Phenology Analysis for Detection of Vegetation Changes Based on Landsat 8 Images in Nature Park Kopački rit, Croatia
Abstract
This study proposed a method for detecting vegetation changes and establishing geospatial management zones based on the 10-year phenology analysis using normalized difference vegetation index (NDVI) long-term trends from Landsat 8 multispectral imagery in Nature Park Kopački rit. The main components of the proposed method include phenology analysis and NDVI anomaly detection supported by unsupervised k-means classification of vegetation management zones. The reference monthly NDVI values (2013–2019) with three test years (2020–2022) strongly indicated very high heterogeneity in vegetation activity. A 100 m spatial resolution and a monthly temporal resolution were used. The results of unsupervised k-means classification in five vegetation activity classes indicated that three of these classes have considerably high negative NDVI anomalies, covering 64.1% of the study area. While the proposed method ensures the detection of vegetation changes and vegetation activity zones, a comprehensive field observation is required to determine the potential environmental and/or anthropogenic causes. However, the proposed approach significantly reduces the need for extensive fieldwork, allowing biologists to focus their efforts on areas with detected abnormal vegetation activity.
References
Ahmed, M., Seraj, R., & Islam, S. M. S. (2020). The k-means Algorithm: A Comprehensive Survey and Performance Evaluation. Electronics, 9(8), Article 8. https://doi.org/10.3390/electronics9081295
Azizan, F. A., Astuti, I. S., Aditya, M. I., Febbiyanti, T. R., Williams, A., Young, A., & Abdul Aziz, A. (2021). Using multi-temporal satellite data to analyse phenological responses of rubber (Hevea brasiliensis) to climatic variations in South Sumatra, Indonesia. Remote Sensing, 13(15), 2932. https://doi.org/10.3390/rs13152932
Bell, A., Klein, D., Rieser, J., Kraus, T., Thiel, M., & Dech, S. (2023). Scientific Evidence from Space—A Review of Spaceborne Remote Sensing Applications at the Science–Policy Interface. Remote Sensing, 15(4), Article 4. https://doi.org/10.3390/rs15040940
Bjedov, D., Mikuska, A., Begović, L., Bollinger, E., Bustnes, J. O., Deme, T., Mikuška, T., Morocz, A., Schulz, R., Søndergaard, J., & Eulaers, I. (2023). Effects of white-tailed eagle (Haliaeetus albicilla) nestling diet on mercury exposure dynamics in Kopački rit Nature Park, Croatia. Environmental Pollution, 336, 122377. https://doi.org/10.1016/j.envpol.2023.122377
Chávez, R. O., Estay, S. A., Lastra, J. A., Riquelme, C. G., Olea, M., Aguayo, J., & Decuyper, M. (2023). npphen: An R-Package for Detecting and Mapping Extreme Vegetation Anomalies Based on Remotely Sensed Phenological Variability. Remote Sensing, 15(1), Article 1. https://doi.org/10.3390/rs15010073
Cumming, G. S., Allen, C. R., Ban, N. C., Biggs, D., Biggs, H. C., Cumming, D. H. M., De Vos, A., Epstein, G., Etienne, M., Maciejewski, K., Mathevet, R., Moore, C., Nenadovic, M., & Schoon, M. (2015). Understanding protected area resilience: A multi-scale, social-ecological approach. Ecological Applications, 25(2), 299–319. https://doi.org/10.1890/13-2113.1
Dronova, I., & Taddeo, S. (2022). Remote sensing of phenology: Towards the comprehensive indicators of plant community dynamics from species to regional scales. Journal of Ecology, 110(7), 1460–1484. https://doi.org/10.1111/1365-2745.13897
Eisfelder, C., Asam, S., Hirner, A., Reiners, P., Holzwarth, S., Bachmann, M., Gessner, U., Dietz, A., Huth, J., Bachofer, F., & Kuenzer, C. (2023). Seasonal Vegetation Trends for Europe over 30 Years from a Novel Normalised Difference Vegetation Index (NDVI) Time-Series—The TIMELINE NDVI Product. Remote Sensing, 15(14), Article 14. https://doi.org/10.3390/rs15143616
Elsen, P. R., Oakes, L. E., Cross, M. S., DeGemmis, A., Watson, J. E. M., Cooke, H. A., Darling, E. S., Jones, K. R., Kretser, H. E., Mendez, M., Surya, G., Tully, E., & Grantham, H. S. (2023). Priorities for embedding ecological integrity in climate adaptation policy and practice. One Earth, 6(6), 632–644. https://doi.org/10.1016/j.oneear.2023.05.014
Estay, S. A., Chávez, R. O., Lastra, J. A., Rocco, R., Gutiérrez, Á. G., & Decuyper, M. (2023). MODIS Time Series Reveal New Maximum Records of Defoliated Area by Ormiscodes amphimone in Deciduous Nothofagus Forests, Southern Chile. Remote Sensing, 15(14), Article 14. https://doi.org/10.3390/rs15143538
Fick, S. E., & Hijmans, R. J. (2017). WorldClim 2: New 1-km spatial resolution climate surfaces for global land areas. International Journal of Climatology, 37(12), 4302–4315. https://doi.org/10.1002/joc.5086
Garroutte, E., Hansen, A., & Lawrence, R. (2016). Using NDVI and EVI to Map Spatiotemporal Variation in the Biomass and Quality of Forage for Migratory Elk in the Greater Yellowstone Ecosystem. Remote Sensing, 8(5), 404. https://doi.org/10.3390/rs8050404
Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., & Moore, R. (2017). Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote sensing of Environment, 202, 18-27. https://doi.org/10.1016/j.rse.2017.06.031
Granero-Belinchon, C., Adeline, K., Lemonsu, A., & Briottet, X. (2020). Phenological Dynamics Characterization of Alignment Trees with Sentinel-2 Imagery: A Vegetation Indices Time Series Reconstruction Methodology Adapted to Urban Areas. Remote Sensing, 12(4), Article 4. https://doi.org/10.3390/rs12040639
Hemati, M., Hasanlou, M., Mahdianpari, M., & Mohammadimanesh, F. (2021). A Systematic Review of Landsat Data for Change Detection Applications: 50 Years of Monitoring the Earth. Remote Sensing, 13(15), Article 15. https://doi.org/10.3390/rs13152869
Hijmans, R. J., Bivand, R., Pebesma, E., & Sumner, M. D. (2024). terra: Spatial Data Analysis (1.7-71) [Computer software]. https://cran.r-project.org/web/packages/terra/index.html
Hu, P., Sharifi, A., Tahir, M. N., Tariq, A., Zhang, L., Mumtaz, F., & Shah, S. H. I. A. (2021). Evaluation of Vegetation Indices and Phenological Metrics Using Time-Series MODIS Data for Monitoring Vegetation Change in Punjab, Pakistan. Water, 13(18), Article 18. https://doi.org/10.3390/w13182550
Jiao, K.-W., Gao, J.-B., Liu, Z.-H., Wu, S.-H., & Fletcher, T. L. (2021). Revealing climatic impacts on the temporal and spatial variation in vegetation activity across China: Sensitivity and contribution. Advances in Climate Change Research, 12(3), 409–420. https://doi.org/10.1016/j.accre.2021.04.006
Karger, D. N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R. W., Zimmermann, N. E., Linder, H. P., & Kessler, M. (2017). Climatologies at high resolution for the earth’s land surface areas. Scientific Data, 4(1), Article 1. https://doi.org/10.1038/sdata.2017.122
Khanal, S., Kc, K., Fulton, J. P., Shearer, S., & Ozkan, E. (2020). Remote Sensing in Agriculture—Accomplishments, Limitations, and Opportunities. Remote Sensing, 12(22), Article 22. https://doi.org/10.3390/rs12223783
Li, J., Pei, Y., Zhao, S., Xiao, R., Sang, X., & Zhang, C. (2020). A Review of Remote Sensing for Environmental Monitoring in China. Remote Sensing, 12(7), Article 7. https://doi.org/10.3390/rs12071130
Li, X., Zhou, Y., Asrar, G. R., & Meng, L. (2017). Characterizing spatiotemporal dynamics in phenology of urban ecosystems based on Landsat data. Science of The Total Environment, 605–606, 721–734. https://doi.org/10.1016/j.scitotenv.2017.06.245
ElMasry, M. G., & Nakauchi, S. (2016). Image analysis operations applied to hyperspectral images for non-invasive sensing of food quality – A comprehensive review. Biosystems Engineering, 142, 53–82. https://doi.org/10.1016/j.biosystemseng.2015.11.009
Misra, G., Cawkwell, F., & Wingler, A. (2020). Status of Phenological Research Using Sentinel-2 Data: A Review. Remote Sensing, 12(17), Article 17. https://doi.org/10.3390/rs12172760
Ntukey, L. T., Munishi, L. K., Kohi, E., & Treydte, A. C. (2022). Land Use/Cover Change Reduces Elephant Habitat Suitability in the Wami Mbiki–Saadani Wildlife Corridor, Tanzania. Land, 11(2), Article 2. https://doi.org/10.3390/land11020307
Panđa, L., Radočaj, D., & Milošević, R. (2024). Methods of Land Cover Classification Using Worldview-3 Satellite Images in Land Management. Tehnički Glasnik, 18(1), 142–147. https://doi.org/10.31803/tg-20221006135311
Pardela, Ł., Kowalczyk, T., Bogacz, A., & Kasowska, D. (2020). Sustainable Green Roof Ecosystems: 100 Years of Functioning on Fortifications—A Case Study. Sustainability, 12(11), Article 11. https://doi.org/10.3390/su12114721
Pereira, O. J. R., Merino, E. R., Montes, C. R., Barbiero, L., Rezende-Filho, A. T., Lucas, Y., & Melfi, A. J. (2020). Estimating Water pH Using Cloud-Based Landsat Images for a New Classification of the Nhecolândia Lakes (Brazilian Pantanal). Remote Sensing, 12(7), Article 7. https://doi.org/10.3390/rs12071090
Phiri, D., Simwanda, M., Salekin, S., Nyirenda, V. R., Murayama, Y., & Ranagalage, M. (2020). Sentinel-2 Data for Land Cover/Use Mapping: A Review. Remote Sensing, 12(14), Article 14. https://doi.org/10.3390/rs12142291
Poggi, S., Vinatier, F., Hannachi, M., Sanz Sanz, E., Rudi, G., Zamberletti, P., Tixier, P., & Papaïx, J. (2021). Chapter Seven—How can models foster the transition towards future agricultural landscapes? In D. A. Bohan & A. J. Vanbergen (Eds.), Advances in Ecological Research (Vol. 64, pp. 305–368). Academic Press. https://doi.org/10.1016/bs.aecr.2020.11.004
Radočaj, D., Gašparović, M., Radočaj, P., & Jurišić, M. (2024). Geospatial prediction of total soil carbon in European agricultural land based on deep learning. Science of The Total Environment, 912, 169647. https://doi.org/10.1016/j.scitotenv.2023.169647
Radočaj, D., Obhođaš, J., Jurišić, M., & Gašparović, M. (2020). Global Open Data Remote Sensing Satellite Missions for Land Monitoring and Conservation: A Review. Land, 9(11), Article 11. https://doi.org/10.3390/land9110402
Radočaj, D., Rapčan, I., & Jurišić, M. (2023). Indoor Plant Soil-Plant Analysis Development (SPAD) Prediction Based on Multispectral Indices and Soil Electroconductivity: A Deep Learning Approach. Horticulturae, 9(12), Article 12. https://doi.org/10.3390/horticulturae9121290
Roux, D. J., Nel, J. L., Freitag, S., Novellie, P., & Rosenberg, E. (2021). Evaluating and reflecting on coproduction of protected area management plans. Conservation Science and Practice, 3(11), e542. https://doi.org/10.1111/csp2.542
Sedona, R., Paris, C., Cavallaro, G., Bruzzone, L., & Riedel, M. (2021). A High-Performance Multispectral Adaptation GAN for Harmonizing Dense Time Series of Landsat-8 and Sentinel-2 Images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14, 10134–10146. https://doi.org/10.1109/JSTARS.2021.3115604
Silveira, E. M. O., Radeloff, V. C., Martínez Pastur, G. J., Martinuzzi, S., Politi, N., Lizarraga, L., Rivera, L. O., Gavier-Pizarro, G. I., Yin, H., Rosas, Y. M., Calamari, N. C., Navarro, M. F., Sica, Y., Olah, A. M., Bono, J., & Pidgeon, A. M. (2022). Forest phenoclusters for Argentina based on vegetation phenology and climate. Ecological Applications, 32(3), e2526. https://doi.org/10.1002/eap.2526
Slingsby, J. A., Moncrieff, G. R., & Wilson, A. M. (2020). Near-real time forecasting and change detection for an open ecosystem with complex natural dynamics. ISPRS Journal of Photogrammetry and Remote Sensing, 166, 15–25. https://doi.org/10.1016/j.isprsjprs.2020.05.017
Šag, M., Turić, N., Vignjević, G., Lauš, B., & Temunović, M. (2016). Prvi nalaz rijetkih i ugroženih saproksilnih kornjaša, Cucujus cinnaberinus (Scopoli, 1763), Rhysodes sulcatus (Fabricius, 1787) i Omoglymmius germari (Ganglbauer, 1891) u Parku prirode Kopački rit. Natura Croatica: Periodicum Musei Historiae Naturalis Croatici, 25(2), 249-258. https://doi.org/10.20302/NC.2016.25.20
Torgbor, B. A., Rahman, M. M., Robson, A., Brinkhoff, J., & Khan, A. (2022). Assessing the Potential of Sentinel-2 Derived Vegetation Indices to Retrieve Phenological Stages of Mango in Ghana. Horticulturae, 8(1), Article 1. https://doi.org/10.3390/horticulturae8010011
Ustin, S. L., & Middleton, E. M. (2021). Current and near-term advances in Earth observation for ecological applications. Ecological Processes, 10(1), 1. https://doi.org/10.1186/s13717-020-00255-4
Wand, M. P., & Jones, M. C. (1994). Kernel Smoothing. CRC Press.
Wang, Y., Lu, Z., Sheng, Y., & Zhou, Y. (2020). Remote Sensing Applications in Monitoring of Protected Areas. Remote Sensing, 12(9), Article 9. https://doi.org/10.3390/rs12091370
Zeng, L., Wardlow, B. D., Xiang, D., Hu, S., & Li, D. (2020). A review of vegetation phenological metrics extraction using time-series, multispectral satellite data. Remote Sensing of Environment, 237, 111511. https://doi.org/10.1016/j.rse.2019.111511
Zhao, J., Huang, S., Huang, Q., Wang, H., Leng, G., & Fang, W. (2020). Time-lagged response of vegetation dynamics to climatic and teleconnection factors. CATENA, 189, 104474. https://doi.org/10.1016/j.catena.2020.104474
Zhou, M., Ma, X., Wang, K., Cheng, T., Tian, Y., Wang, J., Zhu, Y., Hu, Y., Niu, Q., Gui, L., Yue, C., & Yao, X. (2020). Detection of phenology using an improved shape model on time-series vegetation index in wheat. Computers and Electronics in Agriculture, 173, 105398. https://doi.org/10.1016/j.compag.2020.105398