Riparian vegetation mapping for hydraulic roughness estimation using very high resolution remote sensing data fusion

Giovanni Forzieri, Gabriele Moser, Enrique Vivoni, Fabio Castelli, Francesco Canovaro

Research output: Contribution to journalArticle

38 Citations (Scopus)

Abstract

For detailed hydraulic modeling, accurate spatial information of riparian vegetation patterns needs to be derived in automatic fashion. We propose a supervised classification for heterogeneous riparian corridors with a low number of spectrally separate classes using data fusion of a Quickbird image and LIDAR data. The approach considers nine land cover classes including three woody riparian species, brush, cultivated areas, grassland, urban infrastructures, bare soil and water. The classical "stacked vector" approach is adopted for data fusion, while the nonparametric weighted feature-extraction method and the pixel-oriented maximum likelihood algorithm are used for feature-reduction and classification purposes, respectively. We test the approach over a 14-km stretch of the Sieve River (Tuscany Region, Italy). A one-dimensional river modeling is applied over the study reach comparing the results of a classification-derived hydraulic roughness map and a traditional ground-based approach. Despite the complex study reach, the classification method produced encouraging accuracies (OKS=0.77) and represents a useful tool to delineate application domains of flow resistance models suited to different hydrodynamic patterns (e.g., stiff/flexible vegetation). Hydraulic modeling results showed that the remotely derived floodplain roughness parameterization captures the equivalent Manning coefficient over 20 test cross sections with uncertainty distributions described by low mean and standard deviation values.

Original languageEnglish (US)
Pages (from-to)855-867
Number of pages13
JournalJournal of Hydraulic Engineering
Volume136
Issue number11
DOIs
StatePublished - Apr 2010

Fingerprint

vegetation mapping
riparian vegetation
Data fusion
roughness
Remote sensing
Surface roughness
Hydraulics
remote sensing
hydraulics
Rivers
modeling
QuickBird
Sieves
Brushes
image classification
bare soil
Parameterization
extraction method
river
Maximum likelihood

Keywords

  • Flow resistance
  • Hydraulic roughness
  • Hydrodynamics
  • Hydrologic models
  • Remote sensing
  • Vegetation

ASJC Scopus subject areas

  • Water Science and Technology
  • Civil and Structural Engineering
  • Mechanical Engineering

Cite this

Riparian vegetation mapping for hydraulic roughness estimation using very high resolution remote sensing data fusion. / Forzieri, Giovanni; Moser, Gabriele; Vivoni, Enrique; Castelli, Fabio; Canovaro, Francesco.

In: Journal of Hydraulic Engineering, Vol. 136, No. 11, 04.2010, p. 855-867.

Research output: Contribution to journalArticle

Forzieri, Giovanni ; Moser, Gabriele ; Vivoni, Enrique ; Castelli, Fabio ; Canovaro, Francesco. / Riparian vegetation mapping for hydraulic roughness estimation using very high resolution remote sensing data fusion. In: Journal of Hydraulic Engineering. 2010 ; Vol. 136, No. 11. pp. 855-867.
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