Cálculo del área y volumen de agua de dos reservorios de Cuba Central usando métodos de sensores remotos. Una nueva perspectiva

[EN] The availability, quality and management of water constitute essential activities of national, regional and local governments and authorities. Historic annual rain (between 1961 and 2020) in Chambas River Basin (Central Cuba) was evaluated. Two remote sensing methods (Normalized Difference Water Index and RADAR images) were used to calculate the variation of water area and volumes of two reservoirs (Chambas II and Cañada Blanca) of Ciego de Ávila Province at end of wet and dry seasons from 2014-2021. The results showed that mean annual rain was 1330.9 ± 287.4 mm and it did not showed any significant tendency at evaluated period. For both reservoirs, mean water areas measured with two methods were 19 % and 8 % smaller than the mean water area reported by authorities for the same period. The static water storage capacity (water volume) of both reservoirs varied (as area) between seasons with the greatest volume in both reservoirs recorded in October of 2017 (30.5 million of m3 in Chambas II and 45.1 million of m3 in Cañada Blanca reservoir). Large deviations of water area and volumes occurred during the dry season (lower values) and the wet season of 2017 (influenced by rain associated to of Hurricane Irma) and wet season of 2020 (influenced by rain associated to tropical storm Laura). Calculated area volume models with significant statistical correlation are another useful tool that could be used to improve water management in terms of accuracy and to increase reliable results in cases where gauge measurements are scarce or not available.[ES] La disponibilidad, calidad y manejo del agua constituye actividades esenciales de los gobiernos y autoridades regionales y locales.  Fue evaluada La lluvia anual histórica (entre 1961 y 2020) de la Cuenca del Río Chambas. Para el cálculo de la variación de las áreas y volúmenes del agua en dos reservorios de la Provincia de Ciego de Ávila al término de las temporadas lluviosa y poco lluviosa entre 2014 y 2021 fueron usados dos métodos de sensores remotos (Índice Normalizado de Diferencia de Agua e imágenes del RADAR). Los resultados mostraron que la lluvia media anual fue 1330.9±287.4 mm y no mostró tendencia significativa en el período evaluado. Para ambos reservorios, las áreas promedio de agua medidas con los dos métodos fueron 19 % y 8 % menores que el área de agua reportadas por las autoridades para el mismo período. La capacidad estática de almacenamiento de agua (volumen de agua) de los dos reservorios varió (como el área) entre temporadas, con el mayor volumen determinado en ambos reservorios en octubre de 2017 (30.5 millones de m3 en Chambas II y 45.1 millones de m3 en Cañada Blanca). Grandes desviaciones de las áreas y volúmenes del agua ocurrieron durante la temporada poco lluviosa (menores valores) y la temporada lluviosa de 2017 (influenciada por las lluvias asociadas el huracán Irma) y la temporada lluviosa de 2020 (influenciada por la lluvia asociada a la tormenta Laura). Los modelos calculados para la relación área volumen con una significación estadística son otra herramienta útil que podría ser usada para mejorar el manejo del agua en términos de precisión y el incremento de resultados confiables en casos donde la medición de los niveles de agua son escasos o no están disponibles.Valero-Jorge, A.; González-De Zayas, R.; Alcántara-Martín, A.; Álvarez-Taboada, F.; Matos-Pupo, F.; Brown-Manrique, O. (2022). Water area and volume calculation of two reservoirs in Central Cuba using Remote Sensing Methods. A new perspective. Revista de Teledetección. (60):71-87. https://doi.org/10.4995/raet.2022.17770OJS71876

Water area and volume calculation of two reservoirs in Central Cuba using Remote Sensing Methods. A new perspective

[EN] The availability, quality and management of water constitute essential activities of national, regional and local governments and authorities. Historic annual rain (between 1961 and 2020) in Chambas River Basin (Central Cuba) was evaluated. Two remote sensing methods (Normalized Difference Water Index and RADAR images) were used to calculate the variation of water area and volumes of two reservoirs (Chambas II and Cañada Blanca) of Ciego de Ávila Province at end of wet and dry seasons from 2014-2021. The results showed that mean annual rain was 1330.9 ± 287.4 mm and it did not showed any significant tendency at evaluated period. For both reservoirs, mean water areas measured with two methods were 19 % and 8 % smaller than the mean water area reported by authorities for the same period. The static water storage capacity (water volume) of both reservoirs varied (as area) between seasons with the greatest volume in both reservoirs recorded in October of 2017 (30.5 million of m3 in Chambas II and 45.1 million of m3 in Cañada Blanca reservoir). Large deviations of water area and volumes occurred during the dry season (lower values) and the wet season of 2017 (influenced by rain associated to of Hurricane Irma) and wet season of 2020 (influenced by rain associated to tropical storm Laura). Calculated area – volume models with significant statistical correlation are another useful tool that could be used to improve water management in terms of accuracy and to increase reliable results in cases where gauge measurements are scarce or not available.S

Automatic Detection of Low-Backscatter Targets in the Arctic Using Wide Swath Sentinel-1 Imagery

Low backscatter signatures in synthetic aperture radar (SAR) imagery are characteristic to surfaces that are highly smooth and specular reflective of microwave radiation. In the Arctic, these typically represent newly formed sea ice, oil spills, and localized weather phenomena such as low wind or rain cells. The operational monitoring of low backscatter targets can benefit from a stronger integration of freely available SAR imagery from Sentinel-1. We, therefore, propose a detection method applicable to Sentinel-1 extra wide-swath (EW) SAR scenes. Using intensity values coupled with incidence angle and noise-equivalent sigma zero (NESZ) information, the image segmentation method is able to detect the low backscatter targets as one segment across subswaths. We use the Barents Sea as a test site due to the abundant presence of low backscatter targets with different origins, and of long-term operational monitoring services that help cross-validate our observations. Utilizing a large set of scenes acquired in the Barents Sea during the freezing season (November–April), we demonstrate the potential of performing large-scale operational monitoring of local phenomena with low backscatter signatures

Identification of alluvial gold mining in the Colombian Choco region using radar satellite images

Alluvial gold mining is a growing activity in Colombia that severely impacts the loss of vegetation cover with high environmental value. The detection of this activity is essential to define strategies focused on reducing this impact. In previous studies that have used optical satellite imagery, continuous cloud cover, especially in tropical regions, has been a limiting factor that can be overcome thanks to the ability of radar sensors to capture information regardless of whether or illumination conditions. In this project, time series analysis, textures metrics and supervised classification were implemented to identify areas affected by alluvial gold mining in the department of Choco. The approach involves two main steps: In the first, Sentinel-1 radar images with dual polarisation (HH and HV) and acquisition date between November 2020 and November 2021 were analysed, and in the second step, a TanDEM-X image with single polarisation (HH) was used. To evaluate the accuracy, classification results are compared with polygons identified in the region in 2021. The overall accuracy obtained was 97% with Sentinel-1 images and 98% with TanDEM-X. These results suggest that images used are suitable for identifying affected areas by alluvial gold mining. However, the discrimination of this affectation from the bare soil generated by other activities requires the inclusion of additional spatial criteria like the presence of previous affectation, beneficiation ponds and elevation data

Correction Methods for Non-Stationary Noise Floor in Sentinel-1 Images Using Convex Optimization

Synthetic aperture radar (SAR) is a method of creating images of the surface of the Earth by emitting and receiving radar waves. Sentinel-1 is a SAR platform made by the European Space Agency (ESA) that provides a source of SAR images open to the public through the operation of two satellites. Due to the non-uniform radiation pattern projected from the satellite's antenna, there are significant non-stationary noise floor intensity patterns that distract from the desired measurements, which are particularly significant in certain types of image modes, namely Extra Wide and Interferometric Wide modes. While ESA provides a default noise floor estimate with each Sentinel-1 product, with the intention that it be subtracted from the original image so the result is homogeneous, there is clear evidence that it is miscalibrated. This Masters thesis presents two novel methods for estimating the noise floor patterns in the images that are demonstrated to be improvements over the default noise floor. The first method presents a way to dynamically construct and apply linear rescaling to the default noise floor estimate over different sections of the images, called subswaths, by use of least squares optimization. While the method is successful in improving image quality, it is not totally effective because the default noise floor is mis-fit in a non-linear manner. The second method constructs a new noise floor as a power function of the radiation pattern power by using linear programming and least squares optimization. This successfully compensates for the non-linear mis-fit, resulting in an overall increase in image quality, albeit with greater parametric complexity. These methods greatly improve the intrinsic value of Sentinel-1 images in scenarios where the noise floor dominates, such as in cross-polarized images and images where the physical materials result in lower backscatter intensity

Variações de área das geleiras e o estado atual da linha de neve transitória dos campos de gelo da ilha Rei George, Antártica, usando sensores remotos orbitais

Geleiras Antárticas e subantárticas possuem o balanço de massa altamente sensível, principalmente as geleiras marinhas que têm altas temperaturas na Altitude de Linha de Equilíbrio. Assim, é essencial o entendimento das respostas destas às mudanças climáticas e o contínuo monitoramento por sensoriamento remoto. Este trabalho objetivou investigar a Altitude de Linha de Neve (ALN) transitória para 2020 e estimou a perda de área dos campos de gelo na ilha Rei George (IRG) no período 1988-2020, utilizando dados de sensores remotos ativos e passivos. Foram aplicados índices como o NDSI e o NDWI e o mapeamento de imagens Landsat 4 TM e Sentinel-2. As zonas de radar e a ALN (neve úmida e transitória) foram determinadas pelo retroespalhamento e pela elevação obtidos na imagem Sentinel-1 IW (verão de 2020) e no MDE. O processamento da imagem Sentinel-1 envolveu a remoção do ruído termal, a calibração, a correção do terreno e a filtragem speckle. Foram analisados dados de média e erro padrão para as perdas de área das geleiras voltadas para os setores da Passagem de Drake (PD) e do Estreito de Bransfield (EB). Assim como, foram elaborados gráficos de dispersão e correlação para fatores como perda de área glacial, distâncias de determinadas cotas batimétricas e área percentual acima da ALN transitória. Foram obtidos valores de retroespalhamento de ≤ -13 dB para a zona de neve úmida e a ALN transitória foi identificada em uma altitude mínima de 300 metros (abrangendo 55% da área glacial da ilha). O mapeamento resultou em uma área glacial total de 1006,04 km² para 2020 (erro <1%). A perda de área glacial desde 1988 foi de 101,34 km² (9%). A taxa de perda de área para o período de 32 anos foi de 3,17 km²/ano. As geleiras na península Keller e no Domo Bellingshausen, com ausência de ALN transitória, mostraram os maiores percentuais de perda (28,2% e 17,4% respectivamente), seguido do campo de gelo Warszawa (15,6%) e Kraków (13%). Dentre os campos que possuem a ALN transitória, destacam-se os percentuais de perda do Campo de Gelo Warszawa. A Parte Oriental possui expressivas perdas percentuais (10,4%). As perdas de área percentual nas cotas ≤150m de elevação é maior nos campos de gelo Kraków, Warszawa e Domo Bellingshausen. Os percentuais de perda de área por campo de gelo aumentam com a diminuição do percentual de área acima da ALN transitória, da área total e da elevação máxima. Há menores variações nas geleiras com sua frente em porções marinhas mais rasas (PD). A maior variação é encontrada nas geleiras de desprendimento dos setores de maior profundidade de água (EB) devido ao controle de diferentes aspectos na estabilidade da frente da geleira e nas taxas de desprendimento de gelo. O campo de gelo com maior área total é a Parte Oriental, seguido da Parte Central e do Arctowski, os quais também possuem maior área percentual acima da ALN e menores perdas de área. Além das forçantes climáticas no período, há fatores que operam modulando o comportamento das frentes e que explicam as diferenças de retração e de ALN encontradas em cada campo de gelo.The Antarctic and Subantarctic glaciers have higher mass balance sensitivity, mainly maritime glaciers that have higher temperatures at the Equilibrium Line Altitud. Thus, it is essential to understand glaciers responses to climate change and the continuous monitoring by Remote Sensing data. This work investigates the Snow Line Altitud (2020) and the glacial area loss of the King George icefields in the period of 1988- 2020 using active and passive remote sensing data. The Sentinel-2 and LANDSAT satellite images were applied in glacier mapping and index (NDSI and NDWI) to determine the glacier area variations in the period. The radar zones and Transient Snow Line (TSL) altitude were identified using Sentinel-1 IW (2020 summer) and the digital elevation model data to analyze their sensitivity to recent environmental changes. The S-1 was processed with thermal noise removal, calibration, terrain correction and speckle filtering steps. The area loss mean and pattern error values were analyzed by Drake Passage (DP) and Bransfield Strait (BS) sectors. The area loss was associated with environmental characteristics such as bathymetry, elevation, area above the TSL altitude using graphs of the comparison and correlation. The backscattering values of the ≤ -13 dB was identified as a wet snow zone and 300 m was determined to TSL altitude. 55% of glacial coverage is located above the TSL altitude in summer 2020. Eastern, Central and Arctowski icefields have more dimensions and the highest values of the glacial coverage located above the TSL altitude. The island has 1006.04 km² (error <1%) of the total glacial area for 2020 and had 101.34 km² (9%) area loss since 1988/1989. The glaciers have retreated 3.17 km²/year in the last 32 years. The Keller Peninsula glaciers and Bellingshausen Dome had a more significant loss (28% and 17%) and had not been verified TSL altitude in 2020 for both. The Warszawa, Kraków and Eastern icefields had 15.6%, 13% and 10.4% of the loss and had an area above the TSL altitude. The Kraków, Warszawa and Bellingshausen Dome icefields had the highest area loss in the 150m elevation range in this period. The area loss values (%) increased with decrease of the dimensions, total area above TSL and lower values for maximum elevation of the icefields. The lowest glacial shrinkage occurs in coastal shallow sectors. The calving glaciers with ice-flow toward deeper and steeper submarine sectors (associated to BS) had higher glacier variations in comparison with others (associated to DP) due ice- margin stabilization and calving rate changes. The climatic and ocean input and multiple environmental factors have influence in TSL altitude and retreat difference between glaciers

Combined Use of Space Borne Optical and SAR Data to Improve Knowledge about Sea Ice for Shipping

As a first step towards a new combined product for sea ice classification based on optical/thermal data collected by Sentinel-3 satellites and SAR data from Sentinel-1 satellites, which can be used as an appropriate support for navigation in Arctic and sub-Arctic waters, two existing classification algorithms are adapted to these data. The classification based on optical data has improved, so it is expected that the results will be ideally suited to be processed together with SAR data into significantly improved sea ice information products to support marine navigation. The usefulness of the combined processing is demonstrated by means of two simple algorithms and a more sophisticated approach is outlined, which will be realized in the future in order to form the basis for an integration into an operational service with the involvement of further partners and users

SPICES – Sea ice type maps from Fram Strait, Barents and Kara Sea

<p>Sea ice type classification of Sentinel-1 SAR dual polarization EW images. </p> <p>Prior to classification, Sentinel-1 SAR images have been pre-processed to remove noise [1]. The procedure of classification is comprised of the following steps: 1. Haralick texture features are computed, 2. Principal Component Analysis (PCA) is applied, 3. K-means clustering is used to group the data into 15 clusters using factor scores of the PCA as input, 4. The 15 clusters are analysed by an ice expert and classified as open water and different types of sea ice.</p> <p> </p> <p>[1] J.-W. Park, A. A. Korosov, M. Babiker, S. Sandven, J.-S. Won, Efficient Thermal Noise Removal for Sentinel-1 TOPSAR Cross-Polarization Channel, IEEE Transactions on Geoscience and Remote Sensing, 2018; 56, 3, DOI:10.1109/TGRS.2017.2765248.</p

Flood Extent and Volume Estimation using Multi-Temporal Synthetic Aperture Radar.

Ph. D. Thesis.Satellite imagery has the potential to monitor flooding across wide geographical regions. Recent launches have improved the spatial and temporal resolution of available data, with the European Space Agency (ESA) Copernicus programme providing global imagery at no end-user cost. Synthetic Aperture Radar (SAR) is of particular interest due to its ability to map flooding independent of weather conditions. Satellite-derived flood observations have real-world application in flood risk management and validation of hydrodynamic models. This thesis presents a workflow for estimating flood extent, depth and volume utilising ESA Sentinel-1 SAR imagery. Flood extents are extracted using a combination of change detection, variable histogram thresholding and object-based region growing. An innovative technique has been developed for estimating flood shoreline heights by combining the inundation extents with high-resolution terrain data. A grid-based framework is used to derive the water surface from the shoreline heights, from which water depth and volume are calculated. The methodology is applied to numerous catchments across the north of England that suffered from severe flooding throughout the winter of 2015-16. Extensive flooding has been identified throughout the study region, with peak inundation occurring on 29th December 2015. On this date, over 100 km2 of flooding is identified in the Ouse catchment, equating to a water volume of 0.18 km3. The SAR flood extents are validated against satellite optical imagery, achieving a Total Accuracy of 91% and a Critical Success Index of 77%. The derived water surfaces have an average error of 3 cm and an RMSE of 98 cm compared to river stage measurements. The methods developed are robust and globally applicable, shown with an additional study along the Mackenzie River in Australia. The presented methodology, alongside the increased temporal resolution provided by Sentinel-1, highlights the potential for accurate, reliable mapping of flood dynamics using satellite imagery.NERC, (DREAM) CD