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Analyzing Land Conversion in South East Minahasa, Indonesia, Under Landsat Satellite Image Data Using the Supervised Classification Method

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Analyzing Land Conversion in South East Minahasa, Indonesia, Under Landsat Satellite Image Data Using the Supervised Classification Method Analyzing Land Conversion in South East Minahasa, Indonesia, under Landsat Satellite Image Data using the Supervised Classification Method Eko Sediyono, Gregorius Anung Hanindito Adi Setiawan Master of Information System Dept. of Science and Math Satyawacana Christian University Satyawacana Christian University Salatiga, Indonesia Salatiga, Indonesia [email protected], [email protected] [email protected] Abstract— South East Minahasa is recent regency located in Therefore, to ensure the geographic observation in the the North Sulawesi Province, Indonesia. It is officially announced concerned area, image data processing is performed by Landsat in 2007 as the expansion of South Minahasa Regency. This area satellite using remote sensing method. The remote sensing has a superior commodity in the plantation sector, especially for image is the primary focus in the spatial analysis about the coconut plantation. This study is analyzing the expansion pattern plantation land conversion in South East Minahasa. of plantation land and conversion of forest to plantation in South East Minahasa, Indonesia. The image data are taken from The purpose of this study is to monitor the conversion of Landsat Satellite from the year of 2002, 2007, 2010, and 2014. forest into plantation land and to observe the dissemination The results show that during 2002-2014 there are forest pattern of plantation land in South East Minahasa. It is degradation trend in South East Minahasa; from 42.906,25 Ha in conducted by analyzing the land use and land cover change 2002 to 26.392,87 ha in 2014. It is hoped that the results can give during 2002 - 2014 using four samples Landsat image data in valuable input for the related government in establishing the land 2002, 2007, 2010, and 2014. The result of this study can be management regulation in the upcoming years. used by the Government to monitor and control the dissemination pattern of plantation land in South East Keywords— Remote Sensing; land use; land cover; Industrial Minahasa. plantation II. STUDY AREA I. INTRODUCTION South East Minahasa Regency is located in 124°32”56’ For many years, Indonesia has struggled with land East Longitude - 124°57”3’ East Longitude and 0°50”28’ management cases which impacted on deforestation. One of North Longitude - 1°7”17’ North Latitude, with the total area is many crucial causes of deforestation in Indonesia is the 730.62 km2. The topography of most parts in this Regency is plantation and agricultural land expansion on the forest area. mountain range area [5]. South East Minahasa has great The expansion cannot be conducted on the conservation forest potential in developing the plantation products, with coconut [1]. This similar expansion problem also occurs in South East plant as the primary commodity. Minahasa Regency, Indonesia This study focuses on the dissemination pattern and South East Minahasa is one of the recent municipalities in comparison of the plantation land during 2002-2014. The North Sulawesi Province as the expansion result of South administrative location is on South East Minahasa Regency Minahasa Regency. Ratahan is chosen as the capital city. On This Regency is situated on the south of North Sulawesi May 23rd, 2007, the ad interim Minister of Internal State Province which is directly on the verge of Maluku Ocean. Affairs officially announced the South East Minahasa Regency with several other cities and municipalities such as Kotamobagu City, South Bolaang Mongondow Regency, East III. LAND USE AND LAND COVER CHANGE Bolaang Mongondow Regency and Sitaroe Regency [2]. This The land cover change is influenced by human and nature Regency has been established under the Law of Republic factors. The nature factor such as climate change, atmosphere Indonesia No. 9 of 2007 [3]. change, and pests attack can influence the land cover change. South East Minahasa is one of the potential municipalities, Meanwhile, the human factors such as deforestation and especially for its plantation field. Its specialized sector is the infrastructure development can also; trigger land cover change coconut plantation. The provided data records that this [6]. Regency produced 37.687 tons of coconut in 2011 [4]. The land use is any human activity that can influence the identity is still unknown into a certain class [13]. There are dissemination pattern of the land cover [7]. Another definition some advantages to using the supervised classification states that the land use is the human purpose in land method: the users have full control in determining the training utilization, whereas the land cover refers to the biophysical site, the classification results are more specific, and the users attributes of the earth surface that can be directly monitored by does not face difficulties in matching the image class [14]. aerial photographs and satellite sensor [8]. From the Based on those particular reasons, this study applies the definitions, it can be concluded that the land use and the land supervised classification method as it aims to gain the accurate cover are closely related. The land use will affect the land level of the image classification result. cover change. The observation detection about the land use and land V. METHODOLOGY cover change can be conducted by remote sensing. It provides more detail information in monitoring the land use and the A. Data Requirement land cover [9]. The land use and the land cover research have The data are taken from Landsat satellite. The data are been started since 1970s by launching the Landsat satellite in selected based on the cloud cover percentage in the selected the early 1970s. It provides semi temporal data such as area. If the cloud cover percentage is high, the classification Multispectral Scanner (MSS), Thematic Mapper (TM), and the analysis process of the land use and land cover will be Enhanced Thematic Mapper Plus (ETM+) and Landsat Data more difficult. Table 1 shows the data characteristic in this Continuity Mission (LDCM) [10]. study. Geographic information system and remote sensing is one of the methods that can be used in doing the observation of the TABLE I. THE DATA CHARACTERISTICS land use and land cover. Several steps that have been done in Satellite Acquisition Date Cloud Cover detecting the land use and land cover change are data Landsat 7 28 July 2002 < 10% acquisition, image classification, and post-classification Landsat 7 23 May 2007 < 10% comparison. In the analysis process, the map comparison is Landsat 7 12 March 2010 < 10 % conducted by overlaying two analyzed maps [11]. Landsat 8 28 December 2014 < 10% This study is performed by several steps. The first step is Four data sets were taken out of the different acquisition image classification of remote sensing. This classification time. To facilitate the classification and analysis process, the employs the supervised classification method using a data used in this study have only less than 10% cloud cover. maximum likelihood technique which converts the pixel values of an image into the land cover class. B. Research Design The next step is the analysis of the forest conversion to the This research is conducted in several steps as described in plantation. The intersection technique is performed by Fig. 1. comparing the forest with the plantation land. The intersection technique is used to find out the conversion pattern of the Data Collection and Data Selection forest to the plantation land within certain periods. Remote sensing data processing IV. SATELLITE IMAGE CLASSIFICATION METHOD Image Image cropping Contrast image Image Image classification is a process to determine the image Step I Conversion adjusment classification pixel into certain classes. It is performed by collecting some Step II identical pixel group and converting into certain classes in Classified image accordance with the information categories by the users [12]. Remote sensing presents two types of classification methods, raster data to vector conversion namely supervised classification and unsupervised Analysis classification [13]. Conclusion Unsupervised classification is an identification of certain pixel group in a multispectral image without information class gained by the users [13]. There are some disadvantages to Fig. 1. Flow Diagram of the Research Design using the unsupervised classification method, such as the At the first stage, data collection, data selection, and result of the classification may not correspond with the remote sensing data process are performed to gain the information in the real classes, the users have limited control classified image. Data are collected from Landsat satellite and for the class chosen through the classification process, and the selected by considering the cloud cover percentage. The relationship between the spectral class and information class remote sensing data process consists of four stages: image are not always related [14]. conversion, image cropping, contrast image adjustment, and Supervised classification is a classification process using image classification. The spatial resolution in each data is 30 the training site based on the information class identified by meters explains that each real area that covers 30x30 m2 is the users. The training site are used to classify the pixel that its represented by one pixel in the satellite image. The supervised classification method is used in the image be used in land use and land cover change analysis [18]. The classification. It requires sufficient information of the Study observation using the intersection technique will result on the area to gain the expected classes of the land cover class group dissemination pattern of the analyzed land cover [19]. In this [16]. The information about the area study is converted into study, the analysis is performed in the following images: training site made into digits based on the interpretation result of the satellite image which accuracy has been assured through 1. The forest land cover image in 2002 with the interpreting both aerial image and survey [17]. plantation land use image in 2007 The supervised classification method applies the maximum 2.
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