scholarly journals Automated location of active fire perimeters in aerial infrared imaging using unsupervised edge detectors

2018 ◽  
Vol 27 (4) ◽  
pp. 241 ◽  
Author(s):  
M. M. Valero ◽  
O. Rios ◽  
E. Pastor ◽  
E. Planas
Keyword(s):  
Forest Fires ◽  
Large Scale ◽  
Infrared Imaging ◽  
Weather Conditions ◽  
Automated System ◽  
Rate Of Spread ◽  
Active Fire ◽  
Edge Detectors ◽  
Spatio Temporal ◽  
Manual Tasks

A variety of remote sensing techniques have been applied to forest fires. However, there is at present no system capable of monitoring an active fire precisely in a totally automated manner. Spaceborne sensors show too coarse spatio-temporal resolutions and all previous studies that extracted fire properties from infrared aerial imagery incorporated manual tasks within the image processing workflow. As a contribution to this topic, this paper presents an algorithm to automatically locate the fuel burning interface of an active wildfire in georeferenced aerial thermal infrared (TIR) imagery. An unsupervised edge detector, built upon the Canny method, was accompanied by the necessary modules for the extraction of line coordinates and the location of the total burned perimeter. The system was validated in different scenarios ranging from laboratory tests to large-scale experimental burns performed under extreme weather conditions. Output accuracy was computed through three common similarity indices and proved acceptable. Computing times were below 1 s per image on average. The produced information was used to measure the temporal evolution of the fire perimeter and automatically generate rate of spread (ROS) fields. Information products were easily exported to standard Geographic Information Systems (GIS), such as GoogleEarth and QGIS. Therefore, this work contributes towards the development of an affordable and totally automated system for operational wildfire surveillance.

scholarly journals Near Real-Time Automated Early Mapping of the Perimeter of Large Forest Fires from the Aggregation of VIIRS and MODIS Active Fires in Mexico

2020 ◽  
Vol 12 (12) ◽  
pp. 2061 ◽  
Author(s):  
Carlos Ivan Briones-Herrera ◽  
Daniel José Vega-Nieva ◽  
Norma Angélica Monjarás-Vega ◽  
Jaime Briseño-Reyes ◽  
Pablito Marcelo López-Serrano ◽  
...  
Keyword(s):  
Real Time ◽  
Forest Fires ◽  
Large Scale ◽  
Infrared Imaging ◽  
Activity Patterns ◽  
Burned Area ◽  
Management Decision ◽  
Coarse Scale ◽  
Early Evaluation ◽  
Active Fire

In contrast with current operational products of burned area, which are generally available one month after the fire, active fires are readily available, with potential application for early evaluation of approximate fire perimeters to support fire management decision making in near real time. While previous coarse-scale studies have focused on relating the number of active fires to a burned area, some local-scale studies have proposed the spatial aggregation of active fires to directly obtain early estimate perimeters from active fires. Nevertheless, further analysis of this latter technique, including the definition of aggregation distance and large-scale testing, is still required. There is a need for studies that evaluate the potential of active fire aggregation for rapid initial fire perimeter delineation, particularly taking advantage of the improved spatial resolution of the Visible Infrared Imaging Radiometer (VIIRS) 375 m, over large areas and long periods of study. The current study tested the use of convex hull algorithms for deriving coarse-scale perimeters from Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS) active fire detections, compared against the mapped perimeter of the MODIS collection 6 (MCD64A1) burned area. We analyzed the effect of aggregation distance (750, 1000, 1125 and 1500 m) on the relationships of active fire perimeters with MCD64A1, for both individual fire perimeter prediction and total burned area estimation, for the period 2012–2108 in Mexico. The aggregation of active fire detections from MODIS and VIIRS demonstrated a potential to offer coarse-scale early estimates of the perimeters of large fires, which can be available to support fire monitoring and management in near real time. Total burned area predicted from aggregated active fires followed the same temporal behavior as the standard MCD64A1 burned area, with potential to also account for the role of smaller fires detected by the thermal anomalies. The proposed methodology, based on easily available algorithms of point aggregation, is susceptible to be utilized both for near real-time and historical fire perimeter evaluation elsewhere. Future studies might test active fires aggregation between regions or biomes with contrasting fuel characteristics and human activity patterns against medium resolution (e.g., Landsat and Sentinel) fire perimeters. Furthermore, coarse-scale active fire perimeters might be utilized to locate areas where such higher-resolution imagery can be downloaded to improve the evaluation of fire extent and impact.

scholarly journals Fractal properties of forest fires in Amazonia as a basis for modelling pan-tropical burnt area

2014 ◽  
Vol 11 (6) ◽  
pp. 1449-1459 ◽  
Author(s):  
I. N. Fletcher ◽  
L. E. O. C. Aragão ◽  
A. Lima ◽  
Y. Shimabukuro ◽  
P. Friedlingstein
Keyword(s):  
Forest Fires ◽  
Large Scale ◽  
Fire Spread ◽  
Small Scale ◽  
Burnt Area ◽  
Active Fire ◽  
Fractal Properties ◽  
Dynamic Global Vegetation Models ◽  
Vegetation Models ◽  
Global Vegetation

Abstract. Current methods for modelling burnt area in dynamic global vegetation models (DGVMs) involve complex fire spread calculations, which rely on many inputs, including fuel characteristics, wind speed and countless parameters. They are therefore susceptible to large uncertainties through error propagation, but undeniably useful for modelling specific, small-scale burns. Using observed fractal distributions of fire scars in Brazilian Amazonia in 2005, we propose an alternative burnt area model for tropical forests, with fire counts as sole input and few parameters. This model is intended for predicting large-scale burnt area rather than looking at individual fire events. A simple parameterization of a tapered fractal distribution is calibrated at multiple spatial resolutions using a satellite-derived burnt area map. The model is capable of accurately reproducing the total area burnt (16 387 km2) and its spatial distribution. When tested pan-tropically using the MODIS MCD14ML active fire product, the model accurately predicts temporal and spatial fire trends, but the magnitude of the differences between these estimates and the GFED3.1 burnt area products varies per continent.

scholarly journals Long-term temporal changes in the occurrence of a high forest fire danger in Finland

2012 ◽  
Vol 12 (8) ◽  
pp. 2591-2601 ◽  
Author(s):  
H. M. Mäkelä ◽  
M. Laapas ◽  
A. Venäläinen
Keyword(s):  
Forest Fire ◽  
Forest Fires ◽  
Large Scale ◽  
Weather Conditions ◽  
Temporal Variations ◽  
Fire Danger ◽  
Specific Information ◽  
Climate Data ◽  
Forest Fire Danger

Abstract. Climate variation and change influence several ecosystem components including forest fires. To examine long-term temporal variations of forest fire danger, a fire danger day (FDD) model was developed. Using mean temperature and total precipitation of the Finnish wildfire season (June–August), the model describes the climatological preconditions of fire occurrence and gives the number of fire danger days during the same time period. The performance of the model varied between different regions in Finland being best in south and west. In the study period 1908–2011, the year-to-year variation of FDD was large and no significant increasing or decreasing tendencies could be found. Negative slopes of linear regression lines for FDD could be explained by the simultaneous, mostly not significant increases in precipitation. Years with the largest wildfires did not stand out from the FDD time series. This indicates that intra-seasonal variations of FDD enable occurrence of large-scale fires, despite the whole season's fire danger is on an average level. Based on available monthly climate data, it is possible to estimate the general fire conditions of a summer. However, more detailed input data about weather conditions, land use, prevailing forestry conventions and socio-economical factors would be needed to gain more specific information about a season's fire risk.

Spatio-Temporal Typology of Land and Forest Fire in Sumatra

2016 ◽  
Vol 4 (1) ◽  
pp. 83 ◽  
Author(s):  
Israr Albar ◽  
I. Nengah Surati Jaya ◽  
Bambang Hero Saharjo ◽  
Budi Kuncahyo
Keyword(s):  
Forest Fires ◽  
Geographic Location ◽  
Temporal Analysis ◽  
Forest Area ◽  
Single Linkage ◽  
Active Fire ◽  
Linkage Method ◽  
Spatio Temporal ◽  
Key Indicators ◽  
Typology Analysis

<p>The characteristic of land and forest fires occurred in Indonesia are varied widely, following the variation of time within a year and geographic location. This paper describes how the spatio-temporal of forest and land fire typology was developed. The main objective of this study was to develop a spatio-temporal typology of forest and land fire by considering several key indicators that directly related to the density of active fire occurrence, such as percentage of forest area (x<sub>1</sub>), population density (x<sub>2</sub>), ratio of forest area to population (x<sub>3</sub>), ratio of plantation area to population (x<sub>4</sub>), ratio of agriculture area to population (x<sub>5</sub>), GRDP (x<sub>6</sub>), population growth (x<sub>7</sub>), deforestation growth (x<sub>8</sub>), plantation growth (x<sub>9</sub>) and dry agriculture growth (x<sub>10</sub>) as well as  MODIS-based fire hotspot. The typology analysis was performed using clustering techniques with Euclidean distance dissimilarity measure, where the grouping process was drawn with single linkage method. The temporal analysis showed that the highest occurrence of the fire hotspot was mainly found in the third quarter. It was found that the forest and land fire typology could be developed into three classes using variables x<sub>6</sub> and x<sub>7</sub> with overall accuracy of 78.15% or x<sub>1</sub>-x<sub>6</sub>-x<sub>7</sub> with overall accuracy of 78.8%.  No accuracy improvement was obtained when the typology was developed using five variables x<sub>1</sub>-x<sub>3</sub>-x<sub>4</sub>-x<sub>6</sub>-x<sub>7.</sub><em></em></p>

scholarly journals A High-Resolution Flood Inundation Archive (2016–the Present) from Sentinel-1 SAR Imagery over CONUS

2021 ◽  
pp. 1-40
Author(s):  
Qing Yang ◽  
Xinyi Shen ◽  
Emmanouil N. Anagnostou ◽  
Chongxun Mo ◽  
Jack R. Eggleston ◽  
...  
Keyword(s):  
High Resolution ◽  
Large Scale ◽  
Weather Conditions ◽  
Automated System ◽  
Flood Inundation ◽  
Spatiotemporal Resolution ◽  
Dynamic Surface ◽  
Critical Success ◽  
Sar Imagery ◽  
Mapping Techniques

AbstractMost existing inundation inventories are based on surveys, news, or passive remote sensing imagery. Affected by spatiotemporal resolution or weather conditions, these inventories are limited in spatial details or coverage. Satellite Synthetic Aperture Radar (SAR) data has recently enabled flood mapping at unprecedented spatiotemporal resolution. However, the bottleneck in producing SAR-based flood maps is the requirement of expert manual processing to maintain acceptable accuracy by most SAR-driven mapping techniques. To fill the vacancy, we generate a high-resolution (10 m) flood inundation dataset over the contiguous United States (CONUS) from nearly the entire Sentinel-1 SAR archive (from January 2016 to the present), using a recently developed automated Radar Produced Inundation Diary (RAPID) system. RAPID uses U.S. Geological Survey (USGS) water watch system and accumulated precipitation to identify SAR images that potentially overlap a flood event. The dataset includes inundation events with the temporal scale from several days to months. Concluded from all 559 overlapping images in the period from 2016 to the first half of 2019, the comparison of the proposed dataset against the USGS Dynamic Surface Water Extent (DSWE) product yields an overall, user, producer agreements, and critical success index of 99.06%, 87.63%, 91.76%, and 81.23%, respectively, demonstrating the high accuracy of the proposed dataset and the robustness of the automated system. We anticipate this archive to facilitate many applications, including large-scale flood loss and risk assessment, and inundation model calibration and validation.

scholarly journals K-Prototypes Segmentation Analysis on Large-Scale Ridesourcing Trip Data

2020 ◽  
Vol 2674 (9) ◽  
pp. 383-394 ◽  
Author(s):  
Jason Soria ◽  
Ying Chen ◽  
Amanda Stathopoulos
Keyword(s):  
Spatial Data ◽  
Large Scale ◽  
Weather Conditions ◽  
Data Types ◽  
Adverse Weather Conditions ◽  
Segmentation Analysis ◽  
Adverse Weather ◽  
Mobility System ◽  
Spatio Temporal

Shared mobility-on-demand services are expanding rapidly in cities around the world. As a prominent example, app-based ridesourcing is becoming an integral part of many urban transportation ecosystems. Despite the centrality, limited public availability of detailed temporal and spatial data on ridesourcing trips has limited research on how new services interact with traditional mobility options and how they affect travel in cities. Improving data-sharing agreements are opening unprecedented opportunities for research in this area. This study examined emerging patterns of mobility using recently released City of Chicago public ridesourcing data. The detailed spatio-temporal ridesourcing data were matched with weather, transit, and taxi data to gain a deeper understanding of ridesourcing’s role in Chicago’s mobility system. The goal was to investigate the systematic variations in patronage of ridehailing. K-prototypes was utilized to detect user segments owing to its ability to accept mixed variable data types. An extension of the K-means algorithm, its output was a classification of the data into several clusters called prototypes. Six ridesourcing prototypes were identified and discussed based on significant differences in relation to adverse weather conditions, competition with alternative modes, location and timing of use, and tendency for ridesplitting. The paper discusses the implications of the identified clusters related to affordability, equity, and competition with transit.

scholarly journals Large-Scale Station-Level Crowd Flow Forecast with ST-Unet

2019 ◽  
Vol 8 (3) ◽  
pp. 140 ◽  
Author(s):  
Yirong Zhou ◽  
Hao Chen ◽  
Jun Li ◽  
Ye Wu ◽  
Jiangjiang Wu ◽  
...  
Keyword(s):  
Spatial Dependence ◽  
Traffic Management ◽  
Large Scale ◽  
Weather Conditions ◽  
Temporal Dependence ◽  
Flow Information ◽  
Spatio Temporal ◽  
Real World Datasets ◽  
Temporal Events ◽  
Flow Forecast

High crowd mobility is a characteristic of transportation hubs such as metro/bus/bike stations in cities worldwide. Forecasting the crowd flow for such places, known as station-level crowd flow forecast (SLCFF) in this paper, would have many benefits, for example traffic management and public safety. Concretely, SLCFF predicts the number of people that will arrive at or depart from stations in a given period. However, one challenge is that the crowd flows across hundreds of stations irregularly scattered throughout a city are affected by complicated spatio-temporal events. Additionally, some external factors such as weather conditions or holidays may change the crowd flow tremendously. In this paper, a spatio-temporal U-shape network model (ST-Unet) for SLCFF is proposed. It is a neural network-based multi-output regression model, handling hundreds of target variables, i.e., all stations’ in and out flows. ST-Unet emphasizes stations’ spatial dependence by integrating the crowd flow information from neighboring stations and the cluster it belongs to after hierarchical clustering. It learns the temporal dependence by modeling the temporal closeness, period, and trend of crowd flows. With proper modifications on the network structure, ST-Unet is easily trained and has reliable convergency. Experiments on four real-world datasets were carried out to verify the proposed method’s performance and the results show that ST-Unet outperforms seven baselines in terms of SLCFF.

scholarly journals Spatio-temporal variation in the wintering associations of an alpine bird

2021 ◽  
Vol 288 (1951) ◽  
pp. 20210690
Author(s):  
María del Mar Delgado ◽  
Raphaël Arlettaz ◽  
Chiara Bettega ◽  
Mattia Brambilla ◽  
Miguel de Gabriel Hernando ◽  
...  
Keyword(s):  
Large Scale ◽  
Weather Conditions ◽  
Costs And Benefits ◽  
Winter Climate ◽  
Winter Conditions ◽  
Trade Offs ◽  
Common Strategy ◽  
Spatio Temporal ◽  
Wintering Conditions ◽  
Shed Light

Many animals make behavioural changes to cope with winter conditions, being gregariousness a common strategy. Several factors have been invoked to explain why gregariousness may evolve during winter, with individuals coming together and separating as they trade off the different costs and benefits of living in groups. These trade-offs may, however, change over space and time as a response to varying environmental conditions. Despite its importance, little is known about the factors triggering gregarious behaviour during winter and its change in response to variation in weather conditions is poorly documented. Here, we aimed at quantifying large-scale patterns in wintering associations over 23 years of the white-winged snowfinch Montifringilla nivalis nivalis . We found that individuals gather in larger groups at sites with harsh wintering conditions. Individuals at colder sites reunite later and separate earlier in the season than at warmer sites. However, the magnitude and phenology of wintering associations are ruled by changes in weather conditions. When the temperature increased or the levels of precipitation decreased, group size substantially decreased, and individuals stayed united in groups for a shorter time. These results shed light on factors driving gregariousness and points to shifting winter climate as an important factor influencing this behaviour.

scholarly journals Weather Conditions and Warm Air Masses in Southern Sakha During Active Forest Fire Periods

2019 ◽  
Vol 14 (4) ◽  
pp. 641-648 ◽  
Author(s):  
Hiroshi Hayasaka ◽  
Koji Yamazaki ◽  
Daisuke Naito ◽  
◽  
◽  
...  
Keyword(s):  
Forest Fire ◽  
Forest Fires ◽  
Weather Conditions ◽  
Reanalysis Data ◽  
Strong Wind ◽  
Air Masses ◽  
Weather Patterns ◽  
Active Fire ◽  
Fire Activity ◽  
Moderate Resolution Imaging Spectroradiometer

Forest fires are a common and destructive natural disaster in Russia. Weather conditions during active forest fire periods in southern Sakha (Eastern Siberia) at high latitudes (58–65°N, 120–140°E) were evaluated. Periods of high fire activity during 2002 to 2016 were identified using MODIS (moderate resolution imaging spectroradiometer) hotspot data by considering the number of daily hotspots and their continuity. Weather conditions during the top seven periods of high fire activity were analyzed using atmospheric reanalysis data for upper (500 hPa) and lower levels (925 hPa). Our results showed that active fires occurred under varied weather conditions and it was difficult to find common weather patterns at both upper- and lower-levels during the seven most active fire periods. Furthermore, it was apparent that the northward movement of warm air masses (cTe: continental temperate) from lower latitudes (∼40°N) toward southern Sakha tended to exacerbate fires mainly due to strong wind conditions during the seven most active fire periods. In particular, on peak hotspot days, warm air masses from the south existed commonly near southern Sakha. This northward movement of warm air masses can be used to forecast fire and predict future fires in the region.

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