Vegetation Change Detection Analysis Using Multi-sensor Hyperspectral Imagery
Abstract
Vegetation is a fundamental component of ecosystems that maintains carbon levels, hydrological cycles, mitigating greenhouse gases, and ensures climate stability. In recent years, the impacts of global climate change have led to changes in vegetation cover at various levels. Efforts to monitor changes in vegetation are important and beneficial for various fields such as forest monitoring, agriculture, and plantations, among others. The main objective of this research is to detect changes both increase and decrease in vegetation using multi-sensor hyperspectral imagery. The hyperspectral images used in this study are Hyperion 2014 and PRISMA 2021. The method involves creating different levels of spectral resolution simulations from hyperspectral images to detect vegetation changes. Meanwhile, the vegetation change Clustering method employs unsupervised (k-means) techniques. The cluster results can indicate vegetation changes such as vegetation degradation, vegetation, devegetation, or no change, though they currently have low accuracy. The highest accuracy is by Simulated RapidEye image simulations, is 33.5%. The low accuracy results attributed insufficient preprocessing, particularly in topographic correction. Additionally, this research indicates that the spectral resolution levels do not have a significant impact on vegetation change detection, as the differences in change classes at each level are very small.
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Ayuningtyas, E. A. (2022). Pemetaan Partisipatif untuk Bahaya Longsor dan Jalur Evakuasi di Desa Hargomulyo, Kabupaten Kulonprogo, DIY. Jurnal Geografika (Geografi Lingkungan Lahan Basah), 3(2), 78-91. https://doi.org/10.20527/jgp.v3i2.6789
Cavalli, R. M. (2023). The Weight of Hyperion and PRISMA Hyperspectral Sensor Characteristics on Image Capability to Retrieve Urban Surface Materials in the City of Venice. Sensors, 23 (1). https://doi.org/10.3390/s23010454
Celik, T. (2009). Unsupervised change detection in satellite images using principal component analysis and κ-means clustering. IEEE Geoscience and Remote Sensing Letters, 6 (4), 772–776. https://doi.org/10.1109/LGRS.2009.2025059
Chughtai, A.H., Abbasi, H., & Karas, I.R. (2021). A review on change detection method and accuracy assessment for land use land cover. Remote Sensing Applications: Society and Environment, 22 (March), 100482. https://doi.org/10.1016/j.rsase.2021.100482
Chunhui, Z., Bing, G., Lejun, Z., & Xiaoqing, W. (2018). Classification of Hyperspectral Imagery based on spectral gradient, SVM ,and spatial random forest. Infrared Physics and Technology, 95 (January), 61–69. https://doi.org/10.1016/j.infrared.2018.10.012
El Abbassi, M., Overbeck, J., Braun, O., Calame, M., van der Zant, HSJ, & Perrin, M.L. (2021). Benchmark and application of unsupervised classification approaches for univariate data. Communications Physics, 4 (1), 1–9. https://doi.org/10.1038/s42005-021-00549-9
Fang, L., He, N., Li, S., Plaza, A. J., & Plaza, J. (2018). A New Spatial-Spectral Feature Extraction Method for Hyperspectral Images Using Local Covariance Matrix Representation. IEEE Transactions on Geoscience and Remote Sensing, 56 (6), 3534–3546. https://doi.org/10.1109/TGRS.2018.2801387
Fang, Y., Zhao, J., Liu, L., & Wang, J. (2020). Comparison of eight topographic correction algorithms applied to Landsat-8 oil imagery based on the dem. IOP Conference Series: Earth and Environmental Science, 428 (1). https://doi.org/10.1088/1755-1315/428/1/012051
Ghamisi, P., Plaza, J., Chen, Y., Li, J., & Plaza, A. J. (2017). Advanced Spectral Classifiers for Hyperspectral Images: A review. IEEE Geoscience and Remote Sensing Magazine, 5 (1), 8–32. https://doi.org/10.1109/MGRS.2016.2616418
Ghamisi, P., Yokoya, N., Li, J., Liao, W., Liu, S., Plaza, J., Rasti, B., & Plaza, A. (2017). Advances in Hyperspectral Image and Signal Processing: A Comprehensive Overview of the State of the Art. In IEEE Geoscience and Remote Sensing Magazine (Vol. 5, Issue 4, pp. 37–78). https://doi.org/10.1109/MGRS.2017.2762087
Ghosh, S., Patra, S., & Ghosh, A. (2009). An unsupervised context-sensitive change detection technique based on modified self-organizing feature map neural network. International Journal of Approximate Reasoning, 50 (1), 37–50. https://doi.org/10.1016/j.ijar.2008.01.008
González-González, A., Villegas, J.C., Clerici, N., & Salazar, J.F. (2021). Spatial-temporal dynamics of deforestation and its drivers indicate the need for locally adapted environmental governance in Colombia. Ecological Indicators, 126. https://doi.org/10.1016/j.ecolind.2021.107695
Hasanlau, M., & Seydi, ST (2018). Sensitivity analysis on the performance of different unsupervised threshold selection methods in hyperspectral change detection. 2018 10th IAPR Workshop on Pattern Recognition in Remote Sensing, PRRS 2018, 6–9. https://doi.org/10.1109/PRRS.2018.8486355
Jensen, J.R. (2013a). Remote Sensing of the Environment: An Earth Resource Perspective: Pearson New International Edition. Pearson Education. https://books.google.co.id/books?id=fhGpBwAAQBAJ
Jensen, J.R. (2013b). Remote Sensing of the Environment: An Earth Resource Perspective: Pearson New International Edition. Pearson Education.
Khan, M.J., Khan, H.S., Yousaf, A., Khurshid, K., & Abbas, A. (2018). Modern Trends in Hyperspectral Image Analysis: A Review. IEEE Access, 6 (June), 14118–14129. https://doi.org/10.1109/ACCESS.2018.2812999
Kuzera, K., Rogan, J., & Eastman, J.R. (nd). Monitoring Vegetation Regeneration and Deforestation Using Change Vector Analysis: Mt. St. Helens Study Area.
Lehnert, L.W., Meyer, H., Obermeier, W.A., Silva, B., Regeling, B., & Bendix, J. (2018). Hyperspectral Data Analysis in R: the hsdar Package. https://doi.org/10.18637/jss.v089.i12
Leutner, B., Horning, N., Schwalb-Willmann, J., Hijmans, R.J., & Maintainer, ]. (2019). Package “RStoolbox” Title Tools for Remote Sensing Data Analysis.
Li, H., Xu, L., Zhang, Z., Shen, H., Li, W., & Cao, L. (2015). A land cover adaptive topographic correction and evaluation method for remote sensing data. International Geoscience and Remote Sensing Symposium (IGARSS), 2015-Novem (1), 3850–3853. https://doi.org/10.1109/IGARSS.2015.7326664
Lillesand, T., Kiefer, R. W., & Chipman, J. (2015a). Remote Sensing and Image Interpretation, 7th Edition. Wiley.
Lillesand, T., Kiefer, R. W., & Chipman, J. (2015b). Remote Sensing and Image Interpretation, 7th Edition. Wiley. https://books.google.co.id/books?id=eQXYBgAAQBAJ
Liu, S. (2015). Sequential Spectral Change Vector Analysis for Iteratively Discovering and Detecting Multiple Changes in Hyperspectral Images. March. https://doi.org/10.1109/TGRS.2015.2396686
Liu, S., Bruzzone, L., Bovolo, F., & Du, P. (2012). Unsupervised hierarchical spectral analysis for change detection in hyperspectral images. Workshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing, June. https://doi.org/10.1109/WHISPERS.2012.6874245
Lu, D., Li, G., & Moran, E. (2014). Current situation and need for change detection techniques. International Journal of Image and Data Fusion , 5 (1), 13–38. https://doi.org/10.1080/19479832.2013.868372
Lu, D., Mausel, P., Brondízio, E., & Moran, E. (2004). Change detection techniques. International Journal of Remote Sensing, 25 (12), 2365–2401. https://doi.org/10.1080/0143116031000139863
Seydi, ST, & Hasanlou, M. (2017). A new land-cover match-based change detection for hyperspectral imagery. European Journal of Remote Sensing , 50 (1), 517–533. https://doi.org/10.1080/22797254.2017.1367963
Sinaga, KP, & Yang, MS (2020). Unsupervised K-means clustering algorithm. IEEE Access, 8, 80716–80727. https://doi.org/10.1109/ACCESS.2020.2988796
Sun, L., Zhao, D., Zhang, G., Wu, X., Yang, Y., & Wang, Z. (2022). Using SPOT VEGETATION for analyzing dynamic changes and influencing factors on vegetation restoration in the Three-River Headwaters Region in the last 20 years (2000–2019), China. Ecological Engineering, 183 (July), 106742. https://doi.org/10.1016/j.ecoleng.2022.106742
Tripathi, M. K., Govil, H., & Diwan, P. (2019). Petrography, XRD analysis, and identification of talc minerals near Chhabadiya village of Jahajpur region, Bhilwara, India through Hyperion hyperspectral remote sensing data. 2019 2nd International Conference on Intelligent Communication and Computational Techniques, ICCT 2019, 75–78. https://doi.org/10.1109/ICCT46177.2019.8969008
Umarhadi, DA, & Danoedoro, P. (2019). Correcting topographic effect on Landsat-8 images: an evaluation of using different DEMs in Indonesia. November 2019, 41. https://doi.org/10.1117/12.2549109
Visual Information Solutions, I. (1988). FLAASH Module User's Guide 20FLA43DOC. http://www.csie.ntu.edu.tw/~cjlin/libsvm
Wu, J. (2012). K-means Based Consensus Clustering. Springer Science & Business Media. https://doi.org/10.1007/978-3-642-29807-3_7
Zheng, Z., Wan, Y., Zhang, Y., Xiang, S., Peng, D., & Zhang, B. (2021). ISPRS Journal of Photogrammetry and Remote Sensing CLNet: Cross-layer convolutional neural network for change detection in optical remote sensing imagery. ISPRS Journal of Photogrammetry and Remote Sensing, 175 (March), 247–267. https://doi.org/10.1016/j.isprsjprs.2021.03.005
DOI: https://doi.org/10.20527/jgp.v5i1.11709
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