258 research outputs found

    Assessing the utility of geospatial technologies to investigate environmental change within lake systems

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    Over 50% of the world's population live within 3. km of rivers and lakes highlighting the on-going importance of freshwater resources to human health and societal well-being. Whilst covering c. 3.5% of the Earth's non-glaciated land mass, trends in the environmental quality of the world's standing waters (natural lakes and reservoirs) are poorly understood, at least in comparison with rivers, and so evaluation of their current condition and sensitivity to change are global priorities. Here it is argued that a geospatial approach harnessing existing global datasets, along with new generation remote sensing products, offers the basis to characterise trajectories of change in lake properties e.g., water quality, physical structure, hydrological regime and ecological behaviour. This approach furthermore provides the evidence base to understand the relative importance of climatic forcing and/or changing catchment processes, e.g. land cover and soil moisture data, which coupled with climate data provide the basis to model regional water balance and runoff estimates over time. Using examples derived primarily from the Danube Basin but also other parts of the World, we demonstrate the power of the approach and its utility to assess the sensitivity of lake systems to environmental change, and hence better manage these key resources in the future

    Overview of ESA’s Earth Observation upcoming small satellites missions

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    The “New Space” paradigm, has enabled the creation of many new opportunities in the space sector like the development of a large number of missions based on small and nano-satellites. The European Space Agency (ESA) is supporting these new development approaches and technology advancements, including use of Commercial-Off-The-Shelf (COTS) components to enable missions based on small and nano satellites. ESA’s Earth Observation Programmes Directorate (ESA-EOP) is already involved not only in the implementation of technologies exploiting the capabilities offered by small and nano-satellites as a complement to the EOP scientific and application-driven flagship satellites, but also in the quick validation of new approaches like A.I, super resolution or more in general in orbit data processing. ESA-EOP developments in the area of small and nano satellites are spread in three different programmatic lines, each with its own objectives: Scout and F-sat Missions and the InCubed Programme. This paper presents the overall ESA-EOP small missions strategy providing a brief insight on the genesis of each programmatic line and their selection processes including an update of the status of the first initiatives and missions under development or study

    Monitoring vegetation using remote sensing time series data: a review of the period 1996-2017

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    Analyzing time series data with remote sensing provides a better understanding of vegetation dynamics, since previous conditions and changes that have occurred over a given period are known. The objective of this paper was to analyze the current status and recent advances in the use of time series data obtained from remote sensors for vegetation monitoring. A systematic search of scientific papers was performed and 167 papers were found, published during the period 1996 to 2017. No significant difference in the amount of years analyzed was found between time series analyzed with a single sensor and those analyzed with a combination of several sensors (i.e. Landsat and SPOT, Landsat and Sentinel, among others). However, the combination of data from different sensors (fusion of images) can improve the quality of the results. Specialattention must also be given to the fusion of optical and radar data, since this offers more unique spectral and structural information for land cover and land use assessments. Highlights Remote sensing provides a better understanding of vegetation dynamics. The number of vegetation monitoring papers published using time series data are becoming more frequent. The fusion of Landsat and Sentinel-2 satellite data shows great potential for timely monitoring of rapid changes. The fusion of optical and radar data points to a new trend in remote sensing, including the use of geospatial open data sources.Analyzing time series data with remote sensing provides a better understanding of vegetation dynamics, since previous conditions and changes that have occurred over a given period are known. The objective of this paper was to analyze the current status and recent advances in the use of time series data obtained from remote sensors for vegetation monitoring. A systematic search of scientific papers was performed and 167 papers were found, published during the period 1996 to 2017. No significant difference in the amount of years analyzed was found between time series analyzed with a single sensor and those analyzed with a combination of several sensors (i.e. Landsat and SPOT, Landsat and Sentinel, among others). However, the combination of data from different sensors (fusion of images) can improve the quality of the results. Specialattention must also be given to the fusion of optical and radar data, since this offers more unique spectral and structural information for land cover and land use assessments. Highlights Remote sensing provides a better understanding of vegetation dynamics. The number of vegetation monitoring papers published using time series data are becoming more frequent. The fusion of Landsat and Sentinel-2 satellite data shows great potential for timely monitoring of rapid changes. The fusion of optical and radar data points to a new trend in remote sensing, including the use of geospatial open data sources

    A first assessment of the Sentinel-2 Level 1-C cloud mask product to support informed surface analyses

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    Abstract Cloud detection in optical remote sensing images is a crucial problem because undetected clouds can produce misleading results in the analyses of surface and atmospheric parameters. Sentinel-2 provides high spatial resolution satellite data distributed with associated cloud masks. In this paper, we evaluate the ability of Sentinel-2 Level-1C cloud mask products to discriminate clouds over a variety of biogeographic scenarios and in different cloudiness conditions. Reference cloud masks for the identification of misdetection were generated by applying a local thresholding method that analyses Sentinel-2 Band 2 (0.490â€ŻÎŒm) and Band 10 (1.375â€ŻÎŒm) separately; histogram-based thresholds were locally tuned by checking the single bands and the natural color composite (B4B3B2); in doubtful cases, NDVI and DEM were also analyzed to refine the masks; the B2B11B12 composite was used to separate snow. The analysis of the cloud classification errors obtained for our test sites allowed us to get important inferences of general value. The L1C cloud mask generally underestimated the presence of clouds (average Omission Error, OE, 37.4%); this error increased (OE > 50%) for imagery containing opaque clouds with a large transitional zone (between the cloud core and clear areas) and cirrus clouds, fragmentation emerged as a major source of omission errors (R2 0.73). Overestimation was prevalently found in the presence of holes inside the main cloud bodies. Two extreme environments were particularly critical for the L1C cloud mask product. Detection over Amazonian rainforests was highly inefficient (OE > 70%) due to the presence of complex cloudiness and high water vapor content. On the other hand, Alpine orography under dry atmosphere created false cirrus clouds. Altogether, cirrus detection was the most inefficient. According to our results, Sentinel-2 L1C users should take some simple precautions while waiting for ESA improved cloud detection products

    Evaluation of Landsat-8 and Sentinel-2A Aerosol Optical Depth Retrievals Across Chinese Cities and Implications for Medium Spatial Resolution Urban Aerosol Monitoring

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    In urban environments, aerosol distributions may change rapidly due to building and transport infrastructure and human population density variations. The recent availability of medium resolution Landsat-8 and Sentinel-2 satellite data provide the opportunity for aerosol optical depth (AOD) estimation at higher spatial resolution than provided by other satellites. AOD retrieved from 30 m Landsat-8 and 10 m Sentinel-2A data using the Land Surface Reflectance Code (LaSRC) were compared with coincident ground-based Aerosol Robotic Network (AERONET) Version 3 AOD data for 20 Chinese cities in 2016. Stringent selection criteria were used to select contemporaneous data; only satellite and AERONET data acquired within 10 min were considered. The average satellite retrieved AOD over a 1470 m1470 m window centered on each AERONET site was derived to capture fine scale urban AOD variations. AERONET Level 1.5 (cloud-screened) and Level 2.0 (cloud-screened and also quality assured) data were considered. For the 20 urban AERONET sites in 2016 there were 106 (Level 1.5) and 67 (Level 2.0) Landsat-8 AERONET AOD contemporaneous data pairs, and 118 (Level 1.5) and 89 (Level 2.0) Sentinel-2A AOD data pairs. The greatest AOD values (>1.5) occurred in Beijing, suggesting that the Chinese capital was one of the most polluted cities in China in 2016. The LaSRC Landsat-8 and Sentinel-2A AOD retrievals agreed well with the AERONET AOD data (linear regression slopes > 0.96; coefficient of determination r(exp 2) > 0.90; root mean square deviation < 0.175) and demonstrate that the LaSRC is an effective and applicable medium resolution AOD retrieval algorithm over urban environments. The Sentinel-2A AOD retrievals had better accuracy than the Landsat-8 AOD retrievals, which is consistent with previously published research.The implications of the research and the potential for urban aerosol monitoring by combining the freely available Landsat-8 and Sentinel-2 satellite data are discussed

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

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    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

    Evaluating the feasibility of using Sentinel-2 imagery for water clarity assessment in a reservoir

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    The new Sentinel-2 satellites present a significant scientific opportunity for the study of water quality. The objective of this study was to evaluate the suitability of Sentinel-2 imagery for estimating and mapping Secchi disk transparency (SDT) in RĂ­o Tercero reservoir (CĂłrdoba-Argentina). Field observations and a dataset of atmospherically corrected Sentinel-2 images were used to generate and validate an algorithm to estimate water clarity in the studied reservoir. As a real application of the used methodology, the validated algorithm was used to obtain a spatial representation of water clarity in the reservoir during sampling campaigns. Results demonstrate capabilities of Sentinel-2 mission to make a substantial contribution to the current assessment and understanding of aquatic systems by estimating and mapping a water quality characteristic.Fil: Bonansea, Matias. Universidad Nacional de RĂ­o Cuarto. Facultad de Ciencias Exactas FisicoquĂ­micas y Naturales. Instituto de Ciencias de la Tierra, Biodiversidad y Ambiente - Consejo Nacional de Investigaciones CientĂ­ficas y TĂ©cnicas. Centro CientĂ­fico TecnolĂłgico Conicet - CĂłrdoba. Instituto de Ciencias de la Tierra, Biodiversidad y Ambiente; Argentina. Universidad Nacional de RĂ­o Cuarto. Facultad de AgronomĂ­a y Veterinaria. Departamento de Estudios BĂĄsicos y Agropecuarios; ArgentinaFil: Ledesma, Micaela. Universidad Nacional de RĂ­o Cuarto. Facultad de AgronomĂ­a y Veterinaria. Departamento de Estudios BĂĄsicos y Agropecuarios; ArgentinaFil: BazĂĄn, Raquel. Universidad Nacional de CĂłrdoba; ArgentinaFil: Ferral, Anabella. Comision Nacional de Actividades Espaciales. Instituto de Altos Estudios Espaciales "Mario Gulich"; Argentina. Consejo Nacional de Investigaciones CientĂ­ficas y TĂ©cnicas. Centro CientĂ­fico TecnolĂłgico Conicet - CĂłrdoba; ArgentinaFil: German, Alba. Comision Nacional de Actividades Espaciales. Instituto de Altos Estudios Espaciales "Mario Gulich"; Argentina. Ministerio de Agua, Ambiente y Servicios PĂșblicos. - Gobierno de la Provincia de Cordoba. Ministerio de Agua, Ambiente y Servicios Publicos.; ArgentinaFil: O Mill, Patricia. Universidad Nacional de CĂłrdoba; Argentina. Ministerio de Agua, Ambiente y Servicios PĂșblicos. - Gobierno de la Provincia de Cordoba. Ministerio de Agua, Ambiente y Servicios Publicos.; ArgentinaFil: Rodriguez, Claudia. Universidad Nacional de RĂ­o Cuarto. Facultad de AgronomĂ­a y Veterinaria. Departamento de Estudios BĂĄsicos y Agropecuarios; ArgentinaFil: Pinotti, Lucio Pedro. Universidad Nacional de RĂ­o Cuarto. Facultad de Ciencias Exactas FisicoquĂ­micas y Naturales. Instituto de Ciencias de la Tierra, Biodiversidad y Ambiente - Consejo Nacional de Investigaciones CientĂ­ficas y TĂ©cnicas. Centro CientĂ­fico TecnolĂłgico Conicet - CĂłrdoba. Instituto de Ciencias de la Tierra, Biodiversidad y Ambiente; Argentina. Universidad Nacional de CĂłrdoba; Argentin
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