Mapping mixed vegetation communities in salt marshes using airborne spectral data

Wang, Cheng; Menenti, Massimo; Stoll, M. P.; Belluco, Enrica; Marani, Marco

doi:10.1016/j.rse.2006.10.007

Cited by 69 publications

(51 citation statements)

References 30 publications

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“…The usual method for quality control is cross validation: separating the ground truth polygon dataset, reserving some pixels for automatic validation and using the rest for calibration [30,56,89]. In our case, a different approach was chosen because the relatively low spatial resolution (2.5 × 2.5 m) of the output wetland classification raster restricted the number of pixels available from the 10 × 10 m area of the ground truth polygons.…”

Section: Discussion Of Quality Control Methodsmentioning

confidence: 99%

Categorizing Wetland Vegetation by Airborne Laser Scanning on Lake Balaton and Kis-Balaton, Hungary

et al. 2012

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Outlining patches dominated by different plants in wetland vegetation provides information on species succession, microhabitat patterns, wetland health and ecosystem services. Aerial photogrammetry and hyperspectral imaging are the usual data acquisition methods but the application of airborne laser scanning (ALS) as a standalone tool also holds promises for this field since it can be used to quantify 3-dimensional vegetation structure. Lake Balaton is a large shallow lake in western Hungary with shore wetlands that have been in decline since the 1970s. In August 2010, an ALS survey of the shores of Lake Balaton was completed with 1 pt/m 2 discrete echo recording. The resulting ALS dataset was processed to several output rasters describing vegetation and terrain properties, creating a sufficient number of independent variables for each raster cell to allow for basic multivariate classification. An expert-generated decision tree algorithm was applied to outline wetland areas, and within these, patches dominated by Typha sp. Carex sp., and Phragmites australis. Reed health was mapped into four categories: healthy, stressed, ruderal and die-back. The output map was tested against a set of 775 geo-tagged ground photographs and had a user's accuracy of >97% for detecting non-wetland features (trees, artificial surfaces and low density Scirpus stands), >72% for dominant genus detection and >80% for most reed health categories (with 62% for one category). Overall classification OPEN ACCESSRemote Sens. 2012, 4 1618 accuracy was 82.5%, Cohen's Kappa 0.80, which is similar to some hyperspectral or multispectral-ALS fusion studies. Compared to hyperspectral imaging, the processing chain of ALS can be automated in a similar way but relies directly on differences in vegetation structure and actively sensed reflectance and is thus probably more robust. The data acquisition parameters are similar to the national surveys of several European countries, suggesting that these existing datasets could be used for vegetation mapping and monitoring.

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Section: Discussion Of Quality Control Methodsmentioning

confidence: 99%

Categorizing Wetland Vegetation by Airborne Laser Scanning on Lake Balaton and Kis-Balaton, Hungary

et al. 2012

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“…Training data that are not carefully selected may introduce error. Collection of training data is the crucial step for image classification and it directly influences the classification accuracy (Wang et al, 2007 attribute of a training set. Some of the literature suggests the use of a minimum of 10-30p cases per-class for training, where p is the number of wavebands used (Piper, 1992& VanNiel et al, 2005.…”

Section: Training Areas Developmentmentioning

confidence: 99%

Analysis of Land Cover Classification in Arid Environment: A Comparison Performance of Four Classifiers

Mustapha¹,

Lim²,

MatJafri³

2012

Remote Sensing of Planet Earth

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“…The evaluation of new neural network classifier introduced by (Wang et al, 2007) by using spectrally sampled image data to map mixed halophytic vegetation in tidal environments. The work is based on the concept of vegetation communities, mixtures of several species, characteristic of salt marshes.…”

Section: Airborne Hyperspectral Imaging For Different Applicationsmentioning

confidence: 99%

Mapping of Individual Oil Palm Trees Using Airborne Hyperspectral Sensing: An Overview

Jusoff¹,

Pathan²

2009

APR

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This overview represents a preamble step for developing an approach for mapping individual oil palm trees from airborne hyperspectral imaging. The study generally describes airborne hyperspectral sensors in different fields particularly in agriculture by comparing and analyzing their uniqueness for different applications. The emphasis is on the image processing in identifying and mapping of the individual oil palm trees with the utilization of image histogram to examine the RGB bands. An algorithm is design to discover the involvement of different materials in a single mixed pixel and converting it into a pure pixel. The techniques employ in this connection are Linear Spectral Mixture Analysis (LSMA), Mix to Pure Converter (MPC) and Euclidean Norm.

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Mapping mixed vegetation communities in salt marshes using airborne spectral data

Cited by 69 publications

References 30 publications

Categorizing Wetland Vegetation by Airborne Laser Scanning on Lake Balaton and Kis-Balaton, Hungary

Categorizing Wetland Vegetation by Airborne Laser Scanning on Lake Balaton and Kis-Balaton, Hungary

Analysis of Land Cover Classification in Arid Environment: A Comparison Performance of Four Classifiers

Mapping of Individual Oil Palm Trees Using Airborne Hyperspectral Sensing: An Overview

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