scholarly journals Assessment of post-fire changes in land surface temperature and surface albedo, and their relation with fire - burn severity using multitemporal MODIS imagery

2012 ◽  
Vol 21 (3) ◽  
pp. 243 ◽  
Author(s):  
Sander Veraverbeke ◽  
Willem W. Verstraeten ◽  
Stefaan Lhermitte ◽  
Ruben Van De Kerchove ◽  
Rudi Goossens
Keyword(s):  
Time Series ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Surface Albedo ◽  
Remotely Sensed ◽  
Burn Severity ◽  
Night Time ◽  
Fire Event ◽  
Induced Changes

This study evaluates the effects of the large 2007 Peloponnese (Greece) wildfires on changes in broadband surface albedo (α), daytime land surface temperature (LSTd) and night-time LST (LSTn) using a 2-year post-fire time series of Moderate Resolution Imaging Spectroradiometer satellite data. In addition, it assesses the potential of remotely sensed α and LST as indicators for fire–burn severity. Immediately after the fire event, mean α dropped up to 0.039 (standard deviation = 0.012) (P < 0.001), mean LSTd increased up to 8.4 (3.0) K (P < 0.001), and mean LSTn decreased up to –1.2 (1.5) K (P < 0.001) for high-severity plots (P < 0.001). After this initial alteration, fire-induced changes become clearly smaller and seasonality starts governing the α and LST time series. Compared with the fire-induced changes in α and LST, the post-fire NDVI drop was more persistent in time. This temporal constraint restricts the utility of remotely sensed α and LST as indicators for fire–burn severity. For the times when changes in α and LST were significant, the magnitude of changes was related to fire–burn severity, revealing the importance of vegetation as a regulator of land surface energy fluxes.

Analyzing trends in Land Surface Temperature using remotely sensed time series data and the BFAST method

2020 ◽  
Author(s):  
Alexandra Gemitzi ◽  
George Falalakis
Keyword(s):  
Time Series ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Missing Values ◽  
Structural Changes ◽  
Time Series Data ◽  
Remotely Sensed ◽  
Series Data ◽  
Global Changes

&lt;p&gt;The present work deals with the time series analysis of remotely sensed Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature (LST). While many works have been published concerning the trends of nighttime and daytime LST at the regional or local scale, little attention has been paid to structural changes observed within the LST time series in various sub-periods. This could be of much interest not only for climate studies but also for unveiling the possible relation between natural disasters such as wildfires and global changes. In this work we tested the hypothesis of a constant trend in LST time series from 2000 to 2019 and highlighted the existence of periods with changing trends. The methodology was applied in an area of approximately 17.000 km&lt;sup&gt;2&lt;/sup&gt; located in NE Greece and South Bulgaria. The nighttime and daytime LST time series data were initially subjected to a gap filling algorithm to account for missing values and were then aggregated at the catchment level. Furthermore, LST time series were analyzed using the Breaks For Additive Season and Trend (BFAST) method. Results indicated that an abrupt change in both nighttime and daytime LST trends was observed in all examined time series, indicating a transition from a decreasing LST regime from 2002 to 2006 to an abrupt increasing thereafter until today. An initial comparison with the existing inventory of wildfires in the area for the last 20 years indicated an increase of wildfire events which coincides with the LST breakpoint, indicating thus possible connections between rising LST and wildfire events.&lt;/p&gt;

scholarly journals A Review of Reconstructing Remotely Sensed Land Surface Temperature under Cloudy Conditions

2021 ◽  
Vol 13 (14) ◽  
pp. 2838
Author(s):  
Yaping Mo ◽  
Yongming Xu ◽  
Huijuan Chen ◽  
Shanyou Zhu
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Spatial Scales ◽  
Remotely Sensed ◽  
Urban Heat Islands ◽  
Reconstruction Algorithms ◽  
Gap Filling ◽  
Practical Applications ◽  
Data Gaps

Land surface temperature (LST) is an important environmental parameter in climate change, urban heat islands, drought, public health, and other fields. Thermal infrared (TIR) remote sensing is the main method used to obtain LST information over large spatial scales. However, cloud cover results in many data gaps in remotely sensed LST datasets, greatly limiting their practical applications. Many studies have sought to fill these data gaps and reconstruct cloud-free LST datasets over the last few decades. This paper reviews the progress of LST reconstruction research. A bibliometric analysis is conducted to provide a brief overview of the papers published in this field. The existing reconstruction algorithms can be grouped into five categories: spatial gap-filling methods, temporal gap-filling methods, spatiotemporal gap-filling methods, multi-source fusion-based gap-filling methods, and surface energy balance-based gap-filling methods. The principles, advantages, and limitations of these methods are described and discussed. The applications of these methods are also outlined. In addition, the validation of filled LST values’ cloudy pixels is an important concern in LST reconstruction. The different validation methods applied for reconstructed LST datasets are also reviewed herein. Finally, prospects for future developments in LST reconstruction are provided.

scholarly journals Exploring the Variation Trend of Urban Expansion, Land Surface Temperature, and Ecological Quality and Their Interrelationships in Guangzhou, China, from 1987 to 2019

2021 ◽  
Vol 13 (5) ◽  
pp. 1019
Author(s):  
Jianhui Xu ◽  
Yi Zhao ◽  
Caige Sun ◽  
Hanbin Liang ◽  
Ji Yang ◽  
...  
Keyword(s):  
Time Series ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Urban Expansion ◽  
Impervious Surface ◽  
Ecological Quality ◽  
Urban Center ◽  
Urban Centers ◽  
Rapid Urbanization

This study explored the model of urban impervious surface (IS) density, land surface temperature (LST), and comprehensive ecological evaluation index (CEEI) from urban centers to suburbs. The interrelationships between these parameters in Guangzhou from 1987 to 2019 were analyzed using time-series Landsat-5 TM (Thematic Mapper), Landsat-8 OLI (Operational Land Imager), and TIRS (Thermal Infrared Sensor) images. The urban IS densities were calculated in concentric rings using time-series IS fractions, which were used to construct an inverse S-shaped urban IS density function to depict changes in urban form and the spatio-temporal dynamics of urban expansion from the urban center to the suburbs. The results indicated that Guangzhou experienced expansive urban growth, with the patterns of urban spatial structure changing from a single-center to a multi-center structure over the past 32 years. Next, the normalized LST and CEEI in each concentric ring were calculated, and their variation trends from the urban center to the suburbs were modeled using linear and nonlinear functions, respectively. The results showed that the normalized LST had a gradual decreasing trend from the urban center to the suburbs, while the CEEI showed a significant increasing trend. During the 32-year rapid urban development, the normalized LST difference between the urban center and suburbs increased gradually with time, and the CEEI significantly decreased. This indicated that rapid urbanization significantly expanded the impervious surface areas in Guangzhou, leading to an increase in the LST difference between urban centers and suburbs and a deterioration in ecological quality. Finally, the potential interrelationships among urban IS density, normalized LST, and CEEI were also explored using different models. This study revealed that rapid urbanization has produced geographical convergence between several ISs, which may increase the risk of the urban heat island effect and degradation of ecological quality.

scholarly journals Temporal Variations and Associated Remotely Sensed Environmental Variables of Dengue Fever in Chitwan District, Nepal

2018 ◽  
Vol 7 (7) ◽  
pp. 275 ◽  
Author(s):  
Bipin Acharya ◽  
Chunxiang Cao ◽  
Min Xu ◽  
Laxman Khanal ◽  
Shahid Naeem ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Dengue Fever ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Environmental Variables ◽  
Vegetation Index ◽  
Generalized Additive Model ◽  
Additive Model ◽  
Remotely Sensed ◽  
Cross Correlation Analysis

Dengue fever is one of the leading public health problems of tropical and subtropical countries across the world. Transmission dynamics of dengue fever is largely affected by meteorological and environmental factors, and its temporal pattern generally peaks in hot-wet periods of the year. Despite this continuously growing problem, the temporal dynamics of dengue fever and associated potential environmental risk factors are not documented in Nepal. The aim of this study was to fill this research gap by utilizing epidemiological and earth observation data in Chitwan district, one of the frequent dengue outbreak areas of Nepal. We used laboratory confirmed monthly dengue cases as a dependent variable and a set of remotely sensed meteorological and environmental variables as explanatory factors to describe their temporal relationship. Descriptive statistics, cross correlation analysis, and the Poisson generalized additive model were used for this purpose. Results revealed that dengue fever is significantly associated with satellite estimated precipitation, normalized difference vegetation index (NDVI), and enhanced vegetation index (EVI) synchronously and with different lag periods. However, the associations were weak and insignificant with immediate daytime land surface temperature (dLST) and nighttime land surface temperature (nLST), but were significant after 4–5 months. Conclusively, the selected Poisson generalized additive model based on the precipitation, dLST, and NDVI explained the largest variation in monthly distribution of dengue fever with minimum Akaike’s Information Criterion (AIC) and maximum R-squared. The best fit model further significantly improved after including delayed effects in the model. The predicted cases were reasonably accurate based on the comparison of 10-fold cross validation and observed cases. The lagged association found in this study could be useful for the development of remote sensing-based early warning forecasts of dengue fever.

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.

scholarly journals Evaluation of Seven Atmospheric Profiles from Reanalysis and Satellite-Derived Products: Implication for Single-Channel Land Surface Temperature Retrieval

2020 ◽  
Vol 12 (5) ◽  
pp. 791 ◽  
Author(s):  
Jingjing Yang ◽  
Si-Bo Duan ◽  
Xiaoyu Zhang ◽  
Penghai Wu ◽  
Cheng Huang ◽  
...  
Keyword(s):  
Time Series ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Single Channel ◽  
Landsat 8 ◽  
Atmospheric Profiles ◽  
Atmospheric Profile

Land surface temperature (LST) is vital for studies of hydrology, ecology, climatology, and environmental monitoring. The radiative-transfer-equation-based single-channel algorithm, in conjunction with the atmospheric profile, is regarded as the most suitable one with which to produce long-term time series LST products from Landsat thermal infrared (TIR) data. In this study, the performances of seven atmospheric profiles from different sources (the MODerate-resolution Imaging Spectroradiomete atmospheric profile product (MYD07), the Atmospheric Infrared Sounder atmospheric profile product (AIRS), the European Centre for Medium-range Weather Forecasts (ECMWF), the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA2), the National Centers for Environmental Prediction (NCEP)/Global Forecasting System (GFS), NCEP/Final Operational Global Analysis (FNL), and NCEP/Department of Energy (DOE)) were comprehensively evaluated in the single-channel algorithm for LST retrieval from Landsat 8 TIR data. Results showed that when compared with the radio sounding profile downloaded from the University of Wyoming (UWYO), the worst accuracies of atmospheric parameters were obtained for the MYD07 profile. Furthermore, the root-mean-square error (RMSE) values (approximately 0.5 K) of the retrieved LST when using the ECMWF, MERRA2, NCEP/GFS, and NCEP/FNL profiles were smaller than those but greater than 0.8 K when the MYD07, AIRS, and NCEP/DOE profiles were used. Compared with the in situ LST measurements that were collected at the Hailar, Urad Front Banner, and Wuhai sites, the RMSE values of the LST that were retrieved by using the ECMWF, MERRA2, NCEP/GFS, and NCEP/FNL profiles were approximately 1.0 K. The largest discrepancy between the retrieved and in situ LST was obtained for the NCEP/DOE profile, with an RMSE value of approximately 1.5 K. The results reveal that the ECMWF, MERRA2, NCEP/GFS, and NCEP/FNL profiles have great potential to perform accurate atmospheric correction and generate long-term time series LST products from Landsat TIR data by using a single-channel algorithm.

scholarly journals Improving the Classification Accuracy of Annual Crops Using Time Series of Temperature and Vegetation Indices

2020 ◽  
Vol 12 (19) ◽  
pp. 3202
Author(s):  
Xinran Chen ◽  
Yulin Zhan ◽  
Yan Liu ◽  
Xingfa Gu ◽  
Tao Yu ◽  
...  
Keyword(s):  
Time Series ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Classification Accuracy ◽  
Spectral Feature ◽  
Temperature Index ◽  
Long Time Series ◽  
Ndvi Time Series ◽  
Long Time

Accurate cropland classification is important for agricultural monitoring and related decision-making. The commonly used input spectral features for classification cannot be employed to effectively distinguish crops that have similar spectro-temporal features. This study attempted to improve the classification accuracy of crops using both the thermal feature, i.e., the land surface temperature (LST), and the spectral feature, i.e., the normalized difference vegetation index (NDVI), for classification. To amplify the temperature differences between the crops, a temperature index, namely, the modified land surface temperature index (mLSTI) was built using the LST. The mLSTI was calculated by subtracting the average LST of an image from the LST of each pixel. To study the adaptability of the proposed method to different areas, three study areas were selected. A comparison of the classification results obtained using the NDVI time series and NDVI + mLSTI time series showed that for long time series from June to November, the classification accuracy when using the mLSTI and NDVI time series was higher (85.6% for study area 1 in California, 96.3% for area 2 in Kansas, and 91.2% for area 3 in Texas) than that when using the NDVI time series alone (82.0% for area 1, 94.7% for area 2, and 90.9% for area 3); the same was true in most of the cases when using the shorter time series. With the addition of the mLSTI time series, the shorter time series achieved higher classification accuracy, which is beneficial for timely crop identification. The sorghum and soybean crops, which exhibit similar NDVI feature curves in this study, could be better distinguished by adding the mLSTI time series. The results demonstrated that the classification accuracy of crops can be improved by adding mLSTI long time series, particularly for distinguishing crops with similar NDVI characteristics in a given study area.

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