Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 383

The DOI matches its Crossref record.

Integration of remote sensing data and GIS for accurate mapping of flooded areas

Brivio, P. A., Colombo, R., Maggi, M., Tomasoni, R.·2002·International Journal of Remote Sensing

Abstract

This paper describes a synergetic use of satellite radar images and ancillary information to detect flooded areas at their peak and evaluates its potential with mapping. The procedure was tested on the catastrophic flood that occurred in Regione Piemonte in Italy in November 1994. Two ERS-1 synthetic aperture radar (SAR) images were processed, one acquired one month before the flood and the other acquired three days after the event. Visual interpretation and two different thresholding techniques were performed. The flood map derived shows only a small fraction (20%) of the actually flooded lands because of the time delay between the flood peak and the satellite overpass. To overcome this limitation, the authors developed a new procedure to estimate the flooded area at the peak time by integrating the flooded area from SAR imagery with digital topographic data from a GIS technique. This method allowed inundated areas covering 96.7% of the flooded area officially recorded by the local government to be mapped. The proposed procedure is suitable for mapping flooded areas even when satellite data are acquired some days after the event, thus overcoming the constraint of temporal resolution in the application of SAR imagery in hydrology.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1080/01431160010014729

Cites

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cited by

See all 383

Metadata

Authors
Brivio, P. A., Colombo, R., Maggi, M., & Tomasoni, R.
Venue
International Journal of Remote Sensing
Published
2002-01
Volume
23
Issue
3
Pages
429-441
ISSN
  • 0143-1161 (ISSN-L)
  • 1366-5901
OpenAlex
W2006108538
Cited by
383
References
0
FWCI
4.09
Type
journal article
Language
en
Open access
closed

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: Integration of remote sensing data and GIS for accurate mapping of flooded areas. https://app.cochat.ai/papers/10.1080/01431160010014729
The paper
Brivio, P. A., Colombo, R., Maggi, M., & Tomasoni, R. (2002). Integration of remote sensing data and GIS for accurate mapping of flooded areas. International Journal of Remote Sensing. https://doi.org/10.1080/01431160010014729

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.