Land-Cover Change Detection in Batur Catchment Area Using Remote Sensing
Abstract
Land cover information is an essential aspect in the planning and management of earth modeling and understanding. Land cover changes impact the physical and social environment, such as hydrological conditions and ecological systems. This study aimed to identify spatial differences in the land cover of the Batur catchment area from 2015-2021 by using a remote sensing approach to describe the existing land-cover site and to detect its changes. The methods used in this study are a combination of the vegetation index and a supervised classification maximum likelihood algorithm with Landsat 8 OLI/TIRS in 2015 and 2021. Furthermore, the Change Detection Feature, identified from two image periods in 2015-2021 and processed, is used to detect changes in land cover. The accuracy assessment utilized QuickBird imagery recorded in 2015; field survey data were taken in 2021. The results showed that between 2015 to 2021, built-up area, bare land, shrubs, and lake have increased by 102,66% (306,01 ha), 27,95% (452,25 ha), 15,20% (215,72 ha) and 4,05 % (62,73 ha) while dryland forest and dry-dry-field have decreased by -25,84% (-606,29 ha) and -14.59% (-430,42 ha), respectively. The overall accuracy of the multispectral classification results in 2015 and 2021 was 82,63% and 89,57%.
Keywords: Land-Cover Change; Batur; Catchment Area; Remote Sensing
Full Text:
PDFReferences
Abdullah, A. Y. M., Masrur, A., Gani Adnan, M. S., Al Baky, M. A., Hassan, Q. K., & Dewan, A. (2019). Spatio-temporal patterns of land use/land cover change in the heterogeneous coastal region of Bangladesh between 1990 and 2017. Remote Sensing, 11(7). https://doi.org/10.3390/rs11070790
Adebayo, H. O., Otun, W. O., & Daniel, I. S. (2019). Change Detection in Landuse/ Landcover of Abeokuta Metropolitan Area, Nigeria Using Multi-Temporal Landsat Remote Sensing. Indonesian Journal of Geography, 51(2), 217–223.
Alam, S. M. R., & Hossain, M. S. (2020). A Rule-Based Classification Method for Mapping Saltmarsh Land-Cover in South-Eastern Bangladesh from Landsat-8 OLI. Canadian Journal of Remote Sensing, 1–25. https://doi.org/10.1080/07038992.2020.1789852
Alkaradaghi, K., Ali, S. S., Al-Ansari, N., & Laue, J. (2018). Evaluation of Land Use & Land Cover Change Using Multi-Temporal Landsat Imagery: A Case Study Sulaimaniyah Governorate, Iraq. Journal of Geographic Information System, 10(03), 247–260. https://doi.org/10.4236/jgis.2018.103013
Aslami, F., & Ghorbani, A. (2018). Object-based land-use/land-cover change detection using Landsat imagery: a case study of Ardabil, Namin, and Nir counties in northwest Iran. Environmental Monitoring and Assessment, 190(7). https://doi.org/10.1007/s10661-018-6751-y
Barakat, A., Ouargaf, Z., Khellouk, R., El Jazouli, A., & Touhami, F. (2019). Land Use/Land Cover Change and Environmental Impact Assessment in Béni-Mellal District (Morocco) Using Remote Sensing and GIS. Earth Systems and Environment, 3(1), 113–125. https://doi.org/10.1007/s41748-019-00088-y
Bektas Balcik, F., & Karakacan Kuzucu, A. (2016). Determination of land cover/land use using spot 7 data with supervised classification methods. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives, 42(2W1), 143–146. https://doi.org/10.5194/isprs-archives-XLII-2-W1-143-2016
Berihun, M. L., Tsunekawa, A., Haregeweyn, N., Meshesha, D. T., Adgo, E., Tsubo, M., Masunaga, T., Fenta, A. A., Sultan, D., Yibeltal, M., & Ebabu, K. (2019). Hydrological responses to land use/land cover change and climate variability in contrasting agro-ecological environments of the Upper Blue Nile basin, Ethiopia. Science of the Total Environment, 689, 347–365. https://doi.org/10.1016/j.scitotenv.2019.06.338
Campbell, J.B., & Wynne, R. . (2011). Introduction to remote sensingTitle. Guilford Press.
Chen, J., Lu, M., Chen, X., Chen, J., & Chen, L. (2013). A spectral gradient difference based approach for land cover change detection. ISPRS Journal of Photogrammetry and Remote Sensing, 85, 1–12. https://doi.org/10.1016/j.isprsjprs.2013.07.009
Danoedoro, Projo., Kristian, Gerry., Rahmi, K. N. . (2015). Pengaruh Metode Koreksi Radiometrik ALOS AVNIS-2 Terhadap Akurasi Hasil Estimasi Karbon Vegetasi Tegakan di Wilayah Kota Semarang Bagian Timur. Prosiding PIT MAPIN XX.
Danoedoro, P., Ananda, I. N., Kartika, C. S. D., Umela, A. F., & Alvidita. (2020). Testing a detailed classification scheme for land-cover/ land-use mapping of typical Indonesian landscapes: Case study of Sorolangun, Jambi, and Salatiga, Central Java. Indonesian Journal of Geography, 52(3), 327–340.
Foody, G. M. (2020). Explaining the unsuitability of the kappa coefficient in the assessment and comparison of the accuracy of thematic maps obtained by image classification. Remote Sensing of Environment, 239. https://doi.org/10.1016/j.rse.2019.111630
Garg, V., Nikam, B. R., Thakur, P. K., Aggarwal, S. P., Gupta, P. K., & Srivastav, S. K. (2019). Human-induced land use land cover change and its impact on hydrology. HydroResearch, 1, 48–56. https://doi.org/10.1016/j.hydres.2019.06.001
Ghorbani, A., & Ouri, A. E. (2012). Utility of the NDVI for land/canopy cover mapping in Khalkhal County (Iran). Annals of Biological Research, 3(12), 5494–5503. https://www.researchgate.net/publication/284777424
Giri, C. P. (2012). Remote Sensing of Land Use and Land Cover: Principles and Applications (Vol. 1).
Herold, M., See, L., Tsendbazar, N. E., & Fritz, S. (2016). Towards an integrated global land cover monitoring and mapping system. Remote Sensing, 8(12), 1–11. https://doi.org/10.3390/rs8121036
Heydari, S. S., & Mountrakis, G. (2018). Effect of classifier selection, reference sample size, reference class distribution and scene heterogeneity in per-pixel classification accuracy using 26 Landsat sites. Remote Sensing of Environment, 204, 648–658. https://doi.org/10.1016/j.rse.2017.09.035
Hsiao, L. H., & Cheng, K. S. (2016). Assessing uncertainty in LULC classification accuracy by using bootstrap resampling. Remote Sensing, 8(9). https://doi.org/10.3390/rs8090705
Hua, W., Chen, H., Sun, S., & Zhou, L. (2015). Assessing climatic impacts of future land use and land cover change projected with the CanESM2 model. International Journal of Climatology, 35(12), 3661–3675. https://doi.org/10.1002/joc.4240
Jazouli, A. El, Barakat, A., Khellouk, R., Rais, J., & Baghdadi, M. El. (2019). Remote sensing and GIS techniques for prediction of land use land cover change effects on soil erosion in the high basin of the Oum Er Rbia River (Morocco). Remote Sensing Applications: Society and Environment, 13, 361–374. https://doi.org/10.1016/j.rsase.2018.12.004
Ji, D. R. H. Y. W. F. B. Z. Q. (2012). Crop diseases and pests monitoring based on remote sensing: A survey. World Automation Congress 2012, Puerto Vallarta, Mexico, 177–181.
Kementerian Lingkunga Hidup (KLH). (2014). Gerakan Penyelamatan Danau (GERMADAN) Danau Batur.
Khatami, R., Mountrakis, G., & Stehman, S. V. (2016). A meta-analysis of remote sensing research on supervised pixel-based land-cover image classification processes: General guidelines for practitioners and future research. Remote Sensing of Environment, 177, 89–100. https://doi.org/10.1016/j.rse.2016.02.028
Letsoin, S. M. A., Herak, D., Rahmawan, F., & Purwestri, R. C. (2020). Land cover changes from 1990 to 2019 in Papua, Indonesia: Results of the remote sensing imagery. Sustainability (Switzerland), 12(16), 1–18. https://doi.org/10.3390/su12166623
Lin, L., Hao, Z., Post, C. J., Mikhailova, E. A., Yu, K., Yang, L., & Liu, J. (2020). Monitoring land cover change on a rapidly urbanizing island using google earth engine. Applied Sciences (Switzerland), 10(20), 1–16. https://doi.org/10.3390/app10207336
Lu, M., Chen, J., Tang, H., Rao, Y., Yang, P., & Wu, W. (2016). Land cover change detection by integrating object-based data blending model of Landsat and MODIS. Remote Sensing of Environment, 184, 374–386. https://doi.org/10.1016/j.rse.2016.07.028
Millard, K., & Richardson, M. (2015). On the importance of training data sample selection in Random Forest image classification: A case study in peatland ecosystem mapping. Remote Sensing, 7(7), 8489–8515. https://doi.org/10.3390/rs70708489
Mohamed, M. A., Anders, J., & Schneider, C. (2020). Monitoring of changes in land use/land cover in Syria from 2010 to 2018 using multitemporal landsat imagery and GIS. Land, 9(7). https://doi.org/10.3390/land9070226
Mohammadi, A., Shahabi, H., & Ahmad, B. Bin. (2019). Land-cover change detection in a part of cameron highlands, malaysia using ETM+ satellite imagery and support vector machine (SVM) algorithm. EnvironmentAsia, 12(2), 145–154. https://doi.org/10.14456/ea.2019.36
Moser, G., Serpico, S. B., & Benediktsson, J. A. (2013). Land-cover mapping by markov modeling of spatial-contextual information in very-high-resolution remote sensing images. Proceedings of the IEEE, 101(3), 631–651. https://doi.org/10.1109/JPROC.2012.2211551
Mukherjee, F., & Singh, D. (2020). Assessing Land Use–Land Cover Change and Its Impact on Land Surface Temperature Using LANDSAT Data: A Comparison of Two Urban Areas in India. Earth Systems and Environment, 4(2), 385–407. https://doi.org/10.1007/s41748-020-00155-9
P3E Bali Nusra. (2018). Rencana Pengelolaan Sumber Daya Air Dan Lahan Di Danau Batur Serta Daerah Tangkapan Airnya Berbasis Daya Dukung Lingkungan Hidup. November.
Patil, M. B., Desai, C. G., & Umrikar, B. N. (2012). IMAGE CLASSIFICATION TOOL FOR LAND USE / LAND COVER ANALYSIS: A COMPARATIVE STUDY OF MAXIMUM LIKELIHOOD AND MINIMUM DISTANCE METHOD. International Journal of Geology, Earth, and Environmental Sciences, 2(3), 189–196. http://www.cibtech.org/jgee.htm
Patil, R. J. (2018). Spatial Techniques for Soil Erosion Estimation. https://doi.org/10.1007/978-3-319-74286-1_4
Phiri, D., Morgenroth, J., Xu, C., & Hermosilla, T. (2018). Effects of pre-processing methods on Landsat OLI-8 land cover classification using OBIA and random forests classifier. International Journal of Applied Earth Observation and Geoinformation, 73, 170–178. https://doi.org/10.1016/j.jag.2018.06.014
Pontius, R. G., & Millones, M. (2011). Death to Kappa: Birth of quantity disagreement and allocation disagreement for accuracy assessment. International Journal of Remote Sensing, 32(15), 4407–4429. https://doi.org/10.1080/01431161.2011.552923
Potapov, P., Hansen, M. C., Kommareddy, I., Kommareddy, A., Turubanova, S., Pickens, A., Adusei, B., Tyukavina, A., & Ying, Q. (2020). Landsat analysis ready data for global land cover and land cover change mapping. Remote Sensing, 12(3). https://doi.org/10.3390/rs12030426
Rahman, A., Kumar, S., Fazal, S., & Siddiqui, M. A. (2012). Assessment of Land use/land cover Change in the North-West District of Delhi Using Remote Sensing and GIS Techniques. Journal of the Indian Society of Remote Sensing, 40(4), 689–697. https://doi.org/10.1007/s12524-011-0165-4
Rathnayake, C. W. M., Jones, S., & Soto-Berelov, M. (2020). Mapping land cover change over a 25-year period (1993-2018) in Sri Lanka using landsat time-series. Land, 9(1). https://doi.org/10.3390/land9010027
Rawat, J. S., & Kumar, M. (2015). Monitoring land use/cover change using remote sensing and GIS techniques: A case study of Hawalbagh block, district Almora, Uttarakhand, India. Egyptian Journal of Remote Sensing and Space Science, 18(1), 77–84. https://doi.org/10.1016/j.ejrs.2015.02.002
Russell G. Congalton, K. G. (2019). Assessing the Accuracy of Remotely Sensed Data. CRC Press.
Rwanga, S. S., & Ndambuki, J. M. (2017). Accuracy Assessment of Land Use/Land Cover Classification Using Remote Sensing and GIS. International Journal of Geosciences, 08(04), 611–622. https://doi.org/10.4236/ijg.2017.84033
Shao, Y., & Lunetta, R. S. (2012). Comparison of support vector machine, neural network, and CART algorithms for the land-cover classification using limited training data points. ISPRS Journal of Photogrammetry and Remote Sensing, 70, 78–87. https://doi.org/10.1016/j.isprsjprs.2012.04.001
Sharma, A., Liu, X., Yang, X., & Shi, D. (2017). A patch-based convolutional neural network for remote sensing image classification. Neural Networks, 95, 19–28. https://doi.org/10.1016/j.neunet.2017.07.017
Sood, V., Gusain, H. S., Gupta, S., & Singh, S. (2021). Topographically derived subpixel-based change detection for monitoring changes over rugged terrain Himalayas using AWiFS data. Journal of Mountain Science, 18(1), 126–140. https://doi.org/10.1007/s11629-020-6151-y
Sun, C., Fagherazzi, S., & Liu, Y. (2018). Classification mapping of salt marsh vegetation by flexible monthly NDVI time-series using Landsat imagery. Estuarine, Coastal and Shelf Science, 213, 61–80. https://doi.org/10.1016/j.ecss.2018.08.007
Stasiun Klimatologi Jembrana. (2012). Badan Meteorologi Klimatologi dan Geofisika (BMKG). Informasi Hujan
Sy, S., & Quesada, B. (2020). Anthropogenic land cover change impact on climate extremes during the 21st century. Environmental Research Letters, 15(3). https://doi.org/10.1088/1748-9326/ab702c
Taati, A., Sarmadian, F., & Mousavi, A. (2016). Land Use Classification using Support Vector Machine and Maximum Likelihood Algorithms by Landsat 5 TM Images. Walailak Journal, 12(8), 681–687. https://doi.org/10.14456/WJST.2015.33
Tadele, H., Mekuriaw, A., Selassie, Y. G., & Tsegaye, L. (2017). Land Use/Land Cover Factor Values and Accuracy Assessment Using a GIS and Remote Sensing in the Case of the Quashay Watershed in Northwestern Ethiopia. Journal of Natural Resources and Development, 38–44. https://doi.org/10.5027/jnrd.v7i0.05
Thomas M. Lillesand, Ralph W. Kiefer, J. W. C. (2015). Remote Sensing and Image Interpretation (Seventh Ed). WILEY.
Thyagharajan, K. K., & Vignesh, T. (2019). Soft Computing Techniques for Land Use and Land Cover Monitoring with Multispectral Remote Sensing Images: A Review. Archives of Computational Methods in Engineering, 26(2), 275–301. https://doi.org/10.1007/s11831-017-9239-y
Vinayak, B., Lee, H. S., & Gedem, S. (2021). Prediction of land use and land cover changes in Mumbai city, India, using remote sensing data and a multilayer perceptron neural network-based Markov Chain model. Sustainability (Switzerland), 13(2), 1–22. https://doi.org/10.3390/su13020471
Weih, R. C., & Riggan, N. D. (2010). The International Archives of the Photogrammetry. In Remote Sensing and Spatial Information Sciences.
Wu, K., Du, Q., Wang, Y., & Yang, Y. (2017). Supervised sub-pixel mapping for change detection from remotely sensed images with different resolutions. Remote Sensing, 9(3). https://doi.org/10.3390/rs9030284
Wulder, M. A., Loveland, T. R., Roy, D. P., Crawford, C. J., Masek, J. G., Woodcock, C. E., Allen, R. G., Anderson, M. C., Belward, A. S., Cohen, W. B., Dwyer, J., Erb, A., Gao, F., Griffiths, P., Helder, D., Hermosilla, T., Hipple, J. D., Hostert, P., Hughes, M. J., … Zhu, Z. (2019). Current status of Landsat program, science, and applications. Remote Sensing of Environment, 225, 127–147. https://doi.org/10.1016/j.rse.2019.02.015
Zhai, H., Lv, C., Liu, W., Yang, C., Fan, D., Wang, Z., & Guan, Q. (2021). Understanding Spatio-Temporal Patterns of Land Use / Land. Remote Sensing, 2000–2019.
Zhu, L., Gao, D., Jia, T., & Zhang, J. (2021). Using Eco-geographical zoning data and crowdsourcing to improve the detection of spurious land cover changes. Remote Sensing, 13(16). https://doi.org/10.3390/rs13163244
Zylshal, Z., Susanto, H., & Hidayat, S. (2016). Ekstraksi Informasi Penutup Lahan Area Luas Dengan Metode Expert Knowledge Object-Based Image Analysis (Obia) Pada Citra Landsat 8 OLI Pulau Kalimantan. Majalah Ilmiah Globe, 18(1), 09. https://doi.org/10.24895/mig.2016.18-1.390
DOI: https://doi.org/10.24114/jg.v15i1.32670
Article Metrics
Abstract view : 288 timesPDF - 244 times
Refbacks
- There are currently no refbacks.
Accredited Journal, Based on Decree of the Minister of Research, Technology and Higher Education, Republic of Indonesia Number 36/E/KPT/2019
Copyright ©2020 Jurusan Pendidikan Geografi Fakultas Ilmu Sosial Universitas Negeri Medan dan Ikatan Geograf Indonesia (IGI)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.