Early detection of biodiversity degradation risks: The critical role of remote sensing

Abstract

The latest UN environmental report warns that the world is facing three interconnected crises: climate change, environmental pollution, and biodiversity loss. Notably, under the dual pressures of extreme weather events and long-term environmental pollution, both the rate of species extinction and the extent of habitat degradation have reached unprecedented levels. Efficient monitoring and prediction of high-risk areas and critical periods are essential to mitigating biodiversity loss. Traditional methods, which rely heavily on in-situ ground observations, often provide only localized snapshots of ecosystem change. These approaches fall short in addressing the need for global, cross-scale identification and early warning of biodiversity degradation risks. A multi-scale remote sensing network offers a powerful solution, enabling early detection and timely mitigation by integrating satellite observations, aerial drones, ground-based monitoring stations, and oceanic sensors. The fusion of complementary data sources enhances both spatial resolution and coverage, allowing for dynamic tracking of biodiversity across scales. To operationalize such a system globally, international collaboration, open data access, standardized indicators, and strategic technological investments are crucial. Taken together, this integrated framework will strengthen real-time ecological forecasting and support proactive policy and conservation responses.

1. A planetary risks of biodiversity loss

Recent assessments from the United Nations warn of accelerating global environmental degradation: approximately 75% of terrestrial land has been significantly altered, 66% of marine ecosystems are experiencing intensifying cumulative pressures, and > 85% of wetland areas have been lost. These staggering changes are contributing to an unprecedented decline in global biodiversity—eroding ecological stability and undermining human well-being. Under the dual stressors of climate change and land-use transformation, terrestrial ecosystems have witnessed a marked decline in biodiversity, resulting in a cumulative loss of nearly 14.6 gigatonnes of carbon storage capacity globally [1]. Projections suggest that by 2070, 33%–68% of terrestrial ecosystems could undergo major shifts in plant community composition, further threatening ecosystem functions such as water purification, soil stabilization, and habitat provision [2].

Freshwater ecosystems are similarly in decline: over 85% of global wetlands have been lost since the Industrial Revolution, dramatically weakening their capacity for flood mitigation, nutrient cycling, and water filtration [3]. In the marine domain, live coral cover has plummeted by almost 50% since the 1950s, imperiling one of the most biodiverse ecosystems on the planet [4].

Critically, these ecological losses are manifest across all scales. According to the World Wide Fund for Nature (WWF), populations of monitored vertebrate species declined by an average of 69% between 1970 and 2018, with Latin America and the Caribbean experiencing losses as high as 94% [3]. At the national level, China exemplifies this trend: nearly 90% of natural grasslands have experienced degradation, 53% of coastal wetlands have disappeared, and over 80% of coral reefs have been severely damaged since the 1950s [5]. The Amazon rainforest—one of the most critical global biodiversity hotspots—has retained only 36% of its original forest cover, and remains under imminent threat from land conversion, illegal logging, and intensifying climatic extremes [6].

These cross-scale biodiversity losses point toward a potential ecological tipping point, beyond which restoration may become infeasible. To prevent further systemic collapse, the global community must act with urgency. However, current biodiversity monitoring efforts remain fragmented, reactive, and uneven in coverage—lacking the capacity to detect early-warming signals and inform timely interventions. As such, there is a pressing need for an integrated, scalable, and proactive biodiversity monitoring framework. Leveraging the spatiotemporal reach of remote sensing technologies provides a crucial opportunity to bridge these gaps. It offers a pathway toward building a global early warning system capable of anticipating risks and enabling conservation before irreversible damage occurs.

2. Remote sensing potential and limits

Remote sensing has greatly advanced biodiversity monitoring by enabling frequent, scalable, and non-invasive observation of ecosystems. High-resolution optical and radar imagery now support near real-time tracking of deforestation, coral bleaching, and habitat loss across broad regions. Recent studies have pushed technical boundaries; for example, combining satellite-derived land-use data with GPS telemetry enabled fine-resolution assessments of species exposure across nine mammal species in Africa, at 30-meter spatial and daily temporal resolution [7]. In marine systems, the Reef Cover framework, applied to Landsat data, mapped over 1900 km² of coral reefs in the Great Barrier Reef and processed 3 million km² of spectral data across the Pacific [8]. These case studies highlight the growing capacity of remote sensing to capture biodiversity patterns across spatial scales.

However, despite this progress, key limitations persist across three dimensions. First, data fusion across platforms and scales remains a significant challenge. Disparities in spatial resolution, revisit frequency, and biological observability between satellite, drone, ground, and marine systems often prevent seamless integration. This limits the ability to synchronously track biodiversity dynamics from individuals to landscapes. For example, remote sensing cannot directly observe cryptic or microhabitat-dwelling taxa such as soil fauna or understorey amphibians [9].

Second, feature extraction methods are still constrained. While AI-driven ecological parameter inversion shows promise, most models require vast training datasets and struggle with transferability across ecosystems [10]. Furthermore, most remote sensing indicators remain indirect remotely sensed proxies for biodiversity—necessitating ground-truthing, which is often lacking in data-poor regions.

Third, real-time early warning remains underdeveloped. Traditional model-based forecasting relies heavily on precise boundary conditions and environmental inputs, which can be difficult to obtain at large scales. Although remote sensing–AI hybrid approaches are emerging, linking image inversion, real-time monitoring, and risk tracing, their operational implementation is still in its early stages.

These challenges raise critical questions: Can conservation actions keep pace with the accelerating rate of biodiversity loss? Are current tools sufficient to detect early warning signals of ecological collapse amid rapid environmental change? While remote sensing opens unprecedented monitoring possibilities, building an effective early warning system will require multi-source data fusion, model innovation, and strong policy support.

3. Toward a multi-scale early-detection system

To overcome these challenges, the conservation community must move toward an integrated, multi-scale biodiversity monitoring framework. This entails linking observations from satellites, airborne platforms, ground stations and ocean-based sensors into a unified, interoperable network. Each data source complements the others; for example, broad satellite maps can guide ground-based surveys and drone flights, while field data can, in turn, calibrate and validate the satellite-derived models. By combining these observation scales, the system bridges resolution gaps and achieves both the breadth of global coverage and the granularity of local ecological processes—thus laying the foundation for a scale-matching mechanism (Fig. 1).

Fig. 1.

Construction of a Cross-Scale Biodiversity Monitoring Network Integrating Space, Sky, Land, and Ocean (SSEO).

Building a global biodiversity early warning system requires coordinated efforts across multiple fronts. Some key components include:

Multi-platform monitoring must integrate satellite observations, aerial drones, ecological plots, acoustic sensors, and even repurposed infrastructures, such as weather radars. These platforms occupy complementary spatio-temporal niches, and together they enable a near-real-time, cross-validated picture of ecosystem dynamics.

Advanced analytics for risk prediction, including AI and machine learning, can process large volumes of data to detect early signs of biodiversity loss. By jointly analyzing habitat shifts, climatic anomalies, and species distribution patterns, such tools can anticipate emerging risks and forecast tipping points.

Standardized metrics and open data are crucial for ensuring interoperability. Building on efforts like Essential Biodiversity Variables, global standards can enable consistent measurement and reporting across platforms. Equally important are data-sharing infrastructures, ensuring observations from remote regions are accessible to scientists and decision-makers worldwide.

International collaboration and policy alignment must underpin the entire framework. Long-term funding, capacity building in under-monitored regions, and synergy with frameworks like the Kunming-Montreal Global Biodiversity Framework are crucial to operationalizing early warnings at scale.

By implementing these steps, we can shift conservation from a reactive mode to a proactive one. Instead of simply documenting extinctions and habitat loss after the fact, an early warning system would allow us to anticipate problems and take preventive action. For instance, if remote sensing data indicate a rapid drying of wetlands or a sharp decline in forest greenness, conservation agencies could be alerted to intervene or investigate causes immediately. Over time, this approach will minimize the magnitude and spatial extent of biodiversity losses by addressing them before they escalate.

Remote sensing offers an unprecedented opportunity to safeguard global biodiversity. The future of biodiversity conservation lies in harnessing this technology-driven, multi-scale approach, one that enables us to detect early warnings, mobilize resources swiftly, and ultimately steer our planet’s ecosystems away from collapse. Achieving this vision will require strong international collaboration and the political will to invest in our planet’s life support systems. A narrow window remains for effective intervention; yet, the establishment of a global biodiversity early warning system offers a vital pathway to enhance ecological risk detection, guide science-based decision-making, and support proactive conservation and governance responses at multiple scales.

CRediT authorship contribution statement

Libo Wang: Writing – review & editing, Writing – original draft, Validation, Formal analysis, Data curation, Conceptualization. Yinglan A: Writing – review & editing, Writing – original draft, Funding acquisition, Formal analysis, Data curation, Conceptualization. Mimi Gong: Writing – review & editing, Formal analysis. Jin Wu: Writing – review & editing, Writing – original draft. Qiao Wang: Writing – review & editing, Writing – original draft, Conceptualization. Meng Zhang: Writing – review & editing. Yuntao Wang: Writing – review & editing.

Declaration of competing interest

The authors declare that they have no conflicts of interest in this work.

Biographies

Libo Wang is a Ph.D. candidate at the College of Water Sciences, Beijing Normal University. Her research focuses on ecohydrological modeling, hydrological model development, and environmental risk simulation and early detection based on remote sensing.

Yinglan A (BRID:02896.00.52367) is the lecture at the Advanced Interdisciplinary Institute of Satellite Applications, Faculty of Geographical Science, Beijing Normal University. Her research focuses on ecohydrology, watershed modeling, river ecosystems, and environmental risks. In 2024, he received the Young Scientists Fund (Category B) from the National Natural Science Foundation of China.