Using InSAR coherence for investigating the interplay of fluvial and aeolian features in arid lands: implications for groundwater potential in Egypt

Despite the fact that the Sahara is considered the most arid region on Earth, it has witnessed prolonged fluvial and aeolian depositional history, and might harbor substantial fresh groundwater resources. Its ancient fluvial surfaces are, however, often concealed by aeolian deposits, inhibiting the discovery and mapping of potential groundwater recharge areas. However, recent advances in synthetic aperture radar (SAR) imaging offer a novel approach for detecting partially hidden and dynamic landscape features. Interferometry SAR coherence change detection (CCD) is a fairly recent technique that allows the mapping of very slight surface changes between multidate SAR images. Thus, this work explores the use of the CCD method to investigate the fluvial and aeolian morphodynamics along two paleochannels in Egypt. The results show that during wetter climates, runoff caused the erosion of solid rocks and the rounding of sand-sized grains, which were subsequently deposited in depressions further downstream. As an alternating dry climate prevailed, the sand deposits were reshaped into migrating linear dunes. These highly dynamic features are depicted on the CCD image with very low coherence values close to 0 (high change), while the deposits within the associated ephemeral wadis show low to moderate coherence values ranging from 0.2 to 0.4 (high to moderate change), and the country rocks show a relative absence of change with high coherence values close to 1. These linear dunes crossed their parent’s stream courses and dammed the runoff to form lakes during rainy seasons. Part of the dammed surface water would have infiltrated the ground to recharge the permeable wadi deposits. The alternation of fluvial and aeolian depositional environments produced unique hydromorphometrically trapped lakes that are very rare in arid regions, but of great interest because of their significance to groundwater recharge

Automated detection of archaeological mounds using machine-learning classification of multisensor and multitemporal satellite data.

This paper presents an innovative multisensor, multitemporal machine-learning approach using remote sensing big data for the detection of archaeological mounds in Cholistan (Pakistan). The Cholistan Desert presents one of the largest concentrations of Indus Civilization sites (from ca 3300 to 1500 BC). Cholistan has figured prominently in theories about changes in water availability, the rise and decline of the Indus Civilization, and the transformation of fertile monsoonal alluvial plains into an extremely arid margin. This paper implements a multisensor, multitemporal machine-learning approach for the remote detection of archaeological mounds. A classifier algorithm that employs a large-scale collection of synthetic-aperture radar and multispectral images has been implemented in Google Earth Engine, resulting in an accurate probability map for mound-like signatures across an area that covers ca 36,000 km2 The results show that the area presents many more archaeological mounds than previously recorded, extending south and east into the desert, which has major implications for understanding the archaeological significance of the region. The detection of small (30 ha) suggests that there were continuous shifts in settlement location. These shifts are likely to reflect responses to a dynamic and changing hydrological network and the influence of the progressive northward advance of the desert in a long-term process that culminated in the abandonment of much of the settled area during the Late Harappan period.ER

Long‐Wavelength Sinuosity of Linear Dunes on Earth and Titan and the Effect of Underlying Topography

AbstractOn both Earth and Titan, some linear dunefields are characterized by curvilinear patterning atypical of the regularity and straightness of typical longitudinal dunefields. We use remotely sensed imagery and an automated dune crestline detection algorithm to analyze the controls on spatial patterning. Here it is shown that topography can influence the patterning, as dune alignments bend to deflect downslope under the influence of gravity. The effect is pronounced in a terrestrial dunefield (the Great Sandy desert, Australia) where substantial topography underlies, but is absent where the dunefield is underlain by subdued relief (southwestern Kalahari). This knowledge allows the inference of subtle topographic changes underlying dunefields from dunefield patterning, where other sources of elevation data may be absent. This methodology is explored using the Belet Sand Sea of Titan, where likely areas of topographic change at resolutions finer than those currently available from radar altimetry are inferred.</jats:p

Land use/land cover optimized SAR coherence analysis for rapid coastal disaster monitoring: the impact of the Emma Storm in southern Spain

The high exposure of coastal areas worldwide to natural and anthropogenic disasters emphasizes the relevance of disaster management processes that ensure a prompt damage detection and identification of affected areas. This paper aimed to develop a novel approach for disaster monitoring in coastal areas using SAR data. The method was based on an interferometric coherence difference analysis of Sentinel 1 data. To calibrate and validate the method, the Emma Storm, a severe coastal storm that affected the southwest coast of the Iberian Peninsula in 2018, was chosen as a case study. A coastal land use/land cover method optimization by optical and UAV field data resulted in an overall improvement of about 20% in the identification of disaster-affected areas by reducing false alarms by up to 33%. Finally, the method achieved hit and false alarm rates of about 80% and 20%, respectively, leading to the identification of approximately 30% (7000 ha) of the study area as being affected by the storm. Marshes and vegetated dunes were the most significantly impacted covers. In addition, SAR data enabled the impact assessment with a time lag of 2 days, contrasting the 25-day delay of optical data. The proposed method stands out as a valuable tool for regional-scale coastal disaster monitoring. In addition, it can be automated and operated at a low cost, making it a valuable tool for decision-making support.Fil: Garzo, Pedro Andrés. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata. Instituto de Investigaciones Marinas y Costeras. Universidad Nacional de Mar del Plata. Facultad de Ciencias Exactas y Naturales. Instituto de Investigaciones Marinas y Costeras; Argentina. Universidad Nacional de Mar del Plata. Facultad de Ciencias Exactas y Naturales. Instituto de Geología de Costas y del Cuaternario. Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas. Instituto de Geología de Costas y del Cuaternario; ArgentinaFil: Fernández Montblanc, Tomás. Universidad de Cádiz; España. Universidad de Cadiz. Centro Andaluz Superior de Estudios Marinos.; Españ

GEOSPATIAL-BASED ENVIRONMENTAL MODELLING FOR COASTAL DUNE ZONE MANAGEMENT

Tomaintain biodiversity and ecological functionof coastal dune areas, itis important that practical and effective environmentalmanagemental strategies are developed. Advances in geospatial technologies offer a potentially very useful source of data for studies in this environment. This research project aimto developgeospatialdata-basedenvironmentalmodellingforcoastaldunecomplexestocontributetoeffectiveconservationstrategieswithparticularreferencetotheBuckroneydunecomplexinCo.Wicklow,Ireland.Theprojectconducteda general comparison ofdifferent geospatial data collection methodsfor topographic modelling of the Buckroney dune complex. These data collection methodsincludedsmall-scale survey data from aerial photogrammetry, optical satellite imagery, radar and LiDAR data, and ground-based, large-scale survey data from Total Station(TS), Real Time Kinematic (RTK) Global Positioning System(GPS), terrestrial laser scanners (TLS) and Unmanned Aircraft Systems (UAS).The results identifiedthe advantages and disadvantages of the respective technologies and demonstrated thatspatial data from high-end methods based on LiDAR, TLS and UAS technologiesenabled high-resolution and high-accuracy 3D datasetto be gathered quickly and relatively easily for the Buckroney dune complex. Analysis of the 3D topographic modelling based on LiDAR, TLS and UAS technologieshighlighted the efficacy of UAS technology, in particular,for 3D topographicmodellingof the study site.Theproject then exploredthe application of a UAS-mounted multispectral sensor for 3D vegetation mappingof the site. The Sequoia multispectral sensorused in this researchhas green, red, red-edge and near-infrared(NIR)wavebands, and a normal RGB sensor. The outcomesincludedan orthomosiac model, a 3D surface model and multispectral imageryof the study site. Nineclassification strategies were usedto examine the efficacyof UAS-IVmounted multispectral data for vegetation mapping. These strategies involved different band combinations based on the three multispectral bands from the RGB sensor, the four multispectral bands from the multispectral sensor and sixwidely used vegetation indices. There were 235 sample areas (1 m × 1 m) used for anaccuracy assessment of the classification of thevegetation mapping. The results showed vegetation type classification accuracies ranging from 52% to 75%. The resultdemonstrated that the addition of UAS-mounted multispectral data improvedthe classification accuracy of coastal vegetation mapping of the Buckroney dune complex

Understanding the Threshold of Detection of Aeolian Features in Magellan Synthetic Aperture Radar Data Using Venusian and Terrestrial Dune Fields

The spatial resolution required for the detection of geomorphologic features on planetary surfaces is critical to developing a reproducible process for surface feature mapping and identification. The Magellan synthetic aperture radar (SAR) data of Venus have a spatial resolution of 75 m/pixel, which obfuscates features below about a kilometer in scale, while allowing analysis of larger-scale features (Saunders et al., 1991; Rader et al., 2019). Smaller-scale features, such as aeolian bedforms, are important for understanding erosional processes and the redistribution of sediment on the surface. (Rader et al., 2019). In order to properly detect and describe aeolian features on the surface of Venus and get a handle on their implications (e.g., for long-term wind patterns), we must understand the unique, diagnostic characteristics of aeolian features in low resolution SAR images and how these Magellan images can be interpreted (Rader et al., 2019). Here we show how to identify the diagnostic characteristics of dune fields in both visible and radar wavelength images and quantify the differences between them, using Earth as an analog for Venus. Aeolian features of the scale observed on Venus are also found in hyper-arid desert regions on Earth, with this study focusing on the Simpson Desert in Australia and the Namib Desert in Namibia (e.g., Bristow et al., 2007; Lancaster, 2009). The crestlines of linear dunes at these two terrestrial sites were mapped in optical (VIS) images at high resolution and in both high and low resolution SAR images as analogs for the venusian fields. The five venusian localities selected had a representative population of features mapped and then compared to the terrestrial dune fields (Greeley et al., 1992; Weitz et al., 1994; Rader et al., 2019). The mapped images were compared to identify diagnostic characteristics that can be used to identify other potential aeolian fields. These results demonstrate how dune fields can be identified on Venus and suggest possible reasons for the scarcity of potential venusian aeolian fields

High Resolution Mapping of Soils and Landforms for the Desert Renewable Energy Conservation Plan (DRECP)

The Desert Renewable Energy Conservation Plan (DRECP), a major component of California's renewable energy planning efforts, is intended to provide effective protection and conservation of desert ecosystems, while allowing for the sensible development of renewable energy projects. This NASA mapping report was developed to support the DRECP and the Bureau of Land Management (BLM). We outline in this document remote sensing image processing methods to deliver new maps of biological soils crusts, sand dune movements, desert pavements, and sub-surface water sources across the DRECP area. We focused data processing first on the largely unmapped areas most likely to be used for energy developments, such as those within Renewable Energy Study Areas (RESA) and Solar Energy Zones (SEZs). We used imagery (multispectral and radar) mainly from the years 2009-2011

Remote Sensing Applications in Coastal Environment

Coastal regions are susceptible to rapid changes, as they constitute the boundary between the land and the sea. The resilience of a particular segment of coast depends on many factors, including climate change, sea-level changes, natural and technological hazards, extraction of natural resources, population growth, and tourism. Recent research highlights the strong capabilities for remote sensing applications to monitor, inventory, and analyze the coastal environment. This book contains 12 high-quality and innovative scientific papers that explore, evaluate, and implement the use of remote sensing sensors within both natural and built coastal environments