scholarly journals An Automatic Processing Chain for Near Real-Time Mapping of Burned Forest Areas Using Sentinel-2 Data

2020 ◽  
Vol 12 (4) ◽  
pp. 674 ◽  
Author(s):  
Luca Pulvirenti ◽  
Giuseppe Squicciarino ◽  
Elisabetta Fiori ◽  
Paolo Fiorucci ◽  
Luca Ferraris ◽  
...  
Keyword(s):  
Image Processing ◽  
Real Time ◽  
Vegetation Index ◽  
Commission Error ◽  
Management Service ◽  
Image Processing Algorithm ◽  
Burned Areas ◽  
Processing Chain ◽  
Sentinel 2 ◽  
Burned Forest

A fully automated processing chain for near real-time mapping of burned forest areas using Sentinel-2 multispectral data is presented. The acronym AUTOBAM (AUTOmatic Burned Areas Mapper) is used to denote it. AUTOBAM is conceived to work daily at a national scale for the Italian territory to support the Italian Civil Protection Department in the management of one of the major natural hazards, which affects the territory. The processing chain includes a Sentinel-2 data procurement component, an image processing algorithm, and the delivery of the map to the end-user. The data procurement component searches every day for the most updated products into different archives. The image processing part represents the core of AUTOBAM and implements an algorithm for burned forest areas mapping that uses, as fundamental parameters, the relativized form of the delta normalized burn ratio and the normalized difference vegetation index. The minimum mapping unit is 1 ha. The algorithm implemented in the image processing block is validated off-line using maps of burned areas produced by the Copernicus Emergency Management Service. The results of the validation shows an overall accuracy (considering the classes of burned and unburned areas) larger than 95% and a kappa coefficient larger than 80%. For what concerns the class of burned areas, the commission error is around 1%−3%, except for one case where it reaches 25%, while the omission error ranges between 6% and 25%.

Image Processing Algorithm for Real-Time Crack Inspection in Hole Expansion Test

2019 ◽  
Vol 20 (7) ◽  
pp. 1139-1148 ◽  
Author(s):  
Seungho Choi ◽  
Kwangyoon Kim ◽  
Jaeho Lee ◽  
Sung Hyuk Park ◽  
Hye-Jin Lee ◽  
...  
Keyword(s):  
Image Processing ◽  
Real Time ◽  
Processing Algorithm ◽  
Image Processing Algorithm ◽  
Hole Expansion ◽  
Expansion Test ◽  
Hole Expansion Test

scholarly journals Cartografía de la afección y recuperación vegetal del incendio de Las Peñuelas en Moguer (Huelva) con imágenes satelitales. Año 2017

2020 ◽  
pp. 79
Author(s):  
J.J. Vales ◽  
I. Pino ◽  
L. Granado ◽  
R. Prieto ◽  
E. Méndez ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Fire Severity ◽  
Burned Areas ◽  
Area Of Interest ◽  
Fire Environment ◽  
Recovery Strategies ◽  
Aerial Vehicle ◽  
Index Index ◽  
Sentinel 2

<p>Deep knowledge of the regeneration processes after a forest fire is key to addressing their adverse environmental impacts, these are especially evident in the vegetation. In the post-fire environment context, the fire severity constitutes a critical variable that affects the ecosystem response in terms of vegetation recovery and hydrogeomorphological dynamics after the fire. Therefore, the severity accurate assessment is essential for the burned areas management because of it allows the identification of priority areas and, therefore, it helps to carry out recovery strategies and measures. The area of interest is located in the natural place of Las Peñuelas (Huelva), where a large fire took place on June 24, 2017 that affected almost 10 000 ha. The methodology was based on the calculation of the RBR (Relativized Burn Ratio) spectral index to estimate the severity of the fire, and the NDVI (Normalized Difference Vegetation Index) index to evaluate the recovery of vegetal vigor. For the work, images from the Sentinel-2 and Pleiades satellites, images acquired by UAV (Unmanned Aerial Vehicle) and field samplings were used. The result was a cartography showing the levels of recovery or degradation of the affected vegetation.</p>

Autonomous Real-Time Monitoring of Crops in Controlled Environment Agriculture

2019 ◽  
Author(s):  
Saba Faryadi ◽  
Mohammadreza Davoodi ◽  
Javad Mohammadpour Velni
Keyword(s):  
Image Processing ◽  
Real Time ◽  
Directed Graphs ◽  
Controlled Environment ◽  
Real Time Monitoring ◽  
Collection System ◽  
Image Processing Algorithm ◽  
Ground Vehicle ◽  
Controlled Environment Agriculture ◽  
And Performance

Abstract In this work, we develop a system that can be used for real-time monitoring of multiple important areas in controlled environment agriculture (and in particular greenhouses) using an autonomous ground vehicle (AGV). To model the greenhouse layout, as well as the tasks that should be accomplished by the AGV, we generate two weighted directed graphs. Based on those graphs, an algorithm is then proposed for finding the optimal (in the sense of traveled distance) trajectory of the vehicle with the goal of precisely monitoring important areas in the greenhouse. Furthermore, a data collection system and image processing algorithm is proposed and implemented so that the vehicle: (i) can capture images and detect changes that have occurred on the crops in real time, and (ii) construct (if needed) a map of the plant rows, when arriving at each one of the important areas. Based on this work, the images can either be stitched onboard the vehicle and then sent to a server or be sent directly to the server and then processed (stitched) there. Both simulation and experimental results are provided to demonstrate the effectiveness and performance of the proposed system.

scholarly journals Exploitation of Sentinel-2 Time Series to Map Burned Areas at the National Level: A Case Study on the 2017 Italy Wildfires

2019 ◽  
Vol 11 (6) ◽  
pp. 622 ◽  
Author(s):  
Federico Filipponi
Keyword(s):  
Time Series ◽  
Spatial Resolution ◽  
Satellite Data ◽  
High Spatial Resolution ◽  
National Level ◽  
Burned Area ◽  
Commission Error ◽  
Burned Areas ◽  
Sentinel 2

Satellite data play a major role in supporting knowledge about fire severity by delivering rapid information to map fire-damaged areas in a precise and prompt way. The high availability of free medium-high spatial resolution optical satellite data, offered by the Copernicus Programme, has enabled the development of more detailed post-fire mapping. This research study deals with the exploitation of Sentinel-2 time series to map burned areas, taking advantages from the high revisit frequency and improved spatial and spectral resolution of the MSI optical sensor. A novel procedure is here presented to produce medium-high spatial resolution burned area mapping using dense Sentinel-2 time series with no a priori knowledge about wildfire occurrence or burned areas spatial distribution. The proposed methodology is founded on a threshold-based classification based on empirical observations that discovers wildfire fingerprints on vegetation cover by means of an abrupt change detection procedure. Effectiveness of the procedure in mapping medium-high spatial resolution burned areas at the national level was demonstrated for a case study on the 2017 Italy wildfires. Thematic maps generated under the Copernicus Emergency Management Service were used as reference products to assess the accuracy of the results. Multitemporal series of three different spectral indices, describing wildfire disturbance, were used to identify burned areas and compared to identify their performances in terms of spectral separability. Result showed a total burned area for the Italian country in the year 2017 of around 1400 km2, with the proposed methodology generating a commission error of around 25% and an omission error of around 40%. Results demonstrate how the proposed procedure allows for the medium-high resolution mapping of burned areas, offering a benchmark for the development of new operational downstreaming services at the national level based on Copernicus data for the systematic monitoring of wildfires.

Automating an Image Processing Chain of the Sentinel-2 Satellite

2018 ◽  
pp. 216-224
Author(s):  
Rodrigo Rodriguez-Ramirez ◽  
María Guadalupe Sánchez ◽  
Juan Pablo Rivera-Caicedo ◽  
Daniel Fajardo-Delgado ◽  
Himer Avila-George
Keyword(s):  
Image Processing ◽  
Processing Chain ◽  
Sentinel 2
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