Mapping Regional Inundation with Spaceborne L-Band SAR

Shortly after the launch of ALOS PALSAR L-band SAR by the Japan Space Exploration Agency (JAXA), a program to develop an Earth Science Data Record (ESDR) for inundated wetlands was funded by NASA. Using established methodologies, extensive multi-temporal L-band ALOS ScanSAR data acquired bi-monthly by the PALSAR instrument onboard ALOS were used to classify the inundation state for South America for delivery as a component of this Inundated Wetlands ESDR (IW-ESDR) and in collaboration with JAXA’s ALOS Kyoto and Carbon Initiative science programme. We describe these methodologies and the final classification of the inundation state, then compared this with results derived from dual-season data acquired by the JERS-1 L-band SAR mission in 1995 and 1996, as well as with estimates of surface water extent measured globally every 10 days by coarser resolution sensors. Good correspondence was found when comparing open water extent classified from multi-temporal ALOS ScanSAR data with surface water fraction identified from coarse resolution sensors, except in those regions where there may be differences in sensitivity to widespread and shallow seasonal flooding event, or in areas that could be excluded through use of a continental-scale inundatable mask. It was found that the ALOS ScanSAR classification of inundated vegetation was relatively insensitive to inundated herbaceous vegetation. Inundation dynamics were examined using the multi-temporal ALOS ScanSAR acquisitions over the Pacaya-Samiria and surrounding areas in the Peruvian Amazon

Flooding dynamics within an Amazonian floodplain: water circulation patterns and inundation duration

Hydrodynamic

On the use of global flood forecasts and satellite-derived inundation maps for flood monitoring in data-sparse regions

Early flood warning and real-time monitoring systems play a key role in flood risk reduction and disaster response decisions. Global-scale flood forecasting and satellite-based flood detection systems are currently operating, however their reliability for decision making applications needs to be assessed. In this study, we performed comparative evaluations of several operational global flood forecasting and flood detection systems, using 10 major flood events recorded over 2012-2014. Specifically, we evaluated the spatial extent and temporal characteristics of flood detections from the Global Flood Detection System (GFDS) and the Global Flood Awareness System (GloFAS). Furthermore, we compared the GFDS flood maps with those from NASA’s two Moderate Resolution Imaging Spectroradiometer (MODIS) sensors. Results reveal that: 1) general agreement was found between the GFDS and MODIS flood detection systems, 2) large differences exist in the spatio-temporal characteristics of the GFDS detections and GloFAS forecasts, and 3) the quantitative validation of global flood disasters in data-sparse regions is highly challenging. Overall, the satellite remote sensing provides useful near real-time flood information that can be useful for risk management. We highlight the known limitations of global flood detection and forecasting systems, and propose ways forward to improve the reliability of large scale flood monitoring tools.JRC.H.7-Climate Risk Managemen

Monitoring of an Indonesian Tropical Wetland by Machine Learning-Based Data Fusion of Passive and Active Microwave Sensors

In this study, a novel data fusion approach was used to monitor the water-body extent in a tropical wetland (Lake Sentarum, Indonesia). Monitoring is required in the region to support the conservation of water resources and biodiversity. The developed approach, random forest database unmixing (RFDBUX), makes use of pixel-based random forest regression to overcome the limitations of the existing lookup-table-based approach (DBUX). The RFDBUX approach with passive microwave data (AMSR2) and active microwave data (PALSAR-2) was used from 2012 to 2017 in order to obtain PALSAR-2-like images with a 100 m spatial resolution and three-day temporal resolution. In addition, a thresholding approach for the obtained PALSAR-2-like backscatter coefficient images provided water body extent maps. The validation revealed that the spatial patterns of the images predicted by RFDBUX are consistent with the original PALSAR-2 backscatter coefficient images (r = 0.94, RMSE = 1.04 in average), and that the temporal pattern of the predicted water body extent can track the wetland dynamics. The PALSAR-2-like images should be a useful basis for further investigation of the hydrological/climatological features of the site, and the proposed approach appears to have the potential for application in other tropical regions worldwide

Detection of seasonal inundations by satellite data at Shkoder Urban Area, North Albania for sustainable management

The European Space Agency satellites Sentinel-1 radar and Sentinel-2 optical data are widely used in water surface mapping and management. In this work, we exploit the potentials of both radar and optical images for satellite-based quick detection and extent mapping of inundations/water raising events over Shkodër area, which occurred in the two last years (2017–2018). For instance, in March 2018 the Shkodër district (North Albania) was affected twice by the overflow of the Drin and Buna (Bojana) Rivers and by the Shkodër lake plain inundation. Sentinel-1 radar data allowed a rapid mapping of seasonal fluctuations and provided flood extent maps by discriminating water surfaces (permanent water and flood areas) from land/non-flood areas over all the informal zones of Shkodër city. By means of Sentinel-2 data, two color composites maps were produced and the Normalized Difference Water Index was estimated, in order to further distinguish water/moisturized soil surfaces from built-up and vegetated areas. The obtained remote sensing-based maps were combined and discussed with the urban planning framework in order to support a sustainable urban and environmental management. The provided multi-temporal analysis could be easily exploited by the local authorities for flood prevention and management purposes in the inherited territorial context. The proposed approach outputs were validated by comparing them with official Copernicus EMS (Emergency Management Service) maps available for one of the chosen events. The comparison shows good accordance results. As for a further enhancement in the future perspective, it is worth to highlight that a more accurate result could be obtained by performing a post-processing edit to further refine the flooded areas, such as water mask application and supervised classification to filter out isolated flood elements, to remove possible water-lookalikes and weed out false positives

Mapeamento das áreas inundáveis do Médio São Francisco utilizando técnicas de processamento digital de imagens de sensoriamento remoto e modelo HAND

Dissertação (mestrado)—Universidade de Brasília, Instituto de Ciências Humanas, Departamento de Geografia, Programa de Pós-graduação, 2019.As áreas inundáveis desempenham funções ecológicas primordiais para a manutenção do equilíbrio ecológico dos ecossistemas aquáticos e terrestres. Ademais as áreas inundáveis são fundamentais para o sustento de diversas atividades humanas. No entanto, essas áreas vêm sofrendo diversos distúrbios decorrentes das ações antrópicas. O presente trabalho visou realizar a análise da dinâmica fluvial em um trecho do rio São Francisco, localizado entre os municípios de Barra, Pilão Arcado e Xique-Xique, Bahia. Dessa forma, foi calculada a Linha Média das Enchentes Ordinárias (LMEO) e aplicadas técnicas de processamento digital nas imagens Landsat-8/OLI-TIRS e Sentinel-1 (SAR). Os índices espectrais MNDWI, NDWI e AWEI foram aplicados em duas imagens Landsat-8, uma representando a cota do rio próxima à LMEO e a outra um período de seca. A detecção dos alvos de água nas imagens foi feita a partir da técnica de threshould. O índice MNDWI demonstrou maior valor de acurácia, com índice kappa superior a 0,9. Também foi realizada uma análise multitemporal da dinâmica fluvial entre os anos de 2005 e 2019, empregando imagens Landsat 5 e Landsat 8. Em seguida, foram obtidas duas imagens Sentinel-1 representando a cota máxima e mínima do rio, entre os anos 2016 e 2017. Aplicou-se a técnica de threshould para a classificação da água nas imagens. O maior valor de acurácia demonstrado pelo índice kappa nas imagens Sentinel-1 foi 0,47. Além disso, foi gerado o modelo digital HAND da região e delimitada os terrenos marginais, a fim de realizar o levantamento das áreas inundáveis. Por último, foram realizadas simulações de cotas do rio no modelo HAND, as quais demonstraram valor de acurácia superior a 96,67%.Wetlands play a key role in ecological balance process of aquatic and terrestrial ecosystems. In addition, wetlands are crucial because support various human activities. However, anthropogenic actions have impacted these areas. The objective of the present study was to map the wetlands in a section of São Francisco River, using radar (SAR) and optical image processing. The study area is located between the counties of Barra, Pilão Arcado and Xique-Xique, Bahia. Were employed Sentinel-1, Landsat-8/OLI and Landsat-5/TM images. A HAND model was also generated from DEM to map the wetlands. Data from the São Francisco historical series were used to calculate the Limit from Ordinary Flood (LFOF). MNDWI, NDWI and AWEI were applied on two Landsat-8 images, one image representing the flood, with river level like LFOF, and the other image representing the driest period. This process was taken to determine which index demonstrated the best result for water detection. We used threshold technique to water extraction. The MNDWI showed the highest accuracy, Kappa index was greater than 0.9. A multitemporal analysis of river dynamics, between 2005 and 2019, was also performed, using Landsat images. Two Sentinel-1 images, representing the maximum and minimum level of the river, between 2016 and 2017, were obtained. Threshold technique was applied in Sentinel-1 images for open water extraction. The highest accuracy demonstrated by Kappa on Sentinel-1 images was 0.47. River simulations were performed in HAND model, which presented an accuracy higher than 96.67%

Flood monitoring based on the study of RADAR SAR (Sentinel-1)

La cuenca del Ebro es objeto de periódicas crecidas debido, principalmente, a eventos de intensas lluvias y al deshielo. Este estudio se centra en las inundaciones de mediados de abril de 2018 en un área que abarca desde la población de Novillas hasta El Burgo de Ebro, en la provincia de Zaragoza. Para ello, se han utilizado imágenes RADAR del sensor Sentinel-1 como herramienta para delimitar la extensión de la inundación. Dadas las particulares características que poseen los sensores SAR, que pueden actuar bajo circunstancias adversas de luz y en todo tipo de condiciones climáticas, obtenemos imágenes con una correcta resolución espacial y temporal para llevar a cabo el análisis. Es necesario un pretratamiento para reducir el speckle, que dificulta la interpretación de las imágenes y reduce la calidad de las mismas, utilizando filtros que conservan la textura y suavizan los límites. A continuación, con imágenes anteriores y posteriores al evento se han generado mapas de inundación para las polarizaciones VV y VH mediante dos metodologías distintas: composición RGB y umbrales de calibración. A pesar de las limitaciones, los resultados obtenidos confirman la idoneidad de las imágenes RADAR para cartografiar inundaciones, especialmente con la polarización VH.Máster Universitario en Hidrología y Gestión de Recursos Hídrico

Assessing Global Surface Water Inundation Dynamics Using Combined Satellite Information from SMAP, AMSR2 and Landsat

A method to assess global land surface water (fw) inundation dynamics was developed by exploiting the enhanced fw sensitivity of L-band (1.4 GHz) passive microwave observations from the Soil Moisture Active Passive (SMAP) mission. The L-band fw (fw(sub LBand)) retrievals were derived using SMAP H-polarization brightness temperature (Tb) observations and predefined L-band reference microwave emissivities for water and land endmembers. Potential soil moisture and vegetation contributions to the microwave signal were represented from overlapping higher frequency (Tb) observations from AMSR2. The resulting (fw(sub LBand)) global record has high temporal sampling (1-3 days) and 36-km spatial resolution. The (fw(sub LBand)) annual averages corresponded favourably (R=0.84, p<0.001) with a 250-m resolution static global water map (MOD44W) aggregated at the same spatial scale, while capturing significant inundation variations worldwide. The monthly (fw(sub LBand)) averages also showed seasonal inundation changes consistent with river discharge records within six major US river basins. An uncertainty analysis indicated generally reliable (fw(sub LBand)) performance for major land cover areas and under low to moderate vegetation cover, but with lower accuracy for detecting water bodies covered by dense vegetation. Finer resolution (30-m) (fw(sub LBand)) results were obtained for three sub-regions in North America using an empirical downscaling approach and ancillary global Water Occurrence Dataset (WOD) derived from the historical Landsat record. The resulting 30-m (fw(sub LBand)) retrievals showed favourable spatial accuracy for water (70.71%) and land (98.99%) classifications and seasonal wet and dry periods when compared to independent water maps derived from Landsat-8 imagery. The new (fw(sub LBand)) algorithms and continuing SMAP and AMSR2 operations provide for near real-time, multi-scale monitoring of global surface water inundation dynamics and potential flood risk

Determinación de áreas inundables en la reserva forestal protectora nacional del Río León (RFPN-Río León) por medio de imágenes de radar

La Reserva Forestal Protectora Nacional del Río León (RFPN-Río León) fue declarada bajo esta categoría por medio del acuerdo INDERENA No. 023 de 1971. La reserva se encuentra ubicada entre la jurisdicción del departamento de Antioquia y Chocó y cuenta con una extensión de 38.938 ha (SIDAP, 2010). Dentro de la reserva atraviesan 2 afluentes importantes, el río León y el río Tumaradocito. El presente estudio tiene como fin determinar áreas susceptibles a inundación, mediante el uso de imágenes satelitales, las cuales proporcionan métodos directos de estudio para la dinámica terrestre. Se aplicó la metodología planteada por Castro-Gómez, (2015); Podest, (2018); Song, Sohn, & Park, (2007), por medio de la cual se estimó los valores de umbral de retrodispersión para clasificación, a partir de imágenes tomadas por los sensores Alos-Palsar 1 (ALP1) y Sentinel 1-A, 1-B. La matriz de co-ocurrencia que permitió identificar los valores de retrodispersión para la clasificación, se determinó a partir de la ecuación definida por Pathak & Barooah, (2013) después del pre-procesamiento (Reducción de Ruido Térmico, Calibración Radiométrica, Calibración Radiométrica de Terreno, Corrección de Ruido Speckle y Corrección Geométrica de Terreno) y procesamiento (Conversión de valores de retrodispersión a Decibeles) de las polarizaciones disponibles de cada sensor. Las áreas definidas como susceptibles a inundación obtenidas por medio de la metodología planteada, fueron evaluadas por medio de datos tomados in situ y comparándolos con el mapa de humedales de Colombia. Se encontró que de acuerdo a los datos tomados en campo existe una precisión del 90% en la determinación de áreas susceptibles a inundación y, acorde al análisis por medio del mapa de humedales de Colombia se presenta que el 85% del área definida como susceptible a inundación se encuentra dentro del área definida como humedal temporal, que equivale al 75% de la zona de estudio definidaPregradoINGENIERO(A) TOPOGRAFIC