Renata Libonati

An Algorithm for Burned Area Detection in the Brazilian Cerrado Using 4 µm MODIS Imagery

The Brazilian Cerrado is significantly affected by anthropic fires every year, which makes the region an important source of pyrogenic emissions. This study aims at generating improved 1 km monthly burned area maps for Cerrado based on remote-sensed information. The algorithm relies on a burn-sensitive vegetation index based on MODIS daily values of near and middle infrared reflectance and makes use of active fire detection from multiple sensors. Validation is performed using reference burned area (BA) maps derived from Landsat imagery. Results are also compared with MODIS standard BA products. A monthly BA database for the Brazilian Cerrado is generated covering the period 2005–2014. Estimated value of BA is 1.3 times larger than the value derived from reference data, making the product suitable for applications in fire emission studies and ecosystem management. As expected the intra and inter-annual variability of estimated BA over the Brazilian Cerrado is in agreement with the regime of precipitation. This work represents the first step towards setting up a regional database of BA for Brazil to be developed in the framework of BrFLAS, an R and D project in the areas of fire emissions and ecosystem management planning.

Validation of a temperature emissivity separation hybrid method from airborne hyperspectral scanner data and ground measurements in the SEN2FLEX field campaign

This paper presents an assessment of the performance of a hybrid method that allows a simultaneous retrieval of land‐surface temperature (LST) and emissivity (LSE) from remotely‐sensed data. The proposed method is based on a synergistic usage of the split‐window (SW) algorithm and the two‐temperature method (TTM) and combines the advantages of both procedures while mitigating their drawbacks. The method was implemented for thermal channels 76 (10.56 µm) and 78 (11.72 µm) of the Airborne Hyperspectral Scanner (AHS), which was flown over the Barrax test site (Albacete, Spain) in the second week of July 2005, within the framework of the Sentinel‐2 and Fluorescence Experiment (SEN2FLEX) field campaign. A set of radiometric measurements was performed in the thermal infrared region in coincidence with aircraft overpasses for different surface types, e.g. bare soil, water body, corn, wheat, grass. The hybrid method was tested and compared with a standard SW algorithm and the results obtained show that the hybrid method is able to provide better estimates of LST, with values of bias (RMSE) of the order of 0.8 K (1.9 K), i.e. about one third (one half) of the corresponding values of 2.7 K (3.4 K) that were obtained for bias (RMSE) when using the SW algorithm. These figures provide a sound indication that the developed hybrid method is particularly useful for surface and atmospheric conditions where SW algorithms cannot be accurately applied.

Future Projections of Fire Occurrence in Brazil Using EC-Earth Climate Model

Fire has a fundamental role in the Earth system as it influences global and local ecosystem patterns and processes, such as vegetation distribution and structure, the carbon cycle and climate. Since, in the global context, Brazil is one of the regions with higher fire activity, an assessment is here performed of the sensitivity of the wildfire regime in Brazilian savanna and shrubland areas to changes in regional climate during the 21st Century, for an intermediate scenario (RCP4.5) of climate change. The assessment is based on a spatial and temporal analysis of a meteorological fire danger index specifically developed for Brazilian biomes, which was evaluated based on regional climate simulations of temperature, relative humidity and precipitation using the Rossby Centre Regional Climate Model (RCA4) forced by the EC-Earth earth system model. Results show a systematic increase in the extreme levels of fire danger throughout the 21st Century that mainly results from the increase in maximum daily temperature, which rises by about 2 °C between 2005 and 2100. This study provides new insights about projected fire activity in Brazilian woody savannas associated to climate change and is expected to benefit the user community, from governmental policies to land management and climate researches.

Retrieving Middle-Infrared Reflectance Using Physical and Empirical Approaches: Implications for Burned Area Monitoring

A systematic comparison is carried out between retrieved values of middle-infrared (MIR) reflectance by means of the complete radiative transfer equation (RTE) and the simplified algorithm proposed by Kaufman and Remer in 1994 (KR94). The added value to be expected when using RTE is assessed both within and beyond the region where KR94 produces usable estimates of MIR reflectance, paying special attention to their application for discriminating burned areas (BAs) in tropical environments, where KR94 is the most common approach. For large values of land surface temperature (LST) and solar zenith angle (SZA), the retrieval of MIR reflectance based either on RTE or KR94 is an ill-posed problem, i.e., small perturbations due to sensor noise and uncertainties in atmospheric profiles and LST may induce large errors in the retrieved values. It is found that the RTE approach leads to better estimates in virtually all cases, with the exception of high values of LST and SZA, where results from KR94 are also not usable. Impacts on BA discrimination were finally evaluated using Moderate Resolution Imaging Spectroradiometer imagery showing a large fire event in southern Brazil. Synthetic values were generated, assuming a hot tropical environment, and MIR reflectance was retrieved using the two approaches. Whereas retrieved values of MIR reflectance via KR94 did not allow an effective discrimination between burned and unburned areas, those obtained via RTE have shown to be usable for BA monitoring, opening good perspectives for successful applications in hot tropical environments.

A User-Oriented Simplification of the (

Remote sensing from spaceborne sensors combining near- and middle-infrared information has proved to be an efficient means to monitor the effects of vegetation fires. Burn-sensitive spectral indices, such as the (V, W) index system, have been developed and successfully applied for burned area discrimination. The (V, W) index system provides useful capability to discriminate burned pixels, but the elaborate numerical computations involved are a major drawback in operational applications. This letter presents a simplified algorithm to compute the approximate values of indices V and W. The methodology developed is tested in a region located in the Brazilian Cerrado using remote-sensed data from the MODIS instrument. The simplification allows performing burned area discrimination with the same quality as the original algorithm. The methodology may be extended to other sensors and different combinations of bands and opens new perspectives to the generation of synergic longterm databases of burned area.

V,W

A procedure is presented that allows using information from the MODerate resolution Imaging Spectroradiometer (MODIS) sensor to improve the quality of emissivity maps for the Meteosat Second Generation/Spinning Enhanced Visible and Infrared Imager (SEVIRI) currently in use as input to a generalized split window (SW) algorithm for land-surface temperature (LST) retrievals in the operational chain of the Satellite Application Facility on Land Surface Analysis (LSA SAF). Information from MODIS is incorporated by means of linear regression models expressing emissivity in SEVIRI thermal-infrared channels as a linear combination of emissivities in MODIS bands. The linear models are applied to the MODIS emissivity product MOD11C3, and a comparison is performed with the operational LSA-SAF product. Special attention is devoted to the semiarid and arid regions of North Africa where emissivity is highly variable. When compared with the new emissivity maps, the LSA-SAF product displays more uniform emissivity values over these regions, leading to higher retrievals for all channels (bias around 0.03) except for IR3.9 (bias from -0.05 to -0.08). The root-mean-square error (RMSE) varies from 0.06 to 0.09 (0.02 to 0.03) for IR3.9 (IR10.8 and IR12.0) and is about 0.06 for IR8.7. The impact on LST is assessed by comparing the retrievals from a SW algorithm using as input the following: 1) the SEVIRI emissivity LSA-SAF product and 2) SEVIRI emissivity maps from MOD11C3. The uncertainty in the LSA-SAF emissivity product results into LST values with bias ranging from -0.4 to -1.0 K and RMSE around 1.6 K. The new emissivity maps based on MODIS data may be an alternative to the standard LSA-SAF emissivity product over semiarid and arid areas, which cover 26% of the land surfaces within the SEVIRI full disk.

) Burn-Sensitive Vegetation Index System

We used the Visible Infrared Imaging Radiometer Suite (VIIRS) active fire data (375 m spatial resolution) to automatically extract multispectral samples and train a One-Class Support Vector Machine for burned area mapping, and applied the resulting classification algorithm to 300-m spatial resolution imagery from the Project for On-Board Autonomy-Vegetation (PROBA-V). The active fire data were screened to prevent extraction of unrepresentative burned area samples and combined with surface reflectance bi-weekly composites to produce burned area maps. The procedure was applied over the Brazilian Cerrado savanna, validated with reference maps obtained from Landsat images and compared with the Collection 6 Moderate Resolution Imaging Spectrometer (MODIS) Burned Area product (MCD64A1) Results show that the algorithm developed improved the detection of small-sized scars and displayed results more similar to the reference data than MCD64A1. Unlike active fire-based region growing algorithms, the proposed approach allows for the detection and mapping of burn scars without active fires, thus eliminating a potential source of omission error. The burned area mapping approach presented here should facilitate the development of operational-automated burned area algorithms, and is very straightforward for implementation with other sensors.

Land-Surface Emissivity Retrieval in MSG–SEVIRI TIR Channels Using MODIS Data

Global temperatures have increased considerably over the last decades, directly impacting the number, intensity and duration of extreme events such as heat waves. Climate model projections accounting for anthropogenic factors indicate that deadly mega-heat waves are likely to become more frequent in the future. Although the atmospheric features and social-economic related impacts of heat waves have already been documented in various regions around the world, for other highly populated regions, such as the Metropolitan Region of Rio de Janeiro (MRRJ), a similar objective assessment is still needed. Heat waves directly impact the public health sector and particularly the less wealthy and elderly population groups. During February 2010, an elevated mortality peak occurred during a 8-day period (from 2 to 9 Feb 2010) characterized as a heat wave episode in MRRJ. A total excess of 737 deaths was recorded with the elderly group registering the highest mortality incidence. During this heat wave period, a quasi-stationary anticyclonic anomaly forced in altitude by a Rossby wave train was established over the south Brazilian coast. At the surface, the meteorological scenario from January 2010 to the heat wave period was marked by clear sky conditions, large precipitation deficits, and enhanced diabatic heating. During the heat wave period, warm and dry air masses were advected from interior regions towards the MRRJ, exacerbating temperature conditions by pronounced subsidence and adiabatic heating mechanisms. All these conditions contributed to pronounced positive temperature anomalies, reinforced by land-atmosphere feedbacks.

Burned Area Mapping in the Brazilian Savanna Using a One-Class Support Vector Machine Trained by Active Fires

An automated procedure is here presented that allows identifying and dating burned areas in Portugal using values of daily reflectance from near-infrared and middle-infrared bands, as obtained from the MODIS instrument. The algorithm detects persistent changes in monthly composites of the so-called (V,W) Burn-Sensitive Index and the day of maximum change in daily time series of W is in turn identified as the day of the burning event. The procedure is tested for 2005, the second worst fire season ever recorded in Portugal. Comparison between the obtained burned area map and the reference derived from Landsat imagery resulted in a Proportion Correct of 95.6%. Despite being applied only to the months of August and September, the algorithm is able to identify almost two-thirds of all scars that have occurred during the entire year of 2005. An assessment of the temporal accuracy of the dating procedure was also conducted, showing that 75% of estimated dates presented deviations between -5 and 5 days from dates of hotspots derived from the MODIS instrument. Information about location and date of burning events as provided by the proposed procedure may be viewed as complementary to the currently available official maps based on end-of-season Landsat imagery.