Vegetation Continuous Fields Research Articles

Large area forest stem volume mapping in the boreal zone using synergy of ERS-1/2 tandem coherence and MODIS vegetation continuous fields

ERS-1/2 tandem coherence was reported to have high potential for the mapping of boreal forest stem volume (e.g. Santoro et al., 2002, 2007a; Wagner et al., 2003; Askne & Santoro, 2005). Large-scale application of the data for forest stem volume mapping, however, is hindered by the variability of coherence with meteorological and environmental acquisition conditions. The traditional way of stem volume retrieval is based on the training of models, relating coherence to stem volume, with the aid of forest inventory data which is generally available for a few small test sites but not for large areas. In this paper a new approach is presented that allows model training using the MODIS Vegetation Continuous Fields canopy cover product (Hansen et al., 2003) without further need for ground data. A comparison of the new approach with the traditional regression-based and ground-data dependent model training is presented in this paper for a multi-seasonal ERS-1/2 tandem dataset covering several well known Central Siberian forest sites. As a test scenario for large-area application, the approach was applied to a multi-seasonal ERS-1/2 tandem dataset of 223 ERS-1 and ERS-2 image pairs covering Northeast China (~ 1.5 million km 2) to map four stem volume classes (0–20, 20–50, 50–80, and > 80 m 3/ha).

The use of remote sensing products for environmental analysis in the South America

Land use and land cover play a major role in several biogeochemical models that simulate the processes and connections with terrestrial systems, being an important component for taking decisions in ecosystem management. The advance of techniques of obtaining data from remote sensing (RS), combined with the emergence of new operational products, offers alternatives to improve the environmental analysis and monitoring. In this manner, the objective of this study is to analyze the environmental characteristics of South America during 2000 to 2005 time periods, using four remote sensing databases: the GLOBCOVER, the Vegetation Continuous Field (VCF), MODIS Fire Radiative Power (FRP) and the Tropical Rainfall Measuring Mission (TRMM). Initially, GLOBCOVER and VCF products were analyzed and utilized afterwards for quantitative analysis of the spatial distribution of the FRP database. The results show that GLOBCOVER has a tendency to overestimate forest classes and to underestimate urban and mangroves areas. The analysis of hot pixels on the GLOBCOVER product indicates that the highest incidence of fires are located in the deforestation arc, in the Amazon forest border, occurring mainly over vegetation cover composed mainly of broadleaved evergreen or semi-deciduous forest. A time series analysis of FRP database indicates that biomass burning spreads over broadleaved evergreen or semi-deciduous forest and savannah areas.

Continuous fields of land cover for the conterminous United States using Landsat data: first results from the Web-Enabled Landsat Data (WELD) project

Vegetation Continuous Field (VCF) layers of 30 m percent tree cover, bare ground, other vegetation and probability of water were derived for the conterminous United States (CONUS) using Landsat 7 Enhanced Thematic Mapper Plus (ETM+) data sets from the Web-Enabled Landsat Data (WELD) project. Turnkey approaches to land cover characterization were enabled due to the systematic WELD Landsat processing, including conversion of digital numbers to calibrated top of atmosphere reflectance and brightness temperature, cloud masking, reprojection into a continental map projection and temporal compositing. Annual, seasonal and monthly WELD composites for 2008 were used as spectral inputs to a bagged regression and classification tree procedure using a large training data set derived from very high spatial resolution imagery and available ancillary data. The results illustrate the ability to perform Landsat land cover characterizations at continental scales that are internally consistent while retaining local spatial and thematic detail.

Retrieval of growing stock volume in boreal forest using hyper-temporal series of Envisat ASAR ScanSAR backscatter measurements

Methods for the estimation of forest growing stock volume (GSV) are a major topic of investigation in the remote sensing community. The boreal zone contains almost 30% of global forest by area but measurements of forest resources are often outdated. Although past and current spaceborne synthetic aperture radar (SAR) backscatter data are not optimal for forest-related studies, a multi-temporal combination of individual GSV estimates can improve the retrieval as compared to the single-image case. This feature has been included in a novel GSV retrieval approach, hereafter referred to as the BIOMASAR algorithm. One innovative aspect of the algorithm is its independence from in situ measurements for model training. Model parameter estimates are obtained from central tendency statistics of the backscatter measurements for unvegetated and dense forest areas, which can be selected by means of a continuous tree canopy cover product, such as the MODIS Vegetation Continuous Fields product. In this paper, the performance of the algorithm has been evaluated using hyper-temporal series of C-band Envisat Advanced SAR (ASAR) images acquired in ScanSAR mode at 100 m and 1 km pixel size. To assess the robustness of the retrieval approach, study areas in Central Siberia (Russia), Sweden and Québec (Canada) have been considered. The algorithm validation activities demonstrated that the automatic approach implemented in the BIOMASAR algorithm performed similarly to traditional approaches based on in situ data. The retrieved GSV showed no saturation up to 300 m 3/ha, which represented almost the entire range of GSV at the study areas. The relative root mean square error (RMSE) was between 34.2% and 48.1% at 1 km pixel size. Larger errors were obtained at 100 m because of local errors in the reference datasets. Averaging GSV estimates over neighboring pixels improved the retrieval statistics substantially. For an aggregation factor of 10 × 10 pixels, the relative RMSE was below 25%, regardless of the original resolution of the SAR data.

Pan-Arctic Land Cover Mapping and Fire Assessment for the ESA Data User Element Permafrost

The paper presents first results of a pan-boreal scale land cover harmonization and classification. A methodology is presented that combines global and regional vegetation datasets to extract percentage cover information for different vegetation physiognomy and barren for the pan-arctic region within the ESA Data User Element Permafrost. Based on the legend description of each land cover product the datasets are harmonized into four LCCS (Land Cover Classification System) classifiers which are linked to the MODIS Vegetation Continuous Field (VCF) product. Harmonized land cover and Vegetation Continuous Fields products are combined to derive a best estimate of percentage cover information for trees, shrubs, herbaceous and barren areas for Russia. Future work will concentrate on the expansion of the developed methodology to the pan-arctic scale. Since the vegetation builds an isolation layer, which protects the permafrost from heat and cold temperatures, a degradation of this layer due to fire strongly influences the frozen conditions in the soil. Fire is an important disturbance factor which affects vast processes and dynamics in ecosystems (e.g. biomass, biodiversity, hydrology, etc.). Especially in North Eurasia the fire occupancy has dramatically increased in the last 50 years and has doubled in the 1990s with respect to the last five decades. A comparison of global and regional fire products has shown discrepancies between the amounts of burn scars detected by different algorithms and satellite data

Treeline vegetation composition and change in Canada's western Subarctic from AVHRR and canopy reflectance modeling

Abstract Climate change is expected to have significant impacts on northern vegetation, particularly along transition zones such as the treeline. Studies of vegetation composition and change in this ecotone have largely focussed on local analysis of individual trees using labour intensive stand reconstruction techniques, which are spatially limited and do not consider vegetation types other than trees. Remote sensing may be well suited to monitoring recent changes across the treeline because it captures integrated changes of all vegetation life forms over large spatial extents. This research examines treeline vegetation composition and change along the western subarctic treeline mapped by Timoney et al. (1992) using a 1 km resolution, 22-year AVHRR archive from 1985–2006. While most remote sensing studies on vegetation change in arctic and subarctic regions only exploit information contained in the Normalized Difference Vegetation Index (NDVI), we examine long-term reflectance trends in AVHRR bands 1 and 2 in addition to NDVI. The GeoSail canopy reflectance model is used to map treeline composition by combining information from 22-year summertime and early springtime composite images. A set of spectral change vectors are then generated from GeoSail simulations and used to classify trends in AVHRR along the treeline to estimate vegetation change. Evaluation of vegetation composition against the MODIS Vegetation Continuous Fields (VCF) product that has been recently validated along the treeline reveals good spatial correspondence. Temporal trends are shown to agree with literature on tundra–taiga vegetation dynamics in recent decades. Evidence is presented that suggests replacement of bare surfaces with herb, conifer decline along the southern treeline, increased shrubiness, and increased conifer recruitment and growth in the north.

Comparison of MODIS vegetation continuous field — based forest density maps with IRS-LISS III derived maps

The study compared forest cover maps derived using coarse resolution vegetation continuous fields (MODIS VCF; 500m resolution) with the maps derived from medium resolution (24m; IRS LISS-III) data. The comparison of VCF, per cent tree cover product, for the years 2000 to 2004 with LISS III forest density class maps of 2001 and 2003 was carried out for two sites representing hilly (Uttarakhand) and undulating terrains (Madhya Pradesh). Slicing VCF to corresponding forest crown cover, i.e., 0–10%, 10–40%, 40–70% and >70% produced considerable difference in forest area estimates when compared to original LISS III derived crown cover area. The corresponding value range in VCF for 0–10% of actual forest cover were 0–31% and 0–25% in 2 sites respectively, and the respective limit was consistent at 1–20% when VCF range were sliced with respect to upscaled LISS III at 500m resolution. Similarly, all other class limits were also found through iterative process. These limits were similar, within a site, across five years. Spatial Kappa match between these two data indicated higher match in 40–70% class, and also in undulating site. When compared at same resolution, similar forest area cover estimated with weighted area upscaling gave closest match. The study is useful in knowing the usability and limits of VCF product, and utility of spatial Kappa.

MODIS tree cover validation for the circumpolar taiga–tundra transition zone

A validation of the 2005 500 m MODIS vegetation continuous fields (VCF) tree cover product in the circumpolar taiga–tundra ecotone was performed using high resolution Quickbird imagery. Assessing the VCF's performance near the northern limits of the boreal forest can help quantify the accuracy of the product within this vegetation transition area. The circumpolar region was divided into 7 longitudinal zones and validation sites were selected in areas of varying tree cover where Quickbird imagery is available in Google Earth. Each site was linked to the corresponding VCF pixel and overlaid with a regular dot grid within the VCF pixel's boundary to estimate percent tree crown cover in the area. Percent tree crown cover was estimated using Quickbird imagery for 396 sites throughout the circumpolar region and related to the VCF's estimates of canopy cover for 2000–2005. Regression results of VCF inter-annual comparisons (2000–2005) and VCF-Quickbird image-interpreted estimates indicate that: (1) Pixel-level, inter-annual comparisons of VCF estimates of percent canopy cover were linearly related (mean R 2 = 0.77) and exhibited an average root mean square error (RMSE) of 10.1% and an average root mean square difference (RMSD) of 7.3%. (2) A comparison of image-interpreted percent tree crown cover estimates based on dot counts on Quickbird color images by two different interpreters were more variable ( R 2 = 0.73, RMSE = 14.8%, RMSD = 18.7%) than VCF inter-annual comparisons. (3) Across the circumpolar boreal region, 2005 VCF-Quickbird comparisons were linearly related, with an R 2 = 0.57, a RMSE = 13.4% and a RMSD = 21.3%, with a tendency to over-estimate areas of low percent tree cover and anomalous VCF results in Scandinavia. The relationship of the VCF estimates and ground reference indicate to potential users that the VCF's tree cover values for individual pixels, particularly those below 20% tree cover, may not be precise enough to monitor 500 m pixel-level tree cover in the taiga–tundra transition zone.

Modeling the Effects of Anthropogenic Habitat Change on Savanna Snake Invasions into African Rainforest

We used a species-distribution modeling approach, ground-based climate data sets, and newly available remote-sensing data on vegetation from the MODIS and Quick Scatterometer sensors to investigate the combined effects of human-caused habitat alterations and climate on potential invasions of rainforest by 3 savanna snake species in Cameroon, Central Africa: the night adder (Causus maculatus), olympic lined snake (Dromophis lineatus), and African house snake (Lamprophis fuliginosus). Models with contemporary climate variables and localities from native savanna habitats showed that the current climate in undisturbed rainforest was unsuitable for any of the snake species due to high precipitation. Limited availability of thermally suitable nest sites and mismatches between important life-history events and prey availability are a likely explanation for the predicted exclusion from undisturbed rainforest. Models with only MODIS-derived vegetation variables and savanna localities predicted invasion in disturbed areas within the rainforest zone, which suggests that human removal of forest cover creates suitable microhabitats that facilitate invasions into rainforest. Models with a combination of contemporary climate, MODIS- and Quick Scatterometer-derived vegetation variables, and forest and savanna localities predicted extensive invasion into rainforest caused by rainforest loss. In contrast, a projection of the present-day species-climate envelope on future climate suggested a reduction in invasion potential within the rainforest zone as a consequence of predicted increases in precipitation. These results emphasize that the combined responses of deforestation and climate change will likely be complex in tropical rainforest systems.

Aboveground biomass assessment in Colombia: A remote sensing approach

This paper presents a method to increase the level of detail of aboveground biomass estimates at a regional scale. Methods are based on empirical relationships while materials are based on MODIS products and field measurements; the area covers from 4° south up to 12° north of the Equator with a total of 1,139,012 km 2 corresponding to the continental area of Colombia. Vegetation was classified in three broad classes: grasslands, secondary forests and primary forests which have been proved to enhance biomass estimates. MOD44 vegetation continuous fields (VCFs) was used as an explanatory variable for primary and secondary forests following an exponential relationship, while MOD13A1 enhanced vegetation index (EVI) was used as explanatory variable for grasslands following a linear relationship; biomass for this vegetation class was estimated every 16 days given its large variation throughout the year. EVI–biomass relationships were established from 2001 to 2006. Vegetation maps were used to separate primary forests from secondary forest, since the latter has shown lower biomass levels. Confidence intervals of the exponential regression are larger as the biomass values increases, for this reason the uncertainty is quite high ranging from 3.7 to 25.2 millions of Mg with a mean of 16.2 million of Mg. Despite the uncertainty our biomass results are within the estimates of previous studies.

Near real time monitoring of biomass burning particulate emissions (PM2.5) across contiguous United States using multiple satellite instruments

Biomass burning is a major source of aerosols that affect air quality and the Earth's radiation budget. Current estimates of biomass burning emissions vary markedly due to uncertainties in biomass density, combustion efficiency, emission factor, and burned area. This study explores the modeling of biomass burning emissions using satellite-derived vegetative fuel loading, fuel moisture, and burned area across Contiguous United States (CONUS). The fuel loading is developed from Moderate-Resolution Imaging Spectroradiometer (MODIS) data including land cover type, vegetation continuous field, and monthly leaf area index. The weekly fuel moisture category is retrieved from AVHRR (Advanced Very High Resolution Radiometer) Global Vegetation Index (GVIx) data for the determination of fuel combustion efficiency and emission factor. The burned area is simulated using half-hourly fire sizes obtained from the GOES (Geostationary Operational Environmental Satellites) Wildfire Automated Biomass Burning Algorithm (WF_ABBA) fire product. By integrating all these parameters, quantities of PM2.5 (particulate mass for particles with diameter <2.5 μm) aerosols are calculated for each individual fire at an interval of half hour from 2002–2005 across CONUS. The PM2.5 estimates indicate that the annual PM2.5 emissions are 3.49 × 10 5, 3.30 × 10 5, 1.80 × 10 5, and 2.24 × 10 5 tons for 2002 (April to December), 2003, 2004, and 2005, respectively. Among various ecosystems, forest fires release more than 44% of the emissions although the related burned areas only account for less than 30%. Spatially, PM2.5 emissions are larger in California for all these years, but only for some individual years in Oregon, Montana, Arkansas, Florida, Arizona, Louisiana, and Idaho. Finally, the calculated PM2.5 emissions are evaluated using national wildfire emission inventory data (NWEI) and compared with estimates from different fuel loadings. The difference between NWEI and GOES fire-based estimate is less than 20% if the same fuel data are used. The evaluation suggests that the biomass burning emissions derived from multiple satellite data provide realistic spatiotemporal patterns and can be assimilated into air quality models for forecasts in real or near real time.

Evaluation of global land cover data sets over the tundra–taiga transition zone in northernmost Finland

The remote sensing‐based continental to global scale land cover data sets provide several land cover depictions over the circumpolar tundra–taiga transition zone. The aim of this study was to evaluate three data sets in northernmost Finland: the Global Land Cover 2000 Northern Eurasia map (GLC2000‐NE), the MODIS global land cover map (MODIS‐IGBP) and the tree cover layer of the MODIS vegetation continuous fields product (MODIS‐VCF). The data sets were first compared both visually and statistically to biotope inventory data including tree cover, height, species composition and shrub cover information as continuous variables. The agreement with reference data was poor because the classifications do not correspond to the class descriptions. The MODIS‐VCF tree cover overestimates the tree cover in the low values and underestimates it in the high values. The agreement was relatively good when the global data sets were aggregated to a forest–non‐forest level and compared to the Finnish CORINE Land Cover 2000 map over a larger area. However, the inaccurate mapping of the deciduous broadleaf forests and mires reduced the agreement at the forest–non‐forest level. The vegetation transitions are difficult to map using low‐resolution satellite data and further improvements to the land cover characterization over the tundra–taiga transition zone are required.

A method for integrating MODIS and Landsat data for systematic monitoring of forest cover and change in the Congo Basin

In this paper we demonstrate a new approach that uses regional/continental MODIS (MODerate Resolution Imaging Spectroradiometer) derived forest cover products to calibrate Landsat data for exhaustive high spatial resolution mapping of forest cover and clearing in the Congo River Basin. The approach employs multi-temporal Landsat acquisitions to account for cloud cover, a primary limiting factor in humid tropical forest mapping. A Basin-wide MODIS 250 m Vegetation Continuous Field (VCF) percent tree cover product is used as a regionally consistent reference data set to train Landsat imagery. The approach is automated and greatly shortens mapping time. Results for approximately one third of the Congo Basin are shown. Derived high spatial resolution forest change estimates indicate that less than 1% of the forests were cleared from 1990 to 2000. However, forest clearing is spatially pervasive and fragmented in the landscapes studied to date, with implications for sustaining the region's biodiversity. The forest cover and change data are being used by the Central African Regional Program for the Environment (CARPE) program to study deforestation and biodiversity loss in the Congo Basin forest zone. Data from this study are available at http://carpe.umd.edu.

MODIS tasselled cap: land cover characteristics expressed through transformed MODIS data

The tasselled cap concept is extended to Moderate Resolution Imaging Spectroradiometer (MODIS) Nadir BRDF‐Adjusted Reflectance (NBAR, MOD43) data. The transformation is based on a rigid rotation of principal component axes (PCAs) derived from a global sample spanning one full year of NBAR 16‐day composites. To provide a standard for MODIS tasselled cap axes, we recommend an orientation in MODIS spectral band space as similar as possible to the orientation of the Landsat Thematic Mapper (TM) tasselled cap axes. To achieve this we first transformed our global sample of MODIS NBAR reflectance values to TM tasselled cap values using the existing TM transformation, then used an existing algorithm (Procrustes) to compute the transformation that minimizes the mean square difference between the TM transformed NBAR values and NBAR PCA values. This transformation can then be used as a standard to rotate the MODIS NBAR PCA axes into a new MODIS Kauth–Thomas (KT) orientation. Global land cover patterns in tasselled cap space are demonstrated graphically by linking the global sample with several other products, including the MODIS Land Cover product (MOD12) and the MODIS Vegetation Continuous Fields product (MOD44). Patterns seen at this global scale agree with previous explorations of TM tasselled cap space, but are shown here in greater detail with a globally representative sample. Temporal trends of eight smaller‐scale BigFoot Project (www.fsl.orst.edu/larse/bigfoot) sites were also examined, confirming the spectral shifts in tasselled cap space related to phenology.

Spatially and temporally continuous LAI data sets based on an integrated filtering method: Examples from North America

Leaf Area Index (LAI) is an important biophysical variable for characterizing the land surface vegetation. Global LAI product has been routinely produced from the MODerate resolution Imaging Spectroradiometer (MODIS) aboard the Terra and Aqua satellite platforms. However, the MODIS standard LAI product is not continuous both spatially and temporally. To fill the gaps and improve the quality, we have developed a data filtering algorithm. This filter, called the temporal spatial filter (TSF), integrates both spatial and temporal characteristics for different plant functional types. The spatial gaps are first filled with the multi-year averages of the same day. If the values are missing over all years, the pixel is filled with a new estimate using the vegetation continuous field–ecosystem curve fitting method. The TSF integrates both the multi-seasonal average trend (background) and the seasonal observation. We implement this algorithm using the MODIS Collection 4 LAI product over North America. Comparison of the TSF results with the Savitzky–Golay filter indicates that the TSF performs much better in restoring the spatial and temporal distribution of seasonal LAI trends. The new LAI product has been validated by comparing with field measurements and the derived LAI maps from ETM+ data at a broadleaf forest site and an agricultural site. The validation results indicate that the new LAI product agrees better with both the field measurements and LAI values obtained from the ETM+ than does the MODIS LAI standard product, which usually shows higher LAI values.

Integração de dados de sensoriamento remoto multi resoluções para a representação da cobertura da terra utilizando campos contínuos de vegetação e classificação por árvores de decisão

Este trabalho objetiva desenvolver uma metodologia de integração de multisensores para um sistema de monitoramento da Amazônia. O sistema proposto baseia-se no Vegetation Continuous Fields (VCF) que utiliza um algoritmo de árvore de decisão. O algoritmo utiliza um conjunto de variáveis independentes, no caso métricas multitemporais do MODIS, para recursivamente particionar uma variável dependente, no caso dados de treinamentos provenientes de classes de uso da terra, em subconjuntos, que maximizam a redução do quadrado da soma residual. Os dados de treinamentos são obtidos pela classificação de imagens de alta resolução (Landsat/TM, ETM+ e CBERS 2/CCD). Neste estudo, um algoritmo foi desenvolvido a partir da linguagem IDL, no programa ENVI, e uma rotina estatística do programa S-PLUS. A área de estudo é o Estado do Mato Grosso com uma extensa área de cobertura de Floresta Amazônica. As cenas são classificadas em três classes: floresta, não floresta e água. Comparações do produto final com o mapa regional de uso da terra derivado do PRODES revelam uma concordância geral. Portanto, os resultados desse estudo sugerem que a metodologia é apropriada para a determinação da cobertura da terra na Floresta Amazônica.

Evaluation of ISLSCP Initiative II satellite‐based land cover data sets and assessment of progress in land cover data for global modeling

As an important component of the International Satellite Land Surface Climatology Project (ISLSCP) Initiative II data collection, eight state‐of‐the‐art land cover/use data sets have been compiled and made consistent with the ISLSCP Initiative II land/water mask in support of global modeling efforts. These data sets contain new and improved global data sets at coarse resolutions (1/4, 1/2 and 1°) describing historical, recent and present land cover conditions and are a testament to the tremendous progress made in this area over the past decade. In addition to the historical data, data describing the subcell heterogeneity in land cover are also provided, both in terms of subcell proportions of land cover classes and vegetation continuous fields such as % tree, grass and bare cover. Here we present the various ISLSCPII land cover data sets and compare the principal satellite‐derived data sets and the effect of their respective aggregation methods. We find that despite some notable disagreements among similar classes, the satellite‐based data sets agree remarkably well over large portions of the Earth's surface (over 50% for all resolutions). We also find that the methods of aggregation, whether done by a strictly dominant type, or using more information on subcell tree cover, can have an important impact on the final output and need to be considered by the user. Finally, by integrating the vegetation continuous fields data into our analyses we are able to show that the principal differences in terms of discrete land cover classes are in fact transition zones between similar classes.

Mapping woody plant cover in desert grasslands using canopy reflectance modeling and MISR data

A simplified geometric‐optical model (SGM) was inverted using red band reflectance data acquired at 275 m in nine viewing angles from the Multiangle Imaging SpectroRadiometer (MISR) flown on NASA's Terra satellite, to provide estimates of fractional woody plant cover for large areas (over 3519 km2) in parts of the Chihuahuan Desert in New Mexico, USA. The use of the model in these semi‐arid environments was enabled by the derivation of a priori estimates of the soil/understory background reflectance response. This was made possible by determining relationships between the kernel weights from a LiSparse‐RossThin model adjusted against the same MISR data – together with spectral reflectance data derived from MISR's nadir‐viewing camera – and the parameters of the Walthall model used to represent the background. Spatial distributions of retrieved fractional woody plant cover match those of % tree cover in the global MODIS Vegetation Continuous Fields product but also include shrubs. Good relationships were obtained with fractional shrub cover measured in pastures in the USDA, ARS Jornada Experimental Range but tree cover in higher elevation and riparian zones was dramatically over‐estimated as a result of the fixing of crown height and shape parameters.

Estimating emissions from fires in North America for air quality modeling

Fires contribute substantial emissions of trace gases and particles to the atmosphere. These emissions can impact air quality and even climate. We have developed a modeling framework to estimate the emissions from fires in North and parts of Central America (10–71 °N and 55–175 °W) by taking advantage of a combination of complementary satellite and ground-based data to refine estimates of fuel loadings. Various satellite drivers, including the MODIS Thermal Anomalies Product, the Global Land Cover Characteristics 2000 dataset, and the MODIS Vegetation Continuous Fields Product were used in conjunction with data mined from literature to determine fire location and timing, fuel loadings, and emission factors. Daily emissions of particulate matter and numerous trace gases from fires were estimated using this method for three years (2002–2004). Annual emission estimates differ by as much as a factor of 2 (CO emissions for North America ranged from 22.6 to 39.5 Tg yr −1). Regional variations in emissions correspond to different fire seasons within the region. For example, the highest emissions from Central America and Mexico occur in the late spring whereas the highest emissions from the United States and Canada occur during the summer months. Comparisons of these results with other published estimates of CO emission estimates from fire show reasonable agreement, but substantial uncertainties remain in the estimation techniques. We suggest methods whereby future emissions models can reduce these uncertainties.

Estimating forest biomass in the USA using generalized allometric models and MODIS land products

Spatially‐distributed forest biomass components are essential to understand carbon cycle and the impact of biomass burning emissions on air quality. We estimated the density of forest biomass components (foliage biomass, branch biomass, and aboveground biomass) at a spatial resolution of 1 km across the Contiguous United States using foliage‐based generalized allometric models and Moderate‐Resolution Imaging Spectroradiometer (MODIS) land data. The foliage biomass for each forest type was calculated from MODIS leaf‐area indices, land cover types, and vegetation continuous fields. The foliage‐based models were developed using available diameter‐based allometric equations and used to estimate branch biomass and aboveground biomass. The resultant aboveground biomass density matches well with the data from Forest Inventory and Analysis program at both state and pixel levels.