scholarly journals Assessing Across-Scale Optical Diversity and Productivity Relationships in Grasslands of the Italian Alps

2019 ◽  
Vol 11 (6) ◽  
pp. 614 ◽  
Author(s):  
Karolina Sakowska ◽  
Alasdair MacArthur ◽  
Damiano Gianelle ◽  
Michele Dalponte ◽  
Giorgio Alberti ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Large Scale ◽  
Ecosystem Dynamics ◽  
Optical Data ◽  
Integrated Analysis ◽  
Optical Remote Sensing ◽  
Scale Dependency ◽  
Ecosystem Productivity ◽  
Large Scale Assessment ◽  
Wide Range

The linearity and scale-dependency of ecosystem biodiversity and productivity relationships (BPRs) have been under intense debate. In a changing climate, monitoring BPRs within and across different ecosystem types is crucial, and novel remote sensing tools such as the Sentinel-2 (S2) may be adopted to retrieve ecosystem diversity information and to investigate optical diversity and productivity patterns. But are the S2 spectral and spatial resolutions suitable to detect relationships between optical diversity and productivity? In this study, we implemented an integrated analysis of spatial patterns of grassland productivity and optical diversity using optical remote sensing and Eddy Covariance data. Across-scale optical diversity and ecosystem productivity patterns were analyzed for different grassland associations with a wide range of productivity. Using airborne optical data to simulate S2, we provided empirical evidence that the best optical proxies of ecosystem productivity were linearly correlated with optical diversity. Correlation analysis at increasing pixel sizes proved an evident scale-dependency of the relationships between optical diversity and productivity. The results indicate the strong potential of S2 for future large-scale assessment of across-ecosystem dynamics at upper levels of observation.

scholarly journals Introduction to Urban Sensing

2021 ◽  
pp. 311-314
Author(s):  
Wenzhong Shi
Keyword(s):  
Remote Sensing ◽  
Synthetic Aperture Radar ◽  
Indoor Positioning ◽  
User Generated Content ◽  
Light Detection And Ranging ◽  
Optical Remote Sensing ◽  
Interferometric Synthetic Aperture Radar ◽  
Wide Range ◽  
Urban Sensing ◽  
Ambient Sensing

AbstractThis chapter overviews the urban sensing technologies for unban informatics to be introduced in the subsequent chapters under Part III of this book. To be covered is a wide range of technologies for urban sensing from the space, the air, the ground, the underground, and on individuals, including optical remote sensing, interferometric synthetic aperture radar, light detection and ranging, photogrammetry, underground sensing, mobile mapping, indoor positioning, ambient sensing, and the use of user-generated content.

Integrated analysis of remote sensing and numerical oil drift simulations for improved oil spill preparedness capabilities

2021 ◽  
Author(s):  
Camilla Brekke ◽  
Martine Espeseth ◽  
Knut-Frode Dagestad ◽  
Johannes Röhrs ◽  
Lars Hole ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Soybean Oil ◽  
Oil Spill ◽  
Mineral Oil ◽  
The Arctic ◽  
Optical Data ◽  
Integrated Analysis ◽  
Geophysical Research ◽  
Oil Emulsion ◽  
Detection Capabilities

<p><strong>Integrated analysis of remote sensing and numerical oil drift simulations for </strong><strong>improved </strong><strong>oil spill preparedness capabilities</strong></p><p>Camilla Brekke<sup>1</sup>, Martine M. Espeseth<sup>1</sup>, Knut-Frode Dagestad<sup>2</sup>, Johannes Röhrs<sup>2</sup>, Lars Robert Hole<sup>2</sup>, and Andreas Reigber<sup>3</sup></p><p> </p><p><sup>1</sup>UiT The Arctic University of Norway, Tromsø, Norway</p><p><sup>2</sup>The Norwegian Meteorological Institute, Oslo, Norway</p><p><sup>3</sup>DLR, Microwaves and Radar Institute, Oberpfaffenhofen-Weßling, Germany</p><p> </p><p>We present results from a successfully conducted free-floating oil spill field experiment followed by an integrated analysis of remotely sensed data and drift simulations. The experiment took place in the North Sea in the summer of 2019 during Norwegian Clean Seas Association for Operating Companies’ annual oil-on-water exercise. Two types of oils were applied: a mineral oil emulsion and a soybean oil emulsion. The dataset collected contains a collection of close-in-time radar (aircraft and space-borne) and optical data (aircraft, aerostat, and drone) acquisitions of the slicks. We compare oil drift simulations, applying various configurations of wind, wave, and current information, with observed slick positions and shape. We describe trajectories and dynamics of the spills, slick extent, and their evolution, and the differences in detection capabilities in optical instruments versus multifrequency quad-polarimetric synthetic aperture radar (SAR) imagery acquired by DLRs large-scale airborne SAR facility (F-SAR). When using the best available forcing from in situ data and forecast models, good agreement with the observed position and extent are found in this study. The appearance in the optical images and the SAR time series from F-SAR were found to be different between the soybean and mineral oil types. Differences in mineral oil detection capabilities are found between SAR and optical imagery of thinner sheen regions. From a drifting perspective, the biological oil emulsions could replace the viscous similar mineral oil emulsion in future oil spill preparedness campaigns. However, from a remote sensing and wildlife perspective, the two oils have different properties. Depending on the practical application, further investigation on how the soybean oil impact the seabirds must be conducted in order to recommend the soybean oil as a viable substitute for mineral oil.</p><p> </p><p>This study is published as open access in Journalof Geophysical Research: Oceans[1], and we encourage the audience to read this article for detailed acquaintance with the work.</p><p> </p><p>Reference:</p><p>[1]Brekke, C., Espeseth, M. M., Dagestad, K.-F., Röhrs, J., Hole, L. R., & Reigber,A. (2021). Integrated analysis of multisensor datasets and oil driftsimulations—a free-floating oil experiment in the open ocean. Journalof Geophysical Research: Oceans, 126, e2020JC016499. https://doi.org/10.1029/2020JC016499</p>

Support Irrigation Water Management of Cereals Using Optical Remote Sensing and Modeling in a Semi-Arid Region

2019 ◽  
pp. 124-145 ◽  
Author(s):  
Tarik Benabdelouahab ◽  
Hayat Lionboui ◽  
Rachid Hadria ◽  
Riad Balaghi ◽  
Abdelghani Boudhar ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Large Scale ◽  
Near Infrared ◽  
Irrigated Agriculture ◽  
Optical Remote Sensing ◽  
Crop Modeling ◽  
Management Tools ◽  
Irrigated Perimeter ◽  
Semi Arid Region ◽  
Semi Arid

Irrigated agriculture is an important strategic sector for Morocco, contributing to food security and employment. Nowadays, irrigation scheme managers shall ensure that water is optimally used. The main objective was to support the irrigation monitoring and management of wheat in the irrigated perimeter using optical remote sensing and crop modeling. The potential of spectral indices derived from SPOT-5 images was explored for quantifying and mapping surface water content changes at large scale. Indices were computed using the reflectance in red, near infrared, and shortwave infrared bands. A field crop model (AquaCrop) was adjusted and tested to simulate the grain yield and the temporal evolution of soil moisture status. This research aimed at providing a scientific and technical approach to assist policymakers and stakeholders to improve monitoring irrigation and mitigating wheat water stress at field and irrigation perimeter levels in semi-arid areas. The approach could lead to operational management tools for an efficient irrigation at field and regional levels.

scholarly journals Automatic Ship Detection in Optical Remote Sensing Images Based on Anomaly Detection and SPP-PCANet

2018 ◽  
Vol 11 (1) ◽  
pp. 47 ◽  
Author(s):  
Nan Wang ◽  
Bo Li ◽  
Qizhi Xu ◽  
Yonghua Wang
Keyword(s):  
Remote Sensing ◽  
Anomaly Detection ◽  
Superior Performance ◽  
Optical Remote Sensing ◽  
Remote Sensing Images ◽  
Low Contrast ◽  
Ship Detection ◽  
Wide Range ◽  
Spatial Pyramid Pooling ◽  
Spatial Pyramid

Automatic ship detection technology in optical remote sensing images has a wide range of applications in civilian and military fields. Among most important challenges encountered in ship detection, we focus on the following three selected ones: (a) ships with low contrast; (b) sea surface in complex situations; and (c) false alarm interference such as clouds and reefs. To overcome these challenges, this paper proposes coarse-to-fine ship detection strategies based on anomaly detection and spatial pyramid pooling pcanet (SPP-PCANet). The anomaly detection algorithm, based on the multivariate Gaussian distribution, regards a ship as an abnormal marine area, effectively extracting candidate regions of ships. Subsequently, we combine PCANet and spatial pyramid pooling to reduce the amount of false positives and improve the detection rate. Furthermore, the non-maximum suppression strategy is adopted to eliminate the overlapped frames on the same ship. To validate the effectiveness of the proposed method, GF-1 images and GF-2 images were utilized in the experiment, including the three scenarios mentioned above. Extensive experiments demonstrate that our method obtains superior performance in the case of complex sea background, and has a certain degree of robustness to external factors such as uneven illumination and low contrast on the GF-1 and GF-2 satellite image data.

FRF-Net: Land Cover Classification From Large-Scale VHR Optical Remote Sensing Images

2020 ◽  
Vol 17 (6) ◽  
pp. 1057-1061 ◽  
Author(s):  
Qianbo Sang ◽  
Yin Zhuang ◽  
Shan Dong ◽  
Guanqun Wang ◽  
He Chen
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Large Scale ◽  
Land Cover Classification ◽  
Optical Remote Sensing ◽  
Remote Sensing Images

SAR and optical remote sensing: Assessment of complementarity and interoperability in the context of a large-scale operational forest monitoring system

2015 ◽  
Vol 156 ◽  
pp. 335-348 ◽  
Author(s):  
Eric A. Lehmann ◽  
Peter Caccetta ◽  
Kim Lowell ◽  
Anthea Mitchell ◽  
Zheng-Shu Zhou ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Monitoring System ◽  
Large Scale ◽  
Forest Monitoring ◽  
Optical Remote Sensing

scholarly journals Timely prediction potential of landslide early warning systems with multispectral remote sensing: a conceptual approach tested in the Sattelkar, Austria

2021 ◽  
Vol 21 (9) ◽  
pp. 2753-2772
Author(s):  
Doris Hermle ◽  
Markus Keuschnig ◽  
Ingo Hartmeyer ◽  
Robert Delleske ◽  
Michael Krautblatter
Keyword(s):  
Remote Sensing ◽  
Early Warning ◽  
Lead Time ◽  
Early Warning Systems ◽  
Image Correlation ◽  
Optical Data ◽  
Optical Remote Sensing ◽  
Warning Systems ◽  
Conceptual Approach ◽  
Landslide Early Warning

Abstract. While optical remote sensing has demonstrated its capabilities for landslide detection and monitoring, spatial and temporal demands for landslide early warning systems (LEWSs) had not been met until recently. We introduce a novel conceptual approach to structure and quantitatively assess lead time for LEWSs. We analysed “time to warning” as a sequence: (i) time to collect, (ii) time to process and (iii) time to evaluate relevant optical data. The difference between the time to warning and “forecasting window” (i.e. time from hazard becoming predictable until event) is the lead time for reactive measures. We tested digital image correlation (DIC) of best-suited spatiotemporal techniques, i.e. 3 m resolution PlanetScope daily imagery and 0.16 m resolution unmanned aerial system (UAS)-derived orthophotos to reveal fast ground displacement and acceleration of a deep-seated, complex alpine mass movement leading to massive debris flow events. The time to warning for the UAS/PlanetScope totals 31/21 h and is comprised of time to (i) collect – 12/14 h, (ii) process – 17/5 h and (iii) evaluate – 2/2 h, which is well below the forecasting window for recent benchmarks and facilitates a lead time for reactive measures. We show optical remote sensing data can support LEWSs with a sufficiently fast processing time, demonstrating the feasibility of optical sensors for LEWSs.

scholarly journals Reflections on Analytical Choices in the Scaling Model for Test Scores in International Large-Scale Assessment Studies

2021 ◽  
Author(s):  
Alexander Robitzsch ◽  
Oliver Lüdtke
Keyword(s):  
Large Scale ◽  
International Student ◽  
Student Assessment ◽  
Individual Student ◽  
Scaling Model ◽  
Large Scale Assessment ◽  
International Student Assessment ◽  
Wide Range ◽  
Analytical Strategies ◽  
The Individual

International large-scale assessments (LSAs) such as the Programme for International Student Assessment (PISA) provide important information about the distribution of student proficiencies across a wide range of countries. The repeated assessments of these content domains offer policymakers important information for evaluating educational reforms and received considerable attention from the media. Furthermore, the analytical strategies employed in LSAs often define methodological standards for applied researchers in the field. Hence, it is vital to critically reflect the conceptual foundations of analytical choices in LSA studies. This article discusses methodological challenges in selecting and specifying the scaling model used to obtain proficiency estimates from the individual student responses in LSA studies. We distinguish design-based inference from model-based inference. It is argued that for the official reporting of LSA results, design-based inference should be preferred because it allows for a clear definition of the target of inference (e.g., country mean achievement) and is less sensitive to specific modeling assumptions. More specifically, we discuss five analytical choices in the specification of the scaling model: (1) Specification of the functional form of item response functions, (2) the treatment of local dependencies and multidimensionality, (3) the consideration of test-taking behavior for estimating student ability, and the role of country differential items functioning (DIF) for (4) cross-country comparisons, and (5) trend estimation. This article's primary goal is to stimulate discussion about recently implemented changes and suggested refinements of the scaling models in LSA studies.

scholarly journals Carbon / nitrogen interactions in European forests and semi-natural vegetation. Part I: Fluxes and budgets of carbon, nitrogen and greenhouse gases from ecosystem monitoring and modelling

2019 ◽  
Author(s):  
Chris R. Flechard ◽  
Andreas Ibrom ◽  
Ute M. Skiba ◽  
Wim de Vries ◽  
Marcel van Oijen ◽  
...  
Keyword(s):  
Large Scale ◽  
Ghg Emissions ◽  
C Sequestration ◽  
Natural Vegetation ◽  
N Saturation ◽  
Net Ecosystem Productivity ◽  
Ecosystem Productivity ◽  
Ecosystem Monitoring ◽  
Wide Range ◽  
The Impact

Abstract. The impact of atmospheric reactive nitrogen (Nr) deposition on carbon (C) sequestration in soils and biomass of unfertilised, natural, semi-natural and forest ecosystems has been much debated. Many previous results of this dC / dN response were based on changes in carbon stocks from periodical soil and ecosystem inventories, associated with estimates of Nr deposition obtained from large-scale chemical transport models. This study and a companion paper (Flechard et al., 2019) strive to reduce uncertainties of N effects on C sequestration by linking multi-annual gross and net ecosystem productivity estimates from 40 eddy covariance flux towers across Europe to local measurement-based estimates of dry and wet Nr deposition from a dedicated collocated monitoring network. To identify possible ecological drivers and processes affecting the interplay between C and Nr inputs and losses, these data were also combined with in situ flux measurements of NO, N2O and CH4 fluxes, soil NO3− leaching sampling, as well as results of soil incubation experiments for N and greenhouse gas (GHG) emissions, surveys of available data from online databases and from the literature, together with forest ecosystem (BASFOR) modelling. Multi-year averages of net ecosystem productivity (NEP) in forests ranged from −70 to 826 g (C) m−2 yr−1 at total wet + dry inorganic Nr deposition rates (Ndep) of 0.3 to 4.3 g (N) m−2 yr−1; and from −4 to 361 g (C) m−2 yr−1 at Ndep rates of 0.1 to 3.1 g (N) m−2 yr−1 in short semi-natural vegetation (moorlands, wetlands and unfertilised extensively managed grasslands). The GHG budgets of the forests were strongly dominated by CO2 exchange, while CH4 and N2O exchange comprised a larger proportion of the GHG balance in short semi-natural vegetation. Nitrogen losses in the form of NO, N2O and especially NO3− were of the order of 10–20 % of Ndep at sites with Ndep  3 g (N) m−2 yr−1, indicating that perhaps one third of the sites were in a state of early to advanced N saturation. Net ecosystem productivity increased with Nr deposition up to 2–2.5 g (N) m−2 yr−1, with large scatter associated with a wide range in carbon sequestration efficiency (CSE, defined as the NEP / GPP ratio). At elevated Ndep levels (> 2.5 g (N) m−2 yr−1), where inorganic Nr losses were also increasingly large, NEP levelled off and then decreased. The apparent increase in NEP at low to intermediate Ndep levels was partly the result of geographical cross-correlations between Ndep and climate, indicating that the actual mean dC / dN response at individual sites was significantly lower than would be suggested by a simple, straightforward regression of NEP vs. Ndep.

scholarly journals ESTIMATION OF PHYSICAL PARAMETERS OF A MULTILAYERED MULTI-SCALE VEGETATED SURFACE

2016 ◽  
Vol XLI-B1 ◽  
pp. 453-460
Author(s):  
I. Hosni ◽  
L. Bennaceur Farah ◽  
M. S. Naceur ◽  
I. R. Farah
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Cloud Model ◽  
Optical Data ◽  
Vegetation Coverage ◽  
Physical Parameters ◽  
Atmospheric Effects ◽  
Optical Remote Sensing ◽  
Statistical Parameters ◽  
The Impact

Soil moisture is important to enable the growth of vegetation in the way that it also conditions the development of plant population. Additionally, its assessment is important in hydrology and agronomy, and is a warning parameter for desertification. <br><br> Furthermore, the soil moisture content affects exchanges with the atmosphere via the energy balance at the soil surface; it is significant due to its impact on soil evaporation and transpiration. Therefore, it conditions the energy transfer between Earth and atmosphere. <br><br> Many remote sensing methods were tested. For the soil moisture; the first methods relied on the optical domain (short wavelengths). Obviously, due to atmospheric effects and the presence of clouds and vegetation cover, this approach is doomed to fail in most cases. Therefore, the presence of vegetation canopy complicates the retrieval of soil moisture because the canopy contains moisture of its own. <br><br> This paper presents a synergistic methodology of SAR and optical remote sensing data, and it’s for simulation of statistical parameters of soil from C-band radar measurements. Vegetation coverage, which can be easily estimated from optical data, was combined in the backscattering model. The total backscattering was divided into the amount attributed to areas covered with vegetation and that attributed to areas of bare soil. <br><br> Backscattering coefficients were simulated using the established backscattering model. A two-dimensional multiscale SPM model has been employed to investigate the problem of electromagnetic scattering from an underlying soil. The water cloud model (WCM) is used to account for the effect of vegetation water content on radar backscatter data, whereof to eliminate the impact of vegetation layer and isolate the contributions of vegetation scattering and absorption from the total backscattering coefficient.

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