Use of lidar to study changes associated with Spartina invasion in San Francisco Bay marshes

Rosso, Pablo; Ustin, Susan L.; Hastings, Alan

doi:10.1016/j.rse.2005.10.012

Cited by 135 publications

(111 citation statements)

References 24 publications

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“…found that the accuracy that could be reached by vegetation filtering of the point dataset was sufficient for identification of stand expansion and sediment accumulation [50]. In forests, the accuracy of vegetation categorization based on ALS can be similar to this study: separating coniferous and deciduous trees is possible on the basis of full-waveform data with 85% overall accuracy [94], while the identification of the three main deciduous tree genera had an accuracy of 64% [65] (Kappa values not published).…”

Section: Als-based Vegetation Surveysmentioning

confidence: 99%

“…Object-based analysis of derivatives of the point cloud has been successfully used to outline riparian vegetation and streambed extent [49]. Multiple wavelength or multi-temporal surveys have proved to contain sufficient information for vegetation classification beyond the level of growth forms: Spartina stands and the sediment accumulation they facilitate in a saltmarsh environment were successfully outlined using two consecutive ALS surveys [50]. Dual wavelength ALS has been applied by Collin [51] for mapping saltmarsh vegetation (17 categories) and very high accuracy (92%) was reached by multivariate classification of rasterized spectral and spatial ALS products as pseudo-bands.…”

Section: Wetland Vegetation Mapping Based On Als As a Standalone Toolmentioning

confidence: 99%

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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: Als-based Vegetation Surveysmentioning

confidence: 99%

Section: Wetland Vegetation Mapping Based On Als As a Standalone Toolmentioning

confidence: 99%

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

et al. 2012

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“…Worldwide, invasions by Spartina spp. have resulted in altered benthic community structure (Hedge andKriwoken 2000, Neira et al 2006), conversion of low-marsh habitats to high-marsh habitats through increased accretion (Cottet et al 2007), and reduction in mudflats, channels, and shorebird foraging habitats (Callaway and Josselyn 1992, Daehler and Strong 1997, Rosso et al 2006. Invasive Spartina in SF Bay was also projected to negatively impact endangered plant and animal species, delay or prevent native salt marsh restoration or alter restoration trajectory toward non-native habitat conditions, and increase the risk of urban flooding from storm runoff (Strong and Ayres 2013).…”

Section: Invasive Spartina and The California Rail In San Francisco Baymentioning

confidence: 99%

Endangered species management and ecosystem restoration: finding the common ground

Casazza¹,

Overton²,

Bui³

et al. 2016

E&S

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ABSTRACT. Management actions to protect endangered species and conserve ecosystem function may not always be in precise alignment. Efforts to recover the California Ridgway's Rail (Rallus obsoletus obsoletus; hereafter, California rail), a federally and statelisted species, and restoration of tidal marsh ecosystems in the San Francisco Bay estuary provide a prime example of habitat restoration that has conflicted with species conservation. On the brink of extinction from habitat loss and degradation, and non-native predators in the 1990s, California rail populations responded positively to introduction of a non-native plant, Atlantic cordgrass (Spartina alterniflora). California rail populations were in substantial decline when the non-native Spartina was initially introduced as part of efforts to recover tidal marshes. Subsequent hybridization with the native Pacific cordgrass (Spartina foliosa) boosted California rail populations by providing greater cover and increased habitat area. The hybrid cordgrass (S. alterniflora × S. foliosa) readily invaded tidal mudflats and channels, and both crowded out native tidal marsh plants and increased sediment accretion in the marsh plain. This resulted in modification of tidal marsh geomorphology, hydrology, productivity, and species composition. Our results show that denser California rail populations occur in invasive Spartina than in native Spartina in San Francisco Bay. Herbicide treatment between 2005 and 2012 removed invasive Spartina from open intertidal mud and preserved foraging habitat for shorebirds. However, removal of invasive Spartina caused substantial decreases in California rail populations. Unknown facets of California rail ecology, undesirable interim stages of tidal marsh restoration, and competing management objectives among stakeholders resulted in management planning for endangered species or ecosystem restoration that favored one goal over the other. We have examined this perceived conflict and propose strategies for moderating harmful effects of restoration while meeting the needs of both endangered species and the imperiled native marsh ecosystem.

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“…Some biologists and geographers have conducted various researches on ecological characteristics of S. alterniflora and its impacts on environment [1][2][3][4][5][6][7]. In recent years, some evidence has been reported that S. alterniflora could compete with native plants, threaten native ecosystems and coastal aquaculture, and cause declines in local biodiversity [2][3][4]. In invasive studies research, the invasion and control of S. alterniflora has been important and drawn attention in China and abroad [1,5].…”

Section: Introductionmentioning

confidence: 99%

Mapping Spartina alterniflora Biomass Using LiDAR and Hyperspectral Data

Wang

Liu

et al. 2017

Remote Sensing

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Large-scale coastal reclamation has caused significant changes in Spartina alterniflora (S. alterniflora) distribution in coastal regions of China. However, few studies have focused on estimation of the wetland vegetation biomass, especially of S. alterniflora, in coastal regions using LiDAR and hyperspectral data. In this study, the applicability of LiDAR and hypersectral data for estimating S. alterniflora biomass and mapping its distribution in coastal regions of China was explored to attempt problems of wetland vegetation biomass estimation caused by different vegetation types and different canopy height. Results showed that the highest correlation coefficient with S. alterniflora biomass was vegetation canopy height (0.817), followed by Normalized Difference Vegetation Index (NDVI) (0.635), Atmospherically Resistant Vegetation Index (ARVI) (0.631), Visible Atmospherically Resistant Index (VARI) (0.599), and Ratio Vegetation Index (RVI) (0.520). A multivariate linear estimation model of S. alterniflora biomass using a variable backward elimination method was developed with R squared coefficient of 0.902 and the residual predictive deviation (RPD) of 2.62. The model accuracy of S. alterniflora biomass was higher than that of wetland vegetation for mixed vegetation types because it improved the estimation accuracy caused by differences in spectral features and canopy heights of different kinds of wetland vegetation. The result indicated that estimated S. alterniflora biomass was in agreement with the field survey result. Owing to its basis in the fusion of LiDAR data and hyperspectral data, the proposed method provides an advantage for S. alterniflora mapping. The integration of high spatial resolution hyperspectral imagery and LiDAR data derived canopy height had significantly improved the accuracy of mapping S. alterniflora biomass.

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Use of lidar to study changes associated with Spartina invasion in San Francisco Bay marshes

Cited by 135 publications

References 24 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

Endangered species management and ecosystem restoration: finding the common ground

Mapping Spartina alterniflora Biomass Using LiDAR and Hyperspectral Data

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