the area of study. The input data set used in this modeling were factors influencing 
erosion, i.e. erosivity (R), erodibility (K), slope length and steepness (LS), vegetation coverage and 
management (C), land management (P). 

 
 

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Figure 1. Appearance of Landforms (SPOT, 2014 and Analysis, 2016) (a) Structural rocky hills of andesite 

breccia (401494 mT, 9140214 mU), (b) and (c) Structural rocky hills of andesite (399202mT, 913234 mU), 

(d) Alluvial plain (401908 mT, 9,129279 mU), (e) Sermo reservoir, (f) Hills of the remains of a volcano 
(400833 mT, 9139855 mU) 

   
(1). Rainfall erosivity factor (R) 

Erosivity index was calculated using 10 years of daily rainfall data from 13 rain stations around the 
research location, Utomo (1994) was calculated erosivity index using the equation (1) by Bols: 

𝑅 = 6,12(𝑅𝐴𝐼𝑁)1,21𝐷𝐴𝑌𝑆−0,47𝑀𝐴𝑋𝑃0,53     ……………………………………………………………………………… (1) 
 
Where, RAIN = average annual rainfall (cm), DAYS = total day of average rain per year (day), MAXP= 
maximum average rainfall in 24 hours per month within one year (cm), Rm = monthly erosivity index, Ry 
= annual erosivity index 
 

(2). Soil erodibility factor (K) 
K value was determined by the equation (2) used in RUSLE model developed by Renard et al. (1991) as 
follows: 

         K =        7.594 {0.0017 + 0.049 exp [−
1

2
(

log(𝐷𝑔)+1.675

0.6986
)

2
]}  …………………………………………………….. (2) 

 
Where, K = soil erodibility, Dg=Diameter of soil geometric particle (mm) 
 

(3). Slope length and steepness factor (LS) 
Length of slope was calculated using the equation (3) developed by Wischmeier et al. (1978) while 
steepness (S) was calculated using the LS equation (4-5) for USLE model developed by McCool et al., 
(1989) 
 

       L = (
𝜆

22.13
)𝛽                   ……………………………………………………………………………….. (3) 

 

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       β = 
(

𝑠𝑖𝑛𝜃

0.0896
)/[3∗(𝑠𝑖𝑛𝜃)0.8+0.56]

1+(
𝑠𝑖𝑛𝜃

0.0896
)/[3∗(𝑠𝑖𝑛𝜃)0.8+0.56]

               ……………………………………………………………………………….. (4) 

         

         S = {
10.8 𝑠𝑖𝑛𝜃 + 0.03 𝜃     𝜃 < 5°

       16.8 𝑠𝑖𝑛𝜃 − 0.5     5° ≤ 𝜃 < 10°
21.9𝑠𝑖𝑛𝜃 − 0.96      𝜃 ≥ 10°

 ……………………………………………………………………………….. (5)        

                       
Where, L = Slope length; S = Slope steepness; ɵ = Slope value of DEM; λ = Slope horizontal length; β = 
slope index, cell size = size of grid cell 
 

(4). Vegetation coverage and management factor (C), Xu, Xu, & Meng (2012) explained that C is defined as 
the ratio of soil loss from land cropped under spesific conditions to the corresponding loss from clean-
tilled, continous fallow. C value was calculated using Gutman & Ignatov (1998) equation (6): 
 

C = 1-
𝑁𝐷𝑉𝐼−𝑁𝐷𝑉𝐼𝑚𝑖𝑛

𝑁𝐷𝑉𝐼𝑚𝑎𝑥−𝑁𝐷𝑉𝐼𝑚𝑖𝑛
                ………………………………………………………………………………. (6)      

 
(5). Support practices factor (P) 

The support practice factor (P-factor) is the soil-loss ratio with a specific support practice to 
corresponding soil loss with up and down slope tillage. The erosion level due to land management and 
conservation activities (P) varied, especially depending on slope steepness. Classification of p values 
based on classification of slope developed by Shin (1999), show in Table 1. 

Table 1.Classification of P-values (Modified from Shin (1999)) 
Slope (%) P-values 

0 - 8 0.55 
8 - 15 0.60 

15 - 25 0.80 
25 - 40 0.90 

40 > 1.00 

Calculation of erosion factors was performed on ArcGIS 10 platform and converted into raster format. 
The result of quantitative calculation was validated using qualitative approach by observing erosion 
indicators in the field. The erosion factors as the input data of the model were put in four combinations to 
examine the influential factors (Table 2). Additional data of the input layer were added to Combination 1 
(C1), namely the map of solum depth and the map of organic matter. Both factors were considered 
affecting the ability of eroded soil. Organic matter do not only greatly affect the health of the soil but also 
soil properties, both the chemical properties and the physical properties, including the soil structure (Bot & 
Benites, 2005). While the depth of the soil affects the soil-water-plant ecosystem so as to affect the quality 
and yield of plants (Jabro et al., 2010). Combination 2 (C2) was a combination of five erosion factors used in 
the RUSLE model equations, Combination 3 (C3) was comprised of only four factors without the factor of 
land management (P). As for Combination 4 (C4), it only used three erosion factors, namely slope length 
and steepness factor (LS), erodibility (K), and the vegetation factor (C). Overall, the conceptual framework is 
illustrated in the form of a diagram shown in Figure 2. 

Table 2. Input parameters of three different combinations for the erosion risk assessment (Analysis, 2016) 

Factors Controlling Erosion 
Combinations 
C1 C2 C3 C4 

Topographic factor(LS)         
Soil properties     

- erodibility (K)         
- Solum depth      
- Organic matter (OM)      

Rainfall erosivity  ( R)        
Cover management factor (C)         
Practice factor (P)      

 

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Figure 2. The Conceptual Framework of the Research 
 
 

3. RESULT AND DISCUSSION 

3.1. Evaluation of Influential Erosion Factors 

R-factor value was made using spline interpolation method because the sample points were not 
spread evenly. This method has sufficient accuracy despite using a small amount of data. The spatial 
distribution of R-factor in Serang watershed ranged from the highest erosivity index value of 2,078.52 to 
the lowest 1,156.58 (Figure 3a).The equation used to calculate K-factor relied on soil texture data from the 
laboratory test results of soil samples. Soil texture is the most influential soil attributing to erodibility. Low 
erosion happened in soil with dominant element of sand (coarse texture) and soil with dominant fraction of 
loamy, while soil with main elements of dust and fine sand was easily eroded. The erodibility index in 
Serang watershed ranged from 0.46 to 0.09 (Figure 3b).  

The spatial distribution of LS-factor ranged from the lowest value of 0.03 to the highest 427.50 (Figure 
4a). The low slope steepness will have small contribution to LS value. If LS value is small, the erosion 
potential is equally small. The spatial distribution of C-factor show values between 0.01 and 1.43 (Figure 
4b). C value approaches 0 for areas with denser vegetation (forest and mix plantation). Factor C value gives 
contribution to interpretation and land use. Remote sensing through the SPOT 5 satellite could give 
solution to the extraction of factor C value without performing measurement in the field. However, there 
was difference of factor C value with previous researchers in the same area (Arsyad, 2010). Month of 
recording the images in use and climate difference, including rainfall, influence C index value. The spatial 
distribution of P-factor with minimum index 0.55 in the flat slope and maximum index 1 in the steep slope 
(Figure 5a). 

Soil organic matter and soil depth are additional data in C1. Soil organic matter in research area was 
obtained from laboratory test result on several samples while the soil depth of measurement result was 
obtained from the field. The sample value of soil organic matter and soil depth were then interpolated 
using kriging method to represent the maximum and minimum value of sample data. The spatial 
distribution of soil organic matter in Serang watershed showed values between 0.06 and 6.55 (Figure 5b). 
The spatial distribution of soil depth ranged from the lowest value of 19 to the highest 135 (Figure 6).  

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After a comprehensive analysis of the entire combinations, the area of study is an erosion-prone area 
as indicated by the wide spread distribution of extremely to moderate erosion, whereas slight and very 
slight erosions occurred in a smaller percentage (Table 3, Figure 7). The model for C1 involved two soil 
attributes other than erodibility, namely organic matter and soil depth while the other combinations 
solitary used the factor of erodibility. However, C1 had a merely similar percentage of spatial distribution 
with the C3 and C4 for the slight erosion class (Figure 8b) and the severe erosion class for C4 (Figure 8d). This 
means that soil attributes other than erodibility did not significantly affect the erosion risk in the area of 
study. 

 

 

 

 
       
 

 

 

 

 

 

 

 

 
 

Figure 3. Spatial distribution: a.) rainfall erosivity factor (R) and b.) K factor 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 
Figure 4. Spatial distribution: a.) slope length and steepness factor (LS) and b.) crop management factor (C) 

 

   a   b 

   a   b 

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Figure 5. Spatial distribution: a.) support practice factor (P) and b.) organic matter factor (OM) 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 
Figure 6. Spatial distribution of solum depth factor  

The distribution of C2 was almost the same as that of C4 for the entire erosion classes (Figure 8). C2 
added the factors of erosivity (R) and land management (P) in addition to the factors used in C4. It means 
that these two factors, namely factors R and P, did not have a significant influence on the control of erosion 
in the area of study. The P factor map (Figure 5a) has the same distribution pattern as the LS factor map 
(Figure 4a) since both factors are derived from the same contour data, so the LS factor can replace the 
representation of factor P. C3 and C4 had an almost equal distribution percentage for the very slight erosion 
class (Figure 8a). Both combinations used different factors, in which C4 did not use the factor of erosivity. In 
this case, it can be concluded that the factor of erosivity did not have a significant influence on the erosion 
in the area of study. 

   a   b 

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                             Table 3. Distribution of the affected area by erosion classes (Analysis, 2016) 

 Model combination (%) 

 C1 C2 C3 C4 

Very slight 8.37 4.60 2.75 3.26 

Slight 10.08 8.07 11.63 10.18 

Moderate 20.07 30.54 26.12 32.16 

Severe 18.77 22.04 13.29 18.96 

Extremely 42.71 34.75 46.22 35.44 

 
 
 
 
 
 
 
 
 
 
 
 

Figure 7. Distribution of erosion risk classes (Analysis, 2016) 
 
 
 
 
 
 
 
 
 

 

 

 

 

 

 

 

 

 

 
 
 
 
 

Figure 8. Distribution of the erosion risk model based on erosion risk classes (Analysis, 2016) 
(a). very slight, (b) slight, (c) moderate, (d) severe, (e) extremely 

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Based on analysis results of various combinations (Figure 8) showed that the factors affecting erosion 
in the area of study were the slope length and steepness factor (LS) and erodibility (K). Kamaludin et al. 
(2013) showed the same thing that factors which potentially trigger erosion are LS and K. The factors of 
erodibility (R) and vegetation cover (C) affect erosion if they take place simultaneously with the two 
influential factors (LS and K) as illustrated in Combination 2 (C2). Farhan, Zregat, & Farhan (2013) drew the 
same conclusion that a combination of the factors of soil, slopes, and vegetation can describe the risk of 
erosion. The rainfall factor in significantly affected erosion in the area of study, except if high erosivity takes 
place steep slopes, the erosion risk will change into moderate up to extremely as in some areas of 
Girimulyo and Kokap subdistricts in the north (Figure 9). 

The classification results based on the map of erosion risk distribution (Figure 9) reveal that the 
distribution of erosion in the research site was dominated by the following erosion classes, namely 
extremely erosion spreading across most of the area of Kokap Sub-district, Girimulyo Sub-district, and some 
of the area of Pengasih Sub-district; severe erosion spreading all over Panjatan Sub-district, Pengasih Sub-
district, Nanggulan Sub-district and some of the area of Kokap Sub-district; moderate erosion spreading 
across Pengasih Sub-district, some of the area in Wates Sub-district, Panjatan Sub-district and some of the 
area of Kokap Sub-district; as well as slight erosion and very slight erosion spreading all over Temon Sub-
district and Wates Sub-district.  

 
 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

     
 
  

Figure 9. Spatial distribution of erosion risk (C2) 
 

3.2. Validation 
Accuracy was ensured by testing 53 plots in the sample location in the erosion map generated using 

the four combinations using qualitative indicators in the field and the highest accuracy was generated by 
Combination 2 (Table 4) where the factors used were consisted of the five factors of erosion used in the 
model of erosion (R, K, LS, C, P). The lowest of accuracy, i.e. by 83.02%, still can be used as a reference even 
though only three erosion factors were used, namely the slope length and steepness factor (LS), erodibility 
(K), and vegetation (C). 

 

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Erosion classes were also determined by the slope steepness factor. Despite the indicators of erosion 
in the field, if they exist in a flat and sloping slope, the erosion will belong to the slight erosion class. 
Vrieling, Sterk, & Vigiak (2006) argued that the slope factor and the occurrence of erosion significantly 
correlate; a very steep slope belongs to the severe erosion class. The more steepness slope is, the higher 
the number of particles that spreads to the lower slope so as to result in splash and rill erosion (Assouline & 
Ben-Hur, 2006). 

The erosion indicators showed the vulnerability of soil to erosion. Tree root exposure occurred in a 
place where plants or trees grow in an eroded area and, likewise, pedestals indicated a high erosion rate as 
they take place in soil that is easily eroded (high erodibility) by rainfall of high intensity (Stocking & 
Murnaghan, 2000). Sheet erosion belonged to the slight and moderate categories because the runoff flow 
rate was not faster than that taking place in the rill and the gully, the resulting erosion did not lead to the 
formation of a rill and gully. Like the gully erosion, the rill erosion is one of the indicators of severe erosion, 
but the gully erosion cannot be removed through normal soil cultivation, like in the rill erosion. Therefore, 
the occurrence of gully erosion in an area indicates extremely erosion despite the absence of observation 
of other indicators such as pedestals (Figure 10) and tree root exposure (Figure 11). 

                  Table 4. Comparison of the accuracy (analysis, 2016) 

 Overall accuracy (%) Index Kappa 

C1 86.79 0.80 

C2 90.57 0.86 

C3 86.79 0.80 

C4 83.02 0.73 

 

 

 

 

 

 

 

 

Figure 10. Appearance of Pedestals and Armour Layers Slight erosion risk (405113 mU, 9131329 mT) 

 

 

 

 

 

 

 

 

 

Figure 11. Tree root exposure, Location (410800 mU, 9140780 mT), Severe erosion risk (Analysis, 2016) 

Armour 

Pedestal 

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4. CONCLUSION 

Results of testing of the four combinations revealed that the area of Serang Watershed was dominated 
by the extremely erosion class with the most influential factors consisting of the slope length and steepness 
factor (LS) and erodibility (K). Results of the trial showed that the factor of soil management and cultivation 
(P) did not have a significant influence on the occurrence of erosion in the area of study because the P 
value is derived from the same data to obtain the LS value of the contour data, so that the LS factor can 
replace the representation of factor P as input data. Likewise, the addition of soil attributes in C1, namely 
organic matter and soil depth, in this research did not improve the accuracy value. 

5. ACKNOWLEDGMENTS 

The authors would like to thank National Institute of Aeronautics and Space, Indonesia and 
Meteorological Climate and Geophysics Agency for providing the data. Fieldwork assistance was provided 
by Alfiatun Nur Khasana, Bagus Pamungkas, Lesan Purnomojati, Natassa Soeroso and Iwuk Lestari. The 
authors thank the financial support from Department of Higher Education of Indonesia, which has provided 
postgraduate scholarship in Universitas Gadjah Mada, Yogyakarta. 

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	doi: 10.14710/geoplanning.4.2.131-142
	Copyright © 2017 GJGP-UNDIP  This open access article is distributed under a  Creative Commons Attribution (CC-BY-NC-SA) 4.0 International license.
	How to cite (APA 6th Style):
	Arif, N., Danoedoro, P., & Hartono, H. (2017). Remote Sensing and GIS Approaches to a Qualitative Assessment of Soil Erosion Risk in Serang Watershed, Kulon Progo, Indonesia. Geoplanning: Journal of Geomatics and Planning, 4(2), 131-142. doi:10.14710/...
	1. INTRODUCTION
	Keywords:
	Remote sensing, Serang Watershed, soil erosion
	Corresponding Author:
	Nursida Arif
	Faculty of Science and Technology, Universitas Muhammadiyah Gorontalo, Indonesia
	Email: nursida.arif@gmail.com
	2. DATA AND METHODS
	3. RESULT AND DISCUSSION
	4. CONCLUSION
	5. ACKNOWLEDGMENTS
	6. REFERENCES

