360 research outputs found

    Effects of atmospheric, topographic, and BRDF correction on imaging spectroscopy-derived data products

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    Surface reflectance is an important data product in imaging spectroscopy for obtaining surface information. The complex retrieval of surface reflectance, however, critically relies on accurate knowledge of atmospheric absorption and scattering, and the compensation of these effects. Furthermore, illumination and observation geometry in combination with surface reflectance anisotropy determine dynamics in retrieved surface reflectance not related to surface absorption properties. To the best of authors’ knowledge, no comprehensive assessment of the impact of atmospheric, topographic, and anisotropy effects on derived surface information is available so far.This study systematically evaluates the impact of these effects on reflectance, albedo, and vegetation products. Using three well-established processing schemes (ATCOR F., ATCOR R., and BREFCOR), high-resolution APEX imaging spectroscopy data, covering a large gradient of illumination and observation angles, are brought to several processing states, varyingly affected by mentioned effects. Pixel-wise differences of surface reflectance, albedo, and spectral indices of neighboring flight lines are quantitatively analyzed in their respective overlapping area. We found that compensation of atmospheric effects reveals actual anisotropy-related dynamics in surface reflectance and derived albedo, related to an increase in pixel-wise relative reflectance and albedo differences of more than 40%. Subsequent anisotropy compensation allows us to successfully reduce apparent relative reflectance and albedo differences by up to 20%. In contrast, spectral indices are less affected by atmospheric and anisotropy effects, showing relative differences of 3% to 10% in overlapping regions of flight lines.We recommend to base decisions on the use of appropriate processing schemes on individual use cases considering envisioned data products

    Method of Validating Satellite Surface Reflectance Product Using Empirical Line Method

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    Atmospherically corrected surface reflectance (SR) products are used for reliable monitoring of land surfaces and are the standard products of Landsat sensors. Due to increased demand for SR products, a need exists to verify that the L2C2 (Level-2 Collection-2) SR products are precise and accurate. The Level-2 Collection 2 (L2C2) SR Product is processed satellite imagery data that corrects for atmospheric effects such as absorption and scattering, providing a more accurate representation of Earth\u27s surface. The validation of SR products using ground truth measurement is essential. This study aims to develop and evaluate a validation methodology for satellite SR products. Thus, the Empirical Line Method (ELM) is used here for atmospheric validation of remotely sensed data. Validation is performed using the SR derived from ELM tied to ground truth measurement. Absolute surface reflectance models of Algodones Dunes and the Salton Sea located in North America Sonoran Desert are developed to extend the temporally limited ground truth measurements. This model can give ground truth reflectance in any time frame independent of time constraints. The result of the absolute surface reflectance model of Algodones Dunes indicates that the model predicts the response of Algodones Dunes with an average accuracy of 0.0041 and precision of 0.0063 and gives ground measurements across all multispectral between 350-2500nm. For the Salton Sea the model predicts the response of the Salton Sea with mean absolute error (MAE) of 0.0035 and gives ground measurements across all multispectral between 350-2500nm. The ELM generates atmospheric coefficients (gain and bias) which are applied to an image to obtain SR. Validation results indicated for L9-OLI-2, L8-OLI, and L5-TM-SR products give the RMSE range of 0.0019 to 0.0106, 0.0019 to 0.0148 and 0.0026 to 0.0045 reflectance unit, respectively, and accuracy within 0.0038, 0.0022, and 0.0055 reflectance unit across all spectral bands of L9, L8, and L5 respectively. On average, the validation result showed a strong linear relation between the L2C2 SR products and ELM SR within 0.5 to 1 reflectance units. These results demonstrate the high accuracy and reliability of the L2C2 SR product, providing valuable information for a wide range of remote sensing applications, including land cover and land use mapping, vegetation monitoring, and climate change studies

    Utilizing Ground-Penetrating Radar to Estimate the Spatial Distribution of Snow Depth over Lake Ice in Canada’s Sub-Arctic

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    With the expected rise in air temperature, it becomes important to understand how snow will respond in different climate scenarios. The presence of snow over lake ice largely influences the ice thickness, and as Canada’s Arctic and sub-arctic regions are experiencing warming at twice the global rate, concerns rise as changes in the snowpack will significantly impact northern communities that rely on lake ice as a means of transportation, source for drinking water, and feeding their families. The distribution of snow depth is highly sensitive to changes in climate over time, as such a slight increase in air temperature or change in precipitation can substantially alter snowpack dynamics, which in-turn, directly impacts the rate of lake ice growth. The heterogeneity of snow depth over lake ice is driven by wind redistribution and snowpack metamorphism which creates an inconsistent ice thickness across the lake. Currently, daily snow depth measurements are represented as one value, collected at a weather station on land, near lake shorelines, but previous studies show that this data is not representative of the distribution of snow across different landscapes, more specifically lake ice. Due to the exposed nature of lakes, it is shown that snow depth will be redistributed greatly over lake ice, as there is a lack of vegetation compared to land surfaces with differences in topography. To identify the snow spatial distribution, extensive snow depth measurements must be collected across the entire lake. However, the collection of accurate snow depth measurements over lake ice is challenging and requires a great deal of time spent in the field. Studies have explored the use of remote sensing techniques to map snow distribution over land, however our understanding of such over lake ice is minimal. Accurate measurements of the spatial distribution of snow depth over lake ice is limited due to logistical difficulties in manual measurement techniques (i.e., ruler, snow depth probe). This study presents the use of ground-penetrating radar (GPR) and in-situ observations (snow depth and density) to develop a systematic method to estimate the spatial distribution of snow depth over lake ice. Focused on four lakes located in the North Slave Region, Northwest Territories (Landing Lake, Finger Lake, Vee Lake, Long Lake) the snow depth is derived using GPR two-way travel time. Through utilizing a combination of ground-based techniques, this study proposed a methodology to ease the collection process required to get accurate snow depth measurements on a larger spatial scale than current methods allow. The findings of this thesis will benefit the snow and ice community as we can increase our availability of accurate snow depth data over lake ice through an efficient method of collecting larger snow depth datasets. Specifically, with the availability of snow depth data over lake ice, the accuracy of thermodynamic lake ice model can be improved significantly

    Land Surface Monitoring Based on Satellite Imagery

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    This book focuses attention on significant novel approaches developed to monitor land surface by exploiting satellite data in the infrared and visible ranges. Unlike in situ measurements, satellite data provide global coverage and higher temporal resolution, with very accurate retrievals of land parameters. This is fundamental in the study of climate change and global warming. The authors offer an overview of different methodologies to retrieve land surface parameters— evapotranspiration, emissivity contrast and water deficit indices, land subsidence, leaf area index, vegetation height, and crop coefficient—all of which play a significant role in the study of land cover, land use, monitoring of vegetation and soil water stress, as well as early warning and detection of forest ïŹres and drought

    Spatio-temporal variability in Southern Hemisphere glacier snowline altitudes from 2000-2020

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    The glacierised Southern Hemisphere is vulnerable to continued shrinkage under climate change, but representation of these mountainous regions in climate research is limited by hemispheric and altitudinal scarcity of meteorological observations. End-of-summer snowline altitude (SLAEOS) indicates glacier response to climatic forcing, though has been estimated with low spatio-temporal coverage for the Southern Hemisphere. This study presents the first Southern Hemisphere-wide quantification of SLAEOS, with analysis of regional and intra-regional trends. An automated approach was implemented in Google Earth Engine, in which glacier snow cover was classified in Landsat scenes using Otsu image segmentation and SLAEOS was estimated as the lowest altitude from which snow cover ratio was continuously > 0.5. Results encompassed 6485 glaciers of the Southern Alps, Andes, and Antarctic Peninsula, with trends calculated from 2000-2020. Snowlines underwent widespread retreat in this period; mean rates of SLAEOS rise were between 2.19 and 6.28 m yr-1 for regions, between 1.63 and 7.55 m yr-1 for east/west sub-regions, and were mostly accelerated for the recent decade (2010-2020). Mean SLAEOS lowering (-30 to -1 m yr-1) indicated stability in the southernmost Andes, contrasting to rapid SLAEOS rise (10 to 30 m yr-1) in the southern Central Chilean Andes, and eastern slopes generally experienced increased rates of SLAEOS rise compared to western slopes. SLAEOS variability was reflected in periods of summer warming and reductions in summer snowfall, though correlation with these variables was not consistently identified. East-west and north-south disparities in absolute SLAEOS and rates of SLAEOS change were linked to spatial variability in terrain elevation and prevailing moisture transport, with the latter evidencing the variability and impact of large-scale climatic modes. Given implications of observed trends for glacier mass loss, continued research may involve developing an annually-updated global dataset, investigating additional drivers of SLAEOS variability, and estimating glacier response times

    Geo-Information Technology and Its Applications

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    Geo-information technology has been playing an ever more important role in environmental monitoring, land resource quantification and mapping, geo-disaster damage and risk assessment, urban planning and smart city development. This book focuses on the fundamental and applied research in these domains, aiming to promote exchanges and communications, share the research outcomes of scientists worldwide and to put these achievements better social use. This Special Issue collects fourteen high-quality research papers and is expected to provide a useful reference and technical support for graduate students, scientists, civil engineers and experts of governments to valorize scientific research

    Towards COP27: The Water-Food-Energy Nexus in a Changing Climate in the Middle East and North Africa

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    Due to its low adaptability to climate change, the MENA region has become a "hot spot". Water scarcity, extreme heat, drought, and crop failure will worsen as the region becomes more urbanized and industrialized. Both water and food scarcity are made worse by civil wars, terrorism, and political and social unrest. It is unclear how climate change will affect the MENA water–food–energy nexus. All of these concerns need to be empirically evaluated and quantified for a full climate change assessment in the region. Policymakers in the MENA region need to be aware of this interconnection between population growth, rapid urbanization, food safety, climate change, and the global goal of lowering greenhouse gas emissions (as planned in COP27). Researchers from a wide range of disciplines have come together in this SI to investigate the connections between water, food, energy, and climate in the region. By assessing the impacts of climate change on hydrological processes, natural disasters, water supply, energy production and demand, and environmental impacts in the region, this SI will aid in implementation of sustainable solutions to these challenges across multiple spatial scales

    VGC 2023 - Unveiling the dynamic Earth with digital methods: 5th Virtual Geoscience Conference: Book of Abstracts

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    Conference proceedings of the 5th Virtual Geoscience Conference, 21-22 September 2023, held in Dresden. The VGC is a multidisciplinary forum for researchers in geoscience, geomatics and related disciplines to share their latest developments and applications.:Short Courses 9 Workshops Stream 1 10 Workshop Stream 2 11 Workshop Stream 3 12 Session 1 – Point Cloud Processing: Workflows, Geometry & Semantics 14 Session 2 – Visualisation, communication & Teaching 27 Session 3 – Applying Machine Learning in Geosciences 36 Session 4 – Digital Outcrop Characterisation & Analysis 49 Session 5 – Airborne & Remote Mapping 58 Session 6 – Recent Developments in Geomorphic Process and Hazard Monitoring 69 Session 7 – Applications in Hydrology & Ecology 82 Poster Contributions 9

    Measuring and modelling fAPAR for satellite product validation

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    This thesis presents a comprehensive approach to satellite Fraction of Absorbed Photosynthetically Active Radiation (fAPAR) product validation. This draws on 3D radiative transfer modelling and metrology to characterise the biases associated with a satellite fAPAR algorithm and the uncertainty associated with fAPAR estimates. This extends existing approaches which tend to assume that the in situ measurement technique produces the same fAPAR quantity as the satellite product. The validation procedure involves creating a closure experiment where every aspect of the satellite product definition and its associated assumptions can be tested from the perspective of the in situ and satellite sensors. The intrinsic differences created by the satellite product assumptions are also assessed, where a new reference is created. This is known as the “true” fAPAR since it is perfectly knowable within the context of the radiative transfer model used. Correction factors between the in situ and satellite-derived fAPAR are created to correct data collected over Wytham Woods. The results indicate that the corrections reduce differences of >10% to near zero. However, the uncertainty estimates for the satellite-derived fAPAR show that it does not meet the requirements given by Global Climate Observing System (GCOS) (≀(10% or 0.05)). The wider implications of the retrieved uncertainties are also presented showing that it is unlikely that the GCOS requirements associated with downstream applications that use satellite fAPAR can be met currently. This work represents an important step forward in the validation of satellitederived fAPAR because it is the first time that the absence of satellite and in situ data uncertainty and traceability, and satellite product definition differences have been addressed. This paves the way for the improvement of satellite fAPAR products because their uncertainties can now be quantified effectively and their validation conducted fairly, meaning there is now a benchmark to base improvements on
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