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Item Detecting demolished buildings after a natural hazard using high resolution RGB satellite imagery and modified U-Net convolutional neural networks(MDPI AG) Rashidian, Vahid; Baise, Laurie G.; Koch, Magaly; Moaveni, BabakCollapsed buildings are usually linked with the highest number of human casualties reported after a natural disaster; therefore, quickly finding collapsed buildings can expedite rescue operations and save human lives. Recently, many researchers and agencies have tried to integrate satellite imagery into rapid response. The U.S. Defense Innovation Unit Experimental (DIUx) and National Geospatial Intelligence Agency (NGA) have recently released a ready-to-use dataset known as xView that contains thousands of labeled VHR RGB satellite imagery scenes with 30-cm spatial and 8-bit radiometric resolutions, respectively. Two of the labeled classes represent demolished buildings with 1067 instances and intact buildings with more than 300,000 instances, and both classes are associated with building footprints. In this study, we are using the xView imagery, with building labels (demolished and intact) to create a deep learning framework for classifying buildings as demolished or intact after a natural hazard event. We have used a modified U-Net style fully convolutional neural network (CNN). The results show that the proposed framework has 78% and 95% sensitivity in detecting the demolished and intact buildings, respectively, within the xView dataset. We have also tested the transferability and performance of the trained network on an independent dataset from the 19 September 2017 M 7.1 Pueblo earthquake in central Mexico using Google Earth imagery. To this end, we tested the network on 97 buildings including 10 demolished ones by feeding imagery and building footprints into the trained algorithm. The sensitivity for intact and demolished buildings was 89% and 60%, respectively.Item Mapping the groundwater potentiality of West Qena area, Egypt, using integrated remote sensing and hydro-geophysical techniques(MDPI AG, 2020-05-14) Gaber, Ahmed; Mohamed, Adel Kamel; ElGalladi, Ahmed; Abdelkareem, Mohamed; Beshr, Ahmed M.; Koch, MagalyThe integrated use of remote sensing imagery and hydro-geophysical field surveys is a well-established approach to map the hydrogeological framework, and thus explore and evaluate the groundwater potentiality of desert lands, where groundwater is considered as the main source of freshwater. This study uses such integrated approach to map the groundwater potentiality of the desert alluvial floodplain of the Nile Valley west of Qena, Egypt, as alternative water source to the River Nile. Typically ground gradient, faults and their stress field, lateral variation of rock permeability, drainage patterns, watersheds, rainfall, lithology, and soil types are the main factors believed to affect the groundwater recharge and storage from the infiltration of present-time and paleo-runoff. Following this generally accepted approach, different remote sensing data sets (SRTM DEM, Landsat-8, ALOS/PALSAR-1, Sentinel-1, and TRMM) as well as auxiliary maps (geological and soil maps) were used to identify and map these factors and prepare thematic maps portraying the different influences they exert on the groundwater recharge. These thematic maps were overlaid and integrated using weights in a GIS framework to generate the groundwater potentiality map which categorizes the different recharge capabilities into five zones. Moreover, the aeromagnetic data were processed to map the deep-seated structures and estimate the depth to basement rocks that may control the groundwater occurrence. In addition, the vertical electrical sounding (VES) measurements were applied and calibrated with the available borehole data to delineate the subsurface geological and hydrogeological setting as well as the groundwater aquifers. Different geoelectric cross-sections and hydro-geophysical maps were constructed using the borehole information and VES interpretation results to show the lateral extension of the different lithological units, groundwater-bearing zones, water table, and the saturated thickness of the aquifer. The GIS model and geophysical results show that the southwest part of Nag’a Hammadi-El-Ghoneimia stretch has very high recharge and storage potentiality and is characterized by the presence of two groundwater-bearing zones. The shallow groundwater aquifer is located at a depth of 30 m with a saturation thickness of more than 43 m. However, there are NW–SE faults crossing the study area and most likely serve as recharge conduits by connecting the shallow aquifer with the deeper ones. Such aquifers connection has been confirmed by investigating the chemical and isotopic composition of their groundwater.Item Diffused matrix format: a new storage and processing format for airborne hyperspectral sensor images(Molecular Diversity Preservation International, 2010) Martínez, P.; Cristo, A.; Koch, M.; Pérez, R.Mª.; Schmid, T.; Hernández, L.M.At present, hyperspectral images are mainly obtained with airborne sensors that are subject to turbulences while the spectrometer is acquiring the data. Therefore, geometric corrections are required to produce spatially correct images for visual interpretation and change detection analysis. This paper analyzes the data acquisition process of airborne sensors. The main objective is to propose a new data format called Diffused Matrix Format (DMF) adapted to the sensor's characteristics including its spectral and spatial information. The second objective is to compare the accuracy of the quantitative maps derived by using the DMF data structure with those obtained from raster images based on traditional data structures. Results show that DMF processing is more accurate and straightforward than conventional image processing of remotely sensed data with the advantage that the DMF file structure requires less storage space than other data formats. In addition the data processing time does not increase when DMF is used.Item Linking satellite remote sensing based environmental predictors to disease: an application to the spatiotemporal modelling of schistosomiasis in Ghana(2016) Wrable, M.; Liss, A.; Kulinkina, A.; Koch, Magaly; Biritwum, Nana-Kwadwo; Ofosu, A.; Kosinski, K.C.; Gute, D.M.; Naumova, E.N.90% of the worldwide schistosomiasis burden falls on sub-Saharan Africa. Control efforts are often based on infrequent, small-scale health surveys, which are expensive and logistically difficult to conduct. Use of satellite imagery to predictively model infectious disease transmission has great potential for public health applications. Transmission of schistosomiasis requires specific environmental conditions to sustain freshwater snails, however has unknown seasonality, and is difficult to study due to a long lag between infection and clinical symptoms. To overcome this, we employed a comprehensive 8-year time-series built from remote sensing feeds. The purely environmental predictor variables: accumulated precipitation, land surface temperature, vegetative growth indices, and climate zones created from a novel climate regionalization technique, were regressed against 8 years of national surveillance data in Ghana. All data were aggregated temporally into monthly observations, and spatially at the level of administrative districts. The result of an initial mixed effects model had 41% explained variance overall. Stratification by climate zone brought the R2 as high as 50% for major zones and as high as 59% for minor zones. This can lead to a predictive risk model used to develop a decision support framework to design treatment schemes and direct scarce resources to areas with the highest risk of infection. This framework can be applied to diseases sensitive to climate or to locations where remote sensing would be better suited than health surveys.Item Spatiotemporal modeling of schistosomiasis in Ghana: linking remote sensing data to infectious disease(AMER SOC TROP MED & HYGIENE, 2017-11-01) Wrable, Madeline R.; Liss, Alexander; Kulinkina, Alexandra; Koch, Magaly; Biritwum, Nana-Kwadwo; Kosinski, Karen; Gute, David M.; Naumova, ElenaMore than 90% of the worldwide schistosomiasis burden falls on sub-Saharan Africa. Control efforts are often based on infrequent, small-scale health surveys, which are expensive and logistically difficult to conduct. The use of satellite imagery to predictively model infectious disease transmission has great potential for public health applications. The transmission of schistosomiasis, a disease acquired from contact with contaminated surface water, requires specific environmental conditions to sustain freshwater snails. If a connection between schistosomiasis and remotely sensed environmental variables can be established, then cost effective and current disease risk predictions can be made available. Schistosomiasis transmission has unknown seasonality, and the disease is difficult to study due to a long lag between infection and clinical symptoms. To overcome these challenges, we employed a comprehensive 15-year time-series built from remote sensing feeds, which is the longest environmental dataset to be used in the application of remote sensing to schistosomiasis. The following environmental variables will be used in the model: accumulated precipitation, land surface temperature, vegetative growth indices, and climate zones created from a novel climate regionalization technique. This technique, improves upon the conventional Köppen-Geiger method, which has been the primary climate classification system in use the past 100 years. These predictor variables will be regressed against 8 years of national health data in Ghana, the largest health dataset of its kind to be used in this context, and acquired from freely available satellite imagery data. A benefit of remote sensing processing is that it only requires training and time in terms of resources. The results of a fixed effects model can be used to develop a decision support framework to design treatment schemes and direct scarce resources to areas with the highest risk of infection. This framework can be applied to diseases sensitive to climate or to locations where remote sensing would be better suited than health surveys.Item Using high resolution optical imagery to detect earthquake-induced liquefaction: the 2011 Christchurch earthquake(MDPI AG) Rashidian, Vahid; Baise, Laurie G.; Koch, MagalyUsing automated supervised methods with satellite and aerial imageries for liquefaction mapping is a promising step in providing detailed and region-scale maps of liquefaction extent immediately after an earthquake. The accuracy of these methods depends on the quantity and quality of training samples and the number of available spectral bands. Digitizing a large number of high-quality training samples from an event may not be feasible in the desired timeframe for rapid response as the training pixels for each class should be typical and accurately represent the spectral diversity of that specific class. To perform automated classification for liquefaction detection, we need to understand how to build the optimal and accurate training dataset. Using multispectral optical imagery from the 22 February, 2011 Christchurch earthquake, we investigate the effects of quantity of high-quality training pixel samples as well as the number of spectral bands on the performance of a pixel-based parametric supervised maximum likelihood classifier for liquefaction detection. We find that the liquefaction surface effects are bimodal in terms of spectral signature and therefore, should be classified as either wet liquefaction or dry liquefaction. This is due to the difference in water content between these two modes. Using 5-fold cross-validation method, we evaluate performance of the classifier on datasets with different pixel sizes of 50, 100, 500, 2000, and 4000. Also, the effect of adding spectral information was investigated by adding once only the near infrared (NIR) band to the visible red, green, and blue (RGB) bands and the other time using all available 8 spectral bands of the World-View 2 satellite imagery. We find that the classifier has high accuracies (75%–95%) when using the 2000 pixels-size dataset that includes the RGB+NIR spectral bands and therefore, increasing to 4000 pixels-size dataset and/or eight spectral bands may not be worth the required time and cost. We also investigate accuracies of the classifier when using aerial imagery with same number of training pixels and either RGB or RGB+NIR bands and find that the classifier accuracies are higher when using satellite imagery with same number of training pixels and spectral information. The classifier identifies dry liquefaction with higher user accuracy than wet liquefaction across all evaluated scenarios. To improve classification performance for wet liquefaction detection, we also investigate adding geospatial information of building footprints to improve classification performance. We find that using a building footprint mask to remove them from the classification process, increases wet liquefaction user accuracy by roughly 10%.Item Advances in mapping ice-free surfaces within the Northern Antarctic peninsula region using polarimetric RADARSAT-2 data(IEEE, 2018-01-01) Schmid, Thomas; Guillaso, Stephane; Lopez-Martinez, Jeronimo; Nieto, Ana; Mink, Sandra; Koch, MagalyIce-free areas within the Northern Antarctic Peninsula region are of interest for studying changes occurring to surface covers, including those related to glacial coverage, raised beach deposits and periglacial processes and permafrost. The objective of this work is to map the main surface covers within ice-free areas of King George Island, the largest island of the South Shetlands archipelago, using fully polarimetric RADARSAT-2 SAR data. Surface covers such as rock outcrops and glacial till, stone fields, patterned ground, and sand and gravel deposits form the most representative classes and account for 84 km2 of the ice-free areas on the island. A distribution of complex geomorphological features and landforms was obtained, being some of them considered indicators of periglacial processes and presence of permafrost.Item Damage mapping after the 2017 Puebla Earthquake in Mexico using high-resolution Alos2 Palsar2 data(IEEE, 2018-07) Adriano, Bruno; Koshimura, Shunichi; Karimzadeh, Sadra; Matsuoka, Masashi; Koch, MagalyOn September 19, 2017, the Mw7.1 Puebla Earthquake caused significant destruction in several cities in central Mexico. In this paper, two pre- and one post-event ALOS2-PALSAR2 data were used to detect the damaged area around Izucar de Matamoros town in Mexico. First, we identify the built-up areas using pre-event data. Second, we evaluate the earthquake-induced damage areas using an RGB color-coded image constructed from the pre- and co-event coherence images. Our analysis showed that the green and red bands display a great potential to discriminate the damaged areas.Item The use of remotely sensed environmental parameters for spatial and temporal schistosomiasis prediction across climate zones in Ghana(2019-06-28) Wrable, Madeline; Kulinkina, Alexandra V.; Liss, Alexander; Koch, Magaly; Cruz, Melissa S.; Biritwum, Nana-Kwadwo; Ofosu, Anthony; Gute, David M.; Kosinski, Karen C.; Naumova, Elena N.Schistosomiasis control in sub-Saharan Africa is enacted primarily through preventive chemotherapy. Predictive models can play an important role in filling knowledge gaps in the distribution of the disease and help guide the allocation of limited resources. Previous modeling approaches have used localized cross-sectional survey data and environmental data typically collected at a discrete point in time. In this analysis, 8 years (2008-2015) of monthly schistosomiasis cases reported into Ghana's national surveillance system were used to assess temporal and spatial relationships between disease rates and three remotely sensed environmental variables: land surface temperature (LST), normalized difference vegetation index (NDVI), and accumulated precipitation (AP). Furthermore, the analysis was stratified by three major and nine minor climate zones, defined using a new climate classification method. Results showed a downward trend in reported disease rates (~ 1% per month) for all climate zones. Seasonality was present in the north with two peaks (March and September), and in the middle of the country with a single peak (July). Lowest disease rates were observed in December/January across climate zones. Seasonal patterns in the environmental variables and their associations with reported schistosomiasis infection rates varied across climate zones. Precipitation consistently demonstrated a positive association with disease outcome, with a 1-cm increase in rainfall contributing a 0.3-1.6% increase in monthly reported schistosomiasis infection rates. Generally, surveillance of neglected tropical diseases (NTDs) in low-income countries continues to suffer from data quality issues. However, with systematic improvements, our approach demonstrates a way for health departments to use routine surveillance data in combination with publicly available remote sensing data to analyze disease patterns with wide geographic coverage and varying levels of spatial and temporal aggregation.Item Assessing water availability in Mediterranean regions affected by water conflicts through MODIS data time series analysis(MDPI AG) Marco-Dos Santos, Gema; Melendez-Pastor, Ignacio; Navarro-Pedreño, Jose; Koch, MagalyWater scarcity is a widespread problem in arid and semi-arid regions such as the western Mediterranean coastal areas. The irregularity of the precipitation generates frequent droughts that exacerbate the conflicts among agriculture, water supply and water demands for ecosystems maintenance. Besides, global climate models predict that climate change will cause Mediterranean arid and semi-arid regions to shift towards lower rainfall scenarios that may exacerbate water conflicts. The purpose of this study is to find a feasible methodology to assess current and monitor future water demands in order to better allocate limited water resources. The interdependency between a vegetation index (NDVI), land surface temperature (LST), precipitation (current and future), and surface water resources availability in two watersheds in southeastern Spain with serious difficulties in meeting water demands was investigated. MODIS (Moderate Resolution Imaging Spectroradiometer) NDVI and LST products (as proxy of drought), precipitation maps (generated from climate station records) and reservoir storage gauging information were used to compute times series anomalies from 2001 to 2014 and generate regression images and spatial regression models. The temporal relationship between reservoir storage and time series of satellite images allowed the detection of different and contrasting water management practices in the two watersheds. In addition, a comparison of current precipitation rates and future precipitation conditions obtained from global climate models suggests high precipitation reductions, especially in areas that have the potential to contribute significantly to groundwater storage and surface runoff, and are thus critical to reservoir storage. Finally, spatial regression models minimized spatial autocorrelation effects, and their results suggested the great potential of our methodology combining NDVI and LST time series to predict future scenarios of water scarcity.Item Improving spatial prediction of Schistosoma haematobium prevalence in southern Ghana through new remote sensors and local water access profiles(PUBLIC LIBRARY SCIENCE, 2018-06-01) Walz, Yvonne; Koch, Magaly; Biritwum, Nana-Kwadwo; Utzinger, Jurg; Kulinkina, Alexandra V.; Naumova, Elena N.BACKGROUND: Schistosomiasis is a water-related neglected tropical disease. In many endemic low- and middle-income countries, insufficient surveillance and reporting lead to poor characterization of the demographic and geographic distribution of schistosomiasis cases. Hence, modeling is relied upon to predict areas of high transmission and to inform control strategies. We hypothesized that utilizing remotely sensed (RS) environmental data in combination with water, sanitation, and hygiene (WASH) variables could improve on the current predictive modeling approaches. METHODOLOGY: Schistosoma haematobium prevalence data, collected from 73 rural Ghanaian schools, were used in a random forest model to investigate the predictive capacity of 15 environmental variables derived from RS data (Landsat 8, Sentinel-2, and Global Digital Elevation Model) with fine spatial resolution (10-30 m). Five methods of variable extraction were tested to determine the spatial linkage between school-based prevalence and the environmental conditions of potential transmission sites, including applying the models to known human water contact locations. Lastly, measures of local water access and groundwater quality were incorporated into RS-based models to assess the relative importance of environmental and WASH variables. PRINCIPAL FINDINGS: Predictive models based on environmental characterization of specific locations where people contact surface water bodies offered some improvement as compared to the traditional approach based on environmental characterization of locations where prevalence is measured. A water index (MNDWI) and topographic variables (elevation and slope) were important environmental risk factors, while overall, groundwater iron concentration predominated in the combined model that included WASH variables. CONCLUSIONS/SIGNIFICANCE: The study helps to understand localized drivers of schistosomiasis transmission. Specifically, unsatisfactory water quality in boreholes perpetuates reliance on surface water bodies, indirectly increasing schistosomiasis risk and resulting in rapid reinfection (up to 40% prevalence six months following preventive chemotherapy). Considering WASH-related risk factors in schistosomiasis prediction can help shift the focus of control strategies from treating symptoms to reducing exposure.Item Using InSAR coherence for investigating the interplay of fluvial and aeolian features in arid lands: implications for groundwater potential in Egypt(MDPI AG, 2018-05-25) Gaber, Ahmed; Abdelkareem, Mohamed; Abdelsadek, Ismail; El-Baz, Farouk; Koch, M.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.Item A framework of rapid regional tsunami damage recognition from post-event TerraSAR-X imagery using deep neural networks(IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2018-01-01) Bai, Yanbing; Gao, Chang; Singh, Sameer; Koch, Magaly; Adriano, Bruno; Mas, Erick; Koshimura, ShunichiNear real-time building damage mapping is an indispensable prerequisite for governments to make decisions for disaster relief. With high-resolution synthetic aperture radar (SAR) systems, such as TerraSAR-X, the provision of such products in a fast and effective way becomes possible. In this letter, a deep learning-based framework for rapid regional tsunami damage recognition using post-event SAR imagery is proposed. To perform such a rapid damage mapping, a series of tile-based image split analysis is employed to generate the data set. Next, a selection algorithm with the SqueezeNet network is developed to swiftly distinguish between built-up (BU) and nonbuilt-up regions. Finally, a recognition algorithm with a modified wide residual network is developed to classify the BU regions into wash away, collapsed, and slightly damaged regions. Experiments performed on the TerraSAR-X data from the 2011 Tohoku earthquake and tsunami in Japan show a BU region extraction accuracy of 80.4% and a damage-level recognition accuracy of 74.8%, respectively. Our framework takes around 2 h to train on a new region, and only several minutes for prediction.Item Minimizing the residual topography effect on interferograms to improve DInSAR results: estimating land subsidence in Port-Said City, Egypt(MDPI AG, 2017-07-21) Gaber, Ahmed; Darwish, Noura; Koch, MagalyThe accurate detection of land subsidence rates in urban areas is important to identify damage-prone areas and provide decision-makers with useful information. Meanwhile, no precise measurements of land subsidence have been undertaken within the coastal Port-Said City in Egypt to evaluate its hazard in relationship to sea-level rise. In order to address this shortcoming, this work introduces and evaluates a methodology that substantially improves small subsidence rate estimations in an urban setting. Eight ALOS/PALSAR-1 scenes were used to estimate the land subsidence rates in Port-Said City, using the Small BAse line Subset (SBAS) DInSAR technique. A stereo pair of ALOS/PRISM was used to generate an accurate DEM to minimize the residual topography effect on the generated interferograms. A total of 347 well distributed ground control points (GCP) were collected in Port-Said City using the leveling instrument to calibrate the generated DEM. Moreover, the eight PALSAR scenes were co-registered using 50 well-distributed GCPs and used to generate 22 interferogram pairs. These PALSAR interferograms were subsequently filtered and used together with the coherence data to calculate the phase unwrapping. The phase-unwrapped interferogram-pairs were then evaluated to discard four interferograms that were affected by phase jumps and phase ramps. Results confirmed that using an accurate DEM (ALOS/PRISM) was essential for accurately detecting small deformations. The vertical displacement rate during the investigated period (2007–2010) was estimated to be −28 mm. The results further indicate that the northern area of Port-Said City has been subjected to higher land subsidence rates compared to the southern area. Such land subsidence rates might induce significant environmental changes with respect to sea-level rise.Item The application of predictive modelling for determining bio-environmental factors affecting the distribution of blackflies (Diptera: Simuliidae) in the Gilgel Gibe watershed in Southwest Ethiopia(2014) Ambelu, Argaw; Mekonen, Seblework; Koch, Magaly; Addis, Taffere; Boets, Pieter; Everaert, Gert; Goethals, PeterBlackflies are important macroinvertebrate groups from a public health as well as ecological point of view. Determining the biological and environmental factors favouring or inhibiting the existence of blackflies could facilitate biomonitoring of rivers as well as control of disease vectors. The combined use of different predictive modelling techniques is known to improve identification of presence/absence and abundance of taxa in a given habitat. This approach enables better identification of the suitable habitat conditions or environmental constraints of a given taxon. Simuliidae larvae are important biological indicators as they are abundant in tropical aquatic ecosystems. Some of the blackfly groups are also important disease vectors in poor tropical countries. Our investigations aim to establish a combination of models able to identify the environmental factors and macroinvertebrate organisms that are favourable or inhibiting blackfly larvae existence in aquatic ecosystems. The models developed using macroinvertebrate predictors showed better performance than those based on environmental predictors. The identified environmental and macroinvertebrate parameters can be used to determine the distribution of blackflies, which in turn can help control river blindness in endemic tropical places. Through a combination of modelling techniques, a reliable method has been developed that explains environmental and biological relationships with the target organism, and, thus, can serve as a decision support tool for ecological management strategies.Item Water resources assessment and management in drylands(Multidisciplinary Digital Publishing Institute, 2016) Koch, Magaly; Missimer, Thomas M.Drylands regions of the world face difficult issues in maintaining water resources to meet current demands which will intensify in the future with population increases, infrastructure development, increased agricultural water demands, and climate change impacts on the hydrologic system. New water resources evaluation and management methods will be needed to assure that water resources in drylands are optimally managed in a sustainable manner. Development of water management and conservation methods is a multi-disciplinary endeavor. Scientists and engineers must collaborate and cooperate with water managers, planners, and politicians to successfully adopt new strategies to manage water not only for humans, but to maintain all aspects of the environment. This particularly applies to drylands regions where resources are already limited and conflicts over water are occurring. Every aspect of the hydrologic cycle needs to be assessed to be able to quantify the available water resources, to monitor natural and anthropogenic changes, and to develop flexible policies and management strategies that can change as conditions dictate. Optimal, sustainable water management is achieved by cooperation and not conflict, thereby necessitating the need for high quality scientific research and input into the processItem Spectral and thermal mapping of desert surface sediments for agricultural development(Field Science Center, Graduate School of Agricultural Science, Tohoku University, 2015-03) Koch, Mary; Gaber, AhmedA combination of multispectral, thermal and microwave data obtained from space and supported by ground measurements are used to investigate the surface sediment characteristics of a desert plain area in Egypt (El-Gallaba Plain, NW of Aswan). This plain once hosted an ancestral river system that is nowadays largely covered by aeolian and gravelly sands, and thus, only detectible with radar and thermal images. The methodology consists of extracting thermo-physical and textural parameters to guide and improve supervised spectral classification results. The results show that surface mineralogy (obtained from spectral information) correlates strongly with surface emissivity, whereas grain size and surface roughness strongly correlates with apparent thermal inertia. Furthermore, several broad strips of thermal cooling-anomalies are arranged in a linear fashion and diagonally crossing the alluvial basin. The sediments within these strips show very different textural, thermo-physical and compositional characteristics with respect to the surrounding areas suggesting that they were deposited under different depositional environments such as structurally controlled linear basins. These tectonic depressions were confirmed by ground penetrating radar and could be promising areas for groundwater accumulation and exploration enabling agricultural development in the El-Gallaba Plain of the Western Desert in Egypt.