Local isotropy indicator for SAR image filtering: application to Envisat/ASAR images of the Doñana Wetland

©2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.This paper explores a geometrical and computationally simple operator, named Ds, for local isotropy assessment on SAR images. It is assumed that isotropic intensity distributions in natural areas, either textured or nontextured, correspond to a single cover class. Ds is used to measure isotropy in processing neighborhoods and decide if they can be considered as belonging to a unique cover class. The speckle statistical properties are used to determine suitable Ds thresholds for discriminating heterogeneous targets from isotropic cover types at different window sizes. An assessment of Ds as an edge detector showed sensitivities similar to those of the ratio edge operator for straight, sharp boundaries, centered in the processing window, but significantly better sensitivity for detecting heterogeneities during the window expansion in multiresolution filtering. Furthermore, Ds presents the advantage versus the ratio edge coefficient of being rotationally invariant, and its computation indicates the direction of the main intensity gradient in the processing window. The Ds operator is used in a multiresolution fashion for filtering ASAR scenes of the Doñana wetland. The intensities in isotropic areas are averaged in order to flatten fluctuations within cover types and facilitate a subsequent land cover classification. The results show high degree of smoothing within textured cover classes, plus effective spatial adaptation to gradients and irregular boundaries, substantiating the usefulness of this operator for filtering SAR data of natural areas with the purpose of classification.Peer ReviewedPostprint (author's final draft

Estimating flooded area and mean water level using active and passive microwaves: the example of Paraná River Delta floodplain

This paper describes a procedure to estimate both the fraction of flooded area and the mean water level in vegetated river floodplains by using a synergy of active and passive microwave signatures. In particular, C band Envisat ASAR in Wide Swath mode and AMSR-E at X, Ku and Ka band, are used. The method, which is an extension of previously developed algorithms based on passive data, exploits also model simulations of vegetation emissivity. The procedure is applied to a long flood event which occurred in the Paraná River Delta from December 2009 to April 2010. Obtained results are consistent with in situ measurements of river water level

Morphological Development of the German Wadden Sea from 1996 to 2009 Determined with the Waterline Method and SAR and Landsat Satellite Images

The Dutch, German, and Danish Wadden Sea contains some of the largest undisturbed tidal flats in the world of about 10,000 km2. The research areas covered in this thesis are the North Frisian, Neuwerk, and Cuxhaven regions of the German Wadden Sea. The goal of the thesis is to use the waterline method with SAR and optical images to derive topographic maps in order to analyze the morphological development of this valuable ecological system on large spatial and engineering time scales (90 km and 14 years). Compared to earlier applications, the method is improved with respect to the geocoding step and the data coverage of the complete tidal range. The results also allow analyzing smaller scale s developmental details, such as sandbars and estuaries. Topographical maps from 1996 to 1999, and 2004 to 2009 were generated. The largest morphological differences occurred between 2009 and 1996, also observed in the -2 m isobaths map. The Bed Elevation Range of the tidal flats includes all the elevation information from 1996 to 2009 in order to identify the maximum changes during the investigation period. It shows high morphodynamic regions are outer parts of the tidal flat, sandbars, and estuaries. Vertical nodal linear regression gives the direction of the morphological development (erosion or sedimentation). Our result shows that the rate of change is mostly between -0.1 to 0.1 m/yr. Extreme erosion rate reaches over -0.3 m/yr, while extreme sedimentation rate is up to 0.36 m/yr. The absolute amount of elevation change called turnover height has a growth rate of 8.2 mm/yr, indicating the growing morphodynamic activity over the investigation period. The net balance height of the whole investigation region shows an increasing trend of 6.8 mm/yr, demonstrating an overall sedimentation. According to large-scale analyses, the most dynamic areas are the sandbars. Tertiussand, D-Steert, Gelbsand, and Medemgrund/Medemsand are given detailed discussion in this thesis. The west side of the sandbars except for Medemgrund/Medemsand face the high wave and tidal energy arriving from the open North sea, and cause large erosion towards east, while Medemgrund/Medemsand located in the Elbe estuary show migration in the opposite direction. The three cross sections of Tertiussand, Gelbsand and Medemgrund all show clearly increasing elevation if comparing the average elevation over the years 1996-1999 and 2004-2009. Since the areas of Tertiussand and Gelbsand decreased, their increased elevation might relate to internal sediment redistribution. Medemgrund increasead in area, so its increased elevation could be compensated by the adjacent tidal flat Medemsand which has significant erosion towards the north and the sediment brought from Elbe River

Investigation of Coastal Vegetation Dynamics and Persistence in Response to Hydrologic and Climatic Events Using Remote Sensing

Coastal Wetlands (CW) provide numerous imperative functions and provide an economic base for human societies. Therefore, it is imperative to track and quantify both short and long-term changes in these systems. In this dissertation, CW dynamics related to hydro-meteorological signals were investigated using a series of LANDSAT-derived normalized difference vegetation index (NDVI) data and hydro-meteorological time-series data in Apalachicola Bay, Florida, from 1984 to 2015. NDVI in forested wetlands exhibited more persistence compared to that for scrub and emergent wetlands. NDVI fluctuations generally lagged temperature by approximately three months, and water level by approximately two months. This analysis provided insight into long-term CW dynamics in the Northern Gulf of Mexico. Long-term studies like this are dependent on optical remote sensing data such as Landsat which is frequently partially obscured due to clouds and this can that makes the time-series sparse and unusable during meteorologically active seasons. Therefore, a multi-sensor, virtual constellation method is proposed and demonstrated to recover the information lost due to cloud cover. This method, named Tri-Sensor Fusion (TSF), produces a simulated constellation for NDVI by integrating data from three compatible satellite sensors. The visible and near-infrared (VNIR) bands of Landsat-8 (L8), Sentinel-2, and the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) were utilized to map NDVI and to compensate each satellite sensor\u27s shortcomings in visible coverage area. The quantitative comparison results showed a Root Mean Squared Error (RMSE) and Coefficient of Determination (R2) of 0.0020 sr-1 and 0.88, respectively between true observed and fused L8 NDVI. Statistical test results and qualitative performance evaluation suggest that TSF was able to synthesize the missing pixels accurately in terms of the absolute magnitude of NDVI. The fusion improved the spatial coverage of CWs reasonably well and ultimately increases the continuity of NDVI data for long term studies

Monitoring wetlands and water bodies in semi-arid Sub-Saharan regions

Surface water in wetlands is a critical resource in semi-arid West-African regions that are frequently exposed to droughts. Wetlands are of utmost importance for the population as well as the environment, and are subject to rapidly changing seasonal fluctuations. Dynamics of wetlands in the study area are still poorly understood, and the potential of remote sensing-derived information as a large-scale, multi-temporal, comparable and independent measurement source is not exploited. This work shows successful wetland monitoring with remote sensing in savannah and Sahel regions in Burkina Faso, focusing on the main study site Lac Bam (Lake Bam). Long-term optical time series from MODIS with medium spatial resolution (MR), and short-term synthetic aperture radar (SAR) time series from TerraSAR-X and RADARSAT-2 with high spatial resolution (HR) successfully demonstrate the classification and dynamic monitoring of relevant wetland features, e.g. open water, flooded vegetation and irrigated cultivation. Methodological highlights are time series analysis, e.g. spatio-temporal dynamics or multitemporal-classification, as well as polarimetric SAR (polSAR) processing, i.e. the Kennaugh elements, enabling physical interpretation of SAR scattering mechanisms for dual-polarized data. A multi-sensor and multi-frequency SAR data combination provides added value, and reveals that dual-co-pol SAR data is most recommended for monitoring wetlands of this type. The interpretation of environmental or man-made processes such as water areas spreading out further but retreating or evaporating faster, co-occurrence of droughts with surface water and vegetation anomalies, expansion of irrigated agriculture or new dam building, can be detected with MR optical and HR SAR time series. To capture long-term impacts of water extraction, sedimentation and climate change on wetlands, remote sensing solutions are available, and would have great potential to contribute to water management in Africa

Clasificación de coberturas en humedales utilizando datos de Sentinel-1 (Banda C): un caso de estudio en el delta del río Paraná, Argentina

[EN] With the launch of the Sentinel-1 mission, for the first time, multitemporal and dual-polarization C-band SAR data with a short revisit time is freely available. How can we use this data to generate accurate vegetation cover maps on a local scale? Our main objective was to assess the use of multitemporal C-Band Sentinel-1 data to generate wetland vegetation maps. We considered a portion of the Lower Delta of the Paraná River wetland (Argentina). Seventy-four images were acquired and 90 datasets were created with them, each one addressing a combination of seasons (spring, autumn, winter, summer, complete set), polarization (VV, HV, both), and texture measures (included or not). For each dataset, a Random Forest classifier was trained. Then, the kappa index values (k) obtained by the 90 classifications made were compared. Considering the datasets formed by the intensity values, for the winter dates the achieved kappa index values (k) were higher than 0.8, while all summer datasets achieved k up to 0.76. Including feature textures based on the GLCM showed improvements in the classifications: for the summer datasets, the k improvements were between 9% and 22% and for winter datasets improvements were up to 15%. Our results suggest that for the analyzed context, winter is the most informative season. Moreover, for dates associated with high biomass, the textures provide complementary information.[ES] Con el lanzamiento de la misión Sentinel-1, por primera vez, datos SAR de banda C multitemporales y de polarización dual, con un tiempo de revisión corto, están disponibles de forma gratuita. ¿Cómo podemos utilizar estos datos para generar mapas precisos de cobertura vegetal a escala local? Nuestro principal objetivo fue evaluar el uso de datos multitemporales de banda C Sentinel-1 para generar mapas de vegetación en humedales. Consideramos una porción del humedal del Bajo Delta del Río Paraná (Argentina). Utilizamos setenta y cuatro imágenes y creamos noventa conjuntos de datos distintos con ellas, cada uno abordando una combinación de estaciones (primavera, otoño, invierno, verano, conjunto completo), polarización (VV, HV, ambas) y medidas de textura (incluidas o no). Para cada conjunto de datos, se entrenó un clasificador Random Forest. Luego, se compararon los valores de índice kappa (k) obtenidos por las 90 clasificaciones realizadas. Teniendo en cuenta los conjuntos de datos formados por los valores de intensidad de la señal del radar, para las fechas de invierno los valores k obtenidos fueron superiores a 0,8, mientras que los conjuntos de datos de verano obtuvieron k menores a 0,76. La inclusión de los atributos de texturas basados en las matrices de GLCM mostraron mejoras en las clasificaciones: para los conjuntos de datos de verano, las mejoras de k estuvieron entre un 9% y un 22% y para los de invierno, las mejoras fueron de hasta un 15%. Nuestros resultados sugieren que para el contexto analizado, el invierno es la temporada más informativa. Además, para las fechas asociadas con alta biomasa, las texturas proporcionan información complementaria.Rajngewerc, M.; Grimson, R.; Bali, L.; Minotti, P.; Kandus, P. (2022). Cover classifications in wetlands using Sentinel-1 data (Band C): a case study in the Parana river delta, Argentina. Revista de Teledetección. (60):29-46. https://doi.org/10.4995/raet.2022.1691529466

Detection of temporarily flooded vegetation using time series of dual polarised C-band synthetic aperture radar data

The intense research of the last decades in the field of flood monitoring has shown that microwave sensors provide valuable information about the spatial and temporal flood extent. The new generation of satellites, such as the Sentinel-1 (S-1) constellation, provide a unique, temporally high-resolution detection of the earth's surface and its environmental changes. This opens up new possibilities for accurate and rapid flood monitoring that can support operational applications. Due to the observation of the earth's surface from space, large-scale flood events and their spatiotemporal changes can be monitored. This requires the adaptation of existing or the development of new algorithms, which on the one hand enable precise and computationally efficient flood detection and on the other hand can process a large amounts of data. In order to capture the entire extent of the flood area, it is essential to detect temporary flooded vegetation (TFV) areas in addition to the open water areas. The disregard of temporary flooded vegetation areas can lead to severe underestimation of the extent and volume of the flood. Under certain system and environmental conditions, Synthetic Aperture Radar (SAR) can be utilized to extract information from under the vegetation cover. Due to multiple backscattering of the SAR signal between the water surface and the vegetation, the flooded vegetation areas are mostly characterized by increased backscatter values. Using this information in combination with a continuous monitoring of the earth's surface by the S-1 satellites, characteristic time series-based patterns for temporary flooded vegetation can be identified. This combination of information provides the foundation for the time series approach presented here. This work provides a comprehensive overview of the relevant sensor and environmental parameters and their impact on the SAR signal regarding temporary open water (TOW) and TFV areas. In addition, existing methods for the derivation of flooded vegetation are reviewed and their benefits, limitations, methodological trends and potential research needs for this area are identified and assessed. The focus of the work lies in the development of a SAR and time series-based approach for the improved extraction of flooded areas by the supplementation of TFV and on the provision of a precise and rapid method for the detection of the entire flood extent. The approach developed in this thesis allows for the precise extraction of large-scale flood areas using dual-polarized C-band time series data and additional information such as topography and urban areas. The time series features include the characteristic variations (decrease and/or increase of backscatter values) on the flood date for the flood-related classes compared to the whole time series. These features are generated individually for each available polarization (VV, VH) and their ratios (VV/VH, VV-VH, VV+VV). The generation of the time series features was performed by Z-transform for each image element, taking into account the backscatter values on the flood date and the mean value and standard deviation of the backscatter values from the nonflood dates. This allowed the comparison of backscatter intensity changes between the image elements. The time series features constitute the foundation for the hierarchical threshold method for deriving flood-related classes. Using the Random Forest algorithm, the importance of the time series data for the individual flood-related classes was analyzed and evaluated. The results showed that the dual-polarized time series features are particularly relevant for the derivation of TFV. However, this may differ depending on the vegetation type and other environmental conditions. The analyses based on S-1 data in Namibia, Greece/Turkey and China during large-scale floods show the effectiveness of the method presented here in terms of classification accuracy. Theiv supplementary integration of temporary flooded vegetation areas and the use of additional information resulted in a significant improvement in the detection of the entire flood extent. It could be shown that a comparably high classification accuracy (~ 80%) was achieved for the flood extent in each of study areas. The transferability of the approach due to the application of a single time series feature regarding the derivation of open water areas could be confirmed for all study areas. Considering the seasonal component by using time series data, the seasonal variability of the backscatter signal for vegetation can be detected. This allows for an improved differentiation between flooded and non-flooded vegetation areas. Simultaneously, changes in the backscatter signal can be assigned to changes in the environmental conditions, since on the one hand a time series of the same image element is considered and on the other hand the sensor parameters do not change due to the same acquisition geometry. Overall, the proposed time series approach allows for a considerable improvement in the derivation of the entire flood extent by supplementing the TOW areas with the TFV areas

Remote sensing as a tool for the prevention, monitoring and evaluation of fires and floods

Los incendios y las inundaciones son dos de los disturbios que más frecuentemente afectan a la población humana y a los recursos naturales. La teledetección, a través de sensores remotos activos y pasivos, constituye una herramienta muy útil para el desarrollo de sistemas de prevención, seguimiento y evaluación a diferentes escalas espaciales y temporales. En este trabajo se reseñan algunos de los principales avances logrados en el campo de la teledetección de áreas quemadas e inundadas, y en el análisis de sus condiciones predisponentes y de su dinámica posterior a la perturbación. Se ha dado especial énfasis en describir los alcances y las limitaciones de algunos productos derivados de la teledetección que ya están disponibles para los usuarios en general.Fires and floods are among the most frequent perturbations that negatively affect human societies and natural resources. The availability of prevention, monitoring and evaluation systems is therefore crucial to diminish their consequences. Active and passive remote sensing instruments are a valuable tool to achieve these goals because they provide information on different spatial and temporal scales. In this article we review the progress experienced in the field of remote sensing of burnt or flooded areas, its predisposing conditions and its post perturbation dynamics. Special emphasis is given to the description of the strengths and weaknesses of some of currently available remote sensing products.Inter-American Institute for Global Change Research (CRN-2031 - US NSF GEO-0452325), el INTA (AERN4 y AERN4642) y el MINCyT (PICT 08-13931 y PICT No 32415)

Super-resolution mapping of wetland inundation from remote sensing imagery based on integration of back-propagation neural network and genetic algorithm

Mapping the spatio-temporal characteristics of wetland inundation has an important significance to the study of wetland environment and associated flora and fauna. High temporal remote sensing imagery is widely used for this purpose with the limitations of relatively low spatial resolutions. In this study, a novel method based on integration of back-propagation neural network (BP) and genetic algorithm (GA), so-called IBPGA, is proposed for super-resolution mapping of wetland inundation (SMWI) from multispectral remote sensing imagery. The IBPGA-SMWI algorithm is developed, including the fitness function and integration search strategy. IBPGA-SMWI was evaluated using Landsat TM/ETM + imagery from the Poyanghu wetland in China and the Macquarie Marshes in Australia. Compared with traditional SMWI methods, IBPGA-SMWI consistently achieved more accurate super-resolution mapping results in terms of visual and quantitative evaluations. In comparison with GA-SMWI, IBPGA-SMWI not only improved the accuracy of SMWI, but also accelerated the convergence speed of the algorithm. The sensitivity analysis of IBPGA-SMWI in relation to standard crossover rate, BP crossover rate and mutation rate was also carried out to discuss the algorithm performance. It is hoped that the results of this study will enhance the application of median-low resolution remote sensing imagery in wetland inundation mapping and monitoring, and ultimately support the studies of wetland environment.This paper was supported by the National Natural Science Foundation of China (Grant No. 41371343 and Grant No. 41001255) and the scholarship provided by the China Scholarship Council (Grant No. 201308420290)

The agricultural impact of the 2015–2016 floods in Ireland as mapped through Sentinel 1 satellite imagery

peer-reviewedIrish Journal of Agricultural and Food Research | Volume 58: Issue 1 The agricultural impact of the 2015–2016 floods in Ireland as mapped through Sentinel 1 satellite imagery R. O’Haraemail , S. Green and T. McCarthy DOI: https://doi.org/10.2478/ijafr-2019-0006 | Published online: 11 Oct 2019 PDF Abstract Article PDF References Recommendations Abstract The capability of Sentinel 1 C-band (5 cm wavelength) synthetic aperture radio detection and ranging (RADAR) (abbreviated as SAR) for flood mapping is demonstrated, and this approach is used to map the extent of the extensive floods that occurred throughout the Republic of Ireland in the winter of 2015–2016. Thirty-three Sentinel 1 images were used to map the area and duration of floods over a 6-mo period from November 2015 to April 2016. Flood maps for 11 separate dates charted the development and persistence of floods nationally. The maximum flood extent during this period was estimated to be ~24,356 ha. The depth of rainfall influenced the magnitude of flood in the preceding 5 d and over more extended periods to a lesser degree. Reduced photosynthetic activity on farms affected by flooding was observed in Landsat 8 vegetation index difference images compared to the previous spring. The accuracy of the flood map was assessed against reports of flooding from affected farms, as well as other satellite-derived maps from Copernicus Emergency Management Service and Sentinel 2. Monte Carlo simulated elevation data (20 m resolution, 2.5 m root mean square error [RMSE]) were used to estimate the flood’s depth and volume. Although the modelled flood height showed a strong correlation with the measured river heights, differences of several metres were observed. Future mapping strategies are discussed, which include high–temporal-resolution soil moisture data, as part of an integrated multisensor approach to flood response over a range of spatial scales