scholarly journals The Quality of Remote Sensing Optical Images from Acquisition to Users

2021 ◽  
Vol 13 (7) ◽  
pp. 1295
Author(s):  
Massimo Selva
Keyword(s):  
Remote Sensing ◽  
Optical Sensors ◽  
Constant Increase ◽  
Optical Images

The need to observe and characterize the environment leads to a constant increase of the spatial, spectral, and radiometric resolution of new optical sensors [...]

Fluorescent Nanotechnology: An Evolution in Optical Sensors

2020 ◽  
Vol 17 ◽  
Author(s):  
Dilawar Hassan ◽  
Hadi Bakhsh ◽  
Asif M. Khurram ◽  
Shakeel A. Bhutto ◽  
Nida S. Jalbani ◽  
...  
Keyword(s):  
Optical Properties ◽  
Optical Sensors ◽  
Light Emission ◽  
Environmental Contaminants ◽  
Fluorescent Nanoparticles ◽  
Sensing Applications ◽  
Properties Of Materials ◽  
Optical Properties Of Materials ◽  
Fluorescent Nanomaterials

Background: The optical properties of nanomaterials have evolved enormously with the introduction of nanotechnology. The property of materials to absorb and/or emit specific wavelength has turned them into one of the most favourite candidates to be effectively utilized in different sensing applications e.g organic light emission diodes (OLEDs) sensors, gas sensors, biosensors and fluorescent sensors. These materials have been reported as a sensor in the field of tissue and cell imaging, cancer detection and detection of environmental contaminants etc. Fluorescent nanomaterials are heling in rapid and timely detection of various contaminants that greatly impact the quality of life and food, that is exposed to these contaminants. Later, all the contaminants have been investigated to be most perilous entities that momentously affect the life span of the animals and humans who use those foods which have been contaminated. Objective: In this review, we will discuss about various methods and approaches to synthesize the fluorescent nanoparticles and quantum dots (QDs) and their applications in various fields. The application will include the detection of various environmental contaminants and bio-medical applications. We will discuss the possible mode of action of the nanoparticles when used as sensor for the environmental contaminants as well as the surface modification of some fluorescent nanomaterials with anti-body and enzyme for specific detection in animal kingdom. We will also describe some RAMAN based sensors as well as some optical sensing-based nanosensors. Conclusion: Nanotechnology has enabled to play with the size, shape and morphology of materials in the nanoscale. The physical, chemical and optical properties of materials change dramatically when they are reduced to nanoscale. The optical properties can become choosy in terms of emission or absorption of wavelength in the size range and can result in production of very sensitive optical sensor. The results show that the use of fluorescent nanomaterials for the sensing purposes are helping a great deal in the sensing field.

scholarly journals New Orthophoto Generation Strategies from UAV and Ground Remote Sensing Platforms for High-Throughput Phenotyping

2021 ◽  
Vol 13 (5) ◽  
pp. 860
Author(s):  
Yi-Chun Lin ◽  
Tian Zhou ◽  
Taojun Wang ◽  
Melba Crawford ◽  
Ayman Habib
Keyword(s):  
Remote Sensing ◽  
Control Strategies ◽  
Target Function ◽  
Canopy Cover ◽  
Surface Model ◽  
Flowering Date ◽  
Sensing Platforms ◽  
Geolocation Accuracy ◽  
New Strategies

Remote sensing platforms have become an effective data acquisition tool for digital agriculture. Imaging sensors onboard unmanned aerial vehicles (UAVs) and tractors are providing unprecedented high-geometric-resolution data for several crop phenotyping activities (e.g., canopy cover estimation, plant localization, and flowering date identification). Among potential products, orthophotos play an important role in agricultural management. Traditional orthophoto generation strategies suffer from several artifacts (e.g., double mapping, excessive pixilation, and seamline distortions). The above problems are more pronounced when dealing with mid- to late-season imagery, which is often used for establishing flowering date (e.g., tassel and panicle detection for maize and sorghum crops, respectively). In response to these challenges, this paper introduces new strategies for generating orthophotos that are conducive to the straightforward detection of tassels and panicles. The orthophoto generation strategies are valid for both frame and push-broom imaging systems. The target function of these strategies is striking a balance between the improved visual appearance of tassels/panicles and their geolocation accuracy. The new strategies are based on generating a smooth digital surface model (DSM) that maintains the geolocation quality along the plant rows while reducing double mapping and pixilation artifacts. Moreover, seamline control strategies are applied to avoid having seamline distortions at locations where the tassels and panicles are expected. The quality of generated orthophotos is evaluated through visual inspection as well as quantitative assessment of the degree of similarity between the generated orthophotos and original images. Several experimental results from both UAV and ground platforms show that the proposed strategies do improve the visual quality of derived orthophotos while maintaining the geolocation accuracy at tassel/panicle locations.

More accurate less meaningful? A critical physical geographer’s reflection on interpreting remote sensing land-use analyses

2021 ◽  
pp. 030913332199181
Author(s):  
Andreas Christian Braun
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Geographic Information Systems ◽  
Image Classification ◽  
Satellite Image ◽  
Physical Geography ◽  
Data Availability ◽  
Scientific Results ◽  
Normative Considerations

Land-use and land-cover analyses based on satellite image classification are used in most, if not all, sub-disciplines of physical geography. Data availability and increasingly simple image classification techniques – nowadays, even implemented in simple geographic information systems – increase the use of such analyses. To assess the quality of such land-use analyses, accuracy metrics are applied. The results are considered to have sufficient quality, exceeding thresholds published in the literature. A typical practice in many studies is to confuse accuracy in remote sensing with quality, as required by physical geography. However, notions such as quality are subject to normative considerations and performative practices, which differ between scientific domains. Recent calls for critical physical geography have stressed that scientific results cannot be understood separately from the values and practices underlying them. This article critically discusses the specific understanding of quality in remote sensing, outlining norms and practices shaping it and their relation to physical geography. It points out that, as a seeming paradox, results considered more accurate in remote sensing terms can be less informative – or meaningful – in geographical terms. Finally, a roadmap of how to apply remote sensing land-use analyses more constructively in physical geography is proposed.

Unsupervised labelling of remote sensing images based on force field clustering

2021 ◽  
pp. 1-14
Author(s):  
Zhenggang Wang ◽  
Jin Jin
Keyword(s):  
Remote Sensing ◽  
Force Field ◽  
Image Data ◽  
Model Parameters ◽  
Remote Sensing Images ◽  
Initial Cluster ◽  
Data Points ◽  
Global Optimal ◽  
Density Force

Remote sensing image segmentation provides technical support for decision making in many areas of environmental resource management. But, the quality of the remote sensing images obtained from different channels can vary considerably, and manually labeling a mass amount of image data is too expensive and Inefficiently. In this paper, we propose a point density force field clustering (PDFC) process. According to the spectral information from different ground objects, remote sensing superpixel points are divided into core and edge data points. The differences in the densities of core data points are used to form the local peak. The center of the initial cluster can be determined by the weighted density and position of the local peak. An iterative nebular clustering process is used to obtain the result, and a proposed new objective function is used to optimize the model parameters automatically to obtain the global optimal clustering solution. The proposed algorithm can cluster the area of different ground objects in remote sensing images automatically, and these categories are then labeled by humans simply.

scholarly journals Estimation of the HEC-HMS model parameters in data-scarce regions. Application to the Ouergha watershed (Sebou, Morocco)

2021 ◽  
Vol 314 ◽  
pp. 05002
Author(s):  
Hasna Moumni ◽  
Karima Sebari ◽  
Laila Stour ◽  
Abdellatif Ahbari
Keyword(s):  
Remote Sensing ◽  
Hydrological Model ◽  
Model Parameters ◽  
Flow Data ◽  
Random Parameters ◽  
Quality Of Data ◽  
Initial Values ◽  
Digital Elevation ◽  
Elevation Model

The availability, accessibility and quality of data are significant obstacles to hydrological modelling. Estimating the initial values of the hydrological model´’ ’s parameters is a laborious and determining task requiring much attention. Geographic information systems (GIS) and spatial remote sensing are prometting tools for processing and collecting data. In this work, we use an innovative approach to estimate the HEC-HMS hydrological model parameters from the soil map of Africa (250m), the land use map GLC30, the depth to bedrock map, the digital elevation model and observed flow data. The estimation approach is applied to the Ouergha basin (Sebou, Morocco). The proposed approach’s interest is to feed the HEC-HMS hydrological model with initial values of parameters close to the study area reality instead of using random parameters.

Valuing Radiometric Quality of Remote Sensing Data for Decisions

2021 ◽  
Author(s):  
Afreen Siddiqi ◽  
Sheila Baber ◽  
Olivier De Weck
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Sensing Data

Emerging technologies for integrated nematode management: remote sensing or proximal sensing as a potential tool to detect and identify nematode infestation.

2021 ◽  
pp. 414-420
Author(s):  
Matheus T. Kuska ◽  
Matthias Daub ◽  
Anne-Katrin Mahlein
Keyword(s):  
Remote Sensing ◽  
Optical Sensors ◽  
Developmental Stages ◽  
Remote Sensing Data ◽  
Bursaphelenchus Xylophilus ◽  
Heterodera Schachtii ◽  
Proximal Sensing ◽  
The Difference ◽  
Sugarbeet Cyst Nematode ◽  
Physiological Reactions

Abstract Remote or proximal sensing defines the use of optical sensors, in combination with a carrier platform, to obtain information from objects in a non-invasive manner. Optical properties of plants provide valuable information on the health status, vitality or developmental stages of plants. The difference among remote-sensing and proximal-sensing technologies is mainly characterized by the distance between the measurement system and the object of interest. This chapter discusses physiological reactions influencing optical characteristics in nematode infested plants, remote sensing with satellites, the use of robots and drones for a more flexible infield assessment, as well as the analysis and interpretation of remote-sensing data. Some case studies with pine wood nematode (Bursaphelenchus xylophilus) and sugarbeet cyst nematode (Heterodera schachtii) are presented. Further use of remote and proximal sensing for the advancement of agriculture is also mentioned.

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