Identification and mapping of submerged plants in a shallow lake using quickbird satellite data

Dogan, Ozge Karabulut; Akyürek, Zuhal; Beklioğlu, Meryem

doi:10.1016/j.jenvman.2007.06.022

Cited by 59 publications

(30 citation statements)

References 24 publications

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“…Many remote sensing image classification analysis techniques have been tested, including supervised maximum likelihood classification [16], decision-tree classification [3], approaches based on artificial neural networks and fuzzy logic [17], unsupervised clustering classification [18], and the combination of remote sensing analysis with pre-acquired environmental data [19]. Vegetation indices can be used to analyze the vegetation signal in vegetation monitoring.…”

Section: Introductionmentioning

confidence: 99%

“…In addition, macrophytes and phytoplankton may coexist for a long period of time in certain lakes undergoing eutrophication [9,31,32]. Given the ecological and economic value of aquatic vegetation, the monitoring of aquatic vegetation may be extremely useful in conservation planning and management of eutrophic shallow lakes [18]. Shallow lakes usually exhibit intrinsic spatial and temporal variability in the distribution of underwater light and aquatic vegetation [9,33].…”

Section: Introductionmentioning

confidence: 99%

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Mapping Aquatic Vegetation in a Large, Shallow Eutrophic Lake: A Frequency-Based Approach Using Multiple Years of MODIS Data

et al. 2015

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Aquatic vegetation serves many important ecological and socioeconomic functions in lake ecosystems. The presence of floating algae poses difficulties for accurately estimating the distribution of aquatic vegetation in eutrophic lakes. We present an approach to map the distribution of aquatic vegetation in Lake Taihu (a large, shallow eutrophic lake in China) and reduce the influence of floating algae on aquatic vegetation mapping. the 11 years. Our findings suggest that (1) the proposed approach can be used to map the distribution of aquatic vegetation in eutrophic algae-rich waters and (2) dramatic changes occurred in the distribution of aquatic vegetation in Lake Taihu during the 11-year study.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Mapping Aquatic Vegetation in a Large, Shallow Eutrophic Lake: A Frequency-Based Approach Using Multiple Years of MODIS Data

et al. 2015

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“…Gilmore et al [72] utilized LiDAR and multi-temporal Quickbird imagery with an object-oriented classification approach to classify three marsh vegetation species. Dogan et al [73] and Yuan and Zhang [74] used 2.8 m pixel QuickBird imagery to classify submerged plants within wetlands. Thus, overall the accuracies achieved in this study for classification of within wetland vegetation and open water compare to other studies but, as stated above, the minimum mapping unit of this study is generally much smaller, providing capability to detect and map smaller patches of these features as well as logs that could not be achieved with high-resolution satellite imagery.…”

Section: Discussionmentioning

confidence: 99%

Blanding’s Turtle (Emydoidea blandingii) Potential Habitat Mapping Using Aerial Orthophotographic Imagery and Object Based Classification

Barker

King

2012

Remote Sensing

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Blanding's turtle (Emydoidea blandingii) is a threatened species under Canada's Species at Risk Act. In southern Québec, field based inventories are ongoing to determine its abundance and potential habitat. The goal of this research was to develop means for mapping of potential habitat based on primary habitat attributes that can be detected with high-resolution remotely sensed imagery. Using existing spring leaf-off 20 cm resolution aerial orthophotos of a portion of Gatineau Park where some Blanding's turtle observations had been made, habitat attributes were mapped at two scales: (1) whole wetlands; (2) within wetland habitat features of open water, vegetation (used for camouflage and thermoregulation), and logs (used for spring sun-basking). The processing steps involved initial pixel-based classification to eliminate most areas of non-wetland, followed by object-based segmentations and classifications using a customized rule sequence to refine the wetland map and to map the within wetland habitat features. Variables used as inputs to the classifications were derived from the orthophotos and included image brightness, texture, and segmented object shape and area. Independent validation using field data and visual interpretation showed classification accuracy for all habitat attributes to be generally over 90% with a minimum of 81.5% for the producer's accuracy of logs. The maps for each attribute were combined to produce a habitat suitability map for Blanding's turtle. Of the 115 existing turtle observations, 92.3% were closest to a wetland of the two highest suitability classes. High-resolution imagery combined with object-based classification and habitat suitability mapping methods such as those presented provide a much more spatially OPEN ACCESSRemote Sens. 2012, 4 195 explicit representation of detailed habitat attributes than can be obtained through field work alone. They can complement field efforts to document and track turtle activities and can contribute to species inventory planning, conservation, and management.

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“…Other studies conducting accuracy assessments [31,38,39,[60][61][62] show kappa coefficients ranging between 0.57 and 0.92. The result of our study is at the lower end of the scale (kappa = 0.61).…”

Section: Evaluation Of Sav Mappingmentioning

confidence: 99%

“…Several studies therefore evaluate mappings qualitatively [16,19,27,59]. Studies which determined discrete classes (e.g., less dense SAV, bare substrate, submerged, floating vegetation) collected field data by boat or ancillary maps to tabulate error matrices and associated accuracy measures [38,39,[60][61][62]. Dekker et al [26] recommend a minimum patch size of at least three times the pixel size covering homogenous coverage.…”

Section: Evaluation Of Sav Mappingmentioning

confidence: 99%

Mapping Submerged Aquatic Vegetation Using RapidEye Satellite Data: The Example of Lake Kummerow (Germany)

Fritz

Dörnhöfer

Schneider

et al. 2017

Water

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Submersed aquatic vegetation (SAV) is sensitive to changes in environmental conditions and plays an important role as a long-term indictor for the trophic state of freshwater lakes. Variations in water level height, nutrient condition, light availability and water temperature affect the growth and species composition of SAV. Detailed information about seasonal variations in littoral bottom coverage are still unknown, although these effects are expected to mask climate change-related long-term changes, as derived by snapshots of standard monitoring methods included in the European Water Framework Directive. Remote sensing offers concepts to map SAV quickly, within large areas, and at short intervals. This study analyses the potential of a semi-empirical method to map littoral bottom coverage by a multi-seasonal approach. Depth-invariant indices were calculated for four Atmospheric & Topographic Correction (ATCOR2) atmospheric corrected RapidEye data sets acquired at Lake Kummerow, Germany, between June and August 2015. RapidEye data evaluation was supported by in situ measurements of the diffuse attenuation coefficient of the water column and bottom reflectance. The processing chain was able to differentiate between SAV and sandy sediment. The successive increase of SAV coverage from June to August was correctly monitored. Comparisons with in situ and Google Earth imagery revealed medium accuracies (kappa coefficient = 0.61, overall accuracy = 72.2%). The analysed time series further revealed how water constituents and temporary surface phenomena such as sun glint or algal blooms influence the identification success of lake bottom substrates. An abundant algal bloom biased the interpretability of shallow water substrate such that a differentiation of sediments and SAV patches failed completely. Despite the documented limitations, mapping of SAV using RapidEye seems possible, even in eutrophic lakes.

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Identification and mapping of submerged plants in a shallow lake using quickbird satellite data

Cited by 59 publications

References 24 publications

Mapping Aquatic Vegetation in a Large, Shallow Eutrophic Lake: A Frequency-Based Approach Using Multiple Years of MODIS Data

Mapping Aquatic Vegetation in a Large, Shallow Eutrophic Lake: A Frequency-Based Approach Using Multiple Years of MODIS Data

Blanding’s Turtle (Emydoidea blandingii) Potential Habitat Mapping Using Aerial Orthophotographic Imagery and Object Based Classification

Mapping Submerged Aquatic Vegetation Using RapidEye Satellite Data: The Example of Lake Kummerow (Germany)

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