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Journals / Turkish Journal of Forest Science / 2025 / Cilt: 9 Sayı: 2

AI-DRIVEN GEOSPATIAL ANALYSIS IN ECOSYSTEM MANAGEMENT: INTEGRATION OF MACHINE LEARNING METHODS

EKOSİSTEM YÖNETİMİNDE YAPAY ZEKÂ DESTEKLİ COĞRAFİ MEKÂN ANALİZİ: MAKİNE ÖĞRENİMİ YÖNTEMLERİNİN ENTEGRASYONU

Can Saygıner(IZMIR DEMOCRACY UNIVERSITY)0000-0002-1680-392X
Engin Nurlu(Ege Üniversitesi)0000-0001-5458-7749
Pages
560–584
Publication Date
2025-10-27
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

This synthesis illustrates the promise of the consolidation of AI and geospatial analysis to support adaptive, transparent, and efficient ecosystem management amid the rapid global change.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:The escalating complexity of environmental pressures, including climate change, loss of biodiversity, and land degradation, demands new and integrated, science-based solutions for managing ecosystems.
📊
Key Findings & Data:A select group of fifteen case studies illustrates successful applications across various ecological scenarios, including assessments of deforestation, biodiversity conservation, land use and land cover classification, and evaluation of ecosystem services.
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Conclusion & Impact:This synthesis illustrates the promise of the consolidation of AI and geospatial analysis to support adaptive, transparent, and efficient ecosystem management amid the rapid global change.
🔬Design:Review & Systematic Survey
📍Scope:Comprehensive Sample
⚙️Variables:Coğrafi mekân analizi · makine öğrenimi · ekosistem yönetimi

Abstract

The escalating complexity of environmental pressures, including climate change, loss of biodiversity, and land degradation, demands new and integrated, science-based solutions for managing ecosystems. In this study, it is explored how the integration of machine learning (ML) and geospatial analysis is a truly transformative approach to understand, monitor, and manage ecosystems. Through reviewing a wide spectrum of recent research and cases, the paper evaluates the integration of ML processes—e.g., Random Forest, Support Vector Machines, Artificial Neural Networks, and deep learning models—alongside geospatial technologies like GIS, remote sensing, and satellite image technology. Importantly, the research focuses on Python and R as strong programming platforms for developing ecological AI solutions and underscores their importance for data preprocessing, predictive models, visualization, and decision support. A select group of fifteen case studies illustrates successful applications across various ecological scenarios, including assessments of deforestation, biodiversity conservation, land use and land cover classification, and evaluation of ecosystem services. These articles were selected to illustrate methodological variety, geographic coverage, and applied relevance of advances in ecosystem management. Furthermore, the review covers significant methodological progress, toolkits, and platform integration (such as Google Earth Engine and TensorFlow), which are used across geospatial AI workflows. It also highlights ongoing issues—e.g., data quality, computational demand, model interpretability, and ethical considerations—and possible avenues for future research and interdisciplinary collaboration and outlines future directions to foster cross-disciplinary research and sustainable AI application. This synthesis illustrates the promise of the consolidation of AI and geospatial analysis to support adaptive, transparent, and efficient ecosystem management amid the rapid global change.

Özet

Artan çevresel baskıların – özellikle iklim değişikliği, biyoçeşitlilik kaybı ve arazi bozulmasının – yarattığı karmaşıklık, ekosistem yönetiminde daha bütüncül ve bilimsel yaklaşımları zorunlu kılmaktadır. Bu çalışma, makine öğrenimi ile coğrafi mekân analizlerin bütüncül bir şekilde bir araya getirilmesinin, ekosistemlerin anlaşılması, izlenmesi ve etkin bir şekilde yönetilmesi açısından yenilikçi ve dönüştürücü bir yaklaşım sunduğunu ortaya koymaktadır. Makale, yakın dönemli araştırmalar ve örnek uygulamalar üzerinden, Rastgele Orman, Destek Vektör Makineleri, Yapay Sinir Ağları ve Derin Öğrenme modelleri gibi makine öğrenimi yöntemlerinin, CBS (Coğrafi Bilgi Sistemleri), uzaktan algılama ve uydu görüntüleme teknolojileriyle nasıl entegre edildiğini değerlendirmektedir. Ayrıca, yapay zekâ temelli ekolojik çözümler geliştirmede güçlü platformlar olan Python ve R dillerininin; veri ön işleme, tahmine dayalı modelleme, görselleştirme ve karar destek sistemlerindeki rollerine dikkat çekilmektedir. Seçilen on beş örnek uygulama, ormansızlaşma değerlendirilmeleri, biyoçeşitliliğin korunması, arazi kullanımı ve arazi örtüsü sınıflandırması ile ekosistem hizmetlerinin değerlendirilmesi gibi çeşitli ekolojik senaryolarda bu teknolojilerin başarılı kullanımını ortaya koymaktadır. Bu çalışmalar, ekosistem yönetiminde yöntemsel çeşitlilik, coğrafi kapsam ve uygulamaya dönük değer açısından örnek teşkil etmektedir. Ayrıca, inceleme; Google Earth Engine, TensorFlow gibi platformların entegrasyonunu, coğrafi mekân yapay zekâ iş akışlarında kullanılan araç setlerini ve yöntemsel gelişmeleri kapsamaktadır. Veri kalitesi, hesaplama yükü, model yorumlanabilirliği ve etik kaygılar gibi süregelen sorunlara dikkat çekilmekte; gelecekteki araştırma alanları ve disiplinlerarası iş birlikleri için olası yönelimler sunulmaktadır. Bu sentez, yapay zekâ ile coğrafi mekân analizlerinin birleşiminin, hızla değişen küresel koşullar karşısında uyarlanabilir, şeffaf ve etkin bir ekosistem yönetimini destekleme potansiyelini ortaya koymaktadır.

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Cite This Article

Saygıner, C., Nurlu, E. (2025). AI-DRIVEN GEOSPATIAL ANALYSIS IN ECOSYSTEM MANAGEMENT: INTEGRATION OF MACHINE LEARNING METHODS. Turkish Journal of Forest Science, Cilt: 9 Sayı: 2, 560–584. https://doi.org/10.32328/turkjforsci.1704059

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References (91)

  1. Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., Isard, M., Kudlur, M., Levenberg, J., Monga, R., Moore, S., Murray, D. G., Steiner, B., Tucker, P., Vasudevan, V., Warden, P., Wicke, M., Yu, Y., & Zheng, X. (2016). TensorFlow: A system for large-scale machine learning. In Proceedings of the 12th USENIX Symposium on operating systems design and implementation (OSDI 16), 265–283, USENIX Association. https://doi.org/10.48550/arXiv.1603.04467
  2. Alshari, E. A., Abdulkareem, M. B., & Gawali, B. W. (2023). Classification of land use/land cover using artificial intelligence (ANN-RF). Frontiers in Artificial Intelligence, 5: 964279. https://doi.org/10.3389/frai.2022.964279
  3. Bivand, R. S., Pebesma, E., & Gomez-Rubio, V. (2013). Applied Spatial Data Analysis with R. Springer, Berlin, ISBN: 0387781706, https://doi.org/10.1007/978-1-4614-7618-4
  4. Bojer, A.K., Al-Quraishi, A.M.F. & Nadarajah, S. (2024) Evaluating ecological impacts through geospatial analysis of land use land cover dynamics in Jarmet wetland ecosystems in Western Ethiopia. Environmental Systems Research, 13, 57. https://doi.org/10.1186/s40068-024-00381-4
  5. Borrelli, P., Robinson, D. A., Fleischer, L. R., Lugato, E., Ballabio, C., Alewell, C., Meusburger, K., Modugno, S., Schütt, B., Ferro, V., Bagarello, V., Van Oost, K., Montanarella, L., & Panagos, P. (2018). Land use and climate change impacts on global soil erosion by water (2015-2070). Proceedings of the National Academy of Sciences of the United States of America, 117(36), 21994–22001. https://doi.org/10.1073/pnas.2001403117
  6. Chapin, F. S., Matson, P. A. & Vitousek, P. M. (2011). Principal of Terrestrial Ecosystem Ecology. Second Edition, Springer Science+Business Media, LLC 2011, ISBN 978-1-4419-9503-2
  7. Chen, M., Claramunt, C., Çöltekin, A., Liu, X., Peng, P., Robinson, A. C., Wang, D., Strobl, J., Wilson, J. P., Batty, M., Kwan, M.-P., Lotfian, M., Golay, F., Joost, S., Ingensand, J., Senousi, A. M., Cheng, T., Bandrova, T., Konecny, M., Torrens, P. M., Kliper, A., Li, S., Zhang, F., He, L., Wang, J., Ratti, C., Koditz, O., Lin, H., Lü, O. (2023). Artificial intelligence and visual analytics in geographical space and cyberspace: Research opportunities and challenges. Earth-Science Reviews, 241, 104438. https://doi.org/10.1016/j.earscirev.2023.104438
  8. Cheng, T., Liu, G., Ma, Z., & Liang, X. (2018). Real-time participatory mapping and stakeholder engagement for spatial decision-making. Journal of Spatial Information Science, (16), 45-63.
  9. Chollet, F. (2017). Deep Learning with Python. Manning Publications. ISBN 9781617294433
  10. Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of the 2019 conference of the North American chapter of the association for computational linguistics: Human language technologies (NAACL-HLT), 1, 4171–4186. Association for Computational Linguistics. https://doi.org/10.18653/v1/N19-1423
  11. Dias, F., Suhadolnik, N., Camargo, H., & Da Silva, S. (2024). Predicting the pulse of the Amazon: Machine learning insights into deforestation dynamics, Journal of Environmental Management, 362, 121359. https://doi.org/10.1016/j.jenvman.2024.121359
  12. Dominguez, D., de Juan del Vilar, L., Pantoja, O., & González-Rodríguez, M. (2022). Forecasting Amazon rain-forest deforestation using a hybrid machine learning model. Sustainability, 14(2), 691. https://doi.org/10.3390/su14020691
  13. Dritsas, E., & Trigka, M. (2025). Remote sensing and geospatial analysis in the big data era: A survey. Remote Sensing, 17(3), 550. https://doi.org/10.3390/rs17030550
  14. EEAGER Project. (2023). Spying on beavers from space could help save California. In WIRED Magazine. https://www.wired.com/story/spying-on-beavers-from-space-could-help-save-california/
  15. Elith, J., Graham, C. H., Anderson, R. P., Dudík, M., Ferrier, S., Guisan, A., Hijmans, R. J., Huettmann, F., Leathwick, J. R., Lehmann, A., Li, J., Lohmann, L. G., Loiselle, B. A., Manion, G., Moritz, C., Nakamura, M., Nakazawa, Y., Overton, J. M., Peterson, A. T., Phillips, S. J., Richardson, K., Scachetti-Pereira, R., Schapire, R. E., Soberón, J., Williams, S., Wisz, M. S., & Zimmermann, N. E. (2006). Novel methods improve prediction of species’ distributions from occurrence data. Ecography, 29(2), 129-151. https://doi.org/10.1111/j.2006.0906-7590.04596.x
  16. Erdoğan, N., Carrer, F., Tonyaloğlu, E. E., Çavdar, B., Varinlioğlu, G., Şerifoğlu, T. E., Nurlu, E & Turner, S. (2020). Simulating change in cultural landscapes: The integration of historic landscape characterisation and computer modelling. Landscapes, 21(2), 168-182.
  17. Ersoy Tonyaloğlu, E., Erdoğan, N., Çavdar, B., Kurtşan, K., & Nurlu, E. (2021). Comparison of pixel and object based classification methods on Rapideye satellite image. Turkish Journal of Forest Science, 5(1), 1-11. https://doi.org/10.32328/turkjforsci.741030
  18. Ersoy Tonyaloğlu, E. (2025). Future land use/land cover and its impacts on ecosystem services: Case of Aydın, Turkey. International Journal of Environmental Science and Technology, 222, 4601-4617, https://doi.org/10.1007/s13762-024-05907-y
  19. Floridi, L., Cowls, J., Beltrametti, M., Chatila, R., Chazerand, P., Dignum, V., Luetge, C., Madelin, R., Pagallo, U., Rossi, F., Schafer, B., Valcke, P., & Vayena, E. (2018). AI4People-An ethical framework for a good AI society: Opportunities, risks, principles, and recommendations. Minds and Machines, 28(4), 689-707. https://doi.org/10.1007/s11023-018-9482-5
  20. GeoPandas Development Team. (2014). GeoPandas: Python tools for geographic data. GitHub. https://geopandas.org
  21. Giri, C., Ochieng, E., Tieszen, L. L., Zhu, Z., Singh, A., Loveland, T., Masek, J., & Duke, N. (2010). Status and distribution of mangrove forests of the world using earth observation satellite data. Global Ecology and Biogeography, 20(1), 154-159. https://doi.org/10.1111/j.1466-8238.2010.00584.x
  22. Gutiérrez Caloir, B. E., Abebe, Y. A., Vojinovic, Z., Sanchez, A., Mubeen, A., Ruangpan, L., Manojlovic, N., Plavsic, J., Djordjevic, S. (2023). Combining machine learning and spatial data processing techniques for allocation of large-scale nature-based solutions. Blue-Green Systems, 5(2), 186–198. https://doi.org/10.2166/bgs.2023.040
  23. Haripavan, N., Dey, S., & Chandana, C. H. M. (2025). Integration of geospatial techniques and machine learning in land parcel prediction. Geosystems and Geoenvironment, 4(2), 100371. https://doi.org/10.1016/j.geogeo.2025.100371
  24. Hesselbarth, M., Schmidtlein, S., & Hauffe, T. (2019). Landscapemetrics: An R package to calculate landscape metrics. Ecological Indicators, 105, 212-220. https://doi.org/10.1016/j.ecolind.2019.05.002
  25. Hijmans, R. J. (2025). Raster: Geographic data analysis and modeling (Version 3.6-32) [Computer software]. R package. Foundation, Vienna. https://CRAN.R-project.org/package=raster
  26. Hochschild, V., Braun, A., Sommer, C., Warth, G., & Omran, A. (2020). Visualizing landscapes by geospatial techniques. In D. Edler, C. Jenal, & O. Kühne (Eds.), Modern Approaches to the Visualization of Landscapes, RaumFragen: Stadt – Region – Landschaft. Springer VS, Wiesbaden, 47-78. https://doi.org/10.1007/978-3-658-30956-5_4
  27. Huang, W., Kazemzadeh, K., Mansourian, A., & Harrie, L. (2020). Towards knowledge-based geospatial data integration and visualization: A case of visualizing urban bicycling suitability. IEEE Access, 8, 85473-85489. https://doi.org/10.1109/access.2020.2992023
  28. Hunter, J. D. (2007). Matplotlib: A 2D graphics environment. Computing in Science & Engineering, 9(3), 90-95. https://doi.org/10.1109/MCSE.2007.55
  29. Jordan, M. I., & Mitchell, T. M. (2015). Machine learning: Trends, perspectives, and prospects. Science, 349(6245), 255-260. https://doi.org/10.1126/science.aaa8415 IUCN CEM (2025). IUCN commission on ecosystem management annual report 2024. Gland, Switzerland: IUCN. Retrieved from https://www.iucn.org/our-work/commission-ecosystem-management
  30. Kosherbay, K., Mussagaliyeva, A., & Strobl, J. (2022). Analysis of green zones and heat islands of Almaty city based on satellite images. Journal of Geography and Environmental Management, 67(4), 80-93. https://doi.org/10.26577/jgem.2022.v67.i4.07
  31. Koldasbayeva, S., Smith, A. M., & Liu, H. (2023). Challenges in data-based geospatial modeling for environmental research. arXiv preprint arXiv:2311.11057. https://arxiv.org/abs/2311.11057
  32. Lary, D. J., Alavi, A. H., Gandomi, A. H., & Walker, A. L. (2016). Machine learning in geosciences and remote sensing. Geosciences Frontiers, 7(1), 3-10. https://doi.org/10.1016/j.gsf.2015.07.003
  33. Li, S., Dragićević, S., Castro, F. A., Sester, M., Winter, S., Coltekin, A., Pettit, C., Jiang, B., Haworth, J., Stein, A., & Cheng, T. (2016). Geospatial big data handling theory and methods: A review and research challenges. ISPRS Journal of Photogrammetry and Remote Sensing, 115, 119-133. https://doi.org/10.1016/j.isprsjprs.2015.10.012
  34. Li, X., Yuan, W., & Dong, W. (2021). A machine learning method for predicting vegetation indices in China. Remote Sensing, 13(6), 1147. https://doi.org/10.3390/rs13061147
  35. Liu, J., Zuo, Y., Wang, N., Yuan, F., Zhu, X., Zhang, L., Zhang, J., Sun, Y., Guo, Z., Guo, Y., Song, X., Song, C., & Xu, X. (2021). Comparative analysis of two machine learning algorithms in predicting site-level net ecosystem exchange in major biomes. Remote Sensing, 13(2), 2242 https://doi.org/10.3390/rs13122242
  36. Liu, Y., Huang, X., & Liu, Y. (2024). Detection of long-term land use and ecosystem services dynamics in the Loess Hilly-Gully region based on artificial intelligence and multiple models. Journal of Cleaner Production, 447, 141560. https://doi.org/10.1016/j.jclepro.2024.141560
  37. Lovelace, R., Nowosad, J., & Muenchow, J. (2025). Geocomputation with R. (2nd ed.), CRC Press, ISBN 978-1032248882.
  38. Lowell, K., & Calder, B. (2022). Operational performance of a combined density-and clustering-based approach to extract bathymetry returns from LIDAR point clouds. International Journal of Applied Earth Observation and Geoinformation, 107, 102699. https://doi.org/10.1016/j.jag.2022.102699
  39. Lundberg, S. M., & Lee, S.-I. (2017). A unified approach to interpreting model predictions. 30th Conference on Advances in Neural Information Processing Systems (NIPS 2017), Long Beach, CA, USA. https://doi.org/10.48550/arXiv.1705.07874
  40. Ma, Y., Hu, Y., Moncrieff, G. R., Slingsby, J. A., Wilson, A. M., Maitner, B., & Zhou, R. Z. (2022). Forecasting vegetation dynamics in an open ecosystem by integrating deep learning and environmental variables. International Journal of Applied Earth Observations and Geoinformation, 114, 103060.
  41. Malkoç, E. & Nurlu, E. (2024). Coğrafi bilgi sistemleri ve IDRISI Selva LCM aracı ile ormansızlaşma modellemesi: Bozdağlar, Türkiye. Turkish Journal of Forest Science, 8(2), 163-176.
  42. Mahmoud, R., Hassanin, M., Al Feel, H., & Badry, R. M. (2023). Machine learning-based land use and land cover mapping using multi-spectral satellite imagery: A case study in Egypt. Sustainability, 15(12), 9467. https://doi.org/10.3390/su15129467.
  43. Mardani, M., Mardani, H., Simone, L. D., Varas, S., Kita, N., & Saito, T. (2019). Integration of machine learning and open access geospatial data for land cover mapping. Remote Sensing, 11(16), 1907. https://doi.org/10.3390/rs11161907
  44. Maxwell, A. E., Warner, T. A., & Fang, F. (2018). Implementation of machine-learning classification in remote sensing: An applied review. International Journal of Remote Sensing, 39(9), 2784-2817. https://doi.org/10.1080/01431161.2018.1433343
  45. Mitchell, T.M. (1997). Machine Learning. McGraw-Hill Science, ISBN 0070428077.
  46. Millennium Ecosystem Assessment. (2005). Ecosystems and human well-being: Synthesis. Island Press, Washington, DC.
  47. Riedmiller, M., Fidjeland, A. K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., & Hassabis, D. (2015). Human-level control through deep reinforcement learning. Nature, 518, 529-533. https://doi.org/10.1038/nature14236
  48. Mukherjee, M., Paul, A., & Mukerji, M. (2023). Biotic assessment of crowdsourced data defines four ecoregions in Thar: A novel approach for citizen engagement in conservation. Global Ecology and Conservation, 46, e02559, https://doi.org/10.1016/j.gecco.2023.e02559
  49. Norouzzadeh, M. S., Nguyen, A., Kosmala, M., Swanson, A., Palmer, M. S., Packer, C., & Clune, J. (2018). Automatically identifying, counting, and describing wild animals in camera-trap images with deep learning. Proceedings of the National Academy of Sciences, 115(25), E5716–E5725. https://doi.org/10.1073/pnas.1719367115
  50. Obermeyer, Z., Powers, B., Vogeli, C., & Mullainathan, S. (2019). Dissecting racial bias in an algorithm used to manage the health of populations. Science, 366(6464), 447-453. https://doi.org/10.1126/science.aax2342
  51. Obiorah, C. A., Ndubuisi, O. G., Ali, S. E., Aku, T., Nesiama, O., Agbakhamen, C. O., & Okoro, P. (2025). Geospatial technologies for environmental management. International Journal of Innovative Human Ecology & Nature Studies 13(1):89-99.
  52. Oğuz, H., & Çınar, S. (2023). Using remote sensing and machine learning for biodiversity conservation. Biodiversity Conservation, 32(8), 2659-2675.
  53. Oğuz, H. & Yıldız, E. (2023). Land use change analysis using machine learning approaches in agricultural landscapes. Journal of Agriculture, Ecosystems & Environment, 335, 107936.
  54. Pandey, B., Choudhary, S., & Sharma, M. (2023). Artificial intelligence, machine learning, and big data in natural resources management: A comprehensive bibliometric review of literature spanning 1975–2022. Energy Policy, 178, 113604. https://doi.org/10.1016/j.enpol.2023.113604
  55. Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., &
  56. Chintala, S. (2019). PyTorch: An imperative style, high-performance deep learning library. In Advances in neural information processing systems 32 (NeurIPS 2019) Vancouver, Canada. https://papers.nips.cc/paper_files/paper/2019.
  57. Patterson, M. W., & Hoalst-Pullen, N. (2010). Geotechnologies in environmental management. In: Hoalst-Pullen, N., Patterson, M. (eds) Geospatial Technologies in Environmental Management. Geotechnologies and the Environment, 3. Springer, Dordrecht. https://doi.org/10.1007/978-90-481-9525-1_1
  58. Pebesma, E. (2018). Simple features for R: Standardized support for spatial vector data. The R Journal, 10(1), 439-446. https://doi.org/10.32614/RJ-2018-009
  59. Pebesma, E., & Bivand, R. (2023). Spatial data science: With applications in R. (1st ed.), Chapman and Hall/CRC, Boca Raton. https://doi.org/10.1201/9780429459016
  60. Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., & Duchesnay, É. (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12(85), 2825–2830. https://doi.org/10.48550/arXiv.1201.0490
  61. Pettorelli, N., Williams, J., Schulte to Bühne, H., & Crowson, M. (2024). Deep learning and satellite remote sensing for biodiversity monitoring and conservation. In T. Sankey & Y. Ke (Eds.), Remote Sensing in Ecology and Conservation. John Wiley & Sons Ltd. https://doi.org/10.1002/rse2.415
  62. Prodanovic, V., Bach, P. M. & Stojkovic, M. (2024). Urban nature-based solutions planning for biodiversity outcomes: Human, ecological, and artificial intelligence perspectives. Urban Ecosystems 27, 1795–1806. https://doi.org/10.1007/s11252-024-01558-6
  63. Raihan, A., (2023). Artificial intelligence and machine learning applications in forest management and biodiversity conservation. Natural Resources Conservation and Research, 6(2), 1-16. https://doi.org/10.24294/nrcr.v6i2.3825
  64. Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016). You only look once: Unified, real-time object detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Las Vegas, NV, USA, 779-788. IEEE. https://doi.org/10.1109/CVPR.2016.91
  65. Ribeiro, M. T., Singh, S., & Guestrin, C. (2016). Why should I trust you? Explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD '16), 1135-1144. https://doi.org/10.1145/2939672.2939778
  66. Rolnick, D., Donti, P. L., Kaack, L. H., Kochanski, K., Lacoste, A., Sankaran, K., Ross, A. S., Milojevic-Dupont, N., Jaques, N., Waldman-Brown, A., Luccioni, A. S., Maharaj, T., Sherwin, E. D., Mukkavilli, S. K., Kording, K. P., Gomes, C. P., Ng, A. Y., Hassabis, D., Platt, J. C., Creutzig, F., Chayes, J., & Bengio, Y. (2022). Tackling climate change with machine learning. ACM Computing Surveys (CSUR), 55(2), 1-96. https://doi.org/10.1145/3485128
  67. Sample, A. (2013). Remote sensing and GIS in ecosystem management. Island Press, ISBN 978-1559632843.
  68. Sarker, I. H. (2021). Machine learning: Algorithms, real-world applications and research directions. SN Computer Science, 2(160). https://doi.org/10.1007/s42979-021-00592-x
  69. Shivaprakash, K. N., Swami, N., Mysorekar, S., Arora, R., Gangadharan, A., Vohra, K., Jadeyegowda, M., & Kiesecker, J. M. (2022). Potential for artificial intelligence (AI) and machine learning (ML) applications in biodiversity conservation, managing forests, and related services in India. Sustainability, 14(12), 7154. https://doi.org/10.3390/su14127154
  70. Silvestro, D., Goria, S., Sterner, T., & Antonelli, A. (2022). Improving biodiversity protection through artificial intelligence. Nature Sustainability, 5, 415-424. https://doi.org/10.1038/s41893-022-00851-6
  71. Song, Y., Kalacska, M., Gasarovic, M., Yao, J., & Najibi, N. (2023). Advances in geocomputation and geospatial artificial intelligence (GeoAI) for mapping. International Journal of Applied Earth Observation and Geoinformation, 120, 103300. https://doi.org/10.1016/j.jag.2023.103300
  72. Stals, T. A., Bardule, A., Dumiņs, K., Makovskis, K., & Lazdiņa, D. (2023). Remote-sensed tree crown diameter as a predictor of stem diameter and above-ground biomass in Betula pendula Roth and Populus tremuloides Michx. × Populus tremula L. plantations. Land, 12(11), 2006. https://doi.org/10.3390/land12112006
  73. Strubell, E., Ganesh, A., & McCallum, A. (2019). Energy and policy considerations for deep learning in NLP. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (ACL 2019) (pp. 3645-3650). Association for Computational Linguistics, 3645-3650. https://doi.org/10.18653/v1/P19-1355
  74. Szczepanska, A., Kazmierczak, R., & Myszkowska, M. (2021). Virtual reality as a tool for public consultations in spatial planning and management. Energies, 14(19), 6046. https://doi.org/10.3390/en14196046
  75. Talukdar, S., Singha, P., Mahato, S., Shahfahad, Pal, S., Liou, Y.-A., & Rahman, A. (2020). Land-use land-cover classification by machine learning classifiers for satellite observations. A review. Remote Sensing, 12(7).
  76. Taylor, S.J., & Letham, B. (2018). Forecasting at scale. The American Statistician. 72(1), 37-45. https://doi.org/10.1080/00031305.2017.1380080
  77. Tennekes, M. (2018). "tmap: Thematic Maps in R." Journal of Statistical Software, 84(6), 1-39. doi:10.18637/jss.v084.i06.
  78. Tuia, D., Kellenberger, B., Beery, S., Costelloe, B. R., Zuffi, S., Risse, B., Mathis, A., Mathis, M.W., van Langevelde, F., Burghardt, T., Kays, R., Klick, H., Wikelski, M., Couzin, I. D., van Horn, G., Crofoot, M.C., Stewart, C.V., & Berger-Wolf, T. (2022). Perspectives in machine learning for wildlife conservation. Nature Communications, 13, 792. https://doi.org/10.1038/s41467-022-27980-y
  79. Şenay, D., Kesgin Atak, B., Saygıner, C., Ersoy Tonyaloğlu, E., & Nurlu, E. (2024). Evaluation of spatio-temporal changes in landscape: The case of Seferihisar, Turkiye. In E. Malkoç True & M. Özeren Alkan (Eds.), International Congress on Landscape Architecture "City and Human" (pp. 477-490). IKSAD Publishing.
  80. UNEP (United Nations Environment Programme) (n.d.). Ecosystem management. United Nations Environment Programme. Retrieved from https://www.unep.org/topics/nature-action/conservation-restoration-and-sustainable-use/ecosystem-management
  81. Wang, H. (2022) Temporal and spatial evolution pattern of terrestrial ecosystem based on machine learning algorithm. 2022 International Conference on Artificial Intelligence of Things and Crowdsensing (AIoTCs), Nicosia, Cyprus, 567-572.
  82. Wang, J., Bretz, M., Dewan, A. A., Delavar, M. A. (2022) Machine learning in modelling land-use and land cover-change (LULCC): Current status, challenges and prospects. Science of the Total Environment, 822, 1-17, 153559.
  83. Wang, L., Zhang, M., Gao, X., & Shi, W. (2024) Advances and challenges in deep learning-based change detection for remote sensing images: A review through various learning paradigms. Remote Sensing, 16(5), 804. https://doi.org/10.3390/rs16050804
  84. Wickham, H. (2016) ggplot2: Elegant graphics for data analysis. Springer. https://doi.org/10.1007/978-3-319-24277-4
  85. Xu, X., Yu, J., & Wang, F. (2022) Analysis of ecosystem service drivers based on interpretive machine learning: a case study of Zhejiang Province, China. Environmental Science and Pollution Research International, 29(42), 64060–64076. https://doi.org/10.1007/s11356-022-20311-0
  86. Yin, D., Liu, Y., Hu, H., Terstriep, J., Hong, X., Padmanabhan, A., & Wang, S. (2018) CyberGIS-Jupyter for reproducible and scalable geospatial analytics. Concurrency and Computation: Practice and Experience, 31(11). https://doi.org/10.1002/cpe.5040
  87. Yu, Y., Cao, Y., Hou, D., Disse, M., Brieden, A., Zhang, H., & Yu, R. (2022) The study of artificial intelligence for predicting land use changes in an arid ecosystem. Journal of Geographical Sciences, 32, 717–734. https://doi.org/10.1007/s11442-022-1969-6
  88. Yuan, Q., Shen, H., Li, T., Li, Z., Li, S., Jiang, Y., Xu, H., Tan, W., Yang, Q., Wang, J., Gao, J., & Zhang, L. (2020) Deep learning in environmental remote sensing: Achievements and challenges. Remote Sensing of Environment, 241, 111716.
  89. Zhang, L., Wang, Z., Liu, Y., & Chen, Y. (2023) Assessing regional ecosystem conditions using geospatial techniques: A review. Sensors, 23(8), 4101.
  90. Zhang, Y., & Yu, W. (2022) Comparison of DEM super-resolution methods based on interpolation and neural networks. Sensors, 22(3), 745.
  91. Zhao, P., Jimenez, J. R. C., Brovelli, M. A., & Mansourian, A. (2022) Towards geospatial blockchain: A review of research on blockchain technology applied to geospatial data. AGILE: GIScience Series, 3, 1-6. https://doi.org/10.5194/agile-giss-3-71-2022