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IEEE Draft Guide for the Design, Construction, and Operation of Electric Power Substations for Community Acceptance and Environmental Compatibility

IEEE P1127/D8, September 2013, 2013

Significant community acceptance and environmental compatibility items to be considered during the planning and design phases, the construction period, and the operation of electric supply substations are identified, and ways to address these concerns to obtain community acceptance and environmental compatibility are documented. On-site generation and telecommunication facilities are not considered.


Automated extraction of inland surface water extent from Sentinel-1 data

2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2017

Two automated approaches, including Bayesian probability thresholding and regression tree based methods were utilized to detect the surface water extent with training dataset from prior class probabilities of water and non-water from two datasets. First, prior water and non-water masks were classified using SRTM Water Body Dataset (SWBD) and long-term summarized Dynamic Surface Water Extent (DSWE) class probabilities. Then, fully ...


Applying remote sensing to urban ecosystem dynamics: Opportunities for understanding and managing the ballona wetland system in Los Angeles

2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2017

Anthropogenic impacts on ecosystems are prevalent and pose significant risks to vulnerable systems. In particular, wetland ecosystems are often found in urban centers with dense populations and rapid changes. Wetlands provide opportunities to study impacts of rapid urbanization and expansion, climate effects and pollution. The outcomes of human activities directly impact wetlands through coupled human-natural processes, including activities such as ...


Monitoring floods in the Kafue flats with TanDEM-X data

2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2015

Wetlands are very valuable ecosystem which may present fast dynamics. This study is focused on the observation of the Kafue Flats exploiting TanDEM-X polarimetric and interfer-ometric data. Several analysis are carried out considering classification, change detection and Digital Elevation Model (DEM) estimation. Specifically, it is possible to observe that differences between single-pass DEM acquired during different dates can provide promising ...


The study of SROI of the Cheng-Long Wetlands International Environmental Art Project and Setouchi Triennale Art Festival

2017 International Conference on Applied System Innovation (ICASI), 2017

The world is currently facing serious social and environmental problems, causing an upward trend in social design. The Cheng-Long Wetlands International Environmental Art Project, in Taiwan's Yunlin County, attempts to promote awareness of the environment in the local community. The Setouchi Triennale in Japan encourages tourism on the islands of the Seto Sea, regeneration of the local environment as well ...


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IEEE-USA E-Books

  • IEEE Draft Guide for the Design, Construction, and Operation of Electric Power Substations for Community Acceptance and Environmental Compatibility

    Significant community acceptance and environmental compatibility items to be considered during the planning and design phases, the construction period, and the operation of electric supply substations are identified, and ways to address these concerns to obtain community acceptance and environmental compatibility are documented. On-site generation and telecommunication facilities are not considered.

  • Automated extraction of inland surface water extent from Sentinel-1 data

    Two automated approaches, including Bayesian probability thresholding and regression tree based methods were utilized to detect the surface water extent with training dataset from prior class probabilities of water and non-water from two datasets. First, prior water and non-water masks were classified using SRTM Water Body Dataset (SWBD) and long-term summarized Dynamic Surface Water Extent (DSWE) class probabilities. Then, fully automatic algorithms were developed to derive water probability and classify surface water extent using Sentinel-1 data. Results over three representative study regions, including the Delmarva Peninsula, Florida Everglades and Prairie Pothole regions, indicate that the automated algorithm is efficient in monitoring open water inundation extent, and detection of partial water extent is possible using Sentienl-1 SAR data.

  • Applying remote sensing to urban ecosystem dynamics: Opportunities for understanding and managing the ballona wetland system in Los Angeles

    Anthropogenic impacts on ecosystems are prevalent and pose significant risks to vulnerable systems. In particular, wetland ecosystems are often found in urban centers with dense populations and rapid changes. Wetlands provide opportunities to study impacts of rapid urbanization and expansion, climate effects and pollution. The outcomes of human activities directly impact wetlands through coupled human-natural processes, including activities such as agriculture, oil extraction and resulting land cover changes, as well as indirect effects across the entire global biosphere. Here we examine human impacts of the last several years on the La Ballona Wetland Complex in Los Angeles, California, and discuss how remote sensing techniques and modeling can provide better understanding not only for this important wetland area in a megacity but for other wetland areas in other parts of the world.

  • Monitoring floods in the Kafue flats with TanDEM-X data

    Wetlands are very valuable ecosystem which may present fast dynamics. This study is focused on the observation of the Kafue Flats exploiting TanDEM-X polarimetric and interfer-ometric data. Several analysis are carried out considering classification, change detection and Digital Elevation Model (DEM) estimation. Specifically, it is possible to observe that differences between single-pass DEM acquired during different dates can provide promising estimations of the flood water level and extent (provided that the water is partially covered by vegetation). Additionally, the polarimetric information can add details regarding small areas in the data where swamps are presents also in dry seasons.

  • The study of SROI of the Cheng-Long Wetlands International Environmental Art Project and Setouchi Triennale Art Festival

    The world is currently facing serious social and environmental problems, causing an upward trend in social design. The Cheng-Long Wetlands International Environmental Art Project, in Taiwan's Yunlin County, attempts to promote awareness of the environment in the local community. The Setouchi Triennale in Japan encourages tourism on the islands of the Seto Sea, regeneration of the local environment as well as the local economy. This study assesses the social return on investment (SROI) and analyzes the current and future trends of these festivals.

  • ANN Based High Spatial Resolution Remote Sensing Wetland Classification

    RS (Remote Sensing) image classification based on ANN (Artificial Neural Network) is carried out with high spatial resolution images of the wetland, which is the most important ecological environment element within the land components. Wetland dynamic change monitoring is often built upon its classification result concerned here. The typical high spatial resolution image of the wetland in Nanjing is used as a study case by ANN method in comparison with MLC (Maximum Likelihood Classification). Furthermore, the optimal number of ANN hidden neurons are simulated for enhance the classification effectivity. Totally, the results show classification method of ANN with optimal hidden neurons can effectively distinguish ground objects and improve the classification accuracy. The overall accuracy of the ANN classification is up to 93% and the Kappa coefficient is over 0.89.

  • Comparison of Adaboost.M2 and perspective based model ensemble in multispectral image classification

    AdaBoost is a popular ensemble method utilized in pattern recognition problems that are considered tough. Besides being a robust technique it does suffer from few limitations viz. size of training data and presence of noise in training data. In this context, we proposed a novel technique called Perspective Based Model (PBM) for ensemble creation in case of multispectral data analysis. In the present paper, we evaluate its performance in terms of classification accuracy against AdaBoost.M2. Preliminary results show higher accuracy through PBM compared to a single classifier but also a lower classification performance for PBM compared to AdaBoost.M2. An improved performance is also observed for PBM on adding new data features.

  • Monitoring of Inundation Dynamics in the North-American Prairie Pothole Region using Sentinel-1 Time Series

    Monitoring of wetland inundation dynamics is important for flood management and the characterisation of hydrological connectivity. SAR-based inundation extent monitoring in wetlands is often challenging due to different factors, such as waves, vegetation cover and wet snow. The presented study targets the mapping of inundation dynamics in the Prairie Pothole Region (PPR) of North Dakota, USA. A 3-year water extent time series was derived from Sentinel-1 SAR data by first delineating permanent water bodies using a clustering approach. In a second step, water body dynamics were mapped using region growing and automatic thresholding. Results suggest that there is considerable potential for mapping surface water dynamics in late spring, summer and autumn, whereas confusion with wet snow may take place in early spring.

  • A dynamic hierarchical feature selection method for object-based classification of wetlands

    Wetland classification has always been a challenging task among remote sensing experts. Typically, wetland classes have low accuracies regardless of the applied dataset, as they have many spectral and ecological similarities. In this paper, a method is developed particularly effective for distinguishing spectrally similar classes such as wetlands. In this method, feature selection and object-based classification are not done in one step, but instead several feature selections and classifications are applied, and in each level a target class is classified and masked out. While classifying the target class, other spectrally resembling classes are merged so that feature selection is mainly concentrated on separating two classes only. Object-based features were extracted from several SAR and optical images, including RADARSAT-2, ALOS-1, ALOS-2, RapidEye and Landsat-8 images. 15 and 10 percent improvement was obtained in wetlands' average producer and user accuracies compared to the typical feature selection by using the proposed method.

  • Analysis of multi-aspect and fully polarimetric L-band SAR data from uavsar over spacex rocket debris site

    On September 21 and 22, 2016, the L-band UAVSAR airborne Synthetic Aperture Radar (SAR) imaged the SpaceX rocket debris field at Kennedy Space Center, Cape Canaveral Florida. This debris field was the result of an explosion of a SpaceX Falcon 9 rocket during its launch preparations on September 1. The data collected by UAVSAR allowed us to investigate methods for the detection of small ground targets in a complex wetland environment using multi-aspect, fully polarimetric, and high resolution SAR data. We developed a new time domain processor to focus the SAR data directly to a predefined map grid instead of traditional radar coordinates. This approach allowed us to form co- registered SAR images acquired from 8 flight headings to a common map grid without additional resampling. We computed various polarimetric observables for each observed aspect angle, then calculated statistical deviations to identify candidate locations of debris.



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