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         Geoplanning 
Vol 4, No. 2, 2017, 187-200                                                                                                                                                            Journal of Geomatics and Planning 

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

doi: 10.14710/geoplanning.4.2.187-200 

 

LAND-SOIL CHARACTERISTICS FOR MAPPING PADDY CROPPING INTENSITY USING 

DECISION TREE ANALYSIS FROM SINGLE DATE ALI IMAGERY IN MAGELANG, 

CENTRAL JAVA, INDONESIA  

S. Arjasakusumaa,b       , P. Danoedoroa,b       , S. Herumurtia,b, Y.A. Nugrohoa, P.A. Aryagunaa,b 

a Remote Sensing Dept., Geographic Information Science Major, Faculty of Geography, Gadjah Mada University, Indonesia 
b Graduate School of Remote Sensing, Faculty of Geography, Gadjah Mada University, Indonesia 
 

Abstract: Paddy field area and its cropping intensity are main information used to measure 

the crop production and the response of crop to changing climate conditions. Remote sensing 
technology has been widely used to map cropping pattern of paddy mostly using spectral 
analysis of multi-date multispectral data of remote sensing. However, the cropping intensity of 
paddy was also influenced by the characteristics of planted land to paddy field which defines the 
level of land suitability for planting paddy.  This research aimed to map paddy rotation using 
single date ALI imagery by assessing the land and soil characteristics based on the land 
suitability parameters for planting paddy.  Soil characteristics such as texture, acidity level, P205 
(phosphor) and C-organic level collected from field work and terrain characteristics such as 
landform, surface water, and drainage density from visual delineation of SRTM 90 m were 
collected as inputs for the decision tree analysis to map the repetition of paddy planting 
throughout the year. The results showed the overall accuracy of 85% ± 8% (95 % level of 
confidence) for the final paddy rotation map where 2-times paddy per year was mostly found in 
the study area. 
 

 Copyright © 2017 GJGP-UNDIP  

This open access article is distributed under a  

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

Arjasakusuma, S., Danoedoro, P., Herumurti, S., Nugroho, Y.A., Aryaguna, P.A., (2017). Land-Soil Characteristics For Mapping Paddy Cropping 

Intensity Using Decision Tree Analysis From Single Date Ali Imagery In Magelang, Central Java, Indonesia. Geoplanning: Journal of 

Geomatics and Planning, 4(2), 187-200. doi: 10.14710/geoplanning.4.2.187-200 

1. INTRODUCTION 

Rice is the product of the paddy (Oryza sativa) which is the world dominant staple food for human.  
Around 480 million tons of rice were annually produced to supply the demand of global citizen especially 
the poor fulfilling up to 50 % of their calories (Muthayya, Sugimoto, Montgomery, & Maberly, 2014). Same 
situation occurred in Indonesia, rice is one of the main foods for most of the poor citizen of the country 
where 20-25 % of total expenditure was used for rice consumption (Timmer, 2004). Considering the large 
rice consumption, population increase and the declining of paddy field area due to land use conversion in 
Indonesia, accurate and annual information about the area of paddy field and the intensity of paddy 
planting is important to measure the potential of annual rice production. These two main parameters can 
be used to measure the actual and potential deficit of rice production so that further policy can be carried 
to prevent further problem such as inflation. In addition, information regarding crop intensity can be used 
as baseline information for measuring the impact of climate variability to the agricultural area as the 
dependence of agricultural area to climate conditions (Xiao et al., 2006) as well as the effect of intensified 
agriculture pressure to soil quality and biogeochemical cycles (Yan et al., 2014). 

Remote sensing has been considered as valuable technology to provide important information 
regarding precise crop management with the ability to monitor the seasonality of crop and soil conditions 
and time dependence crop management (Moran, Inoue, & Barnes, 1997). Seasonal and time dependence 
cropping pattern in paddy field area is one of the main information that can be monitored using remote 
sensing. Various remote sensing methods have been employed to map the cropping pattern information of 

OPEN ACCESS 

Article Info: 
Received: 15 Dec  2016 
in revised form: 1 May 2017 
Accepted: 7 July 2017 
Available Online:  30 Oct 2017 
 

Keywords:  
Decision Tree, Cropping Intensity, 
Paddy Field, Land Characteristics 
 

Corresponding Author: 
Sanjiwana Arjasakusuma 

Gadjah Mada University 

Email: 

sanjiwana.arjasakusuma@uqconn

ect.edu.au  

  

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https://doi.org/10.14710/geoplanning.4.2.187-200
mailto:sanjiwana.arjasakusuma@uqconnect.edu.au
mailto:sanjiwana.arjasakusuma@uqconnect.edu.au


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paddy field using single date or multi temporal remote sensing data (Foerster, et al., 2012; Li, et al., 2012; 
Pan et al., 2015; Panigrahy & Sharma, 1997; Wang et al., 2015; Xiao et al., 2006). Analysis of paddy field 
distribution and crop rotation was mainly carried out using multi temporal data due to differences in 
spectral value in the paddy field during the phase of flooding, paddy growth and harvesting (Panigrahy & 
Sharma, 1997; Wang et al., 2015). 

Current analysis of remote sensing data using multi temporal analysis relies on the high temporal 
remote sensing data in order to be able to catch the seasonality pattern in spectral values at paddy field 
area. High temporal resolution remote sensing usually have coarse spatial resolution which consequently 
mixed pixel. This becomes problematic when it is implemented on monitoring the smallholder agricultural 
area generally having smaller area compared to the remote sensing pixel size (Jain, et al., 2013).  
Meanwhile, medium spatial resolution remote sensing data tends to have less intensive revisit time to 
capture the seasonality in the paddy field (Wang et al., 2015).  

Cropping intensity at detailed scale is influenced by the socio-economic factor such as farmer’s 
decision and crop price, and climate such as precipitation (Biradar & Xiao, 2011). In addition to climate and 
socio-economic factor, land physical constraints such as soil and water availability to supplement rainfall 
plays role in the farmer’s decision for cropping intensity (Osman, et al., 2015; Panigrahy & Sharma, 1997). 
Effort to map cropping pattern information using landscape-ecological approach from remote sensing data 
has been demonstrated to be able to map cropping intensity with decent accuracy (Danoedoro, n.d.) 

In this study, we aimed to predict the potential cropping intensity information (1) by combining 
ancillary data such as soil characteristics and land morphology;(2) by employing data mining algorithm using 
decision tree analysis; and (3) by further measuring how accurate the potential cropping intensity map 
matches with the reality in the world. The decision tree algorithm was used because it gives comparable 
accuracy with the other remote sensing classification algorithm (Friedl & Brodley, 1997; XU, et al., 2005).  
Furthermore, it is also able to handle data with different scales, no statistical assumption and fast, with the 
output of tree model that is easy to be interpreted (Tso & Mather, 2009). 

 

2. DATA AND METHODS 

2.1. Study area 

This study took place in Magelang districts, Central Java, Indonesia located between 110° 01’ 51” - 110° 

26’ 58” E and 7° 19' 13” - 7° 42’ 16” S with total area of 1112 km2  (Figure 1). Furthermore, this area has 

various land morphologies which can be seen from the hill shade background in the Figure 1. Therefore, 

considering the variety of land morphology, this area is suitable for assessing the land physical 

characteristics relationships to paddy cropping intensity. 

2.2.  Data 
2.2.1. Earth Observation - ALI Imagery 

Earth Observation – Advanced Land Imagery (EO ALI) is an experimental satellite that was launched 

by NASA in November 2000. The main objective of this satellite was to test the new system of hyper-

spectral and multispectral sensor for the next Landsat mission. The data has similar orbit with previous 

Landsat system with 30 m spatial resolution. However ALI has more bands with extra Near Infrared and 

Middle Infrared bands sensors with the total of 9 bands of multispectral (Bicknell, et al., 1999).  Details of 

EO ALI sensors and technical configuration are shown in the following Table 1. 

2.2.2.  Field Data 

Field data was conducted to collect the input data for decision tree analysis and validation data for 
accuracy assessment. Input data that was collected from the field data were the soil characteristics such 
acidity level (pH), phosphor level (P2O5), C-organic level, soil permeability and soil texture while the 
collection of validation data included the land use data and cropping pattern mapping data to assess the 
accuracy of crop intensity map. 

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Figure 1. Map of study area, Magelang District, Central Java, Indonesia with SRTM 90 m hill shade as 

the background  in the inzet map (analysis, 2015) 

 
Table 1. Technical configuration of EO-ALI (Lencioni, et al., 2005) 

Characteristics Specification Details 

Numbers of Bands 
 (pixel size) 

Panchromatic (0.48 – 0.69 µm) 1 band (10 m) 

Visible Near Infrared (0.433 – 0.89 µm) 6 bands (30 m) 

Shortwave Infrared (1.2 – 2.35 µm) 3 bands (30 m) 

Orbit Sun-synchronous with 705 km altitude aligned with Landsat 7 satellite 

Scanning system Push-broom scanning 

Field of View (FOV) 15o cross-track by 1.26O in-track 

Swath width 37 km 

Temporal resolution 16 days 

Radiometric resolution 12 bits 

 

2.3.  Methods 

There are three main analyses in methods that cover 1.) identification of mapping unit boundary using 
visual inspection of land morphology from SRTM data, 2.)  implementation of two-stage decision tree 
analysis on remotely sensed data where the first decision tree was used to extract paddy field area using 
based on at surface radiance-spectral values and the second decision tree was used to add cropping 
intensity information to the extracted paddy field area, and 3.) accuracy assessment of the paddy field area 
as well as the cropping intensity. Systematic general workflow of the aforementioned analyses is shown in 
the Figure 2. 

2.3.1. Identification of Mapping Unit Boundary 

In this study, land terrain unit acted as the mapping unit which is the smallest unit used in the 
analysis. The consideration of using terrain unit as the mapping unit was based on the assumption of 
homogenous terrain morphology having homogeneous lithological unit. Therefore, when the area was 
undertaken by the geomorphological processes such as erosion and sedimentation, it will create similar 

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output of land morphology depending on the resistance of rock material to the erosion process and the 
direction of the sedimentation. Terrain attributes were identified as important variables for soil properties 
mapping as used by (Elnaggar & Noller, 2009) which shows the importance of using of terrain unit as 
mapping unit.  

The terrain was modeled by SRTM hill shade in which delineation was visually made by looking at the 
similar pattern of morphology such as slope, stream flow, hills and canyon. SRTM used in this research to 
generate hill shade has the spatial resolution of 90 m so that the mapping unit was identified at medium 
scale.  

 

 

 

 

 

 

 

 

 

 

 

 

 

 
 

Figure 2. Workflow to extract cropping intensity from ALI – imagery using decision tree analysis (analysis, 

2015) 

2.3.2. Two Stages Decision Tree Analysis 

Decision tree is a method to categorize data using hierarchical splitting procedures. This method offers 
the ability to comprehensively understand the relationship between the data attributes for data 
classification (Tso & Mather, 2009). The final classification is represented in a form of a model tree 
composed of root, interior, and terminal nodes depicting the decision stages that were taken to categorize 
the data. The creation of the tree model can be manually done based on the user’s knowledge or 
automatically using a set of training area or known as supervised decision tree or tree induction. Many 
algorithms have been developed for creating automatic tree model such as ID3, C.45, CART and See5.0. 

See5.0/C5.0 algorithm applied in the software of see5® were used in this study in the formation of tree 
model. This algorithm was a development of the previous C4.5 algorithm which uses information or 
normalized gain ratios to perform the splitting process. The information gain was calculated based on the 
Shannon’s entropy measures. See 5.0 offers boosting techniques which are able to increase the model 
accuracy by repeatedly performing a tree-generating process. This process assigned weight to the training 
area with the ratio value of misclassified training area to the correctly labeled area.  

In this study, two stages of decision tree induction were performed to produce land cover data and 
paddy rotation map. The first tree induction to produce land cover associated with paddy field and non-

Srtm 90 m Hillshade EO1 – ALI Imagery 

Visual Delineation Spectral based Decision Tree 

Field Sampling Reclassification  

Reclassification 

Decision Tree Analysis 

Land Cover Map Terrain Unit Map 

Tentative Paddy Field Map Soil Properties 

 
Cropping 

Intensity 

 

Terrain 

Characteristics 

Paddy Field Map 

Cropping Intensity Map 

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paddy field employed the input value taken from each bands spectral values of the training area. The 
tentative map of the paddy rice distribution was further reclassified using the terrain unit derived from 
visual delineation of SRTM 90m using Boolean logic from field observation. The second stage of tree 
induction was carried out to map the frequency of paddy rotation by employing the set of training area 
from (1) land characteristics such as drainage density which qualitatively estimate stream flow density, 
relief, surface water and morphology and (2) soil characteristics such as texture, acidity level, P2O5 and C-
organic level taken from the field work. The land characteristics that described the terrain configuration 
were considered useful to identify the distribution of soil properties (Sumfleth & Duttmann, 2008) and 
water availability (Samson, Ali, Rashid, Mazid, & Wade, 2004). In the other hand, soil characteristics 
resembles the crop management such as cropping intensity and tillage which affect the dynamic of soil 
chemical, nutrient and quality (Liebig, Tanaka, & Wienhold, 2004; Mikha et al., 2006). Therefore, mapping 
cropping intensity using the combination of land-soil characteristics and remote sensing observation is 
possible to be conducted. 
2.3.3. Mapping paddy field and reclassification process 

To extract paddy field category from the single date ALI Imagery, cautious selection of training area 
were taken to map the land cover information from the imagery. Sixty training areas were taken to 
resemble various land cover visible in the 30 m of spatial resolution ALI Imagery. Average spectral value 
from each training area was used as the input for decision tree analysis with the standard deviation less 
than three to ensure the training area representing homogeneous land cover class. Land cover class 
information that was derived from the imagery was used as the base information for next classification of 
paddy and non-paddy field classes. To successfully deriving this information using single date imagery data, 
the possibility of visual appearance of paddy field at different states of condition should be taken into 
account. Paddy field which is recently planted will have water body that dominates spectral pattern 
meanwhile paddy field which is dominated with paddy cover had vegetation spectral pattern. Lastly paddy 
field that is recently harvested had bare soil typical spectral pattern (Figure 3). To accommodate those 
three characteristics of paddy field in single date imagery, different training classes were assigned to 
specifically record and training the algorithm to recognize these spectral pattern. 

According to the crop calendar from the Indonesian Ministry of Agriculture, in the time when ALI 
imagery that was recorded in June 2004 during this study, the paddy fields in the area were mostly at the 
growth stage. In line with that, the paddy fields with the vegetation spectral pattern were mostly found in 
the imagery even though several unplanted and wet paddy fields were correspondingly found at this time. 
To capture the variability of paddy field in the study area, from 60 training areas that has been randomly 
collected, 23 of the training area were affiliated to paddy field in which 12 of the training areas are affiliated 
to the planted paddy fields dominated with vegetation cover, 4 training areas represent wet or moist soil 
covered paddy fields and 7 training areas affiliated to bare soil covered paddy fields. Those classes related 
paddy field were easily distinguished from other existing land cover classes in the 30 m spatial resolution of 
the imagery due to the homogenous shape and association with the pathway and the linearity of possible 
irrigation network. Although other non-paddy vegetation cover such as woody vegetation and smallholder 
plantation equally exists in the imagery, this vegetation can also be easily distinguished by looking at the 
tone and density of vegetation for woody vegetation and location for the smallholder non-paddy 
plantation. However, misclassification especially for smallholder plantation and vegetation covered paddy 
field was still possible due to the use of spectral information solely as an input during the classification 
process. Thus, additional post-processing was performed to rectify this misclassification using matching 
method based on the identified terrain unit. 

Tree model was produced using See5.0(c) software which average values from each training area were 
employed as the base information to do the splitting process in decision tree in this study. There are 75 
times of trial with 25 % pruning to avoid excessive leaf nodes in the output tree model so that over-fitting in 
the output model can be avoided. From 75 trials, there are 75 output tree models with different number of 
leaf node, splitting decision and error. Error of each model was counted by assessing the number of 
misclassified input training areas that did not match with the output tree model.  

The Land cover classification can be linked into 2 main land use categories which are paddy and non-
paddy fields.  By performing this simple reclassification, tentative spatial distribution of paddy field was able 

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to be extracted. Visual assessment matched with the ground checking revealed misclassification in the 
tentative map especially between covered paddy field and dry smallholder plantation. Both have similar 
spectral signature due to vegetation coverage although both were associated with different landform unit. 
Based on ground checking, smallholder plantation is generally located at higher elevation with steeper 
slope and thin soil solum. With minimum need of irrigation for the majority of the crops in the smallholder 
plantation, planting these crops in this landform is feasible. On the other hand, developing paddy field in 
this area would be problematic as water availability is a limiting factor. With distinct differences in terms of 
landform for smallholder plantation and paddy field, binary matching using land terrain unit from the 
previous analysis was used to remove the falsely identified paddy field. 
 

 

 

 

 

 

 

 

 

 

Figure 3. Different visual appearance of paddy field at different states of condition as appeared in ALI 
multispectral imagery false color composite such as (a). paddy field at flooding stage, (b). paddy field with 

bare soil  reflectance, and (c). paddy field with vegetation (analysis, 2015) 

2.3.4. Accuracy Assessment 

Accuracy assessment was carried using error matrix based calculation developed by (Olofsson, Foody, 
Stehman, & Woodcock, 2013) in order to derive the producer’s, user’s and overall accuracy in percentage. 
The confidence interval for the producer’s, user’s and overall accuracy were calculated using this method 
which gives deviation of error range for the accuracy. As accuracy assessment of the map was calculated 
from statistical analysis using sample data representing the entire population in the map, the confidence 
interval calculation was necessary. Therefore, the accuracy value should not be merely represented by a 
single number but the margin of error should be included in the accuracy assessment. This was to give the 
value of uncertainty of the final accuracy number (Olofsson et al., 2013). The research implemented 
conventional error matrix as the baseline information of the accuracy calculation. However instead of using 
normal pixel or point count calculation in the conventional error matrix (Table 2), another error matrix was 
developed using the estimated area proportion for class i,j (Pij) in the cell entries (Table 3).  

Table 2. Conventional error matrix using sample counts (n) with additional information of Area and Area 

Proportion (Wi) to transform sample counts into estimated area proportion 
Class 1 2 .. Q Total Area  Wi 

1 n11 n21 .. nq1 n1. A1 A1/A.Tot 

2 n12 n22 .. nq2 n2. A2 A2/A.Tot 

.. .. .. .. .. .. .. .. 

q n1q n2q .. Nqq nq. Aq Aq/A.Tot 

Total n.1 n.2 .. n.q N A.tot 1 

 

 

 

 

A B C 

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Table 3. Modified error matrix using estimated area proportion (p) in the cell entries (Olofsson et al. 2013) 

Class 1 2 .. Q Total 

1 p11 p21 .. pq1 p1. 
2 p12 p22 .. pq2 p2. 
.. .. .. .. .. .. 
Q p1q n2q .. Pqq pq. 

Total p.1 p.2 .. p.q P 

 
Here, rice field data was collected using ground checking combined with Google Earth© observation 

while cropping intensity validation was collected by performing interview to the local farmers. There are 
6171 pixels for validation area from the ground checking and extrapolation process for validating rice field 
area and 42 points of validation data for estimating the accuracy of cropping intensity map. 

3. RESULTS AND DISCUSSION 

3.1. Identification of Mapping Unit Boundary 

There are approximately 26 main land units that were identifiable by visual interpretation from SRTM 
90 m hillshade data (Figure 4). These land units represented various morphology, surface roughness and 
slope steepness indicating different process and intensity of geomorphological processes occurring in the 
background. The study area was dominated by main geomorphological processes such as volcanic (V), 
fluvial (F) and denudation (D). Paddy field seems to be centered on fluvial landform with least steep slopes 
and good water availability in the surface. While in the upper volcanic neck and denudation hill with rough 
surface roughness and intensive stream flow indicating the undergoing process of gully erosion and less 
capability of soil to hold water, dry smallholder plantation are often discovered. The association of land 
physical unit characteristics with paddy field and dry plantation is useful for performing reclassification 
process to further eliminate possible misclassification in the land use mapping. 

3.2. Mapping and Reclassification of Paddy Field 

First stage of decision tree analysis resulted in 75 tree models with different nodes and error. From 
these trees the best model having least nodes and lower error was chosen (Figure 5). This particular model 
had 12 nodes and 8.25 % of error indicating that from the total of 60 training areas, 5 of them were 
misclassified or did not match with the output tree model. The output of tree model depends highly on the 
input training area so that changes in training area will correspondingly change the output of the tree 
model. Therefore, the binary splitting decision in the tree model is area-specific and cannot be applied to 
other imagery and other areas. 

 

Figure 4. Map of terrain unit identified from SRTM 90 m hillshade data and their corresponding land 
morphology and topography attributes (analysis, 2015) 

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Figure 5. Output tree model produced by using average spectral value of different land cover in the training 
area (analysis, 2015) 

 

The association of paddy field to certain land terrain characteristics (Figure 3) was developed based on 
the ground checking in order to map the paddy field. In the study area, we observed that paddy fields were 
mostly distributed in flat to least steep with less surface stream flow. This indicated low gully erosion in this 
area indicating thick soil solum and the ability to sustain water. These characteristics are mostly associated 
with fluvial process even though some paddy fields were also found in volcanic and denudation landforms 
such as low to middle volcanic plain, and denudation landform. Using these observations, the association 
was transformed into binary rules (Table 4) to rule out the false identified paddy field from the tentative 
map.  

Table 4. Binary matching to rule out false identified paddy field from the tentative map (analysis, 2015) 

Class Mapping Unit 

D11 D12 F11 F12 S1 V1 V2 V31 V32 

Non-Paddy Field + + + + + + + + + 

Paddy Field - + + + - - - - + 

Class Mapping Unit 

V33 V41 V42 V51 V52 V53 V61 V62 V63 

Non Paddy Field + + + + + + + + + 

Paddy Field + - + - + - - + + 

Legends : + = exist, - = not exist 

 

The resulted map showed the removal of pre-identified paddy field especially in the area with higher 

latitude and coarse surface roughness which agricultural related land cover were associated to smallholder 

non-paddy plantation (Figure 6). This process reduced the detected paddy field although it still has to be 

justified by conducting the accuracy assessment. It is worth noted that the use of the binary matching 

resulted in a discrete distribution of where paddy fields are located. While in the real world, the boundary 

might be fuzzy even though it is still controlled by the landform characteristics. 

 

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(A)        (B) 

Figure 6. Paddy field distribution derived by using (A) decision tree model employing the spectral values and 
further corrected by using (B) binary matching by considering the land morphology – land use relationships 

3.3. Mapping Paddy Cropping Intensity  

The remaining paddy fields after reclassification process were labeled with the paddy cropping 
intensity. This analysis was performed using similar decision tree analysis with land and soil characteristics 
as the training data to select the significant variables influencing the paddy cropping intensity. Eighteen 
soils samples were taken from different landform where paddy field was found. These samples carrying the 
information of land physical and soil characteristics were used as an input for the decision tree analysis.  
Seventy five trials of decision tree analysis were executed to produce 75 decision tree models. To assess 
one best output model, error and the number of leaf nodes were used as the criteria. Here, tree model with 
5.6 % error and 6 leaf nodes were selected where the phosphate level (P2O5), soil texture and pH were 
significant variables that influence paddy field cropping intensity (Figure 7). 

The selected soil parameters of phosphate level, acidity level and soil textures in the tree model 
indicated the significance of those parameters for supporting cropping intensity although the relationships 
between cropping intensity and the parameters are most likely indirect. The parameters detected in the 
tree model are often used for indicating the chemical and physical properties needed to study the soil 
nutrients in the paddy fields among other parameters (Cho & Han, 2002). In particular, P2O5 is often used 
in the chemical fertilizer in which its amount of substance in the soil is needed to sustain and/or to improve 
paddy productivity (Inthavong, Fukai, & Tsubo, 2011; Mishima, Taniguchi, & Komada, 2006). In addition, the 
productivity of paddy fields is also influenced by the water holding capacity factor. One of the parameters 
that control the water availability is soil texture besides the topographic configuration and rainfall intensity 
(Inthavong et al., 2011).  Furthermore, soil texture with less sand will have higher productivity due to its 
capability to preserve waters, nutrients and high electric conductivity (Mzuku et al., 2005).  

In the above tree model, sandy loam was the threshold category of soil texture which is still able to be 
planted maximum 2 times per year. The other condition of soil texture with P2O5 below 97 could only yield 
rice for 1 time per year according to the model in the left side branch. In the right side of the branch, in 
order to obtain maximum 3 times per year planting intensity, pH level close to neutral (equal or above 5) 
and high soil nutrient of phosphate (equal or above 97) is needed. Otherwise, land is only able to be planted 
once per year due to the acidic condition of the soil. Output tree model showed that soil characteristics 
have more influence compare to land physical characteristics that were also included as the training area. 
The conclusion of whether soil characteristics has more influence to paddy cropping intensity compared to 
land physical characteristics cannot be uniquely justified based on this analysis. This is due to the nature of 

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decision tree analysis that is sensitive to the input training area. Thus, the need for more samples to 
become more stable is indispensable. However, these selected variables in the tree results seems to align 
with the statement of soil characteristics that influenced more on the paddy field productivity compared to 
the location or the spatial distribution of paddy field (Keersebilck & Soeprapto, 1985). However, similar to 
the previous decision tree model, this output model is highly sensitive to the input of training area. It 
cannot be implemented for another study using different input even though the same parameters were 
used. 

 

 

 

 

  

 

 

 

 

Figure 7. Output tree model to map the cropping intensity using land physical and soil characteristics as the 
input training data (analysis, 2015) 

Cropping intensity using these rule sets from decision tree analysis showed that 2 times a year paddy 
cropping is common in this area which occupies 47 % (13,520 ha) of the total paddy field (Figure 8). The 
boundary of different crop intensity category in the map is distinct due to the use of land terrain unit as the 
smallest mapping unit for mapping cropping intensity. Two to three times a year, paddy seems to be 
clustered around the alluvial plain and at the lower level of Merapi volcanic slopes.  

 

Figure 8. Final cropping intensity map where dominant of 2 times a year paddy field was found in the area, 
the distribution of different types of cropping intensity appear distinct due to the use of terrain unit as the 

mapping unit (analysis, 2015) 

 

P205 <= 97 

Texture = Sandy Loam pH => 5 

1 Times/Year 

 

2 Times/Year 

 

1 Times/Year 

 

3 Times/Year 

 

: Yes 

: No 

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3.4. Accuracy Assessment  
Accuracy assessment was performed to both tentative and reclassified maps of paddy field distribution 

using 6171 pixel count based on the field observation and extrapolated using Google Earth imagery. Based 
on the calculation performed, the tentative map produced the overall accuracy of 80 % ± 1% with the 79 % 
± 1.5 % and 76 % ± 1.4 % for user accuracy and producer accuracy, respectively (Table 5). After using 
landform association with the paddy field existence to remove the possible false identified paddy field from 
the tentative map, 9 % increase in the overall accuracy has been achieved (Table 6). The overall accuracy for 
the reclassified map is now 89 % ± 1 % with 86 % ± 1.2 % and 82 % ± 1.6 % for the user accuracy and 
producer accuracy. This accuracy is at par with the accuracy of rice field mapping of different studies that 
were able to map rice field at the accuracy around 78 - 90 % (Gumma, Thenkabail, Maunahan, Islam, & 
Nelson, 2014; Wang et al., 2015). The increasing of accuracy after the post processing is the proof that using 
the relation between landform and land use was able to remove possible paddy field mistake in the map. 
 

Table 5. Accuracy before post processing using landform approach (analysis, 2015) 

  Reference Area (Ha) Wi Class Accuracy CI 

Paddy Field Non Paddy Field Total Sample Ui Pi Ui Pi 

Map Paddy Field 2353 620 2972 43287.97 0.431 0.79 0.76 0.015 0.014 

Non Rice Field 599 2600 3199 57033.25 0.569 0.81 0.84 0.014 0.010 

Total Sample 2951 3219 6171 100321.21 Overall Accuracy : 0.80 ± 0.01 

 

Table 6. Accuracy after reclassification using landform approach (analysis, 2015) 

  Reference Area (Ha) Wi Class Accuracy CI 

Paddy Field Non Paddy Field Total Sample Ui Pi Ui Pi 

Map Paddy Field 2646 425 3071 33790.78 0.337 0.86 0.82 0.012 0.016 

Non Rice Field 306 2794 3100 66530.76 0.663 0.90 0.93 0.010 0.006 

Total Sample 2951 3219 6171 100321.55 Overall Accuracy : 0.89 ± 0.01 

 

Table 7.  Accuracy of Cropping Intensity of Paddy Field (analysis, 2015) 

  Reference   Area (Ha) Wi Class Accuracy CI 

1 Times/Year 2 Times/Year 3 Times/Year Total Sample Ui Pi Ui Pi 

Map 1 Times/Year 4 0 0 4 3699.05 0.129 1.00 0.66 0.00 0.21 

2 Times/Year 4 20 5 29 13520.70 0.470 0.69 1.00 0.17 0.00 

3 Times/Year 0 0 9 9 11555.41 0.402 1.00 0.83 0.00 0.11 

Total Sample 8 20 14 42 28775.16 Overall Accuracy : 0.85 ± 0.08 

 

 
Second accuracy assessment was performed to estimate the accuracy of cropping intensity map using 

decision tree analysis from 42 samples collected by interviewing local farmers on the area. Here, overall 
accuracy of the final cropping map showed 85 % ± 8 % which is good for the classification using medium 
resolution imagery (Table 7). However, in the individual class accuracies, producer accuracy for cropping 
intensity of 1 times of paddy per year and user accuracy for class 2 times a year is rather moderate to low 
with big deviation for the confidence interval. Producer accuracy of 1 times paddy per year was 66 % ± 21% 
and user accuracy for class 2 times a year was 69 % ± 17 %. This moderate to low accuracy was compared to 
related research using multidate imagery that able to map 90-97 % of producer accuracy of crop rotation 
(Nguyen, 2013; Panigrahy & Sharma, 1997). The low accuracy for individual class might be caused by the 
coarse unit of mapping that was used for generalizing the cropping intensity for paddy field within the 
specific landscape. 

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The drawback of using 90 m SRTM as the base data for identifying the boundary of  land terrain is that 

only major terrain can be identified resulting in general landform mapping unit. Cropping intensity in paddy 

field was mostly influenced by the availability of water so that if the paddy field has a good irrigation 

system, then 2 or 3 times paddy per year can be achieved. However, variation of cropping intensity within 

clustered paddy field in the same area also possible. Topography controls the distribution of the surface 

water flows so that the distribution of water in the same paddy field may be different. In slope or terraced 

paddy fields will consistently receive water compared to paddy fields in the plain and higher elevation 

which will sustain less water especially in the dry season. In term of irrigation management, terraced paddy 

field will arranged to be keep inundated especially in the dry season to prevent the landslide which gives 

the farmer the chance to plant another crop (Adachi, 2007). In addition, further assessment to include to 

climate data such as rainfall and temperature in the analysis to the dynamic of cropping intensity can be 

performed with the dependence of paddy field to water availability. 

 

4. CONCLUSION 

The accuracy of predicted cropping intensity map revealed the possibility of using the landform 
approach and characteristics. It was used to improve the identification of paddy field and its cropping 
intensity attributes with respectable accuracy. Decision tree was used as the algorithm to develop the rule 
set from the collected training areas. Binary matching using landform approach was able to increase the 
overall accuracy of 9 % indicating the benefit of using logical relationships between landform and land 
use/land cover to omit the misclassification from the result. Rule set developed from decision tree analysis 
using soil parameters was also able to map cropping intensity with respectable level. However, future 
assessment using additional parameters such as monthly rainfall and temperature data is required to 
improve the result as well as socio-economic parameters such as the availability of labor to work on the 
land. In addition, the infrastructures of the paddy field such as irrigation systems require more detailed data 
in the future to show the irrigation systems. Detailed land morphology delineation could also be made by 
using higher spatial resolution of terrain data such as SRTM 30 m or Aster GDEM to enhance the detail of 
land unit map and identify the micro-topography that affect the water distribution especially in rain-fed 
paddy field. The main drawback of this study lies particularly in the algorithm of decision tree, although 
decision tree can give respectable accuracy, the algorithm was strongly sensitive to the input of training 
area where small changes in the input can entirely change the output model. This also suggests that 
collection of training area should be taken carefully and the more number of samples should be taken to 
give more valid output of the tree model. Further assessment using ensemble method, random forest or 
neural network analysis also could be explored in this study application.  

 

5. ACKNOWLEDGMENTS 

The authors would like to acknowledge Graduate School of Remote Sensing, Faculty of Geography, 
Gadjah Mada University, Yogyakarta, Indonesia for the support in this study. The authors also would like to 
extend the gratitude to Statistical Agency (BPS) of Magelang Districts, Central Java, Indonesia and BAPPEDA 
Magelang Districts for the permission to conduct field research and the support of the statistical data. The 
author is grateful for being granted with the Beasiswa Unggulan DIKTI for providing support during the 
study. We also thank anonymous reviewers for the comments and suggestions which significantly improve 
the quality of the manuscript. 

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