MOSEV: a global burn severity database  from MODIS (2000–2020)

Abstract. To make advances in the fire discipline, as well as in the study of CO2 emissions, it is of great interest to develop a global database with estimators of the degree of biomass consumed by fire, which is defined as burn severity. In this work we present the first global burn severity database (MOSEV database), which is based on Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance and burned area (BA) products from November 2000 to near real time. To build the database we combined Terra MOD09A1 and Aqua MYD09A1 surface reflectance products to obtain dense time series of the normalized burn ratio (NBR) spectral index, and we used the MCD64A1 product to identify BA and the date of burning. Then, we calculated for each burned pixel the difference of the NBR (dNBR) and its relativized version (RdNBR), as well as the post-burn NBR, which are the most commonly used burn severity spectral indices. The database also includes the pre-burn NBR used for calculations, the date of the pre- and post-burn NBR, and the date of burning. Moreover, in this work we have compared the burn severity metrics included in MOSEV (dNBR, RdNBR and post-burn NBR) with the same ones obtained from Landsat-8 scenes which have an original resolution of 30 m. We calculated the Pearson's correlation coefficients and the significance of the relationships using 13 pairs of Landsat scenes randomly distributed across the globe, with a total BA of 6904 km2 (n=32 163). Results showed that MOSEV and Landsat-8 burn severity indices are highly correlated, particularly the post-burn NBR (R=0.88; P<0.001), and dNBR (R=0.74; P<0.001) showed stronger relationships than RdNBR (R=0.42; P<0.001). Differences between MOSEV and Landsat-8 indices are attributable to variability in reflectance values and to the different temporal resolution of both satellites (MODIS: 1–2 d; Landsat: 16 d). The database is structured according to the MODIS tiling system and is freely downloadable at https://doi.org/10.5281/zenodo.4265209 (Alonso-González and Fernández-García, 2020).

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MOSEV: a global burn severity database from MODIS (2000–2020)

10.5194/essd-2020-341 ◽

2020 ◽

Author(s):

Esteban Alonso-González ◽

Víctor Fernández-García

Keyword(s):

Correlation Coefficients ◽

Burn Severity ◽

Burned Area ◽

Landsat 8 ◽

Surface Reflectance ◽

Global Database ◽

Time To Build ◽

Moderate Resolution Imaging Spectroradiometer ◽

The Difference ◽

Highly Correlated

Abstract. To advance in the fire discipline as well as in the study of CO2 emissions it is of great interest to develop a global database with estimators of the degree of biomass consumed by fire, which is defined as burn severity. In this work we present the first global burn severity database (MOSEV database), which is based on Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance and burned area (BA) products since November 2000 to near real time. To build the database we combined Terra MOD09A1 and Aqua MYD09A1 surface reflectance products to obtain dense time series of the Normalized Burn Ratio (NBR) spectral index, and we used the MCD64A1 product to identify BA and the date of burning. Then, we calculated for each burned pixel the difference of the NBR (dNBR), and its relativized version (RdNBR), as well as the post-burn NBR which are the most commonly used burn severity spectral indices. The database also includes the pre-burn NBR used for calculations, the date of the pre- and post-burn NBR and the date of burning. Moreover, in this work we have compared the burn severity metrics included in MOSEV (dNBR, RdNBR and post-burn NBR) with the same ones obtained from Landsat-8 scenes, which have an original resolution of 30 m. We calculated the Pearson's correlation coefficients and the significance of the relationships using 13 pairs of Landsat scenes randomly distributed across the globe, with a total BA of 6,904 km2 (n = 32,163). Results showed that MOSEV and Landsat-8 burn severity indices are highly correlated, particularly the post-burn NBR (R = 0.88; P

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Determination of land surface reflectance using the AATSR dual-view capability

Atmospheric Measurement Techniques Discussions ◽

10.5194/amtd-7-7451-2014 ◽

2014 ◽

Vol 7 (7) ◽

pp. 7451-7494

Author(s):

L. Sogacheva ◽

P. Kolmonen ◽

T. H. Virtanen ◽

E. Rodriguez ◽

A.-M. Sundström ◽

...

Keyword(s):

Land Surface ◽

Atmospheric Correction ◽

Correlation Coefficients ◽

Absolute Error ◽

Surface Reflectance ◽

Imaging Spectrometer ◽

Comparison Results ◽

Moderate Resolution ◽

The Difference ◽

Along Track

Abstract. In this study, a method is presented to retrieve the surface reflectance using reflectance measured at the top of the atmosphere for the two views provided by the Along-Track Scanning Radiometer (AATSR). In the first step, the aerosol optical depth (AOD) is obtained using the AATSR dual view algorithm (ADV) by eliminating the effect of the surface on the measured radiances. Hence the AOD is independent of surface properties and can thus be used in the second step to provide the aerosol part of the atmospheric correction which is needed for the surface reflectance retrieval. The method is applied to provide monthly maps of both AOD and surface reflectance at two wavelengths (555 and 659 nm) for the whole year of 2007. The results are validated vs. surface reflectance provided by the AERONET-based Surface Reflectance Validation Network (ASRVN). Correlation coefficients are 0.8 and 0.9 for 555 and 659 nm, respectively. The standard deviation is 0.001 for both wavelengths and the absolute error is less than 0.02. Pixel-by-pixel comparison with MODIS (MODerate resolution Imaging Spectrometer) monthly averaged surface reflectances show a good correlation (0.91 and 0.89 for 555 and 659 nm, respectively) with some (up to 0.05) overestimation by ADV over bright surfaces. The difference between the ADV and MODIS retrieved surface reflectance is smaller than ±0.025 for 68.3% of the collocated pixels at 555 nm and 79.9% of the collocated pixels at 659 nm. An application of the results over Australia illustrates the variation of the surface reflectances for different land cover types. The validation and comparison results suggest that the algorithm can be successfully used for the both AATSR and ATSR-2 (which has characteristics similar to AATSR) missions, which together cover 17 years period of measurements (1995–2012), as well as a prototype for The Sea and Land Surface Temperature Radiometer (SLSTR) to be launched in 2015 onboard the Sentinel-3 satellite.

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FUZZY LOGIC BASED BURNED SEVERITY CLASSIFICATION AND MAPPING WITH LANDSAT-8 DATA

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-2-w16-259-2019 ◽

2019 ◽

Vol XLII-2/W16 ◽

pp. 259-265

Author(s):

B. Valipour Shokouhi ◽

M. Eslami

Keyword(s):

Fuzzy Logic ◽

Urban Infrastructure ◽

Training Data ◽

Burn Severity ◽

Burned Area ◽

Landsat 8 ◽

Area Index ◽

Average Accuracy ◽

Multi Temporal ◽

Normalized Burn Ratio

Abstract. Wildfire has a strong effect on both land use and land cover so that every year, thousands of hectares of forests, farms, and urban infrastructure are destroyed. Mapping and estimation of damages are crucial for planning and decision making. The aim of this study is classification and mapping burn severity using multi-temporal Landsat data and well-known burn severity indices including Normalized Burn Ratio (NBR) and Burned Area Index (BAI) calculated for pre- and post- Landsat 8 images. Subtracted images such as dNBR (Difference Normalized Burn Ratio), RBR (Relativized Burn Ratio) and dBAI (Difference Burned Area Index) were produced on the bases of indices as classification input. Among classification methods, the fuzzy supervised classification was utilized with three classes. The result shows the strong performance of the Fuzzy Logic system (FLS) in the detection of the area with the limited number of training data so that the average accuracy of the classes is 85%; plus, from the human logic perspective, the result was meaningful so that recognition of the features and changes visually were understandable.

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LANDSAT-BASED DETECTION AND SEVERITY ANALYSIS OF BURNED SUGARCANE PLOTS IN TARLAC, PHILIPPINES USING DIFFERENCED NORMALIZED BURN RATIO (dNBR)

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-4-w1-173-2016 ◽

2016 ◽

Vol XLII-4/W1 ◽

pp. 173-179 ◽

Cited By ~ 2

Author(s):

A. B. Baloloy ◽

A. C. Blanco ◽

B. S. Gana ◽

R. C. Sta. Ana ◽

L. C. Olalia

Keyword(s):

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

The Philippines ◽

Local Economy ◽

Burn Severity ◽

Burned Area ◽

Landsat 8 ◽

Potential Yield ◽

Differenced Normalized Burn Ratio ◽

Normalized Burn Ratio

The Philippines has a booming sugarcane industry contributing about PHP 70 billion annually to the local economy through raw sugar, molasses and bioethanol production (SRA, 2012). Sugarcane planters adapt different farm practices in cultivating sugarcane, one of which is cane burning to eliminate unwanted plant material and facilitate easier harvest. Information on burned sugarcane extent is significant in yield estimation models to calculate total sugar lost during harvest. Pre-harvest burning can lessen sucrose by 2.7% - 5% of the potential yield (Gomez, et al 2006; Hiranyavasit, 2016). This study employs a method for detecting burn sugarcane area and determining burn severity through Differenced Normalized Burn Ratio (dNBR) using Landsat 8 Images acquired during the late milling season in Tarlac, Philippines. Total burned area was computed per burn severity based on pre-fire and post-fire images. Results show that 75.38% of the total sugarcane fields in Tarlac were burned with post-fire regrowth; 16.61% were recently burned; and only 8.01% were unburned. The monthly dNBR for February to March generated the largest area with low severity burn (1,436 ha) and high severity burn (31.14 ha) due to pre-harvest burning. Post-fire regrowth is highest in April to May when previously burned areas were already replanted with sugarcane. The maximum dNBR of the entire late milling season (February to May) recorded larger extent of areas with high and low post-fire regrowth compared to areas with low, moderate and high burn severity. Normalized Difference Vegetation Index (NDVI) was used to analyse vegetation dynamics between the burn severity classes. Significant positive correlation, rho = 0.99, was observed between dNBR and dNDVI at 5% level (p = 0.004). An accuracy of 89.03% was calculated for the Landsat-derived NBR validated using actual mill data for crop year 2015-2016.

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IDENTIFICATION OF BURNED AREAS BY SPECIAL INDEX IN A CERRADO REGION OF THE STATE OF TOCANTINS, BRAZIL

FLORESTA ◽

10.5380/rf.v48i4.57362 ◽

2018 ◽

Vol 48 (4) ◽

pp. 553

Author(s):

Ingridy Mikaelly Pereira Sousa ◽

Edmar Vinicius de Carvalho ◽

Antonio Carlos Batista ◽

Igor Eloi Silva Machado ◽

Maira Elisa Ferreira Tavares ◽

...

Keyword(s):

Near Infrared ◽

Short Wave ◽

Burned Area ◽

Landsat 8 ◽

Visual Interpretation ◽

Burned Areas ◽

Area Of Interest ◽

Before And After ◽

Kappa Value ◽

The Difference

Obtaining information on burned areas has been studied and improved in the last decades, and the biggest question is the acquisition of consistent and detailed information about the occurrence of burnings in a simple and effective way. In view of this, remote sensing is a very interesting tool because it allows obtaining information in large areas of difficult access. The identification of areas burned by orbital data is directly related to their spectral behavior. The objective of this study was to analyze the performance of spectral indices in the identification of burned area in OLI/Landsat-8 satellite images. The indices for the before and after fire images were calculated using bands of red and near infrared: NDVI, MSAVI, SAVI, and GEMI, and bands of near infrared and short wave infrared: NBR, BAIMmod, and MIRBImod. The difference between pre and post-fire index was also calculated: dNDVI, dMSAVI, dSAVI, dGEMI, dNBR, dBAIMmod, and dMIRBImod. From these indices, six different compositions (RGB) were created and later they were segmented and classified in a non-supervised way and soon after made the extraction of the area of interest. The results of this classification were validated with the reference data obtained through the visual interpretation of the image. The methods had shown a good quality of classification, with a percentage of accuracy ranging from 85.54 to 92.46% and Kappa value of 0.70 to 0.89. The best method was the dNBR, NBRpost-fire, and dMIRBImod indices in the RGB composite.

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A multi-angle aerosol optical depth retrieval algorithm for geostationary satellite data over the United States

Atmospheric Chemistry and Physics ◽

10.5194/acp-11-11977-2011 ◽

2011 ◽

Vol 11 (23) ◽

pp. 11977-11991 ◽

Cited By ~ 27

Author(s):

H. Zhang ◽

A. Lyapustin ◽

Y. Wang ◽

S. Kondragunta ◽

I. Laszlo ◽

...

Keyword(s):

Aerosol Optical Depth ◽

Optical Depth ◽

Atmospheric Correction ◽

Correlation Coefficients ◽

The United States ◽

Retrieval Algorithm ◽

High Temporal Resolution ◽

Surface Reflectance ◽

Moderate Resolution Imaging Spectroradiometer ◽

Polar Orbiting Satellite

Abstract. Aerosol optical depth (AOD) retrievals from geostationary satellites have high temporal resolution compared to the polar orbiting satellites and thus enable us to monitor aerosol motion. However, current Geostationary Operational Environmental Satellites (GOES) have only one visible channel for retrieving aerosols and hence the retrieval accuracy is lower than those from the multichannel polar-orbiting satellite instruments such as the Moderate Resolution Imaging Spectroradiometer (MODIS). The operational GOES AOD retrieval algorithm (GOES Aerosol/Smoke Product, GASP) uses 28-day composite images from the visible channel to derive surface reflectance, which can produce large uncertainties. In this work, we develop a new AOD retrieval algorithm for the GOES imager by applying a modified Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm. The algorithm assumes the surface Bidirectional Reflectance Distribution Function (BRDF) in the channel 1 of GOES is proportional to seasonal average MODIS BRDF in the 2.1 μm channel. The ratios between them are derived through time series analysis of the GOES visible channel images. The results of AOD and surface reflectance retrievals are evaluated through comparisons against those from Aerosol Robotic Network (AERONET), GASP, and MODIS. The AOD retrievals from the new algorithm demonstrate good agreement with AERONET retrievals at several sites across the US with correlation coefficients ranges from 0.71 to 0.85 at five out of six sites. At the two western sites Railroad Valley and UCSB, the MAIAC AOD retrievals have correlations of 0.8 and 0.85 with AERONET AOD, and are more accurate than GASP retrievals, which have correlations of 0.7 and 0.74 with AERONET AOD. At the three eastern sites, the correlations with AERONET AOD are from 0.71 to 0.81, comparable to the GASP retrievals. In the western US where surface reflectance is higher than 0.15, the new algorithm also produces larger AOD retrieval coverage than both GASP and MODIS.

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Determination of land surface reflectance using the AATSR dual-view capability

Atmospheric Measurement Techniques ◽

10.5194/amt-8-891-2015 ◽

2015 ◽

Vol 8 (2) ◽

pp. 891-906 ◽

Cited By ~ 6

Author(s):

L. Sogacheva ◽

P. Kolmonen ◽

T. H. Virtanen ◽

E. Rodriguez ◽

A.-M. Sundström ◽

...

Keyword(s):

Land Surface ◽

Atmospheric Correction ◽

Correlation Coefficients ◽

Absolute Error ◽

Surface Reflectance ◽

Imaging Spectrometer ◽

Comparison Results ◽

Moderate Resolution ◽

The Difference ◽

Along Track

Abstract. In this study, a method is presented to retrieve the surface reflectance using the radiances measured at the top of the atmosphere for the two views provided by the Advanced Along-Track Scanning Radiometer (AATSR). In the first step, the aerosol optical depth (AOD) is obtained using the AATSR dual-view algorithm (ADV) by eliminating the effect of the surface on the measured radiances. Hence the AOD is independent of surface properties and can thus be used in the second step to provide the aerosol part of the atmospheric correction which is needed for the surface reflectance retrieval. The method is applied to provide monthly maps of both AOD and surface reflectance at two wavelengths (555 and 659 nm) for the whole year of 2007. The results are validated versus surface reflectance provided by the AERONET-based Surface Reflectance Validation Network (ASRVN). Correlation coefficients are 0.8 and 0.9 for 555 and 659 nm, respectively. The standard deviation is 0.001 for both wavelengths and the absolute error is less than 0.02. Pixel-by-pixel comparison with MODIS (Moderate Resolution Imaging Spectrometer) monthly averaged surface reflectances show a good correlation (0.91 and 0.89 for 555 and 659 nm, respectively) with somewhat higher values (up to 0.05) obtained by ADV over bright surfaces. The difference between the ADV- and MODIS-retrieved surface reflectances is smaller than ±0.025 for 68.3% of the collocated pixels at 555 nm and 79.9% of the collocated pixels at 659 nm. An application of the results over Australia illustrates the variation in the surface reflectances for different land cover types. The validation and comparison results suggest that the algorithm can be successfully used for both the AATSR and ATSR-2 (which has characteristics similar to AATSR) missions, which together cover a 17-year period of measurements (1995–2012), as well as a prototype for the Sea and Land Surface Temperature Radiometer (SLSTR) planned to be launched in the fall of 2015 onboard the Sentinel-3 satellite.

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Contrasting the Co-variability of Daytime Cloud and Precipitation over Tropical Land and Ocean

10.5194/acp-2017-612 ◽

2017 ◽

Author(s):

Daeho Jin ◽

Lazaros Oreopoulos ◽

Dongmin Lee ◽

Nayeong Cho ◽

Jackson Tan

Keyword(s):

Correlation Coefficients ◽

Tropical Rainfall Measuring Mission ◽

Cloud Type ◽

Convective Systems ◽

Low Cloud ◽

Moderate Resolution Imaging Spectroradiometer ◽

The Difference ◽

Histogram Bins ◽

Positive Correlations ◽

The Relationship

Abstract. The co-variability of cloud and precipitation in the extended tropics (35° N–35° S) is investigated using contemporaneous datasets for a 13-year period. The goal is to quantify the relationship between cloud types and precipitation events of particular strength. Particular attention is paid to whether the relationship exhibits different characteristics over tropical land and ocean. A major analysis metric is correlation coefficients between fractions of individual cloud types and frequencies within precipitation histogram bins that have been matched in time and space. The cloud type fractions are derived from Moderate Resolution Imaging Spectroradiometer (MODIS) joint histograms of cloud top pressure and cloud optical thickness in one-degree grid cells, and the precipitation frequencies come from the Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) dataset aggregated to the same grid. It is found that the strongest coupling (positive correlation) between clouds and precipitation occurs for cumulonimbus clouds and heaviest rainfall over ocean. While the same cloud type and rainfall bin are also best correlated over land compared to other combinations, the correlation magnitude over land is weaker than over ocean. The difference is attributed to the greater size of convective systems over ocean. It is also found both over ocean and land that the anti-correlation of strong precipitation with weak (i.e., thin and/or low) cloud types is of greater absolute strength than positive correlations between weak cloud types and weak precipitation. Cloud type co-occurrence relationships explain some of the cloud-precipitation anti-correlations. Couplings between weaker rainfall and clouds are also distinct in ocean vs. land, with precipitation predictability when cloud type is known being quite poor in general, particularly over land.

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Operational Validation of Space-based Albedo Products from Upscaled Tower-based Albedometer Measurements

10.5194/egusphere-egu2020-2731 ◽

2020 ◽

Author(s):

Rui Song ◽

Jan-Peter Muller

Keyword(s):

High Resolution ◽

Surface Albedo ◽

Atmospheric Correction ◽

Ground Level ◽

Correction Method ◽

Landsat 8 ◽

Surface Reflectance ◽

Moderate Resolution Imaging Spectroradiometer ◽

Land Surfaces ◽

Sentinel 2

Surface albedo is a fundamental radiative parameter which controls the Earth&#8217;s energy budget by determining the amount of solar radiation which is either absorbed by the surface or reflected back to atmosphere. Satellite observations have long been used to capture the temporal and spatial variations of surface albedo because of their repeated global coverage. In this work, a new method of upscaling surface albedo from ground level footprints of a few tens of metres to coarse satellite scales (&#8776;1km) is reported [1]. Tower-mounted albedometer measurements are upscaled and used to validate global space-based albedo products, including Copernicus Global Land Service (CGLS) 1km albedo data (from Proba-V and previously form VEGETATION-2), MODerate resolution Imaging Spectroradiometer (MODIS) 500m albedo data, and Multi-angle Imaging SpectroRadiometer (MISR) 1.1km albedo data. MODIS albedo retrievals show the closest agreement with tower measurements, followed by the MISR retrievals, and then followed by the CGLS retrievals. The upscaling method uses high-resolution surface reflectance retrievals (from Landsat-8, Sentinel-2) to fill the spatial gaps between the albedometer&#8217;s field-of-view (FoV) and coarse satellite scales. High-resolution surface albedo products are generated by combining high-resolution surface reflectance data and MODIS bi-directional reflectance distribution function (BRDF) climatology data. This upscaling framework also uses a novel Sensor Invariant Atmospheric Correction (SIAC) method [2] to improve the accuracy of upscaled tower albedo values. Total uncertainties of upscaled albedo products are estimated by considering uncertainties from both the tower albedometer raw measurements and SIAC atmospheric corrections. This surface albedo upscaling method can be used over both heterogenous and homogenous land surfaces, and has been examined at the SURFRAD, BSRN and FLUXNET tower sites.Keywords: surface albedo, upscale, CGLS, MODIS, MISR, SIAC[1] Song, R.; Muller, J.-P.; Kharbouche, S.; Woodgate, W. Intercomparison of Surface Albedo Retrievals from MISR, MODIS, CGLS Using Tower and Upscaled Tower Measurements. Remote Sens. 2019, 11, 644, doi:10.3390/rs11060644.[2] Yin, F., Lewis, P. E., Gomez-Dans, J., & Wu, Q. A sensor-invariant atmospheric correction method: application to Sentinel-2/MSI and Landsat 8/OLI. EarthArXiv 2019, https://doi.org/10.31223/osf.io/ps957.

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Harvest chronological planning using a method based on satellite-derived vegetation indices and artificial neural networks

Spanish Journal of Agricultural Research ◽

10.5424/sjar/2019173-14357 ◽

2019 ◽

Vol 17 (3) ◽

pp. e0206

Author(s):

Sepideh Taghizadeh ◽

Hossin Navid ◽

Reza Adiban ◽

Yasser Maghsodi

Keyword(s):

Vegetation Indices ◽

Maximum Yield ◽

Correlation Coefficients ◽

Development Stage ◽

Growth Stages ◽

Landsat 8 ◽

Artificial Neural ◽

The Difference ◽

Artificial Neural Network Ann ◽

Hidden Layer

Aim of study: Wheat appropriate harvest date (WAHD) is an important factor in farm monitoring and harvest campaign schedule. Satellite remote sensing provides the possibility of continuous monitoring of large areas. In this study, we aimed to investigate the strength of vegetation indices (VIs) derived from Landsat-8 for generating the harvest schedule regional (HSR) map using Artificial Neural Network (ANN), a robust prediction tool in the agriculture sector.Area of study: Qorveh plain, Iran.Material and methods: During 2015 and 2016, a total of 100 plots was selected. WAHD was determined by sampling of plots and specifying wheat maximum yield for each plot. The strength of eight Landsat-8 derived spectral VIs (NDVI, SAVI, GreenNDVI, NDWI, EVI, EVI2, CVI and CIgreen) was investigated during wheat growth stages using correlation coefficients between these VIs and observed WAHD. The derived VIs from the required images were used as inputs of ANNs and WAHD was considered as output. Several ANN models were designed by combining various VIs data.Main results: The temporal stage in agreement with dough development stage had the highest correlation with WAHD. The optimum model for predicting WAHD was a Multi-Layer Perceptron model including one hidden layer with ten neurons in it when the inputs were NDVI, NDWI, and EVI2. To evaluate the difference between measured and predicted values of ANNs, MAE, RMSE, and R2 were calculated. For the 3-10-1 topology, the value of R2 was estimated 0.925. A HSR map was generated with RMSE of 0.86 days.Research highlights: Integrated satellite-derived VIs and ANNs is a novel and remarkable methodology to predict WAHD, optimize harvest campaign scheduling and farm management.

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