Assessment of vegetation dynamic and its effects in a large-scale landslide in Central Taiwan with multitemporal Landsat images

Chih-Wei Chuang; Hao-Yu Huang; Chun-Wei Tseng; Chih-Wei Chuang; Hao-Yu Huang; Chun-Wei Tseng

doi:10.3934/geosci.2025014

AIMS Geosciences

2025, Volume 11, Issue 2: 318-342. doi: 10.3934/geosci.2025014

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Assessment of vegetation dynamic and its effects in a large-scale landslide in Central Taiwan with multitemporal Landsat images

1.
Department of Soil and Water Conservation, National Pingtung University of Science and Technology, Pingtung, Taiwan
2.
Forest Management Division, Taiwan Forestry Research Institute, Taipei, Taiwan

Received: 17 November 2024 Revised: 03 February 2025 Accepted: 26 March 2025 Published: 18 April 2025

Large-scale landslides often result in severe soil displacement and the exposure of bedrock, particularly combined with heavy rainfall. This condition significantly increases the risk of sediment-related disasters. Consequently, vegetation restoration and succession following landslide events are critical strategies for mitigating such hazards and enhancing disaster resilience. In this study, we integrated multi-temporal remote sensing imagery, land use classification, and Markov chain change simulations to evaluate the dynamic restoration of vegetation in a large-scale landslide area. Field surveys were conducted to validate the observed patterns of vegetation recovery. The results showed high accuracy in land use classifications derived from eight temporal images, with overall accuracy surpassing 80% and Kappa coefficients exceeding 0.7. The primary areas of vegetation recovery were identified as forests, followed by grasslands. Spatial change simulations indicated that full vegetation stability is expected to be reached after 2075. We emphasized the efficacy of combining remote sensing and modeling techniques for long-term monitoring of vegetation dynamics and offer critical insights for formulating sustainable strategies for disaster management.
- large-scale landslides,
- vegetation dynamic,
- landscape change simulation,
- vegetation restoration,
- NDVI
Citation: Chih-Wei Chuang, Hao-Yu Huang, Chun-Wei Tseng. Assessment of vegetation dynamic and its effects in a large-scale landslide in Central Taiwan with multitemporal Landsat images[J]. AIMS Geosciences, 2025, 11(2): 318-342. doi: 10.3934/geosci.2025014

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Abstract

Large-scale landslides often result in severe soil displacement and the exposure of bedrock, particularly combined with heavy rainfall. This condition significantly increases the risk of sediment-related disasters. Consequently, vegetation restoration and succession following landslide events are critical strategies for mitigating such hazards and enhancing disaster resilience. In this study, we integrated multi-temporal remote sensing imagery, land use classification, and Markov chain change simulations to evaluate the dynamic restoration of vegetation in a large-scale landslide area. Field surveys were conducted to validate the observed patterns of vegetation recovery. The results showed high accuracy in land use classifications derived from eight temporal images, with overall accuracy surpassing 80% and Kappa coefficients exceeding 0.7. The primary areas of vegetation recovery were identified as forests, followed by grasslands. Spatial change simulations indicated that full vegetation stability is expected to be reached after 2075. We emphasized the efficacy of combining remote sensing and modeling techniques for long-term monitoring of vegetation dynamics and offer critical insights for formulating sustainable strategies for disaster management.

References

[1]	Huang CS, Chen MM, Hsu MI (2002) A Preliminary Report on the Chiufenershan Landslide Triggered by the 921 Chichi Earthquake in Nantou, Central Taiwan. Geology 13: 387–395. https://doi.org/10.3319/TAO.2002.13.3.387(CCE) doi: 10.3319/TAO.2002.13.3.387(CCE)
[2]	Lin CY, Lo HM, Chou WC, et al. (2004) Vegetation recovery assessment at the Jou-Jou Mountain landslide area caused by the 921 Earthquake in Central Taiwan. Ecol Model 176: 75–81. https://doi.org/https://doi.org/10.1016/j.ecolmodel.2003.12.037 doi: 10.1016/j.ecolmodel.2003.12.037
[3]	Lin WT (2025) Recovery assessment for earthquake-induced landslides in Central Taiwan: Changes, patterns, and mechanisms. Ecol Eng 212: 107497. https://doi.org/10.1016/j.ecoleng.2024.107497 doi: 10.1016/j.ecoleng.2024.107497
[4]	Chang CH, Liu CH (2015) Action Plan for Prevention and Control of Large-Scale Landslide Disasters. National Science and Technology Center for Disaster Reduction, Taiwan(R.O.C.).
[5]	Kokutse N, Fourcaud T, Kokou K, et al. (2006) 3D numerical modelling and analysis of the influence of forest structure on hill slopes stability. Proceedings of the Interpraevent International Symposiu, Disaster Mitig. Debris Flows, Slope Fail. Landslides. 561–567.
[6]	Ji J, Kokutse N, Genet M, et al. (2012) Effect of spatial variation of tree root characteristics on slope stability. A case study on Black Locust (Robinia pseudoacacia) and Arborvitae (Platycladus orientalis) stands on the Loess Plateau, China. Catena 92: 139–154. https://doi.org/10.1016/j.catena.2011.12.008 doi: 10.1016/j.catena.2011.12.008
[7]	Fan CC, Lai YF (2014) Influence of the spatial layout of vegetation on the stability of slopes. Plant Soil 377: 83–95. https://doi.org/10.1007/s11104-012-1569-9 doi: 10.1007/s11104-012-1569-9
[8]	Mao Z, Bourrier F, Stokes A, et al. (2014) Three-dimensional modelling of slope stability in heterogeneous montane forest ecosystems. Ecol Model 273: 11–22.
[9]	Makoto K, Utsumi S, Zeng R, et al. (2024) Which native legume or non-legume nitrogen-fixing tree is more efficient in restoring post-landslide forests along an environmental gradient? For Ecol Manage 554: 121672. https://doi.org/https://doi.org/10.1016/j.foreco.2023.121672 doi: 10.1016/j.foreco.2023.121672
[10]	Lin SC, Chuang CW, HO SH, et al. (2008) Effect of the Vegetation Index on the Accuracy of Image Classification. J. Soil Water Conserv 40: 181–193.
[11]	Xiong P, Li J, Xue G (2023) Research Progress on Remote Sensing Extraction Methods for Fractional Vegetation Cover. Ecol Environ Prot 6: 62–64.
[12]	Tucker CJ (1979) Red and photographic infrared linear combinations for monitoring vegetation. Remote Sens Environ 8: 127–150. https://doi.org/10.1016/0034-4257(79)90013-0. doi: 10.1016/0034-4257(79)90013-0
[13]	Tian J, Wang L, Li X, et al. (2017) Comparison of UAV and WorldView-2 imagery for mapping leaf area index of mangrove forest. Int J Appl Earth Obs 61: 22–31. https://doi.org/10.1016/j.jag.2017.05.002 doi: 10.1016/j.jag.2017.05.002
[14]	Zhumanova M, Mönnig C, Hergarten C, et al. (2018) Assessment of vegetation degradation in mountainous pastures of the Western Tien-Shan, Kyrgyzstan, using eMODIS NDVI. Ecol Indic 95: 527–543. https://doi.org/10.1016/j.ecolind.2018.07.060 doi: 10.1016/j.ecolind.2018.07.060
[15]	Peng WF, Kuang TT, Taoab S (2019) Quantifying influences of natural factors on vegetation NDVI changes based on geographical detector in Sichuan, western China. J Clean Prod 233: 353–367. https://doi.org/10.1016/j.jclepro.2019.05.355 doi: 10.1016/j.jclepro.2019.05.355
[16]	Almeida DRA, Broadbent EN, Ferreira MP, et al. (2021) Monitoring restored tropical forest diversity and structure through UAV-borne hyperspectral and lidar fusion. Remote Sens Environ 264: 112582. https://doi.org/10.1016/j.rse.2021.112582 doi: 10.1016/j.rse.2021.112582
[17]	Fokeng RM, Fogwe ZN (2022) Landsat NDVI-based vegetation degradation dynamics and its response to rainfall variability and anthropogenic stressors in Southern Bui Plateau, Cameroon. Geosy Geoenviron 1: 100075. https://doi.org/10.1016/j.geogeo.2022.100075 doi: 10.1016/j.geogeo.2022.100075
[18]	Hu X, Wang Z, Zhang Y, et al. (2022) Spatialization method of grazing intensity and its application in Tibetan Plateau. Acta Geographica Sinica 77: 547–558. https://doi.org/10.11821/dlxb202203004. doi: 10.11821/dlxb202203004
[19]	Sun L, Zhao D, Zhang G, et al. (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. Ecol Eng 183: 106742. https://doi.org/10.1016/j.ecoleng.2022.106742. doi: 10.1016/j.ecoleng.2022.106742
[20]	Gu F, Xu G, Wang B, et al. (2023) Vegetation cover change and restoration potential in the Ziwuling Forest Region, China. Ecol Eng 187: 106877. https://doi.org/10.1016/j.ecoleng.2022.106877 doi: 10.1016/j.ecoleng.2022.106877
[21]	Huang KY (2006) Application of geo-spatial information technologies to assessing relationship between debris flow and slope-land for farming use and re-vegetation of landslide scars. J Chinese Soil Water Conserv 37: 305–315.
[22]	Chang YH (2010) Estimation Vegetation Recovery and Landslide Potential with Multi-date Satellite Images in Jou-Jou Mountain. Master Thesis. Department of Civil Engineering, National Chung Hsing University, Taiwan (R.O.C.).
[23]	Shou KJ, Wu CC, Hsu HY (2010) Analysis of Landslide Behavior after 1999 Chi-Chi Earthquake by SPOT Satellite Images. J Photogramm Remote Sens 15: 17–28.
[24]	Chuang CW (2010) Application of Environmental Indices on the Vegetative Restoration of Landslides. Doctoral Dissertation. Department of Soil and Water Conservation, National Chung Hsing University, Taiwan (R.O.C.).
[25]	Lu SY, Lin CY, Hwang LS (2011) Spatial relationships between landslides and topographical factors at the Liukuei Experimental Forest, Southwestern Taiwan after Typhoon Morakot. Taiwan J For Sci 26: 399–408.
[26]	Tsai PS (2015) Landslide Susceptibility and Conservation Benefit Assessment for Chi-Sun watershed. Master Thesis. Department of Soil and Water Conservation, National Chung Hsing University, Taiwan(R.O.C.).
[27]	Chen M, Tang C, Wang X, et al. (2021) Temporal and spatial differentiation in the surface recovery of post-seismic landslides in Wenchuan earthquake-affected areas. Ecol Inform 64: 101356. https://doi.org/10.1016/j.ecoinf.2021.101356 doi: 10.1016/j.ecoinf.2021.101356
[28]	Shooshtari SJ, Gholamalifard M (2015) Scenario-based land cover change modeling and its implications for landscape pattern analysis in the Neka Watershed, Iran. Remote Sens. Appl Soc Environ 1: 1–19. https://doi.org/https://doi.org/10.1016/j.rsase.2015.05.001 doi: 10.1016/j.rsase.2015.05.001
[29]	Verburg PH, Schot PP, Dijst MJ, et al. (2004) Land use change modelling: current practice and research priorities. GeoJournal 61: 309–324. https://doi.org/10.1007/s10708-004-4946-y doi: 10.1007/s10708-004-4946-y
[30]	Forman RTT, Godron M (1986) Landscape ecology. Wiley, New York.
[31]	Aniah P, Bawakyillenuo S, Codjoe SNA, et al. (2023) Land use and land cover change detection and prediction based on CA-Markov chain in the savannah ecological zone of Ghana. Environ Challenges 10: 100664. https://doi.org/10.1016/j.envc.2022.100664. doi: 10.1016/j.envc.2022.100664
[32]	Devkota P, Dhakal S, Shrestha S, et al. (2023) Land use land cover changes in the major cities of Nepal from 1990 to 2020. Environ Sustain Ind 17: 100227. https://doi.org/10.1016/j.indic.2023.100227 doi: 10.1016/j.indic.2023.100227
[33]	Temesgen F, Warkineh B, Hailemicael A (2022) Seasonal land use/land cover change and the drivers in Kafta Sheraro national park, Tigray, Ethiopia. Heliyon 8: e12298. https://doi.org/10.1016/j.heliyon.2022.e12298. doi: 10.1016/j.heliyon.2022.e12298
[34]	Wulder MA, Loveland TR, Roy DP, et al. (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 doi: 10.1016/j.rse.2019.02.015
[35]	Drusch M, Del Bello U, Carlier S, et al. (2012) Sentinel-2: ESA's optical high-resolution mission for GMES operational services. Remote Sens Environ 120: 25–36. https://doi.org/10.1016/j.rse.2011.11.026 doi: 10.1016/j.rse.2011.11.026
[36]	Justice CO, Townshend JRG, Vermote EF, et al. (2002) An overview of MODIS Land data processing and product status. Remote Sens Environ 83: 3–15. https://doi.org/10.1016/S0034-4257(02)00084-6 doi: 10.1016/S0034-4257(02)00084-6
[37]	Lan L, Wang YG, Chen HS, et al. (2024) Improving on mapping long-term surface water with a novel framework based on the Landsat imagery series. J Environ Manage 353: 120202. https://doi.org/10.1016/j.jenvman.2024.120202 doi: 10.1016/j.jenvman.2024.120202
[38]	Bonney MT, He Y, Vogeler J, et al. (2024) Mapping canopy cover for municipal forestry monitoring: Using free Landsat imagery and machine learning. Urban For Urban Green 100: 128490. https://doi.org/10.1016/j.ufug.2024.128490 doi: 10.1016/j.ufug.2024.128490
[39]	Green EP, Mumby PJ, Edwards AJ, et al. (1997) Estimating leaf area index of mangroves from satellite data. Aquat Bot 58: 11–19. https://doi.org/10.1016/S0304-3770(97)00013-2 doi: 10.1016/S0304-3770(97)00013-2
[40]	Price JC, Bausch WC (1995) Leaf area index estimation from visible and near-infrared reflectance data. Remote Sens Environ 52: 55−65. https://doi.org/10.1016/0034-4257(94)00111-Y doi: 10.1016/0034-4257(94)00111-Y
[41]	Rouse JW Jr, Haas RH, Schell JA, et al. (1973) Monitoring vegetation systems in the great plains with ERTS. Third NASA ERTS Symposium, Washington, D.C., NASA SP-351 I, 309–317.
[42]	Elvidge CD, Chen Z (1995) Comparison of broad-band and narrow-band red and near-infrared vegetation indices. Remote Sens Environ 54: 38–48. https://doi.org/10.1016/0034-4257(95)00132-K doi: 10.1016/0034-4257(95)00132-K
[43]	Huang CL, Li X, Lu L (2008) Retrieving soil temperature profile by assimilation MODIS LST products with ensemble Kalman filter. Remote Sens Environ 112: 1320–1336. https://doi.org/10.1016/j.rse.2007.03.028 doi: 10.1016/j.rse.2007.03.028
[44]	Lin CY (2005) Spatial Distribution and Investigation Analysis of Vegetation Restoration in Chiufenershan, Huashan and Caoling Areas by using Ecological Indices. Soil and Water Conservation Bureau Achievement Report, Committee of Agriculture, Executive Yuan, Taiwan (R.O.C.).
[45]	Chou CF, Cheng CC, Chen YC (1991) Application of SPOT data on forest cover type classification. Bull Taiwan For Res Inst 6: 283–297.
[46]	Tu WC, Li SC, Chen HH, et al. (2000) Analyze geomorphology changes in mountain regions of Taiwan with remote sensing image. National Land Information System Communications. 36, 22–29.
[47]	Lei TC, Chou TY, Wan S, et al. (2007) Space characteristic classifier of Support Vector Machine for satellite image classification. J Photogramm Remote Sens 12: 145–163.
[48]	Rashid N, Alam JAMM, Chowdhury MA, et al. (2022) Impact of landuse change and urbanization on urban heat island effect in Narayanganj city, Bangladesh: A remote sensing-based estimation. Environ Chall 8: 100571. https://doi.org/10.1016/j.envc.2022.100571 doi: 10.1016/j.envc.2022.100571
[49]	Halder A, Ghosh A, Ghosh S (2011) Supervised and unsupervised landuse map generation from remotely sensed images using ant based systems. Appl Soft Comput 11: 5770–5781. https://doi.org/10.1016/j.asoc.2011.02.030 doi: 10.1016/j.asoc.2011.02.030
[50]	Lü D, Gao G, Lü Y, et al. (2020) An effective accuracy assessment indicator for credible land use change modelling: Insights from hypothetical and real landscape analyses. Ecol Indic 117: 106552. https://doi.org/10.1016/j.ecolind.2020.106552 doi: 10.1016/j.ecolind.2020.106552
[51]	Liao J, Shao G, Wang C, et al. (2019) Urban sprawl scenario simulations based on cellular automata and ordered weighted averaging ecological constraints. Ecol Indic 107: 105572. https://doi.org/10.1016/j.ecolind.2019.105572 doi: 10.1016/j.ecolind.2019.105572
[52]	You W, Ji Z, Wu L, et al. (2017) Modeling changes in land use patterns and ecosystem services to explore a potential solution for meeting the management needs of a heritage site at the landscape level. Ecol Indic 73: 68–78. https://doi.org/10.1016/j.ecolind.2016.09.027 doi: 10.1016/j.ecolind.2016.09.027
[53]	Peng K, Jiang W, Deng Y, et al. (2020) Simulating wetland changes under different scenarios based on integrating the random forest and CLUE-S models: A case study of Wuhan Urban Agglomeration. Ecol Indic 117: 106671. https://doi.org/10.1016/j.ecolind.2020.106671. doi: 10.1016/j.ecolind.2020.106671
[54]	Janssen LLF, van der Wel FJM (1994) Accuracy assessment of satellite derived land-cover data: a review. Photogramm Eng Rem S 60: 419–426.
[55]	Congalton RG, Green K (1991) Assessing the accuracy of remotely sensed data: Principles and practices. Boca Raton, FL, USA: Lewis Publishers.
[56]	Lin CY, Wang HF, Chuang CW (2006) A study of vegetation recovery for the landslides in the Chiufenershan using ecological index. J Soil Water Conserv 38: 279–286.
[57]	Ahmed SJ, Bramley G, Verburg PH (2014) Key Driving Factors Influencing Urban Growth: Spatial-Statistical Modeling with CLUE-s. Dhaka Megacity. Springer, Netherlands, 123–145. https://doi.org/10.1007/978-94-007-6735-5_7
[58]	Wu J, Xiang WN, Zhao J (2014) Urban ecology in China: Historical developments and future directions. Landscape Urban Plan 125: 222–233. https://doi.org/10.1016/j.landurbplan.2014.02.010 doi: 10.1016/j.landurbplan.2014.02.010
[59]	Panda KC, Singh RM, Singh SK (2024) Advanced CMD predictor screening approach coupled with cellular automata-artificial neural network algorithm for efficient land use-land cover change prediction. J Cleaner Prod 449: 141822. https://doi.org/10.1016/j.jclepro.2024.141822 doi: 10.1016/j.jclepro.2024.141822
[60]	Uwamahoro S, Liu T, Nzabarinda V, et al. (2024) Investigation of Groundwater–Surface water interaction and land use and land cover change in the catchments, A case of Kivu Lake, DRC-Rwanda. Groundwater Sustain Dev 26: 101236. https://doi.org/https://doi.org/10.1016/j.gsd.2024.101236 doi: 10.1016/j.gsd.2024.101236
[61]	Danneels G, Pirard E, Havenith HB (2007) Automatic landslide detection from remote sensing images using supervised classification methods. 2007 IEEE International Geoscience and Remote Sensing Symposium, Barcelona, Spain, 2007, 3014–3017. https://doi.org/10.1109/IGARSS.2007.4423479
[62]	Quevedo RP, Maciel DA, Reis MS, et al. (2024) Land use and land cover changes without invalid transitions: A case study in a landslide-affected area. Remote Sens Appl Soc Environ 36: 101314. https://doi.org/10.1016/j.rsase.2024.101314 doi: 10.1016/j.rsase.2024.101314
[63]	Kondum FA, Rowshon MK, Luqman CA, et al. (2024) Change analyses and prediction of land use and land cover changes in Bernam River Basin, Malaysia. Remote Sens Appl Soc Environ 36: 101281. https://doi.org/10.1016/j.rsase.2024.101281 doi: 10.1016/j.rsase.2024.101281
[64]	Zhao F, Miao F, Wu Y, et al. (2024) Landslide dynamic susceptibility mapping in urban expansion area considering spatiotemporal land use and land cover change. Sci Total Environ 949: 175059. https://doi.org/10.1016/j.scitotenv.2024.175059 doi: 10.1016/j.scitotenv.2024.175059
[65]	Wang Q, Bai X, Zhang D, et al. (2024) Spatiotemporal characteristics and multi-scenario simulation of land use change and ecological security in the mountainous areas: Implications for supporting sustainable land management and ecological planning. Sustain Futures 8: 100286. https://doi.org/10.1016/j.sftr.2024.100286 doi: 10.1016/j.sftr.2024.100286
[66]	Yue W, Qin C, Su M, et al. (2024) Simulation and prediction of land use change in Dongguan of China based on ANN cellular automata - Markov chain model. Environ. Sustain Indic 22: 100355. https://doi.org/10.1016/j.indic.2024.100355 doi: 10.1016/j.indic.2024.100355
[67]	Thompson JN (2022) ecological succession. Encyclopedia Britannica. https://www.britannica.com/science/ecological-succession.
[68]	Congalton RG (1991) A review of assessing the accuracy of classifications of remotely sensed data. Remote Sens Environ 37: 35–46. https://doi.org/10.1016/0034-4257(91)90048-B doi: 10.1016/0034-4257(91)90048-B
[69]	Lin WT, Huang PH, Lu WH (2023) Long-term Monitoring and Assessment of Restoration for the Earthquake-induced Landslide at the Chiufanershan Area. J Soil Water Conserv 53: 3165–3174.
[70]	Lee MH (2023) Vegetation Succession Analysis in Landslide Remediation Areas - Case Study of Landslides in Nantou County. Master's thesis, Department of Soil and Water Conservation, National Chung Hsing University. https://hdl.handle.net/11296/f57y8z
[71]	Lu SY, Wu SW, Sun MY (2023) Soil Depth Estimation and the Influence of Soil Depth and Vegetation Cover on Slope Stability in the Liukuei Experimental Forest. J Chinese Soil Water Conserv 54: 60–69. https://doi.org/10.29417/jcswc.202303_54(1).0006 doi: 10.29417/jcswc.202303_54(1).0006
[72]	Sartohadi J, Pulungan NAH, Nurudin M, et al. (2018) The Ecological Perspective of Landslides at Soils with High Clay Content in the Middle Bogowonto Watershed, Central Java, Indonesia. Appl Environ Soil Sc. 2018: 2648185. https://doi.org/https://doi.org/10.1155/2018/2648185 doi: 10.1155/2018/2648185
[73]	Ho TC, Chu EL, Wang FT, et al. (2019) Plant resources and conservation in Jiufenfrfhan area. Nat Conserv Q 105: 38–49.

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