Korean Journal of Remote Sensing (대한원격탐사학회지)

Korean Society of Remote Sensing (대한원격탐사학회)
권호 표지
DOI QR코드

DOI QR Code

A Review on Remote Sensing and GIS Applications to Monitor Natural Disasters in Indonesia

  • Hakim, Wahyu Luqmanul (Combined MS/PhD Student, Department of Smart Regional Innovation, Kangwon National University) ;
  • Lee, Chang-Wook (Professor, Department of Science Education, Kangwon National University)
  • Received : 2020.11.19
  • Accepted : 2020.12.07
  • Published : 2020.12.31
Download PDF
⟨ Previous Next ⟩

Abstract

Indonesia is more prone to natural disasters due to its geological condition under the three main plates, making Indonesia experience frequent seismic activity, causing earthquakes, volcanic eruption, and tsunami. Those disasters could lead to other disasters such as landslides, floods, land subsidence, and coastal inundation. Monitoring those disasters could be essential to predict and prevent damage to the environment. We reviewed the application of remote sensing and Geographic Information System (GIS) for detecting natural disasters in the case of Indonesia, based on 43 articles. The remote sensing and GIS method will be focused on InSAR techniques, image classification, and susceptibility mapping. InSAR method has been used to monitor natural disasters affecting the deformation of the earth's surface in Indonesia, such as earthquakes, volcanic activity, and land subsidence. Monitoring landslides in Indonesia using InSAR techniques has not been found in many studies; hence it is crucial to monitor the unstable slope that leads to a landslide. Image classification techniques have been used to monitor pre-and post-natural disasters in Indonesia, such as earthquakes, tsunami, forest fires, and volcano eruptions. It has a lack of studies about the classification of flood damage in Indonesia. However, flood mapping was found in susceptibility maps, as many studies about the landslide susceptibility map in Indonesia have been conducted. However, a land subsidence susceptibility map was the one subject to be studied more to decrease land subsidence damage, considering many reported cases found about land subsidence frequently occur in several cities in Indonesia.

Keywords

1.Introduction

The impact of natural disasters in developing countries such as Indonesia (Fig. 1) was capable of producing extensive infrastructure damage and can cause more casualties (Marfai et al., 2008). Indonesia is more prone to several types of natural disasters, such as volcanic eruption (Kadavi et al., 2017; Voight et al., 2000), earthquake (Kerle, 2010; Wang et al., 2012), and tsunami (Gaillard et al., 2008; Rofi et al., 2006) due to the fact that the country is located at the intersection of three major tectonic plates: Eurasian, Indo-Australian, and Pacific plate (Kusumastuti et al., 2014; Marfai et al., 2008). Meanwhile, human activity also contributes to other natural disasters in Indonesia, such as flood (Budiyono et al., 2016; Tambunan, 2017), coastal inundation (Marfai and King, 2008; Takagi et al., 2016), land subsidence (Abidin et al., 2011; Estelle Chaussard et al., 2013; Hakim, Achmad and Lee, 2020; Ng et al., 2012; Yastika et al., 2019), and landslide (Silalahi et al., 2019; Umar et al., 2014).

OGCSBN_2020_v36n6_1_1303_f0001.png 이미지

Fig. 1. The Indonesian archipelago.

In total, Indonesia has endured 33, 177 natural disasters that killed 269, 950 people from 1815 to 2019 (BNPB, 2020). The aftereffect of a natural disaster, such as a deformation of the earth’s surface, can be detected using radar and optical satellites (Achmad et al., 2019; Kadavi et al., 2017). Monitoring natural disasters in Indonesia using satellite imagery has been conducted using several applications of remote sensing and Geographic Information System (GIS) such as InSAR techniques (Hakim et al., 2020; Lee et al., 2017), image classification techniques (Kadavi et al., 2017; Lee et al., 2015; Syifa et al., 2020; Syifa et al., 2019), and susceptibility mapping (Hakim et al., 2020; Silalahi et al., 2019) to detect natural disasters in Indonesia. We will be focused on reviewing articles about natural disasters in Indonesia that used those three techniques.

InSAR (Interferometric Synthetic Aperture Radar) is a technique for mapping the deformation of the earth’s surface that exploit phase information from at least two SAR images acquired at different times over the same area (Crosetto et al., 2016; Osmanoğlu et al., 2016; Pepe and Calò, 2017). This technique can be developed to measure the displacement of the earth’s surface with an accuracy of up to millimeters per year by improving the selection of coherent pixels and reducing atmospheric noise from the time-series of interferograms (Hooper et al., 2012; Li et al., 2019; Osmanoğlu et al., 2016). It has been widely used to monitor many natural disasters in Indonesia, for example, a volcanic eruption in Sinabung volcano (Lee et al., 2017), land subsidence near Lhokseumawe, in Medan, Jakarta, Bandung, Blanakan, Pekalongan, Bungbulang, Semarang, and in the Sidoarjo regency (Estelle Chaussard et al., 2013; Hakim et al., 2020; Ng et al., 2012; Yastika et al., 2019).

The image classification technique is the method used for obtaining an overview of damage sustained in the aftermath of natural disasters (Li et al., 2014). This method has been proven effective to determine the extensive damage over the wide-area with high spatial resolution caused by natural disasters in Indonesia such as the tsunami in Aceh (Aitkenhead et al., 2007), earthquake in Palu (Syifa et al., 2019), a volcanic eruption in Merapi volcano (Cho et al., 2013; Kadavi et al., 2017; Lee et al., 2015), Sinabung volcano (Kadavi et al., 2017; Lee et al., 2017), and Agung volcano (Syifa et al., 2020).

Susceptibility mapping is a method to analyze and predict potential natural disaster occurrence with their related factors (Lee, 2019). This method has been applied in any natural disasters in Indonesia, such as landslide in Bogor (Silalahi et al., 2019), West Sumatera Province (Umar et al., 2014), Pemalang (Oh et al., 2010). Another natural disaster monitored using a susceptibility map is the land subsidence in Jakarta (Hakim et al., 2020) and the flood susceptibility map in Pangkalpinang city (Warlina and Guinensa, 2019).

Thus, the purpose of this study was to review the remote sensing and GIS-based methods that were applied to detect natural disasters in Indonesia to prevent wide-ranging damaged area to the environment. The scope of this study was confined to relevant published studies from 2000-2020. The articles were retrieved from ScienceDirect (www.sciencedirect. com) and Google Scholar (www.scholar.google.com) using the keywords “Indonesia”, “Natural Disaster”, “Susceptibility Map”, “InSAR”, and “Classification”. After excluding several articles that appeared in the search but could not be downloaded, we retrieved 43 articles in a total of 19 articles from radar satellite application, 12 articles from image classification techniques, and 12 articles from GIS-based susceptibility mapping. We selected them based on consideration of the article’s type and their relevance to this study’s scope.

This review paper will first address the fundamentals of optic and radar remote sensing, followed by the application of InSAR techniques, image classification techniques, and susceptibility mapping. After that, we will provide an overview of the recently proposed method to detect any natural disasters in Indonesia. This paper is organized as follows. Section 1 introduces several natural disasters in Indonesia that are monitored using remote sensing and GIS-based method. Section 2 illustrates the basis of remote sensing techniques and the applications for detecting natural disasters in Indonesia. Section 3 describes GIS-based susceptibility mapping to predict natural disasters in Indonesia. Finally, the conclusion is presented in Section 4.

2. Remote Sensing Techniques and Applications

Remote sensing is a technique for obtaining information about an object without direct physical contact with the object. The advantages of remote sensing techniques were implemented to monitor the earth’s surface using satellites (Jensen, 2000). In this section, optic and thermal remote sensing and radar remote sensing were described and followed by their applications to monitor Indonesia’s natural disasters.

1) Optic and Thermal Remote Sensing

Optical remote sensing was one type of satellite sensor used to monitor the earth’s surface by exploiting short-wavelength solar radiation (400 to 2500 nm) (Guyon and Bréda, 2016). The earliest application was aerial photography for surveying and military use, and in the middle of the 1960s, multiple spectral remote sensing was introduced as Imaging Spectroscopy (IS) (Booysen et al., 2020). Multiple spectral or multispectral remote sensing exploits the backscattered energy from the earth’s surface in multiple electromagnetic spectrum bands (Jensen, 2000).

Besides optical remote sensing, thermal remote sensing was also implemented in multispectral remote sensing. The use of thermal remote sensing was to detect the temperature from the earth’s surface by exploiting the reflective infrared (0.7-3.0 μm) or thermal infrared energy (3.0-14 μm) (Jensen, 2000). Thermal infrared remote sensing systems acquired thermal infrared images and used them to determine the type of material and evaluate the changes based on its thermal emission characteristics (Jensen, 2000). The image classification is the optic and thermal remote sensing applications that will be reviewed in this study. Several natural disasters have been monitored using an image classification method, as shown in Fig. 2.

OGCSBN_2020_v36n6_1_1303_f0002.png 이미지

Fig. 2. Several published articles using image classification based on the natural disaster type in Indonesia.

Image classification is an essential part of image processing. Image classification aims to identify a set of categories becomes a new observation based on a training data set containing an example whose category membership is known. The general process of image :classification, as shown in Fig. 3, consists of a training phase and a validation phase. In the training phase, the group of pixel images is labeled for a class. After representing those data, the image classifier will utilize the label information to represent another group pixel of the image into a single image. The validation phase intends to classify the test images in the various classes and predict the classifier’s labels in the training phase. (Li et al., 2014; Tang et al., 2014).

OGCSBN_2020_v36n6_1_1303_f0003.png 이미지

Fig. 3. The general process of image classification (modified from (Tang et al., 2014)).

The ANN method in image classification techniques was used four times in the articles (Aitkenhead et al., 2007; Kadavi et al., 2017; Syifa et al., 2020; Syifa et al., 2019). SVM was two articles (Syifa et al., 2020; Syifa et al., 2019). The fuzzy classification was one article (Dewi et al., 2016). ANN and SVM methods seem to be more popular than the Fuzzy classification method to generate the pre-and post-natural disaster damage in Indonesia. Each method exploits the land cover classification, which samples pixels of an image, and those pixels were labeled as a representative area of a class. ANN, SVM, and Fuzzy Classification were algorithms to identify other pixels with similar pixel value to the sample image. The application of the image classification method is described based on each disaster in the following subsection.

(1) Volcanic Activity

Image classification techniques were widely used to map the aftermath of various disasters in Indonesia using several methods to classify the natural disaster’s impact. The volcanic eruption impact in the form of volcanic ashfall and pyroclastic-flow deposit in Mount Sinabung and Merapi located in Java Island, Indonesia, was observed using Landsat imagery, and the material deposit from the eruption was classified using supervised machine learning algorithm based on Artificial Neural Network (ANN). The combination of RGB bands in Landsat 7 (7, 4, 2) and Landsat 8 (7, 5, 3) was reflected in different colors indicates the value of vegetation, waterbody, or the earth’s surface rock. Thus, they were used to determine some training area (sample) on the image as the representative class covering some areas. The sample area’s pixel value was then used to determine the other pixels using an ANN-based multilayer perceptron model. 4 classes of training data were performed with a backpropagation algorithm for minimizing the error of the output from the calculated network (Kadavi et al., 2017).

Another volcanic eruption that was analyzed using the image classification method was mount Agung in Bali, Indonesia. Volcanic ashfall ad pyroclastic material from the eruption was classified using two different Artificial Neural Network (ANN) and Support Vector Machine (SVM) methods. Landsat Imagery was used to generate a land cover map with four classes: material eruption, vegetation, cloud, and shadow. The ANN and SVM methods are suitable for land cover classification in volcanic eruptions (Syifa et al., 2020).

Merapi volcano has been studied in two articles (Thouret et al., 2015; Yulianto et al., 2013). Both studies have analyzed the 2010 Merapi volcano’s eruption and used a different method to estimate the number of houses destroyed by the eruption. Cross profiling or overlaying the pyroclastic deposits map from ALOS PALSAR with the building sites’ information point from the topographic map from National Coordinating Agency for Surveys and Mapping (BAKOSURTANAL), Jakarta, Indonesia, and SPOT 4 satellite imagery (Yulianto et al., 2013). Another study used GeoEye-1 and Pléiades images to identify and map pyroclastic and lahar deposits by applying Object-Oriented Classification (OOC) based on the Decision Tree algorithm and Normalized Difference Indices of Vegetation, Water, and Soil Redness (NDVI, NDWI, NDRSI). The spatial and temporal change over 2010-2012 were examined to map the damaged area over the Gendol and Opak River basins on the south flank of Merapi volcano (Thouret et al., 2015)

(2) Earthquake

Besides the volcanic eruption, image classification was also used to map other disasters such as earthquakes, tsunami, and coastal inundation. Pre- and Post-earthquake damage in Palu, Central Sulawesi, Indonesia, were mapped using Artificial Neural Network (ANN) and Support Vector Machine (SVM). The remote sensing imagery was taken from Landsat- 8 and Sentinel-2 satellites. Three multispectral bands were selected to generate the false-color combination: shortwave near-infrared (SWIR2) (2.09-2.35 µm), thermal infrared (TIR) (10.40-12.50 µm), and near- infrared (NIR) (0.77-0.90 µm) for Landsat-8 imagery and three visible bands used as the combination of sentinel-2 imagery: NIR (842 nm), red (665 nm), and green (560 nm). A false-color band combination was selected to easily differentiate the affected area from the area unaffected by the earthquake during the classification stage. The result showed land cover classification into five classes: Affected area, Vegetation, Sea and River, Bare land, and Cloud (Syifa et al., 2019).

(3) Tsunami

The urban areas impacted by a tsunami of 26 December 2004 in Aceh, Indonesia, were mapped using Artificial Neural Network (ANN). The RGB composite of IKONOS satellite imagery from pre- tsunami image (10 January 2003) and post-tsunami image (29 December 2004) was acquired and used to classify the land cover classes: Deepwater, Shallow water, Forest, Mud, Urban, Road, Crops, Bare, and Wreckage. The inaccessible urban area analysis could be made to identify the aftermath of the disastrous event (Aitkenhead et al., 2007). Another research about tsunami in Aceh was conducted using the Google Earth pro digital globe by applying the unsupervised LULC (Land Use Land Cover) classification method to assess post-tsunami land cover change (Meilianda et al., 2019).

An earthquake-induced tsunami in Sulawesi, Indonesia, also occurred in 2018. A study of detecting urban changes was conducted using optical satellite images from PlanetScope and Sentinel-2 imagery. The classification was conducted using logistic regression classifier and the inundated area due to tsunami traced by RTK-GNSS (Moya et al., 2020)

(4) Coastal Inundation

Image classification is also used to identify coastal inundation in Central Java, Indonesia. The studies exploit three different sensors (Thematic Mapper (TM), Enhanced Thematic Mapper (ETM), Operational Land Imager (OLI)/Thermal Infra-Red Sensor (TIRS)) to monitor shoreline change between 1994 and 2015. Six spectral bands of Landsat TM and ETM (blue (0.45- 0.515 µm), green (0.525-0.605 µm), red (0.63-0.69 µm), near-infrared (0.75-0.90 µm), shortwave infrared 1 (1.55-1.75 µm) and shortwave infrared 2 (2.09-2.35 µm)) and seven spectral bands of Landsat OLI/TIRS (coastal and aerosol (0.43-0.45 µm), blue (0.45-0.51 µm), green (0.53-0.59 µm), red (0.64-0.67 µm), near- infrared (0.85-0.88 µm), shortwave infrared 1 (1.57-1.65 µm) and shortwave infrared 2 (2.11-2.29 µm)) were used. The classification was conducted using the Fuzzy C-means Classification method to derive the change of non-water area (shorelines) into the water area at Sayung sub-district in the northern coastal area of Central Java Province, Indonesia (Dewi et al., 2016).

(5) Forest Fire

The 1998 forest fires in East Kalimantan (Indonesia) was detected using high-resolution ERS-2 SAR images by performing a principal component analysis (PCA) to those images and applying supervised classification algorithms to the PCA composites (Red, Green, Blue) to classify the different colors of the vegetation type and the burned scar area (Siegert and Hoffmann, 2000). Another forest fire in Jambi Province, Indonesia, between 2000 and 2015 was analyzed using Moderate Resolution Imaging Spectro-radiometer (MODIS) sensor from Terra-Aqua satellites and three sensors of Landsat satellite (TM, ETM, and OLI) imagery. Getis-Ord-Gi* statistic from ArcGIS software spatial analysis tool was applied to analyze the hotspot area distribution, and land cover identification was performed based on visual classification of an object from Landsat Imageries (Prasetyo et al., 2016).

Another land use and land cover change in Indonesia’s forest was conducted over Sumatra and Kalimantan island, Indonesia (Vetrita and Cochrane, 2020). Moderate Resolution Imaging Spectroradiometer (MODIS) Terra/ Aqua combined burned area, and three Landsat-derived LULC maps (Landsat, 1990; Landsat, 2007; Landsat, 2015) were used to analyze the burning area that affected land cover change by applying manual classification and the annual fire-return intervals (FRI) over the specific land-cover type (Vetrita and Cochrane, 2020).

2) Radar Remote Sensing

Synthetic Aperture Radar (SAR) is a technique to monitor the earth’s surface by exploiting radar satellites that transmit their electromagnetic waves and receive the backscattered electromagnetic waves from a target (Jensen, 2000). The strength of the backscattered electromagnetic waves related to the target shape, orientation, and electrical properties was amplified. The amplitude and phase are the two observables in SAR measurement. The radar phase is a periodic phenomenon that changed to the distance traveled by the wavelength’s electromagnetic waves. Wavelength is defined as the distance unit between two points of the radar phase in a periodic wave for SAR measurements (Osmanoğlu et al., 2016).

Interferometry Synthetic Aperture Radar (InSAR) is a remote sensing technique to measure the surface’s topography by exploiting the coherent radar signal phase (Rosen et al., 2000). Most of the natural hazards are capable of changing the earth’s surface, and if the surface change detected by the SAR satellites and compared with the two different SAR images of the same location that natural hazards occur, an interferogram from the phase-shift between the two images will be produced as described in Fig. 4 (Schindler et al., 2016). Nowadays, many radar satellites are currently operating. The amount of SAR data has also increased, making it another advanced analysis of InSAR data to be developed, a multi- temporal or time-series InSAR that observes the displacement of the earth’s surface over time. This technique aims to connect wrapped phase measurements to generate a continuous result of the earth’s surface (Crosetto et al., 2016; Hooper et al., 2012; Osmanoğlu et al., 2016). Those time-series InSAR methods have been applied to various disaster cases in Indonesia, such as volcanic activity or volcanic eruption, landslide, and land subsidence, as shown in Fig. 5.

OGCSBN_2020_v36n6_1_1303_f0005.png 이미지

Fig. 4. Illustration explaining the displacement is derived from the phase shift of two SAR acquisitions. The position of the second acquisition formed a perpendicular (B⊥) and parallel baseline (B‖) which is define the spatial baseline between first and second acquisition (modified from (Osmanoğluet al., 2016)).

OGCSBN_2020_v36n6_1_1303_f0004.png 이미지

Fig. 5. Several published articles using InSAR method based on the natural disaster type in Indonesia.

The review of monitoring natural disaster using the InSAR method in Indonesia is described by each type of disaster in the following subsection. We only focused on articles that implemented time-series method to measure natural disaster in Indonesia that made a gradual change such as volcanic activity, landslide, and land subsidence. We make an exception for earthquake, due to sudden movement of the fault. Monitoring an earthquake only needs an interferogram between two SAR data during the earthquake occurrence date.

(1) Volcanic Activity

Time-Series InSAR has become an essential tool for studies on volcanic activities in Indonesia. Time-series InSAR was applied to monitor the activity of Sinabung, Kerinci, Anak Krakatau, Slamet, Lawu, Lamongan, and Agung volcano (Albino et al., 2020; Chaussard and Amelung, 2012; Chaussard et al., 2013; Lee et al., 2017; Lingyun et al., 2013). The number of published articles using time-series InSAR is shown in Fig. 6.

OGCSBN_2020_v36n6_1_1303_f0006.png 이미지

Fig. 6. Several published articles that monitor volcanic activity using time-series InSAR in Indonesia with respect to the study area.

2006-2009 ALOS Interferometric Synthetic Aperture Radar data with perpendicular baseline <1.5 km over the Sumatra and Java island, Indonesia, was used to derive the time-series of ground deformation from 7 volcanoes: Agung, Anak Krakatau, Kerinci, Lamongan, Lawu, Sinabung, and Slamet volcanoes. The interferograms generated using the ROI_PAC software and Small Baseline (SBAS) without atmospheric filtering methods were applied to perform time-series analysis. Those studies found that Sinabung and Kerinci inflate before an eruption, Agung volcano inflates but does not erupt, and Merapi erupts but does not show precursory deformation (Chaussard and Amelung, 2012). SBAS method (Berardino et al., 2002) was used in the most publication of monitoring volcanic activity in Indonesia (Aditiya et al., 2018; Albino et al., 2020; Albino et al., 2019; Babu and Kumar, 2019; Chaussard and Amelung, 2012; Chaussard et al., 2013; González et al., 2015; Lee et al., 2015; Lee et al., 2017; Lingyun et al., 2013). ALOS Phased Array type L-band Synthetic Aperture Radar (PALSAR) L-band satellite was used in seven articles to monitor volcanic activity in Indonesia (Aditiya et al., 2018; Chaussard and Amelung, 2012; Chaussard et al., 2013; González et al., 2015; Lee et al., 2015; Lee et al., 2017; Lingyun et al., 2013). another three articles were used the Sentinel-1 C-band SAR datasets to monitor volcanic activity in Indonesia (Albino et al., 2020; Albino et al., 2019; Babu and Kumar, 2019).

Sinabung and Agung volcanoes were the most volcano in Indonesia monitored by radar satellite and time-series InSAR analysis. There are six articles (Aditiya et al., 2018; Chaussard and Amelung, 2012; Chaussard et al., 2013; González et al., 2015; Lee et al., 2015; Lee et al., 2017) that used the Sinabung volcano as the study area in the different period of monitoring time such as two articles was monitored Sinabung from 2006 to 2009 (Chaussard and Amelung, 2012) and 2007 to 2009 (Chaussard et al., 2013). Three articles were monitored Sinabung from 2007-2011 (González et al., 2015; Lee et al., 2015; Lee et al., 2017). One article was monitored Sinabung from 2014- 2017 (Aditiya et al., 2018).

Five articles (Albino et al., 2020; Albino et al., 2019; Chaussard and Amelung, 2012; Chaussard et al., 2013; Lingyun et al., 2013) used Agung volcano as their study area, and one of them was monitored Agung volcano from 2006 to 2009 (Chaussard and Amelung, 2012) and two articles were monitored Agung volcano from 2007 to 2009 (Chaussard et al., 2013; Lingyun et al., 2013). Another article was monitored Agung volcano between April-August 2017 (Albino et al., 2019) and between 2017 and 2018 (Albino et al., 2020).

Two articles was monitored Anak Krakatau from 2006-2009 (Chaussard and Amelung, 2012) and 2018- 2019 (Babu and Kumar, 2019). Kerinci volcanic activity was monitored in two articles and monitored Kerinci volcano from 2006 to 2009 (Chaussard and Amelung, 2012) and 2007 to 2009 (Chaussard et al., 2013). Another volcano that has been studied in one articles was, 2006-2009 Slamet volcano (Chaussard and Amelung, 2012), 2006-2009 Lawu volcano (Chaussard and Amelung, 2012), 2007-2009 Merapi volcano (E. Chaussard et al., 2013), and 2006-2009 Lamongan volcano (Chaussard and Amelung, 2012).

(2) Earthquake

InSAR techniques that measure earthquakes’ deformation were the projection of the earth’s surface deformation correlated with the seismogenic faults in the satellite’s line-of-sight (LOS) line. The deformation due to earthquakes are mostly horizontal, and the fault on which the earthquake occurs (Chunyan et al., 2017). A study about the earthquake was conducted in the Bengkulu earthquake (Mw 8.5) that induced a tsunami on 12 September 2007. The study purpose was to observe slip distribution using two interferograms from ALOS/PALSAR data for inversion analysis on Pagai Islands, Indonesia with three months spans on West Sumatra, Indonesia with eight months of periods (Gusman et al., 2010).

Another research with a similar purpose was also conducted in Lombok, Indonesia. The series of earthquakes that occurred on 28 July 2018 (Mw 6.4), 5 August 2018 (Mw 6.9), and 19 August 2018 (Mw 6.9) were observed using Sentinel 1A and 1B data to generate three interferograms between the date of the earthquake occurrence for inversion analysis (Wang et al., 2020). Each research was used the Interferometric Synthetic Aperture Radar (InSAR) data for the surface projection of the inverted slip distribution.

(3) Landslide

InSAR observations of slope movement are used for detecting potentially unstable slopes over the broad areas that lead to a landslide (Dong et al., 2018). The exploitation of SAR satellites to monitor landslides in Indonesia was only found in one article(Isya et al., 2019). The slow ground motion induced landslide was monitored in the Ciloto District, West Java, Indonesia, using Sentinel-1 A/B in ascending track 72 SAR images and descending tracks 68 SAR images from October 2014 until June 2018 by using Small Baseline Subset (SBAS) with Slowly Decorrelating Filter Phase (SDFP) method to derive the time-series analysis (Isya et al., 2019). The availability of Sentinel-1 data from both ascending and descending tracks were used to estimate 2D displacement vectors regarding north- south and east-west motions (Isya et al., 2019).

(4) Land Subsidence

Time-series InSAR has become an established tool for monitoring degradation at the ground level of buildings known as land subsidence in urban areas. Several cities in Indonesia are reportedly affected by this phenomenon, such as near Lhokseumawe, in Medan, Jakarta, Bandung, Blanakan, Pekalongan, Bungbulang, Semarang, and in the Sidoarjo Regency. Several causes of land subsidence have been reported that land subsidence was caused by excessive groundwater and gas extraction. The other reason for land subsidence occurrence in Indonesia was related to geological conditions from the natural consolidation of young alluvium soil that not strong enough to be compressed by a load of buildings. Six articles used different methods to generate time-series land subsidence maps in Indonesia. Persistent Scatterer (PS) InSAR (3 articles) (Hakim et al., 2020; Maghsoudi et al., 2018; Ng et al., 2012) and Small Baseline Subset (SBAS) (3 articles) (Estelle Chaussard et al., 2013; Tolomei et al., 2019; Yastika et al., 2019).

The PSInSAR method was developed from single master interferograms and topographic phase removal using an InSAR processing software. Land subsidence in Jakarta from 2007 to 2010 from 17 ALOS PALSAR L-band radar images and analyzed using GEOS-PSI (Geodesy and Earth Observing Systems –Persistent Scatterer Interferometry) (Ng et al., 2012). The other research of land subsidence monitoring at Jakarta was conducted using Sentinel-1 SAR data from 2017-2020 based on Stanford Method for Persistent Scatterer (StaMPS) (Hakim et al., 2020).

The land subsidence at West Java’s geothermal areas, Indonesia, was analyzed using ALOS PALSAR from 2007 to 2009 and Sentinel-1A between 2015 and 2016. The primary process of PS processing was selecting the PS candidate, estimation and removal of atmospheric phase screen (APS), and selected the final PS with temporal coherence above a reliable threshold (Maghsoudi et al., 2018). The other land subsidence studies in Indonesia were used Small Baseline Subset (SBAS) methods to derive time-series analysis. A study of land subsidence in nine areas in Indonesia has used a maximum spatial baseline of 2600 m ALOS PALSAR data from 2006-2009 (Estelle Chaussard et al., 2013). Monitoring land subsidence from 2003 to 2017 in Semarang, Indonesia, was also analyzed using the SBAS method from Envisat-ASAR, ALOS-PALSAR, and Sentinel-1A SAR data (Yastika et al., 2019). Bandung, Indonesia, the land subsidence was monitored using SBAS methods to process ALOS-1/2, COSMO SkyMed, and Sentinel-1 SAR dataset (Tolomei et al., 2019). The difference between PSInSAR and SBAS method is the number of interferograms used to derive time-series analysis. PSInSAR only used a single master interferogram, and SBAS used interferograms with small orbital baselines to reduce spatial decorrelation and the errors induced by the DEM.

3. GIS Techniques and Applications

Geographic Information Systems (GIS) was a new discipline used to analyze spatial and geographic data in substantive applications to understand earth science (Maguire, 1991). This section reviews the application of GIS-based techniques: Susceptibility mapping with an overview of susceptibility mapping’s general process. The application of susceptibility mapping was described based on the study in each natural disaster that implemented susceptibility mapping to predict the area at risk of a disastrous event.

1) Susceptibility Mapping

Susceptibility mapping is a GIS-based technique to predict the probability of natural disaster occurrences with its related factors. As the number of natural disasters increases, the number of studies related to susceptibility mapping in line with the increasing potential for disasters may occur. The development of susceptibility models is divided into probabilistic, statistical, and machine learning models (Lee, 2019).

The general process of susceptibility mapping is shown in Fig. 7. Two types of data were needed to generate a susceptibility map; Inventory maps and related factors that cause the natural disaster. The inventory map is then randomly divided into 50%/50% or 70%/30% based on the popular method to divided inventory map availability into training and testing data (Arabameri et al., 2020). Train data and conditioning factors are needed to generate susceptibility maps using any training model. The susceptibility maps would be validated using the test data using the Receiver Operating Characteristic (ROC) curve analysis (Fawcett, 2006).

OGCSBN_2020_v36n6_1_1303_f0007.png 이미지

Fig. 7. The general process of susceptibility mapping (modified from (Hakim et al., 2020)).

The essential issue to generate a susceptibility map was the availability of an accurate inventory map to be used as training data and generate the model and testing data to validate the model. The inventory map would be correlated with the related factors that caused the natural disaster (Bianchini et al., 2019). Several studies used field surveys and geodetic measurement techniques to produce the inventory map at the study area from which natural disaster occurrence inventory maps are constructed. For example, a landslide inventory map was produced by a field survey using GPS measurements in Ambon, Indonesia (Aditian et al., 2018). A flood inventory map was produced using GPS measurement and interviews with the citizens around the Pangkalpinang city (Warlina and Guinensa, 2019). Several studies also combined the field survey data with aerial photographs to validate the generated inventory maps (Hadmoko et al., 2017; Umar et al., 2014).

Another method to produce an inventory map was using satellite imagery to measure the natural disaster over broad areas with a high spatial resolution (Fadhillah et al., 2020). The application of satellite imagery to produce an inventory map was conducted to predict the land subsidence susceptibility map in Jakarta. The land subsidence inventory map was generated using the Sentinel-1 SAR satellite and time- series InSAR techniques (Hakim et al., 2020). From all articles, Receiver Operating Characteristic (ROC) curve analysis was commonly used as the validation method of the natural disaster susceptibility map in Indonesia (Aditian et al., 2018; Arifianti and Agustin, 2017; Hadmoko et al., 2017; Hakim et al., 2020; Kamal et al., 2015; Oh et al., 2010; Pamela et al., 2018; Rasyid et al., 2016; Umar et al., 2014; Warlina and Guinensa, 2019; Wati et al., 2010).

Another vital issue to generate a susceptibility map was defining the conditioning factors related to each natural disaster (Lee and Park, 2013; Pradhan et al., 2010). Those conditioning factors would be explained in each natural disaster susceptibility map. Ten studies about landslide susceptibility mapping (Aditian et al., 2018; Arifianti and Agustin, 2017; Hadmoko et al., 2017; Kamal et al., 2015; Oh et al., 2010; Pamela et al., 2018; Rasyid et al., 2016; Silalahi et al., 2019;Umar et al., 2014; Wati et al., 2010) and a study about flood susceptibility mapping (Warlina and Guinensa, 2019) and a study about land subsidence (Hakim et al., 2020) were reviewed to analyze the method, the inventory map, and their related factors to generate a susceptibility map in Indonesia. The number of published articles that implemented susceptibility mapping in Indonesia is shown in Fig. 8.

OGCSBN_2020_v36n6_1_1303_f0008.png 이미지

Fig. 8. Several published articles using susceptibility mapping based on the natural disaster type in Indonesia.

(1) Landslide

The study about the landslide susceptibility map conducted in Indonesia has been conducted in several study areas and acquired the inventory map from a different method such as field surveys, aerial photographs, and satellite imagery. Several studies used field surveys to generate the inventory map, such as in Takengon, Central Aceh, Indonesia (Pamela et al., 2018); Cibeber, Cianjur, Indonesia (Arifianti and Agustin, 2017); Kayangan Catchment, Java, Indonesia (Hadmoko et al., 2017). Another study about landslide susceptibility map in Tawangmangu Sub District, Central Java, Indonesia used field survey and combination of Digital Elevation Model Shuttle Radar Topography Mission (DEM SRTM) to generate the landslide inventory map (Wati et al., 2010). Landslide inventory maps in Ponorogo, East Java, Indonesia, were generated using Google Maps Application Programming Interface (Kamal et al., 2015). Studies about the landslide susceptibility map in Ambon, Indonesia, were generated using field surveys based on GPS measurement (Aditian et al., 2018). The earthquake-induced landslide was mapped in West Sumatera Province, Indonesia using field surveys and QuickBird satellite imagery (Umar et al., 2014). Aerial photographs and field surveys were used in generating inventory maps in the Pemalang area, Indonesia (Oh et al., 2010). Another method to generate an inventory map was conducted in Lompobattang Mountain, Indonesia, using Google earth image interpretation (Rasyid et al., 2016). The study about landslide susceptibility maps in Bogor, Indonesia, has used an inventory map based on previously recorded historic landslide events, documentation of field sites, and satellite imagery interpretation (Silalahi et al., 2019).

The number of published articles and the model used to generate a landslide susceptibility map is shown in Fig. 9. Frequency ratio becomes the most common method to generate landslide susceptibility map since there are five studies about landslide susceptibility mapping in Indonesia that used Frequency Ratio (FR) as their model (Aditian et al., 2018; Oh et al., 2010; Rasyid et al., 2016; Silalahi et al., 2019; Umar et al., 2014). Followed by four studies was using Logistic Regression (LR) as the model to generate landslide susceptibility maps (Aditian et al., 2018; Oh et al., 2010; Rasyid et al., 2016; Umar et al., 2014), followed by two studies that used Analytical Hierarchy Process (AHP) (Hadmoko et al., 2017; Kamal et al., 2015), Artificial Neural Network (ANN) (Aditian et al., 2018; Oh et al., 2010), and Weight of Evidence (WoE) (Arifianti and Agustin, 2017; Pamela et al., 2018). Another model used in landslide susceptibility mapping was Weighted Score (WS) (Wati et al., 2010) and Information Value (IV) (Hadmoko et al., 2017). Analytical Hierarchy Process and Weighted Score are known as the knowledge-driven models; Frequency Ratio, Weight of Evidence, and Information Value were probabilistic models; Logistic Regression is the statistical model; Artificial Neural Network (ANN) was a machine learning model.

OGCSBN_2020_v36n6_1_1303_f0009.png 이미지

Fig. 9. Several published articles with respect to the model used in generating a landslide susceptibility map in Indonesia.

The number of published articles according to the conditioning factor-induced landslide is shown in Fig.10. There are ten articles on landslide susceptibility mapping in Indonesia (Aditian et al., 2018; Arifianti and Agustin, 2017; Hadmoko et al., 2017; Kamal et al., 2015; Oh et al., 2010; Pamela et al., 2018; Rasyid et al., 2016; Silalahi et al., 2019; Umar et al., 2014; Wati et al., 2010) that used slope and lithology as the conditioning factors that cause landslides. Six articles used aspect (Hadmoko et al., 2017; Oh et al., 2010; Pamela et al., 2018; Rasyid et al., 2016; Silalahi et al., 2019; Umar et al., 2014). Eight articles used Land Use and Land Cover (Arifianti and Agustin, 2017; Hadmoko et al., 2017; Kamal et al., 2015; Pamela et al., 2018; Rasyid et al., 2016; Silalahi et al., 2019; Umar et al., 2014; Wati et al., 2010). Four articles used elevation (Aditian et al., 2018; Hadmoko et al., 2017; Pamela et al., 2018; Umar et al., 2014). Four articles used rainfall (Kamal et al., 2015; Pamela et al., 2018; Silalahi et al., 2019; Umar et al., 2014). Six articles used distance to rivers (Aditian et al., 2018; Hadmoko et al., 2017; Oh et al., 2010; Pamela et al., 2018; Rasyid et al., 2016; Umar et al., 2014). Four articles were used distance to roads (Aditian et al., 2018; Hadmoko et al., 2017; Pamela et al., 2018; Rasyid et al., 2016). Three articles used distance to faults (Aditian et al., 2018; Hadmoko et al., 2017; Rasyid et al., 2016). Three articles used distance to lineament (Pamela et al., 2018; Silalahi et al., 2019; Umar et al., 2014). Four articles used curvature (Oh et al., 2010; Pamela et al., 2018; Rasyid et al., 2016; Umar et al., 2014). Two articles were used soil texture (Umar et al., 2014; Wati et al., 2010). Two articles were used peak ground acceleration (Pamela et al., 2018; Umar et al., 2014). Soil type, soil depth, soil permeability, geological density, stream power index, flow direction, and topographic wetness index were other conditioning factors that were not commonly used to generate landslide susceptibility mapping in Indonesia (Aditian et al., 2018; Pamela et al., 2018; Silalahi et al., 2019; Umar et al., 2014; Wati et al., 2010).

OGCSBN_2020_v36n6_1_1303_f0010.png 이미지

Fig.10. Several published articles with respect to the factor used as landslide causes in Indonesia.

(2) Flood

In Indonesia, the flood susceptibility map was only found in one study conducted in Pangkalpinang city, Bangka Belitung, Indonesia (Warlina and Guinensa, 2019). The flood map’s inventory map was made using field surveys based on interviews and GPS measurement. The conditioning factors that used to be correlated with the flood occurrences in Pangkalpinang city, Indonesia, were slope, rainfall, land use, soil type, geological structure, and distance to rivers (Warlina and Guinensa, 2019). The model used to generate the flood susceptibility map was the Weighted Score (WS) model, and the validation was conducted using ROC curve analysis (Warlina and Guinensa, 2019).

(3) Land Subsidence

There is only one study about land subsidence susceptibility mapping in Jakarta, Indonesia. The application of SAR satellite imagery and time-series InSAR was conducted to produce an inventory map and to monitor the land subsidence location in Jakarta. The land subsidence susceptibility models were generated using machine learning models to generate a susceptibility map from the AdaBoost algorithm, LogitBoost algorithm, Multilayer Perceptron, and Logistic Regression (Hakim et al., 2020).

Land subsidence susceptibility maps are not commonly studied in Indonesia. The conditioning factors that used to be correlated with the land subsidence occurrence was the elevation, slope, aspect, profile curvature, plan curvature, topographic wetness index, land use, lithology, distance to faults, distance to rivers, distance to roads, drainage density, groundwater drawdown, and rainfall intensity were used to generate a land subsidence susceptibility map in Jakarta, Indonesia (Hakim et al., 2020).

4. Conclusions

In this study, we reviewed 19 articles on time-series InSAR techniques to monitor natural disasters in Indonesia, 12 articles on image classification, 12 articles on GIS-based susceptibility mapping in Indonesia. Those articles covered many study areas in Indonesia; however, InSAR techniques, mostly time-series InSAR algorithm, were more widely used to monitor the slow deformation over a long time, such as land subsidence (Hakim et al., 2020), volcanic activity (Lee et al., 2017), and slow ground movement (landslide) (Isya et al., 2019). In contrast to InSAR techniques, classification techniques are more widely used to map the aftermath of natural disasters over a study area. The comparison between pre-disaster and post-disaster maps is usually presented in image classification technique in order to observe which urban area are affected by the natural disaster, such as mapping pre-and post-earthquake and tsunami damaged in Palu and Aceh, Indonesia (Aitkenhead et al., 2007; Syifa et al., 2019). The image classification technique’s other capability was to map the material flow from the volcanic eruption in Indonesia, which most of the volcano eruption was pyroclastic flow; image classification techniques can create the areas affected by the eruption. The application of these two methods cannot be found to observe floods in Indonesia. Instead of a flood, an image classification map of the coastal inundation of shorelines was conducted in Central Java, Indonesia (Dewi et al., 2016).

However, on the other hand, a study about the flood was done using susceptibility mapping techniques to predict the area that more prone to flood due to its related factor. The application of the susceptibility map in Indonesia is more widely used to predict prone areas to a landslide. There is only one study about the land subsidence susceptibility map in Indonesia, land subsidence in Jakarta. More studies should be carried out on the susceptibility mapping induced by land subsidence, considering many reported land subsidence cases in big cities in Indonesia to decrease the damage caused by land subsidence.

Acknowledgments

This research was supported by a grant from the National Research Foundation of Korea provided by the government of Korea (No. 2019R1A2C1085686).

References

  1. Abidin, H.Z., H. Andreas, I. Gumilar, Y. Fukuda, Y.E. Pohan, and T. Deguchi, 2011. Land Subsidence of Jakarta (Indonesia) and Its Relation with Urban Development, Natural Hazards, 59(3): 1753-1771. https://doi.org/10.1007/s11069-011-9866-9
  2. Achmad, A.R., M. Syifa, S.J. Park, and C.W. Lee, 2019. Geomorphological Transition Research for Affecting the Coastal Environment Due to the Volcanic Eruption of Anak Krakatau by Satellite Imagery, Journal of Coastal Research, 90(SI): 214-220. https://doi.org/10.2112/SI90-026.1
  3. Aditian, A., T. Kubota, and Y. Shinohara, 2018. Comparison of GIS-Based Landslide Susceptibility Models Using Frequency Ratio, Logistic Regression, and Artificial Neural Network in a Tertiary Region of Ambon, Indonesia, Geomorphology, 318: 101-111. https://doi.org/10.1016/j.geomorph.2018.06.006
  4. Aditiya, A., Y. Aoki, and R.D. Anugrah, 2018. Surface Deformation Monitoring of Sinabung Volcano Using Multi Temporal InSAR Method and GIS Analysis for Affected Area Assessment, Proc. of IOP Conference Series: Materials Science and Engineering, University of Lampung, INA, Sep. 18-20, vol. 344, pp. 1-11.
  5. Aitkenhead, M.J., P. Lumsdon, and D.R. Miller, 2007. Remote Sensing-Based Neural Network Mapping of Tsunami Damage in Aceh, Indonesia, Disasters, 31(3): 217-226. https://doi.org/10.1111/j.1467-7717.2007.01005.x
  6. Albino, F., J. Biggs, C. Yu, and Z. Li, 2020. Automated Methods for Detecting Volcanic Deformation Using Sentinel-1 InSAR Time Series Illustrated by the 2017-2018 Unrest at Agung, Indonesia, Journal of Geophysical Research: Solid Earth, 125(2): 1-18.
  7. Albino, F., J. Biggs, and D.K. Syahbana, 2019. Dyke Intrusion between Neighbouring Arc Volcanoes Responsible for 2017 Pre-Eruptive Seismic Swarm at Agung, Nature Communications, 10(1): 1-11. https://doi.org/10.1038/s41467-018-07882-8
  8. Arabameri, A., O.A. Nalivan, S.C. Pal, R. Chakrabortty, A. Saha, S. Lee, B. Pradhan, and D.T. Bui, 2020. Novel Machine Learning Approaches for Modelling the Gully Erosion Susceptibility, Remote Sensing, 12(17): 2833. https://doi.org/10.3390/rs12172833
  9. Arifianti, Y. and F. Agustin, 2017. An Assessment of the Effective Geofactors of Landslide Susceptibility: Case Study Cibeber, Cianjur, Indonesia. In: GIS Landslide, Springer, Tokyo, Japan, pp. 183-195.
  10. Babu, A. and S. Kumar, 2019. InSAR Coherence and Backscatter Images Based Analysis for the Anak Krakatau Volcano Eruption, Proceedings, 24(1): 21. https://doi.org/10.3390/IECG2019-06216
  11. Berardino, P., G. Fornaro, R. Lanari, and E. Sansosti, 2002. A New Algorithm for Surface Deformation Monitoring Based on Small Baseline Differential SAR Interferograms, IEEE Transactions on Geoscience and Remote Sensing, 40(11): 2375-2383. https://doi.org/10.1109/TGRS.2002.803792
  12. Bianchini, S., L. Solari, M.D. Soldato, F. Raspini, R. Montalti, A. Ciampalini, and N. Casagli, 2019. Ground Subsidence Susceptibility (GSS) Mapping in Grosseto Plain (Tuscany, Italy) Based on Satellite InSAR Data Using Frequency Ratio and Fuzzy Logic, Remote Sensing, 11(17): 1-26.
  13. BNPB(Badan Nasional Penanggulangan Bencana), 2020. Data & Information of Disaster in Indonesia, http://dibi.bnpb.go.id/, Accessed on Nov. 16, 2020.
  14. Booysen, R., R. Gloaguen, S. Lorenz, R. Zimmermann, and P.A.M. Nex, 2021. Geological Remote Sensing, Encyclopedia of Geology, Elsevier, 2(1): 301-314.
  15. Budiyono, Y., J.C. J.H. Aerts, D. Tollenaar, and P.J. Ward, 2016. River Flood Risk in Jakarta under Scenarios of Future Change, Natural Hazards and Earth System Sciences, 16(3): 757-774. https://doi.org/10.5194/nhess-16-757-2016
  16. Chaussard, E., F. Amelung, and Y. Aoki, 2013. Characterization of Open and Closed Volcanic Systems in Indonesia and Mexico Using InSAR Time Series, Journal of Geophysical Research: Solid Earth, 118(8): 3957-3969. https://doi.org/10.1002/jgrb.50288
  17. Chaussard, E. and F. Amelung, 2012. Precursory Inflation of Shallow Magma Reservoirs at West Sunda Volcanoes Detected by InSAR, Geophysical Research Letters, 39(21): 1-6.
  18. Chaussard, E., F. Amelung, H. Abidin, and S.-H. Hong, 2013. Sinking Cities in Indonesia: ALOS PALSAR Detects Rapid Subsidence Due to Groundwater and Gas Extraction, Remote Sensing of Environment, 128: 150-161. https://doi.org/10.1016/j.rse.2012.10.015
  19. Cho, M., Z. Lu, and C.-W. Lee, 2013. Time-Series Analysis of Pyroclastic Flow Deposit and Surface Temperature at Merapi Volcano in Indonesia Using Landsat TM and ETM +, Korean Journal of Remote Sensing, 29(5): 443-459. https://doi.org/10.7780/kjrs.2013.29.5.1
  20. Chunyan, Q.U., X. Shan, D. Zhao, G. Zhang, and X. Song, 2017. Relationships between InSAR Seismic Deformation and Fault Motion Sense, Fault Strike, and Ascending/Descending Modes, Acta Geologica Sinica, 91(1): 93-108. https://doi.org/10.1111/1755-6724.13065
  21. Crosetto, M., O. Monserrat, M.C.-Gonzalez, N. Devanthery, and B. Crippa, 2016. Persistent Scatterer Interferometry: A Review, ISPRS Journal of Photogrammetry and Remote Sensing, 115: 78-89. https://doi.org/10.1016/j.isprsjprs.2015.10.011
  22. Dewi, R., W. Bijker, A. Stein, and M. Marfai, 2016. Fuzzy Classification for Shoreline Change Monitoring in a Part of the Northern Coastal Area of Java, Indonesia, Remote Sensing, 8(3): 190. https://doi.org/10.3390/rs8030190
  23. Dong, J., L. Zhang, M. Tang, M. Liao, Q. Xu, J. Gong, and M. Ao, 2018. Mapping Landslide Surface Displacements with Time Series SAR Interferometry by Combining Persistent and Distributed Scatterers: A Case Study of Jiaju Landslide in Danba, China, Remote Sensing of Environment, 205: 180-198. https://doi.org/10.1016/j.rse.2017.11.022
  24. Fadhillah, M. F., A.R. Achmad, and C.W. Lee, 2020. Integration of Insar Time-Series Data and GIS to Assess Land Subsidence along Subway Lines in the Seoul Metropolitan Area, South Korea, Remote Sensing, 12(21): 1-27.
  25. Fawcett, T., 2006. An Introduction to ROC Analysis, Pattern Recognition Letters, 27(8): 861-874. https://doi.org/10.1016/j.patrec.2005.10.010
  26. Gaillard, J.C., E. Clave, O. Vibert, D. Azhari, J.C. Denain, Y. Efendi, D. Grancher, C. C. Liamzon, D. R. Sari, and R. Setiawan, 2008. Ethnic Groups' Response to the 26 December 2004 Earthquake and Tsunami in Aceh, Indonesia, Natural Hazards, 47(1): 17-38. https://doi.org/10.1007/s11069-007-9193-3
  27. Gonzalez, P.J., K.D. Singh, and K.F. Tiampo, 2015. Shallow Hydrothermal Pressurization before the 2010 Eruption of Mount Sinabung Volcano, Indonesia, Observed by Use of ALOS Satellite Radar Interferometry, Pure and Applied Geophysics, 172(11): 3229-3245. https://doi.org/10.1007/s00024-014-0915-7
  28. Gusman, A.R., Y. Tanioka, T. Kobayashi, H. Latief, and W. Pandoe, 2010. Slip Distribution of the 2007 Bengkulu Earthquake Inferred from Tsunami Waveforms and InSAR Data, Journal of Geophysical Research, 115(12): 1-14.
  29. Guyon, D. and N. Breda, 2016. Applications of Multispectral Optical Satellite Imaging in Forestry, Land Surface Remote Sensing in Agriculture and Forest, 2016: 249-329. https://doi.org/10.1016/B978-1-78548-103-1.50007-8
  30. Hadmoko, D.S., F. Lavigne, and G. Samodra, 2017. Application of a Semiquantitative and GISBased Statistical Model to Landslide Susceptibility Zonation in Kayangan Catchment, Java, Indonesia, Natural Hazards, 87(1): 437-68. https://doi.org/10.1007/s11069-017-2772-z
  31. Hakim, W.L., A.R. Achmad, and C.W. Lee, 2020. Land Subsidence Susceptibility Mapping in Jakarta Using Functional and Meta-ensemble Machine Learning Algorithm Based on Time-series Insar Data, Remote Sensing, 12(21): 1-26.
  32. Hooper, A., D. Bekaert, K. Spaans, and M. Arikan, 2012. Recent Advances in SAR Interferometry Time Series Analysis for Measuring Crustal Deformation, Tectonophysics, 514: 1-13. https://doi.org/10.1016/j.tecto.2011.10.013
  33. Isya, N.H., W. Niemeier, and M. Gerke, 2019. 3D Estimation of Slow Ground Motion Using Insar and the Slope Aspect Assumption, a Case Study: The Puncak Pass Landslide, Indonesia, ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 42: 623-630.
  34. Jensen, J.R., 2000. Remote Sensing of The Environment An Earth Resource Perspective, Upper Saddle River, New Jersey: Prentice-Hall, 1(1): 1-544.
  35. Kadavi, P.R., W.J. Lee, and C.W. Lee, 2017. Analysis of the Pyroclastic Flow Deposits of Mount Sinabung and Merapi Using Landsat Imagery and the Artificial Neural Networks Approach, Applied Sciences (Switzerland), 7(9): 935. https://doi.org/10.3390/app7090935
  36. Kamal, I.M., A. Fariza, and A. Basofi, 2015. Assessment of Landslide Susceptibility Area in Ponorogo, East Java, Indonesia Using Analytical Hierarchy Process and Natural Breaks Classification Assessment of Landslide Susceptibility Area in Ponorogo, East Java, Indonesia Using Analytical Hierarchy, Proc. of In The Fourth Indonesian-Japanese Conference on Knowledge Creation and Intelligent Computing (KCIC), Surabaya, INA, Mar. 25-26, pp. 1-8.
  37. Kerle, N., 2010. Satellite-Based Damage Mapping Following the 2006 Indonesia Earthquake-How Accurate Was It?, International Journal of Applied Earth Observation and Geoinformation, 12(6): 466-476. https://doi.org/10.1016/j.jag.2010.07.004
  38. Kusumastuti, R.D., Viverita, Z.A. Husodo, L. Suardi, and D.N. Danarsari, 2014. Developing a Resilience Index towards Natural Disasters in Indonesia, International Journal of Disaster Risk Reduction, 10(PA): 327-40. https://doi.org/10.1016/j.ijdrr.2014.10.007
  39. Lee, C.W., Z. Lu, and J.W. Kim, 2017. Monitoring Mount Sinabung in Indonesia Using Multi-Temporal InSAR, Korean Journal of Remote Sensing, 33(1): 37-46 (in Korean with English abstract). https://doi.org/10.7780/kjrs.2017.33.1.4
  40. Lee, C.W., Z. L., J.W. Kim, and S.K. Lee, 2015. Volcanic Activity Analysis of Mt. Sinabung in Indonesia Using InSAR and GIS Techniques, Proc. of 2015 IEEE International Geoscience and Remote Sensing Symposium, Milan, ITA, Jul. 26-31, pp. 4793-4796.
  41. Lee, S., 2019. Current and Future Status of GIS-Based Landslide Susceptibility Mapping: A Literature Review, Korean Journal of Remote Sensing, 35(1): 179-93(in Korean With English abstract). https://doi.org/10.7780/KJRS.2019.35.1.12
  42. Lee, S. and I. Park, 2013. Application of Decision Tree Model for the Ground Subsidence Hazard Mapping near Abandoned Underground Coal Mines, Journal of Environmental Management, 127: 166-176. https://doi.org/10.1016/j.jenvman.2013.04.010
  43. Lee, S.K., C.W. Lee, and S. Lee, 2015. A Comparison of the Landsat Image and LAHARZ-Simulated Lahar Inundation Hazard Zone by the 2010 Merapi Eruption, Bulletin of Volcanology, 77(6) :1-13. https://doi.org/10.1007/s00445-014-0893-8
  44. Li, M., S. Zang, B. Zhang, S. Li, and C. Wu, 2014. A Review of Remote Sensing Image Classification Techniques: The Role of Spatio-Contextual Information, European Journal of Remote Sensing, 47(1): 389-411. https://doi.org/10.5721/EuJRS20144723
  45. Li, Z., Y. Cao, J. Wei, M. Duan, L. Wu, J. Hou, and J. Zhu, 2019. Time-Series InSAR Ground Deformation Monitoring: Atmospheric Delay Modeling and Estimating, Earth-Science Reviews, 192: 258-284. https://doi.org/10.1016/j.earscirev.2019.03.008
  46. Lingyun, J., W. Qingliang, and Q. Shanlan, 2013. Present-Day Deformation of Agung Volcano, Indonesia, as Determined Using SBAS-InSAR, Geodesy and Geodynamics, 4(3): 65-70. https://doi.org/10.3724/SP.J.1246.2013.03065
  47. Maghsoudi, Y., F.V.D. Meer, C. Hecker, D. Perissin, and A. Saepuloh, 2018. Using PS-InSAR to Detect Surface Deformation in Geothermal Areas of West Java in Indonesia, International Journal of Applied Earth Observation and Geoinformation, 64: 386-396. https://doi.org/10.1016/j.jag.2017.04.001
  48. Maguire, D.J., 1991. An Overview and Definition of GIS, Geographical Information Systems, 1(1991): 9-20.
  49. Marfai, M.A. and L. King, 2008. Coastal Flood Manage ment in Semarang, Indonesia, Environmental Geology, 55(7): 1507-1518. https://doi.org/10.1007/s00254-007-1101-3
  50. Marfai, M.A., L. King, L.P. Singh, D. Mardiatno, J. Sartohadi, D.S. Hadmoko, and A. Dewi, 2008. Natural Hazards in Central Java Province, Indonesia: An Overview, Environmental Geology, 56(2): 335-351. https://doi.org/10.1007/s00254-007-1169-9
  51. Meilianda, E., B. Pradhan, Syamsidik, L.K. Comfort, D. Alfian, R. Juanda, S. Syahreza, and K. Munadi, 2019. Assessment of Post-Tsunami Disaster Land Use/Land Cover Change and Potential Impact of Future Sea-Level Rise to Low-Lying Coastal Areas: A Case Study of Banda Aceh Coast of Indonesia, International Journal of Disaster Risk Reduction, 41: 101292. https://doi.org/10.1016/j.ijdrr.2019.101292
  52. Moya, L., A. Muhari, B. Adriano, S. Koshimura, E. Mas, L.R.M.-Perez, and N. Yokoya, 2020. Detecting Urban Changes Using Phase Correlation and l1-Based Sparse Model for Early Disaster Response: A Case Study of the 2018 Sulawesi Indonesia Earthquake-Tsunami, Remote Sensing of Environment, 242: 111743. https://doi.org/10.1016/j.rse.2020.111743
  53. Ng, A. H.M., L. Ge, X. Li, H.Z. Abidin, H. Andreas, and K. Zhang, 2012. Mapping Land Subsidence in Jakarta, Indonesia Using Persistent Scatterer Interferometry (PSI) Technique with ALOS PALSAR, International Journal of Applied Earth Observation and Geoinformation, 18(1): 232-242. https://doi.org/10.1016/j.jag.2012.01.018
  54. Oh, H.-J., S. Lee, and G.M. Soedradjat, 2010. Quantitative Landslide Susceptibility Mapping at Pemalang Area, Indonesia, Environmental Earth Sciences, 60(6): 1317-1328. https://doi.org/10.1007/s12665-009-0272-5
  55. Osmanoglu, B., F. Sunar, S. Wdowinski, and E.C.-Cano, 2016. Time Series Analysis of InSAR Data: Methods and Trends, ISPRS Journal of Photogrammetry and Remote Sensing, 115: 90-102. https://doi.org/10.1016/j.isprsjprs.2015.10.003
  56. Pamela, I.A. Sadisun, and Y. Arifianti, 2018. Weights of Evidence Method for Landslide Susceptibility Mapping in Takengon, Central Aceh, Indonesia, Proc. of 2018 IOP Conference Series: Earth and Environmental Science, Bundang, INA, Oct. 18-19, pp. 20-22.
  57. Pepe, A. and F. Calo, 2017. A Review of Interferometric Synthetic Aperture RADAR (InSAR) Multi-Track Approaches for the Retrieval of Earth's Surface Displacements, Applied Sciences, 7(12): 1264. https://doi.org/10.3390/app7121264
  58. Pradhan, B., H.-J. Oh, and M. Buchroithner. 2010. Weights-of-Evidence Model Applied to Landslide Susceptibility Mapping in a Tropical Hilly Area, Geomatics, Natural Hazards and Risk, 1(3): 199-223. https://doi.org/10.1080/19475705.2010.498151
  59. Prasetyo, L.B., A.H. Dharmawan, F.T. Nasdian, and S. Ramdhoni, 2016. Historical Forest Fire Occurrence Analysis in Jambi Province During the Period of 2000-2015: Its Distribution & Land Cover Trajectories, Procedia Environmental Sciences, 33: 450-459. https://doi.org/10.1016/j.proenv.2016.03.096
  60. Rasyid, A.R., N.P. Bhandary, and R. Yatabe, 2016. Performance of Frequency Ratio and Logistic Regression Model in Creating GIS Based Landslides Susceptibility Map at Lompobattang Mountain, Indonesia, Geoenvironmental Disasters, 3(1): 1-16. https://doi.org/10.1186/s40677-016-0036-y
  61. Rofi, A., S. Doocy, and C. Robinson, 2006. Tsunami Mortality and Displacement in Aceh Province, Indonesia, Disasters, 30(3): 340-350. https://doi.org/10.1111/j.0361-3666.2005.00324.x
  62. Rosen, P.A., S. Hensley, I.R. Joughin, F.K. Li, S.N. Madsen, E. Rodriguez, and R.M. Goldstein, 2000. Synthetic Aperture Radar Interferometry, Proceedings of the IEEE, 88(3): 333-382. https://doi.org/10.1109/5.838084
  63. Schindler, S., F. Hegemann, C. Koch, M. Konig, and P. Mark, 2016. Radar Interferometry Based Settlement Monitoring in Tunnelling: Visualisation and Accuracy Analyses, Visualization in Engineering, 4(1): 1-16. https://doi.org/10.1186/s40327-015-0029-z
  64. Siegert, F. and A.A. Hoffmann, 2000. The 1998 Forest Fires in East Kalimantan (Indonesia): A Quantitative Evaluation Using High Resolution, Multitemporal ERS-2 SAR Images and NOOA-AVHRR Hotspot Data, Remote Sensing of Environment, 72(1): 64-77. https://doi.org/10.1016/S0034-4257(99)00092-9
  65. Silalahi, F.E.S., Pamela, Y. Arifianti, and F. Hidayat, 2019. Landslide Susceptibility Assessment Using Frequency Ratio Model in Bogor, West Java, Indonesia, Geoscience Letters, 6(1): 1-17. https://doi.org/10.1186/s40562-019-0131-5
  66. Syifa, M., P.R. Kadavi, and C.W. Lee, 2019. An Artificial Intelligence Application for Post-Earthquake Damage Mapping in Palu, Central Sulawesi, Indonesia, Sensors, 19(3): 542. https://doi.org/10.3390/s19030542
  67. Syifa, M., P.R. Kadavi, C.W. Lee, and B. Pradhan, 2020. Landsat Images and Artificial Intelligence Techniques Used to Map Volcanic Ashfall and Pyroclastic Material Following the Eruption of Mount Agung, Indonesia, Arabian Journal of Geosciences, 13(3): 1-12. https://doi.org/10.1007/s12517-019-5007-7
  68. Takagi, H., M. Esteban, T. Mikami, and D. Fujii, 2016. Projection of Coastal Floods in 2050 Jakarta, Urban Climate, 17: 135-145. https://doi.org/10.1016/j.uclim.2016.05.003
  69. Tambunan, M.P., 2017. The Pattern of Spatial Flood Disaster Region in DKI Jakarta The Pattern of Spatial Flood Disaster Region in DKI Jakarta, Proc. of IOP Conference. Series.: Earth Environ. Science, da Aceh, INA, Nov. 22-24, vol. 56, p. 012014.
  70. Tang, J., S. Alelyani, and H. Liu. 2014. Feature Selection for Classification: A Review. In Data Classification: Algorithms and Applications, 2014: 37-64.
  71. Thouret, J.C., Z. Kassouk, A. Gupta, S.C. Liew, and A. Solikhin, 2015. Tracing the Evolution of 2010 Merapi Volcanic Deposits (Indonesia) Based on Object-Oriented Classification and Analysis of Multi-Temporal, Very High Resolution Images, Remote Sensing of Environment, 170: 350-371. https://doi.org/10.1016/j.rse.2015.09.028
  72. Tolomei, C., S. Salvi, A.T. Yuherdha, G. Prinsen, G. Pezzo, J. Beckers, and S. Atzori, 2019. Multi-Temporal and Multi-Sensor InSAR Results to Support Geohazard Assessment in the Bandung Area, (Western Java, Indonesia), International Geoscience and Remote Sensing Symposium (IGARSS), 2019: 9674-9677.
  73. Umar, Z., B. Pradhan, A. Ahmad, M.N. Jebur, and M.S. Tehrany, 2014. Earthquake Induced Landslide Susceptibility Mapping Using an Integrated Ensemble Frequency Ratio and Logistic Regression Models in West Sumatera Province, Indonesia, Catena, 118: 124-135. https://doi.org/10.1016/j.catena.2014.02.005
  74. Vetrita, Y. and M.A. Cochrane, 2020. Fire Frequency and Related Land-Use and Land-Cover Changes in Indonesia's Peatlands, Remote Sensing, 12(1): 5. https://doi.org/10.3390/rs12010005
  75. Voight, B., E.K. Constantine, S. Siswowidjoyo, and R. Torley, 2000. Historical Eruptions of Merapi Volcano, Central Java, Indonesia, 1768-1998. Journal of Volcanology and Geothermal Research, 100(1-4): 69-138. https://doi.org/10.1016/S0377-0273(00)00134-7
  76. Wang, C., X. Wang, W. Xiu, B. Zhang, G. Zhang, and P. Liu, 2020. Characteristics of the Seismogenic Faults in the 2018 Lombok, Indonesia, Earthquake Sequence as Revealed by Inversion of InSAR Measurements, Seismological Research Letters, 91(2): 733-744. https://doi.org/10.1785/0220190002
  77. Wang, D., J. Mori, and T. Uchide, 2012. Supershear Rupture on Multiple Faults for the M w 8.6 Off Northern Sumatra, Indonesia Earthquake of April 11, 2012, Geophysical Research Letters, 39(21): 1-5.
  78. Warlina, L. and F. Guinensa, 2019. Flood Susceptibility and Spatial Analysis of Pangkalpinang City, Bangka Belitung, Indonesia, Journal of Engineering Science and Technology, 14(6): 3481-3495.
  79. Wati, S.E., T. Hastuti, S. Widjojo, and F. Pinem, 2010. Landslide Susceptibility Mapping With Heuristic Approach In Mountainous Area A Case Study In Tawangmangu Sub District, Central Java, Indonesia. In ISPRS Commission VIII Mid-Term Symposium 'Networking the World with Remote Sensing, 8: 248-253.
  80. Yastika, P.E., N. Shimizu, and H.Z. Abidin, 2019. Monitoring of Long-Term Land Subsidence from 2003 to 2017 in Coastal Area of Semarang, Indonesia by SBAS DInSAR Analyses Using Envisat-ASAR, ALOS-PALSAR, and Sentinel-1A SAR Data, Advances in Space Research, 63(5): 1719-1736. https://doi.org/10.1016/j.asr.2018.11.008
  81. Yulianto, F., P. Sofan, M.R. Khomarudin, and M. Haidar, 2013. Extracting the Damaging Effects of the 2010 Eruption of Merapi Volcano in Central Java, Indonesia, Natural Hazards, 66(2): 229-247. https://doi.org/10.1007/s11069-012-0438-4