scholarly journals Validation of MODIS and AVHRR Fire Detections in Australia

2021 ◽  
Vol 17 (3) ◽  
Author(s):  
A. Forghani ◽  
M. Thankappan ◽  
B. Cechet
Keyword(s):  
High Resolution ◽  
Spatial Information ◽  
Sampling Technique ◽  
Daily Basis ◽  
Earth Observation ◽  
Detection Algorithms ◽  
Mapping Tool ◽  
Active Fire ◽  
High Resolution Imagery ◽  
Moderate Resolution Imaging Spectroradiometer

The Sentinel Bushfire Monitoring System is an internet-based mapping tool which provides timely spatial information to fire agencies across Australia. The mapping system allows users to identify active fire locations that pose a potential risk to communities and property. Sentinel at Geoscience Australia currently provides hotspot information derived from Moderate Resolution Imaging Spectroradiometer (MODIS) and Advanced Very High Resolution Radiometer (AVHRR) sensors on a continent-wide and daily basis enabling the fire community and general public to locate active fires. There has been little validation undertaken of the Sentinel since the system began operating in November 2002. Validation datasets have been collected for this work during the 2003-2007 fire seasons. Five study areas were selected to validate the detection capabilities of the MODIS and AVHRR hotspot product with fire activity that was mapped using high resolution earth observation imagery. The objective is to evaluate the reliability with which hotspots identified in MODIS and AVHRR thermal data can be used to identify fires. This consists of comparing the accuracy of AVHRR versus MODIS and quantifying the accuracy of both products. This objective is achieved by characterising errors through a stratified random sampling technique establishing a relationship between the ‘fire’ and ‘no fire’ condition, and error assessment using multi- source reference datasets over coincident MODIS and AVHRR pixels. The validation framework comprised two key approaches including validation of AVHRR hotspots in relation to MODIS hotspots and validation of both MODIS and AVHRR hotspots using multi-sensor earth observation imagery datasets. The study identified sources of errors associated with the Sentinel hotspots which could be used to improve the performance of hotspot algorithms and provide user-friendly information for the users. Statistical analysis revealed that overall commission errors of MODIS and AVHRR hotspots over the 5% sample data were 15% and 68% respectively, and overall omission errors of MODIS and AVHRR hotspots were 17% and 23% respectively.  An important outcome of this study is the production of a database of fire locations derived from high-resolution imagery, which can serve as a resource for future validation efforts as detection algorithms evolve and sensors change.

scholarly journals Validation of MODIS and AVHRR Fire Detections in Australia

2021 ◽  
Vol 17 (3) ◽  
Author(s):  
A. Forghani ◽  
M. Thankappan ◽  
B. Cechet
Keyword(s):  
High Resolution ◽  
Spatial Information ◽  
Sampling Technique ◽  
Daily Basis ◽  
Earth Observation ◽  
Detection Algorithms ◽  
Mapping Tool ◽  
Active Fire ◽  
High Resolution Imagery ◽  
Moderate Resolution Imaging Spectroradiometer

The Sentinel Bushfire Monitoring System is an internet-based mapping tool which provides timely spatial information to fire agencies across Australia. The mapping system allows users to identify active fire locations that pose a potential risk to communities and property. Sentinel at Geoscience Australia currently provides hotspot information derived from Moderate Resolution Imaging Spectroradiometer (MODIS) and Advanced Very High Resolution Radiometer (AVHRR) sensors on a continent-wide and daily basis enabling the fire community and general public to locate active fires. There has been little validation undertaken of the Sentinel since the system began operating in November 2002. Validation datasets have been collected for this work during the 2003-2007 fire seasons. Five study areas were selected to validate the detection capabilities of the MODIS and AVHRR hotspot product with fire activity that was mapped using high resolution earth observation imagery. The objective is to evaluate the reliability with which hotspots identified in MODIS and AVHRR thermal data can be used to identify fires. This consists of comparing the accuracy of AVHRR versus MODIS and quantifying the accuracy of both products. This objective is achieved by characterising errors through a stratified random sampling technique establishing a relationship between the ‘fire’ and ‘no fire’ condition, and error assessment using multi- source reference datasets over coincident MODIS and AVHRR pixels. The validation framework comprised two key approaches including validation of AVHRR hotspots in relation to MODIS hotspots and validation of both MODIS and AVHRR hotspots using multi-sensor earth observation imagery datasets. The study identified sources of errors associated with the Sentinel hotspots which could be used to improve the performance of hotspot algorithms and provide user-friendly information for the users. Statistical analysis revealed that overall commission errors of MODIS and AVHRR hotspots over the 5% sample data were 15% and 68% respectively, and overall omission errors of MODIS and AVHRR hotspots were 17% and 23% respectively.  An important outcome of this study is the production of a database of fire locations derived from high-resolution imagery, which can serve as a resource for future validation efforts as detection algorithms evolve and sensors change.

scholarly journals 2nd Edition of Instrumenting Smart City Applications with Big Sensing and Earth Observatory Data: Tools, Methods and Techniques

2021 ◽  
Vol 13 (7) ◽  
pp. 1310
Author(s):  
Gabriele Bitelli ◽  
Emanuele Mandanici
Keyword(s):  
High Resolution ◽  
Exponential Growth ◽  
Smart City ◽  
Earth Observation ◽  
Urban Environments ◽  
Observation Data ◽  
High Resolution Imagery ◽  
Observatory Data ◽  
Methods And Techniques ◽  
Earth Observation Data

The exponential growth in the volume of Earth observation data and the increasing quality and availability of high-resolution imagery are increasingly making more applications possible in urban environments [...]

Evaluation and Comparison of MODIS and IMS Snow-Cover Estimates for the Continental United States Using Station Data

2005 ◽  
Vol 6 (6) ◽  
pp. 1002-1017 ◽  
Author(s):  
K. L. Brubaker ◽  
R. T. Pinker ◽  
E. Deviatova
Keyword(s):  
United States ◽  
Snow Cover ◽  
Climate Modeling ◽  
Remotely Sensed ◽  
Daily Basis ◽  
Resource Monitoring ◽  
Detection Algorithms ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Climate Change Assessment ◽  
Year 2000

Abstract Satellite-derived information on fractional snow cover is essential to resource monitoring, hydrologic modeling, and climate change assessment. Evaluating the accuracy of remotely sensed snow-cover products is important but difficult, largely because point-scale surface observations are spatially sparse and generally nonrepresentative of the remote sensor footprint. In this study, two remotely sensed snow-cover products [the Interactive Multisensor Snow and Ice Mapping System (IMS) and the Moderate Resolution Imaging Spectroradiometer (MODIS) Climate Modeling Grid (CMG), v.3] are evaluated against ground observations from the Cooperative Observing Network and SNOTEL on a daily basis over the continental United States for calendar year 2000. Ground observations are treated as points in space and time; no physical modeling or statistical interpolation is applied. Hypothesis tests based on discrete and continuous distributions are developed to assess agreement between ground observations and the remotely sensed snow-cover products at 0.25° resolution. (The MODIS CMG product was degraded from 0.05° for this study, thus its potential is not fully evaluated.) As overall snow extent increases in the course of the season, both MODIS and IMS improve in identifying snow-covered areas (fewer errors of omission), but deteriorate in identifying snow-free areas (more errors of commission). The detection of scattered areas of snow is generally better during ablation than during accumulation. Weaknesses of the statistical methods and assumptions are discussed. This work will help to identify areas for improvement in snow-cover detection algorithms and provides a framework to assess the accuracy of remotely sensed snow cover used as model input and/or confirmation.

scholarly journals Prototype Downscaling Algorithm for MODIS Satellite 1 km Daytime Active Fire Detections

Fire ◽  
2019 ◽  
Vol 2 (2) ◽  
pp. 29 ◽  
Author(s):  
Sanath Sathyachandran Kumar ◽  
Joshua J. Picotte ◽  
Birgit Peterson
Keyword(s):  
Spatial Uncertainty ◽  
Point Spread Functions ◽  
Active Fire ◽  
High Resolution Imagery ◽  
Moderate Resolution ◽  
Science Community ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Shortwave Infrared ◽  
Sky Conditions

This work presents development of an algorithm to reduce the spatial uncertainty of active fire locations within the 1 km MODerate resolution Imaging Spectroradiometer (MODIS Aqua and Terra) daytime detection footprint. The algorithm is developed using the finer 500 m reflective bands by leveraging on the increase in 2.13 μm shortwave infrared reflectance due to the burning components as compared to the non-burning neighborhood components. Active fire presence probability class for each of the 500 m pixels within the 1 km footprint is assigned by locally adaptive contextual tests against its surrounding neighborhood pixels. Accuracy is assessed using gas flares and wildfires in conjunction with available high-resolution imagery. Proof of concept results using MODIS observations over two sites show that under clear sky conditions, over 84% of the 500 m locations that had active fires were correctly assigned to high to medium probabilities, and correspondingly low to poor probabilities were assigned to locations with no visible flaming fronts. Factors limiting the algorithm performance include fire size/temperature distributions, cloud and smoke obscuration, sensor point spread functions, and geolocation errors. Despite these limitations, the resulting finer spatial scale of active fire detections will not only help first responders and managers to locate actively burning fire fronts more precisely but will also be useful for the fire science community.

scholarly journals A Pilot Study to Estimate Forage Mass from Unmanned Aerial Vehicles in a Semi-Arid Rangeland

2020 ◽  
Vol 12 (15) ◽  
pp. 2431
Author(s):  
Alexandria M. DiMaggio ◽  
Humberto L. Perotto-Baldivieso ◽  
J. Alfonso Ortega-S. ◽  
Chase Walther ◽  
Karelys N. Labrador-Rodriguez ◽  
...  
Keyword(s):  
High Resolution ◽  
Unmanned Aerial Vehicles ◽  
Sampling Technique ◽  
Ground Level ◽  
South Texas ◽  
Lower Altitude ◽  
Aerial Vehicles ◽  
High Resolution Imagery ◽  
Non Destructive ◽  
Semi Arid

The application of unmanned aerial vehicles (UAVs) in the monitoring and management of rangelands has exponentially increased in recent years due to the miniaturization of sensors, ability to capture imagery with high spatial resolution, lower altitude platforms, and the ease of flying UAVs in remote environments. The aim of this research was to develop a method to estimate forage mass in rangelands using high-resolution imagery derived from the UAV using a South Texas pasture as a pilot site. The specific objectives of this research were to (1) evaluate the feasibility of quantifying forage mass in semi-arid rangelands using a double sampling technique with high-resolution imagery and (2) to compare the effect of altitude on forage mass estimation. Orthoimagery and digital surface models (DSM) with a resolution <1.5 cm were acquired with an UAV at altitudes of 30, 40, and 50 m above ground level (AGL) in Duval County, Texas. Field forage mass data were regressed on volumes obtained from a DSM. Our results show that volumes estimated with UAV data and forage mass as measured in the field have a significant relationship at all flight altitudes with best results at 30-m AGL (r2 = 0.65) and 50-m AGL (r2 = 0.63). Furthermore, the use of UAVs would allow one to collect a large number of samples using a non-destructive method to estimate available forage for grazing animals.

Mapping and Monitoring of the Land Use/Cover Changes in the Wider Area of ltanos, Crete, Using Very High Resolution EO Imagery With Specific Interest in Archaeological Sites

2019 ◽  
Author(s):  
Sawyer Reid stippa ◽  
George Petropoulos ◽  
Leonidas Toulios ◽  
Prashant K. Srivastava
Keyword(s):  
High Resolution ◽  
Spatial Resolution ◽  
Satellite Images ◽  
Archaeological Site ◽  
Digital Photography ◽  
Archaeological Sites ◽  
Large Area ◽  
High Resolution Imagery ◽  
Site Mapping ◽  
Rule Sets

Archaeological site mapping is important for both understanding the history as well as protecting them from excavation during the developmental activities. As archaeological sites generally spread over a large area, use of high spatial resolution remote sensing imagery is becoming increasingly applicable in the world. The main objective of this study was to map the land cover of the Itanos area of Crete and of its changes, with specific focus on the detection of the landscape’s archaeological features. Six satellite images were acquired from the Pleiades and WorldView-2 satellites over a period of 3 years. In addition, digital photography of two known archaeological sites was used for validation. An Object Based Image Analysis (OBIA) classification was subsequently developed using the five acquired satellite images. Two rule-sets were created, one using the standard four bands which both satellites have and another for the two WorldView-2 images their four extra bands included. Validation of the thematic maps produced from the classification scenarios confirmed a difference in accuracy amongst the five images. Comparing the results of a 4-band rule-set versus the 8-band showed a slight increase in classification accuracy using extra bands. The resultant classifications showed a good level of accuracy exceeding 70%. Yet, separating the archaeological sites from the open spaces with little or no vegetation proved challenging. This was mainly due to the high spectral similarity between rocks and the archaeological ruins. The satellite data spatial resolution allowed for the accuracy in defining larger archaeological sites, but still was a difficulty in distinguishing smaller areas of interest. The digital photography data provided a very good 3D representation for the archaeological sites, assisting as well in validating the satellite-derived classification maps. All in all, our study provided further evidence that use of high resolution imagery may allow for archaeological sites to be located, but only where they are of a suitable size archaeological features.

ASSESSING MICROFOSSIL TAPHONOMY WITH HIGH-RESOLUTION IMAGERY

2017 ◽  
Author(s):  
R. Seth Wood ◽  
◽  
Ashley R. Manning-Berg ◽  
Kenneth H. Williford ◽  
Linda C. Kah
Keyword(s):  
High Resolution ◽  
High Resolution Imagery

scholarly journals Assessing Riyadh’s Urban Change Utilizing High-Resolution Imagery

Land ◽  
2019 ◽  
Vol 8 (12) ◽  
pp. 193
Author(s):  
Ali Alghamdi ◽  
Anthony R. Cummings
Keyword(s):  
Maximum Likelihood ◽  
High Resolution ◽  
Accuracy Assessment ◽  
Middle Eastern ◽  
Object Oriented ◽  
Training Data ◽  
High Resolution Imagery ◽  
Significant Research ◽  
Desert Areas ◽  
Spot 5

The implications of change on local processes have attracted significant research interest in recent times. In urban settings, green spaces and forests have attracted much attention. Here, we present an assessment of change within the predominantly desert Middle Eastern city of Riyadh, an understudied setting. We utilized high-resolution SPOT 5 data and two classification techniques—maximum likelihood classification and object-oriented classification—to study the changes in Riyadh between 2004 and 2014. Imagery classification was completed with training data obtained from the SPOT 5 dataset, and an accuracy assessment was completed through a combination of field surveys and an application developed in ESRI Survey 123 tool. The Survey 123 tool allowed residents of Riyadh to present their views on land cover for the 2004 and 2014 imagery. Our analysis showed that soil or ‘desert’ areas were converted to roads and buildings to accommodate for Riyadh’s rapidly growing population. The object-oriented classifier provided higher overall accuracy than the maximum likelihood classifier (74.71% and 73.79% vs. 92.36% and 90.77% for 2004 and 2014). Our work provides insights into the changes within a desert environment and establishes a foundation for understanding change in this understudied setting.

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