Daniela Stroppiana

An assessment of vegetation fire in Africa (1981–1991): Burned areas, burned biomass, and atmospheric emissions

This paper presents the first published time series of burned area maps of Africa, covering an 8 year period, 1981–1983 and 1985–1991. These maps were derived from the analysis of the advanced very high resolution radiometer (AVHRR) global area coverage (GAC) images at 5 km resolution. The burned area maps for the period 1985–1991 were used with biomass density and burning efficiency figures, to estimate the quantity of burned biomass during this 6 year period. Emission factors were further used to estimate the trace gas and aerosol emissions produced by vegetation fires. Biomass density was estimated based on values found in the literature and on the accumulated normalized difference vegetation index (NDVI) as derived from the remote sensing images. Burning efficiency was assessed with a dryness index that was based on the relative greenness index (RGI), also derived from the NDVI. Average emission factors were retrieved from the literature. The uncertainties in the burned area, biomass density, combustion efficiency, and emission factors were considered, with a total error of 51% for the burned biomass and 58% for the emission estimates. The results obtained for the burned biomass in Africa were compared with other values found in the literature and showed values lower by a factor of 1.1–3.3. The annual burned biomass from vegetation fires in Africa on average was estimated between 704 and 2168 Tg . In the same way, the atmospheric emissions on average ranges are as follows: CO2 (990–3726 Tg), CO (40–151 Tg), CH4 (1.2–4.4 Tg), NOx (2.8–10.6 Tg), and PM (< 2.5 μm) (3.3–12.4Tg).

Radiometric analysis of SPOT-VEGETATION images for burnt area detection in Northern Australia

Abstract Radiometric analysis of SPOT-VEGETATION (VGT) images acquired over Australia was carried out as a basis for the development of an algorithm to map burnt areas in woodland savannas. We analysed the variability of daily ground reflectance and its relationship with illumination and viewing geometry. Finding that the geometrical effects can be parameterised by the phase angle (angle between the illumination and the viewing directions) and the viewing zenith angle (VZA), we fit a simple linear model to the observations. The results show that about 60–70% of the variability in the daily reflectance is caused by geometrical effects. The residual 30–40% of the variability is probably due to changes in vegetation condition, such as senescence, and residual atmospheric contamination. We tested temporal compositing as a practical method of reducing the variability in the reflectance whilst retaining the burnt area signal. We inspected the radiometric and geometrical effects of four different compositing criteria and showed that minimum near infrared (NIR) is the most appropriate for burnt area mapping over the study area. In order to analyse the sensitivity of the VGT spectral bands and derived indices to changes induced by fire, we extracted burnt area spectral signatures for different vegetation types. The persistence of the burnt signal, as observed with each band and index, was analysed. Among the bands, NIR is shown to be the most sensitive to fire occurrence. There is a clear drop in the reflectance immediately after the fire and it remains very low during subsequent weeks. On the other hand, the burnt signal in the short-wave infrared (SWIR) band is showed to be strongly dependent on the vegetation cover type and on the age of the burnt area. Among the indices, the Global Environment Monitoring Index (GEMI) is identified as the most suitable for detecting changes induced by fire on the vegetation cover.

The Global Fire Product: Daily Fire Occurrence from April 1992 to December 1993 Derived from NOAA-AVHRR Data.

Global active fire maps have been produced over a 21-month period from April 1992 to December 1993. A contextual active fire detection algorithm has been applied to the NOAA AVHRR (National Oceanic and Atmospheric Administration Advanced Very High Resolution Radiometer) 1.1 km images provided by the IGBP-DIS (International Geosphere-Biosphere Programme Data and Information System) 1 km AVHRR Global Land Project data set. The Global Fire Product (GFP) is composed of daily fire position tables, 10-day synthesis raster format maps containing fire density and cloud/no-data information; it is now available as the first global scale description of the spatial and temporal distribution of active vegetation fire. In answer to science community requirements the GFP supplies information which can be used to estimate fire impacts on atmospheric chemistry, climate, land use and land cover changes.

Multi-year monitoring of rice crop phenology through time series analysis of MODIS images

Precise phenological calendars, for each cultivated species and variety, are necessary both to highlight anomalous agronomic situations and to feed crop models. This study, conducted in the Italian rice area, focuses on the evaluation of the contribution of remote sensing satellite data to providing phenological information on rice cropping systems. A time series of 5 years (2001–2005) of the Moderate Resolution Imaging Spectrometer (MODIS) Normalized Difference Vegetation Index (NDVI) 16-day composites was analysed with the TIMESAT program in order to automatically retrieve key phenological information such as the start of season (emergence), peak (heading) and end of season (maturity). The procedure involved two steps: (1) interpolation and smoothing of MODIS NDVI temporal profile and (2) the analysis of a temporal signal for the extraction of the phenological metrics. The remote sensing estimates were evaluated using information regarding cultivated variety, sowing dates, management and production directly acquired from rice farmers. A good correlation (R 2 = 0.92, n = 24) has been observed between estimates derived from satellites and estimates produced with the traditional Growing Degree Days (GDD) method based on thermal unit accumulation. Improved estimates of the maturity stage were obtained using a procedure that integrates satellite and GDD methods; however its application requires spatially distributed information on the cultivated varieties. Satellite derived maps of the retrieved phenological parameters showed an intra-seasonal pattern related to different cultivated varieties. Inter-seasonal analysis allowed the anomalous behaviour of the year 2003 to be highlighted, characterized by rapid growth at the beginning of the spring and an early senescence. The results confirm the potential of remotely sensed data for the monitoring of crop status and for the forcing of crop models in a spatially distributed way.

Advanced methods of plant disease detection. A review

Plant diseases are responsible for major economic losses in the agricultural industry worldwide. Monitoring plant health and detecting pathogen early are essential to reduce disease spread and facilitate effective management practices. DNA-based and serological methods now provide essential tools for accurate plant disease diagnosis, in addition to the traditional visual scouting for symptoms. Although DNA-based and serological methods have revolutionized plant disease detection, they are not very reliable at asymptomatic stage, especially in case of pathogen with systemic diffusion. They need at least 1–2 days for sample harvest, processing, and analysis. Here, we describe modern methods based on nucleic acid and protein analysis. Then, we review innovative approaches currently under development. Our main findings are the following: (1) novel sensors based on the analysis of host responses, e.g., differential mobility spectrometer and lateral flow devices, deliver instantaneous results and can effectively detect early infections directly in the field; (2) biosensors based on phage display and biophotonics can also detect instantaneously infections although they can be integrated with other systems; and (3) remote sensing techniques coupled with spectroscopy-based methods allow high spatialization of results, these techniques may be very useful as a rapid preliminary identification of primary infections. We explain how these tools will help plant disease management and complement serological and DNA-based methods. While serological and PCR-based methods are the most available and effective to confirm disease diagnosis, volatile and biophotonic sensors provide instantaneous results and may be used to identify infections at asymptomatic stages. Remote sensing technologies will be extremely helpful to greatly spatialize diagnostic results. These innovative techniques represent unprecedented tools to render agriculture more sustainable and safe, avoiding expensive use of pesticides in crop protection.

Assessing remotely sensed chlorophyll-a for the implementation of the Water Framework Directive in European perialpine lakes

The lakes of the European perialpine region constitute a large water reservoir, which is threatened by the anthropogenic pressure altering water quality. The Water Framework Directive of the European Commission aims to protect water resources and monitoring is seen as an essential step for achieving this goal. Remote sensing can provide frequent data for large scale studies of water quality parameters such as chlorophyll-a (chl-a). In this work we use a dataset of maps of chl-a derived from over 200 MERIS (MEdium Resolution Imaging Spectrometer) satellite images for comparing water quality of 12 perialpine lakes in the period 2003-2009. Besides the different trophic levels of the lakes, results confirm that the seasonal variability of chl-a concentration is particularly pronounced during spring and autumn especially for the more eutrophic lakes. We show that relying on only one sample for the assessment of lake water quality during the season might lead to misleading results and erroneous assignments to quality classes. Time series MERIS data represents a suitable and cost-effective technology to fill this gap, depicting the dynamics of the surface waters of lakes in agreement with the evolution of natural phenomena.

The use of SPOT VEGETATION data in a classification tree approach for burnt area mapping in Australian savanna

An algorithm to map burnt areas has been developed for SPOT VEGETATION (VGT) data in Australian woodland savannas. A time series of daily VGT images (15 May to 15 July 1999) was composited into 10-day periods by applying a minimum value criterion to the near-infrared band (0.78-0.89 @m). The algorithm was developed using a classification tree methodology that was confirmed as a powerful means of image classification. This methodology allowed the identification of three classes of burnt surfaces that appear to be differentiated by the proportion of the pixel that is burnt, the intensity of the fire and the density of the tree layer. The performance of the algorithm was assessed by classification of one VGT composite image (31 May-9 June) using, as representative of the ground truth, burnt areas extracted from two Landsat TM scenes (9 June). We randomly extracted 30 windows (each of ∼14 km by 14 km) for which we compared the percentage of area burnt as derived from TM and VGT. The estimated mean absolute deviation in the percentage of the area burnt in each window is - 6.3%. In the area common to the two datasets a total amount of 6473 km 2 was estimated to be burnt in the VGT classification against 7536 km 2 that was burnt according to TM images. The accuracy of the classification was found to vary with the vegetation type being the most accurate estimate in low woodland with an underestimation error of 8.6%. These results show that VGT could be a very useful sensor for burnt area mapping over large woodland areas, although the low spatial resolution and the lack of a thermal band can be a limitation in certain conditions (e.g. understorey burns). The same methodology will be applied to map burnt areas for the entire Australian continent.

Satellite monitoring of fire in the EXPRESSO study area during the 1996 dry season experiment: Active fires, burnt area, and atmospheric emissions

Fire activity in central Africa was monitored with NOAA advanced very high resolution radiometer (AVHRR) satellite imagery, acquired in situ during the 1996 dry season campaign of the Experiment for Regional Sources and Sinks of Oxidants (EXPRESSO). The extent of the area affected by fire was estimated with a contextual active fire detection algorithm, and with a burnt area mapping approach, based on multiple fuzzy thresholds. The latter was considered to produce more accurate results. In a study area of 2×106 km2, and during a 5-week period, the areas affected were estimated at 112,578 and 525,820 km2 by the active fires and burnt area algorithms, respectively. Biomass densities, combustion factors, and emissions factors for Sudanian savanna, Guinean savanna, and dense tropical forest vegetation types were obtained from the literature, and used to estimate biomass burnt (228–371 Tg), and pyrogenic emissions of aerosols (1.8–2.6 Tg) and of the trace gases CO2 (374–609 Tg), CO (29.2–39.0 Tg), CH4 (2.05–2.73 Tg), and NOx (1.1–1.4 Tg). Because of its high biomass density, the tropical forest was a major source of atmospheric emissions, in spite of the relatively small extent of area burnt in this ecosystem. This highlights the need for particularly accurate estimates of area burnt, biomass density, combustion factor, and emissions factor for the dense tropical forest, as well as the potential for a significant increase in regional pyrogenic emissions as a consequence of deforestation.

An algorithm for mapping burnt areas in Australia using SPOT-VEGETATION data

An algorithm has been developed to map burnt areas over the Australian continent using SPOT-VEGETATION (VGT) S1 satellite images. The algorithm is composed of a set of thresholds applied to each pixels value of the VGT spectral channels, two spectral indices and their temporal difference. The threshold values have been derived by means of a supervised classification methodology based on the classification and regression trees algorithm. A procedure has also been developed specifically for preprocessing the daily S1 images for burnt area mapping purposes. The final product is composed of ten-day and monthly burnt area maps over Australia for the full year 2000.

Evaluation of compositing algorithms over the Brazilian Amazon using SPOT-4 VEGETATION data

The main objective of this study is to evaluate several algorithms to produce monthly composite images of the VEGETATION sensor onboard SPOT-4 over the Brazilian Amazon. The ability of the commonly used Normalized Difference Vegetation Index maximum value composite (MNDVI) and other compositing algorithms (i.e. one- and/or two-step algorithms), in terms of reducing the presence of clouds and cloud shadows and assessing spatial coherence of the composite images was tested. Among the one-step algorithms, the Soil Adjusted Vegetation Index maximum value composite (MSAVI), the minimum value composite of the red band (mRed) and the minimum value composite of the red band with an additional temporal persistence condition (mRedtp) were also analysed. The two-step algorithms included MNDVI or MSAVI followed by the minimum value of the viewing zenith angle, and mRed followed by MNDVI or by MSAVI. These eight compositing algorithms were used to produce monthly (August 2000) composite images of four regions (200 km @ 200 km) representative of different landscape structures in the Brazilian Amazon. The results show that none of the compositing algorithms consistently performs best over all the regions. However, at this stage of the assessment, mRedMNDVI and mRedMSAVI are good candidates for SPOT-4 VGT monthly compositing over the Brazilian Amazon.