Acknowledgment to the Reviewers of Journal of Environmental & Earth Sciences in 2025
2026-02-06
From Data to Decisions: Integrative Frameworks for Environmental Monitoring in the Anthropocene
Authors
-
Fei Zhao
Taiyuan Municipal Ecological Environment Monitoring and Scientific Research Center, Taiyuan 030002, China
DOI:
https://doi.org/10.30564/jees.v8i8.13589Abstract
Environmental monitoring in the Anthropocene is at a pivotal moment from data accumulation towards decision-oriented environmental intelligence. The monitoring systems must be able to integrate multiple data streams and transform these data into actionable, credible and timely knowledge to address the critical issues of rapid climate change, biodiversity loss, pollution, land-use transformation and increasing disaster risks. This review discusses integrative frameworks bringing together the generation of environmental data, analytical synthesis, decision support systems and policy translation. It emphasizes the synergistic use of in situ observations, remote sensing, citizen science, Internet of Things sensors, environmental DNA, bioacoustics and emerging digital platforms. The article examines the role of data fusion, geospatial analysis, statistical modelling, AI, socio-ecological modelling, indicator frameworks, and uncertainty assessment as means to convert observations of diverse nature to usable evidence. It also maintains that interoperability, transparency, institutional capacity, ethical data governance, stakeholder involvement, and environmental justice are equally critical to successful monitoring. The relevance of connecting the outputs of climate monitoring to unambiguous decision pathways is illustrated in applications to climate adaptation, the conservation of biodiversity, pollution control, urban sustainability, water–land systems and disaster risk reduction. The review finds that next-generation environmental monitoring needs to be an adaptive system of knowledge-action that can learn from results, be transparent about uncertainty, and enable accountable governance in a world of dynamic environmental conditions.
Keywords:
Environmental Monitoring; Anthropocene; Decision-Support Systems; Data Integration; Adaptive GovernanceReferences
[1] Ndehedehe, C., 2023. Understanding the Anthropocene. In Hydro-Climatic Extremes in the Anthropocene. Springer International Publishing: Cham, Switzerland. pp. 3–22. DOI: https://doi.org/10.1007/978-3-031-37727-3_1
[2] Malakar, K.D., Kumar, M., Anand, S., et al., 2023. Climate Change and Socio-Ecological Transformation: Applications and Field Narratives. In Climate Change and Socio-Ecological Transformation. Springer Nature: Singapore. pp. 295–374. DOI: https://doi.org/10.1007/978-981-99-4390-6_7
[3] Ascough, J.C., Maier, H.R., Ravalico, J.K., et al., 2008. Future research challenges for incorporation of uncertainty in environmental and ecological decision-making. Ecological Modelling. 219(3–4), 383–399. DOI: https://doi.org/10.1016/j.ecolmodel.2008.07.015
[4] Rashid, S., Singla, A., Yadav, B., et al., 2025. Tracing Environmental Changes Over Time. In: Sankhla, M.S., Awasthi, K.K., Sharma, T., et al. (eds.). Introducing the Synergy Between Forensic Science and Environmental Analysis. Springer Nature: Singapore. pp. 61–80. DOI: https://doi.org/10.1007/978-981-95-0199-1_4
[5] Biermann, F., 2022. The future of ‘environmental’ policy in the Anthropocene: time for a paradigm shift. In Trajectories in Environmental Politics. Routledge: London, UK. pp. 58–77. DOI: https://doi.org/10.4324/9781003213321-4
[6] Fascista, A., 2022. Toward Integrated Large-Scale Environmental Monitoring Using WSN/UAV/Crowdsensing: A Review of Applications, Signal Processing, and Future Perspectives. Sensors. 22(5), 1824. DOI: https://doi.org/10.3390/s22051824
[7] Zhao, T., Song, C., Yu, J., et al., 2025. Leveraging Immersive Digital Twins and AI-Driven Decision Support Systems for Sustainable Water Reserves Management: A Conceptual Framework. Sustainability. 17(8), 3754. DOI: https://doi.org/10.3390/su17083754
[8] Russell, B.J., Soffer, R.J., Ientilucci, E.J., et al., 2023. The Ground to Space CALibration Experiment (G-SCALE): Simultaneous Validation of UAV, Airborne, and Satellite Imagers for Earth Observation Using Specular Targets. Remote Sensing. 15(2), 294. DOI: https://doi.org/10.3390/rs15020294
[9] Fraisl, D., Hager, G., Bedessem, B., et al., 2022. Citizen science in environmental and ecological sciences. Nature Reviews Methods Primers. 2(1), 64. DOI: https://doi.org/10.1038/s43586-022-00144-4
[10] Barbierato, E., Gatti, A., 2024. The Challenges of Machine Learning: A Critical Review. Electronics. 13(2), 416. DOI: https://doi.org/10.3390/electronics13020416
[11] Oakes, L.E., Peterson St‐Laurent, G., Cross, M.S., et al., 2022. Strengthening monitoring and evaluation of multiple benefits in conservation initiatives that aim to foster climate change adaptation. Conservation Science and Practice. 4(6), e12688. DOI: https://doi.org/10.1111/csp2.12688
[12] Ncube, M.M., Ngulube, P., 2024. Enhancing environmental decision-making: a systematic review of data analytics applications in monitoring and management. Discover Sustainability. 5(1), 290. DOI: https://doi.org/10.1007/s43621-024-00510-0
[13] Cao, S., Hu, X., Wang, Y., et al., 2023. Understanding Spatial-Temporal Interactions of Ecosystem Services and Their Drivers in a Multi-Scale Perspective of Miluo Using Multi-Source Remote Sensing Data. Remote Sensing. 15(14), 3479. DOI: https://doi.org/10.3390/rs15143479
[14] Kissling, W.D., Lumbierres, M., Lyche Solheim, A., et al., 2026. Building the backbone for Europe’s biodiversity monitoring. Nature Reviews Biodiversity. 2(4), 242–257. DOI: https://doi.org/10.1038/s44358-026-00140-6
[15] Prasath, C.A., 2025. Big Data-Driven Decision Support System for Urban Climate Adaptation Strategies. Journal of Smart Infrastructure and Environmental Sustainability. 2(2), 65–71.
[16] Chisom, O.N., Biu, P.W., Umoh, A.A., et al., 2024. Reviewing the role of AI in environmental monitoring and conservation: A data-driven revolution for our planet. World Journal of Advanced Research and Reviews. 21(1), 161–171. DOI: https://doi.org/10.30574/wjarr.2024.21.1.2720
[17] Martinez, J., Mahajan, S., 2024. Redefining Environmental Governance: The Socio-Technical Implications of Open-Source Air Quality Monitoring. SSRN Preprint. DOI: https://doi.org/10.2139/ssrn.4991177
[18] Karagiannopoulou, A., Tsertou, A., Tsimiklis, G., et al., 2022. Data Fusion in Earth Observation and the Role of Citizen as a Sensor: A Scoping Review of Applications, Methods and Future Trends. Remote Sensing. 14(5), 1263. DOI: https://doi.org/10.3390/rs14051263
[19] Dong, J., Liang, X., Du, B., et al., 2025. Applications of Geographic Information Systems in Ecological Impact Assessment: A Methods Landscape, Practical Bottlenecks, and Future Pathways. Sustainability. 17(22), 10358. DOI: https://doi.org/10.3390/su172210358
[20] Talukder, K.A., 2025. The impact of data-driven decision support systems on governance and policy implementation in US institutions. ASRC Procedia: Global Perspectives in Science and Scholarship. 1(1), 994–1030. DOI: https://doi.org/10.63125/3v98q104
[21] Wang, D., Xu, P.-Y., An, B.-W., et al., 2024. Urban green infrastructure: bridging biodiversity conservation and sustainable urban development through adaptive management approach. Frontiers in Ecology and Evolution. 12, 1440477. DOI: https://doi.org/10.3389/fevo.2024.1440477
[22] Ilcic, A., Fuentes, M., Lawler, D., 2025. Artificial intelligence, complexity, and systemic resilience in global governance. Frontiers in Artificial Intelligence. 8, 1562095. DOI: https://doi.org/10.3389/frai.2025.1562095
[23] Nazarov, D., Sulimin, V., Shvedov, V., 2024. Advancing environmental stewardship: The role of automation in enhanced environmental monitoring. E3S Web of Conferences. 542, 05005. DOI: https://doi.org/10.1051/e3sconf/202454205005
[24] Mollenhauer, H., Kasner, M., Haase, P., et al., 2018. Long-term environmental monitoring infrastructures in Europe: observations, measurements, scales, and socio-ecological representativeness. Science of The Total Environment. 624, 968–978. DOI: https://doi.org/10.1016/j.scitotenv.2017.12.095
[25] Thenkabail, P. (ed.), 2018. Remote Sensing Systems—Platforms and Sensors: Aerial, Satellite, UAV, Optical, Radar, and LiDAR. In Remote Sensing Handbook - Three Volume Set. CRC Press: Boca Raton, FL, USA. pp. 37–92. DOI: https://doi.org/10.1201/b19355-8
[26] Pettorelli, N., Schulte To Bühne, H., Tulloch, A., et al., 2018. Satellite remote sensing of ecosystem functions: opportunities, challenges and way forward. Remote Sensing in Ecology and Conservation. 4(2), 71–93. DOI: https://doi.org/10.1002/rse2.59
[27] Conrad, C.C., Hilchey, K.G., 2011. A review of citizen science and community-based environmental monitoring: issues and opportunities. Environmental Monitoring and Assessment. 176(1–4), 273–291. DOI: https://doi.org/10.1007/s10661-010-1582-5
[28] Brown, G., Rhodes, J., Lunney, D., et al., 2019. The influence of sampling design on spatial data quality in a geographic citizen science project. Transactions in GIS. 23(6), 1184–1203. DOI: https://doi.org/10.1111/tgis.12568
[29] Narayana, T.L., Venkatesh, C., Kiran, A., et al., 2024. Advances in real time smart monitoring of environmental parameters using IoT and sensors. Heliyon. 10(7), e28195. DOI: https://doi.org/10.1016/j.heliyon.2024.e28195
[30] Darling, J.A., Blum, M.J., 2007. DNA-based methods for monitoring invasive species: a review and prospectus. Biological Invasions. 9(7), 751–765. DOI: https://doi.org/10.1007/s10530-006-9079-4
[31] Velmurugan, T., Manimala, V., Parameshwari, V., et al., 2026. Advanced Signal Processing Techniques in Bioacoustics by Elevating Wildlife Monitoring. In: Subbaraj, K., Balusamy, B., Chithirai Pon Selvan, M., et al. (eds.). Computational Bioacoustic Artificial Intelligence. Springer Nature: Cham, Switzerland. pp. 3–23. DOI: https://doi.org/10.1007/978-3-032-06088-4_1
[32] Yoon, H.J., Seo, J.H., Shin, S.H., et al., 2025. Bioinformation and Monitoring Technology for Environmental DNA Analysis: A Review. Biosensors. 15(8), 494. DOI: https://doi.org/10.3390/bios15080494
[33] Tella, A., Mandu, M., 2025. Ensuring Reliable Data: Innovative Approaches to Metadata Research and Practices. Bibliotechnyi visnyk. (4), 132–145. DOI: https://doi.org/10.15407/bv2025.04.132
[34] Reitz, T., Escriu, J., MInghini, M., 2025. Interoperability Approaches for environmental data sharing and reuse in the European Union. Publications Office of the European Union: Luxembourg, Luxembourg.
[35] Kochanek, A., Generowicz, A., Zacłona, T., 2025. The Role of Geographic Information Systems in Environmental Management and the Development of Renewable Energy Sources—A Review Approach. Energies. 18(17), 4740. DOI: https://doi.org/10.3390/en18174740
[36] Janković, S., 2024. Navigating Uncertainties in the Built Environment: Reevaluating Antifragile Planning in the Anthropocene through a Posthumanist Lens. Buildings. 14(4), 857. DOI: https://doi.org/10.3390/buildings14040857
[37] Goho, S.A., 2023. Advances in Air Monitoring Opportunities and Challenges for Addressing Race, Poverty, and Environmental Justice. American Journal of Law and Equality. 3, 53–63. DOI: https://doi.org/10.1162/ajle_a_00054
[38] Zeng, Y., Hao, D., Huete, A., et al., 2022. Optical vegetation indices for monitoring terrestrial ecosystems globally. Nature Reviews Earth & Environment. 3(7), 477–493. DOI: https://doi.org/10.1038/s43017-022-00298-5
[39] Keyhanipour, A.H., 2026. Uncertainty-aware synthetic data generation: a systematic review of methods and challenges. International Journal of Data Science and Analytics. 22(1), 141. DOI: https://doi.org/10.1007/s41060-026-01120-x
[40] Gabriele, M., Brumana, R., Previtali, M., et al., 2023. A combined GIS and remote sensing approach for monitoring climate change-related land degradation to support landscape preservation and planning tools: the Basilicata case study. Applied Geomatics. 15(3), 497–532. DOI: https://doi.org/10.1007/s12518-022-00437-z
[41] Dritsas, E., Trigka, M., 2025. Remote Sensing and Geospatial Analysis in the Big Data Era: A Survey. Remote Sensing. 17(3), 550. DOI: https://doi.org/10.3390/rs17030550
[42] Brunet, L., 2025. Mapping for Mapping’s Sake? Ecosystems Services Maps and the Modes of (Ir)relevance of Ecological Knowledge for Nature Conservation. Minerva. 63(2), 231–255. DOI: https://doi.org/10.1007/s11024-024-09552-0
[43] Koldasbayeva, D., Tregubova, P., Gasanov, M., et al., 2024. Challenges in data-driven geospatial modeling for environmental research and practice. Nature Communications. 15(1), 10700. DOI: https://doi.org/10.1038/s41467-024-55240-8
[44] Jeliazkov, A., Gavish, Y., Marsh, C.J., et al., 2022. Sampling and modelling rare species: Conceptual guidelines for the neglected majority. Global Change Biology. 28(12), 3754–3777. DOI: https://doi.org/10.1111/gcb.16114
[45] Polasky, S., Carpenter, S.R., Folke, C., et al., 2011. Decision-making under great uncertainty: environmental management in an era of global change. Trends in Ecology & Evolution. 26(8), 398–404. DOI: https://doi.org/10.1016/j.tree.2011.04.007
[46] Villegas-Ch, W., García-Ortiz, J., Sánchez-Viteri, S., 2024. Toward Intelligent Monitoring in IoT: AI Applications for Real-Time Analysis and Prediction. IEEE Access. 12, 40368–40386. DOI: https://doi.org/10.1109/ACCESS.2024.3376707
[47] Tironi, M., Rivera Lisboa, D.I., 2023. Artificial intelligence in the new forms of environmental governance in the Chilean State: Towards an eco-algorithmic governance. Technology in Society. 74, 102264. DOI: https://doi.org/10.1016/j.techsoc.2023.102264
[48] Giupponi, C., Ausseil, A.-G., Balbi, S., et al., 2022. Integrated modelling of social-ecological systems for climate change adaptation. Socio-Environmental Systems Modelling. 3, 18161. DOI: https://doi.org/10.18174/sesmo.18161
[49] Little, J.C., Kaaronen, R.O., Hukkinen, J.I., et al., 2023. Earth Systems to Anthropocene Systems: An Evolutionary, System-of-Systems, Convergence Paradigm for Interdependent Societal Challenges. Environmental Science & Technology. 57(14), 5504–5520.
[50] Mir, Y.H., Mir, S., Ganie, M.A., et al., 2025. Overview of Land Use and Land Cover Change and Its Impacts on Natural Resources. In: Jatav, H.S., Raiput, V.D., Minkina, T. (eds.). Ecologically Mediated Development: Promoting Biodiversity Conservation and Food Security. Springer Nature: Singapore. pp. 101–130. DOI: https://doi.org/10.1007/978-981-96-2413-3_5
[51] Padhiary, M., Kumar, R., 2024. Assessing the Environmental Impacts of Agriculture, Industrial Operations, and Mining on Agro-Ecosystems. In: Azrour, M., Mabrouki, J., Alabdulatif, A., et al. (eds.). Smart Internet of Things for Environment and Healthcare. Springer Nature: Cham, Switzerland. pp. 107–126. DOI: https://doi.org/10.1007/978-3-031-70102-3_8
[52] Nugroho, S., Uehara, T., 2023. Systematic Review of Agent-Based and System Dynamics Models for Social-Ecological System Case Studies. Systems. 11(11), 530. DOI: https://doi.org/10.3390/systems11110530
[53] McCarthy, M.J., Herrero, H.V., Insalaco, S.A., et al., 2025. Satellite remote sensing for environmental sustainable development goals: A review of applications for terrestrial and marine protected areas. Remote Sensing Applications: Society and Environment. 37, 101450. DOI: https://doi.org/10.1016/j.rsase.2025.101450
[54] Schönbeck, L., Arteaga, M., Mirza, H., et al., 2023. Plant physiological indicators for optimizing conservation outcomes. Conservation Physiology. 11(1), coad073. DOI: https://doi.org/10.1093/conphys/coad073
[55] Niemeijer, D., De Groot, R.S., 2008. A conceptual framework for selecting environmental indicator sets. Ecological Indicators. 8(1), 14–25. DOI: https://doi.org/10.1016/j.ecolind.2006.11.012
[56] Hazen, E.L., Savoca, M.S., Clark-Wolf, T.J., et al., 2024. Ecosystem Sentinels as Early-Warning Indicators in the Anthropocene. Annual Review of Environment and Resources. 49(1), 573–598. DOI: https://doi.org/10.1146/annurev-environ-111522-102317
[57] Beven, K., 2018. Environmental Modelling: An Uncertain Future? CRC press: London, UK.
[58] Sebok, E., Henriksen, H.J., Pastén-Zapata, E., et al., 2022. Use of expert elicitation to assign weights to climate and hydrological models in climate impact studies. Hydrology and Earth System Sciences. 26(21), 5605–5625. DOI: https://doi.org/10.5194/hess-26-5605-2022
[59] Guillen-Aguinaga, M., Aguinaga-Ontoso, E., Guillen-Aguinaga, L., et al., 2025. Data Quality in the Age of AI: A Review of Governance, Ethics, and the FAIR Principles. Data. 10(12), 201. DOI: https://doi.org/10.3390/data10120201
[60] Nilsson, M., Persson, A., 2003. Framework for analysing environmental policy integration. Journal of Environmental Policy & Planning. 5(4), 333–359. DOI: https://doi.org/10.1080/1523908032000171648
[61] Chatrabhuj, Meshram, K., 2024. Design of an iterative method for environmental-sustainable development: Integrating bioinspired computing techniques. Environmental Development. 51, 101045. DOI: https://doi.org/10.1016/j.envdev.2024.101045
[62] Sarker, S., 2025. Role of management information systems in environmental risk assessment: A systematic review of geographic and ecological applications. American Journal of Interdisciplinary Studies. 6(1), 95–126. DOI: https://doi.org/10.63125/k27tnn83
[63] Filip, F.G., 2022. Collaborative Decision-Making: Concepts and Supporting Information and Communication Technology Tools and Systems. International Journal of Computers Communications & Control. 17(2). DOI: https://doi.org/10.15837/ijccc.2022.2.4732
[64] Yang, Y., Shen, L., Li, Y., et al., 2022. The Impact of Environmental Information Disclosure on Environmental Governance Satisfaction. Sustainability. 14(13), 7888. DOI: https://doi.org/10.3390/su14137888
[65] Shirk, A.J., Jones, G.M., Yang, Z., et al., 2023. Automated habitat monitoring systems linked to adaptive management: a new paradigm for species conservation in an era of rapid environmental change. Landscape Ecology. 38(1), 7–22. DOI: https://doi.org/10.1007/s10980-022-01457-1
[66] Digkoglou, P., Papathanasiou, J., 2025. Application of multiple criteria decision aiding in environmental policy-making processes. International Journal of Environmental Science and Technology. 22(8), 6967–6982. DOI: https://doi.org/10.1007/s13762-024-06101-w
[67] Gupta, P., Tandekar, K., Mirjha, S.T., et al., 2025. Early Warning Systems: Timely Responses to Extreme Weather Events. In Global Climate Stress on Crop Improvement and Management. CRC Press: London, UK. pp. 436–461. DOI: https://doi.org/10.1201/9781003750437-15
[68] Singh, S.K., Gothania, J., Sansaniwal, S.K., 2026. Early Warning Systems and Real-Time Decision Support: Leveraging GIS and Remote Sensing for Global Disaster Preparedness. In: Guney, İ., Celik, M.A. (eds.). Interdisciplinary Studies on Global Disasters. IGI Global Scientific Publishing: Hershey, PA, USA. pp. 373–398. DOI: https://doi.org/10.4018/979-8-3373-6274-8.ch013
[69] Moallemi, E.A., Gao, L., Eker, S., et al., 2022. Diversifying models for analysing global change scenarios and sustainability pathways. Global Sustainability. 5, e7. DOI: https://doi.org/10.1017/sus.2022.7
[70] Flörke, M., Wimmer, F., Laaser, C., et al., 2011. Climate Adaptation–Modelling Water Scenarios and Sectoral Impacts. CESR – Center for Environmental Systems Research: Kassel, Germany.
[71] Gregory, R., Failing, L., Higgins, P., 2006. Adaptive management and environmental decision making: A case study application to water use planning. Ecological Economics. 58(2), 434–447. DOI: https://doi.org/10.1016/j.ecolecon.2005.07.020
[72] Sahu, Y., Upadhyay, V.K., 2024. Environmental Application of Digital Twins: A Review. In: Talukdar, S., Rahman, A., Bera, S., et al. (eds.). Environmental Risk and Resilience in the Changing World. Springer Nature: Cham, Switzerland. pp. 287–295. DOI: https://doi.org/10.1007/978-3-031-62442-1_18
[73] Morchenko, A.A., 2025. Cognitive Efficiency in Environmental Dashboards: Bridging Visual Perception Theory and Spatiotemporal Sustainability Monitoring. SciQuest Research Database. 5(10), 79–86.
[74] Wesselink, A., Buchanan, K.S., Georgiadou, Y., et al., 2013. Technical knowledge, discursive spaces and politics at the science–policy interface. Environmental Science & Policy. 30, 1–9. DOI: https://doi.org/10.1016/j.envsci.2012.12.008
[75] Huitema, D., Turnhout, E., 2009. Working at the science–policy interface: a discursive analysis of boundary work at the Netherlands Environmental Assessment Agency. Environmental Politics. 18(4), 576–594. DOI: https://doi.org/10.1080/09644010903007427
[76] Andersson, K., Boyes, C., Richards, K., 2023. Diagnosing environmental problems and their possible policy responses: A tool for assessing initial options. Elem Sci Anth. 11(1), 00137. DOI: https://doi.org/10.1525/elementa.2022.00137
[77] Keenleyside, K., 2012. Ecological Restoration for Protected Areas: Principles, Guidelines and Best Practices. IUCN (International Union for Conservation of Nature): Gland, Switzerland.
[78] Brock, T., Reed, M.G., Stewart, K.J., 2023. A practical framework to guide collaborative environmental decision making among Indigenous Peoples, corporate, and public sectors. The Extractive Industries and Society. 14, 101246. DOI: https://doi.org/10.1016/j.exis.2023.101246
[79] Cooke, S.J., Cook, C.N., Nguyen, V.M., et al., 2023. Environmental evidence in action: on the science and practice of evidence synthesis and evidence-based decision-making. Environmental Evidence. 12(1), 10. DOI: https://doi.org/10.1186/s13750-023-00302-5
[80] Pujar, G.S., Taori, A., Chakraborty, A., et al., 2024. Sensing Climate Change Through Earth Observations: Perspectives at Global and National Level. In: Priyadarshan, P.M., Jain, S.M., Penna, S., et al. (eds.). Digital Agriculture. Springer International Publishing: Cham, Switzerland. pp. 225–280. DOI: https://doi.org/10.1007/978-3-031-43548-5_8
[81] Aubrecht, C., Özceylan, D., Steinnocher, K., et al., 2013. Multi-level geospatial modeling of human exposure patterns and vulnerability indicators. Natural Hazards. 68(1), 147–163. DOI: https://doi.org/10.1007/s11069-012-0389-9
[82] Sharma, T., Singh, A., Kumar, N., et al., 2023. Emerging Pollutants in the Environment and Ecological Risks. In: George, N., Dwibedi, V., Rath, S.K., et al. (eds.). Management and Mitigation of Emerging Pollutants. Springer International Publishing: Cham, Switzerland. pp. 1–20. DOI: https://doi.org/10.1007/978-3-031-41005-5_1
[83] Arriazu-Ramos, A., Santamaría, J.M., Monge-Barrio, A., et al., 2025. Health Impacts of Urban Environmental Parameters: A Review of Air Pollution, Heat, Noise, Green Spaces and Mobility. Sustainability. 17(10), 4336. DOI: https://doi.org/10.3390/su17104336
[84] Verburg, P.H., Mertz, O., Erb, K.-H., et al., 2013. Land system change and food security: towards multi-scale land system solutions. Current Opinion in Environmental Sustainability. 5(5), 494–502. DOI: https://doi.org/10.1016/j.cosust.2013.07.003
[85] King, D., Gurtner, Y., 2025. Information and communication for disaster resilience. Australian Journal of Emergency Management. 40(3), 17–28. DOI: https://doi.org/10.47389/40.3.17
[86] Tiggeloven, T., Raymond, C., De Ruiter, M.C., et al., 2026. Multi-hazard risk assessment and management: pathways for the Sendai Framework and beyond. Geoscience Communication. 9(2), 185–221. DOI: https://doi.org/10.5194/gc-9-185-2026
[87] Adepoju, A.H., Austin-Gabriel, B., Hamza, O., et al., 2022. Advancing monitoring and alert systems: A proactive approach to improving reliability in complex data ecosystems. IRE Journals. 5(11), 281–282.
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Zhao, F. (2026). From Data to Decisions: Integrative Frameworks for Environmental Monitoring in the Anthropocene. Journal of Environmental & Earth Sciences, 8(8), 986–1010. https://doi.org/10.30564/jees.v8i8.13589
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