Introduction

Natural risk management increasingly depends on the speed with which environmental change can be detected and converted into an operational response. The World Meteorological Organization reported that 2015–2025 were the eleven warmest years in the observational record and that 2025 remained among the three warmest years, at about 1.43 °C above the 1850–1900 average [1]. Heat, drought, intense precipitation and wildfire conditions create interacting pressures on forests, water systems, settlements and critical infrastructure, which raises the value of continuous environmental monitoring (EM). Satellite imagery, meteorological stations, hydrological gauges, unmanned aerial vehicles (UAVs) and field sensors generate increasingly detailed observations, while fragmentation between data sources can delay interpretation and action. Forest-focused engineering studies indicate that prevention requires monitoring to be combined with forecasting of hazardous change rather than limited to retrospective condition assessment [2].

Similar logic applies to urban ecosystems, where resilience to heatwaves, floods and droughts depends on ecological, climatic, socioeconomic and governance variables considered together [3].

The purpose of this article is to determine how EM can be structured as a tool for natural risk management and to identify the technical, analytical, economic and institutional conditions under which monitoring information supports preventive decisions.

Monitoring architecture for natural risk identification

A risk-oriented monitoring architecture begins with the distinction between observing environmental state and identifying a management-relevant deviation. Remote sensing (RS) provides broad spatial coverage for vegetation stress, burned area, flood extent, surface temperature and land-cover change, while ground networks supply higher-frequency local measurements needed for calibration and threshold control.

Geographic information systems (GIS) connect these streams with exposure data, including settlements, transport corridors, protected areas and productive assets. Recent proposals for global environmental early warning emphasize the need to combine satellite, aerial, terrestrial and marine observations in a multi-scale network rather than maintain isolated monitoring platforms [4].

Artificial intelligence (AI) extends this architecture by converting imagery and sensor streams into classifications, anomaly maps and probability estimates. A methodological review of disaster applications shows that deep-learning segmentation is already used for wildfire delineation, flood mapping and earthquake-damage assessment, although model quality remains sensitive to spatial information loss, feature representation and training data [5]. In forest risk management, current research similarly treats ecological monitoring as an input to ignition-risk assessment, fuel-condition analysis and spread forecasting, which shifts the role of EM from description toward prevention support [6].

The functional relationship between monitoring components and management tasks is summarized in Table 1.

Table 1. Monitoring components and their role in natural risk management

Table 1 shows that no single observation channel is sufficient for natural risk management. RS is efficient for regional detection, yet operational decisions often require local measurements and repeated updates. The value of UAVs and sensor networks is strongest where they close temporal or spatial gaps in satellite coverage, while AI provides a common analytical layer for combining heterogeneous signals.

A 2026 wildfire system that integrated satellite imagery, meteorology, topography and machine-learning detection reached an F1-score of 97.1% ± 0.3% and produced calibrated spread forecasts using only information available at the time of issue [7].

Technical performance still has to be interpreted in relation to the decision horizon. A highly accurate map delivered after the response window has limited preventive value, while a moderately uncertain forecast can be useful when probabilities and trigger rules are explicit. This makes latency, update frequency, uncertainty calibration and data continuity as important as classification accuracy.

Environmental signals, early warning and preventive action

Monitoring becomes a risk-management instrument when observations are linked to thresholds, forecasts and predefined actions. The sequence can be expressed as a continuous chain: environmental state measurement, anomaly identification, risk estimation, warning communication, intervention and post-event verification. The global status of multi-hazard early warning systems (MHEWS) illustrates the management significance of this chain.

In 2025, 119 countries, or about 60% of all countries, reported the existence of MHEWS, while disaster-related mortality in countries with more comprehensive systems was nearly six times lower than in countries with limited capabilities [8].

The warning stage requires more than automated detection. A systematic review of AI in early warning systems (EWS) identifies growing use of AI across risk knowledge and forecasting, while gaps persist in policy, responsible deployment and integration with warning dissemination and preparedness [9]. Flood-related evidence reaches a similar conclusion: effective systems depend on continuity, timeliness, transparency, integration, human capacity and governance, rather than forecasting technology alone [10]. This is directly relevant to EM because data that cannot be translated into an understandable warning and assigned response has weak practical value.

Cross-sector digital management research provides an additional organizational lesson. Integrated project information environments based on project management information systems and building information modeling can improve coordination when data exchange, roles and communication procedures are aligned [11].

The same principle can be applied to natural risk management: monitoring platforms should use common spatial references, synchronized timestamps, documented data quality and an agreed escalation route from analyst to responsible authority.

Recent wildfire statistics demonstrate the scale at which this coordination is required. Copernicus reported 2,242,195 ha of burned area and 23,180 fires across Europe, the Middle East and North Africa in 2025; within the European Union, more than one million hectares burned during the most severe fire season recorded by the European Forest Fire Information System [12]. These values support the use of persistent monitoring before peak hazard periods, with attention to vegetation moisture, fuel condition, heat anomalies and weather-driven spread potential rather than fire detection alone.

Risk interpretation must also account for cascading effects. Heat and drought can reduce vegetation resilience, increase fire susceptibility and alter urban thermal conditions, while heavy precipitation can interact with soil saturation, drainage capacity and previous land degradation. An interdisciplinary view of urban ecosystem resilience treats ecological condition, climate exposure, socioeconomic sensitivity and governance capacity as connected dimensions [3]. EM should reflect this structure by combining hazard indicators with exposure and vulnerability layers instead of producing isolated environmental metrics.

Economic and institutional conditions for integrated monitoring

The economic value of EM arises from avoided losses, reduced field-inspection costs, better allocation of response resources and improved timing of preventive measures.

Research on integrated forest monitoring links economic efficiency with earlier detection of resource degradation and the possibility of acting before losses become irreversible or require costly restoration [13]. This shifts evaluation away from the price of sensors or imagery alone and toward the full cost of the risk-management cycle.

Recent empirical evidence provides a measurable example. A 2026 assessment of near real-time hotspot information for forest and land-fire monitoring in Indonesia estimated the net information benefit at 83.33%, while the proportional willingness to pay for the service reached about IDR 5.73 million among responsible users [14]. Reported benefits were associated with system quality, response time, user satisfaction, cost savings and decision support. Such results indicate that higher monitoring expenditure can be economically rational when it reduces uncertainty and operational search costs at the stage where intervention remains possible.

Institutional design determines whether this technical and economic value is realized. Comparative research on standardized project-management methodologies shows that formal adoption is insufficient when procedures are not adapted to the national regulatory and organizational environment [15].

For EM, an analogous requirement is the explicit allocation of data ownership, validation responsibility, warning authority and response obligations. A technically integrated platform can still fail if thresholds have no legal or administrative consequence, agencies use incompatible classifications, or alerts are transmitted without a designated decision-maker.

A practical risk-oriented model can be organized around four requirements: continuous observation of variables connected to plausible hazards; multi-source validation and uncertainty estimation; trigger levels tied to preventive or emergency measures; and post-event assessment that updates thresholds and models. The monitoring program should also be reviewed against avoided-loss indicators, false-alert costs, detection latency and the proportion of warnings that produce timely action. These criteria keep EM connected to management outcomes rather than to the volume of collected data.

Conclusion

Environmental monitoring is most effective in natural risk management when it operates as an end-to-end decision system rather than as a separate observation function. RS, ground sensors, UAVs and AI can improve detection and forecasting, but their contribution depends on data interoperability, calibrated uncertainty, short information latency and explicit links between risk thresholds and preventive measures. Current climate and wildfire data increase the need for such systems, while international evidence on MHEWS shows that comprehensive warning capacity is associated with substantially lower disaster mortality. The analysis also indicates that monitoring effectiveness has an economic and institutional dimension. Integrated data can reduce inspection and response costs and improve the allocation of resources, while formal responsibilities determine whether information leads to action. A mature EM system should consequently be assessed by the quality of risk reduction it enables: the speed of detection, reliability of interpretation, timeliness of response and reduction of expected environmental and economic losses.

1. World Meteorological Organization. State of the Global Climate 2025. Geneva: WMO, 2026. URL: https://wmo.int/publication-series/state-of-global-climate/state-of-global-climate-2025 (accessed: 11.08.2026).
2. Tarsevich A. Engineering methods for monitoring and forecasting the condition of forest areas for the prevention of forest disasters // Technical Sciences: Problems and Solutions: Collection of Articles Based on the Proceedings of the CVIII International Scientific and Practical Conference “Technical Sciences: Problems and Solutions”. 2026. №5(102). P. 289-299.
3. Gorchak D.E. Assessing the resilience of urban ecosystems to extreme climate events: an interdisciplinary approach // Cold Science. 2025. №24. P. 37-44.
4. Wang L., A Y., Wang G., Xue B., Wu J., Wang Y., Wang Q. Building a global early-warning system for environmental risks with remote sensing // Fundamental Research. 2026. Vol. 6. № 2. P. 580-582.
5. Akhyar A., Zulkifley M.A., Lee J., Song T., Han J., Cho C., Hyun S., Son Y., Hong B.-W. Deep artificial intelligence applications for natural disaster management systems: a methodological review // Ecological Indicators. 2024. Vol. 163. P. 112067.
6. Feng S., Liang H., Liu X. From ecological monitoring to prevention decision support: a critical review of artificial intelligence for forest fire prevention // Forests. 2026. Vol. 17. № 7. P. 817.
7. Moldamurat K., La Spada L., Bakyt M. Geospatial system for wildfire monitoring and prediction using aerospace data // Natural Hazards Research. 2026. Vol. 6. № 2. P. 516-533.
8. United Nations Office for Disaster Risk Reduction, World Meteorological Organization. Global Status of Multi-Hazard Early Warning Systems 2025. 2025. URL: https://www.undrr.org/reports/global-status-mhews-2025 (accessed: 11.08.2026).
9. Tiggeloven T., Pfeiffer S., Matanó A., van den Homberg M., Thalheimer L., Reichstein M., Torresan S. The role of artificial intelligence for early warning systems: status, applicability, guardrails, and ways forward // iScience. 2025. Vol. 28. № 11. P. 113689.
10. Painter P., Semmens K., Maxfield K., Cattoën C., Carr R.H. Designing effective flood early warning systems: a review of barriers, best practices, and key characteristics // Journal of Flood Risk Management. 2025. Vol. 18. № 4. P. e70145.
11. Razgonau A. The impact of PMIS and BIM systems on coordination, communication, and productivity in construction projects: a multifactor analysis // International Journal of Civil Engineering and Architecture Engineering. 2026. Vol. 7(1). P. 80-85.
12. Sedano F., Maianti P., Boca R., Suarez-Moreno M., Broglia M., de Rigo D., Roglia E., Branco A., San-Miguel-Ayanz J., Durrant T., Ferrari D., Scionti N., Nikonovas T., Oom D., Artés Vivancos T. Advance report on forest fires in Europe, Middle East and North Africa 2025. Luxembourg: Publications Office of the European Union, 2026.
13. Tarsevich A. Economic efficiency of integrated forest monitoring in the system of resource loss prevention // Economy and Business: Theory and Practice. 2026. Vol. 7(137). P. 211-217.
14. Soleh M., Indradjad A., Diana S.R., Asyiri A., Pertiwi R. Economic value of near real-time hotspot data products for Indonesian forest and land fire monitoring // Kuwait Journal of Science. 2026. Vol. 53. № 3. P. 100585.
15. Razgonau A. Analysis of the integration of the international FIDIC methodology into national construction project management standards: a comparative study // Journal of Economics, Finance And Management Studies. 2026. Vol. 9(3). P. 1428-1434.

Все статьи автора «author98211»