Assessment of habitat suitability for waterbirds in the West Songnen Plain, China, using remote sensing and GIS

Dong, Zhicheng; Wang, Zongming; Liu, Dianwei; Li, Lin; Chunying, Ren; Tang, Xuguang; Jia, Mingming; Liu, Chunyue

doi:10.1016/j.ecoleng.2013.02.006

Cited by 74 publications

(44 citation statements)

References 24 publications

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“…Smoothing of the local noise at object level was particularly useful in studies using SAR data to map wetland cover types in Amazon floodplain [74][75][76], Brazilian Pantanal [79,80] and northern forested regions of Canada [27] and Siberia [73]. Radar backscatter may exhibit substantial speckle due to complexity of both horizontal and vertical structure of wetland ecosystems which affects relative contributions of different scattering modes and thus the local variation of the modified signal [37,73].…”

Section: Using Image Segmentation To Address Heterogeneity and Noisementioning

confidence: 99%

“…Depending on the sensor wavelength, polarization, incident angle and structure of vegetation, the backscatter response may be dominated by surface scattering, volume scattering, or by "double-bounce" effect caused by the signal's interaction with multiple surfaces inside deeper canopies, e.g., tree trunks and water in flooded forests [37,[74][75][76]. SAR wavelength plays an important role in object-based classifications; for instance, the shorter-wavelength C-band was useful for detecting herbaceous marsh vegetation [37,78] but had limited sensitivity in forests [37,103]; while the longer L-band wavelength was sensitive to size of scattering elements in woody canopies and thus especially informative in forested wetlands [77,82].…”

Section: Spectral Variablesmentioning

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%

“…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%

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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: Using Image Segmentation To Address Heterogeneity and Noisementioning

confidence: 99%

Section: Spectral Variablesmentioning

confidence: 99%

Section: General Characteristics Of Input Data and Classification Accmentioning

confidence: 99%

Section: Research Objectives and Focusmentioning

confidence: 99%

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Object-Based Image Analysis in Wetland Research: A Review

2015

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“…Several studies have previously used image segmentation and classification in order to perform delimitation of habitats [68][69][70][71][72]. In this study, we propose a two-step methodology based on image segmentation followed by a classification of the resulting segments through hierarchical clustering to delineate habitat functional types (HFTs).…”

Section: Retrieval Of Habitat Functional Typesmentioning

confidence: 99%

Biophysical Characterization of Protected Areas Globally through Optimized Image Segmentation and Classification

et al. 2016

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Protected areas (PAs) need to be assessed systematically according to biodiversity values and threats in order to support decision-making processes. For this, PAs can be characterized according to their species, ecosystems and threats, but such information is often difficult to access and usually not comparable across regions. There are currently over 200,000 PAs in the world, and assessing these systematically according to their ecological values remains a huge challenge. However, linking remote sensing with ecological modelling can help to overcome some limitations of conservation studies, such as the sampling bias of biodiversity inventories. The aim of this paper is to introduce eHabitat+, a habitat modelling service supporting the European Commission's Digital Observatory for Protected Areas, and specifically to discuss a component that systematically stratifies PAs into different habitat functional types based on remote sensing data. eHabitat+ uses an optimized procedure of automatic image segmentation based on several environmental variables to identify the main biophysical gradients in each PA. This allows a systematic production of key indicators on PAs that can be compared globally. Results from a few case studies are illustrated to show the benefits and limitations of this open-source tool.

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Assessing Channel Migration, Bank Erosion Vulnerability and Suitable Human Habitation Sites in the Torsa River Basin of Eastern India Using AHP Model and Geospatial Technology

Dey¹,

Mandal

2022

Geography of the Physical Environment

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Assessment of habitat suitability for waterbirds in the West Songnen Plain, China, using remote sensing and GIS

Cited by 74 publications

References 24 publications

Object-Based Image Analysis in Wetland Research: A Review

Object-Based Image Analysis in Wetland Research: A Review

Biophysical Characterization of Protected Areas Globally through Optimized Image Segmentation and Classification

Assessing Channel Migration, Bank Erosion Vulnerability and Suitable Human Habitation Sites in the Torsa River Basin of Eastern India Using AHP Model and Geospatial Technology

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