RESULT AND DISCUSSION 1 Unsupervised classification-ISODATA
Bogor, 21-22 October 2015
252 extent it could have been achieved by chance Lillesand Kiefer, 2000; Salovaara, Thessler,
Malik, Tuomisto, 2005. In addition, to estimate Kappa using sample data, Kappa statistics Khat:
Kˆ is used and the value can be interpreted based on the Table 1 Landis Koch, 1977. It is calculated using Equation 1 Salovaara, et al., 2005.
r 1
i i
i 2
r 1
i r
1 i
i i
ii
x x
N x
x x
N Kˆ
where r is number of the rowscolumns in the error matrix, x
ii
is number of observations in the cell ii row i and column i, x
i+
is marginal totals of row i, x
+i
is marginal totals of column i and N is total number of observations.
Table 1: Interpretation of Kappa statistics Landis Koch, 1977
Kappa Agreement
0.00 –0.20
0.21 – 0.40
0.41 –0.60
0.61 –0.80
0.81 –1.00
Less than chance agreementpoor agreement Slight agreement
Fair agreement Moderate agreement
Substantial agreement Almost perfect agreement
3. RESULT AND DISCUSSION 3.1 Unsupervised classification-ISODATA
Unsupervised classification on Landsat 8 OLI with band combination of 564 NIR, SWIR and Red produced 10 spectral classes presented in Figure 1. The resulting spectral classes are
then given the label or land cover class 10 classes with the help of 384 training samples, i.e.: 1 settlement, 2 paddy field, 3 plantation forest, 4 mangrove forest, 5 natural forest, 6
rubber estate, 7 mixed garden, 8 water body, 9 cloud, and 10 cloud shadow. The map can depict the distribution of settlement, cloud and cloud shadow well. Nevertheless, the
classification results do not yet fully reflect the actual state of the field, for example, water body class is also found on the mainland, which in fact is flooded paddy field based on
reference data. It indicates that there is spectral similarity between the water body and the flooded paddy field.
Spectral similarity same spectral values is not only found in the water body but also on other objects, especially vegetation forests and mixed garden. Thus, in general unsupervised
classification technique produced land usecover map with low level of accuracy. Quantitative analysis of the level of accuracy is presented in Table 2.
3.2 Supervised classification-Maximum Likelihood MLC Landsat 8 OLI in units of reflectance with band combination of 564 NIR, SWIR and Red
was classified using maximum likelihood classifier presented in Figure 2. Based on spectral signatures that have been collected from 384 training samples, the image was classified into
ten land cover classes, i.e.: 1 settlement, 2 paddy field, 3 plantation forest, 4 mangrove forest, 5 natural forest, 6 rubber estate, 7 mixed garden, 8 water body, 9 cloud, and
Bogor, 21-22 October 2015
253 10 cloud shadow. Based on visual comparison as in Figure 2, distribution of paddy field,
water body, rubber estate, and mangrove forest can be mapped more accurately by using this classification technique. However, supervised classification techniques tend to map the
settlement excessively over-classified, especially around the clouds, while plantation forest, natural forest and mixed garden cannot be differentiated accurately.
Figure 1: Land usecover map resulted from unsupervised classification Table 2. Accuracy assessment of Unsupervised Classification - ISODATA
Classification Reference
Total Users
accuracy Commission
error NF
MF PF
MG RE
S PF
W []
[] Cloud
3 5
8 0.00
100.00 Cloud shadow
1 4
5 0.00
100.00 Natural forest
1 2
2 7
3 1
12 28
3.57 96.43
Mangrove forest 1
10 4
7 3
19 44
22.73 77.27
Plantation forest 2
5 8
7 1
1 11
1 36
22.22 77.78
Mixed garden 7
8 2
1 7
25 32.00
68.00 Rubber estate
2 6
8 16
0.00 100.00
Settlement 28
28 100.00
0.00 Paddy field
1 2
21 2
26 80.77
19.23 Water body
20 8
28 28.57
71.43 Total
4 19
27 30
9 36
104 15
244 Producers
accuracy [] 25.00
52.63 29.63
26.67 0.00
77.78 20.19
53.33 Omission error
[] 75.00
47.37 70.37
73.33 100.00
22.22 79.81
46.67 Overall accuracy 34.43
Kappa Statistics 0.26 Note:
NF = Natural Forest RE = Rubber Estate
PF = Plantation Forest PF = Paddy Field
MF = Mangrove Forest S = Settlement
MG = Mixed Garden W = Water Body
Bogor, 21-22 October 2015
254 Figure 2: Land usecover map resulted from supervised classification
Table 3: Accuracy assessment of supervised classification maximum Likelihood
Classification Reference
Total Users
accuracy Commission
error NF
MF PF
MG RE
S PF
W []
[]
Cloud 1
1 0.00
100.00 Natural forest
4 1
2 7
57.14 42.86
Mangrove forest 16
1 2
19 84.21
15.79 Plantation forest
14 12
8 34
41.18 58.82
Mixed garden 5
10 1
9 25
40.00 60.00
Rubber estate 1
9 10
90.00 10.00
Settlement 3
2 34
6 45
75.56 24.44
Paddy field 2
5 2
73 9
91 80.22
19.78 Water body
6 6
12 50.00
50.00 Total
4 19
27 30
9 36
104 15
244 Producers accuracy
[] 100.00 84.21 51.85 33.33
100.00 94.44 70.19 40.00 Omission error []
0.00 15.79 48.15 66.67
0.00 5.56
29.81 60.00
Note: NF = Natural Forest
RE = Rubber Estate PF = Plantation Forest
PF = Paddy Field MF = Mangrove Forest
S = Settlement MG = Mixed Garden
W = Water Body
Overall accuracy: 68.03 Kappa statistics: 0.59
Bogor, 21-22 October 2015
255 Compared to unsupervised classification technique, supervised classification technique
produced land cover map with higher visual accuracy. However, this technique cannot also separate land cover classes that have similarity in spectral values mixed spectral, thus yielded
low accuracy map. Accuracy assessment of supervised classification technique is presented in Table 3.
3.3 Rule-based classification Land cover classification with rule-based technique was performed using spectral information
from Landsat 8 OLI reflectance, NDVI and NDWI, training samples, ASTER-DEM elevation and slope and spatial information on existing thematic maps forest area function
and land usecover. Utilization of spatial information from thematic maps allows separation of land cover classes that have similar spectral characteristics by applying rulesspecific
requirements. In addition, the use of existing land cover and forest area function maps can be used to identify land cover classes under the clouds and cloud shadows. Thus, the
classification of satellite imagery with rule-based technique can produce a complete land cover map Figure 3. In this case, the generated land cover map has eight classes, i.e.: 1 settlement,
2 paddy field, 3 plantation forest, 4 mangrove forest, 5 natural forest, 6 rubber estate, 7 mixed garden, and 8 water body. Visually, the result of rule-based classification has a high
degree of separation for each land cover class. The area of each land usecover class is presented in Table 4. Quantitative analysis of the level of accuracy of the map is discussed in
accuracy assessment section and is presented in Table 5. Table 4: Area of each land use cover resulted from rule-based classification
No. Land usecover
Area [ha]
Percentage []
1 Natural forest
3,628.98 0.78
2 Mangrove forest
10,365.93 2.24
3 Plantation forest
72,179.55 15.57
4 Mixed garden
175,572.45 37.87
5 Rubber estate
14,507.01 3.13
6 Settlement
26,930.61 5.81
7 Paddy field
153,085.32 33.02
8 Water body
7,320.87 1.58
Total 463,590.72
100.00
Based on Table 4, the total area of the Citanduy Watershed is 463,591 ha. The dominant land cover classes are mixed garden 175,572 ha and paddy field 153,085 ha, with a total
percentage of about 71. Plantation forest managed by Perum Perhutani teak and pine is the third dominant land cover class with an area of about 72,180 ha 15.57. Mangrove
forest in Segara Anakan covers an area of 10,366 ha 2.24. Settlements only occupy 26,931 ha, which proportionately only slightly above 5.
Bogor, 21-22 October 2015
256 Figure 3: Land usecover map resulted from rule-based classification
Table 5: Accuracy assessment of rule-based classification
Classification Reference
Total Users
accuracy Commission
error NF
MF PF
MG RE
S PF
W
Natural forest 4
1 5
80.00 20.00
Mangrove forest 19
1 20
95.00 5.00
Plantation forest 25
3 1
29 86.21
13.79 Mixed garden
24 2
5 4
35 68.57
31.43 Rubber estate
1 6
2 9
66.67 33.33
Settlement 25
4 29
86.21 13.79
Paddy field 2
5 1
5 90
3 106
84.91 15.09
Water body 1
10 11
90.91 9.09
Total 4
19 27
30 9
36 104
15 244
Producers accuracy 100.00 100.00
92.59 80.00 66.67 69.44 86.54 66.67 Omission error
0.00 0.00
7.41 20.00 33.33 30.56 13.46 33.33
Note: NF = Natural Forest
RE = Rubber Estate PF = Plantation Forest
PF = Paddy Field MF = Mangrove Forest
S = Settlement MG = Mixed Garden
W = Water Body
Overall accuracy: 83.20 Kappa statistics: 0.78
Bogor, 21-22 October 2015
257
4. CONCLUSION Unsupervised classification-ISODATA and supervised classification-Maximum Likelihood
techniques produced land usecover maps with an overall accuracy of 34.43 and 68.03 and Kappa statistics of 0.26 fair agreement and 0.59 moderate agreement, respectively.
Factors that lead to low level of land cover map accuracy resulted from ISODATA and Maximum Likelihood techniques are: 1 similarity in the spectral characteristics of several
land cover classes and 2 quality of training and testing samples collected from Google Earth, particularly for locations that are away from the location of the field survey.
Additional spatial information obtained from ASTER-DEM elevation and slope, Landsat 8 OLI NDVI and NDWI can be used to improve the accuracy of land cover map generated
using rule-based classification technique.Land cover map resulted from rule-based classification technique has an overall accuracy of 83.20 with a Kappa statistics of 0.78
substantial agreement, which is higher compared to those resulted from ISODATA and Maximum Laikelihood. The dominant land cover in the Citanduy Watershed is mixed garden
175,572 ha and paddy field 153,085 ha, which constitutes 71 of the total watershed area. Natural forest, mangrove forest and plantation forest only occupy an area of about 86,174 ha
18.6.
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Kasus Desa Tanjungsari, Kecamatan Sukaresik, Kabupaten Tasikmalaya, Jawa Barat. Undergraduate S1, Institut Pertanian Bogor IPB, Bogor.
Congalton, R. G. 1991. A review of assessing the accuracy of classifications of remotely sensed data. Remote Sensing of Environment, 371, 35-46.
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ERDAS Inc. 1999. ERDAS Field Guide Fifth Edition: revised and expanded ed.. Atlanta, Georgia: ERDAS Worldwide Headquarters.
Francois, M.J. and Ramirez, I. 1996. Comparison of Land Use Classifications Obtained by Visual Interpretation and Digital Processing. ITC Journal 96 - 34, pp 278-283.
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Indonesia, Food Fertilizer Technology Center, Taiwan and National Institute for Agro-Environmental Sceinces, Japan.
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258 Landis, J. R., Koch, G. G. 1977. The measurement of observer agreement for categorical
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Lu, D., Weng, Q. 2007. A survey of image classification methods and techniques for improving classification performance. International Journal of Remote Sensing, 285, 823 -
870. Prasetyo, L. B. 2004. Deforestasi dan degradasi lahan DAS Citanduy. Bogor: Pusat Studi
Pembangunan - lnstitut Pertanian Bogor and Partnership for Governance Reform in Indonesia - UNDP.
Salovaara, K. J., Thessler, S., Malik, R. N., Tuomisto, H. 2005. Classification of Amazonian primary rain forest vegetation using Landsat ETM+ satellite imagery. Remote
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based and satellite-derived deforestation estimates in the Amazon basin using a hybrid classifier. International Journal of Remote Sensing, 286, 1299 - 1315.
Bogor, 21-22 October 2015
259
PAPER C3 - The Hydrological Function of
Eucalyptus pellita
Plantation, in Riau
Agung B Supangat
1 1
Forestry Technology Research Institute for Watershed Management Jl. A. Yani-Pabelan, PO BOX 295 Surakarta, Central Java, Indonesia
Coressponding Email: maz_goenkyahoo.com
ABSTRACT
Forest plantation development in Indonesia still has many obstacles. One of which is related to issue stating that use of short rotation plants fast growing species are allegedly going to
drain the water for their rapid growth. The aim of the research was to determine hydrological function of E. pellita F.Muell plantation forest. The study was conducted in forest plantation
land of E. pellita, located in Perawang, Riau Province. To evaluate the hydrological data the study of evapotranspiration, surface runoff, sedimentation rate, and water balance was applied.
The results show that ecosystem of E. pellita plantation in Riau are in humid tropical climate zones which are susceptible to degradation, explained by high annual rainfall and degradable
soil of ultisls. The water use evapotranspiration rate of E. pellita plantation is high enough i.e: 1,188
– 1,834 mm.year
-1
or 47.8 – 71.5 of annual rainfall. However, these number is still
below the average of the annual rainfall 2,361 mm.year
-1
, so that the potential of water deficit drought can still be avoided. The loss of water from the ecosystem both through the
interception or runoff are less enough. The interception rate is around 13.3 – 18.7 of
rainfall, whereas runoff rate is around 1,663 – 2,813 mm.year
-1
or 14.2 – 50.6 of annual
rainfall. The average of sediment yield is as low as 3.14 ton.ha
-1
.year
-1
. Analysis within a plantation cycle concluded that there was a critical growth phase prone to hydrological
hazards that were post-logging until 1 year old plants phase. Keywords: E. pellita, forest plantation, evapotranspiration, runoff, sedimentation
1. INTRODUCTION The development of plantation forest is a necessity in Indonesia, in addition to be aimed to
fulfill the need of raw material in the forest industry, it is also expected as a rehabilitation effort in forest area and degraded land in order that its ecological function can be improved
MoF, 2004. However, the development of plantation forest has still many obstacles, one of them is related to the issue stating that the development of plantation forest particularly,
industrial plantation forest HTI is only to cause a lot of problems about environment such as the drought and the soil fertility decline. The environment issue is related to the use of
short rotation plant fast growing species supposed will deplete the nutrient and water to its rapid growth Bruijnzeel, 1997; Colin, et al., 2004.
There are many research into ecological aspects of plantation forest. The development of plantation forest could change the hydrological characteristics such as water yield quantity
and quality, runoff responses to rainfall input, and sediment yield. The conversion of natural forests into plantations significantly altered both streamflow Waterloo, 1994; Bruijnzeel,
2004; Zhao, et al., 2009, Sørensen, et al., 2009; Nóbrega, et al,. 2010; Zegre, 2011; Shamsuddin, et al., 2014, and sediment yield Brown K, 2010 ; Khanal dan Parajuli, 2013.
Bogor, 21-22 October 2015
260 Eucalyptus pellita F. Muell, one of the Eucalyptus species, was widely developed by PT. Arara
Abadi in Riau Province with 6 years of cutting cycle. The plantation activities have allegedly affected the watershed hydrological function. Some experts believe that fast growing species
FGS such as Eucalyptus sp. have effects on becoming lower soil moisture content, reducing soil fertility and declining water yield due to the high consumes of water for their rapid growth
Smith, et al., 1974; Feller, 1981; Lima, et al., 1990; Calder, 1992; and Pudjiharta, 2001. The aim of the research was to determine the hydrological function of the E. pellita F.Muell
plantation forest, in Riau Province. 2. EXPERIMENTAL METHOD
2.1 Time and Research Location The research was conducted from year 2008 to 2012. The location of research was in the area
of industrial timber concession of PT. Arara Abadi, Perawang, Riau Province of Indonesia Figure 1.. The company has been getting kind of E. pellita plantations - scale enterprise for
the purposes of producing raw material for pulp. The geographical location are at 00º 41.656 to 00º 45.361 N and 101º 34.657 to 101º 36.384 E, with altitude ranging between 39-74
meters above sea level. The climate type is A according to Schmidt-Ferguson classification, with annual rainfall ranges from 1,937-3,484 mm average of 2,361 mm yr, average of daily
air temperature is 27.7 ºC, while average of daily humidity is 68.7. Type of soil is Ultisols Red Yellow Podzolic, with soil texture in range from sandy loam to sandy clay loam.
Figure 1: Location of research
DAS SIAK, RIAU
PT. Arara Abadi, Perawang - Siak
Micro catchment Petak 175-B
Bogor, 21-22 October 2015
261
2.2 Material Material used in this research is a 4.62 ha of micro catchment which is consisted E. pellita
forest plantation planted on may 2006. While, tools conducted in this research are a set of automatic water level logger and suspended sediment sampler installed at the catchment
outlet, manual daily rainfall recorder, and a set of interception losses measurement tools. 2.3 Method
There were several studies had been conducted to observe water balances, with parameters measured including crop water requirement evapotranspiration, ET, interception losses, and
water and sediment yields. The method of each study has been conducted by field observation using a 4.62 ha of micro catchment as observation unit, explained below.
2.3.1
Interception losses
The interception losses were studied by measuring the rainfall interception by tree canopy, consist of throughfall and stemflow. Throughfall was measured by using five gauges in the
micro catchment area. The gauges was made from PVC pipe with a circular hole of diameter 10 cm and 30 cm height. They were placed on soil surface at 1.50 m height. Stemflow was
measured with five trees equipped with a spiral plastic gutter after the bark had been removed locally, and water collection from plastic box at below. Throughfall and stemflow volume
ml were measured and converted to depths mm by dividing the surface area occupied by each canopy tree.
2.3.2
Water discharge and sediment consentration
treamflow of the micro catchment was measured by V-notch 90 degree for 0-20 cm of river water level, and Cipolleti weir for 21 up cm of river water level. River water levels were
recorded in 10 minutes interval by tools of Automatic Logger Water Level Recorder. The manual reading of river water levels as the control were conducted by using staff gauge peilskal tools
installed at the side of river, with replications of 3 times every day. The annual discharge m
3
.s
-1
was estimated by using a regression formula to analyse the relationship between water level of the river and the river discharge stage-discharge rating curve.
The regression is Q = a Hb; where a and b are the numbers determined by slope degree of V-notch and Cipolleti weirs USDA, 2001.
The suspended sediment consentrations SSC; g.l
-1
were measured by using tools of sediment sampler installed in the river with height intervals of 5 cm start from 0 cm at spillway. Water
and sediment suspend in the water at each water level were collected in sample bottles, and was brought to laboratory for sediment analysis.
2.3.3
Plant water use
Water consumption by plantation forest crops water use is commonly called the evapotranspiration Al-Kaisi and Broner, 1998. The evapotranspiration rate ET was
measured by using the formula of catchment water balance from Seyhan 1977. Thus, rainfall data precipitation and total runoff data were collected annually to measure the
evapotranspiration rate. 2.4
Data Analysis 2.4.1
Interception losses
The calculation of interception losses by canopy cover will be conducted using an equation as follows Waterloo, 1994:
Bogor, 21-22 October 2015
262 E
i
= P
g
– Tf – Sf 1
Where : E
i
= Interception losses by canopy cover mm.day
-1
P
g
= Daily rainfall mm Tf
= Daily Throughfall mm Sf
= Daily Stemflow mm