Project Paper, Book & Report -

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I. INTRODUCTION

I.1.

B

ACKGROUND

Telaga Warna is a shallow basin lake, situated at 25 km southeast of Bogor. This lake contains freshwater supporting a rich ecosystem, promoting the lives of three primate species of Java such as Hylobates moloch (owa jawa), Presbytis comata (surili), Trachypithecus auratus (lutung), and Macaca fascicularis (monyet ekor panjang) (Hernowo, 2006). Considering to its function, the national government established its status as Nature Reserve in 1981 through Ministry of Forestry Regulation No. 481/Kpts/Um/6/1981.

The sustainability of this ecosystem and its function in the future are questionable by looking the situation around Telaga Warna. The permeable and fertile soils, high rainfall, reliable supply of good quality water, good climatic conditions, availability of cheap labor, and easy access to Bogor city, are the causes of horticultural industry and settlement booming around the lake. The population has increased tremendously around the lake, resulting in a proliferation of unplanned settlements. The rapid development seems not to be decelerated among many speculations on the complex relationships between resources, resource users, and subsequent disputes. Because of that, its function as water catchments area preserving hydrological function to the lower land is facing uncertainty in the longer term. It becomes an interesting case for natural resource management, specifically to answer the question how to preserve this ecosystem which surrounded by a very dynamic landscape (BKSDA Jawa Barat, 2005) .

Sustainable land use is a complex problem which containing several issues in different fields and then need to be solved by integrating the information from different field studies. It requires not only the information come from relation of natural condition and natural process such as land cover change and soil erosion, but also the information from analysis of socio-economic condition of the catchments area. It aims to prevent even a small part of land losing their function or degraded. Lack of information about the condition of land is one of important problem to know why most of soil in mountainous area is degraded (Tuan, 2004). To comprehend such issue, many aspects and levels using information from history to present needs to be considered (Mannaerts 1993). One of the major factors that affect sustainable land use is soil erosion.

The relationship between land cover and soil erosion is obvious. The change of land cover tends to affect the quantity of soil erosion. Hence, the information of soil erosion through land cover change assessment can be used to design sustainable land

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Above rationale encourage the investigation of soil erosion through land cover change assessment in relation to the land use development for conserving Telaga Warna in the long term. Under some limitation, the result of this study is expected to facilitate the design of land use especially its natural condition (land cover) which minimize soil erosion in “Telaga Warna” catchments area.

I.2.

O

BJECTIVES

The objectives of the research are as follows:

1)

To analyze the relation of soil erosion with land cover change in Telaga Warna Nature Reserve using USLE model by means of Remote Sensing and Geographical Information System.

2)

To provide accurate information of critical land / land degradation map for land use planning.

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II. LITERATURE REVIEW

II.1.

FACTORS AND CAUSES OF LAND DEGRADATION

Factors of land degradation are the biophysical processes and attributes that determine the kind of degradation processes. These include land quality as affected by its intrinsic properties of climate, terrain/landscape, vegetation, and biodiversity, especially soil biodiversity. Causes of land degradation are the agents that determine the rate of degradation. These are biophysical, socioeconomic, and political forces that influence the effectiveness of processes and factors of land degradation (Eswaran et al., 2001; Zerabruk, 2003).

Basically soil erosion by water is an interaction of two items, the water and the soil. However, there are subsidiary factors that can influence the interaction between the two items (Zerabruk, 2003). Hence, the rate and magnitude of soil erosion by water is dominantly controlled by the following factors:

A. Rainfall erosivity and runoff

Both rainfall and runoff factors must be considered in assessing a water erosion problem. Erosivity is the capacity of rainfall to cause erosion. It is a function of rainfall intensity, rainfall amount, erosive rain, rainfall duration, and kinetic energy of rain (Shestha, 2002; Zerabruk, 2003). The soil lost due to rainfall is directly related to the duration and intensity. As the intensity increases so does the diameter of each raindrop. In addition, as the size of each raindrop increases, so does the amount of energy transferred to the soil when it hits the soil surface. Thus, the more energy a particular raindrop possesses, the greater erosive capability it has. Surface runoff occurs when rainfall exceeds a soil’s maximum saturation level and all surface depression storage is filled to capacity. The amount of runoff can be increased if infiltration is reduced due to soil compaction (Zerabruk 2003).

B. Soil erodibility

Erodibility of a soil is its vulnerability or susceptibility to erosion. The factors which affect erodibility of a soil fall into three broad groups namely physical features of soils, such as aggregate stability, particle size distribution, base minerals, organic carbon content, clay mineralogy, infiltration capaciity, pore size, pore stability and moisture holding capacity of soil, topographic features and management of the land (Shestha, 2002; Zerabruk, 2003). Generally, soils with faster infiltration rates, higher levels of

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organic matter and improved soil structure have a greater resistance to erosion. Sand, sandy loam and loam-textured soils tend to be less erodible than silt, very fine sand and certain clay textured soils. Tillage and cropping practices, which lower soil organic matter levels, cause poor soil structure, and result compacted soils that contributed to increases in the soil erodibility. Soil erodibility index can be derived from nomographs or determined using different methodes such as the simple field test.

C. Slope gradient and length

Naturally, high gradient of slope, the greater the amount of soil loss from erosion by water will be. Soil erosion by water also increases as the slope length increases due to the greater accumulation of runoff. Slope gradient and length can be determined from field estimation (using clinometers, compas and measuring tape), topographic maps, and DTM (Shestha, 2002; Zerabruk, 2003).

D. Vegetation/crop cover

Soil erosion potential is increased if the soil has no or very little vegetative cover of plants and/or crop residues. Plant and residue cover protects the soil from raindrop impact and splash, tends to slow down the movement of a surface runoff and allows excess surface water to infiltrate. The erosion-reducing effectiveness of a plant and/or residue cover depends on the type, extent and quantity of cover.

E. Conservation measures

Certain conservation measures can reduce water erosion. Tillage and cropping practices, as well as land management practices, directly affect the overall soil erosion problem and solution on a farm.

II.2.

P

OTENTIAL

S

OIL

L

OSS

E

STIMATION

B

ASED ON THE

U

NIVERSAL

S

OIL

L

OSS

E

QUATION

(USLE)

The USLE is a simple multiplicative model to calculate potential soil loss. The USLE can be written as:

A = R * K * LS * C * P with:

 A –computed spatial average soil loss per unit of area, expressed in t/ha/a.

 R – rainfall-runoff erosivity factor – the rainfall erosion index plus a factor for any significant runoff from snowmelt.

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 K – soil erodibility factor – the soil-loss rate per erosion index unit for a specified soil as measured on a standard plot, which is defined as a 22.1 m length of uniform 9 % slope in continuous clean-tilled fallow.

 L – slope length factor – the ratio of soil loss from the field slope length to soil loss from a 22.1 m length under identical conditions.

 S – slope steepness factor – the ratio of soil loss from the field slope gradient to soil loss from a 9 % slope under otherwise identical conditions.

 C – cover-management factor – the ratio of soil loss from an area with specified cover and management to soil loss from an identical area in tilled continuous fallow.

 P – support practice factor – the ratio of soil loss with a support practice like contouring, strip cropping, or terracing, to soil loss with straight-row farming up and down the slope.

II.3.

R

EMOTE

S

ENSING

T

ECHNIQUES TO

L

AND

D

EGRADATION

D

ETECTION

Remote sensing provides a convenient source of information but the data collected by these instruments do not directly correspond to the information we need (Hill et al., 1995b; Torrion, 2002). Remote Sensing has high potential for critical land data collection due to large area coverage, regular time interval, spatial and spectral resolution and which facilitates detection of degraded areas (De Jong, 1994; Torrion, 2002). For degradation mapping, features whether they are directly or indirectly visible on the ground should be considered. For this reason, signs of degradation features should be well considered. Degradation features that must be checked in the field include: 1) signs of degradation on bare ground, 2) signs of degradation provided by vegetation and land use, and 3) signs of degradation provided by the terrain morphology (Torrion, 2002).

Torrion (2002) arrived at two basic approaches dealing with change detection; 1) the comparative analysis of independently produced classification for different dates, and 2) the simultaneous analysis of multi-temporal data. Different change detection techniques include univariate image differencing, vegetation index differencing, image regression, image ratioing, principal component analysis, post classification comparison, direct multi-date comparison, change vector analysis and background substraction. Various researchers use multi-sensor data in monitoring salt affected soils, water-logged and eroded soils, and desertification (Tripathy et al., 1996; Torrion, 2002). Though researchers devised best techniques to serve their purpose, these techniques seem to yield different levels of results for different environmental features and applications.

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III. MATERIALS AND METHOD

III.1.

T

IME AND

L

OCATION OF

S

TUDY

The study area is situated in the Puncak, Bogor District, about 25 km southeast from Bogor (Figures 3.1). Its geographic position is between 106,99° – 107,01° E longitude and 6,68° - 6,70° S latitude. The altitude range of the study area is about 1250 - 2000 meters a.s.l.

Figure 3.1. Location map of the study area

III.2.

E

QUIPMENT

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Table 3.1. Equipment used

No. Item Utilization

1. PC Desktop Intel IV, RAM 1GB

Desktop computer for processing the data

2. Tablet PC, RAM 1 GB Portable computer used for ground truthing

3. GPS (Garmin GPSMap 176C) Global Positioning System receiver used to

acquire earth-referenced coordinates

4. ERDAS Imagine 8.5 Software used for image data processing

5. ArcView 3.2 Software used as a main GIS Desktop

application for map viewer, spatial analysis, and map layouting

6. Spatial Analyst Tool An ArcView extension for grid-based data

analysis

7. Soil Water Assessment Tool

(SWAT)

An ArcView extension for delineating watershed and catchment area

8. MS Excel Office application used to analyze the

statistics of the data or results

III.3.

D

ATA

The data is classified into two types, i.e. digital satellite imagery and vector data. Landsat TM and Landsat ETM+ are the satellite imagery used for land cover extraction and further for land cover change analysis. IKONOS imagery is used to help the identification of land cover types. The data used in this study is shown in the Table 3.2.

Table 3.2. Satellite data

No. Satellite Imagery Acquisition

1. Landsat TM Path: 122, Row: 65, year 1991, 1994, and 1997

2. Landsat ETM+ Path: 122, Row: 65, year 2001, 2004, and 2006

3. IKONOS 2002

Digital vector data used are topographic and soil map. Contour feature from topographic map was used to generate Digital Elevation Model (DEM) data. DEM, geological and soil map is used in the analysis of soil erosion. The detail of the used digital vector data are provided in the table below.

Table 3.3. Digital vector data

No. Data Scale Source

1. Topographic Map (Sheet

No. 142 and 1209-231)

1:25.000 BAKOSURTANAL (1999)

2. Geological map 1:100.000 Geological Research and

Development Center (1993)

3. Soil map and Land Unit

map of Bogor

1:250.000 Center for Soil and Agro-climate

Research (1991)

4. Rainfall Map 1:250.000 Meteorological and Geophysics

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III.4.

M

ETHODOLOGY

Digital imagery data is the primary data of the study. These data must be prepared before further analysis. Figure illustrates the overall process used for handling the data.

Figure 3.2. Overall image processing and analysis procedure

Note:

GC= Geometric corrected RC = Radiometric Corrected

Soil Data Classification

Accuracy Analysis

Soil Erosion Analysis Geometric

Correction

Atmospheric Correction

Surfacing Landsat

Imageries

Ikonos Imagery

Countur Lines Road Data

DEM GC Landsat

Imageries

GC Ikonos Imagery

Topographic Data

RC Landsat Imageries GCP

Data

Image Classification

Watershed Delineation

Ground Truth Data

Land Cover Change Analysis

Watershed Data Land

cover

Assessed Land Cover

Rainfall Data Data Processing

Data Analysis Data Preprocessing

Slope Classes

Slope Length Slope Classes

Generation

Slope Length Generation

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III.5.

D

ATA PREPROCESSING

The aims of data preprocessing are to improve the quality of the images and to produce the intermediate data which will be used in further processing. There are three kind of data preprocessing done, i.e.: geometric correction, atmospheric correction, and DEM generation. The description of the method of each process is given in the subsections below.

A. Geometric Correction

Topographic map (especially road map) and Ground Control Point Data were used as reference data to rectify IKONOS and Landsat data. Polynomial order was used for developing transformation model and the used re-sampling method was cubic convolution.

B. Atmospheric Correction

Atmospheric correction is conducted consider that the time when the satellite captured the earth image, certain atmospheric condition occurred which affecting spectral representation of features in image through the digital number. The atmospheric condition may unique on each features but the reflectance value of features is always constant. Therefore, to compare and process multi-date images data, the images should radiometrically corrected in such that each feature has a similar reflectance value (digital number) throughout the images.

In this research, relative radiometric normalization was used to improve the radiometric quality of multi images efficiently. Another atmospheric correction method used is haze removal. The description below describes the atmospheric correction used in detail.

C. Relative Radiometric Normalization

Relative radiometric normalization (RRN) comprises of many methods, such as pseudo-invariant features, radiometric control set, image regression, no-change set determined from scattergrams, and histogram matching (Yang and Lo, 2000). Image regression (IR) based on pseudo-invariant features (PIF) is preferred consider that it is used to reduce variability throughout image other than land cover changes (Phua and Tsuyuki, 2006) and possible to know the uncertainty level of developed model. It works by using one image as reference and adjust the brightness of the rest image to match the reference.

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The dark object method was carried out upon Landsat images 2006 which is assigned as referenced data of normalization. Even the image was suffered from striping caused by Scan Line Corrector (SLC) failure, the radiometric quality remains intact (USGS, 2006). The complete process of atmospheric correction can be seen in the figure below.

Figure 3.3. The Process Flow of Atmospheric Correction

D. DEM Generation

DEM (Digital Elevation Model) is one of raster data used in this study which generated from contour data. Contour data was came from topographic map (Rupa Bumi Indonesia map), sheet no. 1209-142 and 1209-231, with contour interval 12.5 m. DEM was generated through surfacing process by using non-linear rubber sheeting method. In order to have same in resolution of data, DEM produced have a resolution as same as

Atmospheric Correction: dark object method Data Preprocessing

Landsat 12265_ 2006

Landsat 12265_ 1991 12265_ 1994 12265_ 1997 12265_ 2001 12265_ 2004 RC Landsat

12265_ 2006

PIS Selection

 STD of PIS of reference image (σR(k))

 STD of PIS of subject image (σS(k,y))

 Mean of PIS of reference Image (Rk)

 Mean of PIS of subject image (Sk,y )

Note:

PIS = Pseudo-invariant Set STD = Standard Deviation RC = Radiometric Corrected Transformation

 Transformation Coefficient slope: mk, y= σR(k) / σS(k,y)

intercept: bk, y = Rk - mk, y . Sk,y  Image Regression Model

Yy = mk, y . Xk + bk, y Reference Image

Subject Images (Xk)

Landsat 12265_ 1991 12265_ 1994 12265_ 1997 12265_ 2001 12265_ 2004 Normalized Images(Yy)

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LANDSAT data, that is 30 m x 30 m. In further step of analysis in this study, the DEM was converted to a slope map and then used as a one factor for estimate soil erosion.

III.6.

D

ATA PROCESSING

There are three kinds of data processing are carried out to prepare the data for the main analysis in the study (that is soil erosion analysis), comprise of image classification, watershed delineation, and slope classes and length generation.

A. Watershed Delineation

The watershed delineation tool uses AVSWAT-2000 (version 1.0), the extension for ArcView and Spatial Analysis. It is used to perform watershed delineation based on SWAT (Soil and Water Assessment Tool) model. SWAT is a model developed to predict the impact of land management practices on water, sediment, and agricultural chemical yields in large, complex watershed with varying soils, land use, and management conditions over long period of time (Arnold et al., 1998). The delineation process requires a Digital Elevation Model (DEM) in ArcInfo grid format. The diagram flow below illustrates the steps in the delineation process:

Figure 3.4. Delineation process Topographic Data

Digital Elevation Model (in Erdas format *.img)

Digital Elevation Model (In ArcInfo grid format *.grid)

Surfacing

Export *.img to *.grid

Process in ERDAS

Outputs (*. shp file): 1.Sub-watersheds

area

2.The stream network  Load the DEM

 Preprocess the DEM  Specify the minimum sub-watershed area (catchments area)  Review and edit the

stream network points  Delineation Process

Process in ArcView

(AVSWAT Ext.)

Data Processing Data Preprocessing

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Figure 3.5 shows the catchments area which generated from Digital Elevation Model (DEM). Delineating process obtained three large watersheds, which overlapped with Telaga Warna Nature Reserve, as follows: Ciliwung, Cigundul and Cibeet watersheds. However, the area study is limited on the catchments area (part of watershed) that has an upper stream region on nature reserve area. Figure 3.5.b. shows that area study has eight catchments that have upper stream region on nature reserve area.

Figure 3.5. Delineating catchments area

B. Slope Classes and Length Generation

The length and the gradient of the slope have a major influence on the amount of soil erosion that can be occurred. The DEM was generated using surfacing method used to produce length and gradient of the slope. The length and gradient of slope are illustrated in the figure below:

The example of procedure: interval contour 25 m (rise) and distance 50 m (run), so length of slope is 55.9 m (Pythagoras Equation) and gradient of slope is 50 %.



tan  =

run

rise

run

Percent of slope =

x

100 run

rise

Length of slope _{Degree of slope/gradient =}_

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n11 n12 n1k n1+

n21 n22 n2k n2+

nk1 nk2 nkk nk+

n+1 n+2 n+k n

Row total

Column total

Reference

lass

ion

C. Classification Accuracy Analysis

Essentially, the procedure of accuracy assessment (Congalton and Green, 1999; USGS, 2006b) can be summarized into two general procedures, i.e.: sampling design and the assessment of accuracy parameters.

a. Sampling Design

 Sampling definition

 Sampling unit and size (total area in observation: 9629.23 ha, including watershed)

 Sampling technique

b. Assessment of the Accuracy Parameters

Figure 3.6. Possible Accuracy Categories of Classification

The accuracy parameters involve in building error matrix, estimating accuracy parameters such as total accuracy, user’s accuracy, and producer’s accuracy (Congalton and Green, 1999), KHAT statistic (Kappa estimator) and its variance. Error matrix describes the number of sample units (collected from reference data) assigned to three possible accuracy categories, i.e.: correctly classified, misclassified and unclassified categories (Figure 3.7.).

Figure 3.7. Error Matrix

Misclassified and unclassified category is usually known as commission error (user’s accuracy) and omission error (producer’s accuracy) respectively. Misclassified occurs when an area is included in an incorrect category, but omission error occurs when an area

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is excluded from the category to which it belongs (Congalton and Green, 1999). The design of error matrix is illustrated by Figure 3.7.

The following equations are used to calculate the accuracy parameters (Congalton and Green, 1999):

 Total (Overall) Accuracy (

P

ˆ

n

P

k i ii





ˆ

_;



Producer’s Accuracy (

P

ˆ

)

j jj j A n n P   ˆ



User’s Accuracy (

PˆU

):





i ii Ui

n

P

ˆ

Other accuracy parameter used is Kappa parameter, which estimated by computing KHAT statistic. KHAT value is a measure of agreement (accuracy) based on the difference between actual agreement in the error matrix (the accuracy between classification and reference data as indicated by the major diagonal) and the chance agreement (as indicated by the row and column totals). In short, it is a measure how well the classification agrees with reference data (Congalton and Green, 1999). The equation of KHAT statistic is given below.



          _k i i i i k i i k i ii n n n n n n n K 1 2 1 1 ˆ

The advantage of using KHAT statistic is that confidence interval around KHAT can be computed. Additionally, it has normal distribution. Therefore, the hypothesis (whether the significance of the agreement between the classification and there reference data is greater than 0) can be formulated and tested. The equation used to compute variance of KHAT statistic is shown in the following equations (Congalton and Green, 1999):

nii : diagonal elements of error matrix at i-th row and i-th column

i : the i-th row of error matrix

n : total number of sample units

j A

Pˆ : Producer’s accuracy at j-th class

njj : diagonal elements of error matrix at j-th row and j-th column

n+j : total number of sample units in j-th column

i U

Pˆ : Producer’s accuracy at i-th class

nii : diagonal elements of error matrix at i-th row and i-th column

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





















_























 4 2 2 2 4 2 1 3 2 3 2 1 1 2 2 1 1

1

4

1

2

1

2

1 )

ˆ

(

var



n

K

;





k i ii

n

₁ 1

1 

;



  



k i i i

n

1 2 2

1 

;







  



k i i i ii

n

1 2 3

1 

;







     k i k j i j ij n n

n 1 1

2 3



The test statistic for testing the significance of KHAT statistic from a single error matrix is expressed by:

)

ˆ

(

var

ˆ

K

Z





The null hypothesis is rejected when Z ≥ Zα/2, where α/2 is the confidence level of

the two-tailed Z test.

D. Land Cover Change Analysis

The method used for analyzing land cover change per year was Boolean method. Subsequently, the descriptive statistics in the tabular and graphic format was used to expose the change of each class in sequential years. The analysis was carried out by using MS Excel.

III.7.

S

OIL EROSION ANALYSIS

The erosion is estimated by using USLE (Universal Soil Loss Equation) model (Wischmeier dan Smith, 1978). The model is ordered several factors which presumed affected land erosion, which mathematically describe by the equation below:

A = R x K x LS x C x P

A : Maximum soil erosion rate (ton/ha/year)

R : Rain erosivity factor K : Soil erodibility factor

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C : Plant management factor index

P : Soil conservation techniques factor index

1. Erosivity Factor

Erosivity factor is estimated by considering monthly rainfall intensity (r), rainy days (D), and maximum rainfall intensity (M) in 24 hours. The equation is given below (Bols, 1978):

EI

= 6,119 .r

1,21

.D

-0,47

. M

0,53

2. Erodibility Factor

Soil erodibility is estimated based on soil sensitivity on erosion, which influenced by the nature of soil texture, structure, permeability and contained bio-organic matters.

100 K = 1.292 [2.1 M

1.14

(10

-4

) (12-a)] + 3.25 (b-2) + 2.5 (c-3)

where:

M : (% sand + % dust) x (100 - % clay) a : soil organic matter concentration (%) b : soil structure codes

c : soil permeability classes

3. Slope and length factor

The length slope (LS) is estimated by the following equation below (Ardis and Booth, 2004):





const length slope

slope slope

LS 

  

   



 0.065 0.0456. 0.00654. 2 _

Where:

slope : slope steepness (%) slope_length : length of slope (meter) const : a constant, i.e.: 22.1

NN : constant determined from Table Error! Reference

source not found..

Table 3.4. Slope Range and NN Exponent Constants (Arsyad, 1989)

No. Slope NN

1 < 1% 0.2

2 1% < slope < 3% 0.3

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4. C and P Factor

C and P factor is determined by referring to the investigation of Arsyad (1989) and Wischmeier and Smith (1979 in Arsyad, 1989) and the information obtained from ground check data and image interpretation.

The relationship between land cover change and erosion rate was described by projecting area of each land cover class and erosion rate into time line. The cumulative erosion is also examined on each sub-watershed and illustrated using histogram.

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IV. RESULTS AND DISCUSSION

IV.1.

T

OPOGRAPHIC NORMALIZATION OF LANDSAT IMAGE

The area study is a mountainous area which certain parts have a significant variation of topography. In turn, it affects the result of classification. Therefore, topographic correction has to be done, before conducting classification. Topographic slope, aspect, calculated incidence, and angle existence were merged with the multi-spectral Landsat response for TM/ETM bands. In order to fit a generalized photometric function, Minnaert method, a member of non-Lambertian model in which utilizing regression analysis, was applied (Smith et al., 1980; Riano et al., 2003; Kato, 2003). Figures below show the results of the topographic normalization process.

Figure 4.1. Results of topographic normalization LANDSAT Image

Generally, the effect of topography was reduced. Previously, at the area which has a high slope and aspect in line with solar radiation is darker than others, even they has a same land cover type (Figure 4.1). Minnaert method was able to correct such error, although it was overestimated for a small area which slopes more than 50 (Figure 4.1).

IV.2.

L

ANDSCAPE AND SOIL CLASSIFICATION

Based on Landsystem Map (Landsystem with Suitability and Environmental Hazard, 1989), there are three soil associations found on the site location. They have

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Figure 4.2. Landscape and soil classification

Soils of the Dystrandepts-Hydrandepts association occur in largely inaccessible deep V-shaped drainages and narrow ridges on very steep west/east-facing slopes above an elevation of about 1,500 meter a.s.l (above sea level), and serve primarily as a watershed (Foote et al. 1972) (Figure 4.3.). These are thin, well-drained, light soils derived from volcanic ash that are high in organic matter, can contain more water than other soil when saturated, and are strongly to extremely acid. Because of their characteristics, medium textured soils, such as the silt loam soils, this association have moderate K values, about 0.25 – 0.40. They are moderately susceptible to detachment and produce moderate runoff (http://www.iwr.msu.edu). Their occurrence on steep slopes, potential friability at the surface, and fine silty texture, the soil erosion hazard by water is high for this association, especially in areas where vegetation has been depleted. However, in case a forest as cover vegetation, organic matter in their structure can reduces erodibility because it reduces the susceptibility of the soil to detachment, and it increases infiltration (Armas, 2004).

Association Dystrandepts-Hydrandepts

(Landsystem Type: TGM) Association

Vitrandepts-Eutropepts (Landsystem Type: CGS)

Association Dystropepts-Tropudults (Landsystem Type: BSM)

Ciliwung Hulu _Cibeet

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Figure 4.3. Description for soil type in study area

Vitrandepts-Eutropepts association is a porous soil, which developed from volcanic ash (Hart et al, 2004). Depending on the above parent material and weathering force, those soil type characterized by low soil organic matter, even though they is developed by deposits material from upper area and leaching process not as intensive as upper area. So have low K value, about 0.05 – 0.10, because erodibility can reduced if soil structure in stable caused by organic matter bounding.

Dystropepts-Tropudults association is a mature soil type, which weather process intensively and high in clay.

IV.3.

L

AND COVER CHANGES

Based on Landsat data there are 10 classes of land cover can be identified, which are forest, bush/scrub, mix garden, built up, paddy field, upland, tea plantation, bare land, grass and water.

In total 9629.23 ha of study area were evaluated for land cover changes. Table 4.1 presents about land cover changes from 1991 until 2006. In detail, land cover change

Dystrandepts-Hidrandepts Association

Vitrandepts-Eutropepts Association

Dystropepts-Tropudults Association

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Table 4.1. a. Land cover changes from 1991 until 2006

1991 1994 1997 2001 2004 2006

(hectare)

Forest 3315.51 3162.60 2978.64 2726.73 2474.19 2000.340

Bush 1043.46 1027.62 1286.10 1171.62 1677.87 1393.660

Mix garden 574.74 898.47 422.64 804.78 409.06 824.490

Built up 133.11 255.06 352.35 457.65 743.76 1058.580

Paddy field 30.24 38.88 29.70 137.61 126.36 60.750

Upland 1172.25 904.32 1373.22 1215.99 977.13 920.250

Tea plantation 562.41 968.31 749.79 663.75 923.61 748.290

Bare land 763.83 318.42 301.68 124.29 224.56 582.22

Grass 8.46 29.88 111.24 310.77 53.01 21.24

Water 0.54 0.90 11.25 2.43 1.53 1.26

Cloud 4.14 4.14 4.14 4.14 4.14 4.14

Shadow 8.01 8.01 8.01 8.01 8.01 8.01

b. Trend cover changes from 1991 until 2006

1990-1994 1994-1997 1997-2001 1997-2004 2004-2006 Total Change (percent)

Forest -1.59 -1.91 -2.62 -2.62 -4.92 -13.69

Bush -0.16 2.69 -1.19 5.26 -2.95 3.64

Mixgarden 3.37 -4.95 3.97 -4.11 4.32 2.60

Builtup 1.27 1.01 1.09 2.97 3.27 9.63

Paddy field 0.09 -0.10 1.12 -0.12 -0.68 0.32

Upland -2.79 4.88 -1.63 -2.48 -0.59 -2.62

Tea plantation 4.22 -2.27 -0.89 2.70 -1.82 1.93

Bareland -4.64 -0.17 -1.84 1.04 3.72 -1.89

Grass 0.22 0.85 2.07 -2.68 -0.33 0.13

Water 0.00 0.11 -0.09 -0.01 0.00 0.01

Cloud 0.00 0.00 0.00 0.00 0.00 0.00

Shadow 0.00 0.00 0.00 0.00 0.00 0.00

The rates of land conversion between 1991, 1994, 1997, 2001, 2004 and 2006 are given in Figure 4.4. The negative bars represent land types that decreased in extent, whereas positive bars indicate land type that expanded.

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F o re st Bu sh M ix g a rd e n Bu il tu p Pa d d y fi e ld U p la n d T e a p la n ta ti o n Ba re la n d G ra ss W a te r C lo u d Sh a d o w -6.00 -5.00 -4.00 -3.00 -2.00 -1.00 0.00 1.00 2.00 3.00 4.00 5.00 1990-1994 % C h a n g e Forest Bush Mixgarden Builtup Paddy field Upland Tea plantation Bareland Grass Water Cloud Shadow F o re s t B u s h M ix g a rd e n B u ilt u p P a d d y f ie ld U p la n d T e a p la n ta tio n B a re la n d G ra s s W a te r C lo u d S h a d o w -6.00 -4.00 -2.00 0.00 2.00 4.00 6.00 1994-1997 % C h a n g e Forest Bush Mixgarden Builtup Paddy field Upland Tea plantation Bareland Grass Water Cloud Shadow Fo re s t B u s h M ix g a rd e n B u ilt u p P a d d y f ie ld U p la n d T e a p la n ta ti o n Ba re la n d G ra ss W a te r C lo u d Sh a d o w -3.00 -2.00 -1.00 0.00 1.00 2.00 3.00 4.00 5.00 1997-2001 % C h a n g e Forest Bush Mixgarden Builtup Paddy field Upland Tea plantation Bareland Grass Water Cloud Shadow F o re st B u sh Mi xg a rd e n B u ilt u p P a d d y fi e ld U p la n d Te a p la n ta ti o n B a re la n d G ra s s W a te r C lo u d S h a d o w -6.00 -4.00 -2.00 0.00 2.00 4.00 6.00 1997-2004 % C h a n g e Forest Bush Mixgarden Builtup Paddy field Upland Tea plantation Bareland Grass Water Cloud Shadow F o re st B u sh M ix g a rd e n B u ilt u p P a d d y fi e ld U p la n d T e a p la n ta ti o n B a re la n d G ra ss W a te r C lo u d S h a d o w -6.00 -4.00 -2.00 0.00 2.00 4.00 6.00 2004-2006 % C h a n g e Forest Bush Mixgarden Builtup Paddy field Upland Tea plantation Bareland Grass Water Cloud Shadow

Figure 4.4. The rates of land cover conversion from 1991 until 2006

Based on result of classification, forest, bush, upland and bare land has decreased between 1991 and 1994. However, upland and bare land fluctuate in next year. Its fluctuating happened depends on a season and planting time. Decreasing forest cover happened consistently until 2006, opposite with increasing in built up (settlement) cover.

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0.00 500.00 1000.00 1500.00 2000.00 2500.00 3000.00 3500.00

1991 1994 1997 2001 2004 2006

Year A re a ( he c ta re ) Forest Bush Mixgarden Builtup Paddy field Upland Tea plantation Bareland Grass Water Cloud Shadow 0.0 500.0 1000.0 1500.0 2000.0 2500.0 3000.0 3500.0

1991 1994 1997 2001 2004 2006

A re a ( he c ta re ) 0.0 200.0 400.0 600.0 800.0 1000.0 1200.0 Year A re a ( he c ta re ) Forest Builtup 0.0 200.0 400.0 600.0 800.0 1000.0 1200.0 1400.0 1600.0 1800.0

1991 1994 1997 2001 2004 2006

A re a ( he c ta re ) 0.0 100.0 200.0 300.0 400.0 500.0 600.0 700.0 800.0 900.0 Year A re a ( he c ta re )

Bush Mixgarden Paddy field Upland Tea plantation Bareland Grass Water Cloud Shadow

Figure 4.5. Land conversion processes from 1991 until 2006

Figure 4.5. shows land conversion processes between 1991 and 2006 with all lands grouped in three clusters. Degradation of ecologically valuable land caused by forest cover decreasing between 1991 until 2006 affects more than 13.66 % of the total land area (over 1.315 ha). Moreover, 9.61 % of the built up/settlement (over 925 ha) and 9.48 % of cultivated land always in fluctuated depends on a seasons and planting time (over 912 ha).

Between 1991 until 2006, bush, mix garden, built up, paddy field, tea plantation, and grass increased per year by 0.24 %, 0.17 %, 0.64 %, 0.02 %, 0.13 % and 0.01 %, respectively. Forest cover decreased by 1,315 ha representing decrease of 0.91 % per

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year. Areas of upland and bare land also decreased between 1991 until 2006 by 0.17 % and 0.13 % per year, respectively.

Upper Ciliwung

Cibeet

Cigundul

Telaga Warna Nature Reserve Telaga Warna Lake

Upper Ciliwung

Cibeet

Cigundul

Telaga Warna Nature Reserve Telaga Warna Lake

Upper Ciliwung

Cibeet

Cigundul

Telaga Warna Nature Reserve Telaga Warna Lake

2006

Upper Ciliwung

Cibeet

Cigundul

Telaga Warna Nature Reserve Telaga Warna Lake

2004

1991 1994

1997 2001

2004 2006

Upper Ciliwung

Cibeet

Cigundul

Telaga Warna Nature Reserve Telaga Warna Lake

Upper Ciliwung

Cibeet

Cigundul

Telaga Warna Nature Reserve Telaga Warna Lake

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0.00 100.00 200.00 300.00 400.00 500.00 600.00 700.00 800.00 900.00 1000.00

1991 1994 1997 2001 2004 2006

Year

(

)

0.00 20.00 40.00 60.00 80.00 100.00 120.00 140.00 160.00 180.00

(

)

Figure 4.6. Distributions of Land cover from 1991 until 2006

Figure 4.5. and 4.6. suggests that ecological causes behind almost 25 % (forest and built up cover conversion) of the total area. At the same time, although in fluctuating rate, but cultivated lands for agricultural productions tend to increase. These results suggest that prospects for land preservation of upper Ciliwung, Cigundul and Cibeet catchments area not very good without changes in policy. However, land preservation of area Telaga Warna Nature Reserve is currently good.

Figure 4.7. shows that catchments area in upper Ciliwung, Cigundul and Cibeet has different trend of forest and built up/settlement cover changes. Forest cover of upper Ciliwung watershed has curve decrease stepper than Cigundul and Cibeet watershed. During 5 years, built up cover in upper Cigundul watershed increased significantly.

Figure 4.7. Trend of forest and built up cover change in three catchments area

Increase of degraded land as product of land cover change during 1991–2006 will be analyzed hereafter-using soil erosion approach.

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IV.4.

A

CCURACY ASSESSMENT OF IMAGE CLASSIFICATION

In this study, image data used are Landsat 1991 to 2006 and classified to 10 classes of land cover, but for accuracy assessment only conducted on image 2006, because have limitation for ground control reference in the past.

The general acceptance of the error matrix as the standard descriptive reporting tool for accuracy assessment of remotely sensed data has significantly improved the use of such data. An error matrix is a square array of numbers organized in rows and columns which express the number of sample units (i.e. pixels and clusters of pixels) assigned to a particular category relative to the actual category as indicated by reference data (Congalton, 1996).

An initial error matrix was generated for the initial supervised 2006 land cover map prior to the mode filtering by creating a maximum likelihood report (Tabel 4.2). This report calculates area and percentages of each land class incorporated in a maximum likelihood classification. The ground truth data were utilized in the maximum likelihood report as the independent data set from which the classification accuracy was compared. The accuracy is essentially a measure of how many ground truth pixels were classified correctly.

Table 4.2. Classification Accuracy Assessment Report

Error Matrix Accuracy

No Classified Data Ground truth

Forest Bush Mix garden Built up Paddy field Upland Tea planta Bare land Grass Water (Percent)

1 Forest 25 88.00 8.00 0.00 0.00 0.00 0.00 4.00 0.00 0.00 0.00

2 Bush 7 14.29 85.71 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

3 Mix garden 20 0.00 0.00 90.00 0.00 0.00 5.00 5.00 0.00 0.00 0.00

4 Built up 25 0.00 0.00 4.00 88.00 4.00 4.00 0.00 0.00 0.00 0.00

5 Paddy field 0 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

6 Upland 13 0.00 0.00 0.00 0.00 0.00 100.00 0.00 0.00 0.00 0.00

7 Tea plantation 21 0.00 4.76 0.00 0.00 0.00 0.00 95.24 0.00 0.00 0.00

8 Bare land 4 25.00 0.00 0.00 0.00 0.00 0.00 75.00 0.00 0.00 0.00

9 Grass 0 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

10 Water 0 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Column Total 115 24 9 19 22 1 15 25 0 0 0

Accuracy Result

Class Names Reference Totals

Classified Totals

Number Correct

Producers

Accuracy Users Accuracy

Kappa Accuracy

1. Forest 24 25 22 91.67% 88.00% 0.85

2. Bush 9 7 6 66.67% 85.71% 0.85

3. Mix garden 19 20 18 94.74% 90.00% 0.88

4. Built up 22 25 22 100.00% 88.00% 0.85

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Class Names Reference Totals

Classified Totals

Number Correct

Producers

Accuracy Users Accuracy

Kappa Accuracy

8. Bare land 0 4 0 --- --- 0.00

9. Grass 0 0 0 --- --- 0.00

10. water 0 0 0 --- --- 0.00

Totals 115 115 101

Overall Classification Accuracy = 87.83% Kappa Coefficient = 0.8525

An overall accuracy of 87.83% was achieved with a Kappa coefficient of 0.8525. The overall accuracy is a similar average with the accuracy of each class weighted by the proportion of test samples for that class in the total training of testing sets. Thus, the overall accuracy is a more accurate of accuracy.

The importance and power of the Kappa analysis is that it is possible to test if a land cover map is significantly better than if the map had been generated by randomly assigning labels to areas (Congalton, 1996). It is widely used because all elements in the classification error matrix, and not just the main diagonal, contribute to its calculation and because it compensates for change agreement (Rosenfield & Fitzpatrick-Lins, 1986). The Kappa coefficient represents the proportion of agreement obtained after removing the proportion of agreement that could be expected to occur by chance (Foody, 1992).

This implies that the Kappa value of 0.8525 represents a probable 85% better accuracy than if the classification resulted from a random, unsupervised, assignment instead of the employed maximum likelihood classification.

The Kappa coefficient lies typically on a scale between 0 and 1, where the latter indicates complete agreement, and is often multiplied by 100 to give a percentage measure of classification accuracy. Kappa values are also characterized into 3 groupings: a value greater than 0.80 (80%) represents strong agreement, a value between 0.40 and 0.80 (40 to 80%) represents moderate agreement, and a value below 0.40 (40%) represents poor agreement (Congalton, 1996).

IV.5.

T

REND OF SOIL EROSION

In this study, soil erosion in the whole watershed was estimated with GIS Remote Sensing based on USLE. The rainfall factor R was assumed a constant value in the whole study area, because study area not too wide, and no enough rainfall data available, which accurate to determined rainfall map in detail scale. So, soil erosion rate in this study represented by soil loss risk.

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The result of calculation of soil erosion (soil loss risk) between 1991, 1994, 1997, 2001, 2004 and 2006 are given in Table 4.3 and Figure 4.8.

Table 4.3. Calculation of soil erosion from 1991 until 2006

No Class of

soil erosion

1991 1994 1997 2001 2004 2006

Area (hectare)

1 Very low 4921.47 5082.48 4814.82 4721.67 4467.78 3914.19

2 Low 1235.97 1267.38 1251.81 1257.57 1282.77 1366.65

3 Moderate 1278.90 1194.21 1416.24 1481.31 1721.61 1965.78

4 Heavy 149.13 100.17 149.13 164.52 147.24 259.11

5 Very heavy 111.78 53.01 65.25 72.18 77.85 191.52

1991 1994

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Figure 4.8. Distribution of soil erosion rate from 1991 until 2006

In figure above, shows the effect of land cover change to soil erosion that analyzed spatially and timely from 1991 until 2006. However, in figure above can seen that difficult to make some trend of soil erosion rate from each years, because interact with land cover factor change, and it is depend on season and planting time.

2004 2006

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Figure 4.9. Cover change and very heavy class of soil erosion

Figure 4.9. shows the result of analysis about effect of land cover change to soil erosion in pattern. Most areas with soil erosion rate in very heavy class happened if land cover change to bare land, up land, mix garden and built up, although that location not in steep of slope (more than 40 %). Very heavy class of soil erosion happened in bare land, mix garden, up land, built up and grass. The area for that class in each year showed in table below.

Table 4.4. Relation landcover change and very heavy class of soil erosion

No Type of land cover 91-94 94-97 97-2001 2001-2004 2004-2006

Area (hectare)

1 Mix garden 2.07 1.44 6.66 0.99 5.22

2 Built up 0.72 1.71 5.22 10.98 28.53

3 Upland 3.96 20.70 24.21 13.32 21.60

4 Bare land 27.99 26.19 14.58 19.44 96.21

2004 2006

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V. CONCLUSION

Based on result of classification, decreasing forest cover happened consistently until 2006, opposite with increasing in built up (settlement) cover. Cultivated area always in fluctuated depends on a seasons and planting time. However, land preservation of area Telaga Warna Nature Reserve is currently good.

The result of analysis about effect of land cover changes to soil erosion in pattern has showed that most areas with soil erosion rate in very heavy class happened if land cover change to bare land, up land, mix garden and built up, although that location not in steep of slope (more than 40 %). Very heavy class of soil erosion happened in bare land, mix garden, up land, built up and grass. The area for that class in each year showed in table below.

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IV.4.

A

CCURACY ASSESSMENT OF IMAGE CLASSIFICATION

Table 4.2. Classification Accuracy Assessment Report Error Matrix Accuracy

No Classified Data Ground truth

Forest Bush Mix garden Built up Paddy field Upland Tea planta Bare land Grass Water (Percent)

1 Forest 25 88.00 8.00 0.00 0.00 0.00 0.00 4.00 0.00 0.00 0.00 2 Bush 7 14.29 85.71 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 3 Mix garden 20 0.00 0.00 90.00 0.00 0.00 5.00 5.00 0.00 0.00 0.00 4 Built up 25 0.00 0.00 4.00 88.00 4.00 4.00 0.00 0.00 0.00 0.00 5 Paddy field 0 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 6 Upland 13 0.00 0.00 0.00 0.00 0.00 100.00 0.00 0.00 0.00 0.00 7 Tea plantation 21 0.00 4.76 0.00 0.00 0.00 0.00 95.24 0.00 0.00 0.00 8 Bare land 4 25.00 0.00 0.00 0.00 0.00 0.00 75.00 0.00 0.00 0.00 9 Grass 0 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 10 Water 0 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Column Total 115 24 9 19 22 1 15 25 0 0 0

Accuracy Result

Class Names Reference Totals Classified Totals Number Correct Producers

Accuracy Users Accuracy

Kappa Accuracy

1. Forest 24 25 22 91.67% 88.00% 0.85

2. Bush 9 7 6 66.67% 85.71% 0.85

3. Mix garden 19 20 18 94.74% 90.00% 0.88

4. Built up 22 25 22 100.00% 88.00% 0.85

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Class Names Reference Totals Classified Totals Number Correct Producers

Accuracy Users Accuracy

Kappa Accuracy

8. Bare land 0 4 0 --- --- 0.00

9. Grass 0 0 0 --- --- 0.00

10. water 0 0 0 --- --- 0.00

Totals 115 115 101

Overall Classification Accuracy = 87.83% Kappa Coefficient = 0.8525

IV.5.

T

REND OF SOIL EROSION

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The result of calculation of soil erosion (soil loss risk) between 1991, 1994, 1997, 2001, 2004 and 2006 are given in Table 4.3 and Figure 4.8.

Table 4.3. Calculation of soil erosion from 1991 until 2006 No Class of

soil erosion

1991 1994 1997 2001 2004 2006 Area (hectare)

1 Very low 4921.47 5082.48 4814.82 4721.67 4467.78 3914.19 2 Low 1235.97 1267.38 1251.81 1257.57 1282.77 1366.65 3 Moderate 1278.90 1194.21 1416.24 1481.31 1721.61 1965.78 4 Heavy 149.13 100.17 149.13 164.52 147.24 259.11 5 Very heavy 111.78 53.01 65.25 72.18 77.85 191.52

1991 1994

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Figure 4.8. Distribution of soil erosion rate from 1991 until 2006

2004 2006

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Figure 4.9. Cover change and very heavy class of soil erosion

Table 4.4. Relation landcover change and very heavy class of soil erosion

No Type of land cover 91-94 94-97 97-2001 2001-2004 2004-2006 Area (hectare)

1 Mix garden 2.07 1.44 6.66 0.99 5.22

2 Built up 0.72 1.71 5.22 10.98 28.53

3 Upland 3.96 20.70 24.21 13.32 21.60

4 Bare land 27.99 26.19 14.58 19.44 96.21

2004 2006

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