Mapping salt marsh vegetation using aerial hyperspectral imagery and linear unmixing in Humboldt Bay, California

Judd, Chaeli; Steinberg, Steven J.; Shaughnessy, Frank J.; Crawford, Greg

doi:10.1672/0277-5212(2007)27[1144:msmvua]2.0.co;2

Cited by 26 publications

(21 citation statements)

References 16 publications

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“…Passive and active remote sensing has been widely applied to classify and map coastal wetland vegetation, and been widely reviewed [50][51][52][53]. The broadest extent of literature has emphasized defining individual plant reflectance spectra and optimizing spectral discrimination of vegetation classes [54][55][56][57][58][59][60][61][62][63][64]. Other studies have tested innovative approaches to classifying vegetation, including comparing use of different remote sensing data sources [65][66][67][68][69], testing object-based image analysis techniques [69][70][71][72][73][74][75], and other methods [76][77][78].…”

Section: Invoking Multiple Stable State Theorymentioning

confidence: 99%

Multiple Stable States and Catastrophic Shifts in Coastal Wetlands: Progress, Challenges, and Opportunities in Validating Theory Using Remote Sensing and Other Methods

et al. 2015

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Multiple stable states are established in coastal tidal wetlands (marshes, mangroves, deltas, seagrasses) by ecological, hydrological, and geomorphological feedbacks. Catastrophic shifts between states can be induced by gradual environmental change or by disturbance events. These feedbacks and outcomes are key to the sustainability and resilience of vegetated coastlines, especially as modulated by human activity, sea level rise, and climate change. Whereas multiple stable state theory has been invoked to model salt marsh responses to sediment supply and sea level change, there has been comparatively little empirical verification of the theory for salt marshes or other coastal wetlands. Especially lacking is long-term evidence documenting if or how stable states are established and maintained at ecosystem scales. Laboratory and field-plot studies are informative, but of necessarily limited spatial and temporal scope. For the purposes of long-term, coastal-scale monitoring, remote sensing is the best viable option. This review summarizes the above topics and highlights the emerging promise and challenges of using remote sensing-based analyses to validate coastal OPEN ACCESS Remote Sens. 2015, 7 10185 wetland dynamic state theories. This significant opportunity is further framed by a proposed list of scientific advances needed to more thoroughly develop the field.

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Section: Invoking Multiple Stable State Theorymentioning

confidence: 99%

Multiple Stable States and Catastrophic Shifts in Coastal Wetlands: Progress, Challenges, and Opportunities in Validating Theory Using Remote Sensing and Other Methods

et al. 2015

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“…This can be a limitation, or can be used for species identification by using the differences among species in temporal changes of spectral signatures [14]. In spite of these shortcomings, remote sensing has proved to be a cost-effective tool for studies on vegetation distribution [15,16], and in particular, for the detection and mapping of invasive species [10,[17][18][19][20][21][22][23][24][25][26][27][28]. Some studies have used low spatial resolution images such as Landsat TM [14] or Hyperion [25,29].…”

Section: Introductionmentioning

confidence: 99%

“…However, in the initial stages vegetation patches of invasive species can be small and difficult to detect with medium resolution satellite sensors [30]. So currently, high spatial resolution images from satellite or airborne sensors are needed for early detection [23,24].…”

Section: Introductionmentioning

confidence: 99%

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Hyperspectral Sensors as a Management Tool to Prevent the Invasion of the Exotic Cordgrass Spartina densiflora in the Doñana Wetlands

et al. 2016

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Abstract:We test the use of hyperspectral sensors for the early detection of the invasive denseflowered cordgrass (Spartina densiflora Brongn.) in the Guadalquivir River marshes, Southwestern Spain. We flew in tandem a CASI-1500 (368-1052 nm) and an AHS (430-13,000 nm) airborne sensors in an area with presence of S. densiflora. We simplified the processing of hyperspectral data (no atmospheric correction and no data-reduction techniques) to test if these treatments were necessary for accurate S. densiflora detection in the area. We tested several statistical signal detection algorithms implemented in ENVI software as spectral target detection techniques (matched filtering, constrained energy minimization, orthogonal subspace projection, target-constrained interference minimized filter, and adaptive coherence estimator) and compared them to the well-known spectral angle mapper, using spectra extracted from ground-truth locations in the images. The target S. densiflora was easy to detect in the marshes by all algorithms in images of both sensors. The best methods (adaptive coherence estimator and target-constrained interference minimized filter) on the best sensor (AHS) produced 100% discrimination (Kappa = 1, AUC = 1) at the study site and only some decline in performance when extrapolated to a new nearby area. AHS outperformed CASI in spite of having a coarser spatial resolution (4-m vs. 1-m) and lower spectral resolution in the visible and near-infrared range, but had a better signal to noise ratio. The larger spectral range of AHS in the short-wave and thermal infrared was of no particular advantage. Our conclusions are that it is possible to use hyperspectral sensors to map the early spread S. densiflora in the Guadalquivir River marshes. AHS is the most suitable airborne hyperspectral sensor for this task and the signal processing techniques target-constrained interference minimized filter (TCIMF) and adaptive coherence estimator (ACE) are the best performing target detection techniques that can be employed operationally with a simplified processing of hyperspectral images.

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“…As a consequence new methods have been developed both for monitoring environmental changes and for dynamic adaptation of the crops' technological processes. Prasannakumar et al [11] and Judd et al [7] provide methods to monitor the development and pest influence in a certain crop using for damages evaluations the spectral indices given by remote sensing. A significant contribution in this direction is brought by the monitoring procedures for plant growth that use satellite observations.…”

Section: Introductionmentioning

confidence: 99%

Neuro-fuzzy concepts applied for planning of the cereal crops: applications to the maize hybrids growing in a Romanian region

Constantinescu

2016

Journal of Biological Dynamics

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The aim of our study is to improve the crop planning procedures using neuro-fuzzy concepts. In this paper we design a neuro-fuzzy procedure that offers the suitable maize hybrid, from a set of preferred hybrids, which must be organically farmed in the current year. Our method is a statistical one, on the one hand it processes data provided by the previous years and on the other hand it takes in account the vague character of the environmental factors. Also we present here some experimental results obtained by us on a certain set of real data, results which prove the efficiency of our approach. ARTICLE HISTORY

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Mapping salt marsh vegetation using aerial hyperspectral imagery and linear unmixing in Humboldt Bay, California

Cited by 26 publications

References 16 publications

Multiple Stable States and Catastrophic Shifts in Coastal Wetlands: Progress, Challenges, and Opportunities in Validating Theory Using Remote Sensing and Other Methods

Multiple Stable States and Catastrophic Shifts in Coastal Wetlands: Progress, Challenges, and Opportunities in Validating Theory Using Remote Sensing and Other Methods

Hyperspectral Sensors as a Management Tool to Prevent the Invasion of the Exotic Cordgrass Spartina densiflora in the Doñana Wetlands

Neuro-fuzzy concepts applied for planning of the cereal crops: applications to the maize hybrids growing in a Romanian region

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