Coastal and Estuarine Waters: Optical Sensors and Remote Sensing

2016 ◽  
pp. 656-661 ◽  
Author(s):  
Darryl J. Keith
Keyword(s):  
Remote Sensing ◽  
Optical Sensors ◽  
Estuarine Waters

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 [...]

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.

scholarly journals The Utility of Satellites and Autonomous Remote Sensing Platforms for Monitoring Offshore Aquaculture Farms: A Case Study for Canopy Forming Kelps

2020 ◽  
Vol 7 ◽  
Author(s):  
Tom W. Bell ◽  
Nick J. Nidzieko ◽  
David A. Siegel ◽  
Robert J. Miller ◽  
Kyle C. Cavanaugh ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Optical Sensors ◽  
High Frequency ◽  
Autonomous Vehicles ◽  
Energy Demand ◽  
Spatial Scales ◽  
Long Distance ◽  
Decadal Scale ◽  
Canopy Area ◽  
Kelp Aquaculture

The emerging sector of offshore kelp aquaculture represents an opportunity to produce biofuel feedstock to help meet growing energy demand. Giant kelp represents an attractive aquaculture crop due to its rapid growth and production, however precision farming over large scales is required to make this crop economically viable. These demands necessitate high frequency monitoring to ensure outplant success, maximum production, and optimum quality of harvested biomass, while the long distance from shore and large necessary scales of production makes in person monitoring impractical. Remote sensing offers a practical monitoring solution and nascent imaging technologies could be leveraged to provide daily products of the kelp canopy and subsurface structures over unprecedented spatial scales. Here, we evaluate the efficacy of remote sensing from satellites and aerial and underwater autonomous vehicles as potential monitoring platforms for offshore kelp aquaculture farms. Decadal-scale analyses of the Southern California Bight showed that high offshore summertime cloud cover restricts the ability of satellite sensors to provide high frequency direct monitoring of these farms. By contrast, daily monitoring of offshore farms using sensors mounted to aerial and underwater drones seems promising. Small Unoccupied Aircraft Systems (sUAS) carrying lightweight optical sensors can provide estimates of canopy area, density, and tissue nitrogen content on the time and space scales necessary for observing changes in this highly dynamic species. Underwater color imagery can be rapidly classified using deep learning models to identify kelp outplants on a longline farm and high acoustic returns of kelp pneumatocysts from side scan sonar imagery signal an ability to monitor the subsurface development of kelp fronds. Current sensing technologies can be used to develop additional machine learning and spectral algorithms to monitor outplant health and canopy macromolecular content, however future developments in vehicle and infrastructure technologies are necessary to reduce costs and transcend operational limitations for continuous deployment in an offshore setting.

scholarly journals Remote sensing of plant trait responses to field-based plant–soil feedback using UAV-based optical sensors

2017 ◽  
Vol 14 (3) ◽  
pp. 733-749 ◽  
Author(s):  
Bob van der Meij ◽  
Lammert Kooistra ◽  
Juha Suomalainen ◽  
Janna M. Barel ◽  
Gerlinde B. De Deyn
Keyword(s):  
Remote Sensing ◽  
Chlorophyll Content ◽  
Optical Sensors ◽  
Cover Crop ◽  
Plant Traits ◽  
Field Scale ◽  
Fresh Biomass ◽  
Plant Soil ◽  
Leaf Chlorophyll ◽  
Soil Feedback

Abstract. Plant responses to biotic and abiotic legacies left in soil by preceding plants is known as plant–soil feedback (PSF). PSF is an important mechanism to explain plant community dynamics and plant performance in natural and agricultural systems. However, most PSF studies are short-term and small-scale due to practical constraints for field-scale quantification of PSF effects, yet field experiments are warranted to assess actual PSF effects under less controlled conditions. Here we used unmanned aerial vehicle (UAV)-based optical sensors to test whether PSF effects on plant traits can be quantified remotely. We established a randomized agro-ecological field experiment in which six different cover crop species and species combinations from three different plant families (Poaceae, Fabaceae, Brassicaceae) were grown. The feedback effects on plant traits were tested in oat (Avena sativa) by quantifying the cover crop legacy effects on key plant traits: height, fresh biomass, nitrogen content, and leaf chlorophyll content. Prior to destructive sampling, hyperspectral data were acquired and used for calibration and independent validation of regression models to retrieve plant traits from optical data. Subsequently, for each trait the model with highest precision and accuracy was selected. We used the hyperspectral analyses to predict the directly measured plant height (RMSE  =  5.12 cm, R2  =  0.79), chlorophyll content (RMSE  =  0.11 g m−2, R2  =  0.80), N-content (RMSE  =  1.94 g m−2, R2  =  0.68), and fresh biomass (RMSE  =  0.72 kg m−2, R2  =  0.56). Overall the PSF effects of the different cover crop treatments based on the remote sensing data matched the results based on in situ measurements. The average oat canopy was tallest and its leaf chlorophyll content highest in response to legacy of Vicia sativa monocultures (100 cm, 0.95 g m−2, respectively) and in mixture with Raphanus sativus (100 cm, 1.09 g m−2, respectively), while the lowest values (76 cm, 0.41 g m−2, respectively) were found in response to legacy of Lolium perenne monoculture, and intermediate responses to the legacy of the other treatments. We show that PSF effects in the field occur and alter several important plant traits that can be sensed remotely and quantified in a non-destructive way using UAV-based optical sensors; these can be repeated over the growing season to increase temporal resolution. Remote sensing thereby offers great potential for studying PSF effects at field scale and relevant spatial-temporal resolutions which will facilitate the elucidation of the underlying mechanisms.

scholarly journals Urban Features Identification from Dual-Pol SAR Images with Filter Properties

2020 ◽  
Vol 13 (3) ◽  
pp. 39-62
Author(s):  
Aman Kumar ◽  
Deepak Kumar
Keyword(s):  
Remote Sensing ◽  
Optical Sensors ◽  
Satellite Images ◽  
Median Filter ◽  
Microwave Remote Sensing ◽  
Speckle Noise ◽  
Research Groups ◽  
Feature Identification ◽  
Sar Images ◽  
Definition Of

AbstractThere is no formal definition of feature identification but it depends on the application and context of the problem. This feature acts as primary elements for execution of several algorithms, hence feature identification is one of the significant steps for has been very interesting for several research groups. Various researchers have attempted in this regard for feature identification. The current work presents an approach for urban feature identification from satellite datasets for a detailed analysis of the features for better management of the resources. Several features based feature extraction approach has been attempted to identify the compare with statistical profiling. Microwave remote sensing is one of the significant methods of remote sensing to get the data where our optical sensors usually failed or less capable to provide accurate and timely sensed data. In today’s world, active remote sensing is one of the greatest technologies which is used widely in many application areas. Synthetic aperture radar is the main object to get the actively remote sensed images. Either it’s optical or microwave data, the satellite images has its many errors, in SAR, while receiving the reflected echoes from the target the trouble has occurred in the form of Speckle Noise in an image. In this paper, the focus is on about the Speckle Noise, SLC & GRD data, the filtered images performance with Boxcar and Median filter, degraded and preserving information of an image, reduce speckle noise effect of an image.

scholarly journals Validation of Earth Observation Time-Series: A Review for Large-Area and Temporally Dense Land Surface Products

2019 ◽  
Vol 11 (22) ◽  
pp. 2616 ◽  
Author(s):  
Stefan Mayr ◽  
Claudia Kuenzer ◽  
Ursula Gessner ◽  
Igor Klein ◽  
Martin Rutzinger
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Optical Sensors ◽  
Land Surface ◽  
Global Scale ◽  
Observation Time ◽  
Large Area ◽  
Validation Data ◽  
Information Products ◽  
Global Coverage

Large-area remote sensing time-series offer unique features for the extensive investigation of our environment. Since various error sources in the acquisition chain of datasets exist, only properly validated results can be of value for research and downstream decision processes. This review presents an overview of validation approaches concerning temporally dense time-series of land surface geo-information products that cover the continental to global scale. Categorization according to utilized validation data revealed that product intercomparisons and comparison to reference data are the conventional validation methods. The reviewed studies are mainly based on optical sensors and orientated towards global coverage, with vegetation-related variables as the focus. Trends indicate an increase in remote sensing-based studies that feature long-term datasets of land surface variables. The hereby corresponding validation efforts show only minor methodological diversification in the past two decades. To sustain comprehensive and standardized validation efforts, the provision of spatiotemporally dense validation data in order to estimate actual differences between measurement and the true state has to be maintained. The promotion of novel approaches can, on the other hand, prove beneficial for various downstream applications, although typically only theoretical uncertainties are provided.

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