ArticleEnvironmental monitoring and assessment2020
Assessment of volunteered geographic information for vegetation mapping.
Article in Environmental monitoring and assessment, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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.
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.
Who cites it
2 citing papers in PubMed.
- Mapping wild vascular plant species diversity in urban areas in California using crowdsourcing data by regression kriging: Examining socioeconomic disparities.The Science of the total environment · 2023Article
- Mask R-CNN and OBIA Fusion Improves the Segmentation of Scattered Vegetation in Very High-Resolution Optical Sensors.Sensors (Basel, Switzerland) · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Vegetation mapping requires extensive field data for training and validation. Volunteered geographic information in the form of geotagged photos of identified plants has the potential to serve as a supplemental data source for vegetation mapping projects. In this study, we compare the locations of specific taxa from the iNaturalist platform to locations identified on both a fine-scale vegetation map and high-resolution ortho-imagery in open-canopy shrubland in San Clemente Island, CA. Due to positional uncertainty associated with the iNaturalist observations, as well as the presence-only nature of the data, it was not possible to perform a traditional accuracy assessment. We instead measured the distance between the location recorded by an iNaturalist observer for a given taxon and the closest mapped individual of that taxon. This distance was within 10 m for a majority of the observations (64%). When comparing the iNaturalist location to the closest individual detected through image interpretation, 87% of the observations were within 10 m. The discrepancy in agreement between the vegetation map and imagery is likely due to mapping errors. While iNaturalist data come with important limitations, the platform is an excellent resource for supporting vegetation mapping and other ecological applications.
Indexed as
Identifiers
32737593What OpenQuestion holds
Registered trials
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.