Research

9586 KiB

Open AccessFeature PaperArticle

Hyperspectral Sensors as a Management Tool to Prevent the Invasion of the Exotic Cordgrass Spartina densiflora in the Doñana Wetlands

by Javier Bustamante, David Aragonés, Isabel Afán, Carlos J. Luque, Andrés Pérez-Vázquez, Eloy M. Castellanos and Ricardo Díaz-Delgado

Remote Sens. 2016, 8(12), 1001; https://doi.org/10.3390/rs8121001 - 08 Dec 2016

Cited by 20 | Viewed by 8808

Abstract

We test the use of hyperspectral sensors for the early detection of the invasive dense-flowered cordgrass (Spartina densiflora Brongn.) in the Guadalquivir River marshes, Southwestern Spain. We flew in tandem a CASI-1500 (368–1052 nm) and an AHS (430–13,000 nm) airborne sensors in [...] Read more.

We test the use of hyperspectral sensors for the early detection of the invasive dense-flowered cordgrass (Spartina densiflora Brongn.) in the Guadalquivir River marshes, Southwestern Spain. We flew in tandem a CASI-1500 (368–1052 nm) and an AHS (430–13,000 nm) airborne sensors in an area with presence of S. densiflora. We simplified the processing of hyperspectral data (no atmospheric correction and no data-reduction techniques) to test if these treatments were necessary for accurate S. densiflora detection in the area. We tested several statistical signal detection algorithms implemented in ENVI software as spectral target detection techniques (matched filtering, constrained energy minimization, orthogonal subspace projection, target-constrained interference minimized filter, and adaptive coherence estimator) and compared them to the well-known spectral angle mapper, using spectra extracted from ground-truth locations in the images. The target S. densiflora was easy to detect in the marshes by all algorithms in images of both sensors. The best methods (adaptive coherence estimator and target-constrained interference minimized filter) on the best sensor (AHS) produced 100% discrimination (Kappa = 1, AUC = 1) at the study site and only some decline in performance when extrapolated to a new nearby area. AHS outperformed CASI in spite of having a coarser spatial resolution (4-m vs. 1-m) and lower spectral resolution in the visible and near-infrared range, but had a better signal to noise ratio. The larger spectral range of AHS in the short-wave and thermal infrared was of no particular advantage. Our conclusions are that it is possible to use hyperspectral sensors to map the early spread S. densiflora in the Guadalquivir River marshes. AHS is the most suitable airborne hyperspectral sensor for this task and the signal processing techniques target-constrained interference minimized filter (TCIMF) and adaptive coherence estimator (ACE) are the best performing target detection techniques that can be employed operationally with a simplified processing of hyperspectral images. Full article

(This article belongs to the Special Issue What can Remote Sensing Do for the Conservation of Wetlands?)

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7200 KiB

Open AccessArticle

Effect of Protection Level in the Hydroperiod of Water Bodies on Doñana’s Aeolian Sands

by Javier Bustamante, David Aragonés and Isabel Afán

Remote Sens. 2016, 8(10), 867; https://doi.org/10.3390/rs8100867 - 20 Oct 2016

Cited by 17 | Viewed by 8369

Abstract

Mediterranean temporary ponds on Doñana’s aeolian sands form an extensive system of small dynamic water bodies, dependent on precipitation and groundwater, of considerable importance for biodiversity conservation. Different areas of the aeolian sands have received different levels of environmental protection since 1969, and [...] Read more.

Mediterranean temporary ponds on Doñana’s aeolian sands form an extensive system of small dynamic water bodies, dependent on precipitation and groundwater, of considerable importance for biodiversity conservation. Different areas of the aeolian sands have received different levels of environmental protection since 1969, and this has influenced the degree of conservation and the flooding dynamic of these temporary surface waters. We use the Landsat series of satellite images from 1985 to 2014 to study the temporal dynamic of small temporary water bodies on the aeolian sands in relation to the protection level and to distance to water abstraction pressures from agriculture and residential areas. The results show that even with small and ephemeral water bodies optical remote sensing time-series are an effective way to study their flooding temporal dynamics. The protected areas of the aeolian sands hold a better preserved system of temporary ponds, with a flooding dynamic that fluctuates with precipitation. The unprotected area shows an increase in mean hydroperiod duration, and surface flooded, and a decline in hydroperiod variability. This seems to be due to the creation of irrigation ponds and the artificialization of the flooding regime of the natural temporary ponds, that either receive excess irrigation water or dry-up due to the lowering of the groundwater table level. Although a decline in hydroperiod duration of temporary ponds is seen as negative to the system, an increase in hydroperiod of surface waters due to artificialization, or a decline in variability cannot be considered as positive compensatory effects. Full article

(This article belongs to the Special Issue What can Remote Sensing Do for the Conservation of Wetlands?)

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1775 KiB

Open AccessArticle

Estimating the Total Nitrogen Concentration of Reed Canopy with Hyperspectral Measurements Considering a Non-Uniform Vertical Nitrogen Distribution

by Juhua Luo, Ronghua Ma, Huihui Feng and Xinchuan Li

Remote Sens. 2016, 8(10), 789; https://doi.org/10.3390/rs8100789 - 23 Sep 2016

Cited by 28 | Viewed by 9721

Abstract

The total nitrogen concentration (NC, g/100 g) of wetland plants is an important parameter to estimate the wetland health status and to calculate the nitrogen storage of wetland plants. Remote sensing has been widely used to estimate biophysical, physiological, and biochemical parameters of [...] Read more.

The total nitrogen concentration (NC, g/100 g) of wetland plants is an important parameter to estimate the wetland health status and to calculate the nitrogen storage of wetland plants. Remote sensing has been widely used to estimate biophysical, physiological, and biochemical parameters of plants. However, current studies place little emphasis on NC estimations by only taking nitrogen’s vertical distribution into consideration, resulting in limited accuracy and decreased practical value of the results. The main goal of this study is to develop a model, considering a non-uniform vertical nitrogen distribution to estimate the total NC of the reed canopy, which is one of the wetland’s dominant species, using hyperspectral data. Sixty quadrats were selected and measured based on an experimental design that considered vertical layer divisions within the reed canopy. Using the measured NCs of different leaf layers and corresponding spectra from the quadrats, the results indicated that the vertical distribution law of the NC was distinct, presenting an initial increase and subsequent decrease from the top layer to the bottom layer. The spectral indices MCARI/MTVI2, TCARI/OSAVI, MMTCI, DCNI, and PPR/NDVI had high R² values when related to NC (R² > 0.5) and low R² when related to LAI (R² < 0.2) and could minimize the influence of LAI and increase the sensitivity to changes in NC of the reed canopy. The relative variation rates (R_v, %) of these spectral indices, calculated from each quadrat, also indicated that the top three layers of the reed canopy were an effective depth to estimate NCs using hyperspectral data. A model was developed to estimate the total NC of the whole reed canopy based on PPR/DNVI with R² = 0.88 and RMSE = 0.37%. The model, which considered the vertical distribution patterns of the NC and the effective canopy layers, has demonstrated great potential to estimate the total NC of the whole reed canopy. Full article

(This article belongs to the Special Issue What can Remote Sensing Do for the Conservation of Wetlands?)

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4217 KiB

Open AccessArticle

Long-Term Monitoring of the Flooding Regime and Hydroperiod of Doñana Marshes with Landsat Time Series (1974–2014)

by Ricardo Díaz-Delgado, David Aragonés, Isabel Afán and Javier Bustamante

Remote Sens. 2016, 8(9), 775; https://doi.org/10.3390/rs8090775 - 20 Sep 2016

Cited by 45 | Viewed by 10032

Abstract

This paper presents a semi-automatic procedure to discriminate seasonally flooded areas in the shallow temporary marshes of Doñana National Park (SW Spain) by using a radiommetrically normalized long time series of Landsat MSS, TM, and ETM+ images (1974–2014). Extensive field campaigns for ground [...] Read more.

This paper presents a semi-automatic procedure to discriminate seasonally flooded areas in the shallow temporary marshes of Doñana National Park (SW Spain) by using a radiommetrically normalized long time series of Landsat MSS, TM, and ETM+ images (1974–2014). Extensive field campaigns for ground truth data retrieval were carried out simultaneous to Landsat overpasses. Ground truth was used as training and testing areas to check the performance of the method. Simple thresholds on TM and ETM band 5 (1.55–1.75 μm) worked significantly better than other empirical modeling techniques and supervised classification methods to delineate flooded areas at Doñana marshes. A classification tree was applied to band 5 reflectance values to classify flooded versus non-flooded pixels for every scene. Inter-scene cross-validation identified the most accurate threshold on band 5 reflectance (ρ < 0.186) to classify flooded areas (Kappa = 0.65). A joint TM-MSS acquisition was used to find the MSS band 4 (0.8 a 1.1 μm) threshold. The TM flooded area was identical to the results from MSS 4 band threshold ρ < 0.10 despite spectral and spatial resolution differences. Band slicing was retrospectively applied to the complete time series of MSS and TM images. About 391 flood masks were used to reconstruct historical spatial and temporal patterns of Doñana marshes flooding, including hydroperiod. Hydroperiod historical trends were used as a baseline to understand Doñana’s flooding regime, test hydrodynamic models, and give an assessment of relevant management and restoration decisions. The historical trends in the hydroperiod of Doñana marshes show two opposite spatial patterns. While the north-western part of the marsh is increasing its hydroperiod, the southwestern part shows a steady decline. Anomalies in each flooding cycle allowed us to assess recent management decisions and monitor their hydrological effects. Full article

(This article belongs to the Special Issue What can Remote Sensing Do for the Conservation of Wetlands?)

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25904 KiB

Open AccessArticle

Assessment of Carbon Flux and Soil Moisture in Wetlands Applying Sentinel-1 Data

by Katarzyna Dabrowska-Zielinska, Maria Budzynska, Monika Tomaszewska, Alicja Malinska, Martyna Gatkowska, Maciej Bartold and Iwona Malek

Remote Sens. 2016, 8(9), 756; https://doi.org/10.3390/rs8090756 - 15 Sep 2016

Cited by 36 | Viewed by 9826

Abstract

The objectives of the study were to determine the spatial rate of CO₂ flux (Net Ecosystem Exchange) and soil moisture in a wetland ecosystem applying Sentinel-1 IW (Interferometric Wide) data of VH (Vertical Transmit/Horizontal Receive—cross polarization) and VV (Vertical Transmit/Vertical Receive—like polarization) [...] Read more.

The objectives of the study were to determine the spatial rate of CO₂ flux (Net Ecosystem Exchange) and soil moisture in a wetland ecosystem applying Sentinel-1 IW (Interferometric Wide) data of VH (Vertical Transmit/Horizontal Receive—cross polarization) and VV (Vertical Transmit/Vertical Receive—like polarization) polarization. In-situ measurements of carbon flux, soil moisture, and LAI (Leaf Area Index) were carried out over the Biebrza Wetland in north-eastern Poland. The impact of soil moisture and LAI on backscattering coefficient (σ°) calculated from Sentinel-1 data showed that LAI dominates the influence on σ° when soil moisture is low. The models for soil moisture have been derived for wetland vegetation habitat types applying VH polarization (R² = 0.70 to 0.76). The vegetation habitats: reeds, sedge-moss, sedges, grass-herbs, and grass were classified using combined one Landsat 8 OLI (Operational Land Imager) and three TerraSAR-X (TSX) ScanSAR VV data. The model for the assessment of Net Ecosystem Exchange (NEE) has been developed based on the assumption that soil moisture and biomass represented by LAI have an influence on it. The σ° VH and σ° VV describe soil moisture and LAI, and have been the input to the NEE model. The model, created for classified habitats, is as follows: NEE = f (σ° Sentinel-1 VH, σ° Sentinel-1 VV). Reasonably good predictions of NEE have been achieved for classified habitats (R² = 0.51 to 0.58). The developed model has been used for mapping spatial and temporal distribution of NEE over Biebrza wetland habitat types. Eventually, emissions of CO₂ to the atmosphere (NEE positive) has been noted when soil moisture (SM) and biomass were low. This study demonstrates the importance of the capability of Sentinel-1 microwave data to calculate soil moisture and estimate NEE with all-weather acquisition conditions, offering an important advantage for frequent wetlands monitoring. Full article

(This article belongs to the Special Issue What can Remote Sensing Do for the Conservation of Wetlands?)

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33676 KiB

Open AccessArticle

Submerged and Emergent Land Cover and Bathymetric Mapping of Estuarine Habitats Using WorldView-2 and LiDAR Imagery

by Joanne Halls and Kaitlyn Costin

Remote Sens. 2016, 8(9), 718; https://doi.org/10.3390/rs8090718 - 31 Aug 2016

Cited by 30 | Viewed by 8291

Abstract

Tidal creeks are small estuarine watersheds characterized by low freshwater input, marine to brackish salinity, and subtidal, intertidal, and supratidal habitats. Most people are familiar with large rivers and estuaries, but the smaller tidal watersheds comprise a greater percentage of the coastline. As [...] Read more.

Tidal creeks are small estuarine watersheds characterized by low freshwater input, marine to brackish salinity, and subtidal, intertidal, and supratidal habitats. Most people are familiar with large rivers and estuaries, but the smaller tidal watersheds comprise a greater percentage of the coastline. As the population along coasts rises there is growing concern about water quality and increased sedimentation rates. Therefore, these smaller tidal creek watersheds are at risk to pollution, decreased environmental health, and deterioration of protective salt marshes. The purpose of this study was to test methods for high spatial resolution mapping of benthic (submerged) and emergent habitats as well as the derivation of bathymetry using DigitalGlobe’s WorldView-2 imagery. An intensive field effort was conducted to test and assess several image processing techniques. Results concluded that: (1) supervised habitat classification produced the highest map accuracy (95%); (2) sand, water, scrub/shrub, and docks/rubble were mapped the most accurately at greater than 95%; (3) saltmarsh habitats (high and low density cordgrass, Spartina alterniflora, and black needlerush, Juncus roemerianus), mud, and oyster beds were between 80 and 85% accurate; (4) pan-sharpening and atmospheric correction did not improve map accuracy; (5) LiDAR (light detection and ranging) data increased habitat map accuracy; and (6) WorldView-2 imagery was capable of deriving water depth and these data increased the map accuracy of benthic habitats. The project produced habitat maps for benthic and emergent species at high spatial resolution (4 m²) which will be useful for studying the dynamic processes in this tidal environment. The data and methods developed here could be used by state and local government planning agencies to assess potential long-term changes and develop appropriate management strategies. Full article

(This article belongs to the Special Issue What can Remote Sensing Do for the Conservation of Wetlands?)

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12778 KiB

Open AccessArticle

Mapping Above-Ground Biomass by Integrating Optical and SAR Imagery: A Case Study of Xixi National Wetland Park, China

by Chudong Huang, Xinyue Ye, Chengbin Deng, Zili Zhang and Zi Wan

Remote Sens. 2016, 8(8), 647; https://doi.org/10.3390/rs8080647 - 09 Aug 2016

Cited by 19 | Viewed by 6954

Abstract

Wetlands are important ecosystems as they are known as the “kidney of the earth”. Particularly, urban wetlands play an important role in providing both natural and social beneficial services. However, urban wetlands are suffering from various human impacts, such as excessive land use [...] Read more.

Wetlands are important ecosystems as they are known as the “kidney of the earth”. Particularly, urban wetlands play an important role in providing both natural and social beneficial services. However, urban wetlands are suffering from various human impacts, such as excessive land use conversion, air and water pollution, especially those in developing countries undergoing rapid industrialization and urbanization. Therefore, it is of great necessity to derive timely biomass information for optimal design, management and protection of urban wetlands. In this paper, we develop a set of models for estimating above ground biomass (AGB) in Xixi National Wetland Park in Hangzhou, China by using optical images and Synthetic Aperture Radar (SAR) images. A series of vegetation indices (VIs) derived from optical data is introduced along with spectral data. The modeling methods consist of (1) curve estimation; (2) linear regression for multivariable model; (3) Back Propagation Neural Network (BPNN) modeling. Curve estimation is a combination of linear and nonlinear regressions. It is applied to generate AGB models from a single variable including Normalized Difference Vegetation Index (NDVI) and radar backscatter coefficient. The models are then compared via three accuracy metrics. According to the results, SAR models generally show better accuracy than optical models and BPNN models show the greatest accuracy among all the models. The BPNN model from the combination of Terra Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and European Remote-Sensing Satellite-2 (ERS-2) SAR (Synthetic Aperture Radar) image has the least root mean square (RMSE, 0.396 kg/m²), least mean absolute error (MAE, 0.256 kg/m²) and the greatest correlation coefficient (0.974). The results indicate that AGB can be estimated by integrating optical and SAR imagery. Four maps of AGB are derived to illustrate the distribution of AGB in the study area. The total AGB in the study area is estimated to be between 165,000 and 210,000 kg/m². Full article

(This article belongs to the Special Issue What can Remote Sensing Do for the Conservation of Wetlands?)

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Open AccessArticle

Evaluating the Effectiveness of Conservation on Mangroves: A Remote Sensing-Based Comparison for Two Adjacent Protected Areas in Shenzhen and Hong Kong, China

by Mingming Jia, Mingyue Liu, Zongming Wang, Dehua Mao, Chunying Ren and Haishan Cui

Remote Sens. 2016, 8(8), 627; https://doi.org/10.3390/rs8080627 - 29 Jul 2016

Cited by 49 | Viewed by 10904

Abstract

Mangroves are ecologically important ecosystems and globally protected. The purpose of this study was to evaluate the effectiveness of mangrove conservation efforts in two adjacent protected areas in China that were under the management policies of the Ramsar Convention (Mai Po Marshes Nature [...] Read more.

Mangroves are ecologically important ecosystems and globally protected. The purpose of this study was to evaluate the effectiveness of mangrove conservation efforts in two adjacent protected areas in China that were under the management policies of the Ramsar Convention (Mai Po Marshes Nature Reserve (MPMNR), Hong Kong) and China’s National Nature Reserve System (Futian Mangrove National Nature Reserve (FMNNR), Shenzhen). To achieve this goal, eleven Landsat images were chosen and classified, areal extent and landscape metrics were then calculated. The results showed that: from 1973–2015, the areal extent of mangroves in both reserves increased, but the net change for the MPMNR (281.43 hm²) was much higher than those of the FMNNR (101.97 hm²). In general, the area-weighted centroid of the mangroves in FMNNR moved seaward by approximately 120 m, whereas in the MPMNR, the centroid moved seaward even farther (410 m). Although both reserves saw increased integrality and connectivity of the mangrove patches, the patches in the MPMNR always had higher integrality than those in the FMNNR. We concluded that the mangroves in the MPMNR were more effectively protected than those in the FMNNR. This study may provide assistance to the formulation of generally accepted criteria for remote sensing-based evaluation of conservation effectiveness, and may facilitate the development of appropriate mangrove forest conservation and management strategies in other counties. Full article

(This article belongs to the Special Issue What can Remote Sensing Do for the Conservation of Wetlands?)

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4597 KiB

Open AccessArticle

Monitoring Hydrological Patterns of Temporary Lakes Using Remote Sensing and Machine Learning Models: Case Study of La Mancha Húmeda Biosphere Reserve in Central Spain

by Carolina Doña, Ni-Bin Chang, Vicente Caselles, Juan Manuel Sánchez, Lluís Pérez-Planells, Maria Del Mar Bisquert, Vicente García-Santos, Sanaz Imen and Antonio Camacho

Remote Sens. 2016, 8(8), 618; https://doi.org/10.3390/rs8080618 - 26 Jul 2016

Cited by 21 | Viewed by 7818

Abstract

The Biosphere Reserve of La Mancha Húmeda is a wetland-rich area located in central Spain. This reserve comprises a set of temporary lakes, often saline, where water level fluctuates seasonally. Water inflows come mainly from direct precipitation and runoff of small lake watersheds. [...] Read more.

The Biosphere Reserve of La Mancha Húmeda is a wetland-rich area located in central Spain. This reserve comprises a set of temporary lakes, often saline, where water level fluctuates seasonally. Water inflows come mainly from direct precipitation and runoff of small lake watersheds. Most of these lakes lack surface outlets and behave as endorheic systems, where water withdrawal is mainly due to evaporation, causing salt accumulation in the lake beds. Remote sensing was used to estimate the temporal variation of the flooded area in these lakes and their associated hydrological patterns related to the seasonality of precipitation and evapotranspiration. Landsat 7 ETM+ satellite images for the reference period 2013–2015 were jointly used with ground-truth datasets. Several inverse modeling methods, such as two-band and multispectral indices, single-band threshold, classification methods, artificial neural network, support vector machine and genetic programming, were applied to retrieve information on the variation of the flooded areas. Results were compared to ground-truth data, and the classification errors were evaluated by means of the kappa coefficient. Comparative analyses demonstrated that the genetic programming approach yielded the best results, with a kappa value of 0.98 and a total error of omission-commission of 2%. The dependence of the variations in the water-covered area on precipitation and evaporation was also investigated. The results show the potential of the tested techniques to monitor the hydrological patterns of temporary lakes in semiarid areas, which might be useful for management strategy-linked lake conservation and specifically to accomplish the goals of both the European Water Framework Directive and the Habitats Directive. Full article

(This article belongs to the Special Issue What can Remote Sensing Do for the Conservation of Wetlands?)

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5557 KiB

Open AccessArticle

Potential of ENVISAT Radar Altimetry for Water Level Monitoring in the Pantanal Wetland

by Denise Dettmering, Christian Schwatke, Eva Boergens and Florian Seitz

Remote Sens. 2016, 8(7), 596; https://doi.org/10.3390/rs8070596 - 14 Jul 2016

Cited by 24 | Viewed by 8213

Abstract

Wetlands are important ecosystems playing an essential role for continental water regulation and the hydrologic cycle. Moreover, they are sensitive to climate changes as well as anthropogenic influences, such as land-use or dams. However, the monitoring of these regions is challenging as they [...] Read more.

Wetlands are important ecosystems playing an essential role for continental water regulation and the hydrologic cycle. Moreover, they are sensitive to climate changes as well as anthropogenic influences, such as land-use or dams. However, the monitoring of these regions is challenging as they are normally located in remote areas without in situ measurement stations. Radar altimetry provides important measurements for monitoring and analyzing water level variations in wetlands and flooded areas. Using the example of the Pantanal region in South America, this study demonstrates the capability and limitations of ENVISAT radar altimeter for monitoring water levels in inundation areas. By applying an innovative processing method consisting of a rigorous data screening by means of radar echo classification as well as an optimized waveform retracking, water level time series with respect to a global reference and with a temporal resolution of about one month are derived. A comparison between altimetry-derived height variations and six in situ time series reveals accuracies of 30 to 50 cm RMS. The derived water level time series document seasonal height variations of up to 1.5 m amplitude with maximum water levels between January and June. Large scale geographical pattern of water heights are visible within the wetland. However, some regions of the Pantanal show water level variations less than a few decimeter, which is below the accuracies of the method. These areas cannot be reliably monitored by ENVISAT. Full article

(This article belongs to the Special Issue What can Remote Sensing Do for the Conservation of Wetlands?)

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11262 KiB

Open AccessArticle

Analysis and Mapping of the Spectral Characteristics of Fractional Green Cover in Saline Wetlands (NE Spain) Using Field and Remote Sensing Data

by Manuela Domínguez-Beisiegel, Carmen Castañeda, Bernard Mougenot and Juan Herrero

Remote Sens. 2016, 8(7), 590; https://doi.org/10.3390/rs8070590 - 13 Jul 2016

Cited by 16 | Viewed by 7402

Abstract

Inland saline wetlands are complex systems undergoing continuous changes in moisture and salinity and are especially vulnerable to human pressures. Remote sensing is helpful to identify vegetation change in semi-arid wetlands and to assess wetland degradation. Remote sensing-based monitoring requires identification of the [...] Read more.

Inland saline wetlands are complex systems undergoing continuous changes in moisture and salinity and are especially vulnerable to human pressures. Remote sensing is helpful to identify vegetation change in semi-arid wetlands and to assess wetland degradation. Remote sensing-based monitoring requires identification of the spectral characteristics of soils and vegetation and their correspondence with the vegetation cover and soil conditions. We studied the spectral characteristics of soils and vegetation of saline wetlands in Monegros, NE Spain, through field and satellite images. Radiometric and complementary field measurements in two field surveys in 2007 and 2008 were collected in selected sites deemed as representative of different soil moisture, soil color, type of vegetation, and density. Despite the high local variability, we identified good relationships between field spectral data and Quickbird images. A methodology was established for mapping the fraction of vegetation cover in Monegros and other semi-arid areas. Estimating vegetation cover in arid wetlands is conditioned by the soil background and by the occurrence of dry and senescent vegetation accompanying the green component of perennial salt-tolerant plants. Normalized Difference Vegetation Index (NDVI) was appropriate to map the distribution of the vegetation cover if the green and yellow-green parts of the plants are considered. Full article

(This article belongs to the Special Issue What can Remote Sensing Do for the Conservation of Wetlands?)

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5678 KiB

Open AccessArticle

Mapping and Characterization of Hydrological Dynamics in a Coastal Marsh Using High Temporal Resolution Sentinel-1A Images

by Cécile Cazals, Sébastien Rapinel, Pierre-Louis Frison, Anne Bonis, Grégoire Mercier, Clément Mallet, Samuel Corgne and Jean-Paul Rudant

Remote Sens. 2016, 8(7), 570; https://doi.org/10.3390/rs8070570 - 05 Jul 2016

Cited by 78 | Viewed by 9470

Abstract

In Europe, water levels in wetlands are widely controlled by environmental managers and farmers. However, the influence of these management practices on hydrodynamics and biodiversity remains poorly understood. This study assesses advantages of using radar data from the recently launched Sentinel-1A satellite to [...] Read more.

In Europe, water levels in wetlands are widely controlled by environmental managers and farmers. However, the influence of these management practices on hydrodynamics and biodiversity remains poorly understood. This study assesses advantages of using radar data from the recently launched Sentinel-1A satellite to monitor hydrological dynamics of the Poitevin marshland in western France. We analyze a time series of 14 radar images acquired in VV and HV polarizations from December 2014 to May 2015 with a 12-day time step. Both polarizations are used with a hysteresis thresholding algorithm which uses both spatial and temporal information to distinguish open water, flooded vegetation and non-flooded grassland. Classification results are compared to in situ piezometric measurements combined with a Digital Terrain Model derived from LiDAR data. Results reveal that open water is successfully detected, whereas flooded grasslands with emergent vegetation and fine-grained patterns are detected with moderate accuracy. Five hydrological regimes are derived from the flood duration and mapped. Analysis of time steps in the time series shows that decreased temporal repetitivity induces significant differences in estimates of flood duration. These results illustrate the great potential to monitor variations in seasonal floods with the high temporal frequency of Sentinel-1A acquisitions. Full article

(This article belongs to the Special Issue What can Remote Sensing Do for the Conservation of Wetlands?)

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4555 KiB

Open AccessArticle

Mapping Dynamics of Inundation Patterns of Two Largest River-Connected Lakes in China: A Comparative Study

by Guiping Wu and Yuanbo Liu

Remote Sens. 2016, 8(7), 560; https://doi.org/10.3390/rs8070560 - 30 Jun 2016

Cited by 24 | Viewed by 5632

Abstract

Poyang Lake and Dongting Lake are the two largest freshwater lakes in China. The lakes are located approximately 300 km apart on the middle reaches of the Yangtze River and are differently connected through their respective tributary systems, which will lead to different [...] Read more.

Poyang Lake and Dongting Lake are the two largest freshwater lakes in China. The lakes are located approximately 300 km apart on the middle reaches of the Yangtze River and are differently connected through their respective tributary systems, which will lead to different river–lake water exchanges and discharges. Thus, differences in their morphological and hydrological conditions should induce individual lake spatio-temporal inundation patterns. Quantitative comparative analyses of the dynamic inundation patterns of Poyang Lake and Dongting Lake are of great importance to basic biogeochemical and ecological studies. In this study, using Moderate Resolution Imaging Spectoradiometer (MODIS) satellite imagery and a geographic information system (GIS) analysis method, we systematically compared the spatio-temporal inundation patterns of the two river-connected lakes by analyses of the lake area, the inundation frequencies (IFs) and the water variation rates (WVRs). The results indicate that there was a significant declining trend in the lakes’ inundation area from 2000 to 2011. The inundation areas of Poyang Lake and Dongting Lake, decreased by 54.74% and 40.46%, with an average annual decrease rate of 109.74 km²/y and 52.37 km²/y, respectively. The alluvial regions near Dongting Lake expressed much lower inundation frequencies, averaged over multiple years, than the alluvial regions near Poyang Lake. There was an obvious spatial gradient in the distribution of water inundation for Poyang Lake; the monthly mean IF slowly increased from north to south during the low-water, rising, and high-water periods. However, Dongting Lake expressed a clear zonal distribution of water inundation, especially in the low-water and rising periods. In addition, the WVRs of the two lakes differently changed in space throughout the year, but were in good agreement with the changing processes of water expansion or shrinkage. The different inundation frequencies and water variation rates in the two lakes may possibly depend on many intrinsic factors, including surface discharges from their respective tributaries, river–lake water exchanges and the lakes’ topographical characteristics. These findings are valuable for policymakers because they may lead to different decisions and policies for these two complex river–lake systems. Full article

(This article belongs to the Special Issue What can Remote Sensing Do for the Conservation of Wetlands?)

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15079 KiB

Open AccessArticle

Remote Sensing Measures Restoration Successes, but Canopy Heights Lag in Restoring Floodplain Vegetation

by Samantha K. Dawson, Adrian Fisher, Richard Lucas, David K. Hutchinson, Peter Berney, David Keith, Jane A. Catford and Richard T. Kingsford

Remote Sens. 2016, 8(7), 542; https://doi.org/10.3390/rs8070542 - 24 Jun 2016

Cited by 11 | Viewed by 7028

Abstract

Wetlands worldwide are becoming increasingly degraded, and this has motivated many attempts to manage and restore wetland ecosystems. Restoration actions require a large resource investment, so it is critical to measure the outcomes of these management actions. We evaluated the restoration of floodplain [...] Read more.

Wetlands worldwide are becoming increasingly degraded, and this has motivated many attempts to manage and restore wetland ecosystems. Restoration actions require a large resource investment, so it is critical to measure the outcomes of these management actions. We evaluated the restoration of floodplain wetland vegetation across a chronosequence of land uses, using remote sensing analyses. We compared the Landsat-based fractional cover of restoration areas with river red gum and lignum reference communities, which functioned as a fixed target for restoration, over three time periods: (i) before agricultural land use (1987–1997); (ii) during the peak of agricultural development (2004–2007); and (iii) post-restoration of flooding (2010–2015). We also developed LiDAR-derived canopy height models (CHMs) for comparison over the second and third time periods. Inundation was crucial for restoration, with many fields showing little sign of similarity to target vegetation until after inundation, even if agricultural land uses had ceased. Fields cleared or cultivated for only one year had greater restoration success compared to areas cultivated for three or more years. Canopy height increased most in the fields that were cleared and cultivated for a short duration, in contrast to those cultivated for >12 years, which showed few signs of recovery. Restoration was most successful in fields with a short development duration after the intervention, but resulting dense monotypic stands of river cooba require future monitoring and possibly intervention to prevent sustained dominance. Fields with intensive land use histories may need to be managed as alternative, drier flood-dependent vegetation communities, such as black box (Eucalyptus largiflorens) grasslands. Remotely-sensed data provided a powerful measurement technique for tracking restoration success over a large floodplain. Full article

(This article belongs to the Special Issue What can Remote Sensing Do for the Conservation of Wetlands?)

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5127 KiB

Open AccessArticle

Identification of Woodland Vernal Pools with Seasonal Change PALSAR Data for Habitat Conservation

by Laura L. Bourgeau-Chavez, Yu Man Lee, Michael Battaglia, Sarah L. Endres, Zachary M. Laubach and Kirk Scarbrough

Remote Sens. 2016, 8(6), 490; https://doi.org/10.3390/rs8060490 - 10 Jun 2016

Cited by 18 | Viewed by 8772

Abstract

Woodland vernal pools are important, small, cryptic, ephemeral wetland ecosystems that are vulnerable to a changing climate and anthropogenic influences. To conserve woodland vernal pools for the state of Michigan USA, vernal pool detection and mapping methods were sought that would be efficient, [...] Read more.

Woodland vernal pools are important, small, cryptic, ephemeral wetland ecosystems that are vulnerable to a changing climate and anthropogenic influences. To conserve woodland vernal pools for the state of Michigan USA, vernal pool detection and mapping methods were sought that would be efficient, cost-effective, repeatable and accurate. Satellite-based L-band radar data from the high (10 m) resolution Japanese ALOS PALSAR sensor were evaluated for suitability in vernal pool detection beneath forest canopies. In a two phase study, potential vernal pool (PVP) detection was first assessed with unsupervised PALSAR (LHH) two season change detection (spring when flooded—summer when dry) and validated with 268, 1 ha field-sampled test cells. This resulted in low false negatives (14%–22%), overall map accuracy of 48% to 62% and high commission error (66%). These results make this blind two-season PALSAR approach for cryptic PVP detection of use for locating areas of high vernal pool likelihood. In a second phase of the research, PALSAR was integrated with 10 m USGS DEM derivatives in a machine learning classifier, which greatly improved overall PVP map accuracies (91% to 93%). This supervised approach with PALSAR was found to produce better mapping results than using LiDAR intensity or C-band SAR data in a fusion with the USGS DEM-derivatives. Full article

(This article belongs to the Special Issue What can Remote Sensing Do for the Conservation of Wetlands?)

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7638 KiB

Open AccessArticle

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

by Iryna Dronova, Steven R. Beissinger, James W. Burnham and Peng Gong

Remote Sens. 2016, 8(6), 462; https://doi.org/10.3390/rs8060462 - 31 May 2016

Cited by 37 | Viewed by 7651

Abstract

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 [...] Read more.

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. Full article

(This article belongs to the Special Issue What can Remote Sensing Do for the Conservation of Wetlands?)

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51155 KiB

Open AccessArticle

Mapping Wetlands in Zambia Using Seasonal Backscatter Signatures Derived from ENVISAT ASAR Time Series

by Stefan Schlaffer, Marco Chini, Denise Dettmering and Wolfgang Wagner

Remote Sens. 2016, 8(5), 402; https://doi.org/10.3390/rs8050402 - 12 May 2016

Cited by 47 | Viewed by 9467

Abstract

Wetlands are considered a challenging environment for mapping approaches based on Synthetic Aperture Radar (SAR) data due to their often complex internal structures and the diverse backscattering mechanisms caused by vegetation, soil moisture and flood dynamics contributing to the resulting imagery. In this [...] Read more.

Wetlands are considered a challenging environment for mapping approaches based on Synthetic Aperture Radar (SAR) data due to their often complex internal structures and the diverse backscattering mechanisms caused by vegetation, soil moisture and flood dynamics contributing to the resulting imagery. In this study, a time series of >100 SAR images acquired by ENVISAT during a time period of ca. two years over the Kafue River basin in Zambia was compared to water heights derived from radar altimetry and surface soil moisture from a reanalysis dataset. The backscatter time series were analyzed using a harmonic model to characterize the seasonality in C-band backscatter caused by the interaction of flood and soil moisture dynamics. As a result, characteristic seasonal signatures could be derived for permanent water bodies, seasonal open water, persistently flooded vegetation and seasonally flooded vegetation. Furthermore, the analysis showed that the influence of local incidence angle could be accounted for by a linear shift in backscatter averaged over time, even in wetland areas where the dominant scattering mechanism can change depending on the season. The retrieved harmonic model parameters were then used in an unsupervised classification to detect wetland backscattering classes at the regional scale. A total area of 7800 km² corresponding to 7.6% of the study area was classified as either one of the wetland backscattering classes. The results demonstrate the value of seasonality parameters extracted from C-band SAR time series for wetland mapping. Full article

(This article belongs to the Special Issue What can Remote Sensing Do for the Conservation of Wetlands?)

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9666 KiB

Open AccessArticle

InSAR-Based Mapping of Tidal Inundation Extent and Amplitude in Louisiana Coastal Wetlands

by Talib Oliver-Cabrera and Shimon Wdowinski

Remote Sens. 2016, 8(5), 393; https://doi.org/10.3390/rs8050393 - 07 May 2016

Cited by 34 | Viewed by 10171

Abstract

The Louisiana coast is among the most productive coastal areas in the US and home to the largest coastal wetland area in the nation. However, Louisiana coastal wetlands have been disappearing at an alarming rate due to natural and anthropogenic processes, including sea [...] Read more.

The Louisiana coast is among the most productive coastal areas in the US and home to the largest coastal wetland area in the nation. However, Louisiana coastal wetlands have been disappearing at an alarming rate due to natural and anthropogenic processes, including sea level rise, land subsidence and infrastructure development. Wetland loss occurs mainly along the tidal zone, which varies in width and morphology along the Louisiana shoreline. In this study, we use Interferometric Synthetic Aperture Radar (InSAR) observations to detect the extent of the tidal inundation zone and evaluate the interaction between tidal currents and coastal wetlands. Our data consist of ALOS and Radarsat-1 observations acquired between 2006–2011 and 2003–2008, respectively. Interferometric processing of the data provides detailed maps of water level changes in the tidal zone, which are validated using sea level data from a tide gauge station. Our results indicate vertical tidal changes up to 30 cm and horizontal tidal flow limited to 5–15 km from open waters. The results also show that the tidal inundation is disrupted by various man-made structures, such as canals and roads, which change the natural tidal flow interaction with the coast. Full article

(This article belongs to the Special Issue What can Remote Sensing Do for the Conservation of Wetlands?)

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2521 KiB

Open AccessArticle

Variability and Changes in Climate, Phenology, and Gross Primary Production of an Alpine Wetland Ecosystem

by Xiaoming Kang, Yanbin Hao, Xiaoyong Cui, Huai Chen, Sanxiang Huang, Yangong Du, Wei Li, Paul Kardol, Xiangming Xiao and Lijuan Cui

Remote Sens. 2016, 8(5), 391; https://doi.org/10.3390/rs8050391 - 06 May 2016

Cited by 58 | Viewed by 7686

Abstract

Quantifying the variability and changes in phenology and gross primary production (GPP) of alpine wetlands in the Qinghai–Tibetan Plateau under climate change is essential for assessing carbon (C) balance dynamics at regional and global scales. In this study, in situ eddy covariance (EC) [...] Read more.

Quantifying the variability and changes in phenology and gross primary production (GPP) of alpine wetlands in the Qinghai–Tibetan Plateau under climate change is essential for assessing carbon (C) balance dynamics at regional and global scales. In this study, in situ eddy covariance (EC) flux tower observations and remote sensing data were integrated with a modified, satellite-based vegetation photosynthesis model (VPM) to investigate the variability in climate change, phenology, and GPP of an alpine wetland ecosystem, located in Zoige, southwestern China. Two-year EC data and remote sensing vegetation indices showed that warmer temperatures corresponded to an earlier start date of the growing season, increased GPP, and ecosystem respiration, and hence increased the C sink strength of the alpine wetlands. Twelve-year long-term simulations (2000–2011) showed that: (1) there were significantly increasing trends for the mean annual enhanced vegetation index (EVI), land surface water index (LSWI), and growing season GPP (R² ≥ 0.59, p < 0.01) at rates of 0.002, 0.11 year⁻¹ and 16.32 g·C·m⁻²·year⁻¹, respectively, which was in line with the observed warming trend (R² = 0.54, p = 0.006); (2) the start and end of the vegetation growing season (SOS and EOS) experienced a continuous advancing trend at a rate of 1.61 days·year⁻¹ and a delaying trend at a rate of 1.57 days·year⁻¹ from 2000 to 2011 (p ≤ 0.04), respectively; and (3) with increasing temperature, the advanced SOS and delayed EOS prolonged the wetland’s phenological and photosynthetically active period and, thereby, increased wetland productivity by about 3.7–4.2 g·C·m⁻²·year⁻¹ per day. Furthermore, our results indicated that warming and the extension of the growing season had positive effects on carbon uptake in this alpine wetland ecosystem. Full article

(This article belongs to the Special Issue What can Remote Sensing Do for the Conservation of Wetlands?)

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4133 KiB

Open AccessArticle

Hydrological Response of Alpine Wetlands to Climate Warming in the Eastern Tibetan Plateau

by Wenjiang Zhang, Yonghong Yi, Kechao Song, John S. Kimball and Qifeng Lu

Remote Sens. 2016, 8(4), 336; https://doi.org/10.3390/rs8040336 - 18 Apr 2016

Cited by 36 | Viewed by 6782

Abstract

Alpine wetlands in the Tibetan Plateau (TP) play a crucial role in the regional hydrological cycle due to their strong influence on surface ecohydrological processes; therefore, understanding how TP wetlands respond to climate change is essential for projecting their future condition and potential [...] Read more.

Alpine wetlands in the Tibetan Plateau (TP) play a crucial role in the regional hydrological cycle due to their strong influence on surface ecohydrological processes; therefore, understanding how TP wetlands respond to climate change is essential for projecting their future condition and potential vulnerability. We investigated the hydrological responses of a large TP wetland complex to recent climate change, by combining multiple satellite observations and in-situ hydro-meteorological records. We found different responses of runoff production to regional warming trends among three basins with similar climate, topography and vegetation cover but different wetland proportions. The basin with larger wetland proportion (40.1%) had a lower mean runoff coefficient (0.173 ± 0.006), and also showed increasingly lower runoff level (−3.9% year⁻¹, p = 0.002) than the two adjacent basins. The satellite-based observations showed an increasing trend of annual non-frozen period, especially in the wetland-dominated region (2.64 day·year⁻¹, p < 0.10), and a strong extension of vegetation growing-season (0.26–0.41 day·year⁻¹, p < 0.10). Relatively strong increasing trends in evapotranspiration (ET) (~1.00 mm·year⁻¹, p < 0.01) and the vertical temperature gradient above ground surface (0.043 °C·year⁻¹, p < 0.05) in wetland-dominant areas were documented from satellite-based ET observations and weather station records. These results indicate recent surface drying and runoff reduction of alpine wetlands, and their potential vulnerability to degradation with continued climate warming. Full article

(This article belongs to the Special Issue What can Remote Sensing Do for the Conservation of Wetlands?)

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12289 KiB

Open AccessArticle

Monitoring of the Lac Bam Wetland Extent Using Dual-Polarized X-Band SAR Data

by Linda Moser, Andreas Schmitt, Anna Wendleder and Achim Roth

Remote Sens. 2016, 8(4), 302; https://doi.org/10.3390/rs8040302 - 05 Apr 2016

Cited by 51 | Viewed by 8025

Abstract

Wetlands in semi-arid Africa are vital as water resource for local inhabitants and for biodiversity, but they are prone to strong seasonal fluctuations. Lac Bam is the largest natural freshwater lake in Burkina Faso, its water is mixed with patches of floating or [...] Read more.

Wetlands in semi-arid Africa are vital as water resource for local inhabitants and for biodiversity, but they are prone to strong seasonal fluctuations. Lac Bam is the largest natural freshwater lake in Burkina Faso, its water is mixed with patches of floating or flooded vegetation, and very turbid and sediment-rich. These characteristics as well as the usual cloud cover during the rainy season can limit the suitability of optical remote sensing data for monitoring purposes. This study demonstrates the applicability of weather-independent dual-polarimetric Synthetic Aperture Radar (SAR) data for the analysis of spatio-temporal wetland dynamics. A TerraSAR-X repeat-pass time series of dual-co-polarized HH-VV StripMap data—with intervals of 11 days, covering two years (2013–2015) from the rainy to the dry season—was processed to normalized Kennaugh elements and classified mono-temporally and multi-temporally. Land cover time series and seasonal duration maps were generated for the following four classes: open water, flooded/floating vegetation, irrigated cultivation, and land (non-wetland). The added value of dual-polarimetric SAR data is demonstrated by significantly higher multitemporal classification accuracies, where the overall accuracy (88.5%) exceeds the classification accuracy using single-polarimetric SAR intensity data (82.2%). For relevant change classes involving flooded vegetation and irrigated fields dual-polarimetric data (accuracies: 75%–97%) are favored to single-polarimetric data (42%–87%). This study contributes to a better understanding of the dynamics of semi-arid African wetlands in terms of water areas including water with flooded vegetation, and the location and timing of irrigated cultivations. Full article

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11337 KiB

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An Integrated Field and Remote Sensing Method for Mapping Seagrass Species, Cover, and Biomass in Southern Thailand

by Werapong Koedsin, Wissarut Intararuang, Raymond J. Ritchie and Alfredo Huete

Remote Sens. 2016, 8(4), 292; https://doi.org/10.3390/rs8040292 - 30 Mar 2016

Cited by 50 | Viewed by 11342

Abstract

Accurate and up-to-date maps of seagrass biodiversity are important for marine resource management but it is very challenging to test the accuracy of remote sensing techniques for mapping seagrass in coastal waters with variable water turbidity. In this study, Worldview-2 (WV-2) imagery was [...] Read more.

Accurate and up-to-date maps of seagrass biodiversity are important for marine resource management but it is very challenging to test the accuracy of remote sensing techniques for mapping seagrass in coastal waters with variable water turbidity. In this study, Worldview-2 (WV-2) imagery was combined with field sampling to demonstrate the capability of mapping species type, percentage cover, and above-ground biomass of seagrasses in monsoonal southern Thailand. A high accuracy positioning technique, involving the Real Time Kinematic (RTK) Global Navigation Satellite System (GNSS), was used to record field sample data positions and reduce uncertainties in matching locations between satellite and field data sets. Our results showed high accuracy (90.67%) in mapping seagrass distribution and moderate accuracies for mapping percentage cover and species type (73.74% and 75.00%, respectively). Seagrass species type mapping was successfully achieved despite discrimination confusion among Halophila ovalis, Thalassia hemprichii, and Enhalus acoroides species with greater than 50% cover. The green, yellow, and near infrared spectral channels of WV-2 were used to estimate the above-ground biomass using a multiple linear regression model (RMSE of ±10.38 g·DW/m², R = 0.68). The average total above-ground biomass was 23.95 ± 10.38 g·DW/m². The seagrass maps produced in this study are an important step towards measuring the attributes of seagrass biodiversity and can be used as inputs to seagrass dynamic models and conservation efforts. Full article

(This article belongs to the Special Issue What can Remote Sensing Do for the Conservation of Wetlands?)

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1318 KiB

Open AccessArticle

Using Remotely Sensed Imagery to Document How Land Use Drives Turbidity of Playa Waters in Texas

by Scott M. Starr, Lucas J. Heintzman, Kevin R. Mulligan, Lucia S. Barbato and Nancy E. McIntyre

Remote Sens. 2016, 8(3), 192; https://doi.org/10.3390/rs8030192 - 26 Feb 2016

Cited by 13 | Viewed by 6892

Abstract

Sedimentation (primarily from human land use) is a major threat to runoff-fed wetlands of the Great Plains of North America (playas), but it is unknown how many playas are turbid, how prevalence of turbidity has changed over time, and how turbidity is related [...] Read more.

Sedimentation (primarily from human land use) is a major threat to runoff-fed wetlands of the Great Plains of North America (playas), but it is unknown how many playas are turbid, how prevalence of turbidity has changed over time, and how turbidity is related to surrounding land use. We used remotely sensed imagery to assess sedimentation in the waters of over 7700 playa basins in Texas on four dates during a 29-year span: 25 July 1986 (a regionally wet time), 3 May 2014 (during drought), 4 June 2014 (after the drought was broken), and 25 July 2015 (one year post-drought). Even on the wettest date examined, 64% of playa basins did not hold water. Turbidity varied over time, was already present in over half of the basins examined in 1986, and prevalence of turbidity was not simply proportional to overall wet playa abundance. There was an increase in total and irrigated cropland in our focal region and a statistically significant association between sedimentation and land use within 100 m of a playa: clear playas were associated with more urban development and pasture/grassland, and turbid playas were surrounded mostly by cropland. Full article

(This article belongs to the Special Issue What can Remote Sensing Do for the Conservation of Wetlands?)

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5268 KiB

Open AccessArticle

Mapping Plant Functional Types in Floodplain Wetlands: An Analysis of C-Band Polarimetric SAR Data from RADARSAT-2

by Natalia S. Morandeira, Francisco Grings, Claudia Facchinetti and Patricia Kandus

Remote Sens. 2016, 8(3), 174; https://doi.org/10.3390/rs8030174 - 25 Feb 2016

Cited by 40 | Viewed by 7090

Abstract

The inclusion of functional approaches on wetland characterizations and on biodiversity assessments improves our understanding of ecosystem functioning. In the Lower Paraná River floodplain, we assessed the ability of C-band polarimetric SAR data of contrasting incidence angles to discriminate wetland areas dominated by [...] Read more.

The inclusion of functional approaches on wetland characterizations and on biodiversity assessments improves our understanding of ecosystem functioning. In the Lower Paraná River floodplain, we assessed the ability of C-band polarimetric SAR data of contrasting incidence angles to discriminate wetland areas dominated by different plant functional types (PFTs). Unsupervised H/α and H/A/α Wishart classifications were implemented on two RADARSAT-2 images differing in their incidence angles (FQ24 and FQ08). Obtained classes were assigned to the information classes (open water, bare soil and PFTs) by a priori labeling criteria that involved the expected interaction mechanisms between SAR signal and PFTs as well as the relative values of H and α. The product obtained with the shallow incidence angle scene had a higher accuracy than the one obtained with the steep incidence angle product (61.5% vs. 46.2%). We show how a systematic analysis of the H/A/α space can be used to improve the knowledge about the radar polarimetric response of herbaceous vegetation. The map obtained provides novel ecologically relevant information about plant strategies dominating the floodplain. Since the obtained classes can be interpreted in terms of their functional features, the approach is a valuable tool for predicting vegetation response to floods, anthropic impacts and climate change. Full article

(This article belongs to the Special Issue What can Remote Sensing Do for the Conservation of Wetlands?)

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