Variability of fire‐induced changes in MODIS surface reflectance by land‐cover type in Borneo

This study investigates fire‐induced spectral changes detected by the Moderate Resolution Imaging Spectroradiometer (MODIS) in different land‐cover types in Borneo. Linear discriminant analysis is used to determine the most powerful band combinations among the MODIS reflective bands for discrimination between burnt and unburnt areas in each land‐cover type. The results show that the nature of fire‐induced changes is dependent on pre‐fire vegetation characteristics in this region. Bands 1 (0.64 µm), 2 (0.86 µm), and 7 (2.14 µm) are found to be the most sensitive bands in land‐cover types dominated by green vegetation, and consequently indices or combinations of indices using these three bands are potentially effective for burnt‐area detection in the majority of areas. In land‐cover types dominated by dry vegetation and soil, MODIS band 5 (1.24 µm) alone showed the greatest statistical separability and could not be significantly improved by any multiband index.     

Detection of vegetation fires and burnt areas by remote sensing in insular Southeast Asian conditions: current status of knowledge and future challenges

The humid tropical insular Southeast Asian region is one of the most biologically diverse areas in the world. It contains around 70 Gt of carbon stored in peat deposits susceptible to burning when drained and it has significantly higher population density than any other humid tropical region. This region experiences yearly fire activity of anthropogenic origin with widely varying extent and severity. At the same time, there are several geographic, climatic, and social aspects that complicate fire monitoring in the region. In this review article, we analyse the current knowledge and limitations of active fire detection and burnt area mapping in insular Southeast Asia, highlighting the special characteristics of the region that affect all types of remote-sensing-based regional-level fire monitoring. We conclude that the monitoring methods currently employed have serious limitations that directly affect the reliability of results for fire and burnt area monitoring in this region. With the materials and methods presently available, the regional and global effects of fire activity taking place in insular Southeast Asia are in danger of being underestimated. New approaches utilizing higher spatial and temporal resolution remote-sensing data are needed for more detailed quantification of fire activity and subsequently improved estimation of the effects of fires in this region.     

Evaluating the performance of multitemporal image compositing algorithms for burned area analysis

The main objective of this study was to compare the adequacy of various multitemporal image compositing algorithms to produce composite images suitable for burned area analysis. Satellite imagery from the NOAA Advanced Very High Resolution Radiometer (AVHRR) from three different regions (Portugal, central Africa, and South America) were used to compare six algorithms, two of which involve the sequential application of two criteria. Performance of the algorithms was assessed with the Jeffries-Matusita distance, to quantify spectral separability of the burned and unburned classes in the composite images. The ability of the algorithms to avoid the retention of cloud shadows was assessed visually with red-green-blue colour composites, and the level of radiometric speckle in the composite images was quantified with the Moran's I spatial autocorrelation statistic. The commonly used NDVI maximum value compositing procedure was found to be the least appropriate to produce composites to be used for burned area mapping, from all standpoints. The best spectral separability is provided by the minimum channel 2 (m2) compositing approach which has, however, the drawback of retaining cloud shadows. A two-criterion approach which complements m2 with maximization of brightness temperature in a subset of the data (m2M4) is considered the better method.     

Assessment of multitemporal compositing techniques of MODIS and AVHRR images for burned land mapping

The performance of several criteria to generate multitemporal composites of daily Moderate Resolution Imaging Spectrometer (MODIS) and Advanced Very High Resolution Radiometer (AVHRR) images for burned land mapping was tested using data acquired over the Iberian Peninsula in 2001, 2003 and 2004. The experiment was based on four tests that assessed the discriminability between burned and unburned areas, the presence of artifacts (clouds and clouds shadows), the verticality of the sensor viewing angle, and the spatial coherency of the composite images. Seven different compositing techniques were tested, based on maximizing normalized difference vegetation index (NDVI) and brightness/surface temperature, and minimizing reflectance and sensor zenith angles. The composite criterions that provide the most accurate images for burned land mapping were based on maximizing brightness/surface temperatures, either as the only criterion or in conjunction with minimizing sensor zenith angle or near infrared (NIR) reflectance. These composites present high discrimination capacity between burned and unburned areas, remove most clouds and cloud shadows, offer high spatial coherency and present middle-to-low sensor zenith angles. Traditional compositing criterion based on maximizing NDVI values provided poor results in most tests. Finally, standard NASA MODIS composite provides close to nadir observation angles, and good spatial coherency, but it offered lower discrimination between burned and unburned areas that those composites based on thermal data.     

New 500 m spatial resolution land cover map of the western insular Southeast Asia region

This letter presents the methodology and accuracy assessment of a new 500 m spatial resolution land cover map of the western part of insular Southeast Asia. The map was produced using 250 Moderate Resolution Imaging Spectroradiometer (MODIS) images (acquired 1 January–2 July 2007), elevation information and peatland maps. The map covers the Malaysian Peninsula and the major islands of Sumatra, Java and Borneo, in addition to numerous smaller islands. The classification scheme of 12 classes reflects the special characteristics of land cover of insular Southeast Asia. With an overall accuracy of 82%, the map provides reliable new information on the current land cover distribution in this region, which is experiencing rapid land cover changes.     

Intercalibration of NOAA and Meteosat window channel brightness temperatures

This study presents an intercalibration of Meteosat‐5 11 µm channel and NOAA‐14 10.8 µm and 12.0 µm channels, and their comparison for sea and land pixels. The intercalibration empirical relation is derived for clear‐sky sea measurements, with similar zenith viewing angles. The root mean square difference between NOAA‐14 and Meteosat‐5 intercalibrated brightness temperatures is about 1.4 K (4.7 K) for all clear‐sky sea (land) pixels. The discrepancies over land are analysed in terms of viewing angle, surface type, terrain elevation and exposure to sunlight. The satellite viewing geometry is responsible for two major impacts, namely: the obstruction by neighbouring clouds towards one of the satellites; and differences in surface solar illumination viewed by each sensor. It is also shown that the higher discrepancies between intercalibrated temperatures occur for the most heterogeneous surfaces (e.g. Open Shrublands). The effect of terrain variability is not linear and depends strongly on the surface type.     

Measuring fire severity using UAV imagery in semi-arid central Queensland,Australia

Remote-sensing methods for fire severity mapping have traditionally relied on multispectral imagery captured by satellite platforms carrying passive sensors such as Landsat Thematic Mapper /Enhanced Thematic Mapper Plus or Moderate Resolution Imaging Spectroradiometer. This article describes the analysis of high spatial resolution Unmanned Aerial Vehicle (UAV) imagery to assess fire severity on a 117 ha experimental fire conducted on coal mine rehabilitation in an open woodland environment in semi-arid Central Queensland, Australia. Three band indices, Excess Green Index, Excess Green Index Ratio, and Modified Excess Green Index, were used to derive differenced (d) fire severity maps from UAV data. Fire severity data sets derived from aerial photograph interpretation were used to assess the utility of employing UAV technology to determine fire severity impacts. The dEGI was able to separate high severity, low severity, and unburnt areas with an overall classification accuracy of 58% and Kappa statistic of 0.37; outperforming the dEGIR (overall accuracy 55%, Kappa 0.31) and the dMEGI (overall accuracy 38%, Kappa 0.06). Classification accuracy increased for all indices when canopy shadows were masked, with dEGI improving to an overall accuracy of 68% and 0.48 Kappa. The McNemar’s test indicated that there was no significant difference between the classification accuracies for dEGI and dEGIR (p < 0.05). The test also demonstrated that dMEGI was significantly lower in accuracy compared to dEGI and dEGIR (p < 0.05). We quantified the proportion of burnt area within each severity class and calculated that 32% of the site was burnt at high severity, 34% was burnt at low severity, and 34% of the block was unburnt due to the patchy nature of the fire. We discuss the UAV-specific errors associated with fire severity mapping, and the potential for UAVs to assist land managers to assess the extent and severity of fire and subsequent recovery of burnt ecosystems at local scales (10⁴m²–1 km²).     

Characteristics of the 1 km AVHRR data set for North America

The North America portion of a new global 1 km AVHRR time-series dataset was produced recently by the U.S. Geological Survey, EROS Data Center. Characteristics of the dataset were evaluated for scan-angle distribution, image area distortion as the result of map projection, distribution of high solar zenith angle, and cloud presence in image composites produced using maximum values of normalized difference vegetation index (NDVI). The evaluation showed that the compositing procedure exhibits a bias favouring off-nadir pixels, particularly at post-nadir (forward scanning) positions in the winter months. Results for scan angle distribution and image area distortion provide a basis for calculating the data's effective minimum mapping area for various geographical locations. The amount of missing data due to large solar zenith angle effect varies from 42 per cent in January to 1 per cent in July. Cloud contaminated pixels estimated for the thirty-six 10-day composites range from 7·5 per cent in May to 1·6 per cent in November. Recompositing the North America data set from 10-day cycles to monthly cycles can effectively reduce the amount of cloudy pixels in the data.     

An estimation of the optimum temporal resolution for monitoring vegetation condition on a nationwide scale using MODIS/Terra data

Monitoring vegetation condition is an important issue in the Mediterranean region, in terms of both securing food and preventing fires. The recent abundance of remotely sensed data, such as the daily availability of MODIS imagery, raises the issue of appropriate temporal sampling when monitoring vegetation: under‐sampling may not accurately describe the phenomenon under consideration, whilst over‐sampling would increase the cost of the project without additional benefit. The aim of this work is to estimate the optimum temporal resolution for vegetation monitoring on a nationwide scale using 250 m MODIS/Terra daily images and composites. Specific objectives include: (i) an investigation into the optimum temporal resolution for monitoring vegetation condition during the dry season on a nationwide scale using time‐series analysis of Normalized Difference Vegetation Index, NDVI, datasets, (ii) an investigation into whether this temporal resolution differs between the two major vegetation categories of natural and managed vegetation, and (iii) a quality assessment of multi‐temporal NDVI composites following the proposed optimum temporal resolution. A time‐series of daily NDVI data is developed for Greece using MODIS/Terra 250 m imagery. After smoothing to remove noise and cloud influence, it is subjected to temporal autocorrelation analysis, and its level of significance is the adopted objective function. In addition, NDVI composites are created at various temporal resolutions and compared using qualitative criteria. Results indicate that the proposed optimum temporal resolution is different for managed and natural vegetation. Finally, quality assessment of the multi‐temporal NDVI composites reveals that compositing at the proposed optimum temporal resolution could derive products that are useful for operational monitoring of vegetation.     

Cross‐calibration of MSG1‐SEVIRI infrared channels with Terra‐MODIS channels

This paper addresses the cross‐calibration of the infrared channels 4 (3.9 µm), 9 (10.8 µm) and 10 (12.0 µm) of the Spinning Enhanced Visible and Infra‐Red Imager (SEVIRI) onboard the Meteosat Second Generation 1 (MSG1) satellite with the channels of the MODerate resolution Imaging Spectroradiometer (MODIS) onboard Terra. The cross‐calibrations, including the Ray‐Matching (RM) method and the Radiative Transfer Modelling (RTM) method, were developed and implemented over a tropical area using SEVIRI and MODIS measurements of July 2005 and July 2006 with absolute view zenith angle differences (|ΔVZA|)<0.5°, absolute view azimuth angle differences (|ΔVAA|)<0.5° and absolute time differences (|ΔTime|)<10 min. The results obtained by the RM and RTM methods revealed calibration discrepancies between the two sensors. The results obtained by the RM method were consistent with previously published results. The results obtained by the RTM method were consistent with the results obtained by the RM method if the temperature differences caused by the spectral differences between the two sensors were taken into account. From the cross‐calibration results obtained by the two methods, the use of the results obtained by the RTM method to recalibrate the SEVIRI data is recommended. The recalibrations remove the overestimation of the Land Surface Temperature (LST) retrieved from the SEVIRI data by a split‐window method.     

Developing clear-sky,cloud and cloud shadow mask for producing clear-sky composites at 250-meter spatial resolution for the seven MODIS land bands over Canada and North America

A new technology was developed at the Canada Centre for Remote Sensing (CCRS) for generating Canada-wide and North America continental scale clear-sky composites at 250 m spatial resolution for all seven MODIS land spectral bands (B1–B7). The MODIS Level 1B (MOD02) swath level data are used as input to circumvent the problems with image distortion in the mid latitude and polar regions inherent to the global sinusoidal (SIN) projection utilized for the standard MODIS data products. The MODIS 500 m land bands B3 to B7 are first downscaled to 250 m resolution using an adaptive regression and normalization scheme for compatibility with the 250 m bands B1 and B2. A new method has been developed to produce the mask of clear-sky, cloud and cloud shadow at 250 m resolution. It shows substantial advantages in comparison with the MODIS 250 m standard cloud masks. The testing of new cloud mask showed that it is in reasonable agreement with the MODIS 1-km standard product once it is aggregated to 1-km scale, while the cloud shadow detection looks more reliable with the new methodology. Nevertheless, more quantitative analyses of the presented scene identification technique are required to understand its performance over the range of input scenes in various seasons. The new clear-sky compositing scheme employs a scene-dependent decision matrix. It is demonstrated that this new scheme provides better results than any others based on a single compositing criterion, such as maximum NDVI or minimum visible reflectance. To account for surface bi-directional properties, two clear-sky composites for the same time period are produced by separating backward scattering and forward scattering geometries, which separate pixels with the sun-satellite relative azimuth angles within 90°–270° and outside of this range. Comparison with Landsat imagery and with MODIS standard composite products demonstrated the advantage of the new technique for screening cloud and cloud shadow, and generating high spatial resolution MODIS clear-sky composites. The new data products are mapped in the Lambert Conformal Conic (LCC) projection for Canada and the Lambert Azimuthal Equal-Area (LAEA) projection for North America. Presently this activity is limited to MODIS/TERRA due to known problems with band-to-band registration and noisy SWIR channels on MODIS/AQUA.     

An in situ study of the effects of surface anisotropy on the remote sensing of burned savannah

This Letter presents field‐based evidence of the perturbing effects of surface anisotropy on the remote sensing of burned savannah. The analysis is based on bidirectional spectral reflectance data collected at different solar illumination angles and convolved to Moderate‐resolution Imaging Spectroradiometer (MODIS) reflective bands. Results from a grass savannah site show that burning reduces the anisotropy of the surface compared to its pre‐burn state. In contrast, at a shrub savannah site, burning reduces or increases surface anisotropy. Spectral indices defined from 1.240 µm and 2.130 µm reflectance, and 1.640 µm and 2.130 µm reflectance, provided stronger diurnal separation between burned and unburned areas than individual reflectance bands but do not eliminate anisotropic effects. The Normalized Difference Vegetation Index (NDVI) provides weak diurnal separation relative to these near‐ and mid‐infrared based indices. Implications of the findings are discussed for burned area mapping.     

AVHRR multitemporal compositing techniques for burned land mapping

The performance of several criteria to generate multitemporal composites of daily AVHRR images for burned land mapping was tested on some large fires affecting the Iberian Peninsula. The experiment was based on four tests that assessed the discriminability between burned and unburned areas, the presence of artefacts, the verticality of the viewing angle, and the spatial coherency. The maximum temperature was found to be the most appropriate compositing technique for burned land mapping, since it provides the highest performance for the four assessments, with close to maximum discrimination power, no clouds or cloud shadows, high spatial coherency and close to nadir observation angles. Traditional compositing criterion based on maximizing NDVI values provided the lowest ranks in most tests.     

Characteristics of composited AVHRR data and problems in their classification

Unsupervised classification procedures were applied to a temporal sequence of fifteen bi-weekly composited NDVI images produced from AVHRR LAC data. Individual examination of the input images appeared to show substantial contamination due to clouds which persist through the compositing period. These apparent cloud features dominated the results of the clustering procedures. The composites also include large numbers of far off-nadir pixels. This causes severe spatial smoothing and produces a blurred image appearance. Further combining the data to monthly composites largely eliminated the cloud cover problem, but did not necessarily reduce the frequency of large view zenith angles. Preprocessing of high temporal frequency; low spatial resolution data such as that provided by AVHRR and the planned EOS MODIS instrument must more effectively remove the effects of clouds, correct for anisotropic scattering from the atmosphere and bi-directional reflectance from the surface, and should be biased towards the selection of near-nadir measurements. The design and processing procedures for MODIS will reduce problems associated with atmospheric effects and the geometric distortion of pixels, while enhancing the detection and screening of clouds.     

Spectral shape-based temporal compositing algorithms for MODIS surface reflectance data

Spectral similarity metrics have previously been used to select representative spectra from a class for use in spectral mixture modeling. Since the tasks of spectral selection for spectral mixture modeling and spectral selection for temporal compositing are similar, these metrics may have utility for temporal compositing. This paper explores the use of two spectral similarity metrics, endmember average root mean square error (EAR) and minimum average spectral angle (MASA), for constructing temporal composites. EAR and MASA compositing algorithms were compared against four previously used algorithms, including maximum NDVI, minimum view zenith angle, minimum blue, and median red. A total of 10 different algorithms were used to create 16-day composites of Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance data over a 6-year period. Algorithm performance was assessed based on short-term temporal variability in spectral reflectance and in a selection of indices, both within a southwestern California study area and within five land-cover class subsets. EAR compositing produced the lowest variability for 4 out of 7 MODIS bands, as measured by the root mean square of time series residuals. MASA or EAR compositing produced the lowest root mean square residual values for all of the tested indices. To assess how compositing algorithms might affect remote sensing correlations with biophysical variables, correlations between indices calculated from different composites and live fuel moisture were compared. Correlations between indices and live fuel moisture were higher for shape-based composites compared with the standard composites.     

Comparison and enhancement of MODIS cloud mask products for Southeast Asia

Accurate quality information on cloud occurrence is of utmost importance for a wide range of remote-sensing applications and analyses. This study compares the two existing cloud mask products available for the Moderate Resolution Imaging Spectroradiometer (MODIS), stored in the quality layer of the MOD09 daily surface reflectance product. For both masks, statistics on cloud occurrence are calculated for 1 year of daily surface reflectance data covering the area of southeast Asia. Furthermore, a cloud mask enhancement algorithm is presented for increasing cloud flag reliability by effectively combining the existing cloud labels together with the utilization of annual statistics based on the blue reflectance band. Furthermore, since a lot of compositing algorithms rely on cloud mask information for the filtering of unsuitable observations, the influence of the different cloud masks on 8-day MOD09A1 composite outputs is examined with respect to data availability and average view angles. The results of the statistical analysis show that the accuracy of the two cloud mask products differs significantly for southeast Asia. Particularly, the radiative influence of land cover proves to strongly affect the reliability of the cloud flags, although to varying degrees throughout the year. The enhancement algorithm successfully identifies undetected clear observations in the original masks while simultaneously setting upper limits for atmospheric contamination. In this manner, considerably higher proportions of cloud-free observations can be retained compared to a clear-sky conservative combination of the masks as applied in other compositing algorithms.     

DMSP-OLS estimation of tropical forest area impacted by surface fires in Roraima,Brazil: 1995 versus 1998

A procedure has been developed to locate and estimate the area of heavy forest burning based on the frequency of DMSP-OLS (US Air Force Defense Meteorological Satellite Program Operational Linescan System) fire detection from time series of observations across the fire season. A calibration was developed for Roraima, Brazil, using Landsat Thematic Mapper (TM) data acquired near the end of the 1998 burn season and analysed to identify unburnt, partially burnt and heavily burnt forest areas. A fire detection frequency threshold of five nights was used to map heavily burnt forest using the 3 months of DMSP-OLS observations. The threshold of five fire detections, which could occur anytime during the 3-month time period, was selected to constrain errors of commission involving unburnt forest to 10% of the total area for unburnt forest in the calibration area. At this threshold setting the DMSP-OLS estimate of heavily burnt forest area covered 79% of the Landsat measured area. It was found that 77% of the 1998 heavily burnt forest area was outside of the state's protected areas (national parks, reserves, indigenous areas). Two of the protected areas sustained a substantial increase in heavily burnt forest in 1998 relative to 1995 (Reserva Biologica Mucaja and Parque Ind gena Yanomami). The 1998 forest burning in these two areas was concentrated in their eastern-most sections. The core of the Yanomami area did not sustain extensive burning in 1998. Protected areas in the north-eastern section of the state, where forests are mixed with cerrado, had moderate increases in heavily burnt forest in 1998. Other protected areas were largely free of the heavy forest burning, which was concentrated to the west of the state's primary cerrado zone.     

De‐shadowing of satellite/airborne imagery

A de‐shadowing technique is presented for multispectral and hyperspectral imagery over land acquired by satellite/airborne sensors. The method requires a channel in the visible and at least one spectral band in the near‐infrared (0.8–1?µm) region, but performs much better if bands in the short‐wave infrared region (around 1.6 and 2.2?µm) are available as well. The algorithm consists of these major components: (i) calculation of the covariance matrix and zero‐reflectance matched filter vector, (ii) derivation of the unscaled and scaled shadow function, (iii) histogram thresholding of the unscaled shadow function to define the core shadow areas, (iv) region growing to include the surroundings of the core shadow areas for a smooth shadow/clear transition, and (v) de‐shadowing of the pixels in the final shadow mask. The critical parameters of the method are discussed. Example images from different climates and landscapes are presented to demonstrate the successful performance of the shadow removal process over land surfaces.     

Connection between fire and land cover change in Southeast Asia: a remote sensing case study in Riau,Sumatra

Biomass burning in humid tropical Southeast Asia causes haze problems, environmental degradation and economic losses. These fires have often been connected to land clearance and changes in land cover. This study investigates the relationship between fire and land cover change in humid tropical Southeast Asia. The analysis is based on three sets of land cover classifications and burnt area detections (1998, 2000 and 2002), based on SPOT 2 HRV and SPOT 4 HRVIR images. The results indicate that the connection between land cover change and fire is highly dependent on land cover type. There is a strong correlation between land cover change and fire in the primary vegetation and a slightly weaker correlation in the secondary growth. More than 90% of the severely burnt primary vegetation areas and 45% of the severely burnt secondary growth resulted in land cover type change during the study. Fire is extensively used for conversion on the aforementioned land cover types. In the managed land cover types fire does not have any correlation with land cover changes. This study also revealed substantial land cover changes in the study area. The primary vegetation areas diminish by approximately 5% per year, while the managed land cover types consistently increase their areas.     

Active forest fire monitoring in Uttaranchal State,India using multi‐temporal DMSP‐OLS and MODIS data

This paper gives an account of day–night active forest fire monitoring conducted over the sub‐tropical and moist temperate forests of the Uttaranchal State, India, during 2005 using the Defence Meteorological Satellite Program – Operational Line Scan system (DMSP‐OLS) and Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data. The state experienced heavy fire episodes during May–June 2005 and daily datasets of DMSP‐OLS (night‐time) and selected cloud‐free MODIS (daytime) datasets were used in mapping active fire locations. DMSP‐OLS collects data in visible (0.5 to 0.9 µm) and thermal (10.5 to 12.5 µm) bands and detects dim sources of lighting on the earth's surface, including fires. The enhanced fire algorithm for active fire detection (version 4) was used in deriving fire products from MODIS datasets. Fire locations derived from DMSP‐OLS and MODIS data were validated with limited ground data from forest department and media reports. Results of the study indicated that the state experienced heavy fire episodes, most of them occurring during night‐time rather than daytime. Validation of satellite‐derived fires with ground data showed a high degree of spatial correlation.