Determining Bathymetry of Shallow and Ephemeral Desert Lakes Using Satellite Imagery and Altimetry

©2020. American Geophysical Union. All Rights Reserved. Water volume estimates of shallow desert lakes are the basis for water balance calculations, important both for water resource management and paleohydrology/climatology. Water volumes are typically inferred from bathymetry mapping; however, being shallow, ephemeral, and remote, bathymetric surveys are scarce in such lakes. We propose a new, remote-sensing-based, method to derive the bathymetry of such lakes using the relation between water occurrence, during \u3e30 year of optical satellite data, and accurate elevation measurements from the new Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2). We demonstrate our method at three locations where we map bathymetries with ~0.3 m error. This method complements other remotely sensed, bathymetry-mapping methods as it can be applied to: (a) complex lake systems with subbasins, (b) remote lakes with no in-situ records, and (c) flooded lakes. The proposed method can be easily implemented in other shallow lakes as it builds on publically accessible global data sets

Mapping and monitoring the Akagera wetland in Rwanda

Wetland maps are a prerequisite for wetland development planning, protection, and restoration. The present study aimed at mapping and monitoring Rwanda's Akagera Complex Wetland by means of remote sensing and geographic information systems (GIS). Landsat data, spanning from 1987 to 2015, were acquired from different sensor instruments, considering a 5-year interval during the dry season and the shuttle radar topographic mission (SRTM) digital elevation model (30-m resolution) was used to delineate the wetland. The mapping and delineation results showed that the wetland narrowly extends along the Rwanda-Tanzania border from north to south, following the course of Akagera River and the total area can be estimated at 100,229.76 ha. After waterbodies that occupy 30% of the wetland's surface area, hippo grass and Cyperus papyrus are also predominant, representing 29.8% and 29%, respectively. Floodplain and swamp forest have also been inventoried in smaller proportions. While the wetland extent has apparently remained stable, the inhabiting waterbodies have been subject to enormous instability due to invasive species. Lakes, such as Mihindi, Ihema, Hago and Kivumba have been shrinking in extent, while Lake Rwanyakizinga has experienced a certain degree of expansion. This study represents a consistent decision support tool for Akagera wetland management in Rwanda

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

Mapping Nearshore Bathymetry with Spaceborne Data Fusion and State Space Modeling

Despite numerous techniques for measuring and estimating water depth, bathymetry in the nearshore zone is notoriously difficult to map. Dangerous sea states, noisy environmental conditions, and expensive survey operations, particularly in remote areas, contribute to the difficulties of obtaining data along the coast. Global datasets, derived mainly from satellite altimetry methods, do exist, but they have significant limitations nearshore. Numerous high-resolution datasets, conventionally acquired with acoustic and lidar techniques, also exist, but they cover only a small percentage of the world's coasts. Spaceborne data fusion employing multispectral satellite derived bathymetry (SDB) offers the potential to significantly reduce the global lack of nearshore bathymetry, coined the "white ribbon" by the hydrographic community, referring to the alongshore data gap on many nautical charts. A broad term, multispectral SDB spans a diverse spectrum of methods that have been used extensively in specific case studies, but the application of multispectral SDB on a global or regional scale is significantly limited by the availability of in situ reference depths needed to tune derived values. Additionally, many existing approaches only use a single multispectral image, which can result in significant errors or missing data if the image contains environmental or sensor noise, such as clouds, sediment plumes, or detector-edge artifacts. This dissertation presents two spaceborne empirical multispectral SDB methods to address shortcomings of existing SDB approaches and reduce the global shortage of nearshore bathymetry – (1) active/passive spaceborne data fusion combining MABEL/ICESat-2 and multispectral data and (2) state space modeling of Sentinel-2 and Landsat 8 multispectral data to generate gap-free models of relative SDB (rSDB) with corresponding uncertainty estimates. The recently launched ICESat-2 mission offers an opportunity for a completely spaceborne active-passive data fusion approach to nearshore bathymetry by potentially providing a global source of nearshore reference depths to tune empirical multispectral SDB algorithms. The main objectives of the ICESat-2 mission are to measure ice-sheet elevations, sea-ice thickness, and global biomass, but ICESat-2’s 532-nm wavelength photon-counting Advanced Topographic Laser Altimeter System (ATLAS) was first posited, then demonstrated capable of detecting bathymetry in certain nearshore environments. Presented in two studies conducted prior to ICESat-2’s launch, the active-passive approach is demonstrated with data from MABEL, NASA’s high-altitude ATLAS simulator system. The first study assessed the ability to derive bathymetry from MABEL and then evaluated the accuracy and reliability of MABEL bathymetry using data acquired in Keweenaw Bay, Lake Superior. The study also developed and verified a baseline model to predict numbers of bottom returns as a function of water depth. The second study completed the demonstration of the spaceborne active/passive data fusion method by synergistically fusing MABEL-derived bathymetry and Landsat 8 multispectral Operational Land Imager (OLI) imagery over the entire Keweenaw Bay study site using the Stumpf band-ratio algorithm. The study also assessed the spatiotemporal viability of the data fusion approach by characterizing the variability of global coastal water clarity as interpreted from Visible Infrared Imaging Radiometer Suite (VIIRS) Kd(490) data. The calculated SDB agreed with a high-resolution topobathymetric lidar dataset to within an RMSE of 0.7 m, and the spatiotemporal viability analysis indicated that the spaceborne active-passive data fusion approach may be viable over many regions of the globe throughout the course of a year. State space modeling of empirical multitemporal SDB overcomes limitations of single-image SDB by leveraging the bathymetric signal in multispectral time series to create gap-free models of relative SDB (rSDB) for an arbitrary date, enabling SDB for dates with noisy or no data. State space models (SSMs) are well established in many applications but are absent in empirical SDB literature. Consisting of a state equation, which relates consecutive state vectors, and an observation equation, which relates observations to the state vector, SSMs are typically solved using Kalman filtering techniques, which provide estimates of uncertainties along with state estimates. SSMs also provide a mechanism for data fusion by allowing an observation equation for multiple observed time series. The third study demonstrates a state space approach to empirical multispectral SDB by applying local level SSMs to Landsat 8 OLI and Sentinel-2 MSI rSDB time series, both separately and fused. A representative single-sensor SSM (Landsat 8) was transformed to SDB that agreed with a high-resolution topobathymetric lidar dataset to within an RMSE of 0.29 m, which indicates the promising performance of the state space framework. Internally consistent fused-sensor SSMs verified that state space modeling also offers a data-fusion method capable of incorporating time series from a diverse suite of multispectral sensors

Mapping the surface water storage variation in densely impounded semi-arid NE Brazil with satellite remote sensing approach

Surface water bodies provide vital support to the society and fundamentally affect ecosystems in various manners. Precise knowledge of the spatial extent of surface water bodies (e.g. reservoirs) as well as of the quantity of water they store is necessary for efficient water deployment and understanding of the local hydrology. Remote sensing provides broad opportunities for surface water mapping. The main objectives of this thesis are: 1) delineating surface water area of partly vegetated water bodies only from remote sensing data without field data input; 2) obtaining the surface water storage, and 3) analyzing its spatio-temporal variations for northeastern (NE) Brazil as a representative for a densely dammed semi-arid region. At first, I investigated the potential of digital elevation models (DEMs) generated from TanDEM-X data, which were acquired during the low water level stage, for reservoirs’ bathymetry derivation. I found that the accuracy of such DEMs can reach one meter, both in the absolute and relative respects. It has shown that DEMs derived from TanDEM-X data have great potentials for representing the reservoirs’ bathymetry of temporally dried-out reservoirs. Subsequently, I targeted at developing a method for mapping the water surface beneath canopy independent of field data for further delineation of the effective water surface. Instead of the commonly used backscattering coefficients, I investigated the capability of the Gray-Level Co-Occurrence Matrix (GLCM) texture index to distinguish different types of Radar backscattering taking place in (partly) vegetated reservoirs. This experiment demonstrated that different types of backscattering at the vegetated water surface show distinct statistical characteristics on GLCM variance derived from TerraSAR-X satellite time series data. Furthermore, with the threshold established based on the statistics of the sub-populations dominated by different types of backscattering, the vegetated water surfaces were effectively mapped, and the effective water surface areas were further delineated with an accuracy of 77% to 95%. ii Based on the investigation of the DEMs generated from TanDEM-X data, I derived the formerly unknown bathymetry for 2 105 reservoirs of various sizes in four representative regions of an overall area of 10 000 km2. The spatial distributions of surface water storage capacities in the four regions were subsequently extracted from the combination of the reservoir bathymetry and the water surface extents provided by RapidEye satellite time series. Furthermore, the spatio-temporal variations of surface water storage were derived for the four representative regions on an annual basis in the period of 2009-2017. This study showed that 1) The density of reservoirs in NE Brazil amounts to 0.04-0.23 reservoirs per km2, the corresponding water surface and surface water storage are 1.18-4.13 ha/km2 and 0.01-0.04 hm3 m/km², respectively; 2) On the spatial unit of 5×5 km2, the surface water storage in the region constantly decreased due to a prolonged drought with a rate of 105 m3/year from 2009 to 2017, with a slight increase from 2016 to 2017 in a few reservoirs; 3) Local precipitation deficit controls the variation of the overall surface water storage in the region. In this thesis I demonstrated the great potential of the great potential of SAR and optical satellite time series data for hydrological applications. The method I developed for delineating the effective water extent from the vegetated reservoirs has shown high potential transferability for other similar regions. The data gaps of bathymetry and surface waters storage capacity were filled for 2 105 reservoirs in NE Brazil. The results of the spatio-temporal variations of surface water storage in four representative regions from 2009-2016 can support future water management and improve hydrological prediction in NE Brazil

Comparison of sea-ice freeboard distributions from aircraft data and cryosat-2

The only remote sensing technique capable of obtain- ing sea-ice thickness on basin-scale are satellite altime- ter missions, such as the 2010 launched CryoSat-2. It is equipped with a Ku-Band radar altimeter, which mea- sures the height of the ice surface above the sea level. This method requires highly accurate range measure- ments. During the CryoSat Validation Experiment (Cry- oVEx) 2011 in the Lincoln Sea, Cryosat-2 underpasses were accomplished with two aircraft, which carried an airborne laser-scanner, a radar altimeter and an electro- magnetic induction device for direct sea-ice thickness re- trieval. Both aircraft flew in close formation at the same time of a CryoSat-2 overpass. This is a study about the comparison of the sea-ice freeboard and thickness dis- tribution of airborne validation and CryoSat-2 measure- ments within the multi-year sea-ice region of the Lincoln Sea in spring, with respect to the penetration of the Ku- Band signal into the snow