Evidence map›Paper›PMID 41971784›Full record

ReviewFundamental research2026

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

Libo Wang, Yinglan A, Mimi Gong, Jin Wu, Qiao Wang, Meng Zhang, Yuntao Wang

Abstract readReview
In one paragraph

Review in Fundamental research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Libo WangCollege of Water Sciences, Beijing Normal University, Beijing 100875, China.
Yinglan AAdvanced Interdisciplinary Institute of Satellite Applications, Beijing Normal University, Beijing 100875, China.
Mimi GongAdvanced Interdisciplinary Institute of Satellite Applications, Beijing Normal University, Beijing 100875, China.
Jin WuAdvanced Interdisciplinary Institute of Satellite Applications, Beijing Normal University, Beijing 100875, China.
Qiao WangAdvanced Interdisciplinary Institute of Satellite Applications, Beijing Normal University, Beijing 100875, China.
Meng ZhangAdvanced Interdisciplinary Institute of Satellite Applications, Beijing Normal University, Beijing 100875, China.
Yuntao WangAdvanced Interdisciplinary Institute of Satellite Applications, Beijing Normal University, Beijing 100875, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Biodiversity degradation risksEarly detectionMulti-scale monitoringMulti-source data fusionRemote sensing

Identifiers

PMID41971784
PMCPMC13069837

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.