Satellite Data and Classification Method

44 Figure 1. Illustration of Classification concept in maximum likelihood method 2.3. Pre ‐processing and Classification Plan The pre‐ and processing of ALOSPALSAR‐ data is diagrammatically shown in Fig. . Pre‐processing is performed by the following steps: . Geometric correction consists of geocoding correction and image to image calibration method by collection ground control points GCP that Landsat 8 image is used as reference. The purpose of this process is to convert image’s coordinate to the real coordinate in the Earth. mage resolution is resampled to m resolution in the geocoding process using Digital Elevation Model DEM image. . Layovershadow masking. This process is aimed to decrease layover and shadow effects in the image. Layover effect is a condition where the information received in the satellite are mixed caused by differences in arrival time of lowland’s wavelength reflection and highland plateau ’s wavelength reflection. Shadow is an effect where there is no backscatter or wavelength reflection received by the satellite since the coverage of highlands to lowlands. . n the speckle filtering, Lee filter is applied that is aimed to reduce the speckles effect noise in the image. . Radar section calculation is a process to produce image in dB values. Figure 2. Flow diagram of Classification Process 44 The specification of Landsat 8 and ALOS‐ PALSAR‐ imageries that are used in this study is shown in Table . Table . Specification of Landsat 8 and ALOS‐ PALSAR‐ image No. Monitoring date Sensor Polarization Satellite type Data Level . December , PALSAR‐ , V Active, Non‐Optical . . February , PALSAR‐ , V Active, Non‐Optical . . December , Landsat ‐ Passive‐Optical ‐ . January , Landsat ‐ Passive‐Optical ‐ The pre‐processing of Landsat 8 image is performed by the following steps: . Radiometric correction is aimed to correct pixels’ value that are not fit with the real wavelength of reflected values. We applied the FLAAS algorithm in this process. . Atmospheric correction is aimed to reduce or to get rid of atmospheric disruption such as cloud. . Noise Reduction is aimed to reduce noise in the image using MNF algorithm. The study area is Bandung regency that covers Bandung city, Cileunyi, and Ciparay districts as shown in Fig. . Figure 3. Study area Google, 2015

3. Implementation and Classification Results

3.1. Radiometric Enhancement Landsat 8 and ALOS‐ PALSAR‐ imageries are combined or stacked to produce an RGB image. ALOS‐ PALSAR‐ polarization image, V polarization image, and Landsat 8 band near‐infrared band image are selected as Red, Green, Blue bands, respectively. Band of Landsat 8 is used to detect and to distinguish land cover such as forest and non‐forest, soil, and water. Radiometric process is performed after image stacking to increase the pixels’ intensity using piecewise linear algorithm as shown in Fig. . 447 a b Figure 4. Combined image of a ALOS‐2PALSAR‐2 on December 12, 2014 and Landsat 8 December 11, 2014 b ALOS‐2PALSAR‐2 February 25, 2016 and Landsat 8 January 15, 2016 Since the large scene of both Landsat 8 and ALOS‐ PALSAR‐ , to give more detail information on the study area, the imageries are cropped to Bandung regency only as shown in Fig. . a b Figure 5. Cropped image of a ALOS‐2PALSAR‐2 on Dec. 12, 2014 and Landsat 8 Dec. 11, 2014 image; b ALOS ‐2PALSAR‐2 on Feb. 25, 2016 and Landsat on Jan. 15, 2016 We selected training samples known as Region Of nterests ROs based on Google Earth Google, . Training samples of RO are then converted into .shp vector files and re‐projected into UTM Zone S to match to the area. There are four classes of land cover: forest, barren land in this study barren land consists of unused land and paddy fields that currently are not in planting or harvesting period , residential areas, and paddy fieldsplantations. Training samples are selected according to the dates of satellite data: December , and February , . We selected ROs as training samples for each classification class and divided into two parts: ROs are used for classification process and the other are used for accuracy assessment. 3.2. Maximum Likelihood Method Implementation n the classification, we used threshold value, the minimal probability value for a pixel to be clustered in a specific class. The threshold value used in this study is . for each class for all trials. As a result of this selection, some pixels will not be filtered into a specific 448 class unclassified . These unfiltered pixels mean the areas related to these pixels represent land cover other than forest, barren land, residential areas, or paddy fieldsplantations. We implemented ten simulations using different ROs for classification process and accuracy assessment and the result is shown in Fig. . Unclassified Residential Area Forest Barren Land Paddy fieldsPlantations Figure 6. Classification result of joint processing of Landsat 8 December 11, 2014 and ALOS ‐2PALSAR‐2 December 12, 2014 combined image first simulation Based on classification results, the area for each class is presented in more detail in Table . As shown in the table, in most of the area classified as forest, followed by residential area, paddy fieldsplantations, and barren land. Table . Classification results of combined Landsat 8 on Dec. , and ALOS‐ PALSAR‐ on Dec. , Simulation Area a Residential Area Forest Barren Land Paddy FieldsPlantations , . , . , .8 , . 8, . , . , . , . , 8. , . , . , . , . ,8 . , . 8, . , . , . , . , . , . , . , . 8, . , . , . , . , . 8 , . , . , 8 . 8 ,8 8. , . , 8. ,8 . , . , .8 , . 8 , 8. , . Average , . , . , . , . We implemented the classification method using the data in . The Landsat 8 data was dated on January , and ALOS‐ PALSAR‐ was dated on February , . The date of both data was different since the limited number of good quality of Landsat data. The first simulation using RO as training samples and ROs as testing data is shown in Fig. .