Search

A locally-adapted multitemporal two-phase burned area (BA) algorithm has been developed using as inputs Sentinel-2 MSI reflectance measurements in the short and near infrared wavebands plus the active fires detected by Terra and Aqua MODIS sensors. An initial burned area map is created in the first step, from which tile dependent statistics are extracted for the second step. The whole Sub-Saharan Africa (around 25 M km(2)) was processed with this algorithm at a spatial resolution of 20 m, from January to December 2016. This period covers two half fire seasons on the Northern Hemisphere and an entire fire season in the South. The area was selected as existing BA products account it to include around 70% of global BA. Validation of this product was based on a two-stage stratified random sampling of Landsat multitemporal images. Higher accuracy values than existing global BA products were observed, with Dice coefficient of 77% and omission and commission errors of 26.5% and 19.3% respectively. The standard NASA BA product (MCD64A1 c6) showed a similar commission error (20.4%), but much higher omission errors (59.6%), with a lower Dice coefficient (53.6%). The BA algorithm was processed over > 11,000 Sentinel-2 images to create a database that would also include small fires (< 100 ha). This is the first time a continental BA product is generated from medium resolution sensors (spatial resolution = 20 m), showing their operational potential for improving our current understanding of global fire impacts. Total BA estimated from our product was 4.9 M km(2), around 80% larger area than what the NASA BA product (MCD64A1 c6) detected in the same period (2.7 M km(2)). The main differences between the two products were found in regions where small fires (< 100 ha) account for a significant proportion of total BA, as global products based on coarse pixel sizes (500 m for MCD64A1) unlikely detect them. On the negative side, Sentinel-2 based products have lower temporal resolution and consequently are more affected by cloud/cloud shadows and have less temporal reporting accuracy than global BA products. The product derived from S2 imagery would greatly contribute to better understanding the impacts of small fires in global fire regimes, particularly in tropical regions, where such fires are frequent. This product is named FireCCISFD11 and it is publicly available at: https://www.esa-fire-cci.org/node/262, last accessed on November 2018. This research was carried out within the Fire_cci project (https://www.esa-fire-cci.org/, last accessed on November 2018), contract no. 4000115006/15/I-NB, which has been funded by the European Space Agency (ESA) under the Climate Change Initiative Programme. The FireCCISFD11 product can be downloaded at https://www.esa-fire-cci.org/node/262 (last accessed on November 2018).

This paper shows the interest of laboratory hyperspectral measurements to estimate fuel moisture content for fire risk assessment. Measurements were obtained for two Mediterranean forest species over a controlled water stress experiment. The hyperspectral measurements were performed with a Specim camera, sensible to spectral reflectance from 900 to 1700 nm. We calculated the spectral correlation with variations in the moisture content, both for the camera raw bands as well as for those adjusted to the MODIS (Moderate Resolution Imaging Spectroradiometer) sensor. High correlation values were obtained for moisture content measurements and the Normalized Difference Water Index (NDWI), both for Q. suber (r=0.803 with the FMC and r=0.846 for EWT), and for the Q. robur (r=0.705 with the FMC and r=0.802 for EWT). Our work asserts that both parameters can be estimated with appropriate accuracy using satellite images.

The aim of this research was to monitor transparency in inland water bodies from water reflectance field measurements and also from high and medium spatial resolution sensors. The most suitable band to measure transparency was determined by sampling irradiance, water reflectance and transparency at different depths. The results of this sampling show that the wavelength range 520-600 nm is the most suitable band for measuring transparency. Reflectance in this band was regressed against transparency field data to derive an empirical equation for transparency estimation. This equation was assessed using data from Landsat Thematic Mapper (TM) images from 1992 and Daedalus 1268 Airborne Thematic Mapper (ATM) data from 1997. The difference in the errors in the data observed between the two dates was attributed to the fact that in 1997 the sampling time was less than 3 h (1.5 h before and after the acquisition of the image), which allowed similar solar illumination conditions to be maintained, while in 1992 the sampling time was much longer. Once the equation was verified, water transparency monitoring was performed for a set of satellite images obtained from 1984 to 2000. The maps show that transparency in the water bodies responds to seasonal trends. Furthermore, this analysis enabled trophic state and Ecological Quality Ratio (EQR) transparency maps to be generated, which are very useful for the management of water bodies.

AHVRR Channel 3 data have been used widely for forest fire detection and mapping. However, little attention has been paid to the use of these data for daily fire growth monitoring. A simple method ...

(1994). Postgraduate training in remote sensing—a British perspective. International Journal of Remote Sensing: Vol. 15, No. 15, pp. 3059-3071.

El uso de modelos de simulación de la reflectividad (MSR) para la estimación de parámetros biofísicos mediante imágenes de satélite se ha desarrollado notablemente en los últimos años, gracias a su poder de generalización. No obstante, resultan complejos de parametrizar, lo que dificulta su uso operativo. En este trabajo se presentan los principales problemas que plantea el uso de los MSR en teledetección, resaltando la importancia de considerar relaciones ecofisiológicas entre las variables de entrada de estos modelos. Se presenta como caso práctico la estimación del contenido de humedad de matorrales mediterráneos a partir de imágenes MODIS, empleando los MSR denominados PROSPECT y SAILH. El análisis muestra cómo, al considerar la co-variación de las variables de entrada en la parametrización de los MSR se logran estimaciones más precisas (con errores medios cercanos al 30%) que si las variables se parámetrizan aleatoriamente (errores próximos al 50%). Simulation approaches based on Radiative Transfer Models (RTM) have been widely used in the last few years to estimate biophysical parameters from remotely sensed data, thanks to their greater generalization power. However, the operational use of RTM is constrained by the difficulty of providing correct parameterization of the input variables. This paper reviews the main problems of using RTM to accurately estimate biophysical variables from satellite imagery, highlighting the need for considering ecophysiological relationships between the input variables. A case study, focused on the estimation of shrublands water content from MODIS imagery is presented. The selected RTM were PROSPECT and SAILH. As a result of this exercise, it is shown that the accuracy in the estimations is greatly improved by considering the co-variation between the inputs (average errors about 30% against errors around 50% when using a random variation of input parameters). Este trabajo ha sido financiado por el Programa Nacional de I+D en Medio Ambiente y Clima, del Ministerio de Educación y Ciencia, a través del proyecto Firemap (CGL2004-060490C04-01/CLI) y del Programa de Formación del Profesorado Universitario (FPU), a través de la beca doctoral de Marta Yebra.

International audience; Fire disturbance is a significant component of the climate system. Analysis of satellite-derived burned areas has allowed the identification of fire patches and their morphology as a new resource for tracking fire spread to improve fire models used to assess the impact of fires on climate and the carbon cycle. A critical parameter of the flood-fill algorithm used to create fire patches is the cut-off (in days) below which it aggregates two contiguous burned pixels to the same fire patch. However, the current level of validation is insufficient to understand the effect of the cut-off values and sensor resolutions on the subsequent fire-patch morphology. The FRY v1.0 database of functional fire-patch traits (e.g., size, elongation, and direction) emanates from the analyses of two global burned-area products derived from MODIS and MERIS sensors with different spatial and temporal resolutions and with cut-off values of 3, 5, 9, and 14 days. To evaluate whether the FRY products are accurately identifying the spatial features of fire patches and what are the most realistic cut-off values to use in different sub-regions of North America, we propose a new functional diversity trait-based approach, which compares the satellite-derived fire patches to forest service perimeters as reference data. This paper shows the accuracy of the FRY fire patches ≥300 ha in North America during 2005–2011. Our analysis demonstrates that fire patches with a high cut-off of 14 days and those derived from the MODIS sensor, with their high temporal resolution, better identify the fire diversity in North America. In conclusion, our statistical framework can be used for assessing satellite-derived fire patches. Furthermore, the temporal resolution of satellite sensors is the most important factor in identifying fire patches — thus space agencies should consider it when planning the future development of cost-effective climate observation systems.