Analyzing the habitat suitability for migratory birds at the Chongming Dongtan Nature Reserve in Shanghai, China

Tian, Bo; Zhou, Yunxuan; Zhang, Liquan; Lin, Yu‐Ching

doi:10.1016/j.ecss.2008.08.014

Cited by 93 publications

(54 citation statements)

References 19 publications

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“…Typological classification of delineated wetland units: assignment of mapped wetland bodies into hydrological, geomorphological and ecological categories without detailed mapping of within-wetland cover [6,27,29,37,43,44,55,65,66,73,79,80]; (3). Classification of within-wetland cover types and/or vegetation: mapping within-wetland surface composition and vegetation types, sometimes targeting specific classes such as invasive plant species (e.g., [5,8,12,13,17,[23][24][25]28,30,32,34,41,45,[48][49][50]52,53,57,68,69,[74][75][76][77][79][80][81][82][83][84][85]); (4). Analysis of wetland change over a particular period of time...…”

Section: Research Objectives and Focusmentioning

confidence: 99%

“…Seventeen studies used the data from synthetic aperture radar (SAR) either as the primary input or in combination with Landsat or other sensors [27,37,40,43,47,53,54,71,[73][74][75][76][77][78][79][80]82] Natural log of pixel size in m overall accuracy less than 85% overall accuracy equal or greater than 85% overall accuracy not reported and ALOS PALSAR [40,79,80]; spatial resolution of these datasets varied between 10 and 100 m. Radar images as inputs to OBIA have been especially critical for wetland analyses in humid regions with frequent cloud cover limiting the availability of multi-spectral images and the consistency of seasonal time series [37,75,82]. Several high-resolution studies using multi-spectral imagery additionally incorporated light detection and ranging (lidar) datasets to facilitate the overall wetland classification or discrimination of specific classes and landscape features [6,25,28,32,59,71,77,80,89].…”

Section: General Characteristics Of Input Data and Classification Accmentioning

confidence: 99%

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Object-Based Image Analysis in Wetland Research: A Review

2015

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Abstract:The applications of object-based image analysis (OBIA) in remote sensing studies of wetlands have been growing over recent decades, addressing tasks from detection and delineation of wetland bodies to comprehensive analyses of within-wetland cover types and their change. Compared to pixel-based approaches, OBIA offers several important benefits to wetland analyses related to smoothing of the local noise, incorporating meaningful non-spectral features for class separation and accounting for landscape hierarchy of wetland ecosystem organization and structure. However, there has been little discussion on whether unique challenges of wetland environments can be uniformly addressed by OBIA across different types of data, spatial scales and research objectives, and to what extent technical and conceptual aspects of this framework may themselves present challenges in a complex wetland setting. This review presents a synthesis of 73 studies that applied OBIA to different types of remote sensing data, spatial scale and research objectives. It summarizes the progress and scope of OBIA uses in wetlands, key benefits of this approach, factors related to accuracy and uncertainty in its applications and the main research needs and directions to expand the OBIA capacity in the future wetland studies. Growing demands for higher-accuracy wetland characterization at both regional and local scales together with advances in very high resolution remote sensing and novel tasks in wetland restoration monitoring will likely continue active exploration of the OBIA potential in these diverse and complex environments.

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Section: Research Objectives and Focusmentioning

confidence: 99%

Section: General Characteristics Of Input Data and Classification Accmentioning

confidence: 99%

Object-Based Image Analysis in Wetland Research: A Review

2015

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“…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]. In addition to automated analysis methods, vegetation classes have long been digitized by hand [79][80][81].…”

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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“…Water level may be such a key missing factor which often determines spatial location of waterbirds within the study area and access to food resources [8,43,45]; however, spatially explicit water level data have not been monitored at the sub-lake scale in the Poyang Lake region. Finally, study results could be affected by more general constraints of remote sensing analyses in wetlands, such as heterogeneity of wetland surface as a challenge to classification accuracy that may be partially addressed by OBIA [29,57,85] and relatively coarse resolution of 30-m Landsat data obscuring local patterns of vegetation composition and micro-topography [57].…”

Section: Uncertainties In Models and Study Limitationsmentioning

confidence: 99%

“…Novel object-based image analysis (OBIA) methods and machine learning image classification algorithms offer promise to enhance the quality of wetland cover mapping and interpretation, although these techniques are still relatively under-utilized [29][30][31][32]. Another caveat to spatial analyses of ecosystem and habitat properties is presented by potential spatial dependence (autocorrelation), which may violate the assumptions of independent and identically distributed errors in landscape models but in some cases indicate ecological processes such as dispersal or species interactions [33][34][35][36][37][38][39].…”

Section: Introductionmentioning

confidence: 99%

Landscape-Level Associations of Wintering Waterbird Diversity and Abundance from Remotely Sensed Wetland Characteristics of Poyang Lake

et al. 2016

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Poyang Lake, the largest freshwater wetland in China, provides critical habitat for wintering waterbirds from the East Asian Flyway; however, landscape drivers of non-uniform bird diversity and abundance are not yet well understood. Using a winter 2006 waterbird survey, we examined the relationships among metrics of bird community diversity and abundance and landscape characteristics of 51 wetland sub-lakes derived by an object-based classification of Landsat satellite data. Relative importance of predictors and their sets was assessed using information-theoretic model selection and the Akaike Information Criterion. Ordinary least squares regression models were diagnosed and corrected for spatial autocorrelation using spatial autoregressive lag and error models. The strongest and most consistent landscape predictors included Normalized Difference Vegetation Index for mudflat (negative effect) and emergent grassland (positive effect), total sub-lake area (positive effect), and proportion of submerged vegetation (negative effect). Significant spatial autocorrelation in linear regression was associated with local clustering of response and predictor variables, and should be further explored for selection of wetland sampling units and management of protected areas. Overall, results corroborate the utility of remote sensing to elucidate potential indicators of waterbird diversity that complement logistically challenging ground observations and offer new hypotheses on factors underlying community distributions.

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Analyzing the habitat suitability for migratory birds at the Chongming Dongtan Nature Reserve in Shanghai, China

Cited by 93 publications

References 19 publications

Object-Based Image Analysis in Wetland Research: A Review

Object-Based Image Analysis in Wetland Research: A Review

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

Landscape-Level Associations of Wintering Waterbird Diversity and Abundance from Remotely Sensed Wetland Characteristics of Poyang Lake

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