We evaluated the feasibility for operational snow drought monitoring over Europe based on the near-real-time snow water equivalent (SWE) satellite product from the EUMETSAT Satellite Application Facility on Support to Operational Hydrology and Water Management (H-SAF). To do so, the consistency of this dataset with the consolidated dataset of the Canadian Meteorological Centre (CMC), as well as with the ERA5 reanalysis dataset from the European Centre for Medium-Range Weather Forecasts, was tested in terms of both spatial snow coverage and detection of anomalies from the long-term climatology. The analysis confirms a general good agreement among the three products as well as substantial differences over mountainous terrains, with the H-SAF product capturing only about 30% of the areas identified by CMC as snow-covered in those areas, while a better match between the ERA5 and the CMC spatial coverage is observed. However, significant inconsistencies in the correlation between all three SWE anomalies are observed over mountain areas. Due to the lack of a reliable reference dataset, the observed inconsistencies and the coarse spatial resolution (0.25 deg) of all three products limit the possibility for snow drought monitoring over key European regions such as the Alps.
The values of the Normalized Difference Vegetation Index obtained from NOAA Advanced Very High Resolution Radiometer (AVHRR) have often been used for forestry application, including the assessment of fire risk. Forest fire risk estimates were based mainly on the decrease of NDVI values during the summer in areas subject to summer drought. However, the inter-annual variability of the vegetation response has never been extensively taken into account. The present work was based on the assumption that Mediterranean vegetation is adapted to summer drought and one possible estimator of the vegetation stress was the inter-annual variability of the vegetation status, as reflected by NDVI values. This article presents a novel methodology for the assessment of fire risk based on the comparison of the current NDVI values, on a given area, with the historical values along a time series of 13 years. The first part of the study is focused on the characterization of the Minimum and Maximum long term daily images. The second part is centered on the best method to compare the long term Maximum and Minimum with the current NDVI. A statistical index, Dynamic Relative Greenness, DRG, was tested on as a novel potential fire risk indicator.
Each year thousands of hectares of forest burnt across Southern Europe. To date, remote sensing assessments of this phenomenon have focused on the use of optical satellite imagery. However, the presence of clouds and smoke prevents the acquisition of this type of data in some areas. It is possible to overcome this problem by using synthetic aperture radar (SAR) data. Principal component analysis (PCA) was performed to quantify differences between pre- and post- fire images and to investigate the separability over a European Remote Sensing (ERS) SAR time series. Moreover, the transformation was carried out to determine the best conditions to acquire optimal SAR imagery according to meteorological parameters and the procedures to enhance burnt area discrimination for the identification of fire damage assessment. A comparative neural network classification was performed in order to map and to assess the burnts using a complete ERS time series or just an image before and an image after the fire according to the PCA. The results suggest that ERS is suitable to highlight areas of localized changes associated with forest fire damage in Mediterranean landcover.
Forest fires in Southern Europe are a major source of concern for environmental security. Every year several hundred thousand hectares of forest are burned. These fires put at risk, not only human life and property but also the sustainability of forests. Hence, it is important to have an accurate and timely knowledge of the total area burned during the fire season as well as the type of forest that is burned. Until now, IRS-WiFS 180 meters spatial resolution images were used to map the burnt areas after the fire season in Southern Europe. However, the lack of a short-wave infrared band is in certain cases a limitation for the detectability of the burnt areas. Fusion of IRS-WiFS with MODIS 500 meters spatial resolution images, that has short-wave infrared bands, could improve the mapping of burned areas. We present results on the data fusion of both images over the Iberian Peninsula on September 25th of 2000. The fused images were obtained through local correlation modeling resulting in MODIS bands of 180 meters pixels. The preliminary results show a good potential to improve the burned area mapping in Southern Europe by using the IRS-WiFS higher spatial resolution images in conjunction with the MODIS short-wave infrared bands.
Legislation was introduced in Portugal to enable the Portuguese Forest Services (DGF) to implement a management strategy to control forest plantation in forest burned areas. Under this legislation, the forest owners have to notify DGF after a fire in order to replant the same species, or to seek DGF’s permission if the previous species is to be replaced. We developed a methodology that uses Earth Observation (EO) data to identify the species composition of the forests before being burned, and to yearly supervise the burnt areas in order to identify new reforestations and check if the forest owners are in compliance with the Portuguese legislation on burnt areas. Our methodology is based on vegetation indices differencing and map algebra leading to the production of maps where potential illegal areas are identified. These areas are then field checked to identify the forest species that was planted and compare it with the pre-fire forest cover to detect legal and illegal situations. The methodology was successfully tested in a study area in central Portugal with an extension of 640 km2. The results are encouraging for an operational implementation of the methodology by DGF, leading to an efficient application of this specific legislation.
Forest fires are a major problem in Portugal, consuming thousands of hectares per year. A great number of fires are due to arson, which most of the times is related to land use change purposes. Different Government Agencies are responsible for checking if the forest owners are in compliance with the legislation that regulates land use change after fire occurrence. Earth observation data can play a very important role in monitoring land cover transitions occurring in burnt forest areas. An exploratory analysis of a Landsat 5 Thematic Mapper (TM) multi-temporal dataset was carried out to see if pre-defined land cover transitions, within a burnt forest area, could be separated and identified. Three vegetation indices (VI) were used for this propose: NDVI, MSAVI and ARVI. The capabilities of these VI were evaluated on test areas that had pine forest before the fire, followed by a transition into eucalyptus planted in the first or second year after fire or shrub land. The three VI were ranked, in terms of separability, between these three types of transition. ARVI was found to be the one that discriminated better between the two eucalyptus transitions and shrub land.
A methodology to detect vegetation burned areas is presented together with the results obtained for the African continent between November 1990 and October 1991. NOAA-AVHRR-GAC-5 Km images were used in this study. The spectral bands and indices used were land surface temperature (Ts) and the Global Environment Monitoring Index (GEMI). The time series was composited in weekly images using the minimum value composite of albedo (MiVCA). After analysis of the weekly profiles on the main vegetation types that are affected by burning, a multitemporal multithreshold technique to detect burned pixels was developed [Burned Area Algorithm (BAA)]. This technique was based on the increase of Ts and a decrease in the GEMI after a fire occurrence. The results showed good agreement at the continental scale with the temporal and spatial patterns of active fires from the IGBP-DIS Global Fire Product. Comparison with a Landsat TM image classification showed good performance of the algorithm.
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