Remote Sensing Of Oil Slicks

1969 ◽  
Vol 1969 (1) ◽  
pp. 297-307
Author(s):  
L.G. Swaby ◽  
A.F. Forziati
Keyword(s):  
Remote Sensing ◽  
Oil Spill ◽  
Santa Barbara ◽  
Advantages And Disadvantages ◽  
Remote Sensing Methods

Abstract An overview of remote sensing methods is presented and illustrated with data taken at the Santa Barbara oil spill The conclusion is reached that such methods possess monitoring capability but so far the ability to identify the slick material as oil has not been demonstrated. The advantages and disadvantages of each method are briefly discussed.

ECONOMIC ANALYSIS OF QUARRY SURVEYING

2020 ◽  
Author(s):  
Vivita Pukite ◽  
Vita Celmina ◽  
Dainora Jankauskiene
Keyword(s):  
Remote Sensing ◽  
Economic Analysis ◽  
Land Surface ◽  
Laser Scanning ◽  
Market Price ◽  
Mineral Resources ◽  
Volume Calculation ◽  
Advantages And Disadvantages ◽  
Remote Sensing Methods ◽  
Geodetic Surveying

There are several surveying methods whose practical function is to determine the areas of land, distances, heights, the amount of earthwork, and to produce reduced images of the earth's surface. The research looks at how geodetic and remote sensing methods can be used, and the results they provide in quarry surveying. The most important in quarry surveying is to get an accurate land surface for calculation of the volume of mineral resources. After quarries surveying, it is possible to calculate the amount of remaining mineral resources. Within the framework of the research, were performed surveying in quarries with geodetic surveying and remote sensing methods. For geodetic surveying was used GNSS receiver and a robotic total station, but from remote sensing methods were used aerial photography and aerial laser scanning. The most important reason why it is important to get an accurate surface and make an accurate volume calculation in quarry surveying is the economic factor. The economic analysis was carried out using a comparison method based on volume, market price and natural resources tax. The research presents the advantages and disadvantages of each surveying method and explains the results obtained, based on economic calculations. The main conclusion is that the accuracy of the preparation of land surface relief models is based mainly on economic calculations because mineral resources are a commodity that is bought and sold for which tax is payable.

scholarly journals APPLICATION OF ACTIVE REMOTE SENSING METHODS FOR OIL SPILL DETECTION

2021 ◽  
Vol 6 ◽  
pp. 213-218
Author(s):  
Pavel I. Nazdrachev ◽  
Alexander Yu. Chermoshentsev
Keyword(s):  
Remote Sensing ◽  
Oil Spill ◽  
Oil Spills ◽  
Oil Spill Detection ◽  
Radar Images ◽  
Remote Sensing Methods

The article describes the implementation of the method for processing radar images from the Sentinel-1 satellite on the territory of the Sakhalin Region, the purpose of which is to detect oil spills. The possibility of using this technique for the prompt detection of oil spills in water areas, as well as for monitoring is shown.

scholarly journals Continuous Oil Spill Remote Sensing and Autonomous Monitoring

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
David B. Chenault ◽  
Justin P. Vaden
Keyword(s):  
Remote Sensing ◽  
Oil Spill ◽  
Environmental Conditions ◽  
Material Properties ◽  
Detection System ◽  
Unmanned Aircraft ◽  
Infrared Sensor ◽  
Santa Barbara ◽  
Web Camera ◽  
The Difference

ABSTRACT The Pyxis camera is a polarized thermal infrared sensor that provides area detection at all times of day in a variety of conditions. It exploits the difference in oil and water material properties rather than temperature differences and is therefore far more robust for detection and false alarm rejection. It is small and has been integrated with drones, mounted at fixed sites, and used as a handheld for spill detection and monitoring. Pyxis has been tested extensively at Ohmsett and successfully demonstrated for oil detection at the MC20 site and at Santa Barbara in both manned and unmanned aircraft. Pyxis has now been integrated into the Polarization Oil Detection System (PODS) for autonomous oil spill detection and monitoring. PODS essentially operates as a web camera and continuously monitors the user defined area for oil entering the scene while adapting to changing environmental conditions. PODS is well-suited for monitoring fixed sites at processing or transfer points, unmanned rigs and platforms, and along waterways and pipelines.

Application of Remote Sensing in Agriculture

2014 ◽  
Vol 13 (1) ◽  
Author(s):  
Jan Piekarczyk
Keyword(s):  
Remote Sensing ◽  
Precision Agriculture ◽  
Crop Production ◽  
Satellite Images ◽  
Crop Yields ◽  
Spatial Scales ◽  
Strong Relationship ◽  
Physical Parameters ◽  
Agricultural Crop ◽  
Remote Sensing Methods

AbstractWith increasing intensity of agricultural crop production increases the need to obtain information about environmental conditions in which this production takes place. Remote sensing methods, including satellite images, airborne photographs and ground-based spectral measurements can greatly simplify the monitoring of crop development and decision-making to optimize inputs on agricultural production and reduce its harmful effects on the environment. One of the earliest uses of remote sensing in agriculture is crop identification and their acreage estimation. Satellite data acquired for this purpose are necessary to ensure food security and the proper functioning of agricultural markets at national and global scales. Due to strong relationship between plant bio-physical parameters and the amount of electromagnetic radiation reflected (in certain ranges of the spectrum) from plants and then registered by sensors it is possible to predict crop yields. Other applications of remote sensing are intensively developed in the framework of so-called precision agriculture, in small spatial scales including individual fields. Data from ground-based measurements as well as from airborne or satellite images are used to develop yield and soil maps which can be used to determine the doses of irrigation and fertilization and to take decisions on the use of pesticides.

Application of remote sensing methods for assessment of anthropogenic transformation of rangelands

2000 ◽  
pp. 16-25
Author(s):  
E. I. Rachkovskaya ◽  
S. S. Temirbekov ◽  
R. E. Sadvokasov
Keyword(s):  
Remote Sensing ◽  
New Technology ◽  
Field Studies ◽  
Digital Data ◽  
Aerial Images ◽  
Expert Assessment ◽  
Automated Classification ◽  
Anthropogenic Transformation ◽  
Initial Processing ◽  
Remote Sensing Methods

Capabilities of the remote sensing methods for making maps of actual and potential vegetation, and assessment of the extent of anthropogenic transformation of rangelands are presented in the paper. Study area is a large intermountain depression, which is under intensive agricultural use. Color photographs have been made by Aircraft camera Wild Heerburg RC-30 and multispectral scanner Daedalus (AMS) digital aerial data (6 bands, 3.5m resolution) have been used for analysis of distribution and assessment of the state of vegetation. Digital data were processed using specialized program ENVI 3.0. Main stages of the development of cartographic models have been described: initial processing of the aerial images and their visualization, preliminary pre-field interpretation (classification) of the images on the basis of unsupervised automated classification, field studies (geobotanical records and GPS measurements at the sites chosen at previous stage). Post-field stage had the following sub-stages: final geometric correction of the digital images, elaboration of the classification system for the main mapping subdivisions, final supervised automated classification on the basis of expert assessment. By systematizing clusters of the obtained classified image the cartographic models of the study area have been made. Application of the new technology of remote sensing allowed making qualitative and quantitative assessment of modern state of rangelands.

scholarly journals Convolutional Neural Network for the Semantic Segmentation of Remote Sensing Images

2021 ◽  
Vol 26 (1) ◽  
pp. 200-215
Author(s):  
Muhammad Alam ◽  
Jian-Feng Wang ◽  
Cong Guangpei ◽  
LV Yunrong ◽  
Yuanfang Chen
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Neural Networks ◽  
Image Processing ◽  
Deep Learning ◽  
Semantic Segmentation ◽  
Natural Scene ◽  
Remote Sensing Images ◽  
Advantages And Disadvantages ◽  
Target Segmentation

AbstractIn recent years, the success of deep learning in natural scene image processing boosted its application in the analysis of remote sensing images. In this paper, we applied Convolutional Neural Networks (CNN) on the semantic segmentation of remote sensing images. We improve the Encoder- Decoder CNN structure SegNet with index pooling and U-net to make them suitable for multi-targets semantic segmentation of remote sensing images. The results show that these two models have their own advantages and disadvantages on the segmentation of different objects. In addition, we propose an integrated algorithm that integrates these two models. Experimental results show that the presented integrated algorithm can exploite the advantages of both the models for multi-target segmentation and achieve a better segmentation compared to these two models.

Sign in / Sign up

Export Citation Format

Share Document