Evidence map›Paper›PMID 42684487›Full record

ReviewEnvironmental monitoring and assessment2026

Remote sensing for monitoring and managing Eichhornia crassipes: a review of methods, applications, and decision-support frameworks.

Daniel H C Salim, Caio C S Mello, Gabriel Pereira, Raian V Maretto, Camila C Amorim

Abstract readReview
In one paragraph

Review in Environmental monitoring and assessment, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

5 authors.

Daniel H C SalimDepartment of Sanitary and Environmental Engineering, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil.
Caio C S MelloDepartment of Sanitary and Environmental Engineering, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil.
Gabriel PereiraDepartment of Geosciences, Universidade Federal de São João del-Rei, São João del-Rei, Minas Gerais, Brazil.
Raian V MarettoFaculty of Geo-Information Science and Earth Observation, University of Twente, Enschede, the Netherlands.
Camila C AmorimDepartment of Sanitary and Environmental Engineering, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil. camila@desa.ufmg.br.ORCID https://orcid.org/0000-0001-6132-0866

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Eichhornia crassipes (water hyacinth) is among the world's most aggressive aquatic invasive plants, with severe ecological and socioeconomic impacts on freshwater ecosystems. Over the last two decades, remote sensing has emerged as a critical tool for monitoring its spread and supporting management decisions, offering scalable, repeatable, and increasingly precise assessments. This review synthesizes 56 studies published between 2004 and 2025, examining how different platforms (satellites, UAVs, airborne, proximal), sensor types (multispectral, hyperspectral, SAR), and analytical methods (index thresholding, object-based classification, machine learning, deep learning) have been applied to detect, classify, and quantify E. crassipes. We highlight the evolution from early single-platform approaches to recent multimodal frameworks that integrate optical, radar, and UAV data, enabling both large-scale surveillance and fine-resolution diagnostics. Evidence demonstrates that species-level discrimination, phenological tracking, and biomass estimation are now feasible, with promising developments in linking spectral responses to pollutants and stress indicators. Despite these advances, key challenges remain in harmonizing data across platforms, reducing misclassification in mixed vegetation assemblages, and translating remote sensing outputs into decision-relevant information for adaptive management. We propose a decision-support framework built on five strategic pillars: multi-scale sensor fusion, ecologically interpretable classification schemes, scalable analytical ladders, early-warning indicators, and feedback loops linking monitoring to management actions. Together, these directions show how remote sensing can move beyond detection toward more responsive and decision-oriented approaches that support the sustainable management of E. crassipes invasions across diverse aquatic environments.

Indexed as

EichhorniaEnvironmental MonitoringRemote Sensing TechnologyConservation of Natural ResourcesDecision Support TechniquesEcosystemIntroduced SpeciesAdaptive managementAquatic invasive plantsEarly-warning indicatorsFreshwater ecosystemsMulti-sensor integrationSpecies discrimination

Identifiers

PMID42684487
PMCPMC13538195

What OpenQuestion holds

Textmetadata
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.