Fire and smoke observed from the Earth Observing System MODIS instrument--products,validation, and operational use

The Moderate Resolution Imaging Spectroradiometer (MODIS) sensor, launched on the National Aeronautics and Space Administration Terra satellite at the end of 1999, was designed with 36 spectral channels for a wide array of land, ocean, and atmospheric investigations. MODIS has a unique ability to observe fires, smoke, and burn scars globally. Its main fire detection channels saturate at high brightness temperatures: 500 K at 4 µm and 400 K at 11 µm, which can only be attained in rare circumstances at the 1 km fire detection spatial resolution. Thus, unlike other polar orbiting satellite sensors with similar thermal and spatial resolutions, but much lower saturation temperatures (e.g. Advanced Very High Resolution Radiometer and Along Track Scanning Radiometer), MODIS can distinguish between low intensity ground surface fires and high intensity crown forest fires. Smoke column concentration over land is for the first time being derived from the MODIS solar channels, extending from 0.41 µm to 2.1 µm. The smoke product has been provisionally validated both globally and regionally over southern Africa and central and south America. Burn scars are observed from MODIS even in the presence of smoke, using the 1.2 to 2.1 µm channels. MODIS burned area information is used to estimate pyrogenic emissions. A wide range of these fire and related products and validation are demonstrated for the wild fires that occurred in northwestern USA in Summer 2000. The MODIS rapid response system and direct broadcast capability is being developed to enable users to obtain and generate data in near real-time. It is expected that health and land management organizations will use these systems for monitoring the occurrence of fires and the dispersion of smoke within two to six hours after data acquisition.     

Using multiple endmember spectral mixture analysis to retrieve subpixel fire properties from MODIS

The Moderate-Resolution Imaging Spectroradiometer (MODIS) sensors on NASA's Terra and Aqua satellites image most of the Earth multiple times each day, providing useful data on fires that cannot be practically acquired using other means. Unfortunately, current fire products from MODIS and other sensors leave large uncertainties in measurements of fire sizes and temperatures, which strongly influence how fires spread, the amount and chemistry of their gas and aerosol emissions, and their impacts on ecosystems. In this study, we use multiple endmember spectral mixture analysis (MESMA) to retrieve subpixel fire sizes and temperatures from MODIS, possibly overcoming some limitations of existing methods for characterizing fire intensities such as estimating the fire radiative power (FRP). MESMA is evaluated using data from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) to assess the performance of FRP and MESMA retrievals of fire properties from a simultaneously acquired MODIS image, for a complex of fires in Ukraine from August 21, 2002. The MESMA retrievals of fire size described in this paper show a slightly stronger correlation than FRP does to fire pixel counts from the coincident ASTER image. Prior to this work, few studies, if any, had used MESMA for retrieving fire properties from a broad-band sensor like MODIS, or compared MESMA to higher-resolution fire data or other measures of fire properties like FRP. In the future, MESMA retrievals could be useful for fire spread modeling and forecasting, reducing hazards that fires pose to property and health, and enhancing scientific understanding of fires and their effects on ecosystems and atmospheric composition.     

Study of the impact of wildfire emissions,through MOPITT total CO column,at different spatial scales

The estimation of total carbon monoxide (CO) column has been identified as essential to improve our understanding of its role in the global climate system. The Earth Observing System (EOS) Science Steering Committee and the World Meteorological Organization (WMO) has suggested that a satellite-borne CO sensor, which would operate for extended periods, would be useful for that task. Measurements of Pollution in the Troposphere (MOPITT), on board the Terra spacecraft, is a correlation radiometer for estimating CO vertical profiles and total CO column in the lower atmosphere, through the thermal radiance received in the 4.7 μm spectral region. One of the main sources of CO in the atmosphere is the fires and global biomass-burning emissions that are produced when combustion is not complete, especially in the smouldering phase. This article presents a methodology based on a Fourier technique and spatial analysis in order to estimate the total CO column contribution of wildfires at three different spatial scales. First, in a seasonal study, a Mediterranean country (Spain) is selected, and the main regions affected by fire during four years in the summer season are analysed. Second, in order to estimate CO emissions at a local scale, a large fire (in Spain) and a cluster of fires (in North China) are selected. Third, for a global study at large scale and for comparing with CO and carbon dioxide (CO₂) data from Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY), locations in North China, equatorial Africa, and Amazonia are selected. Results obtained show that MOPITT data are suitable to assess and to discriminate CO emissions at local spatial scales. Finally, a qualitative agreement between CO behaviour obtained by MOPITT and CO and CO₂ obtained by SCIAMACHY is found.     

Integration of geostationary FRP and polar-orbiter burned area datasets for an enhanced biomass burning inventory

Earth Observation (EO) sensors play an important role in quantifying biomass burning related fuel consumption and carbon emissions, and capturing their spatial and temporal dynamics. Typically, biomass burning emissions inventories are developed by exploiting either burned area (BA) or active fire (AF) measures of fire radiative energy (FRE). These approaches have both advantages and limitations. For example, methods based on burned area data typically require hard-to-obtain estimates of fuel load and combustion completeness, and the accuracy of the BA algorithm may deteriorate for small fires or those in densely forested terrain. Conversely, ‘raw’ FRE-based methods are typically low-biassed due to the non-detection of low intensity fires, and are also hindered by cloud cover. Here we develop a methodology integrating these two EO data types to deliver a high temporal resolution emissions inventory, maximising the benefit of each data type without requiring additional information. We focus on Africa, the most fire affected continent, and combine daily FRE observations provided by Meteosat Spinning Enhanced Visible and Infrared Imager (SEVIRI) with BA measures delivered by Moderate Resolution Imaging Spectroradiometer (MODIS). For individual fires detected by both types of data, we estimate fuel consumption per unit area (FC_A: g·m^− 2) via the ratio of FRE-derived total fuel consumption (FC_T) to BA. These values are then extrapolated to fires that were mapped using the BA data but which remained undetected in the SEVIRI AF product, thus correcting for the ‘low spatial resolution bias’ inherent in geostationary AF datasets. Calculated daily continental scale FC_T for Africa varies between 0.3 and 20 Tg for the period February 2004-January 2005. We estimate annual continental FC_T to be 1418 Tg, far closer to the 2272 Tg provided by the widely used Global Fire Emissions Database (version 3; GFEDv3) than is obtained when using ‘raw’ FRE data alone. This synergistic approach has substantially narrowed the gap between GFEDv3 and FRE-derived emissions inventories, whilst the geostationary FRP observations offer the advantage that the daily emissions estimates can be distributed more accurately over the diurnal fire cycle if required for linking to atmospheric transport models.     

Assessing two large area burnt area products across Australian Southern Forests

Burnt area is a critical parameter for estimating emissions of greenhouse gases associated with biomass burning. Several burnt area products (BAPs) derived from Earth Observation satellites/sensors have been released; these are based on different spatial resolutions and derived using different methodologies so that accuracies can vary amongst them. This study validates a global (MODIS) and a national (AVHRR) BAP across Australian southern forests using two reference datasets: state fire histories (SFHs) from 2000 to 2013 and a forest cover map derived through high resolution air photo interpretation (API). The spatial and temporal agreement between fires in the BAPs and reference SFH were analysed based on 2610 sample points representative of Australian southern forest types (successful detection was evaluated according to fire type: planned burn vs. wildfire, size of fire, and land tenure). Results show that both BAPs were most successful when identifying large wildfires (>5000 ha). Overall accuracy for AVHRR and MODIS was 73.9% and 62.5%, respectively. When compared to the API derived forest cover map as reference dataset, both products achieved higher overall accuracies (94.1% for AVHRR and 87.1% for MODIS); an expected result given that the fires detected in this dataset are known to be observable using Earth observation data. But regardless of reference dataset, the AVHRR BAP which is tailored to Australian conditions achieved better results than the MODIS global BAP. Also, the AVHRR archive in Australia goes back to 1988, which is an important consideration for calculating wildfire history for greenhouse gas accounting.     

Multi year satellite remote sensing of particulate matter air quality over Sydney,Australia

Particulate matter (PM) air‐quality information is usually derived from ground‐based instruments. These measurements, while valuable, are not well suited to provide air‐quality information over large spatial scales. In this study, using 4 years of satellite aerosol optical thickness (AOT) at 0.55 µm derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) on board NASA's Terra and Aqua satellites, we present a multi‐year air analysis of PM air quality over Sydney, Australia. We then compare the satellite data with PM_2.5 mass concentration measurements from six ground‐based stations in the area. Our results indicate significant diurnal variations and an overall increase in PM_2.5 during Southern Hemisphere spring and summer seasons due to bush fires. The air quality in Sydney, Australia is good throughout the year except during major bushfires when PM_2.5 mass loading can increase from normal (<20 µg m^?3) to unhealthy conditions (>70 µg m^?3). The satellite data also show corresponding AOT changes from less than 0.1 to greater than 1.0 during bushfire events. We conclude that satellite data are an excellent tool for studying PM air quality over large areas, especially when ground measurements are not available. While this is the first multi‐year combined satellite and ground‐based air quality analysis over Sydney, ancillary information from lidars, sun photometers, and size‐resolved chemistry measurements will further enhance our capability to monitor and forecast air quality in and around Sydney.     

Estimation of particulate matter from satellite- and ground-based observations over Hyderabad,India

Long-term trends in surface-level particulate matter of dynamic diameter ≤2 µm (PM₂) in regard to air quality observations over Greater Hyderabad Region (GHR), India are estimated by the synergy of ground-based measurements and satellite observations during the period 2001–2013 (satellite) and July 2009–Dec 2013 (ground-based). Terra Moderate Resolution Imaging Spectroradiometer (MODIS)-derived aerosol optical thickness (AOT) (MODIS-AOTs) was validated against that measured from Microtops-II Sunphotometer (MTS) AOTs (MTS-AOTs) and then utilized to estimate surface-level PM₂ concentrations over GHR using regression analysis between MODIS-AOTs, MTS-AOTs, and measured PM₂. In general, the MODIS-estimated PM₂ concentrations fell within the uncertainty of the measurements, thus allowing the estimate of PM₂ from MODIS, although in some cases they differed significantly due to vertical heterogeneity in aerosol distribution and the presence of distinct elevated aerosol layers of different origin and characteristics. Furthermore, significant spatial and temporal heterogeneity in the AOT and PM₂ estimates is observed in urban environments, especially during the pre-monsoon and monsoon seasons, which reduces the accuracy of the PM₂ estimates from MODIS. The estimates of PM₂ using MTS or MODIS-AOT exhibit a root mean square deference (RMSD) of about 8–16% against measured PM₂ on a seasonal basis. Furthermore, a tendency of increasing PM₂ concentrations is observed, which however is difficult to quantify for urban areas due to uncertainties in PM₂ estimations and gaps in the data set. Examination of surface and columnar aerosol concentrations, along with meteorological parameters from radiosonde observations on certain days, reveals that changes in local emissions and boundary-layer dynamics, and the presence or arrival of distinct aerosol plumes aloft, are major concerns in the accurate estimation of PM₂ from MODIS, while the large spatial distribution of aerosol and pollutants in the urban environment makes such estimates a considerable challenge.     

Power law correlations in time series of wild-land and forest fires in Brazil

We study temporal correlations in daily hot pixel time series detected in Brazil in the period 1998–2007, using detrended fluctuation analysis (DFA). We find that power law correlations decay following two distinct scaling regimes with exponents a ₁ ? 0.8 and a ₂ ? 1.3, with a crossover point at approximately 45 days. These results agree with previous studies of burned area time series of forest fires in other regions of the world and should be taken into account when modelling wild-land and forest fires in Brazil.     

Assessment of tropical forest degradation by selective logging and fire using Landsat imagery

Many studies have assessed the process of forest degradation in the Brazilian Amazon using remote sensing approaches to estimate the extent and impact by selective logging and forest fires on tropical rain forest. However, only a few have estimated the combined impacts of those anthropogenic activities. We conducted a detailed analysis of selective logging and forest fire impacts on natural forests in the southern Brazilian Amazon state of Mato Grosso, one of the key logging centers in the country. To achieve this goal a 13-year series of annual Landsat images (1992-2004) was used to test different remote sensing techniques for measuring the extent of selective logging and forest fires, and to estimate their impact and interaction with other land use types occurring in the study region. Forest canopy regeneration following these disturbances was also assessed. Field measurements and visual observations were conducted to validate remote sensing techniques. Our results indicated that the Modified Soil Adjusted Vegetation Index aerosol free (MSAVI_af) is a reliable estimator of fractional coverage under both clear sky and under smoky conditions in this study region. During the period of analysis, selective logging was responsible for disturbing the largest proportion (31%) of natural forest in the study area, immediately followed by deforestation (29%). Altogether, forest disturbances by selective logging and forest fires affected approximately 40% of the study site area. Once disturbed by selective logging activities, forests became more susceptible to fire in the study site. However, our results showed that fires may also occur in undisturbed forests. This indicates that there are further factors that may increase forest fire susceptibility in the study area. Those factors need to be better understood. Although selective logging affected the largest amount of natural forest in the study period, 35% and 28% of the observed losses of forest canopy cover were due to forest fire and selective logging combined and to forest fire only, respectively. Moreover, forest areas degraded by selective logging and forest fire is an addition to outright deforestation estimates and has yet to be accounted for by land use and land cover change assessments in tropical regions. Assuming that this observed trend of land use and land cover conversion continues, we predict that there will be no undisturbed forests remaining by 2011 in this study site. Finally, we estimated that 70% of the total forest area disturbed by logging and fire had sufficiently recovered to become undetectable using satellite data in 2004.     

Fire radiative energy for quantitative study of biomass burning: derivation from the BIRD experimental satellite and comparison to MODIS fire products

A major focus in global change research is to quantify the amount of gaseous and particulate pollutants emitted from terrestrial vegetation fires. Determination of the emitted radiant energy released during biomass combustion episodes (the so-called fire radiative energy or FRE) has been suggested as a new tool for determining variations in biomass combustion rates and the rate of production of atmospheric pollutants. We review the physical principals behind the remote determination of FRE and present an alternative method for its derivation via analysis of ‘fire pixel’ radiances in the middle infrared spectral region. We compare our method to the existing FRE retrieval approach used in the EOS Moderate Resolution Imaging Spectro-radiometer (MODIS) fire products, and to retrievals of FRE based on derived fire temperature and area made via the so-called Bi-spectral method. We test each FRE retrieval method using both simulated data and imagery from a new experimental space mission, the Bi-spectral InfraRed Detection (BIRD) small satellite, which has sensors specifically designed for the study of active fires. We analyse near simultaneous MODIS and BIRD data of the fires that burned around Sydney, Australia in January 2002. Despite the markedly different pixel size and spectral coverage of these sensors, where the spatial extent of the fire pixel groups detected by MODIS and BIRD are similar, the derived values of FRE for these fires agree to within ±15 %. However, in certain fires, the lower spatial resolution of MODIS appears to prevent many of the less intensely radiating fire pixels being detected as such, meaning MODIS underestimates FRE for these fires by up to 46% in comparison to BIRD. Though the FRE release of each of these low intensity fire pixels is relatively low, their comparatively large number makes their overall FRE significant. Thus, total FRE release of the Sydney fires on 5 January 2002 is estimated to be 6.5×10⁹ J s⁻¹ via BIRD but 4.0×10⁹ J s⁻¹ via MODIS. The ability of BIRD to resolve individual fire fronts further allows the first accurate calculation of ‘radiative’ fireline intensity from spaceborne measurements, providing values of 15-75 kJ s⁻¹ m⁻¹ for fire fronts that are up to 9 km in length. Finally, we analyse the effectiveness of the satellite-based FRE retrieval methods in estimating the FRE from the active flaming and smouldering components only (FRE_Active, believed to be proportional to the rate of biomass combustion), despite the sensor receiving additional radiance from the ‘cooling ground’. The MIR radiance method appears particularly strong in this regard, allowing FRE_Active to be estimated to within ±30% in the range 100-100,000 J s⁻¹ m⁻². These results provide further confidence in the ability of spaceborne missions to derive physically meaningful values of FRE that could be used to support biomass burning emissions inventories. Future comparisons between FRE derived via MODIS and those from higher spatial resolution BIRD or airborne imagery may allow the MODIS-derived FRE values to be ‘calibrated’ for any systematic underestimation. We therefore expect FRE to become an important tool for enhancing global studies of terrestrial vegetation fires with infrared remote sensing, particularly as the majority of large fires are now imaged four times per day via the MODIS instruments on the Terra and Aqua spacecraft.     

Detection rates of the MODIS active fire product in the United States

MODIS active fire data offer new information about global fire patterns. However, uncertainties in detection rates can render satellite-derived fire statistics difficult to interpret. We evaluated the MODIS 1 km daily active fire product to quantify detection rates for both Terra and Aqua MODIS sensors, examined how cloud cover and fire size affected detection rates, and estimated how detection rates varied across the United States. MODIS active fire detections were compared to 361 reference fires (≥ 18 ha) that had been delineated using pre- and post-fire Landsat imagery. Reference fires were considered detected if at least one MODIS active fire pixel occurred within 1 km of the edge of the fire. When active fire data from both Aqua and Terra were combined, 82% of all reference fires were found, but detection rates were less for Aqua and Terra individually (73% and 66% respectively). Fires not detected generally had more cloudy days, but not when the Aqua data were considered exclusively. MODIS detection rates decreased with fire size, and the size at which 50% of all fires were detected was 105 ha when combining Aqua and Terra (195 ha for Aqua and 334 ha for Terra alone). Across the United States, detection rates were greatest in the West, lower in the Great Plains, and lowest in the East. The MODIS active fire product captures large fires in the U.S. well, but may under-represent fires in areas with frequent cloud cover or rapidly burning, small, and low-intensity fires. We recommend that users of the MODIS active fire data perform individual validations to ensure that all relevant fires are included.     

Peat fires detected by the BIRD satellite

Tropical peat swamp forests store huge amounts of carbon, which is released to the atmosphere as carbon dioxide during fires. Recurrent peat swamp forest fires are local catastrophic events that can have a serious impact on the global carbon balance. Urgent tasks in this regard are the provision of information on the fire locations and magnitude of the carbon emissions. The experimental Bi-spectral InfraRed Detection (BIRD) satellite enables early detection of peat swamp forest fires and retrieval of their quantitative characteristics, such as the effective fire temperature, area and radiative energy release. The combination of ground truth measurements and data obtained by BIRD can improve the accuracy of estimates of carbon emissions into the atmosphere and related trace gas composition.     

Satellite remote sensing of fine particulate matter (PM2.5) air quality over Beijing using MODIS

Fine particulate matter (aerodynamic diameters of less than 2.5 µm, PM_2.5) air pollution has become one of the major environmental challenges, causing severe environmental issues in urban visibility, climate, and public health. In this study, ground-level PM_2.5 concentrations, air-quality categories (AQCs), and health risk categories (HRCs) over Beijing, China, have been estimated based on mid-visible column aerosol optical depth (AOD) measurements extracted from Moderate Resolution Imaging Spectroradiometer (MODIS) data on board both Terra and Aqua satellites. Our results indicate that the MODIS AOD retrievals at 550 nm (AOD₅₅₀) match hourly aerosol robotic network (AERONET) measurements with correlation coefficients (r) of 0.950 for Terra and 0.895 for Aqua. The relationship between ground-level PM_2.5 and MODIS AOD₅₅₀ from March 2012 to February 2013 showed correlation coefficients of 0.69, 0.60, and 0.73 for spring, summer, and autumn, respectively. The atmospheric boundary layer height and relative humidity (RH) adjustments improved the AOD–PM_2.5 relationship in summer months. The estimates of daily average PM_2.5 from satellite measurements were used to predict both AQCs and HRCs, which are well matched with observations. Satellite remote sensing of atmospheric aerosols continues to show great potential for estimating ground-level PM_2.5 concentrations and can be further used to monitor the atmospheric environment in China.     

Fraction images derived from Terra Modis data for mapping burnt areas in Brazilian Amazonia

The objective of this paper is to present a method for mapping burnt areas in Brazilian Amazonia using Terra MODIS data. The proposed approach is based on image segmentation of the shade fraction images derived from MODIS, using a non‐supervised classification algorithm followed by an image editing procedure for minimizing misclassifications. Acre State, the focus of this study, is located in the western region of Brazilian Amazonia and undergoing tropical deforestation. The extended dry season in 2005 affected this region creating conditions for extensive forest fires in addition to fires associated with deforestation and land management. The high temporal resolution of MODIS provides information for studying the resulting burnt areas. Landsat 5 TM images and field observations were also used as ground data for supporting and validating the MODIS results. Multitemporal analysis with MODIS showed that about 6500 km² of land surface were burnt in Acre State. Of this, 3700 km² corresponded to the previously deforested areas and 2800 km² corresponded to areas of standing forests. This type of information and its timely availability are critical for regional and global environmental studies. The results showed that daily MODIS sensor data are useful sources of information for mapping burnt areas, and the proposed method can be used in an operational project in Brazilian Amazonia.     

Monitoring forest fires over the Indian region using Defense Meteorological Satellite Program-Operational Linescan System nighttime satellite data

Monitoring and management of forest fires is very important in countries like India where 55% of the total forest cover is prone to fires annually. The present study aims at effective monitoring of forest fires over the Indian region using Defense Meteorological Satellite Program-Operational Linescan System (DMSP-OLS) nighttime satellite data and to evaluate the active fire detection capabilities of the sensor. Nightly DMSP-OLS fire products were generated from February to May 2005 (peak fire season) and analyzed to study the occurrence and behavior of fires over different forest physiognomies in Indian region. Fire products generated from DMSP-OLS were validated with ground observations of fire records from state forest departments to evaluate the accuracy of fire products. Further, inter-comparison of the DMSP-OLS derived fire products with contemporary fire products from Moderate resolution Imaging Spectroradiometer (MODIS) (both daytime and nighttime products) in addition to fires and burnt areas derived from Indian Remote sensing Satellite (IRS-P6) Advanced Wide Field Sensor (AWiFS) data has been done to analyze spatial agreement of fire locations given by the above sensors.Results from the DMSP-OLS fire products (derived from February to May 2005) over Indian region showed high forest fires in southern dry deciduous forests during February-March; central Indian dry and mixed deciduous forests during March-April; northeastern tropical forests during February-April and northern pine forests during May. Spatial pattern in fires showed a typical seasonal shift in fire activity from the southern dry deciduous forests to the northern pine forests and temperate forests as the fire season progressed. Statistical evaluation of DMSP-OLS fire products with ground observations showed an over all accuracy of 98%. Comparison of DMSP-OLS derived fires with consecutive MODIS and AWiFS derived fires for individual days indicated that 69% of the fires continued from current day (DMSP-OLS pass around ∼ 7 pm to ∼ 10 pm local time) to the next day (MODIS and AWiFS pass ∼ 10:30 am local time). Comparison of DMSP-OLS derived fires with burnt areas estimated from AWiFS showed that 98% of DMSP-OLS derived fires on the current day fell within the burnt area of AWiFS on subsequent day. Since the worst forest fires are those that extend from the current to the consecutive days, DMSP-OLS derived fires provide a valuable augmentation to the fires derived from other sensors operating in daytime.     

Atmospheric conditions for monitoring the long-term vegetation dynamics in the Amazon using normalized difference vegetation index

This study examined the effect of biomass-burning aerosols and clouds on the temporal dynamics of the normalized difference vegetation index (NDVI) exhibited by two widely used, time-series NDVI data products: the Pathfinder AVHRR land (PAL) dataset and the NASA Global Inventory Monitoring and Modeling Studies (GIMMS) dataset. The PAL data are 10-day maximum-value NDVI composites from 1982 to 1999 with corrections for Rayleigh scattering and ozone absorption. The GIMMS data are 15-day maximum-value NDVI composites from 1982 to 1999. In our analysis, monthly maximum-value NDVI was extracted from these datasets. The effects were quantified by comparing time-series of NDVI from PAL and GIMMS with observations from the SPOT/VEGETATION sensor and aerosol index data from the Total Ozone Mapping Spectrometer (TOMS), and results from radiative transfer simulation. Our analysis suggests that the substantial large-scale NDVI seasonality observed in the south and east Amazon forest region with PAL and GIMMS is primarily caused by variations in atmospheric conditions associated with biomass-burning aerosols and cloudiness. Reliable NDVI data can be typically acquired from April to July when such effects are relatively low, whereas there is a few effective NDVI data from September to December. In the central Amazon forest region, where aerosol loads are relatively low throughout the year, large-scale NDVI seasonality results primarily from seasonal variations in cloud cover. Careful treatment of these aerosol and cloud effects is required when using NDVI from PAL and GIMMS (or other source) to determine large-scale seasonal and interannual dynamics of vegetation greenness and ecosystem-atmosphere CO₂ exchange in the Amazon region.     

Comparison of burnt area estimates derived from satellite products and national statistics in Europe

Burnt area maps based on satellite observations are frequently used in calculations related to fire regime, such as those of carbon dioxide emissions. Nevertheless, burnt area estimates between products vary widely, and validation against independent data is scarce, especially for Europe. Here we compare two active fire maps (the ATSR World Fire Atlas and the Moderate Resolution Imaging Spectroradiometer (MODIS) Active Fire Product) and two fire scars maps (the L3JRC and the MODIS Burned Area Product) to independent national statistics taken from 22 European countries between 1997 and 2008. We also tested the coincidence between satellite products derived by calculation of the fraction of active fires that were confirmed by a subsequent drop in reflectance. As a large proportion of fire pixels (between 40% and 66%, depending on the product) is located on urban land or crop fields, filtering out fires located on these land uses greatly improves the agreement between satellite-based burnt area estimates and national statistics and it also improves the coincidence between satellite products. The MODIS Active Fire Product appears to be most suitable for use as a proxy for burnt area patterns, showing a high correlation to national statistics (R² = 0.9), relatively low spatial and temporal heterogeneity and only a slight underestimation of the total burnt area (19 000 ha year^–1). Unfiltered products show cases of substantial wildfire overestimation in all products, mainly attributable to anthropogenic activity, in the case of active fire products, and drought-induced vegetation dieback, in that of fire scar maps. Thus, filtering out fires on anthropogenic land uses seems to be essential when analysing patterns of forest fires from satellite observations. However, if agricultural fires are to be included, a combination of MODIS Active Fire and MODIS Burned Area products is recommended. We obtained that such combination shows low temporal and spatial heterogeneity and the highest coincidence between satellite products (25%), although the correlation to national statistics is not very high (R² = 0.67) and clearly underestimates the total burnt area (187 000 ha year^–1).     

Comparison of multiple models for remote sensing of carbon exchange using MODIS data in conifer-dominated forests

Boreal forests in the northern hemisphere provide important sinks for storing carbon dioxide (CO₂). However, the size and distribution of these sinks remain uncertain. In particular, many remote-sensing models show a strong bias in the simulation of carbon fluxes for evergreen needleleaf forest. The objective of this study is to improve these predictive models for accurately quantifying temporal changes in the net ecosystem exchange (NEE) of conifer-dominated forest solely based on satellite remote sensing, including the Moderate Resolution Imaging Spectroradiometer (MODIS) Terra daytime land-surface temperature (LST), night-time LST′, enhanced vegetation index (EVI), land–surface water index (LSWI), fraction of absorbed photosynthetically active radiation (FPAR), and leaf area index (LAI). Considering that the component fluxes, gross primary production (GPP), and ecosystem respiration (R_e), are strongly influenced by vegetation phenology, seasonality information was extracted from time-series MODIS EVI data based on non-linear least-squares fits of asymmetric Gaussian model functions with a software package for analysing the time-series of satellite sensor data (TIMESAT). The results indicated that models directly incorporating phenological information failed to improve their performance for temperate deciduous forest. Instead, three methods to retrieve the component fluxes – GPP and R_e – including direct estimates, models incorporating the phenological information, and models developed based on the threshold value (LST 273 K), were explored respectively. All methods improved NEE estimates markedly and models developed based on the threshold value performed best, and provided a future framework for accurate remote sensing of NEE in evergreen forest.     

Monitoring intense dust storms over the Indian region using satellite data – a case study

Dust storms are normally considered to be natural hazards. During such events, dust aerosol is loaded into the atmosphere, directly reducing visibility and effectively reflecting solar radiation back to space. In the present study, an intense dust storm was monitored during the first week of June 2010 using Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua data over the Indian region. A dust cloud was detected using a combination of MODIS reflective and emissive channels and moving trace/spread monitored by its multi-temporal data. The MODIS Terra-derived aerosol optical depth at 550 nm (AOD₅₅₀) and the aerosol index (AI) obtained from the Ozone Monitoring Instrument (OMI) were used in conjunction with National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis wind fields for the monitoring of dust clouds. The study reveals that the movement of a high concentration of dust clouds coincided with the NCEP/NCAR reanalysis meridional and zonal wind fields (>8 m s^?1) at pressure levels of 700 hPa. The Cloud–Aerosol Lidar Pathfinder Satellite Observations (CALIPSOs) that derive vertical feature mask images also suggested that the vertical extent of the dust aerosol layer was at a height of about 6 km over northern India on 2 June 2010. The roles of long-range transport of dust over the entire Gangetic plane are analysed using back trajectories from the National Oceanic and Atmospheric Administration (NOAA) Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model. Back trajectory analysis suggests that dust clouds moving over long distances entered from the western side of India on 1 June 2010.     

Modeling gross primary production of temperate deciduous broadleaf forest using satellite images and climate data

Net ecosystem exchange (NEE) of CO₂ between the atmosphere and forest ecosystems is determined by gross primary production (GPP) of vegetation and ecosystem respiration. CO₂ flux measurements at individual CO₂ eddy flux sites provide valuable information on the seasonal dynamics of GPP. In this paper, we developed and validated the satellite-based Vegetation Photosynthesis Model (VPM), using site-specific CO₂ flux and climate data from a temperate deciduous broadleaf forest at Harvard Forest, Massachusetts, USA. The VPM model is built upon the conceptual partitioning of photosynthetically active vegetation and non-photosynthetic vegetation (NPV) within the leaf and canopy. It estimates GPP, using satellite-derived Enhanced Vegetation Index (EVI), Land Surface Water Index (LSWI), air temperature and photosynthetically active radiation (PAR). Multi-year (1998-2001) data analyses have shown that EVI had a stronger linear relationship with GPP than did the Normalized Difference Vegetation Index (NDVI). Two simulations of the VPM model were conducted, using vegetation indices from the VEGETATION (VGT) sensor onboard the SPOT-4 satellite and the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor onboard the Terra satellite. The predicted GPP values agreed reasonably well with observed GPP of the deciduous broadleaf forest at Harvard Forest, Massachusetts. This study highlighted the biophysical performance of improved vegetation indices in relation to GPP and demonstrated the potential of the VPM model for scaling-up of GPP of deciduous broadleaf forests.