scholarly journals Processing of Near Real Time Land Surface Temperature and Its Application in Forecasting Forest Fire Danger Conditions

Sensors ◽  
2020 ◽  
Vol 20 (4) ◽  
pp. 984 ◽  
Author(s):  
M. Ahmed ◽  
Quazi Hassan ◽  
Masoud Abdollahi ◽  
Anil Gupta
Keyword(s):  
Surface Temperature ◽  
Real Time ◽  
Land Surface Temperature ◽  
Forest Fires ◽  
Land Surface ◽  
Fire Danger ◽  
Naming Convention ◽  
Area Of Interest ◽  
Data Files ◽  
Moderate Resolution Imaging Spectroradiometer

Near real time (NRT) remote sensing derived land surface temperature (Ts) data has an utmost importance in various applications of natural hazards and disasters. Space-based instrument MODIS (moderate resolution imaging spectroradiometer) acquired NRT data products of Ts are made available for the users by LANCE (Land, Atmosphere Near real-time Capability) for Earth Observing System (EOS) of NASA (National Aeronautics and Space Administration) free of cost. Such Ts products are swath data with 5 min temporal increments of satellite acquisition, and the average latency is 60-125 min to be available in public domain. The swath data of Ts requires a specialized tool, i.e., HEG (HDF-EOS to GeoTIFF conversion tool) to process and make the data useful for further analysis. However, the file naming convention of the available swath data files in LANCE is not appropriate to download for an area of interest (AOI) to be processed by HEG. In this study, we developed a method/algorithm to overcome such issues in identifying the appropriate swath data files for an AOI that would be able to further processes supported by the HEG. In this case, we used Terra MODIS acquired NRT swath data of Ts, and further applied it to an existing framework of forecasting forest fires (as a case study) for the performance evaluation of our processed Ts. We were successful in selecting appropriate swath data files of Ts for our study area that was further processed by HEG, and finally were able to generate fire danger map in the existing forecasting model. Our proposed method/algorithm could be applied on any swath data product available in LANCE for any location in the world.

scholarly journals Relating Spatiotemporal Patterns of Forest Fires Burned Area and Duration to Diurnal Land Surface Temperature Anomalies

2018 ◽  
Vol 10 (11) ◽  
pp. 1777 ◽  
Author(s):  
Carmine Maffei ◽  
Silvia Alfieri ◽  
Massimo Menenti
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Forest Fires ◽  
Land Surface ◽  
Fire Danger ◽  
Parametric Models ◽  
Burned Area ◽  
Informative Content

Forest fires are a major source of ecosystem disturbance. Vegetation reacts to meteorological factors contributing to fire danger by reducing stomatal conductance, thus leading to an increase of canopy temperature. The latter can be detected by remote sensing measurements in the thermal infrared as a deviation of observed land surface temperature (LST) from climatological values, that is as an LST anomaly. A relationship is thus expected between LST anomalies and forest fires burned area and duration. These two characteristics are indeed controlled by a large variety of both static and dynamic factors related to topography, land cover, climate, weather (including those affecting LST) and anthropic activity. To investigate the predicting capability of remote sensing measurements, rather than constructing a comprehensive model, it would be relevant to determine whether anomalies of LST affect the probability distributions of burned area and fire duration. This research approached the outlined knowledge gap through the analysis of a dataset of forest fires in Campania (Italy) covering years 2003–2011 against estimates of LST anomaly. An LST climatology was first computed from time series of daily Aqua-MODIS LST data (product MYD11A1, collection 6) over the longest available sequence of complete annual datasets (2003–2017), through the Harmonic Analysis of Time Series (HANTS) algorithm. HANTS was also used to create individual annual models of LST data, to minimize the effect of varying observation geometry and cloud contamination on LST estimates while retaining its seasonal variation. LST anomalies where thus quantified as the difference between LST annual models and LST climatology. Fire data were intersected with LST anomaly maps to associate each fire with the LST anomaly value observed at its position on the day previous to the event. Further to this step, the closest probability distribution function describing burned area and fire duration were identified against a selection of parametric models through the maximization of the Anderson-Darling goodness-of-fit. Parameters of the identified distributions conditional to LST anomaly where then determined along their confidence intervals. Results show that in the study area log-transformed burned area is described by a normal distribution, whereas log-transformed fire duration is closer to a generalized extreme value (GEV) distribution. The parameters of these distributions conditional to LST anomaly show clear trends with increasing LST anomaly; significance of this observation was verified through a likelihood ratio test. This confirmed that LST anomaly is a covariate of both burned area and fire duration. As a consequence, it was observed that conditional probabilities of extreme events appear to increase with increasing positive deviations of LST from its climatology values. This confirms the stated hypothesis that LST anomalies affect forest fires burned area and duration and highlights the informative content of time series of LST with respect to fire danger.

Developing a Wildfire danger index based on the satellite-derived parameters 

2021 ◽  
Author(s):  
Suresh Babu ◽  
Vernon Visser ◽  
Glenn Moncrieff ◽  
Jasper Slingsby ◽  
Res Altwegg
Keyword(s):  
Surface Temperature ◽  
Real Time ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Thermal Anomaly ◽  
Shuttle Radar Topography Mission ◽  
Fire Danger ◽  
Mitigation Measures ◽  
Fire Season ◽  
Time Basis

<p>Wildfires are occurring throughout the world, causing immense damage to forest resources, flora, and fauna. The Fire Danger indices are used as a tool for the decision-makers to issue warnings to the public, based on the level of fire danger classes for implementing mitigation measures to control wildfires. In this study, a Wildfire danger index (WDI) is developed from static and dynamic factors, which are derived from satellite datasets. The Static fire danger Index (SFDI) is generated using MODIS Land cover type (MCD12Q1), Shuttle Radar Topography Mission (SRTM) DEM, and Open Street Map datasets. The Random Forest algorithm is used to generate SFDI from the parameters LULC map, slope map, aspect map, and elevation maps based on the historical MODIS active thermal anomaly product (MCD14). Dynamic Fire Danger Index (DFDI) is developed from the MODIS Terra datasets such as Land Surface Temperature (MOD11A2) and surface reflectance (MOD09A1) datasets.  The DFDI is developed from four parameters viz. Land surface temperature, Visible Atmospherically Resistant Index (VARI) and Normalized Multiband Drought Index (NMDI), and Normalized Difference Infrared Index – B6 (NDIIB6). Finally, the wildfire danger index is calculated by integrating SFDI and DFDI and found that the accuracy is more than 80% during the 2018-19 fire season. Therefore, the WDI can be useful for disseminating daily fire danger maps on near real-time basis using the MODIS TERRA Near Real-Time datasets so that the fire officials to take necessary actions to control the spread of wildfires.</p><p> </p><p> </p><p> </p>

scholarly journals Assimilating Satellite Land Surface States Data from Fengyun-4A

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Chunlei Meng ◽  
Huoqing Li
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Surface Albedo ◽  
Surface Emissivity ◽  
Land Surface Emissivity ◽  
China Academy ◽  
Moderate Resolution ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Academy Of Sciences

AbstractFengyun-4A is the new generation of Chinese geostationary meteorological satellites. Land surface albedo, land surface emissivity and land surface temperature are key states for land surface modelling. In this paper, the land surface albedo, land surface emissivity and land surface temperature data from Fengyun-4A were assimilated into the Integrated Urban land Model. The Fengyun-4A data are one of the data sources for the land data assimilation system which devoted to produce the high spatial and temporal resolution, multiple parameters near real-time land data sets. The Moderate-Resolution Imaging Spectroradiometer (MODIS) LSA and LSE data, the Institute of Atmospheric Physics, China Academy of Sciences (IAP) 325 m tower observation data and the observed 5 cm and 10 cm soil temperature data in more than 100 sites are used for validation. The results indicate the MODIS land surface albedo is much smaller than the Fengyun-4A and is superior to the Fengyun-4A for the Institute of Atmospheric Physics, China Academy of Sciences 325 m tower site. The Moderate-Resolution Imaging Spectroradiometer land surface emissivity is smaller than the Fengyun-4A in barren land surface and the differences is relatively small for other land use and land cover categories. In most regions of the research area, the Fengyun-4A land surface albedo and land surface emissivity are larger than those of the simulations. After the land surface albedo assimilation, in most regions the simulated net radiation was decreased. After the land surface emissivity assimilation, in most regions the simulated net radiation was increased. After the land surface temperature assimilation, the biases of the land surface temperature were decreased apparently; the biases of the daily average 5 cm and 10 cm soil temperature were decreased.

scholarly journals Introducing a New Remote Sensing-Based Model for Forecasting Forest Fire Danger Conditions at a Four-Day Scale

2019 ◽  
Vol 11 (18) ◽  
pp. 2101 ◽  
Author(s):  
M. Ahmed ◽  
Quazi Hassan ◽  
Masoud Abdollahi ◽  
Anil Gupta
Keyword(s):  
Remote Sensing ◽  
Forest Fire ◽  
Forest Fires ◽  
Land Surface ◽  
Vegetation Index ◽  
Significant Risk ◽  
Fire Danger ◽  
Area Of Interest ◽  
Forest Fire Danger ◽  
Very High

Forest fires are natural disasters that create a significant risk to the communities living in the vicinity of forested landscape. To minimize the risk of forest fires for the resilience of such urban communities and forested ecosystems, we proposed a new remote sensing-based medium-term (i.e., four-day) forest fire danger forecasting system (FFDFS) based on an existing framework, and applied the system over the forested regions in the northern Alberta, Canada. Hence, we first employed moderate resolution imaging spectroradiometer (MODIS)-derived daily land surface temperature (Ts) and surface reflectance products along with the annual land cover to generate three four-day composite for Ts, normalized difference vegetation index (NDVI), and normalized difference water index (NDWI) at 500 m spatial resolution for the next four days over the forest-dominant regions. Upon generating these four-day composites, we calculated the variable-specific mean values to determine variable-specific fire danger maps with two danger classes (i.e., high and low). Then, by assuming the cloud-contaminated pixels as the low fire danger areas, we combined these three danger maps to generate a four-day fire danger map with four danger classes (i.e., low, moderate, high, and very high) over our study area of interest, which was further enhanced by incorporation of a human-caused static fire danger map. Finally, the four-day scale fire danger maps were evaluated using observed/ground-based forest fire occurrences during the 2015–2017 fire seasons. The results revealed that our proposed system was able to detect about 75% of the fire events in the top two danger classes (i.e., high and very high). The system was also able to predict the 2016 Horse River wildfire, the worst fire event in Albertian and Canadian history, with about 67% agreement. The higher accuracy outputs from our proposed model indicated that it could be implemented in the operational management, which would be very useful for lessening the adverse impact of such fire events.

scholarly journals Deep Learning Convolutional Neural Network for the Retrieval of Land Surface Temperature from AMSR2 Data in China

Sensors ◽  
2019 ◽  
Vol 19 (13) ◽  
pp. 2987 ◽  
Author(s):  
Jiancan Tan ◽  
Nusseiba NourEldeen ◽  
Kebiao Mao ◽  
Jiancheng Shi ◽  
Zhaoliang Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Surface Temperature ◽  
Convolutional Neural Network ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Measurement Data ◽  
Retrieval Algorithm ◽  
Observation Data ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Better Than

A convolutional neural network (CNN) algorithm was developed to retrieve the land surface temperature (LST) from Advanced Microwave Scanning Radiometer 2 (AMSR2) data in China. Reference data were selected using the Moderate Resolution Imaging Spectroradiometer (MODIS) LST product to overcome the problem related to the need for synchronous ground observation data. The AMSR2 brightness temperature (TB) data and MODIS surface temperature data were randomly divided into training and test datasets, and a CNN was constructed to simulate passive microwave radiation transmission to invert the surface temperature. The twelve V/H channel combinations (7.3, 10.65, 18.7, 23.8, 36.5, 89 GHz) resulted in the most stable and accurate CNN retrieval model. Vertical polarizations performed better than horizontal polarizations; however, because CNNs rely heavily on large amounts of data, the combination of vertical and horizontal polarizations performed better than a single polarization. The retrievals in different regions indicated that the CNN accuracy was highest over large bare land areas. A comparison of the retrieval results with ground measurement data from meteorological stations yielded R2 = 0.987, RMSE = 2.69 K, and an average relative error of 2.57 K, which indicated that the accuracy of the CNN LST retrieval algorithm was high and the retrieval results can be applied to long-term LST sequence analysis in China.

scholarly journals Assessment of agricultural drought in Morocco based on a composite of the Vegetation Health Index (VHI) and Standardized Precipitation Evapotranspiration Index (SPEI)

2021 ◽  
Vol 314 ◽  
pp. 04003
Author(s):  
Sara Moutia ◽  
Mohamed Sinan ◽  
Brahim Lekhlif
Keyword(s):  
Surface Temperature ◽  
Real Time ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Vegetation Index ◽  
Agricultural Drought ◽  
Health Index ◽  
Spatial Coverage ◽  
Vegetation Health ◽  
Vegetation Health Index

According to IPCC, Morocco is a highly vulnerable country to extreme climate events, especially droughts; this will affect different socioeconomic sectors, mainly the agriculture sector. Droughts are controlled by the variability of precipitation and evapotranspiration but also not neglecting the effect of land surface conditions such as land surface temperature. In this present study, the remote sense observations MODIS Normalized Difference Vegetation Index (NDVI) and CMSAF Land Surface Temperature (LST) were used for calculating the Vegetation Health Index (VHI). The main advantage of remote sensing products is that they are reasonably efficient in terms of temporal and spatial coverage, and they are useful for the monitoring and assessment of drought in the near real-time. Furthermore, ERA5 Reanalysis-based SPEI is calculated. The goal of this study is to assess the spatial and temporal patterns of drought, this study offers the composite of SPEI and VHI drought monitoring obtained by plotting maps and graphs to show the monthly and annual variability of drought for the period 2000–2015 over the whole of Morocco. This monitoring can be used as a near real-time warning system in a changing climate.

scholarly journals Pattern and Trend of Land Surface Temperature Change on New Guinea Island

2020 ◽  
Vol 28 (4) ◽  
Author(s):  
Munawar ◽  
Tofan Agung Eka Prasetya ◽  
Rhysa McNeil ◽  
Rohana Jani
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
New Guinea ◽  
Global Scale ◽  
Surface Temperature Change ◽  
Sea Levels ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Rising Sea Levels ◽  
The Many

Global warming will have an impact on nature in many ways, including rising sea levels and an increasing spread of infectious diseases. Land surface temperature is one of the many indicators that can be used to measure climate change on both a local and global scale. This study aims to analyze the change in land surface temperatures on New Guinea Island using a cubic spline method, autoregressive model, and multivariate regression. New Guinea Island was divided into 5 regions each consisting of 9 subregions. The data of each subregion was obtained from the National Aeronautics and Space Administration moderate resolution imaging spectroradiometer database from 2000 to 2019. The average change in temperature was +0.012°C per decade. However, the changes differed by region; significantly decreasing in the northwest at -0.107°C per decade (95% CI: -0.207, -0.007), significantly increasing in the south at 0.201°C per decade (95% CI: 0.069, 0.333), and remaining stable in the centralnorth, southeast and northeast.

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