Burned Area Index Research Articles

On a new coordinate system for improved discrimination of vegetation and burned areas using MIR/NIR information

A transformation defined on the near-infrared (NIR) and middle-infrared (MIR) space is presented that allows deriving a new coordinate system appropriate for vegetation and burned area discrimination. The transformation is based on the difference between MIR and NIR in conjunction with the distance from a convergence point in the MIR/NIR space, representative of a totally burnt surface. One of the derived coordinates presents a small scatter for pixels associated to vegetated surfaces (strict scale) whereas the other one covers a wide range of values (large scale) that suggest its use as a proxy of water content of vegetation. The strict scale character of the first coordinate together with the large scale character of the second one make the coordinate system especially adequate to discriminate vegetated surfaces and rank them according to the water content, from green and dry to burned vegetation. The performance of the new coordinate system is then assessed against than traditional ratio or modified ratio indices (namely the Vegetation Index, the Burned Area Index and the Global Environmental Monitoring Index, modified to the MIR/NIR space) and it is shown that the new coordinate system provides better information than traditional indices, opening interesting perspectives for burned area discrimination and other applications like drought monitoring.

Integration of AWiFS and MODIS active fire data for burn mapping at regional level using the Burned Area Synergic Algorithm (BASA)

The present paper presents an algorithm that synergistically combines data from four different parts of the spectrum (near-, shortwave, middle- and thermal infrared) to produce a reliable burned-area map. It is based on the use of a modified version of the BAIM (MODIS – Moderate Resolution Imaging Spectrometer – Burned Area Index) together with active fire information. The following study focusses in particular on an image from the AWiFS (Advanced Wide Field Sensor) sensor dated 21 August 2006 and MODIS active fires detected during the first 20 days of August as well as ancillary maps and information. The methodology was tested in Galicia (north-west Spain) where hundreds of forest fires occurred during the first 20 days of August 2006. Burned area data collected from the present work was compared with official fire statistics from both the Spanish Ministry of the Environment and the Galician Forestry Service. The speed, accuracy and cost-effectiveness of this method suggest that it would be of great interest for use at both regional and national levels.

Discriminating cropping systems and agro-environmental measures by remote sensing

The agrarian policy of the European Union tends to support sustainable agriculture, subsidising only cropping systems that are implemented with specific agro-environmental measures. These actions require a precise follow-up of the crops and of the agricultural practices over a large surface. To that end, remote-sensing techniques are unique and cost-effective. We developed here a digital land cover classification in the Mediterranean dryland, mapping and assessing the main cropping systems and some agro-environmental measures such as cover crops in olive orchards and crop stubble for reducing soil erosion. We analysed a high spatial resolution satellite image (QuickBird) taken in early summer around Montilla, southern Spain. Images of the four broad wavebands, six band ratios and three vegetation indices were extracted from the satellite image and studied for the discrimination of nine land covers. The classified regions were determined by applying adequate boundary digital values to the selected images. Our results show that the land covers were discriminated with an overall accuracy of about 90%. Images of the normalised difference vegetation index and the ratio vegetation index discriminated between vegetation and non-vegetation zones. The visible wavebands discriminated roadside trees and herbaceous crops, and the near-infrared waveband highways and urban soil plus bare soil. The ratios blue/green and red/green were useful for distinguishing non-burnt stubble. The burnt stubble area was discriminated through the adapted burnt area index. Olive orchards were classified once the regions of vegetation, non-vegetation and non-burnt stubble were extracted. This technology will be a useful tool of agroecology control for the administration and will be a substitute for the current follow-up of cropping systems by ground visits. It can also be used on a farm level in order to help farmers and technicians to make decisions about the management of sustainable agricultural practices.

FACTORS AFFECTING FLUID REQUIREMENT ON THE FIRST DAY AFTER SEVERE BURN TRAUMA

Parkland formula (PF) is the most often used schema for calculating intravenous resuscitation fluid requirement in burn patients. Some studies have reported that PF underestimates the fluid requirement in 45-63% of patients. The aim of this retrospective study was to analyse factors influencing first-day intravenous fluid replacement set for a targeted urinary output in severely burnt patients. Data of 47 patients with burn injury affecting equal or more than 15% of body surface area were retrieved from the archived files. The local intensive care protocol rendered the infusion rate of lactated Ringer's solution to achieve a urinary output of 0.5-1.0 mL/kg per hour in the first 24 h after burn trauma. First-day i.v. infusion volume was significantly higher than PF preferred. In the first 24 h the hourly volume of intravenous fluid resuscitation per bodyweight per burnt surface area showed significant negative correlation to the burnt body surface area and body mass index, (r = -0.553, P < 0.001; r = -0.570, P < 0.001, respectively) no correlation was found to bodyweight, height or patient age. Patients having deep-burn injury required higher intravenous fluid resuscitation rate than patients having superficial injury only (P < 0.01). Our data suggest that fluid requirement is higher than predicted by PF if the extent of burn or body mass index is low and less if the extent of burn or body mass index is high. The presence of deep burn increases fluid requirement.

Burnt Area Index (BAIM) for burned area discrimination at regional scale using MODIS data

Burnt Area Index (BAIM) for burned area discrimination at regional scale using MODIS data

Estimating spectral separability of satellite derived parameters for burned areas mapping in the Calabria region by using SPOT-Vegetation data

In the Mediterranean regions, fires are considered a major cause of land degradation. Every year, around 45,000 forest fires break out in the Mediterranean basin causing the destruction of about 2.6 million hectares ( FAO, 2001). In Italy, as in other countries of the Mediterranean Basin, a small number of fires generally destroy a large percentage of the total burned areas every year. In these cases, the use of coarse resolution satellite sensors appears to be very useful for the discrimination of burned areas. In this study, SPOT-Vegetation (SPOT-VGT) data at full spatial resolution were analysed in order to investigate the spectral features of burned areas observed in the Mediterranean ecosystems in the Calabria Region during the 1998 fire season. Among the total fire events occurred in the considered period wildland fires larger than 1000 ha were selected for this study. SPOT-VGT imagery acquired before and after fire events were considered. Single channels or spectral indices suitable/or specifically designed for burned areas mapping were analysed. In particular near-infrared (NIR), short-wave infrared reflectance (SWIR), albedo, normalized difference of vegetation index (NDVI), normalized difference of infrared index (NDII), burned area index (BAI), global environmental monitoring index (GEMI) and soil adjusted vegetation index (SAVI), were considered in this study. The changes observed before and after fire occurrence in the considered parameters were presented and discussed. Results showed that among the spectral indices considered in this work, the highest discrimination capability was generally observed for NDII, SAVI, GEMI, BAI and NIR, nevertheless, strongly differences were observed from one fire event to another, and this fact suggests that the discrimination capability must be analysed coupled with the specific land covers affected by fire.

Assessment of the potential of SAC-C/MMRS imagery for mapping burned areas in Spain

Early and detailed information regarding the location and extension of areas affected by forest fires is a critical issue for assessing their effects at several scales. Remote sensing is a valuable tool for burned area mapping, providing spatially explicit information on the scorched areas, even for remote regions. Various sensors have been used for burned land mapping in recent years, covering local (10–30 m pixel size) and global scales (500–1000 m pixel size). Regional inventories require higher detail than global studies, but typically cover larger territories than local studies. For this purpose, the use of medium spatial resolution sensors (100–300 m pixel size) is highly advisable. Few studies have evaluated these sensors, and none have specifically assessed the performance of images acquired by the Argentinian Satellite for Scientific Applications-C/Multispectral Medium Resolution Scanner (SAC-C/MMRS) for mapping burned areas, which is the main objective in this paper. Since information on calibration parameters was not available, raw data was converted into ground reflectance values after a “cross-calibration” method, using Landsat Enhanced Thematic Mapper (ETM+) data as reference. Estimated radiance values showed very high correlation with those for the ETM+ ( r 2>0.9). Several spectral indices were generated and tested. The Burned Area Index (BAI), derived from red and near infrared (NIR) reflectances showed the greatest sensitivity to discriminate burned areas from other land cover types, although the Normalized Difference Infrared Index (NDII), based on the Red and Short Wave Infrared (SWIR) bands also provided good discrimination capability. Image segmentation was performed by using a multi-threshold approach, based on the BAI and the NDII since these two spectral indices have the highest sensitivity to (1) discriminate burned areas and (2) to avoid confusion with water and cloud shadows. Burned perimeters delineated using a seeded region growing algorithm showed good agreement with the perimeters digitised on Landsat ETM+ data, providing accurate estimation of the burned areas compared to statistics provided by the Spanish forest fire authorities. In the first study area (Almerı́a, south-eastern Spain), the perimeters showed a 90% agreement. In the second study site (Madrid), two fires were studied. The first one (Cadalso, south–west of Madrid) showed an agreement of 64%, and for the second one (Patones, north–east of Madrid), the agreement was 84%. The lower accuracy of the former was caused by the great spatial discontinuity of that fire, which includes many patches of unaffected vegetation within the scar perimeter.

Assessment of different spectral indices in the red-near-infrared spectral domain for burned land discrimination

A new spectral index named Burned Area Index (BAI), specifically designed for burned land discrimination in the red-near-infrared spectral domain, was tested on multitemporal sets of Landsat Thematic Mapper (TM) and NOAA Advanced Very High Resolution Radiometer (AVHRR) images. The utility of BAI for burned land discrimination was assessed against other widely used spectral vegetation indices: Normalized Difference Vegetation Index (NDVI), Soil Adjusted Vegetation Index (SAVI) and Global Environmental Monitoring Index (GEMI). BAI provided the highest discrimination ability among the indices tested. It also showed a high variability within scorched areas, which reduced the average normalized distances with respect to other indices. A source of potential confusion between burned land areas and low-reflectance targets, such as water bodies and cloud shadows, was identified. Since BAI was designed to emphasize the charcoal signal in post-fire images, this index was highly dependent on the temporal permanence of charcoal after fires.