Integrating AI/ML, Geospatial, and Ecosystem Service Modelling for the Economic Valuation of Forest Ecosystems within the SEEA-EA Framework:A Systematic Literature Review

Disalin dari bagian Reference. Entri duplikat dan variasi penulisan pada draft dipertahankan; daftar ini bukan daftar seleksi 136 studi yang sudah direkonsiliasi.

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Allen, M. J., Moreno-Fernández, D., Ruiz-Benito, P., Grieve, S. W. D., & Lines, E. R. (2024). Low-cost tree crown dieback estimation using deep learning-based segmentation. Environmental Data Science, 3. https://doi.org/10.1017/eds.2024.16

Anees, S., Mehmood, K., Khan, W., Sajjad, M., Alahmadi, T., Alharbi, S., & Luo, M. (2024). Integration of machine learning and remote sensing for above ground biomass estimation through Landsat-9 and field data in temperate forests of the Himalayan region. ECOLOGICAL INFORMATICS, 82. https://doi.org/10.1016/j.ecoinf.2024.102732

Angkahad, T., Laosuwan, T., Sangpradid, S., Prasertsri, N., Uttaruk, Y., Phoophathong, T., & Nuchthapho, J. (2025). Developing a Drone-Based Machine Learning for Spatial Modeling and Analysis of Biomass and Carbon Sequestration in Forest Ecosystems. Engineered Science, 35. https://doi.org/10.30919/es1508

Antúnez, P., Wehenkel, C., Basave-Villalobos, E., Calixto-Valencia, C. G., Valenzuela-Encinas, C., Ruiz-Aquino, F., & Sarmiento-Bustos, D. (2025). Predictive Modeling of Volume and Biomass in Pinus pseudostrobus Using Machine Learning and Allometric Approaches. Forest Science and Technology, 21(1), 110–122. Scopus. https://doi.org/10.1080/21580103.2025.2456295

Aranguren, I., González-Audícana, M., Montero, E., Sanz, J., & Alvarez-Mozos, J. (2025). Assessing the Synergistic Use of Sentinel-1, Sentinel-2, and LiDAR Data for Forest Type and Species Classification. REMOTE SENSING, 17(12). https://doi.org/10.3390/rs17122028

Araza, A., de Bruin, S., Hein, L., & Herold, M. (2023). Spatial predictions and uncertainties of forest carbon fluxes for carbon accounting. Scientific Reports, 13(1). Scopus. https://doi.org/10.1038/s41598-023-38935-8

Astola, H., Seitsonen, L., Halme, E., Molinier, M., & Lönnqvist, A. (2021). Deep Neural Networks with Transfer Learning for Forest Variable Estimation Using Sentinel-2 Imagery in Boreal Forest. REMOTE SENSING, 13(12). https://doi.org/10.3390/rs13122392

Avtar, R., Rinamalo, A., Umarhadi, D., Gupta, A., Khedher, K., Yunus, A., Singh, B., Kumar, P., Sahu, N., & Sakti, A. (2022). Land Use Change and Prediction for Valuating Carbon Sequestration in Viti Levu Island, Fiji. LAND, 11(8). https://doi.org/10.3390/land11081274

Aziz, T., & van Cappellen, P. (2020). Valuation of Ontario’s ecosystem services and relevance for decision-making. In Can.: Past, Present and Future Perspect. (pp. 131–165). Nova Science Publishers, Inc.; Scopus. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85107554799&partnerID=40&md5=c27b182972adcc326eb791aaa6f034c7

Bagstad, K. J., Balbi, S., Adamo, G., Athanasiadis, I. N., Affinito, F., Willcock, S., Magrach, A., Hayashi, K., Harmáčková, Z. V., Niamir, A., Smets, B., Buchhorn, M., Drakou, E. G., Alfieri, A., Edens, B., Morales, L. G., Vári, Á., Sanz, M.-J., & Villa, F. (2025). Interoperability for ecosystem service assessments: Why, how, who, and for whom? Ecosystem Services, 72. https://doi.org/10.1016/j.ecoser.2025.101705

Bagstad, K., Reed, J., Semmens, D., Sherrouse, B., & Troy, A. (2016). Linking biophysical models and public preferences for ecosystem service assessments: A case study for the Southern Rocky Mountains. REGIONAL ENVIRONMENTAL CHANGE, 16(7), 2005–2018. https://doi.org/10.1007/s10113-015-0756-7

Banerjee, O., Bagstad, K. J., Cicowiez, M., Dudek, S., Horridge, M., Alavalapati, J. R. R., Masozera, M., Rukundo, E., & Rutebuka, E. (2020). Economic, land use, and ecosystem services impacts of Rwanda’s Green Growth Strategy: An application of the IEEM+ESM platform. Science of the Total Environment, 729. Scopus. https://doi.org/10.1016/j.scitotenv.2020.138779

Banerjee, O., Cicowiez, M., Vargas, R., & Horridge, M. (2019). The SEEA-Based Integrated Economic-Environmental Modelling Framework: An Illustration with Guatemala’s Forest and Fuelwood Sector. Environmental and Resource Economics, 72(2), 539–558. Scopus. https://doi.org/10.1007/s10640-017-0205-9

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Bjerreskov, K., Nord-Larsen, T., & Fensholt, R. (2021). Classification of Nemoral Forests with Fusion of Multi-Temporal Sentinel-1 and 2 Data. REMOTE SENSING, 13(5). https://doi.org/10.3390/rs13050950

Boisvenue, C., Smiley, B., White, J., Kurz, W., & Wulder, M. (2016). Integration of Landsat time series and field plots for forest productivity estimates in decision support models. FOREST ECOLOGY AND MANAGEMENT, 376, 284–297. https://doi.org/10.1016/j.foreco.2016.06.022

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Bulut, S., Günlü, A., & Çakir, G. (2023). Modelling some stand parameters using Landsat 8 OLI and Sentinel-2 satellite images by machine learning techniques: A case study in Turkiye. GEOCARTO INTERNATIONAL, 38(1). https://doi.org/10.1080/10106049.2022.2158238

Buthelezi, M., Lottering, R., Peerbhay, K., & Mutanga, O. (2024). Optimising forest rehabilitation and restoration through remote sensing and machine learning: Mapping natural forests in the eThekwini Municipality. REMOTE SENSING APPLICATIONS-SOCIETY AND ENVIRONMENT, 36. https://doi.org/10.1016/j.rsase.2024.101335

Buthelezi, M. N. M., Lottering, R., Peerbhay, K., & Mutanga, O. (2024). Optimising forest rehabilitation and restoration through remote sensing and machine learning: Mapping natural forests in the eThekwini Municipality. Remote Sensing Applications: Society and Environment, 36. https://doi.org/10.1016/j.rsase.2024.101335

Campos, P., Álvarez, A., Oviedo, J. L., Ovando, P., Mesa, B., & Caparrós, A. (2020). Income and ecosystem service comparisons of refined national and agroforestry accounting frameworks: Application to holm oak open woodlands in Andalusia, Spain. Forests, 11(2). Scopus. https://doi.org/10.3390/f11020185

Campos, P., Mesa, B., Álvarez, A., Oviedo, J. L., & Caparrós, A. (2022). Towards measuring environmental income through a refined United Nations SEEA EA: Application to publicly-owned, protected, pine-forest-farm case studies in Andalusia, Spain. Ecological Economics, 201. Scopus. https://doi.org/10.1016/j.ecolecon.2022.107570

Campos, P., Oviedo, J. L., Álvarez, A., & Mesa, B. (2022). Measurement of the Threatened Biodiversity Existence Value Output: Application of the Refined System of Environmental-Economic Accounting in the Pinus pinea Forests of Andalusia, Spain. Land, 11(7). Scopus. https://doi.org/10.3390/land11071119

Campos, P., Oviedo, J. L., Álvarez, A., & Mesa, B. (2025). Ecosystem accounting for measuring linked sustainable physical and economic indicators: Application to Andalusian forests in protected and non-protected areas. Environmental and Sustainability Indicators, 28. Scopus. https://doi.org/10.1016/j.indic.2025.100848

Cao, Y., Coops, N., Murray, B., & Sinclair, I. (2025). Enhancing tree species composition mapping using Sentinel-2 and multi-seasonal deep learning fusion. INTERNATIONAL JOURNAL OF REMOTE SENSING. https://doi.org/10.1080/01431161.2025.2583600

Caparrós, A., Oviedo, J. L., Álvarez, A., & Campos, P. (2017). Simulated exchange values and ecosystem accounting: Theory and application to free access recreation. Ecological Economics, 139, 140–149. Scopus. https://doi.org/10.1016/j.ecolecon.2017.04.011

Capriolo, A., Boschetto, R. G., Mascolo, R. A., Balbi, S., & Villa, F. (2020). Biophysical and economic assessment of four ecosystem services for natural capital accounting in Italy. Ecosystem Services, 46. Scopus. https://doi.org/10.1016/j.ecoser.2020.101207

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Chen, L., Wang, Y., Ren, C., Zhang, B., & Wang, Z. (2019). Optimal Combination of Predictors and Algorithms for Forest Above-Ground Biomass Mapping from Sentinel and SRTM Data. REMOTE SENSING, 11(4). https://doi.org/10.3390/rs11040414

Chen, X., Jiang, K., Zhu, Y., Wang, X., & Yun, T. (2021). Individual tree crown segmentation directly from uav-borne lidar data using the pointnet of deep learning. Forests, 12(2), 1–22. Scopus. https://doi.org/10.3390/f12020131

Chen, X., Sun, Y., Qin, X., Cai, J., Cai, M., Hou, X., Yang, K., & Zhang, H. (2024). Assessing the Potential of UAV for Large-Scale Fractional Vegetation Cover Mapping with Satellite Data and Machine Learning. REMOTE SENSING, 16(19). https://doi.org/10.3390/rs16193587

Chen, Y., Vardon, M., Keith, H., van Dijk, A., & Doran, B. (2023). Linking ecosystem accounting to environmental planning and management: Opportunities and barriers using a case study from the Australian Capital Territory. Environmental Science and Policy, 142, 206–219. https://doi.org/10.1016/j.envsci.2023.02.014

Cheng, F., Ou, G., Wang, M., & Liu, C. (2024). Remote Sensing Estimation of Forest Carbon Stock Based on Machine Learning Algorithms. FORESTS, 15(4). https://doi.org/10.3390/f15040681

Chernenkova, T., Kotlov, I., Belyaeva, N., & Suslova, E. (2021). Spatiotemporal modeling of coniferous forests dynamics along the southern edge of their range in the central russian plain. Remote Sensing, 13(10). https://doi.org/10.3390/rs13101886

Chivulescu, S., Hapa, M., Pitar, D., Lorenţ, A., Marmureanu, L., Leca, S., Radu, R., Cazacu, R., Dobre, A. C., Pascu, I. S., Marcu, C., Verghelet, M., Vezeanu, C., Racoviceanu, T., & Badea, O. (2024). Integrating monetary and non-monetary valuation for ecosystem services in Piatra Craiului national park, Southern Carpathians: A comprehensive approach to sustainability and conservation. Frontiers in Forests and Global Change, 7. Scopus. https://doi.org/10.3389/ffgc.2024.1280793

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