Validation of active forest fires detected by MSG‐SEVIRI by means of MODIS hot spots and AWiFS images

The detection of forest fires and the determination of their parameters have been usually carried out by polar‐orbit sensors: AVHRR, (A)ATSR, BIRD, and MODIS mainly. However, their time resolution prevents them from operating in real time. In contrast, the new geostationary sensors have very appropriate capacities for the observation of the Earth and monitoring of forest fires, as is being proved. GOES, MSG, and MTSAT are already operative, and they have led the international community to think that the global observation network in real time may become a reality. The implementation of this network is the aim of the Global Observations of Forest Cover and Land Cover Dynamics (GOFC/GOLD) FIRE Mapping and Monitoring programme, focused internationally on taking decisions concerning the research of the Global Change. In this Letter, the operation in real time by the MSG‐SEVIRI sensor over the Iberian Peninsula is studied. On the other hand, the reliability of validation results by means of polar sensors, with a finer spatial resolution, is difficult to analyse due to errors caused by confused location of fires. This Letter shows that fires detected by means of MSG‐SEVIRI can be an useful option in order to estimate burnt areas at global scale, considering a spatial resolution of 40 km.     

A comparative analysis of the use of NOAA‐AVHRR NDVI and FWI data for forest fire risk assessment

Fires are a major hazard to forests in the Mediterranean region, where, on average, half a million hectares of forested areas are burned every year. The assessment of fire risk is therefore at the heart of fire prevention policies in the region. The estimation of forest fire risk often involves the integration of meteorological and other fuel‐related variables, leading to an index that assesses the different levels of risk. Two indices frequently used to estimate the level of fire risk are the Fire Weather Index (FWI) and the Normalized Difference Vegetation Index (NDVI). Although a correlation between the number of fires and the level of risk determined by these indices has been demonstrated in previous studies, the analyses focused on the changes in fire risk levels in areas where fires took place. The present study analyses the behaviour of the fire risk indices not only in areas where fires occurred but also in areas where fires did not take place. Specifically, the objective of this work was to compare the potential of the two indices to discriminate different levels of fire risk over large areas. Qualitative and quantitative methods were used to compare the statistical distributions of fire event frequencies with those of fire risk levels. The qualitative method highlights graphically the statistical difference between the values of the indices computed over burnt areas and the overall distribution of the values of the indices. The quantitative method, based on the use of the so‐called performance index, was used to evaluate and compare numerically the potential of the indices. The analyses were performed considering very extensive datasets of fire events, satellite data and meteorological data for Spain during a 10‐year period. Although the NDVI is assumed to describe the vegetation status as related to fire ignition, the results show conclusively an enhanced performance of the FWI over the NDVI in identifying areas at risk of fires.     

Assessment of stand‐wise stem volume retrieval in boreal forest from JERS‐1 L‐band SAR backscatter

JERS‐1 L‐band SAR backscatter from test sites in Sweden, Finland and Siberia has been investigated to determine the accuracy level achievable in the boreal zone for stand‐wise forest stem volume retrieval using a model‐based approach. The extensive ground‐data and SAR imagery datasets available allowed analysis of the backscatter temporal dynamics. In dense forests the backscatter primarily depended on the frozen/unfrozen state of the canopy, showing a ～4 dB difference. In sparse forests, the backscatter depended primarily on the dielectric properties of the forest floor, showing smaller differences throughout the year. Backscatter modelling as a function of stem volume was carried out by means of a simple L‐band Water Cloud related scattering model. At each test site, the model fitted the measurements used for training irrespective of the weather conditions. Of the three a priori unknown model parameters, the forest transmissivity coefficient was most affected by seasonal conditions and test site specific features (stand structure, forest management, etc.). Several factors determined the coefficient's estimate, namely weather conditions at acquisition, structural heterogeneities of the forest stands within a test site, forest management practice and ground data accuracy. Stem volume retrieval was strongly influenced by these factors. It performed best under unfrozen conditions and results were temporally consistent. Multi‐temporal combination of single‐image estimates eliminated outliers and slightly decreased the estimation error. Retrieved and measured stem volumes were in good agreement up to maximum levels in Sweden and Finland. For the intensively managed test site in Sweden a 25% relative rms error was obtained. Higher errors were achieved in the larger and more heterogeneous forest test sites in Siberia. Hence, L‐band backscatter can be considered a good candidate for stand‐wise stem volume retrieval in boreal forest, although the forest site conditions play a fundamental role for the final accuracy.     

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.     

Estimation of the North–South Transect of Eastern China forest biomass using remote sensing and forest inventory data

The assessment of forest biomass is required for the estimation of carbon sinks and a myriad other ecological and environmental factors. In this article, we combined satellite data (Thematic Mapper (TM) and Moderate Resolution Imaging Spectrometer (MODIS)), forest inventory data, and meteorological data to estimate forest biomass across the North–South Transect of Eastern China (NSTEC). We estimate that the total regional forest biomass was 2.306 × 10⁹ Megagrams (Mg) in 2007, with a mean coniferous forest biomass density of 132.78 Mg ha^?1 and a mean broadleaved forest biomass density of 142.32 Mg ha^?1. The mean biomass density of the entire NSTEC was 129 Mg ha^?1. Furthermore, we analysed the spatial distribution pattern of the forest biomass and the distribution of biomass along the latitudinal and longitudinal gradients. The biomass was higher in the south and east and lower in the north and west of the transect. In the northern part of the NSTEC, the forest biomass was positively correlated with longitude. However, in the southern part of the transect, the forest biomass was negatively correlated with latitude but positively correlated with longitude. The biomass had an increasing trend with increases in precipitation and temperature. The results of the study can provide useful information for future studies, including quantifying the regional carbon budget.     

Regenerating boreal forest structure estimation using SPOT‐5 pan‐sharpened imagery

Forested stand structure is an important target variable within the fields of wildlife ecology. Remote sensing has often been suggested as a viable alternative to time consuming field and aerial investigations to determine forest structural attributes. In this study, 44 stands of recently harvested, regenerating, and old growth forest within the Foothills Model Forest in west‐central Alberta were selected to test the ability of pan‐sharpened SPOT‐5 spectral response to classify stand structure. For each stand, a Structural Complexity Index (SCI) was calculated from field data using principal components analysis. To complement the spectral response data set and further increase accuracy, the normalized difference moisture index (NDMI) and three window sizes (5×5, 11×11, and 25×25) of first‐ (mean and standard deviation) and second‐order (homogeneity, entropy, contrast, and correlation) textural measures were calculated over the pan‐sharpened image. Stepwise multivariate regression analysis was used to determine the best explanatory model of the SCI using the spectral and textural data. The NDMI, first‐order standard deviation and second‐order correlation texture measures were better able to explain differences in SCI among the 44 forest stands (r² = 0.79). The most appropriate window size for the texture measures was 5×5 indicating that this is a measure only detectable at a very high spatial resolution. The resulting classified SCI values were comparable to the actual field level SCI (r² = 0.74, p = 0.01) and were limited by the strong variability within stands. Future research may find this measure useful either as a separate parameter or as an indicator of forest age for use in wildlife habitat modelling.     

Calibration and validation of a set of multi‐aspect airborne polarimetric Pi‐SAR data

To implement target reconstruction from multi‐aspect SAR data, a simple method of first calibrating multi‐aspect data is presented. It requires that at least one aspect is calibrated beforehand, and other aspects are then calibrated with respect to this calibrated aspect. A natural object, such as flat bare ground, is usually chosen as a reference target, and is expected to preserve identical scattering for all aspects. Thereafter, the channel imbalance factors are estimated from the distribution of the phase difference and amplitude ratio of co‐polarized, hh and vv, echoes of the reference target, and are then used to compensate the whole SAR images. This approach was applied to the calibration of four‐aspect airborne Pi‐SAR (Polarimetric and Interferometric Synthetic Aperture Radar) data for target reconstruction. Based on the same principle, another potential application is calibration of descending (ascending) data using the calibrated ascending (descending) data.     

Evaluation of linear spectral unmixing and ΔNBR for predicting post‐fire recovery in a North American ponderosa pine forest

The Differenced Normalized Burn Ratio (ΔNBR) is widely used to map post‐fire effects in North America from multispectral satellite imagery, but has not been rigorously validated across the great diversity in vegetation types. The importance of these maps to fire rehabilitation crews highlights the need for continued assessment of alternative remote sensing approaches. To meet this need, this study presents a first preliminary comparison of immediate post‐fire char (black ash) fraction, as measured by linear spectral unmixing, and ΔNBR, with two quantitative one‐year post‐fire field measures indicative of canopy and sub‐canopy conditions: % live tree and dry organic litter weight (gm^?2). Image analysis was applied to Landsat 7 Enhanced Thematic Mapper (ETM+) imagery acquired both before and immediately following the 2000 Jasper Fire, South Dakota. Post‐fire field analysis was conducted one‐year post‐fire. Although the immediate post‐fire char fraction (r ² = 0.56, SE = 28.03) and ΔNBR (r ² = 0.55, SE = 29.69) measures produced similarly good predictions of the % live tree, the standard error in the prediction of litter weight with the char fraction method (r ² = 0.55, SE = 4.78) was considerably lower than with ΔNBR (r ² = 0.52, SE = 8.01). Although further research is clearly warranted to evaluate more field measures, in more fires, and across more fire regimes, the char fraction may be a viable approach to predict longer‐term indicators of ecosystem recovery and may potentially act as a surrogate retrospective measure of the fire intensity.     

A theoretical and empirical investigation of multi‐item on‐line auctions

In this paper we explore and analyze the structure of Internet auctions from an analytical and an empirical perspective. Such web‐based auctions are rapidly emerging as a mercantile process of choice in the electronic marketplace. We observe current Internet auctions for one‐time products, such as rapidly aging hardware, and analyze them within the framework of the existing auction theory. While traditional auction theory focuses on single‐item auctions, we observe that a majority of on‐line auctions are multi‐item auctions. A significant contribution of this work is the theoretical derivation of the structure of the winning bids in multi‐item progressive on‐line auctions. Additionally, for comparative purposes, we explore the structural characteristics of alternative multi‐item auction mechanisms proposed in the auction theory. We derive hypotheses based on our analytical results and compare two different types of auction mechanisms. We test the traditional auction theory assumption regarding the homogeneity of bidders and present the first ever empirically derived classification and performance‐comparison of on‐line bidders. We test our hypotheses using real‐world empirical data obtained by tracking a premier web‐based auction site. Statistical analysis of the data indicates that firms may gain by choosing alternative auction mechanisms. We also provide directions for further exploration of this emerging but important dimension of electronic commerce. This revised version was published online in July 2006 with corrections to the Cover Date.     

Comparison and improvement of wavelet‐based image fusion

The wavelets used in image fusion can be categorized into three general classes: orthogonal, biorthogonal, and non‐orthogonal. Although these wavelets share some common properties, each wavelet also has a unique image decomposition and reconstruction characteristic that leads to different fusion results. This paper focuses on the comparison of the image‐fusion methods that utilize the wavelet of the above three general classes, and theoretically analyses the factors that lead to different fusion results. Normally, when a wavelet transformation alone is used for image fusion, the fusion result is not good. However, if a wavelet transform and a traditional fusion method, such as an IHS transform or a PCA transform, are integrated, better fusion results may be achieved. Therefore, this paper also discusses methods to improve wavelet‐based fusion by integrating an IHS or a PCA transform. As the substitution in the IHS transform or the PCA transform is limited to only one component, the integration of the wavelet transform with the IHS or PCA to improve or modify the component, and the use of IHS or PCA transform to fuse the image, can make the fusion process simpler and faster. This integration can also better preserve colour information. IKONOS and QuickBird image data are used to evaluate the seven kinds of wavelet fusion methods (orthogonal wavelet fusion with decimation, orthogonal wavelet fusion without decimation, biorthogonal wavelet fusion with decimation, biorthogonal wavelet fusion without decimation, wavelet fusion based on the ‘à trous’, wavelet and IHS transformation integration, and wavelet and PCA transformation integration). The fusion results are compared graphically, visually, and statistically, and show that wavelet‐integrated methods can improve the fusion result, reduce the ringing or aliasing effects to some extent, and make the whole image smoother. Comparisons of the final results also show that the final result is affected by the type of wavelets (orthogonal, biorthogonal, and non‐orthogonal), decimation or undecimation, and wavelet‐decomposition levels.     

Estimation of Mediterranean forest attributes by the application of k‐NN procedures to multitemporal Landsat ETM+ images

Routine applications of nonparametric estimation methods to satellite data for assisting the creation of forest inventories in Northern European countries are stimulating interest in the possible extension of these methods to more complex Mediterranean areas. This is the subject of the current work, which presents an experiment based on the integration of remotely sensed images and sample field measurements aimed at producing forest attribute maps in central Italy. Testing was carried out in an area where 370 geocoded field plots, sampled on a single‐stage cluster design, were collected to characterize wood and non‐wood forest attributes. These ground data served to apply various k‐Nearest Neighbour (k‐NN) estimation procedures to multitemporal Landsat 7 ETM+ images in order to map major forest attributes (basal area and simulated leaf area index, LAI). More specifically, the investigation focused on evaluating the effects of using satellite images from different periods of the growing season and spectral metrics of increasing complexity. The results achieved by the examined methods are finally discussed in order to provide guidelines for possible operational utilization.     

Fast over‐land atmospheric correction of visible and near‐infrared satellite images

A rapid atmospheric correction method is proposed to be used for visible and near‐infrared satellite sensor images over land. The method is based on a simplified use of a radiative transfer code (RTC), which is used only a priori, to generate Look‐Up‐Tables (LUTs) of the estimated surface reflectance. A typical scenario and ranges of values for the main atmospheric correction parameters are initially established. Each image pixel is treated as a slight deviation from the reference scenario defined by the vector of the typical values for the parameters. The assumption of the parameter's independence allows the use of one‐dimensional LUTs. The method is suitable for near real‐time processing or whenever a large number of data are to be handled rapidly. The operator intervention is minimal, and the computation time involved in the correction of a whole image is about 1000 times shorter than the full use of the base RTC. A test is performed with advanced very‐high‐resolution radiometer (AVHRR) visible and near‐infrared data, using the Second Simulation of the Satellite Signal in the Solar Spectrum (6S) RTC as the base code. The accuracy of the proposed method was compared with the standard use of the 6S RTC over the same dataset with resulting root mean square errors of 0.0114 and 0.0104 for AVHRR bands 1 and 2 for the estimated surface reflectance, respectively.     

Ground‐based monitoring of high‐risk landslides through joint use of laser scanner and interferometric radar

Terrestrial laser scanner (TLS) and interferometric synthetic aperture radar (InSAR) allow the acquisition of data on an observed surface with high spatial sampling rate. The data provided by TLS observation of a landslide ground surface can be used to generate a very detailed digital model of this surface, and multitemporal observations with TLS or continuous or multitemporal observation with InSAR can provide a reliable displacement map. In order to acquire useful information about the analogies, differences, and capabilities, as well as limitations of these techniques, a joint experimentation of TLS and InSAR was performed over two years in various sites in the Italian Alps. The results have indicated that these techniques can provide high‐quality data, can be very useful in the monitoring intended for the mitigation of hydrogeological risk in a wide range of cases, and must be supported by a topographical georeferenced network.     

Analysis of ENVISAT ASAR data for forest parameter retrieval and forest type classification—a case study over deciduous forests of central India

The present paper gives an account of potential of Environment Satellite‐Advanced Synthetic Aperture Radar (ENVISAT‐ASAR) C‐band data in forest parameter retrieval and forest type classification over deciduous forests of Tadoba Andhari Tiger Reserve (TATR), central India. Ground data on phyto‐sociology and Leaf Area Index (LAI) over the study area was collected in 23 sampling points (20m×20m) over the study area. Phyto‐sociological data collected over the study area was used to compute plot‐wise biometric parameters like basal area, volume, stem density and dominant height. ENVISAT ASAR data covering the study area, pertaining to 24 November 2005, has been geo‐referenced and digital number (DN) values were converted to radar backscatter values. Regression analysis between backscatter and the retrieved biometric variables has been done to explain the relationships between SAR backscatter and forest parameters. Analysis showed a significant correlation between backscatter and biometric parameters and backscatter values typically increased with increase in basal area, volume, stem density and dominant height. The scatter observed between ASAR backscatter and stem density, basal area and dominant height suggested limitation of C‐band data in estimating biometric variables in heterogeneous forest systems. Further, ASAR data was used in conjunction with Indian Remote sensing Satellite (IRS‐P6)—Linear Imaging Self Scanner (LISS) III data of 16 October 2004 to classify the study area into different land use/land cover (LU/LC) classes. Various texture and adaptive filters were applied on ASAR image to reduce speckle noise and enhance image features. An attempt is made to merge ASAR image with LISS‐III to enhance feature discrimination. Training sets corresponding to the ground data have been used to derive confusion matrices for the ASAR and LISS‐III images. Results suggested better discrimination of vegetation types in the merged data suggesting the possible use of ASAR data in forest type discrimination.     

Radar backscattering of forest stands†

Research on the potential applications of microwave remote sensing in agriculture is conducted in the Netherlands by the ROVE team. Since active microwave remote sensing, featuring its all-weather capability, also seems to be a promising tool for forest classification, especially on a global scale, the Wageningen Agricultural University started a new working group in co-operation with the ROVE team in order to explore this field of application.

Results of four X-band SLAR flights have been analysed. The digital radar images obtained are accurately corrected both geometrically and radiometrically and indicate gamma values instead of arbitrary grey tones.

Radar signatures, showing seasonal and angular effects, of 16 classes of forest stands have been derived from the images. Special attention has been paid to the statistical properties of the radar signatures and their impact on classification accuracy. Several interesting phenomena have been observed indicating effects of vegetation structure on radar backscattering.

One of the test areas is a young forest in the Oost-Flevoland polder featuring a substantial variety of species; parcels are relatively large, rectangular in shape, homogeneous in structure and age and with pure species stands making this an ideal test site. Another test area is an old forest located at the Veluwe. Much variation in age is present here, which made it possible to determine relationships between tree age and radar backscatter for several coniferous tree species.

The initial work as presented here clearly demonstrates the appropriateness of X-band ability in the classification of (Dutch) forests. Theoretical considerations suggest that a multitemporal approach is likely to give the most accurate results of tree-type classification. A classification simulation yielded overall error fractions ranging from 10 to 16 per cent at the Oost-Flevoland polder test area and 14 to 28 per cent at the Veluwe test area. This can be demonstrated in multitemporal radar images as well as in actual classified images.     

MISR multi‐angular spectral remote sensing for temperate forest mapping at 1.1‐km resolution

Accurate information about temperate forest distribution and extent is important to quantify the carbon sink in the northern temperate forest. While Advanced Very High Resolution Radiometer (AVHRR) and Moderate Resolution Imaging Spectroradiometer (MODIS) multi‐temporal spectral information has been extensively studied for this purpose, it has not been fully studied whether Multi‐angle Imaging Spectroradiometer (MISR) information is helpful for temperate forest mapping at 1.1‐km resolution. This Letter addresses the potential use of 1.1‐km multi‐angular MISR data to improve temperate forest mapping based on a study area in eastern USA. Classification accuracy using nadir‐only MISR data is compared with results derived from the combined use of some off‐nadir MISR data. The results show a substantial increase in forest mapping accuracy when off‐nadir spectral measurements are used.     

Evaluation of the B‐method for determining actual evapotranspiration in a boreal forest from MODIS data

Boreal forests occupy about 11% of the terrestrial surface and represent an important contribution to global energy balance. The ground measurement of daily evapotranspiration (LE_d) is very difficult due to the limitations on experiments. The objective of this paper is to present and explore the applicability of the B‐method for monitoring actual LE_d in these ecosystems. The method shown in this paper allows us to determine the surface fluxes over boreal forests on a daily basis from instantaneous information registered in a conventional meteorological tower, as well as the canopy temperature (T _c) retrieved by satellite. Images collected by the MODIS (moderate resolution imaging spectroradiometer) on board EOS‐Terra have been used for this study. The parameters of the model were calibrated from the SIFLEX‐2002 (Solar Induced Fluorescence Experiment 2002) campaign dataset in a northern boreal forest in Finland. A study of these parameters was made on an hourly basis in order to make the method applicable, not only at midday but within an interval of 7 h around it. This is an important advance with respect to the original formulation of this approach since the overpass time of satellites can be very variable. The comparison between T _c ground measured with a thermal infrared radiometer, and T _c retrieved from land surface temperature (LST) MODIS data, showed an estimation error of ±1.4°C for viewing angles from 5 to 60°. A complete sensitivity analysis was carried out and an estimation error of about ±35%, corresponding to the interval 10.00–11.00 h UTC, was shown as the lowest in LE_d retrieval. Finally, the method was validated over the study site using 21 MODIS images for 2002 and 2003. The results were compared with eddy‐correlation ground measurements. An accuracy of ±1.0 mm/day and an overestimation of 0.3 mm/day were shown in the LE_d retrieval.     

Estimation and validation of LAI using physical and semi‐empirical BRDF models

This letter deals with estimation of LAI for a wheat crop using physical and semi‐empirical BRDF models and IRS‐1D LISS‐III sensor data. NDVI was computed for both the models with LAI as a free parameter. The model‐computed NDVI was compared with corresponding atmospherically corrected LISS‐III NDVI. The estimation of LAI was carried out on the basis of a look‐up table approach and minimum root mean squared deviation between model computed and observed NDVI. The estimated LAI was validated against field measurements carried out during the months of February and March 2003, at the Central State Farm, Rajasthan, India. It was found that LAI was underestimated in both physical and semi‐empirical models. Results show that inclusion of multiple scattering in physical models may not always lead to a more accurate estimation of LAI and that it may be possible to estimate LAI at early stages of crop growth using semi‐empirical models. The coefficient of determination (R ²) between model estimated and measured LAI was 0.57 (standard error of estimate (SE) 0.156) and 0.63 (SE 0.187) for semi‐empirical and physical models, respectively, in the single scattering approximation, for February data. The corresponding values for March data were 0.57 (SE 0.206) and 0.51 (SE 0.216), respectively.     

Small‐footprint,waveform‐resolving lidar estimation of submerged and sub‐canopy topography in coastal environments

The experimental advanced airborne research lidar (EAARL) is an airborne lidar instrument designed to map near‐shore submerged topography and adjacent land elevations simultaneously. This study evaluated data acquired by the EAARL system in February 2003 and March 2004 along the margins of Tampa Bay, Florida, USA, to map bare‐earth elevations under a variety of vegetation types and submerged topography in shallow, turbid water conditions. A spatial filtering algorithm, known as the iterative random consensus filter (IRCF), was used to extract ground elevations from a point cloud of processed last‐surface EAARL returns. Filtered data were compared with acoustic and field measurements acquired in shallow submerged (0–2.5 m water depth) and sub‐canopy environments. Root mean square elevation errors (RMSEs) ranged from 10–14 cm for submerged topography to 16–20 cm for sub‐canopy topography under a variety of vegetation communities. The effect of lidar sampling angles and global positioning system (GPS) satellite configuration on accuracy was investigated. Results show high RMSEs for data acquired during periods of poor satellite configuration and at large sampling angles along the edges of the lidar scan. The results presented in this study confirm the cross‐environment capability of a green‐wavelength, waveform‐resolving lidar system, making it an ideal tool for mapping coastal environments.