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  • 1.
    Bhardwaj, Anshuman
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Space Technology.
    Sam, Lydia
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Space Technology.
    Martin-Torres, Javier
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Space Technology. Instituto Andaluz de Ciencias de la Tierra (CSIC-UGR), Armilla, Granada, Spain.
    Zorzano Mier, María-Paz
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Space Technology. Centro de Astrobiología (INTA-CSIC), Torrejón de Ardoz, Madrid, Spain.
    Distribution and Morphologies of Transverse Aeolian Ridges in ExoMars 2020 Rover Landing Site2019In: Remote Sensing, ISSN 2072-4292, E-ISSN 2072-4292, Vol. 11, no 8, article id 912Article in journal (Refereed)
    Abstract [en]

    Aeolian processes are believed to play a major role in the landscape evolution of Mars. Investigations on Martian aeolian landforms such as ripples, transverse aeolian ridges (TARs), and dunes, and aeolian sediment flux measurements are important to enhance our understanding of past and present wind regimes, the ongoing dust cycle, landscape evolution, and geochemistry. These aeolian bedforms are often comprised of loose sand and sharply undulating topography and thus pose a threat to mobility and maneuvers of Mars rovers. Here we present a first-hand account of the distribution, morphologies, and morphometrics of TARs in Oxia Planum, the recently selected ExoMars 2020 Rover landing site. The gridded mapping was performed for contiguous stretches of TARs within all the landing ellipses using 57 sub-meter high resolution imaging science experiment (HiRISE) scenes. We also provide the morphological descriptions for all types of TARs present within the landing ellipses. We use HiRISE digital terrain models (DTMs) along with the images to derive morphometric information for TARs in Oxia Planum. In general, the average areal TAR coverage was found to be 5.4% (±4.9% standard deviation), increasing from west to east within the landing ellipses. We report the average TAR morphometrics in the form of crest–ridge width (131.1 ± 106.2 m), down-wind TAR length (17.6 ± 10.1 m), wavelength (37.3 ± 11.6 m), plan view aspect ratio (7.1 ± 2.3), inter-bedform spacing (2.1 ± 1.1), slope (10.6° ± 6.1°), predominant orientations (NE-SW and E-W), and height (1.2 ± 0.8 m). While simple TARs are predominant, we report other TAR morphologies such as forked TAR, wavy TAR with associated smaller secondary ripples, barchan-like TAR, networked TAR, and mini-TARs from the region. Our results can help in planning the rover traverses in terms of both safe passage and scientific returns favoring aeolian research, particularly improving our understanding of TARs.

  • 2.
    Bhardwaj, Anshuman
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Space Technology.
    Sam, Lydia
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Space Technology.
    Martin-Torres, Javier
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Space Technology.
    Zorzano Mier, María-Paz
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Space Technology.
    Ramírez Luque, Juan Antonio
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Space Technology.
    UAV Imaging of a Martian Brine Analogue Environment in a Fluvio-Aeolian Setting2019In: Remote Sensing, ISSN 2072-4292, E-ISSN 2072-4292, Vol. 11, no 18, article id 2104Article in journal (Refereed)
    Abstract [en]

    Understanding extraterrestrial environments and landforms through remote sensing and terrestrial analogy has gained momentum in recent years due to advances in remote sensing platforms, sensors, and computing efficiency. The seasonal brines of the largest salt plateau on Earth in Salar de Uyuni (Bolivian Altiplano) have been inadequately studied for their localized hydrodynamics and the regolith volume transport across the freshwater-brine mixing zones. These brines have recently been projected as a new analogue site for the proposed Martian brines, such as recurring slope lineae (RSL) and slope streaks. The Martian brines have been postulated to be the result of ongoing deliquescence-based salt-hydrology processes on contemporary Mars, similar to the studied Salar de Uyuni brines. As part of a field-site campaign during the cold and dry season in the latter half of August 2017, we deployed an unmanned aerial vehicle (UAV) at two sites of the Salar de Uyuni to perform detailed terrain mapping and geomorphometry. We generated high-resolution (2 cm/pixel) photogrammetric digital elevation models (DEMs) for observing and quantifying short-term terrain changes within the brines and their surroundings. The achieved co-registration for the temporal DEMs was considerably high, from which precise inferences regarding the terrain dynamics were derived. The observed average rate of bottom surface elevation change for brines was ~1.02 mm/day, with localized signs of erosion and deposition. Additionally, we observed short-term changes in the adjacent geomorphology and salt cracks. We conclude that the transferred regolith volume via such brines can be extremely low, well within the resolution limits of the remote sensors that are currently orbiting Mars, thereby making it difficult to resolve the topographic relief and terrain perturbations that are produced by such flows on Mars. Thus, the absence of observable erosion and deposition features within or around most of the proposed Martian RSL and slope streaks cannot be used to dismiss the possibility of fluidized flow within these features

  • 3.
    Kottayil, Ajil
    et al.
    Advanced Centre for Atmospheric Radar Research, Cochin University of Science and Technology, Kerala.
    John, Viju O.
    EUMETSAT, Darmstadt 64295, Germany; Met Office Hadley Centre, Exeter EX1 3PB, UK.
    Buehler, Stefan A.
    Meteorological Institute, University of Hamburg, Hamburg 20146, Germany.
    Mohanakumar, Kesavapillai
    Advanced Centre for Atmospheric Radar Research, Cochin University of Science and Technology, Kerala.
    Evaluating the diurnal cycle of upper tropospheric humidity in two different climate models using satellite observations2016In: Remote Sensing, ISSN 2072-4292, E-ISSN 2072-4292, Vol. 8, no 4, article id 325Article in journal (Refereed)
    Abstract [en]

    The diurnal cycle of upper tropospheric humidity (UTH) is known to be influenced by such processes as convection and the formation of clouds which are parameterized in current global climate models. In this study, we evaluate the performance of two climate models, the Community Atmospheric Model version 5 (CAM-5) and the Global Atmosphere 3.0 (GA-3) model in simulating the diurnal cycle of UTH (represented by a combination of sinusoids of 12 and 24 h periods) by comparing with microwave and infrared (IR) measurements (where available). These comparisons were made over two convective land regions in South America and Africa, and over oceanic regions in the Atlantic, Indian and West Pacific for the month of January 2007. We analyzed how the diurnal cycles from IR and microwave instruments differ, and the reason for the differences. Our study suggests that the differences in the diurnal cycles of IR and microwave UTH result from sampling differences due to the presence of clouds. As noted by earlier studies, the models exhibit considerable discrepancies in diurnal amplitude and phase relative to observations, and these discrepancies have different magnitudes over land and ocean.

  • 4.
    Shahabi, Himan
    et al.
    Department of Geomorphology, Faculty of Natural Resources, University of Kurdistan, Sanandaj, Iran. Board Member of Department of Zrebar Lake Environmental Research, Kurdistan Studies Institute, University of Kurdistan, Sanandaj, Iran.
    Shirzadi, Ataollah
    Department of Rangeland and Watershed Management, Faculty of Natural Resources, University of Kurdistan, Sanandaj, Iran.
    Ghaderi, Kayvan
    Department of Information Technology and Computer Engineering, Faculty of Engineering, University of Kurdistan, Sanandaj, Iran.
    Omidvar, Ebrahim
    Department of Rangeland and Watershed Management, Faculty of Natural Resources and Earth Sciences, University of Kashan, Kashan, Iran.
    Al-Ansari, Nadhir
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Mining and Geotechnical Engineering.
    Clague, John J.
    Department of Earth Sciences Simon Fraser University, Burnaby, BC, Canada.
    Geertsema, Marten
    British Columbia, Ministry of Forests, Lands, Natural Resource Operations and Rural Development, Prince George, BC, Canada.
    Khosravi, Khabat
    School of Engineering, University of Guelph, Guelph, ON , Canada.
    Amini, Ata
    Kurdistan Agricultural and Natural Resources Research and Education Center, AREEO, Sanandaj, Iran.
    Bahrami, Sepideh
    Department of Hydrological Sciences, University of Nevada, Reno, NV, USA.
    Rahmati, Omid
    Kurdistan Agricultural and Natural Resources Research and Education Center, AREEO, Sanandaj, Iran.
    Habibi, Kyoumars
    Department of urban and regional planning, Faculty of Art and Architecture, University of Kurdistan, Sanandaj, Iran.
    Mohammadi, Ayub
    Department of Geoinformation, Faculty of Built Environment and Surveying, Universiti Teknologi Malaysia (UTM), Johor Bahru, Malaysia.
    Nguyen, Hoang
    Institute of Research and Development, Duy Tan University, Da Nang, Vietnam.
    Melesse, Assefa M.
    Department of Earth and Environment, Florida International University, Miami, FL, USA.
    Ahmad, Baharin Bin
    Department of Geoinformation, Faculty of Built Environment and Surveying, Universiti Teknologi Malaysia (UTM),Johor Bahru, Malaysia.
    Ahmad, Anuar
    Department of Geoinformation, Faculty of Built Environment and Surveying, Universiti Teknologi Malaysia (UTM),Johor Bahru, Malaysia.
    Flood Detection and Susceptibility Mapping Using Sentinel-1 Remote Sensing Data and a Machine Learning Approach: Hybrid Intelligence of Bagging Ensemble Based on K-Nearest Neighbor Classifier2020In: Remote Sensing, ISSN 2072-4292, E-ISSN 2072-4292, Vol. 12, no 2, p. 1-30, article id 266Article in journal (Refereed)
    Abstract [en]

    Mapping flood-prone areas is a key activity in flood disaster management. In this paper, we propose a new flood susceptibility mapping technique. We employ new ensemble models based on bagging as a meta-classifier and K-Nearest Neighbor (KNN) coarse, cosine, cubic, and weighted base classifiers to spatially forecast flooding in the Haraz watershed in northern Iran. We identified flood-prone areas using data from Sentinel-1 sensor. We then selected 10 conditioning factors to spatially predict floods and assess their predictive power using the Relief Attribute Evaluation (RFAE) method. Model validation was performed using two statistical error indices and the area under the curve (AUC). Our results show that the Bagging–Cubic–KNN ensemble model outperformed other ensemble models. It decreased the overfitting and variance problems in the training dataset and enhanced the prediction accuracy of the Cubic–KNN model (AUC=0.660). We therefore recommend that the Bagging–Cubic–KNN model be more widely applied for the sustainable management of flood-prone areas.

  • 5.
    Singh, Shaktiman
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Space Technology.
    Bhardwaj, Anshuman
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Space Technology.
    Singh, Atar
    Department of Environmental Science, Sharda University, Greater Noida, India.
    Sam, Lydia
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Space Technology.
    Shekhar, Mayank
    Birbal Sahni Institute of Palaeosciences, Lucknow, India.
    Martin-Torres, Javier
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Space Technology. Instituto Andaluz de Ciencias de la Tierra (CSIC-UGR), Armilla, Granada, Spain.
    Zorzano Mier, María-Paz
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Space Technology. Centro de Astrobiología (INTA-CSIC), Madrid, Spain.
    Quantifying the Congruence between Air and Land Surface Temperatures for Various Climatic and Elevation Zones of Western Himalaya2019In: Remote Sensing, ISSN 2072-4292, E-ISSN 2072-4292, Vol. 11, no 24, article id 2889Article in journal (Refereed)
    Abstract [en]

    The surface and near-surface air temperature observations are primary data for glacio-hydro-climatological studies. The in situ air temperature (Ta) observations require intense logistic and financial investments, making it sparse and fragmented particularly in remote and extreme environments. The temperatures in Himalaya are controlled by a complex system driven by topography, seasons, and cryosphere which further makes it difficult to record or predict its spatial heterogeneity. In this regard, finding a way to fill the observational spatiotemporal gaps in data becomes more crucial. Here, we show the comparison of Ta recorded at 11 high altitude stations in Western Himalaya with their respective land surface temperatures (Ts) recorded by Moderate Resolution Imagining Spectroradiometer (MODIS) Aqua and Terra satellites in cloud-free conditions. We found remarkable seasonal and spatial trends in the Ta vs. Ts relationship: (i) Ts are strongly correlated with Ta (R2 = 0.77, root mean square difference (RMSD) = 5.9 °C, n = 11,101 at daily scale and R2 = 0.80, RMSD = 5.7 °C, n = 3552 at 8-day scale); (ii) in general, the RMSD is lower for the winter months in comparison to summer months for all the stations, (iii) the RMSD is directly proportional to the elevations; (iv) the RMSD is inversely proportional to the annual precipitation. Our results demonstrate the statistically strong and previously unreported Ta vs. Ts relationship and spatial and seasonal variations in its intensity at daily resolution for the Western Himalaya. We anticipate that our results will provide the scientists in Himalaya or similar data-deficient extreme environments with an option to use freely available remotely observed Ts products in their models to fill-up the spatiotemporal data gaps related to in situ monitoring at daily resolution. Substituting Ta by Ts as input in various geophysical models can even improve the model accuracy as using spatially continuous satellite derived Ts in place of discrete in situ Ta extrapolated to different elevations using a constant lapse rate can provide more realistic estimates. 

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