scholarly journals Editorial to Special Issue “Remote Sensing Data Compression”

2021 ◽  
Vol 13 (18) ◽  
pp. 3727
Author(s):  
Benoit Vozel ◽  
Vladimir Lukin ◽  
Joan Serra-Sagristà
Keyword(s):  
Remote Sensing ◽  
Neural Networks ◽  
Data Compression ◽  
Remote Sensing Data ◽  
Low Complexity ◽  
Special Issue ◽  
Huge Amount ◽  
Sensing Data ◽  
The Impact ◽  
Lossless And Lossy Compression

A huge amount of remote sensing data is acquired each day, which is transferred to image processing centers and/or to customers. Due to different limitations, compression has to be applied on-board and/or on-the-ground. This Special Issue collects 15 papers dealing with remote sensing data compression, introducing solutions for both lossless and lossy compression, analyzing the impact of compression on different processes, investigating the suitability of neural networks for compression, and researching on low complexity hardware and software approaches to deliver competitive coding performance.

scholarly journals Analysing the Impact of Climate Change on Hydrological Ecosystem Services in Laguna del Sauce (Uruguay) Using the SWAT Model and Remote Sensing Data

2021 ◽  
Vol 13 (10) ◽  
pp. 2014
Author(s):  
Celina Aznarez ◽  
Patricia Jimeno-Sáez ◽  
Adrián López-Ballesteros ◽  
Juan Pablo Pacheco ◽  
Javier Senent-Aparicio
Keyword(s):  
Climate Change ◽  
Remote Sensing ◽  
Ecosystem Services ◽  
Water Resources ◽  
Swat Model ◽  
Remote Sensing Data ◽  
Erosion Control ◽  
Sensing Data ◽  
Extreme Precipitation Events ◽  
The Impact

Assessing how climate change will affect hydrological ecosystem services (HES) provision is necessary for long-term planning and requires local comprehensive climate information. In this study, we used SWAT to evaluate the impacts on four HES, natural hazard protection, erosion control regulation and water supply and flow regulation for the Laguna del Sauce catchment in Uruguay. We used downscaled CMIP-5 global climate models for Representative Concentration Pathways (RCP) 2.6, 4.5 and 8.5 projections. We calibrated and validated our SWAT model for the periods 2005–2009 and 2010–2013 based on remote sensed ET data. Monthly NSE and R2 values for calibration and validation were 0.74, 0.64 and 0.79, 0.84, respectively. Our results suggest that climate change will likely negatively affect the water resources of the Laguna del Sauce catchment, especially in the RCP 8.5 scenario. In all RCP scenarios, the catchment is likely to experience a wetting trend, higher temperatures, seasonality shifts and an increase in extreme precipitation events, particularly in frequency and magnitude. This will likely affect water quality provision through runoff and sediment yield inputs, reducing the erosion control HES and likely aggravating eutrophication. Although the amount of water will increase, changes to the hydrological cycle might jeopardize the stability of freshwater supplies and HES on which many people in the south-eastern region of Uruguay depend. Despite streamflow monitoring capacities need to be enhanced to reduce the uncertainty of model results, our findings provide valuable insights for water resources planning in the study area. Hence, water management and monitoring capacities need to be enhanced to reduce the potential negative climate change impacts on HES. The methodological approach presented here, based on satellite ET data can be replicated and adapted to any other place in the world since we employed open-access software and remote sensing data for all the phases of hydrological modelling and HES provision assessment.

scholarly journals ANALYSIS OF THE IMPACT OF OIL PRODUCT SPILLS ON NATURAL OBJECTS BASED ON SENTINEL-2 REMOTE SENSING DATA IN EASTERN SIBERIA

2021 ◽  
Vol 6 ◽  
pp. 24-31
Author(s):  
Dmitry A. Baikin
Keyword(s):  
Remote Sensing ◽  
Oil Spills ◽  
Remote Sensing Data ◽  
Oil Products ◽  
Eastern Siberia ◽  
Sensing Data ◽  
Sensing System ◽  
Natural Objects ◽  
The Impact ◽  
Sentinel 2

The article analyzes the impact of oil spills on natural objects according to the remote sensing system Sentinel-2 in Eastern Siberia. Remote sensing data analysis is used to detect traces of oil products in the accident area. Conclusions about the usage of Sentinel-2 data for detecting traces of oil products were made.

scholarly journals Editorial for Special Issue: “Remote Sensing based Building Extraction”

2020 ◽  
Vol 12 (3) ◽  
pp. 549
Author(s):  
Mohammad Awrangjeb ◽  
Xiangyun Hu ◽  
Bisheng Yang ◽  
Jiaojiao Tian
Keyword(s):  
Remote Sensing ◽  
Urban Planning ◽  
Disaster Management ◽  
Remote Sensing Data ◽  
Building Extraction ◽  
Special Issue ◽  
Sensing Data ◽  
Geographic Databases

Building extraction from remote sensing data plays an important role in urban planning, disaster management, navigation, updating geographic databases, and several other geospatial applications [...]

scholarly journals Preface: Remote Sensing for Flood Mapping and Monitoring of Flood Dynamics

2019 ◽  
Vol 11 (8) ◽  
pp. 943 ◽  
Author(s):  
Alessio Domeneghetti ◽  
Guy J.-P. Schumann ◽  
Angelica Tarpanelli
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Flood Mapping ◽  
Special Issue ◽  
Sensing Data ◽  
Wide Range ◽  
Flood Monitoring ◽  
Novel Processing ◽  
Flood Dynamics ◽  
Processing Techniques

This Special Issue is a collection of papers that focus on the use of remote sensing data and describe methods for flood monitoring and mapping. These articles span a wide range of topics; present novel processing techniques and review methods; and discuss limitations and challenges. This preface provides a brief overview of the content.

scholarly journals An Event-Based Inventory Approach in Landslide Hazard Assessment: The Case of the Skolis Mountain, Northwest Peloponnese, Greece

2020 ◽  
Vol 9 (7) ◽  
pp. 457
Author(s):  
Aspasia Litoseliti ◽  
Ioannis K. Koukouvelas ◽  
Konstantinos G. Nikolakopoulos ◽  
Vasiliki Zygouri
Keyword(s):  
Remote Sensing ◽  
Aspect Ratio ◽  
Remote Sensing Data ◽  
Historical Seismicity ◽  
Landslide Hazard ◽  
Disaster Mitigation ◽  
Earthquake Activity ◽  
Sensing Data ◽  
Reach Angle ◽  
The Impact

Assessment of landslide hazard across mountains is imperative for public safety. Pre- and post-earthquake landslide mapping envisage that landslides show significant size changes during earthquake activity. One of the purposes of earthquake-induced landslide investigation is to determine the landslide state and geometry and draw conclusions on their mobility. This study was based on remote sensing data that covered 72 years, and focused on the west slopes of the Skolis Mountains, in the northwest Peloponnese. On 8 June 2008, during the strong Movri Mountain earthquake (Mw = 6.4), we mapped the extremely abundant landslide occurrence. Historical seismicity and remote sensing data indicate that the Skolis Mountain west slope is repeatedly affected by landslides. The impact of the earthquakes was based on the estimation of Arias intensity in the study area. We recognized that 89 landslides developed over the last 72 years. These landslides increased their width (W), called herein as inflation or their length (L), termed as enlargement. Length and width changes were used to describe their aspect ratio (L/W). Based on the aspect ratio, the 89 landslides were classified into three types: I, J, and Δ. Taluses, developed at the base of the slope and belonging to the J- and Δ-landslide types, are supplied by narrow or irregular channels. During the earthquakes, the landslide channels migrated upward and downward, outlining the mobility of the earthquake-induced landslides. Landslide mobility was defined by the reach angle. The reach angle is the arctangent of the landslide’s height to length ratio. Furthermore, we analyzed the present slope stability across the Skolis Mountain by using the landslide density (LD), landslide area percentage (LAP), and landslide frequency (LF). All these parameters were used to evaluate the spatial and temporal landslide distribution and evolution with the earthquake activity. These results can be considered as a powerful tool for earthquake-induced landslide disaster mitigation

Moderate-Resolution Remote Sensing and Geospatial Analyses of Microclimates, Mounds, and Maize in the Northern Great Lakes

2014 ◽  
Vol 2 (3) ◽  
pp. 195-207 ◽  
Author(s):  
Meghan C.L. Howey ◽  
Michael Palace ◽  
Crystal H. McMichael ◽  
Bobby Braswell
Keyword(s):  
Remote Sensing ◽  
Great Lakes ◽  
Remote Sensing Data ◽  
Sensing Data ◽  
Inland Lakes ◽  
Maize Cultivation ◽  
Geospatial Analyses ◽  
Moderate Resolution ◽  
Burial Mounds ◽  
The Impact

AbstractRemote sensing applications are increasingly common in archaeology but they often focus on high resolution imagery and direct archaeological site detection. Moderate spatial resolution remote sensing instruments, which have (near) daily repeat intervals, but contain less detailed spectral and spatial information, have been employed much less frequently in archaeology. However, moderate remote sensing data offer distinct advantages for archaeological research as they can be used to relate archaeological, ecological, and climactic data at vast spatial scales. To show this potential, we use moderate remote sensing data to examine the impact of landscape heterogeneity on the spread of indigenous maize horticulture in the northern Great Lakes during Late Precontact (ca. AD 1200-1600). Analyzing National Aeronautics and Space Administration (NASA) Moderate Resolution Imaging Spectroradiometer (MODIS) imagery, we identify differences in freeze/thaw cycles across inland lakes in Michigan, showing that some large inland lakes produce a microclimatic amelioration, possibly extending the growing season for prehistoric maize cultivation. Conducting geospatial analyses, we find that burial mounds and maize cultivation practices were associated preferentially with larger inland lakes with microclimates. We could not have found these dynamic interrelationships between microclimates, burial mounds, and maize cultivation if not for both the frequent temporal imaging and large spatial coverage provided by moderate resolution remote sensing imagery.

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