Assessing the feasibility of sub-pixel burned area mapping in miombo woodlands of northern Mozambique using MODIS imagery

The goal of this study was to evaluate the feasibility of sub-pixel burned area detection in the miombo woodlands of northern Mozambique, using imagery from the Moderate Resolution Imaging Spectroradiometer (MODIS). Multitemporal Landsat-7 ETM+ data were acquired to produce a high spatial resolution map of areas burned between mid-August and late September 2000, and a field campaign was conducted in early November 2000 to gather ground truth data. Mapping of burned areas was performed with an ensemble of classification trees and yielded a kappa value of 0.896. This map was subsequently degraded to a spatial resolution of 500 m, to produce an estimate of burned area fraction, at the MODIS pixel size. Correlation analysis between the sub-pixel burned area fraction map and the MODIS reflective channels 1-7 yielded low but statistically significant correlations for all channels. The better correlations were obtained for MODIS channels 2 (0.86 µm), 5 (1.24 µm) and 6 (1.64 µm). A regression tree was constructed to predict sub-pixel burned area fraction as a function of those MODIS channels. The resulting tree has nine terminal nodes and an overall root mean square error of 0.252. The regression tree analysis confirmed that MODIS channels 2, 5, and 6 are the best predictors of burned area fraction. It may be possible to improve these results considering, as an alternative to individual channels, some appropriate spectral indices used to enhance the burnt scar signal, and by including MODIS thermal data in the analysis. It may also be possible to improve the accuracy of sub-pixel burned area fraction using MODIS imagery by allowing the regression tree to automatically create linear combinations of individual channels, and by using ensembles of trees.     

Prototyping a global algorithm for systematic fire-affected area mapping using MODIS time series data

The remote sensing of Earth surface changes is an active research field aimed at the development of methods and data products needed by scientists, resource managers, and policymakers. Fire is a major cause of surface change and occurs in most vegetation zones across the world. The identification and delineation of fire-affected areas, also known as burned areas or fire scars, may be considered a change detection problem. Remote sensing algorithms developed to map fire-affected areas are difficult to implement reliably over large areas because of variations in both the surface state and those imposed by the sensing system. The availability of robustly calibrated, atmospherically corrected, cloud-screened, geolocated data provided by the latest generation of moderate resolution remote sensing systems allows for major advances in satellite mapping of fire-affected area. This paper describes an algorithm developed to map fire-affected areas at a global scale using Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance time series data. The algorithm is developed from the recently published Bi-Directional Reflectance Model-Based Expectation change detection approach and maps at 500 m the location and approximate day of burning. Improvements made to the algorithm for systematic global implementation are presented and the algorithm performance is demonstrated for southern African, Australian, South American, and Boreal fire regimes. The algorithm does not use training data but rather applies a wavelength independent threshold and spectral constraints defined by the noise characteristics of the reflectance data and knowledge of the spectral behavior of burned vegetation and spectrally confusing changes that are not associated with burning. Temporal constraints are applied capitalizing on the spectral persistence of fire-affected areas. Differences between mapped fire-affected areas and cumulative MODIS active fire detections are illustrated and discussed for each fire regime. The results reveal a coherent spatio-temporal mapping of fire-affected area and indicate that the algorithm shows potential for global application.     

Comparing ten classification methods for burned area mapping in a Mediterranean environment using Landsat TM satellite data

Various methods have been developed during the past three decades to improve the classification accuracy in burned area mapping using satellite data captured by different sensors. In this article, we compare ten such classification approaches using Landsat Thematic Mapper (TM) imagery on three Mediterranean test sites by evaluating the classification accuracy using (i) a traditional pixel-based approach, (ii) the concept of the Pareto boundary of efficient solution and (iii) linear regression analysis. Additionally, we make a discrimination of errors depending on their distribution and causal factor. The classification approaches compared resulted in not statistically significant differences in the accuracy of the burned area maps. Differences between the methods were also observed when considering the accuracy along the edges of the burned patches; however, again these were not statistically significant. The findings of our study in a Mediterranean environment clearly demonstrate that, for the selection of the most suitable classification approach, other factors could be given more weight, such as computational resources, imagery characteristics, availability of ancillary data, available software and the analyst's experience. Maybe the most important finding of our work is that the variance imposed by the methods is less than the variance imposed by factors differentiated locally in the three study sites since the between-group variance of the overall accuracy is higher than that of the within groups.     

Regionally adaptable dNBR-based algorithm for burned area mapping from MODIS data

Recent advances in instrument design have led to considerable improvements in wildfire mapping at regional and global scales. Global and regional active fire and burned area products are currently available from various satellite sensors. While only global products can provide consistent assessments of fire activity at the global, hemispherical or continental scales, the efficiency of their performance differs in various ecosystems. The available regional products are hard-coded to the specifics of a given ecosystem (e.g. boreal forest) and their mapping accuracy drops dramatically outside the intended area. We present a regionally adaptable semi-automated approach to mapping burned area using Moderate Resolution Imaging Spectroradiometer (MODIS) data. This is a flexible remote sensing/GIS-based algorithm which allows for easy modification of algorithm parameterization to adapt it to the regional specifics of fire occurrence in the biome or region of interest. The algorithm is based on Normalized Burned Ratio differencing (dNBR) and therefore retains the variability of spectral response of the area affected by fire and has the potential to be used beyond binary burned/unburned mapping for the first-order characterization of fire impacts from remotely sensed data. The algorithm inputs the MODIS Surface Reflectance 8-Day Composite product (MOD09A1) and the MODIS Active Fire product (MOD14) and outputs yearly maps of burned area with dNBR values and beginning and ending dates of mapping as the attributive information. Comparison of this product with high resolution burn scar information from Landsat ETM+ imagery and fire perimeter data shows high levels of accuracy in reporting burned area across different ecosystems. We evaluated algorithm performance in boreal forests of Central Siberia, Mediterranean-type ecosystems of California, and sagebrush steppe of the Great Basin region of the US. In each ecosystem the MODIS burned area estimates were within 15% of the estimates produced by the high resolution base with the R² between 0.87 and 0.99. In addition, the spatial accuracy of large burn scars in the boreal forests of Central Siberia was also high with Kappa values ranging between 0.76 and 0.79.     

Logistic regression modelling of multitemporal Thematic Mapper data for burned area mapping

This study focused on the development of a logistic regression model for burned area mapping using two Landsat-5 Thematic Mapper (TM) images. Logistic regression models were structured using the spectral channels of the two images as explanatory variables. The overall accuracy of the results and other statistical indications denote that logisticregression modelling can be usedsuccessfully for burned area mapping. The model that consisted of the spectral channels TM4, TM7 and TM1 and had an overall accuracy of 97.62%, proved to be the most suitable. Moreover, the study concluded that the spectral channel TM4 was the most sensitive to alterations of the spectral response of the burned category pixels, followed by TM7.     

An autologistic regression model for increasing the accuracy of burned surface mapping using Landsat Thematic Mapper data

An autologistic regression model, which takes into account neighbouring associations, was developed and applied for burned land mapping using Landsat-5 Thematic Mapper data. The integration of the autocovariate component (estimated using a moving window of 3 @ 3 pixels) into the ordinary logistic regression model increased significantly the overall accuracy from 88.18% to 92.44%. In contrast, the accuracy derived with application of post-classification majority filters, which follow the same principles, were not significantly different to that derived with ordinary logistic regression.     

Retrieving middle-infrared reflectance for burned area mapping in tropical environments using MODIS

The ephemeral character of the radiative signal together with the presence of aerosols imposes severe limitations on the use of classical approaches, e.g. based on red and near-infrared, to discriminate between burned and unburned surfaces in tropical environments. Surface reflectance in the middle-infrared (MIR) has been used to circumvent these difficulties because the signal is virtually unaffected by the presence of aerosols associated to biomass burning. Retrieval of the MIR reflected component from the total signal is, however, a difficult problem because of the presence of a diversity of radiance sources, namely the surface reflected solar irradiance and the surface emitted radiance that may reach comparable magnitude during daytime. The method proposed by Kaufman and Remer (1994) to retrieve surface MIR reflectance presents the advantage of not requiring auxiliary datasets (e.g. atmospheric profiles) nor major computational means (e.g. for solving radiative transfer models). Nevertheless, the method was specifically designed to retrieve MIR reflectance over dense dark forests in the middle latitudes and, as shown in the present study, severe problems may arise when applying it beyond the range of validity, namely for burned area mapping in tropical environments. The present study consists of an assessment of the performance of the method for a wide range of atmospheric, geometric and surface conditions and of the usefulness of extracted surface reflectances for burned area discrimination. Results show that, in the case of tropical environments, there is a significant decrease in performance of the method for high values of land surface temperature, especially when associated with low sun elevation angles. Burned area discrimination is virtually impaired in such conditions, which are often present when using data from instruments on-board polar orbiters, namely MODIS in Aqua and Terra, to map burned surfaces over the Amazon forest and “cerrado” savanna regions.     

The GBA2000 initiative: Developing a global burnt area database from SPOT-VEGETATION imagery

The scientific community dealing with modelling of emissions of greenhouse gases and aerosols from anthropogenic sources demands reliable and quantitative information on the magnitude of biomass burning at a global scale. It is in this context that the Global Burnt Area -- 2000 (GBA2000) initiative has been launched. The specific objectives of this initiative are to produce a map of the areas burnt globally for the year 2000, using the medium resolution (1.1 km) Système Pour l'Observation de la Terre (SPOT) 4-VEGETATION (SPOT-VGT) satellite imagery and to derive statistics of area burnt per country, per month and per main type of vegetation cover. A series of regional algorithms has been developed and incorporated into a data processing system designed to yield monthly estimates of areas burnt at a global scale. The map data will then be transformed into quantitative information and made publicly available over the World Wide Web at a range of spatial and temporal resolutions to satisfy some of the requirements of the atmospheric and climate change modelling community.     

A global NDVI and EVI reference data set for land-surface phenology using 13 years of daily SPOT-VEGETATION observations

Time series of vegetation indices (VIs) obtained by remote sensing are widely used to study phenology on regional and global scales. The aim of the study is to design a method and to produce a reference data set describing the seasonal and inter-annual variability of the land-surface phenology on a global scale. Specific constraints are inherent in the design of such a global reference data set: (1) the high diversity of vegetation types and the heterogeneous conditions of observation, (2) a near-daily resolution is needed to follow the rapid changes in phenology, (3) the time series used to depict the baseline vegetation cycle must be long enough to be representative of the current vegetation dynamic and encompass anomalies, and (4) a spatial resolution consistent with a land-cover-specific analysis should be privileged. This study focuses on the SPOT (Satellite Pour l’Observation de la Terre)-VEGETATION sensor and its 13-year time series of reflectance values. Five steps addressing the noise and the missing data in the reflectance time series were selected to process the daily multispectral reflectance observations. The final product provides, for every pixel, three profiles for 52 × 7-day periods: a mean, a median, and a standard deviation profile. The mean and median profiles represent the reference seasonal pattern for variation of the vegetation at a specific location whereas the standard deviation profile expresses the inter-annual variability of VIs. A quality flag at the pixel level demonstrated that the reference data set can be considered as a reliable representation of the vegetation phenology in most parts of the Earth.     

Derivation of leaf area index for grassland within alpine upland using multi-temporal RapidEye data

Biophysical parameters such as leaf area index (LAI) are key variables for vegetation monitoring and particularly important for modelling energy and matter fluxes in the biosphere. Therefore LAI has been derived from remote sensing data operationally based on data with a somewhat coarse spatial resolution. This study aims at deriving high-spatial resolution (6.5 m) multi-temporal LAI for grasslands based on RapidEye data by statistical regressions between vegetation indices (VIs) and field samplings. However, the suitability of those data for grassland LAI derivation has not been tested to date. Thus, the potential of RapidEye data in general and its red edge band in particular are investigated, as well as the robustness of the established relationships for different points in time.

LAI was measured repeatedly over summer 2011 at about 30 different meadows in the Bavarian alpine upland using the LAI-2000 and correlated with VI values. The best relationships resulted from using the ratio vegetation index and red edge indices (NDVI_rededge, rededge ratio index 1, and relative length) in non-linear models. Thus the indices based on the red edge channel improved regression modelling. The associated transfer functions achieved R² values ranging from 0.57 to 0.85. The temporal transferability of those transfer functions to other dates was shown to be limited, with the root mean square errors (RMSEs) of several scenes exceeding one. However, when the LAI ranges are similar, a reliable transfer is possible: for example, the transfer of the regression function based on early autumn measurements showed RMSEs of only 0.77–0.95 for the other scenes except for the high-density stage in July, when the LAI reaches unprecedented maximal values. Also, the combination of multi-temporal training data shows no saturation of the selected indices and enables a satisfactory LAI mapping of different dates (RMSE = 0.59 – 1.02).     

An active-fire based burned area mapping algorithm for the MODIS sensor

We present an automated method for mapping burned areas using 500-m Moderate Resolution Imaging Spectroradiometer (MODIS) imagery coupled with 1-km MODIS active fire observations. The algorithm applies dynamic thresholds to composite imagery generated from a burn-sensitive vegetation index and a measure of temporal texture. Cumulative active fire maps are used to guide the selection of burned and unburned training samples. An accuracy assessment for three geographically diverse regions (central Siberia, the western United States, and southern Africa) was performed using high resolution burned area maps derived from Landsat imagery. Mapped burned areas were accurate to within approximately 10% in all regions except the high-tree-cover sub-region of southern Africa, where the MODIS burn maps underestimated the area burned by 41%. We estimate the minimum detectable burn size for reliable detection by our algorithm to be on the order of 120 ha.     

Assessing a predictive model of land change using uncertain data

This paper presents a method to assess models that predict changes among land categories between two points in time. Cross-tabulation matrices show comparisons among three maps: 1) the reference calibration map of an initial time, 2) the reference validation map of a subsequent time, and 3) the model's predicted map of the same subsequent time. The proposed method analyzes these three maps to evaluate the ability of the model to predict land change vis-à-vis a null model, while accounting for the error in the reference maps. We illustrate this method with a prediction of land change from 1971 to 1999 in Central Massachusetts, USA. Results reveal that the land change model predicts a larger quantity of transition from forest to built than the reference maps indicate, and the model allocates the transition erroneously in space, thus causing substantial error where the model predicts built in 1999 but the reference map shows forest. If the accuracy of each category in the 1971 reference map is greater than 81 percent, then the predicted change is larger than the error in the 1971 reference map. If the accuracy of each category in the 1999 reference map is greater than 82 percent, then the model's prediction disagreement with respect to truth is larger than the error in the 1999 reference map. Partial information concerning the accuracy of the reference maps indicates that the maps are likely to be more accurate than the 82 percent threshold. The method is designed to analyze predictions for the common situation when the levels of accuracy in the reference maps are not known precisely.     

Comparison of burned area estimates derived from SPOT-VEGETATION and Landsat ETM+ data in Africa: Influence of spatial pattern and vegetation type

An algorithm for burned area mapping in Africa based on classification trees was developed using SPOT-VEGETATION (VGT) imagery. The derived 1 km spatial resolution burned area maps were compared with 30 m spatial resolution maps obtained with 13 Landsat ETM+ scenes, through linear regression analysis. The procedure quantifies the bias in burned area estimation present in the low spatial resolution burned area map. Good correspondence was observed for seven sites, with values of the coefficient of determination (R²) ranging from 0.787 to 0.983. Poorer agreement was observed in four sites (R² values between 0.257 and 0.417), and intermediate values of R² (0.670 and 0.613) were obtained for two sites. The observed variation in the level of agreement between the Landsat and VGT estimates of area burned results from differences in the spatial pattern and size distribution of burns in the different fire regimes encompassed by our analysis. Small and fragmented burned areas result in large underestimation at 1 km spatial resolution. When large and compact burned areas dominate the landscape, VGT estimates of burned area are accurate, although in certain situations there is some overestimation. Accuracy of VGT burned area estimates also depends on vegetation type. Results showed that in forest ecosystems VGT maps underestimate substantially the amount of burned area. The most accurate estimates were obtained for woodlands and grasslands. An overall linear regression fitted with the data from the 13 comparison sites revealed that there is a strong relationship between VGT and Landsat estimates of burned area, with a value of R² of 0.754 and a slope of 0.803. Our findings indicate that burned area mapping based on 1 km spatial resolution VGT data provides adequate regional information.     

Object-based image classification for burned area mapping of Creus Cape, Spain, using NOAA-AVHRR imagery

Due to the ability of the NOAA-AVHRR sensor to cover a wide area and its high temporal frequency, it is possible to quickly obtain a general overview of the prevailing situation over a large area of terrain and, more specifically, quickly assess the damage caused by a recent large forest fire by mapping the extent of the burned area. The aim of this work was to map a large forest fire that recently took place on the Spanish Mediterranean coast using innovative image classification techniques and low spatial resolution imagery. The methodology involved developing an object-based classification model using spectral as well as contextual object information. The burned area map resulting from the image classification was compared with the fire perimeter provided by the Catalan Environmental Department in terms of spatial overlap and size in order to determine to what extent they were compatible. Results of the comparison indicated a high degree (≈90%) of spatial agreement. The total burned area of the classified image was found to be 6900 ha, compared to a fire perimeter of 6000 ha produced by the Catalan Environmental Department. It was concluded that, although the object-oriented classification approach was capable of affording very promising results when mapping a recent burn on the Spanish Mediterranean coast, the method in question required further assessment to ascertain its ability to map other burned areas in the Mediterranean.     

Comparison of multi-temporal ASTER images for hydrothermal alteration mapping using a fractal-aided SAM method

The shortwave infrared (SWIR) spectral bands of four multi-temporal images acquired by the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) on NASA’s Terra platform were analysed for evaluating the effects of acquisition properties and atmospheric pre-processing levels on the resulting hydrothermal alteration maps a using the fractal-aided Spectral Angle Mapper (SAM) method. Three ASTER level-1B products covering the Sar Cheshmeh area in Iran were used for hydrothermal alteration mapping. These images were converted to surface reflectance using the Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes (FLAASH) method. The low reflectance of band 5 of the level-1B products was compensated for by using the spectra of collected rock samples. Level-2 (AST2B05S) SWIR ASTER images that had already been processed were also used. Reference spectra of the main hydrothermal alteration types were extracted for each product. The threshold angles were determined using the real value–area (RV–A) fractal technique. Then, SAM classification was carried out to map hydrothermal alteration for every product. It is concluded that the level-1B products that had been converted to reflectances have a better spectral contrast than the AST2B05S product. Summer images with lower tilt angle and higher solar elevation should be used to increase the accuracy of the image classification and minimize the effect of vegetation on the spectra of index minerals. By comparing the resulting hydrothermal alteration maps with known alteration types using a confusion matrix, it was shown that the application of the RV–A fractal technique to produce less biased threshold angles increases the accuracy of SAM classification.     

Daily global solar radiation mapping of Turkey using Meteosat satellite data

Many studies have indicated that the estimation of solar irradiation at ground level using meteorological satellite data has been an alternative and easy method compared to classical methods. In the present work, the incident of solar radiation over Turkey has been estimated at ground level between July 1997 and December 1998. Statistical regressions between ground data and digital satellite data, measured in the visible band (0.4–1.1?µm) by Meteosat radiometer, have been determined and these regression parameters have been used to estimate solar radiation at ground level. This is the so-called statistical method, which uses a simple model because satellites measure only a few parameters among the many that govern radiative transfers.

The visible image (C3D) data used in the present work was Meteosat Wefax type. While pursuing our studies the mean daily sum of global solar radiation over Turkey has been determined to be 18.44?MJ?m^?2?d^?1 with a correlation coefficient of 0.96. The rms error for the mean daily sum has been evaluated as 1.92?MJ?m^?2?d^?1. The monthly mean daily sum of solar radiation has been determined with an rms error of 1.82?MJ?m^?2?d^?1 in two years. During this period the maximum value of the daily sum has been found to occur in June 1998 as 28.47?MJ?m^?2?d^?1, whereas the minimum has been found to occur in December 1998 as 7.35?MJ?m^?2?d^?1. The evaluation procedure, results and possible sources of error are suggested and possible ways of improving the method are described and discussed.     

Mapping urbanization dynamics at regional and global scales using multi-temporal DMSP/OLS nighttime light data

Urban areas concentrate people, economic activity, and the built environment. As such, urbanization is simultaneously a demographic, economic, and land-use change phenomenon. Historically, the remote sensing community has used optical remote sensing data to map urban areas and the expansion of urban land-cover for individual cities, with little research focused on regional and global scale patterns of urban change. However, recent research indicates that urbanization at regional scales is growing in importance for economics, policy, land use planning, and conservation. Therefore, there is an urgent need to understand and monitor urbanization dynamics at regional and global scales. Here, we illustrate the use of multi-temporal nighttime light (NTL) data from the U.S Air Force Defense Meteorological Satellites Program/Operational Linescan System (DMSP/OLS) to monitor urban change at regional and global scales. We use independently derived data on population, land use and land cover to test the ability of multi-temporal NTL data to measure regional and global urban growth over time. We apply an iterative unsupervised classification method on multi-temporal NTL data from 1992 to 2008 to map urbanization dynamics in India, China, Japan, and the United States. For two-year intervals between 1992 and 2000, India consistently experienced higher rates of urban growth than China, and both countries exceeded the urban growth rates of the United States and Japan. This is not surprising given that the populations of India and China were growing faster than those of the U.S. and Japan during those periods. For two-year intervals between 2000 and 2008, China experienced higher rates of urban growth than India. Results show that the multi-temporal NTL provides a regional and potentially global measure of the spatial and temporal changes in urbanization dynamics for countries at certain levels of GDP and population-driven growth.     

Coupling the short-term global forecast system weather data with a variable source area hydrologic model

Few current modeling tools are designed to predict short-term, high-risk runoff from hydrologically sensitive areas (HSAs) in watersheds. This study couples the Soil and Water Assessment Tool-Variable Source Area model with the Climate Forecast System Reanalysis model and the Global Forecast System-Model Output Statistics model short term weather forecast, to develop a HSA prediction tool designed to assist producers, landowners, and planners in identifying high-risk areas generating storm runoff and pollution. Short-term predictions for stream flow and soil moisture level were estimated in the South Fork of the Shenandoah river watershed. Daily volumetric flow forecasts were found to be satisfactory four days into the future, and distributed model predictions accurately captured sub-field scale HSAs. The model has the potential to provide valuable forecasts that can be used to improve the effectiveness of agricultural management practices and reduce the risk of non-point source pollution.     

Comparison of multi-temporal NOAA-AVHRR and SPOT-XS satellite data for mapping land-cover dynamics in the west African Sahel

Abstract

Multi-resolution and multi-temporal remote sensing data (SPOT-XS and AVHRR) were evaluated for mapping local land cover dynamics in the Sahel of West Africa. The aim of this research was to evaluate the agricultural information that could be derived from both high and low spatial resolution data in areas where there is very often limited ground information. A combination of raster-based image processing and vector-based geographical information system mapping was found to be effective for understanding both spatial and spectral land-cover dynamics. The SPOT data proved useful for mapping local land-cover classes in a dominantly recessive agricultural region. The AVHRR-LAC data could be used to map the dynamics of riparian vegetation, but not the changes associated with recession agriculture. In areas where there was a complex mixture of recession and irrigated agriculture, as well as riparian vegetation, the AVHRR data did not provide an accurate temporal assessment of vegetation dynamics.