scholarly journals Direct estimation of Byram's fire intensity from infrared remote sensing imagery

2017 ◽  
Vol 26 (8) ◽  
pp. 668 ◽  
Author(s):  
Joshua M. Johnston ◽  
Martin J. Wooster ◽  
Ronan Paugam ◽  
Xianli Wang ◽  
Timothy J. Lynham ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Fire Intensity ◽  
Ancillary Data ◽  
Remote Sensing Imagery ◽  
Method Of Calculation ◽  
Rate Of Spread ◽  
Radiative Power ◽  
Thermal Infrared Imagery ◽  
Remote Sensing Methods

Byram’s fire intensity (IB,tot; kWm–1) is one the most important and widely accepted metrics for quantifying wildfire behaviour. Calculation of IB,tot requires measurement of fuel consumption, heat of combustion and rate of spread; existing methods for obtaining these measurements are either inexact or at times impossible to obtain in the field. This paper presents and evaluates a series of remote sensing methods for directly deriving radiative fire intensity (IB,rad; kWm–1) using the Fire Radiative Power (FRP) approach applied to thermal infrared imagery of spreading vegetation fires. Comparisons between the remote sensing data and ground-sampled measurements were used to evaluate the various estimates of IB,tot, and to determine the radiative fraction (radF) of a fire’s emitted energy. Results indicate that the IB,tot along an advancing flame front can be reasonably estimated (and agrees with traditional methods of estimation (R2=0.34–0.73)) from appropriately collected time-series of remote sensing imagery without the need for ground sampling or ancillary data. We further estimate that the radF of the fire’s emitted energy varies between 0.15 and 0.20 depending on the method of calculation, which is similar to previous estimates.

Estimation of Byram’s Fire Intensity and Rate of Spread from Spaceborne Remote Sensing Data in a Savanna Landscape

Fire ◽  
2021 ◽  
Vol 4 (4) ◽  
pp. 65
Author(s):  
Gernot Ruecker ◽  
David Leimbach ◽  
Joachim Tiemann
Keyword(s):  
Remote Sensing ◽  
Fuel Consumption ◽  
Spatial Resolution ◽  
Fire Behavior ◽  
Remote Sensing Data ◽  
Fire Intensity ◽  
Rate Of Spread ◽  
Radiative Power ◽  
Sentinel 2 ◽  
Fire Radiative Power

Fire behavior is well described by a fire’s direction, rate of spread, and its energy release rate. Fire intensity as defined by Byram (1959) is the most commonly used term describing fire behavior in the wildfire community. It is, however, difficult to observe from space. Here, we assess fire spread and fire radiative power using infrared sensors with different spatial, spectral and temporal resolutions. The sensors used offer either high spatial resolution (Sentinel-2) for fire detection, but a low temporal resolution, moderate spatial resolution and daily observations (VIIRS), and high temporal resolution with low spatial resolution and fire radiative power retrievals (Meteosat SEVIRI). We extracted fire fronts from Sentinel-2 (using the shortwave infrared bands) and use the available fire products for S-NPP VIIRS and Meteosat SEVIRI. Rate of spread was analyzed by measuring the displacement of fire fronts between the mid-morning Sentinel-2 overpasses and the early afternoon VIIRS overpasses. We retrieved FRP from 15-min Meteosat SEVIRI observations and estimated total fire radiative energy release over the observed fire fronts. This was then converted to total fuel consumption, and, by making use of Sentinel-2-derived burned area, to fuel consumption per unit area. Using rate of spread and fuel consumption per unit area, Byram’s fire intensity could be derived. We tested this approach on a small number of fires in a frequently burning West African savanna landscape. Comparison to field experiments in the area showed similar numbers between field observations and remote-sensing-derived estimates. To the authors’ knowledge, this is the first direct estimate of Byram’s fire intensity from spaceborne remote sensing data. Shortcomings of the presented approach, foundations of an error budget, and potential further development, also considering upcoming sensor systems, are discussed.

scholarly journals Forest areas remote monitoring within the limits of transport objects impact

2021 ◽  
pp. 109-118
Author(s):  
Dmytro Liashenko ◽  
◽  
Dmytro Pavliuk ◽  
Vadym Belenok ◽  
Vitalii Babii ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Spatial Data ◽  
Remote Monitoring ◽  
Forest Cover ◽  
Landsat Imagery ◽  
Remote Sensing Data ◽  
Transport Infrastructure ◽  
Nature Management ◽  
Collecting Methods ◽  
Remote Sensing Methods

The article studies the issues of using remote sensing data for the tasks of ensuring sustainable nature management in the territories within the influence of transport infrastructure objects. Peculiarities of remote monitoring for tasks of transport networks design and in the process of their operation are determined. The paper analyzes the development of modern remote sensing methods (satellite imagery, the use of mobile sensors installed on cars or aircraft). A brief overview of spatial data collecting methods for the tasks of managing the development of territories within the influence of transport infrastructure (roads, railways, etc.) has made. The article considers the experience of using remote sensing technologies to monitor changes in the parameters of forest cover in the Transcarpathian region (Ukraine) in areas near to highways, by use Landsat imagery.

Groundwater potential zonation for basaltic watersheds using satellite remote sensing data and GIS techniques

2009 ◽  
Vol 1 (1) ◽  
Author(s):  
Biswajeet Pradhan
Keyword(s):  
Remote Sensing ◽  
Process Model ◽  
Remote Sensing Data ◽  
Groundwater Potential ◽  
Remotely Sensed Data ◽  
Drainage Pattern ◽  
Ancillary Data ◽  
Sensing Data ◽  
Gis Techniques ◽  
Gis Tools

AbstractThis paper summarizes the findings of groundwater potential zonation mapping at the Bharangi River basin, Thane district, Maharastra, India, using Satty’s Analytical Hierarchal Process model with the aid of GIS tools and remote sensing data. To meet the objectives, remotely sensed data were used in extracting lineaments, faults and drainage pattern which influence the groundwater sources to the aquifer. The digitally processed satellite images were subsequently combined in a GIS with ancillary data such as topographical (slope, drainage), geological (litho types and lineaments), hydrogeomorphology and constructed into a spatial database using GIS and image processing tools. In this study, six thematic layers were used for groundwater potential analysis. Each thematic layer’s weight was determined, and groundwater potential indices were calculated using groundwater conditions. The present study has demonstrated the capabilities of remote sensing and GIS techniques in the demarcation of different groundwater potential zones for hard rock basaltic basin.

scholarly journals Deep-learning-based information mining from ocean remote-sensing imagery

2020 ◽  
Vol 7 (10) ◽  
pp. 1584-1605 ◽  
Author(s):  
Xiaofeng Li ◽  
Bin Liu ◽  
Gang Zheng ◽  
Yibin Ren ◽  
Shuangshang Zhang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Remote Sensing Data ◽  
Information Mining ◽  
Remote Sensing Imagery ◽  
Sensing Data ◽  
Sensing Applications ◽  
Data Archives ◽  
Ocean Remote Sensing ◽  
Learning Frameworks

Abstract With the continuous development of space and sensor technologies during the last 40 years, ocean remote sensing has entered into the big-data era with typical five-V (volume, variety, value, velocity and veracity) characteristics. Ocean remote-sensing data archives reach several tens of petabytes and massive satellite data are acquired worldwide daily. To precisely, efficiently and intelligently mine the useful information submerged in such ocean remote-sensing data sets is a big challenge. Deep learning—a powerful technology recently emerging in the machine-learning field—has demonstrated its more significant superiority over traditional physical- or statistical-based algorithms for image-information extraction in many industrial-field applications and starts to draw interest in ocean remote-sensing applications. In this review paper, we first systematically reviewed two deep-learning frameworks that carry out ocean remote-sensing-image classifications and then presented eight typical applications in ocean internal-wave/eddy/oil-spill/coastal-inundation/sea-ice/green-algae/ship/coral-reef mapping from different types of ocean remote-sensing imagery to show how effective these deep-learning frameworks are. Researchers can also readily modify these existing frameworks for information mining of other kinds of remote-sensing imagery.

scholarly journals Identification of Groundwater Potential Zones of Visakhapatnam Urban Area using Integrating Multi Criteria Evaluation with Geographical Information System

2018 ◽  
Vol 7 (3.31) ◽  
pp. 234
Author(s):  
K M. Ganesh ◽  
G Jai Sankar ◽  
M Jagannadha Rao ◽  
R Subba Rao
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Data Interpretation ◽  
Groundwater Potential ◽  
Digital Data ◽  
Geographical Information ◽  
Human Beings ◽  
Groundwater Potential Zones ◽  
Ancillary Data ◽  
Sensing Data

Groundwater forms very little quantity when compared to the total water available on the earth. Therefore it is very vital for all living beings especially for human beings. Visakhapatnam, one of the fastest growing industrial city, is situated on the East Coast of India between longitudes E83o11’ 30” and 83o 22’ 16” and latitudes. N170 39’ 16” and 170 45’ 58”.   The present study is aimed to evaluate the groundwater occurrence using Remote sensing and GIS. Remote sensing data interpretation of visual and digital images gave the immediate information about surface features. From this information the groundwater potential zones are identified. The present study used IRS-IC (March 99) and ID (November 99) LISS-III digital data for comparative land use and land cover categorization and  hydrogeomorphological features identification and lineament study. The layers created from Remote sensing data and available ancillary data for index overlay operations for identification of groundwater potential zones in the study area using GIS.  

scholarly journals UPSCALING AND VALIDATION OF RTK-DIRECT GEOREFERENCED UAV-BASED RGB IMAGE DATA WITH PLANET IMAGERY USING POLYGON GRIDS FOR PASTURE MONITORING

2021 ◽  
Vol XLIII-B3-2021 ◽  
pp. 533-538
Author(s):  
G. Bareth ◽  
C. Hütt
Keyword(s):  
Remote Sensing ◽  
Decision Support ◽  
Data Acquisition ◽  
Remote Sensing Data ◽  
Image Data ◽  
Spatial Decision Support ◽  
Analysis Techniques ◽  
Remote Sensing Methods ◽  
Very High ◽  
Rgb Image

Abstract. The monitoring of managed grasslands with remote sensing methods is becoming more important for spatial decision support. Various remote sensing data acquisition techniques are applied for that purpose in different spatial resolutions ranging from UAV-borne to satellite-based remote sensing. In the last decade, UAV-borne imaging and analysis techniques or in the focus of crop and grassland monitoring and provide very high spatial resolutions. In contrast, satellite data are only available in high to moderate spatial resolutions. In this contribution, we introduce direct georeferenced data acquisition with a Phantom 4 RTK for pasture monitoring and investigate the upscaling of the cm data to satellite resolutions using polygon grids.

scholarly journals Gudalur Spectral Target Detection (GST-D): A New Benchmark Dataset and Engineered Material Target Detection in Multi-Platform Remote Sensing Data

2020 ◽  
Vol 12 (13) ◽  
pp. 2145 ◽  
Author(s):  
Sudhanshu Shekhar Jha ◽  
Rama Rao Nidamanuri
Keyword(s):  
Remote Sensing ◽  
Target Detection ◽  
Satellite Imagery ◽  
Hyperspectral Imagery ◽  
Remote Sensing Data ◽  
Benchmark Dataset ◽  
Remote Sensing Imagery ◽  
Sensing Data ◽  
Detection Algorithms ◽  
Reference Target

Target detection in remote sensing imagery, mapping of sparsely distributed materials, has vital applications in defense security and surveillance, mineral exploration, agriculture, environmental monitoring, etc. The detection probability and the quality of retrievals are functions of various parameters of the sensor, platform, target–background dynamics, targets’ spectral contrast, and atmospheric influence. Generally, target detection in remote sensing imagery has been approached using various statistical detection algorithms with an assumption of linearity in the image formation process. Knowledge on the image acquisition geometry, and spectral features and their stability across different imaging platforms is vital for designing a spectral target detection system. We carried out an integrated target detection experiment for the detection of various artificial target materials. As part of this work, we acquired a benchmark multi-platform hyperspectral and multispectral remote sensing dataset named as ‘Gudalur Spectral Target Detection (GST-D)’ dataset. Positioning artificial targets on different surface backgrounds, we acquired remote sensing data by terrestrial, airborne, and space-borne sensors on 20th March 2018. Various statistical and subspace detection algorithms were applied on the benchmark dataset for the detection of targets, considering the different sources of reference target spectra, background, and the spectral continuity across the platforms. We validated the detection results using the receiver operation curve (ROC) for different cases of detection algorithms and imaging platforms. Results indicate, for some combinations of algorithms and imaging platforms, consistent detection of specific material targets with a detection rate of about 80% at a false alarm rate between 10−2 to 10−3. Target detection in satellite imagery using reference target spectra from airborne hyperspectral imagery match closely with the satellite imagery derived reference spectra. The ground-based in-situ reference spectra offer a quantifiable detection in airborne or satellite imagery. However, ground-based hyperspectral imagery has also provided an equivalent target detection in the airborne and satellite imagery paving the way for rapid acquisition of reference target spectra. The benchmark dataset generated in this work is a valuable resourcefor addressing intriguing questions in target detection using hyperspectral imagery from a realistic landscape perspective.

scholarly journals Power and/or Penury of Visualizations: Some Thoughts on Remote Sensing Data and Products in Archaeology

2020 ◽  
Vol 12 (18) ◽  
pp. 2996
Author(s):  
Włodzimierz Rączkowski
Keyword(s):  
Remote Sensing ◽  
Virtual Reality ◽  
New Technologies ◽  
Remote Sensing Data ◽  
Analytical Techniques ◽  
Sensing Data ◽  
Visualization Technology ◽  
Remote Sensing Methods ◽  
Cognitive Perspectives

Airborne and spaceborne remote sensing in archaeology generates at least two important issues for discussion: technology and visualization. Technology seems to open new cognitive perspectives for archaeology and keeps researchers increasingly fascinated in its capabilities (archaeological science being a case in point). Acquired data, especially via remote sensing methods, can be studied after processing and visualizing. The paper raises several issues related to the new cognitive situation of archaeologists facing the development of new technologies within remote sensing methods. These issues are discussed from ontological, epistemological, and discursive perspectives, supporting an exploration of the role of technology and visualization. The ontological perspective places the visualization of remote sensing data in the context of understanding Virtual Reality and Jean Baudrillard’s simulacra. The epistemological perspective generates questions related to visualization as mimesis, the issue of cultural neutrality, and the use of sophisticated classifications and analytical techniques. The level of discursiveness of visualization includes categories such as persuasion, standardization, and aesthetics. This discussion is framed in relation to Martin Heidegger’s understanding of technology and a dichotomy of naturalism versus antinaturalism.

scholarly journals Roads and Intersections Extraction from High-Resolution Remote Sensing Imagery Based on Tensor Voting under Big Data Environment

2019 ◽  
Vol 2019 ◽  
pp. 1-11 ◽  
Author(s):  
Ke Sun ◽  
Junping Zhang ◽  
Yingying Zhang
Keyword(s):  
Remote Sensing ◽  
Big Data ◽  
High Resolution ◽  
Remote Sensing Data ◽  
Environmental Data ◽  
Tensor Voting ◽  
Remote Sensing Imagery ◽  
Sensing Data ◽  
The Road ◽  
Road Intersections

Currently, big data is a new and hot object of research. In particular, the development of the Internet of things (IoT) results in a sharp increase in data. Enormous amounts of networking sensors are constantly collecting and transmitting data for storage and processing in the cloud including remote sensing data, environmental data, geographical data, etc. Road information extraction from remote sensing data is mainly researched in this paper. Roads are typical man-made objects. Extracting roads from remote sensing imagery has great significance in various applications such as GIS data updating, urban planning, navigation, and military. In this paper a multistage and multifeature method to extract roads and detect road intersections from high-resolution remotely sensed imagery based on tensor voting is presented. Firstly, the input remote sensing image is segmented into two groups including road candidate regions and nonroad regions using template matching; then we can obtain preliminary road map. Secondly, nonroad regions are removed by geometric characteristics of road (large area and long strip). Thirdly, tensor voting is used to overcome the broken roads and discontinuities caused by the different disturbing factors and then delete the nonroad areas that are mixed into the road areas due to mis-segmentation, improving the completeness of extracted roads. And then, all the road intersections are extracted by using tensor voting. The experiments are conducted on different remote sensing images to test the effectiveness of our method. The experimental results show that our method can get more complete and accurate extracted results than the state-of-the-art methods.

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