Spatial - Temporal variability of Land Use Land Cover at Mount Merapi, Indonesia

International Journal of Scientific Research in Science and Technology Print ISSN: 2395-6011 | Online ISSN: 2395-602X (www.ijsrst.com) doi : https://doi.org/10.32628/IJSRST229129 Spatial - Temporal variability of Land Use Land Cover at Mount Merapi, Indonesia A. Rajani*1, Dr. S. Varadarajan2 * Research Scholar, Department of ECE, Sri Venkateswara University College of Engineering, Tirupati, Andhra 1 Pradesh, India 2Professor, Department of ECE, Sri Venkateswara University College of Engineering, Tirupati, Andhra Pradesh, India ABSTRACT One of the most active volcanoes in the island of Java is Merapi mount which was experienced the last major eruption peak on October 26th, 2010. This Article Info volcanic eruption was effusive eruption type where magmatic gas pressure in Volume 9, Issue 1 the crater was not too strong and magma eruption was just flown out past the Page Number : 183-192 slopes of the Merapi mount area. However, magmatic gas pressure and magma volume still result in deformation changes that have a direct impact on Publication Issue residential areas throughout the Merapi mount area. Residential areas were January-February-2022 obtained through supervised classification process from Landsat 7 and 8- satellite imagery in the 2009, 2011 and 2019 acquisition year. The reason of Article History observation year selection was based on pre and post eruption concept to get Accepted : 25 Jan 2022 pattern of Merapi’s mountain body change through deformation analysis. The Published : 03 Feb 2022 work focuses on spatial-temporal variability of land use land cover analysis at Mount Merapi pre and post 2010 eruption event considered here. The technique is based on NDVI (Normalized Difference Vegetation Index), Maximum Likelihood Classification (MLC) and False Colour Composite methodology. Based on change in number of pixels it was analysed. Actually, some portion of land was covered with clouds and its shadows. From the results it was observed that, water body, barren and built up features were miss classified. So finally False Colour Composite (FCC) images are used to identify the misclassified classes. Keywords : NDVI, MLC, False Colour Composite (FCC), Mount Merapi Volcano, Landsat 7 & Landsat 8. I. INTRODUCTION surveys. Both spatial and non-spatial datasets are included in this type of survey. At the local, regional The social and economic development of a society is and national levels, LULC maps play a critical role in completely dependent on its expansion. This is the programme design, management, and monitoring. On primary motivation for conducting socioeconomic the one hand, this type of information aids in the Copyright: © the author(s), publisher and licensee Technoscience Academy. This is an open-access article distributed under the 183 terms of the Creative Commons Attribution Non-Commercial License, which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited  A. Rajani et al Int J Sci Res Sci & Technol. January-February-2022, 9 (1) : 183-192 understanding of land use issues, and on the other, it densely populated area where the island of Java aids in the formulation of policies and programmes became one of the islands with the largest residential necessary for development planning. It is vital to area in Indonesia. monitor the ongoing process of land use land cover pattern throughout time in order to ensure The organization of this document is as follows. sustainable development. To accomplish sustainable Section 2 presents a literature review of the existing urban development and to prevent haphazard methods used to detect Land Use Land Cover change. expansion of towns and cities, authorities involved in Section 3 which discuss about materials methodology urban development must establish planning models i.e. study area and input images for the study purpose that allow every available piece of land to be used in and proposed method for detecting changes. Results the most reasonable and optimal way possible. So it is and discussions were presented in the section 4. necessary to have information about the existing and Finally section 5 states the conclusions and future former land use land cover data of the area. LULC scope. maps also aid in the investigation of changes in our ecology and surroundings. The precise information II. LITERATURE REVIEW regarding the research unit's Land Use Land Cover [1], will be helpful to formulate regulations and Halah Qahtan Hamdy (2018) et.al. Digital change implement programmes to protect our ecosystem. detection method applied to find LU/LC change detection using maximum likelihood estimation. And The Earth is in a perpetual state of flux. Some of this also NDVI (Normalized difference Vegetation Index) transformation happens slowly over millennia, while used to classify two classes vegetation and no- others happen quickly over decades. Volcanoes, vegetation. NDWI (Normalized Difference Water continental shifts, mountain building and erosion, Index) used to identify water and no-water areas [2]. reorganisation of oceans, appearance and NDBI (normalized Difference Built-up Index) used to disappearance of deserts and marshlands, advances classify urban area and no-urban area. Number of and retreats of great ice sheets, rise and fall of sea and indices is used for better identification of each lake levels, and the evolution and extinction of vast individual class. But there is no comparison between numbers of species are all caused by major natural maximum likelihood estimation and indices based forces. Volcanic eruptions are also monitored by using estimation. thermal remote sensing by estimating Land Surface temperature values [5, 10, and 11]. Zubair Saing (2021) et.al. LULC change detection done for the area South Sulawesi Province, Indonesia Mount Merapi is one of the most active volcanos in for the two years 2005 and 2019[7,3]. Here Indonesia, which is located in central Java. It has a classification had done using ISO cluster classification lengthy history of major eruptive episodes. Activity which is unsupervised [13]. Training samples has included lava flows, pyroclastic flows, lahars, considered based on ISO cluster and topographical Plinian explosions with heavy ash-fall, incandescent map of Indonesia. Then finally maximum likelihood block avalanches, block-and-ash flows, and dome classifier [14] applied and results validation had done growth and destruction. Fatalities from these events using random point and field check. Accuracy were reported in 1994, 2006, and in 2010 when achieved was 82% and 86% for June 2005 and March hundreds of thousands of people were evacuated. The 2019 respectively. impact of active volcano activity will be felt in the International Journal of Scientific Research in Science and Technology (www.ijsrst.com) | Volume 9 | Issue 1 184  A. Rajani et al Int J Sci Res Sci & Technol. January-February-2022, 9 (1) : 183-192 Anjan Roy (2019) et.al. used Integrated hybrid Analysis), NDVI and NDWI analysis were classification technique, which comprises of implemented to assess the change scenario [9]. unsupervised and supervised classification techniques, Maximum likelihood supervised classification was applied combined human knowledge. NDVI and technique was performed to create the signature class NDWI [6] maps are used for cross verification of the of significant land cover category (deep water, results. The confusion matrix-based accuracy shallow water, vegetation, and settlement). The assessment and Kappa coefficient were considered for evaluation of accuracy was not taken into account. assessing the performance of the classification system. The results showed an overall accuracy of 91.36% and III. MATERIALS AND METHODOLOGY kappa index of agreement value of 0.91. Study Area: Sophia S. Rwanga, (2017) et.al [8] proposed supervised Mount Merapi, Gunung Merapi (Fire Mountain) It is classification algorithm for Land Use Land Cover the most active volcano in Indonesia and has erupted classified map generation. Finally classified image regularly since 1548. It is located approximately 28 accuracy assessment was done using error matrix kilometres (17 mi) north of Yogyakarta city which (Confusion matrix) by comparing with ground truth has a population of 2.4 million, and thousands of data. The overall classification accuracy of 81.7% and people live on the flanks of the volcano, with villages kappa coefficient of 0.722 observed. Name of the as high as 1,700 meters (5,577 ft) above sea level. For applied supervised classification algorithm was not geographical area of research lies in latitude position mentioned. 110°14’60” E- 110°32’30” E and in longitude position 7°29’47” S-7°47’53” S and can be seen clearly in Figure Md. Inzamul Haque (2017) et.al. Proposed pre- 1. classification approach with CVA (Change Vector Figure 1. Study area of Mount merapi and its surrounding districts Mount Merapi rises to roughly 2,930 metres above sea magmatic gas pressure. For the magma to flow out of level. On the 26th of October, 2010, Mount Merapi the cavity, it must pass through the top of the erupted. The Merapi volcanic eruption is a kind of mountain's slopes. Despite being a type effusive effusive eruption characterised by lava melts and low eruption, this one produces significant physical, International Journal of Scientific Research in Science and Technology (www.ijsrst.com) | Volume 9 | Issue 1 185  A. Rajani et al Int J Sci Res Sci & Technol. January-February-2022, 9 (1) : 183-192 environmental, and economic damage. The lava flow will change the land cover in the area, therefore there Methods: will be changes in land cover before and after Mount The methods utilized to detect changes at Mount Merapi erupts. Prior to the eruption, this results in Merapi volcano before and after the huge eruption on substantial deforestation (deformation). Volcanic October 26, 2010 are depicted in Figure 2 in the form deformation is caused by volcanic activity in the form of flow diagram. The entire procedure is divided into of magma movements beneath the surface, which four sections. 1)Pre-processing of images 2) effect pressure changes in the magma pocket. Classification based on NDVI thresholds Submarine motion is a precursor to eruption and a 3)Classification based on maximum likelihood 4) rise in pressure, both of which will cause ground Change detection using statistical analysis deformation. This study will look at the deformation 5)Comparison using FCC image. pattern that happened on Mount Merapi before and after the eruption. It also includes a study of the Image pre-processing: effects on land cover land use changes in the study The initial step in picture pre-processing is to use the area. landsat toolbox to remove scan-lines errors from Landsat7 band images. Mount Merapi surrounding Materials: areas were selected by using Area Of Interest (AOI) Multispectral band pictures of Landsat8 Level 1 OLI / selection process using map clipping process. For TIRS C1 Level 1 & Landsat 7 (ETM+) were used to subsequent processing, an AOI map is clipped from detect spatiotemporal changes in land use/land cover the needed band images. All the selected images were at Mount Merapi, 2010 pre and post eruption. WGS84 projected onto a UTM (universal Transverse Mercator) is the reference datum. Landsat image path-120 and coordinate system, datum WGS84 in the north zone row-65 of the study area were considered. Landsat 7 49N (WGS_1984_UTM_Zone_49N). has a pixel spatial resolution of 28.5 meters and Landsat 8 has a pixel spatial resolution of 30 meters. NDVI Threshold based Classification: The satellite image acquisition dates are June 21, 2009, The Normalized Difference Vegetation Index is a May 10, 2011 from landsat7 and June 25, 2019 from prominent vegetation index that is used by remote landsat8. Satellite data is shown in Table 1. Images sensing to analyse vegetation health and land use land courtesy of https://earthexplorer.usgs.gov. This study cover [5,6]. The mathematical formula for estimating considered images having <10% cloud coverage for the NDVI using RED band and NearInfraRed (NIR) attaining best classification as well as change band images is given below. detection results. For the development of LULC change detection maps GIS software used is ArcGIS 𝑁𝐼𝑅 − 𝑅𝐸𝐷 𝑁𝐷𝑉𝐼 = 10.3. 𝑁𝐼𝑅 + 𝑅𝐸𝐷 𝐵𝑎𝑛𝑑4 − 𝐵𝑎𝑛𝑑3 Table 1. Spatial Image Sources 𝐿𝑎𝑛𝑑𝑠𝑎𝑡 7 − 𝑁𝐷𝑉𝐼 = 𝐵𝑎𝑛𝑑4 + 𝐵𝑎𝑛𝑑3 𝐵𝑎𝑛𝑑5 − 𝐵𝑎𝑛𝑑4 𝐿𝑎𝑛𝑑𝑠𝑎𝑡 8 − 𝑁𝐷𝑉𝐼 = Data Spatial 𝐵𝑎𝑛𝑑5 + 𝐵𝑎𝑛𝑑4 Sensor Date Bands source resolution LAND 21-06-2009 NDVI values ranges from -1 to +1. The classification ETM+ 28.5 meters 3,4&5 SAT 7 10-05-2011 was done based on NDVI threshold values. It was LAND OLI 25-06-2019 30 meters 4,5 &6 divided into five classes a) Water bodies b) Barren SAT 8 /TIRS International Journal of Scientific Research in Science and Technology (www.ijsrst.com) | Volume 9 | Issue 1 186  A. Rajani et al Int J Sci Res Sci & Technol. January-February-2022, 9 (1) : 183-192 Land c) Built up d) Low density vegetation e) High density vegetation. Maximum Likelihood Classification: Maximum likelihood classification determines the probability that a given pixel belongs to a specific class based on the statistics for each class in each band being normally distributed. All pixels are categorised unless a probability threshold is set. It was chosen because of its ability to appropriately classify items. This is the most widely using algorithm for creating land use and land cover classification maps and detecting changes based on them. Signature files, which can be constructed from NDVI threshold classified image [4]. It was used as base for maximum likelihood classification. Change detection using statistical analysis: Change detection requires understanding the changes in land use land cover that happened as a result of such a huge volcanic eruption [12]. Here, pixel-based variations were calculated to quantify the changes in distinct LULC classes. Images from 2009, 2011 and 2019 were used in this study, which were taken before and after Mount Merapi's massive volcanic eruption in 2010. Comparison with FCC: Finally comparison between Maximum likelihood classified images with False Figure 2. Change detection methodology flow diagram Colour Composite (FCC) image of that study area was done. By visual interpretation of the FCC image with IV. RESULTS AND DISCUSSION classified images results were presented. The goal of this study was to determine how land use and land cover were before and after the 2010 volcanic eruption. This data is required by the government officials to take appropriate action plan in response to the observed changes. They will be able to rebuild and recreate the essential facilities as a result of this. To compensate those who have been affected. Normalized Difference Vegetation Index images for the years 2009, 2011 and 2019 are shown in the figures 3(a), 4(a) and 5(a) respectively. Maximum Likelihood Classified (MLC) images are International Journal of Scientific Research in Science and Technology (www.ijsrst.com) | Volume 9 | Issue 1 187  A. Rajani et al Int J Sci Res Sci & Technol. January-February-2022, 9 (1) : 183-192 shown in the figures 3(b), 4(b) and 5(b) for the years respectively. Basically Composite band images are 2009, 2011 and 2019 respectively. False Colour used for visual interpretation of the changes in Land Composite (FCC) images for the years 2009, 2011 and use and Land cover. This composite band image was 2019 are shown in the figures 3(c), 4(c) and 5(c) generated using 3bands. (a) (b) (c) Figure 3. Study area (a) NDVI Threshold (b) MLC image (c) False Color Composite image for the year 2009 (a) (b) (c) Figure 4. Study area (a) NDVI Threshold (b) MLC image (c) False Color Composite image for the year 2011 (a) (b) (c) Figure 5. Study area (a) NDVI Threshold (b) MLC image (c) False Color Composite image for the year 2019 International Journal of Scientific Research in Science and Technology (www.ijsrst.com) | Volume 9 | Issue 1 188  A. Rajani et al Int J Sci Res Sci & Technol. January-February-2022, 9 (1) : 183-192 Table 2: Classified parameters in terms of no. of pixels count, Area (% ) and differences Difference Difference Land use No. of Area (%) No. of Area (%) No. of Area (%) between between Land cover Pixels 2009 Pixels 2011 Pixels 2019 2011 & 2019 & parameter 2009 2009 Barren 584879 15.0429 1179577 30.34 619042 15.92 15.2971 0.8771 land Low 1340772 34.4843 1126659 28.98 1212421 31.18 -5.5043 -3.3043 vegetation High 75228 1.9348 136442 3.51 173903 4.47 1.5752 2.5352 vegetation Water 226 0.0058 22933 0.59 262547 6.75 0.5842 6.7442 body Built- up 1886964 48.5322 1422458 36.59 1620156 41.67 -11.9422 -6.8622 50.0000 45.0000 Area interms of per-cent 40.0000 35.0000 30.0000 25.0000 20.0000 15.0000 10.0000 5.0000 0.0000 Barren land low high water body built up vegetation vegetation 2009 15.0429 34.4843 1.9348 0.0058 48.5322 2011 30.34 28.98 3.51 0.59 36.59 2019 15.92 31.18 4.47 6.75 41.67 Figure 6. LULC parmeters for the years 2009, 2011 and 2011 interms of Area (%) From the above results it was observed that, due to iv) Low vegetation and v) High vegetation. Table 2 clouds and its corresponding shadows some of classes presents the land use land cover parameters change are misclassified. Which can be clearly observed in in-terms of number pixel count per year, parameter False Colour Composite (FCC) images shown figures 3 wise area (%) occupancy and difference between 2011 (c),4 (c) and 5 (c). The study area was classified into 5 & 2009 and 2019 & 2009. LULC parameters and its classes i) Water body ii) Barren Land iii) Built-up area quantification using area wise (%) occupancy International Journal of Scientific Research in Science and Technology (www.ijsrst.com) | Volume 9 | Issue 1 189  A. Rajani et al Int J Sci Res Sci & Technol. January-February-2022, 9 (1) : 183-192 presented in the figure 6 for the three years data. by comparing the NDVI and MLC classified images After quantifying the results it was observed that, with FCC images. water bodies were very less in the year 2009, due to cloud masking over that area. In the year 2019 built- VI. REFERENCES up was about to 41.67 % actually it was more than that. In the year 2011 barren land was approximately [1]. A.Rajani, S.Varadarajan,” LU/LC Change double to 2009 and 2019. All these observations are Detection Using NDVI & MLC Through made by comparing the NDVI and MLC classified Remote Sensing and GIS for Kadapa Region”, images with FCC images. So, finally for achieving International Conference on Cognitive better classification results it requires cloud free Informatics and Soft Computing (CISC-2019), images. pp. 215-223, (c) Springer Nature Singapore Pt. Ltd, 2020. [https://doi.org/10.1007/978-981-15- V. CONCLUSION 1451-7_24]. [2]. 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After Computer and Mathematics Education,2021, quantifying the results it was observed that, water volume no.12, issue no.5, pp. 607-617. bodies were very less in the year 2009, due to cloud [6]. Halah Qahtan Hamdy, Osamah Hadi Mutlag, masking over that area. In the year 2019 built-up was “Land Cover Change Detection in Al-Karkh / about to 41.67 % actually it was more than that. In Baghdad”, International Journal of Science and the year 2011 barren land was approximately double Research (IJSR), 2020, Volume 9, Issue 1, to 2009 and 2019. All these observations were made pp.412-417. International Journal of Scientific Research in Science and Technology (www.ijsrst.com) | Volume 9 | Issue 1 190  A. Rajani et al Int J Sci Res Sci & Technol. January-February-2022, 9 (1) : 183-192 [7]. Zubair Saing, Herry Djainal, Saiful Deni, “Land [14]. Richard J. Radke, Srinivas Andra, Omar Al- use balance determination using satellite Kofahi, Badrinath Roysam, “Image Change imagery and geographic information system: Detection Algorithms: A Systematic Survey”, case study in South Sulawesi Province, IEEE Transactions on Image processing, 2005, Indonesia”, Geodesy and Geodynamics by 14: 3, pp:294-307. Elsevier, 2021, volume 12, pp.133-147. [8]. Martha Raynolds, Borgþór Magnússon, Sigmar Authors : Metúsalemsson and Sigurður H. Magnússon, “Warming, Sheep and Volcanoes: Land Cover A. Rajani, Research Scholar (part- Changes in Iceland Evident in Satellite NDVI time) in the Department of Trends”, Remote sensing(mpdi), 2015, volume Electronics and Communication no.7, pp.9492-9506. Engineering at Sri Venkateswara [9]. Md. Inzamul Haque, Rony Basak, “Land cover University College of Engineering, change detection using GIS and remote sensing Tirupati, India. She has obtained techniques: A spatio-temporal study on Tanguar her B.E (ECE) from Osmania Haor, Sunamganj, Bangladesh”, The Egyptian University, Hyderabad in 2001 and M.Tech in Journal of Remote Sensing and Space Sciences, Electronic Instrumentation and Communication 2017, volume no.17, pp.251-263. Systems from Sri Venkateswara University, Tirupati [10]. Olutoyin Adeola Fashae, Efosa Gbenga in 2010. She has 11 years of teaching experience. Her Adagbasa, Adeyemi Oludapo Olusola, Rotimi research areas of interest includes Image processing, Oluseyi Obateru, “Land use/land cover change Signal Processing and Embedded Systems and and land surface temperature of Ibadan and Microcontrollers. She has published more than 15 environs, Nigeria” , Environ Monit Assess, journals in national and international journals. She is Spinger, 2020, 192:109, pp.1-18. a member of Institute of Engineers (India). Currently [11]. Ogunjobi, K. O., Adamu, Y., Akinsanola, A. A., working as Assistant Professor at Annamacharya & Orimoloye, I. R. (2018). Spatio-temporal Institute of Technology and Sciences, Tirupati, analysis of land use dynamics and its potential Andhra Pradesh, India. indications on land surface temperature in Email: [email protected] Sokoto Metropolis, Nigeria. Royal Society Open Science, 5(12), 180,661. [12]. Masuma Chowdhury, Mohammad Emran Dr. S. Varadarajan is Hasan, M.M. Abdullah-Al-Mamun, “Land working as a Professor in the use/land cover change assessment of Halda department of Electronics watershed using remote sensing and GIS”, The and Communication Egyptian Journal of Remote Sensing and Space Engineering at Sri Sciences, 2020, Venkateswara University [13]. S Salman, W A Abbas, “Multispectral and college of Engineering, Panchromatic used Enhancement Resolution Tirupati, Andhra Pradesh, and Study Effective Enhancement on India. He has 25 years of Supervised and Unsupervised Classification teaching experience. He received his PhD from Sri Land – Cover”, IOP Conference Series: Journal Venkateswara University, Tirupati, AP, India. His of Physics :1003, 2018. areas of interest are Digital Communication, Image International Journal of Scientific Research in Science and Technology (www.ijsrst.com) | Volume 9 | Issue 1 191  A. Rajani et al Int J Sci Res Sci & Technol. January-February-2022, 9 (1) : 183-192 and Signal Processing. He has published more than 100 papers in national and international conferences and national and international journals. He is a Fellow of the Andhra Pradesh Academy of Sciences (FAPAS), Fellow of IETE and IE. He chaired and served as reviewer for several international conferences and is a member of editorial board of several international conferences and is a member of the editorial board of several international journals. He visited the USA, UK. He worked as secretary, Andhra Pradesh State Council of Higher Education from Jan’2016 to Aug’2019. Email: [email protected] Cite this article as : A. Rajani, Dr. S. Varadarajan, "Spatial - Temporal variability of Land Use Land Cover at Mount Merapi, Indonesia", International Journal of Scientific Research in Science and Technology (IJSRST), Online ISSN : 2395-602X, Print ISSN : 2395-6011, Volume 9 Issue 1, pp. 183-192, January-February 2022. Available at doi : https://doi.org/10.32628/IJSRST229129 Journal URL : https://ijsrst.com/IJSRST229129 International Journal of Scientific Research in Science and Technology (www.ijsrst.com) | Volume 9 | Issue 1 192