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Special Issue "Towards Remote Long-Term Monitoring of Wetland Landscapes"

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A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: closed (31 January 2015)

Special Issue Editor

Guest Editor
Dr. Alisa L. Gallant

Earth Resources Observation and Science (EROS) Center, US Geological Survey, Sioux Falls, SD 57198-0001, USA
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Interests: integrated multiscale research on effects of global change on wetland-upland landscapes, ecoregional landscape dynamics, effects of changes in land-cover type and condition on provision of ecosystem services

Special Issue Information

Dear Colleague,

Wetland landscapes are highly productive and support a host of ecosystem goods and services. At local scales, wetlands provide food, fiber, filtering of contaminants, sediment storage, flood control, wildlife habitat, recreation, and aesthetic value, among other functions. At broader scales, wetland-rich landscapes help regulate regional climate and provide critical habitat for intercontinental migratory species. Despite these important benefits, wetlands have been drained extensively worldwide to increase acreage for cultivation of crops and accommodate expansion of human settlements. Wetlands continue to be vulnerable to changes in land use and management, but accelerated rates of climate change have added to the complexity of maintaining functioning wetlands.

Remote monitoring has been invaluable for gaining large-area perspectives on how the Earth’s surface is changing. The status of wetlands, however, has proven difficult to monitor remotely, because wetlands can be challenging to detect in certain landscapes and often are not mapped with sufficient accuracy and consistency to monitor change. This Special Issue of Remote Sensing focuses on research that improves our capability for remote, large-area monitoring of wetlands by investigating: (1) What approaches and data products are being developed specifically to support regional to global long-term monitoring of wetland landscapes? (2) What are the promising new technologies and sensor/multisensor approaches for more accurate and consistent detection of wetlands? and (3) Are there studies that demonstrate how remote long-term monitoring of wetland landscapes can reveal changes that correspond with changes in land cover and land use and/or changes in climate? Papers that fit the descriptive areas of research listed below are sought for this Special Issue. Not encouraged are papers describing studies where the purpose of the research was to develop a wetland map for a local area.

  • Global and regional wetland mapping;
  • Operational techniques and/or products for remote, large-area monitoring of wetlands.
  • New technologies for consistent mapping and change detection in wetland landscapes.
  • Portable techniques for mapping and detecting changes in wetland landscapes.
  • Multisensor approaches to increase accuracies of wetland characterization and mapping;
  • Long-term, large-area assessments of wetland change related to changes in land cover and land use.
  • Long-term, large-area assessments of wetland responses related to climate.
  • Uncertainty, error, and accuracy in wetland characterization and mapping.

Dr. Alisa L. Gallant
Guest Editor

Submission

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. Papers will be published continuously (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are refereed through a peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed Open Access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs).

Keywords

  • wetlands
  • wetland landscapes
  • wetland mapping
  • wetland monitoring
  • remote sensing
  • long-term monitoring
  • change detection
  • climate change
  • land-cover and land-use change

Published Papers (19 papers)

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Editorial

Jump to: Research, Review

Open AccessEditorial The Challenges of Remote Monitoring of Wetlands
Remote Sens. 2015, 7(8), 10938-10950; doi:10.3390/rs70810938
Received: 13 August 2015 / Accepted: 13 August 2015 / Published: 24 August 2015
Cited by 8 | PDF Full-text (518 KB) | HTML Full-text | XML Full-text
Abstract
Wetlands are highly productive and support a wide variety of ecosystem goods and services. Various forms of global change impose compelling needs for timely and reliable information on the status of wetlands worldwide, but several characteristics of wetlands make them challenging to monitor
[...] Read more.
Wetlands are highly productive and support a wide variety of ecosystem goods and services. Various forms of global change impose compelling needs for timely and reliable information on the status of wetlands worldwide, but several characteristics of wetlands make them challenging to monitor remotely: they lack a single, unifying land-cover feature; they tend to be highly dynamic and their energy signatures are constantly changing; and steep environmental gradients in and around wetlands produce narrow ecotones that often are below the resolving capacity of remote sensors. These challenges and needs set the context for a special issue focused on wetland remote sensing. Contributed papers responded to one of three overarching questions aimed at improving remote, large-area monitoring of wetlands: (1) What approaches and data products are being developed specifically to support regional to global long-term monitoring of wetland landscapes? (2) What are the promising new technologies and sensor/multisensor approaches for more accurate and consistent detection of wetlands? (3) Are there studies that demonstrate how remote long-term monitoring of wetland landscapes can reveal changes that correspond with changes in land cover and land use and/or changes in climate? Full article
(This article belongs to the Special Issue Towards Remote Long-Term Monitoring of Wetland Landscapes)
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Research

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Open AccessArticle Efficient Wetland Surface Water Detection and Monitoring via Landsat: Comparison with in situ Data from the Everglades Depth Estimation Network
Remote Sens. 2015, 7(9), 12503-12538; doi:10.3390/rs70912503
Received: 16 June 2015 / Accepted: 17 September 2015 / Published: 23 September 2015
Cited by 2 | PDF Full-text (2334 KB) | HTML Full-text | XML Full-text
Abstract
The U.S. Geological Survey is developing new Landsat science products. One, named Dynamic Surface Water Extent (DSWE), is focused on the representation of ground surface inundation as detected in cloud-/shadow-/snow-free pixels for scenes collected over the U.S. and its territories. Characterization of DSWE
[...] Read more.
The U.S. Geological Survey is developing new Landsat science products. One, named Dynamic Surface Water Extent (DSWE), is focused on the representation of ground surface inundation as detected in cloud-/shadow-/snow-free pixels for scenes collected over the U.S. and its territories. Characterization of DSWE uncertainty to facilitate its appropriate use in science and resource management is a primary objective. A unique evaluation dataset developed from data made publicly available through the Everglades Depth Estimation Network (EDEN) was used to evaluate one candidate DSWE algorithm that is relatively simple, requires no scene-based calibration data, and is intended to detect inundation in the presence of marshland vegetation. A conceptual model of expected algorithm performance in vegetated wetland environments was postulated, tested and revised. Agreement scores were calculated at the level of scenes and vegetation communities, vegetation index classes, water depths, and individual EDEN gage sites for a variety of temporal aggregations. Landsat Archive cloud cover attribution errors were documented. Cloud cover had some effect on model performance. Error rates increased with vegetation cover. Relatively low error rates for locations of little/no vegetation were unexpectedly dominated by omission errors due to variable substrates and mixed pixel effects. Examined discrepancies between satellite and in situ modeled inundation demonstrated the utility of such comparisons for EDEN database improvement. Importantly, there seems no trend or bias in candidate algorithm performance as a function of time or general hydrologic conditions, an important finding for long-term monitoring. The developed database and knowledge gained from this analysis will be used for improved evaluation of candidate DSWE algorithms as well as other measurements made on Everglades surface inundation, surface water heights and vegetation using radar, lidar and hyperspectral instruments. Although no other sites have such an extensive in situ network or long-term records, the broader applicability of this and other candidate DSWE algorithms is being evaluated in other wetlands using this work as a guide. Continued interaction among DSWE producers and potential users will help determine whether the measured accuracies are adequate for practical utility in resource management. Full article
(This article belongs to the Special Issue Towards Remote Long-Term Monitoring of Wetland Landscapes)
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Open AccessArticle Development of a Bi-National Great Lakes Coastal Wetland and Land Use Map Using Three-Season PALSAR and Landsat Imagery
Remote Sens. 2015, 7(7), 8655-8682; doi:10.3390/rs70708655
Received: 9 February 2015 / Revised: 25 June 2015 / Accepted: 30 June 2015 / Published: 9 July 2015
Cited by 3 | PDF Full-text (7132 KB) | HTML Full-text | XML Full-text
Abstract
Methods using extensive field data and three-season Landsat TM and PALSAR imagery were developed to map wetland type and identify potential wetland stressors (i.e., adjacent land use) for the United States and Canadian Laurentian coastal Great Lakes. The mapped area included
[...] Read more.
Methods using extensive field data and three-season Landsat TM and PALSAR imagery were developed to map wetland type and identify potential wetland stressors (i.e., adjacent land use) for the United States and Canadian Laurentian coastal Great Lakes. The mapped area included the coastline to 10 km inland to capture the region hydrologically connected to the Great Lakes. Maps were developed in cooperation with the overarching Great Lakes Consortium plan to provide a comprehensive regional baseline map suitable for coastal wetland assessment and management by agencies at the local, tribal, state, and federal levels. The goal was to provide not only land use and land cover (LULC) baseline data at moderate spatial resolution (20–30 m), but a repeatable methodology to monitor change into the future. The prime focus was on mapping wetland ecosystem types, such as emergent wetland and forested wetland, as well as to delineate wetland monocultures (Typha, Phragmites, Schoenoplectus) and differentiate peatlands (fens and bogs) from other wetland types. The overall accuracy for the coastal Great Lakes map of all five lake basins was 94%, with a range of 86% to 96% by individual lake basin (Huron, Ontario, Michigan, Erie and Superior). Full article
(This article belongs to the Special Issue Towards Remote Long-Term Monitoring of Wetland Landscapes)
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Open AccessArticle Evaluation of Polarimetric SAR Decomposition for Classifying Wetland Vegetation Types
Remote Sens. 2015, 7(7), 8563-8585; doi:10.3390/rs70708563
Received: 8 February 2015 / Revised: 14 June 2015 / Accepted: 25 June 2015 / Published: 7 July 2015
Cited by 5 | PDF Full-text (16877 KB) | HTML Full-text | XML Full-text
Abstract
The Florida Everglades is the largest subtropical wetland system in the United States and, as with subtropical and tropical wetlands elsewhere, has been threatened by severe environmental stresses. It is very important to monitor such wetlands to inform management on the status of
[...] Read more.
The Florida Everglades is the largest subtropical wetland system in the United States and, as with subtropical and tropical wetlands elsewhere, has been threatened by severe environmental stresses. It is very important to monitor such wetlands to inform management on the status of these fragile ecosystems. This study aims to examine the applicability of TerraSAR-X quadruple polarimetric (quad-pol) synthetic aperture radar (PolSAR) data for classifying wetland vegetation in the Everglades. We processed quad-pol data using the Hong & Wdowinski four-component decomposition, which accounts for double bounce scattering in the cross-polarization signal. The calculated decomposition images consist of four scattering mechanisms (single, co- and cross-pol double, and volume scattering). We applied an object-oriented image analysis approach to classify vegetation types with the decomposition results. We also used a high-resolution multispectral optical RapidEye image to compare statistics and classification results with Synthetic Aperture Radar (SAR) observations. The calculated classification accuracy was higher than 85%, suggesting that the TerraSAR-X quad-pol SAR signal had a high potential for distinguishing different vegetation types. Scattering components from SAR acquisition were particularly advantageous for classifying mangroves along tidal channels. We conclude that the typical scattering behaviors from model-based decomposition are useful for discriminating among different wetland vegetation types. Full article
(This article belongs to the Special Issue Towards Remote Long-Term Monitoring of Wetland Landscapes)
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Open AccessArticle On the Importance of Training Data Sample Selection in Random Forest Image Classification: A Case Study in Peatland Ecosystem Mapping
Remote Sens. 2015, 7(7), 8489-8515; doi:10.3390/rs70708489
Received: 31 March 2015 / Revised: 15 June 2015 / Accepted: 23 June 2015 / Published: 6 July 2015
Cited by 13 | PDF Full-text (20158 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Random Forest (RF) is a widely used algorithm for classification of remotely sensed data. Through a case study in peatland classification using LiDAR derivatives, we present an analysis of the effects of input data characteristics on RF classifications (including RF out-of-bag error, independent
[...] Read more.
Random Forest (RF) is a widely used algorithm for classification of remotely sensed data. Through a case study in peatland classification using LiDAR derivatives, we present an analysis of the effects of input data characteristics on RF classifications (including RF out-of-bag error, independent classification accuracy and class proportion error). Training data selection and specific input variables (i.e., image channels) have a large impact on the overall accuracy of the image classification. High-dimension datasets should be reduced so that only uncorrelated important variables are used in classifications. Despite the fact that RF is an ensemble approach, independent error assessments should be used to evaluate RF results, and iterative classifications are recommended to assess the stability of predicted classes. Results are also shown to be highly sensitive to the size of the training data set. In addition to being as large as possible, the training data sets used in RF classification should also be (a) randomly distributed or created in a manner that allows for the class proportions of the training data to be representative of actual class proportions in the landscape; and (b) should have minimal spatial autocorrelation to improve classification results and to mitigate inflated estimates of RF out-of-bag classification accuracy. Full article
(This article belongs to the Special Issue Towards Remote Long-Term Monitoring of Wetland Landscapes)
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Open AccessArticle Evaluation of ALOS PALSAR Data for High-Resolution Mapping of Vegetated Wetlands in Alaska
Remote Sens. 2015, 7(6), 7272-7297; doi:10.3390/rs70607272
Received: 20 August 2014 / Accepted: 19 May 2015 / Published: 3 June 2015
Cited by 3 | PDF Full-text (22891 KB) | HTML Full-text | XML Full-text
Abstract
As the largest natural source of methane, wetlands play an important role in the carbon cycle. High-resolution maps of wetland type and extent are required to quantify wetland responses to climate change. Mapping northern wetlands is particularly important because of a disproportionate increase
[...] Read more.
As the largest natural source of methane, wetlands play an important role in the carbon cycle. High-resolution maps of wetland type and extent are required to quantify wetland responses to climate change. Mapping northern wetlands is particularly important because of a disproportionate increase in temperatures at higher latitudes. Synthetic aperture radar data from a spaceborne platform can be used to map wetland types and dynamics over large areas. Following from earlier work by Whitcomb et al. (2009) using Japanese Earth Resources Satellite (JERS-1) data, we applied the “random forests” classification algorithm to variables from L-band ALOS PALSAR data for 2007, topographic data (e.g., slope, elevation) and locational information (latitude, longitude) to derive a map of vegetated wetlands in Alaska, with a spatial resolution of 50 m. We used the National Wetlands Inventory and National Land Cover Database (for upland areas) to select training and validation data and further validated classification results with an independent dataset that we created. A number of improvements were made to the method of Whitcomb et al. (2009): (1) more consistent training data in upland areas; (2) better distribution of training data across all classes by taking a stratified random sample of all available training pixels; and (3) a more efficient implementation, which allowed classification of the entire state as a single entity (rather than in separate tiles), which eliminated discontinuities at tile boundaries. The overall accuracy for discriminating wetland from upland was 95%, and the accuracy at the level of wetland classes was 85%. The total area of wetlands mapped was 0.59 million km2, or 36% of the total land area of the state of Alaska. The map will be made available to download from NASA’s wetland monitoring website. Full article
(This article belongs to the Special Issue Towards Remote Long-Term Monitoring of Wetland Landscapes)
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Open AccessArticle Mapping Regional Inundation with Spaceborne L-Band SAR
Remote Sens. 2015, 7(5), 5440-5470; doi:10.3390/rs70505440
Received: 6 November 2014 / Revised: 1 April 2015 / Accepted: 13 April 2015 / Published: 30 April 2015
Cited by 5 | PDF Full-text (8740 KB) | HTML Full-text | XML Full-text
Abstract
Shortly after the launch of ALOS PALSAR L-band SAR by the Japan Space Exploration Agency (JAXA), a program to develop an Earth Science Data Record (ESDR) for inundated wetlands was funded by NASA. Using established methodologies, extensive multi-temporal L-band ALOS ScanSAR data acquired
[...] Read more.
Shortly after the launch of ALOS PALSAR L-band SAR by the Japan Space Exploration Agency (JAXA), a program to develop an Earth Science Data Record (ESDR) for inundated wetlands was funded by NASA. Using established methodologies, extensive multi-temporal L-band ALOS ScanSAR data acquired bi-monthly by the PALSAR instrument onboard ALOS were used to classify the inundation state for South America for delivery as a component of this Inundated Wetlands ESDR (IW-ESDR) and in collaboration with JAXA’s ALOS Kyoto and Carbon Initiative science programme. We describe these methodologies and the final classification of the inundation state, then compared this with results derived from dual-season data acquired by the JERS-1 L-band SAR mission in 1995 and 1996, as well as with estimates of surface water extent measured globally every 10 days by coarser resolution sensors. Good correspondence was found when comparing open water extent classified from multi-temporal ALOS ScanSAR data with surface water fraction identified from coarse resolution sensors, except in those regions where there may be differences in sensitivity to widespread and shallow seasonal flooding event, or in areas that could be excluded through use of a continental-scale inundatable mask. It was found that the ALOS ScanSAR classification of inundated vegetation was relatively insensitive to inundated herbaceous vegetation. Inundation dynamics were examined using the multi-temporal ALOS ScanSAR acquisitions over the Pacaya-Samiria and surrounding areas in the Peruvian Amazon. Full article
(This article belongs to the Special Issue Towards Remote Long-Term Monitoring of Wetland Landscapes)
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Open AccessArticle Supervised Classification of Benthic Reflectance in Shallow Subtropical Waters Using a Generalized Pixel-Based Classifier across a Time Series
Remote Sens. 2015, 7(5), 5098-5116; doi:10.3390/rs70505098
Received: 2 February 2015 / Revised: 10 April 2015 / Accepted: 20 April 2015 / Published: 24 April 2015
Cited by 6 | PDF Full-text (5868 KB) | HTML Full-text | XML Full-text
Abstract
We tested a supervised classification approach with Landsat 5 Thematic Mapper (TM) data for time-series mapping of seagrass in a subtropical lagoon. Seagrass meadows are an integral link between marine and inland ecosystems and are at risk from upstream processes such as runoff
[...] Read more.
We tested a supervised classification approach with Landsat 5 Thematic Mapper (TM) data for time-series mapping of seagrass in a subtropical lagoon. Seagrass meadows are an integral link between marine and inland ecosystems and are at risk from upstream processes such as runoff and erosion. Despite the prevalence of image-specific approaches, the classification accuracies we achieved show that pixel-based spectral classes may be generalized and applied to a time series of images that were not included in the classifier training. We employed in-situ data on seagrass abundance from 2007 to 2011 to train and validate a classification model. We created depth-invariant bands from TM bands 1, 2, and 3 to correct for variations in water column depth prior to building the classification model. In-situ data showed mean total seagrass cover remained relatively stable over the study area and period, with seagrass cover generally denser in the west than the east. Our approach achieved mapping accuracies (67% and 76% for two validation years) comparable with those attained using spectral libraries, but was simpler to implement. We produced a series of annual maps illustrating inter-annual variability in seagrass occurrence. Accuracies may be improved in future work by better addressing the spatial mismatch between pixel size of remotely sensed data and footprint of field data and by employing atmospheric correction techniques that normalize reflectances across images. Full article
(This article belongs to the Special Issue Towards Remote Long-Term Monitoring of Wetland Landscapes)
Open AccessArticle The Effects of Point or Polygon Based Training Data on RandomForest Classification Accuracy of Wetlands
Remote Sens. 2015, 7(4), 4002-4025; doi:10.3390/rs70404002
Received: 1 February 2015 / Revised: 23 March 2015 / Accepted: 27 March 2015 / Published: 2 April 2015
Cited by 3 | PDF Full-text (16975 KB) | HTML Full-text | XML Full-text
Abstract
Wetlands are dynamic in space and time, providing varying ecosystem services. Field reference data for both training and assessment of wetland inventories in the State of Minnesota are typically collected as GPS points over wide geographical areas and at infrequent intervals. This status-quo
[...] Read more.
Wetlands are dynamic in space and time, providing varying ecosystem services. Field reference data for both training and assessment of wetland inventories in the State of Minnesota are typically collected as GPS points over wide geographical areas and at infrequent intervals. This status-quo makes it difficult to keep updated maps of wetlands with adequate accuracy, efficiency, and consistency to monitor change. Furthermore, point reference data may not be representative of the prevailing land cover type for an area, due to point location or heterogeneity within the ecosystem of interest. In this research, we present techniques for training a land cover classification for two study sites in different ecoregions by implementing the RandomForest classifier in three ways: (1) field and photo interpreted points; (2) fixed window surrounding the points; and (3) image objects that intersect the points. Additional assessments are made to identify the key input variables. We conclude that the image object area training method is the most accurate and the most important variables include: compound topographic index, summer season green and blue bands, and grid statistics from LiDAR point cloud data, especially those that relate to the height of the return. Full article
(This article belongs to the Special Issue Towards Remote Long-Term Monitoring of Wetland Landscapes)
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Open AccessArticle Operational Actual Wetland Evapotranspiration Estimation for South Florida Using MODIS Imagery
Remote Sens. 2015, 7(4), 3613-3632; doi:10.3390/rs70403613
Received: 13 October 2014 / Revised: 13 March 2015 / Accepted: 18 March 2015 / Published: 26 March 2015
Cited by 1 | PDF Full-text (4422 KB) | HTML Full-text | XML Full-text
Abstract
Evapotranspiration is a reliable indicator of wetland health. Wetlands are an important and valuable ecosystem on the South Florida landscape. Accurate wetland Actual Evapotranspiration (AET) data can be used to evaluate the performance of South Florida’s Everglades restoration programs. However, reliable AET measurements
[...] Read more.
Evapotranspiration is a reliable indicator of wetland health. Wetlands are an important and valuable ecosystem on the South Florida landscape. Accurate wetland Actual Evapotranspiration (AET) data can be used to evaluate the performance of South Florida’s Everglades restoration programs. However, reliable AET measurements rely on scattered point measurements restricting applications over a larger area. The objective of this study was to validate the ability of the Simplified Surface Energy Balance (SSEB) approach and the Simple Method (also called the Abtew Method) to provide large area AET estimates for wetland recovery efforts. The study used Moderate Resolution Imaging Spectroradiometer (MODIS) sensor spectral data and South Florida Water Management District (SFWMD) solar radiation data to derive weekly AET values for South Florida. The SSEB-Simple Method approach provided acceptable results with good agreement with observed values during the critical dry season period, when cloud cover was low (rave (n = 59) = 0.700, pave < 0.0005), but requires further refinement to be viable for yearly estimates because of poor performance during wet season months, mainly because of cloud contamination. The approach can be useful for short-term wetland recovery assessment projects that occur during the dry season and/or long term projects that compare site AET rates from dry season to dry season. Full article
(This article belongs to the Special Issue Towards Remote Long-Term Monitoring of Wetland Landscapes)
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Open AccessArticle Adjusting Lidar-Derived Digital Terrain Models in Coastal Marshes Based on Estimated Aboveground Biomass Density
Remote Sens. 2015, 7(4), 3507-3525; doi:10.3390/rs70403507
Received: 5 December 2014 / Revised: 10 March 2015 / Accepted: 17 March 2015 / Published: 25 March 2015
Cited by 8 | PDF Full-text (14045 KB) | HTML Full-text | XML Full-text
Abstract
Digital elevation models (DEMs) derived from airborne lidar are traditionally unreliable in coastal salt marshes due to the inability of the laser to penetrate the dense grasses and reach the underlying soil. To that end, we present a novel processing methodology that uses
[...] Read more.
Digital elevation models (DEMs) derived from airborne lidar are traditionally unreliable in coastal salt marshes due to the inability of the laser to penetrate the dense grasses and reach the underlying soil. To that end, we present a novel processing methodology that uses ASTER Band 2 (visible red), an interferometric SAR (IfSAR) digital surface model, and lidar-derived canopy height to classify biomass density using both a three- class scheme (high, medium and low) and a two-class scheme (high and low). Elevation adjustments associated with these classes using both median and quartile approaches were applied to adjust lidar-derived elevation values closer to true bare earth elevation. The performance of the method was tested on 229 elevation points in the lower Apalachicola River Marsh. The two-class quartile-based adjusted DEM produced the best results, reducing the RMS error in elevation from 0.65 m to 0.40 m, a 38% improvement. The raw mean errors for the lidar DEM and the adjusted DEM were 0.61 ± 0.24 m and 0.32 ± 0.24 m, respectively, thereby reducing the high bias by approximately 49%. Full article
(This article belongs to the Special Issue Towards Remote Long-Term Monitoring of Wetland Landscapes)
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Open AccessArticle Surface Freshwater Storage Variations in the Orinoco Floodplains Using Multi-Satellite Observations
Remote Sens. 2015, 7(1), 89-110; doi:10.3390/rs70100089
Received: 29 September 2014 / Accepted: 16 December 2014 / Published: 24 December 2014
Cited by 4 | PDF Full-text (2828 KB) | HTML Full-text | XML Full-text
Abstract
Variations in surface water extent and storage are poorly characterized from regional to global scales. In this study, a multi-satellite approach is proposed to estimate the water stored in the floodplains of the Orinoco Basin at a monthly time-scale using remotely-sensed observations of
[...] Read more.
Variations in surface water extent and storage are poorly characterized from regional to global scales. In this study, a multi-satellite approach is proposed to estimate the water stored in the floodplains of the Orinoco Basin at a monthly time-scale using remotely-sensed observations of surface water from the Global Inundation Extent Multi-Satellite (GIEMS) and stages from Envisat radar altimetry. Surface water storage variations over 2003–2007 exhibit large interannual variability and a strong seasonal signal, peaking during summer, and associated with the flood pulse. The volume of surface water storage in the Orinoco Basin was highly correlated with the river discharge at Ciudad Bolivar (R = 0.95), the closest station to the mouth where discharge was estimated, although discharge lagged one month behind storage. The correlation remained high (R = 0.73) after removing seasonal effects. Mean annual variations in surface water volume represented ~170 km3, contributing to ~45% of the Gravity Recovery and Climate Experiment (GRACE)-derived total water storage variations and representing ~13% of the total volume of water that flowed out of the Orinoco Basin to the Atlantic Ocean. Full article
(This article belongs to the Special Issue Towards Remote Long-Term Monitoring of Wetland Landscapes)
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Open AccessArticle The Influence of Polarimetric Parameters and an Object-Based Approach on Land Cover Classification in Coastal Wetlands
Remote Sens. 2014, 6(12), 12575-12592; doi:10.3390/rs61212575
Received: 19 July 2014 / Revised: 1 December 2014 / Accepted: 5 December 2014 / Published: 15 December 2014
Cited by 6 | PDF Full-text (6844 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
The purpose of this study was to examine how different polarimetric parameters and an object-based approach influence the classification results of various land use/land cover types using fully polarimetric ALOS PALSAR data over coastal wetlands in Yancheng, China. To verify the efficiency of
[...] Read more.
The purpose of this study was to examine how different polarimetric parameters and an object-based approach influence the classification results of various land use/land cover types using fully polarimetric ALOS PALSAR data over coastal wetlands in Yancheng, China. To verify the efficiency of the proposed method, five other classifications (the Wishart supervised classification, the proposed method without polarimetric parameters, the proposed method without an object-based analysis, the proposed method without textural and geometric information and the proposed method using the nearest-neighbor classifier) were applied for comparison. The results indicated that some polarimetric parameters, such as Shannon entropy, Krogager_Kd, Alpha, HAAlpha_T11, VanZyl3_Vol, Derd, Barnes2_T33, polarization fraction, Barnes1_T33, Neuman_delta_mod and entropy, greatly improved the classification results. The shape index was a useful feature in distinguishing fish ponds and rivers. The distance to the sea can be regarded as an important factor in reducing the confusion between herbaceous wetland vegetation and grasslands. Furthermore, the decision tree algorithm increased the overall accuracy by 6.8% compared with the nearest neighbor classifier. This research demonstrated that different polarimetric parameters and the object-based approach significantly improved the performance of land cover classification in coastal wetlands using ALOS PALSAR data. Full article
(This article belongs to the Special Issue Towards Remote Long-Term Monitoring of Wetland Landscapes)
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Open AccessArticle Improved Wetland Classification Using Eight-Band High Resolution Satellite Imagery and a Hybrid Approach
Remote Sens. 2014, 6(12), 12187-12216; doi:10.3390/rs61212187
Received: 13 June 2014 / Revised: 18 November 2014 / Accepted: 27 November 2014 / Published: 8 December 2014
Cited by 5 | PDF Full-text (7584 KB) | HTML Full-text | XML Full-text
Abstract
Although remote sensing technology has long been used in wetland inventory and monitoring, the accuracy and detail level of wetland maps derived with moderate resolution imagery and traditional techniques have been limited and often unsatisfactory. We explored and evaluated the utility of a
[...] Read more.
Although remote sensing technology has long been used in wetland inventory and monitoring, the accuracy and detail level of wetland maps derived with moderate resolution imagery and traditional techniques have been limited and often unsatisfactory. We explored and evaluated the utility of a newly launched high-resolution, eight-band satellite system (Worldview-2; WV2) for identifying and classifying freshwater deltaic wetland vegetation and aquatic habitats in the Selenga River Delta of Lake Baikal, Russia, using a hybrid approach and a novel application of Indicator Species Analysis (ISA). We achieved an overall classification accuracy of 86.5% (Kappa coefficient: 0.85) for 22 classes of aquatic and wetland habitats and found that additional metrics, such as the Normalized Difference Vegetation Index and image texture, were valuable for improving the overall classification accuracy and particularly for discriminating among certain habitat classes. Our analysis demonstrated that including WV2’s four spectral bands from parts of the spectrum less commonly used in remote sensing analyses, along with the more traditional bandwidths, contributed to the increase in the overall classification accuracy by ~4% overall, but with considerable increases in our ability to discriminate certain communities. The coastal band improved differentiating open water and aquatic (i.e., vegetated) habitats, and the yellow, red-edge, and near-infrared 2 bands improved discrimination among different vegetated aquatic and terrestrial habitats. The use of ISA provided statistical rigor in developing associations between spectral classes and field-based data. Our analyses demonstrated the utility of a hybrid approach and the benefit of additional bands and metrics in providing the first spatially explicit mapping of a large and heterogeneous wetland system. Full article
(This article belongs to the Special Issue Towards Remote Long-Term Monitoring of Wetland Landscapes)
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Open AccessArticle An Effective Method for Detecting Potential Woodland Vernal Pools Using High-Resolution LiDAR Data and Aerial Imagery
Remote Sens. 2014, 6(11), 11444-11467; doi:10.3390/rs61111444
Received: 27 August 2014 / Revised: 5 November 2014 / Accepted: 12 November 2014 / Published: 17 November 2014
Cited by 12 | PDF Full-text (10153 KB) | HTML Full-text | XML Full-text
Abstract
Effective conservation of woodland vernal pools—important components of regional amphibian diversity and ecosystem services—depends on locating and mapping these pools accurately. Current methods for identifying potential vernal pools are primarily based on visual interpretation and digitization of aerial photographs, with variable accuracy and
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Effective conservation of woodland vernal pools—important components of regional amphibian diversity and ecosystem services—depends on locating and mapping these pools accurately. Current methods for identifying potential vernal pools are primarily based on visual interpretation and digitization of aerial photographs, with variable accuracy and low repeatability. In this paper, we present an effective and efficient method for detecting and mapping potential vernal pools using stochastic depression analysis with additional geospatial analysis. Our method was designed to take advantage of high-resolution light detection and ranging (LiDAR) data, which are becoming increasingly available, though not yet frequently employed in vernal pool studies. We successfully detected more than 2000 potential vernal pools in a ~150 km2 study area in eastern Massachusetts. The accuracy assessment in our study indicated that the commission rates ranged from 2.5% to 6.0%, while the proxy omission rate was 8.2%, rates that are much lower than reported errors of previous vernal pool studies conducted in the northeastern United States. One significant advantage of our semi-automated approach for vernal pool identification is that it may reduce inconsistencies and alleviate repeatability concerns associated with manual photointerpretation methods. Another strength of our strategy is that, in addition to detecting the point-based vernal pool locations for the inventory, the boundaries of vernal pools can be extracted as polygon features to characterize their geometric properties, which are not available in the current statewide vernal pool databases in Massachusetts. Full article
(This article belongs to the Special Issue Towards Remote Long-Term Monitoring of Wetland Landscapes)
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Open AccessArticle A New Method for Modifying Thresholds in the Classification of Tree Models for Mapping Aquatic Vegetation in Taihu Lake with Satellite Images
Remote Sens. 2014, 6(8), 7442-7462; doi:10.3390/rs6087442
Received: 17 March 2014 / Revised: 24 July 2014 / Accepted: 25 July 2014 / Published: 12 August 2014
Cited by 5 | PDF Full-text (3561 KB) | HTML Full-text | XML Full-text
Abstract
Aquatic vegetation plays an important role in maintaining the balance of lake ecosystems. Thus, classifying and mapping aquatic vegetation is a priority for lake management. Classification tree (CT) approaches have been used successfully to map aquatic vegetation from spectral indices obtained from remotely
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Aquatic vegetation plays an important role in maintaining the balance of lake ecosystems. Thus, classifying and mapping aquatic vegetation is a priority for lake management. Classification tree (CT) approaches have been used successfully to map aquatic vegetation from spectral indices obtained from remotely sensed images. However, due to the effects of extrinsic and intrinsic factors, applying a CT model developed for imagery from one date to imagery from another date or a different dataset likely would reduce the classification accuracy. In this study, three spectral features (SFs) were selected to develop a CT model for identifying aquatic vegetation in Taihu Lake. Three traditional CT models with three SFs were developed using CT analysis based on satellite images acquired on 11 July, 16 August and 26 September 2013, and corresponding ground-truth samples, from the Huangjing-1A/B Charge-Coupled Device (HJ-CCD) images, environment and disaster reduction small satellites that were launched by China Center for Resources Satellite Data and Application (CRESDA). The overall accuracies of traditional CT models were 82%, 80% and 84%. We then tested two methods to modify CT model thresholds to adjust the traditional CT models based on image date to determine if the results would enable us to map and classify aquatic vegetation for periods when no ground-based data were available. We assessed the results with ground-truth samples and area agreement with traditional CT models. Results showed that CT models modified from a linear adjustment based on the relationship between ranked values of SFs between two image dates produced map accuracies comparable with those obtained from the traditional CT models and suggest that the method we proposed is feasible for mapping aquatic vegetation types in lakes when ground data are not available. Full article
(This article belongs to the Special Issue Towards Remote Long-Term Monitoring of Wetland Landscapes)

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Open AccessReview Multiple Stable States and Catastrophic Shifts in Coastal Wetlands: Progress, Challenges, and Opportunities in Validating Theory Using Remote Sensing and Other Methods
Remote Sens. 2015, 7(8), 10184-10226; doi:10.3390/rs70810184
Received: 28 May 2015 / Revised: 3 August 2015 / Accepted: 4 August 2015 / Published: 11 August 2015
Cited by 8 | PDF Full-text (1479 KB) | HTML Full-text | XML Full-text
Abstract
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
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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 wetland dynamic state theories. This significant opportunity is further framed by a proposed list of scientific advances needed to more thoroughly develop the field. Full article
(This article belongs to the Special Issue Towards Remote Long-Term Monitoring of Wetland Landscapes)
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Open AccessReview A Collection of SAR Methodologies for Monitoring Wetlands
Remote Sens. 2015, 7(6), 7615-7645; doi:10.3390/rs70607615
Received: 17 January 2015 / Revised: 12 May 2015 / Accepted: 22 May 2015 / Published: 9 June 2015
Cited by 6 | PDF Full-text (7010 KB) | HTML Full-text | XML Full-text
Abstract
Wetlands are an important natural resource that requires monitoring. A key step in environmental monitoring is to map the locations and characteristics of the resource to better enable assessment of change over time. Synthetic Aperture Radar (SAR) systems are helpful in this way
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Wetlands are an important natural resource that requires monitoring. A key step in environmental monitoring is to map the locations and characteristics of the resource to better enable assessment of change over time. Synthetic Aperture Radar (SAR) systems are helpful in this way for wetland resources because their data can be used to map and monitor changes in surface water extent, saturated soils, flooded vegetation, and changes in wetland vegetation cover. We review a few techniques to demonstrate SAR capabilities for wetland monitoring, including the commonly used method of grey-level thresholding for mapping surface water and highlighting changes in extent, and approaches for polarimetric decompositions to map flooded vegetation and changes from one class of land cover to another. We use the Curvelet-based change detection and the Wishart-Chernoff Distance approaches to show how they substantially improve mapping of flooded vegetation and flagging areas of change, respectively. We recommend that the increasing availability SAR data and the proven ability of these data to map various components of wetlands mean SAR should be considered as a critical component of a wetland monitoring system. Full article
(This article belongs to the Special Issue Towards Remote Long-Term Monitoring of Wetland Landscapes)
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Open AccessReview Definitions and Mapping of East African Wetlands: A Review
Remote Sens. 2015, 7(5), 5256-5282; doi:10.3390/rs70505256
Received: 12 January 2015 / Revised: 13 April 2015 / Accepted: 20 April 2015 / Published: 27 April 2015
Cited by 5 | PDF Full-text (25438 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Wetlands provide invaluable ecosystem services and contribute significantly to food security around the world. To maintain these functions, wetlands need to be protected from rapid transformation and overuse. Spatially-explicit information is required for sustainable wetland management. Development of wetland maps based on remote
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Wetlands provide invaluable ecosystem services and contribute significantly to food security around the world. To maintain these functions, wetlands need to be protected from rapid transformation and overuse. Spatially-explicit information is required for sustainable wetland management. Development of wetland maps based on remote sensing requires a clear-cut definition of wetlands. This review was undertaken to improve the understanding of these habitats from a remote sensing perspective and to determine available wetland map layers for the East African countries of Kenya, Rwanda, Tanzania and Uganda. This study includes three components: (1) a review of the availability and types of wetland definitions from the scientific literature record (including 245 separate references); (2) a systematic analysis of land use/land cover classifications and the conceptual approaches and spatial coverages of wetland classes for each system; and (3) a depiction of wetland layers and a discussion of their suitability for use in regional inventories. Our literature review shows that a standard definition of wetlands is not in use, and a specific definition of wetlands is not given in more than 40% of the reviewed remote sensing publications. Spatial information on East African wetlands is currently insufficient for use in regional wetland management. Full article
(This article belongs to the Special Issue Towards Remote Long-Term Monitoring of Wetland Landscapes)

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