Modeling and sensitivity analysis of fire emissions in southern Africa during SAFARI 2000

This paper uses three recently generated southern African satellite burned area products for the month of September 2000 in a sensitivity study of regional biomass burning emissions for a number of trace gases and particulates. Differences in the extent and location of areas burned among products generated from Moderate Resolution Imaging Spectroradiometer (MODIS), Systeme Pour l'Observation de la Terre (SPOT-VEGETATION), and Along Track Scanning Radiometer (ATSR-2) data are significant and result in different emissions estimates for woodland and grassland land cover types. Due to the different emission profiles in woodlands and grasslands, favoring relatively more products of incomplete combustion in woodlands compared with products of complete combustion in grasslands in the late dry season, these changes are not proportional to the differences in the burned area amounts. The importance of accurate burned area information not just in terms of the total area but also in terms of its spatial distribution becomes apparent from our modeling results. This paper highlights the urgent need for satellite data producers to provide accuracy assessments associated with satellite-derived products. Preferably, these accuracy data will be spatially explicit, or defined in a way that can be applied in a spatially explicit modeling context, to enable emissions uncertainties to be defined with respect to different landscape units in support of greenhouse gas emissions reporting.     

Modeling and sensitivity analysis of neural networks

This paper investigates the use of neural networks for the identification of linear time invariant dynamical systems. Two classes of networks, namely the multilayer feedforward network and the recurrent network with linear neurons, are studied. A notation based on Kronecker product and vector-valued function of matrix is introduced for neural models. It permits to write a feedforward network as a one step ahead predictor used in parameter estimation. A special attention is devoted to system theory interpretation of neural models. Sensitivity analysis can be formulated using derivatives based on the above-mentioned notation.     

半自主式火控系统建模与动态特性分析

对火控系统智能化的发展要求进行了研究,针对现装备中火控系统存在的客观问题,立足现有技术基础,分析了半自主式火控系统体系结构设计的重要现实意义。设计了半自主式火控系统的功能结构和工作流程,并建立了基于Petri网的系统仿真模型,对系统搜索指示、智能规划和精确打击3个功能单元的工作过程进行了详细论证。通过实验进行了系统的动态特性分析,实验结果表明,该系统的Petri网模型是可行和有效的,系统结构和工作流程设计是合理的,为下一步的系统战技性能分析和系统结构优化提供了基础支撑。     

Mapping vegetation-soil-climate complexes in southern Africa using temporal Fourier analysis of NOAA-AVHRR NDVI data

The distribution and phenology of vegetation is largely associated with climate, terrain characteristics and human activity. Satellite data provide the opportunity to monitor continuously the dynamics of vegetation, its changes and its impact on the environment. Monthly normalized difference vegetation index (NDVI) values which had been extracted from NOAA-AVHRR GAC data for Southern Africa (south of the Equator) from 1981 till 1991 were analysed using the Fast Fourier Transform (FFT). The FFT gives a new representation of the time series of images, i.e. a set of images of amplitude and phase (pixelwise) of periodic functions with different frequencies, which allows the analysis of the vegetation phenology using only the amplitude and phase of the most important periodic components. This approach is a powerful way to monitor various dynamic parameters of the vegetation in Southern Africa. The relationship between amplitude values and aridity and vegetation type was studied by correlating the amplitude and phase images with maps of the Budyko Aridity Index (ratio of net radiation to rainfall) and of the vegetation (White map). Images of amplitude and phase values were then used as attributes to map land units homogeneous as regards leaf display phenology. The map of these land units in Southern Africa, which has a 7.6 km resolution, was based on long term (9 years) information of 19 different vegetation-soil-climate complexes. Ten out of the 19 vegetation-soil-climate complexes are described in detail.     

Mapping fire scars in a southern African savannah using Landsat imagery

The spectral, spatial and temporal characteristics of the Landsat data record make it appropriate for mapping fire scars. Twenty-two annual fire scar maps from 1972–2002 were produced from historical Landsat imagery for a semi-arid savannah landscape on the South Africa–Botswana border, centred over Madikwe Game Reserve (MGR) in South Africa. A principal components transformation (PCT) helped differentiate the spectral signal of fire scars in each image. A simple, nonparametric, supervised classification (parallelepiped) of the PCT data differentiated burned and unburned areas. During most years, fire occurrences and the percentage of area burned annually were lowest in Botswana, highest in MGR, and intermediate in South Africa outside MGR. These fire scar maps are aiding MGR managers, who are endeavouring to restore a more active fire regime following decades of fire exclusion.     

Space-borne high resolution fire remote sensing in Benin,West Africa

An experiment on high resolution remote sensing of fires was conducted in the Ouémé catchment area of Benin, West Africa using the Bi-spectral InfraRed Detection (BIRD) satellite. The high spatial resolution capability of the BIRD system allows the detection of fires with active burning areas less than a few hundred square metres in the sub-pixel domain, as well as the estimation of quantitative characteristics of these fires, such as the radiative fire energy release and (less accurately) the effective fire temperature and area.     

An improved approach to fire monitoring in West Africa usingAVHRR data

Data from the Advanced Very High Resolution Radiometer (AVHRR) have been used for the detection of fires in various ecosystems throughout the world. In this study the most commonlyused methods have been applied to a time-series of 63 AVHRR day time images for the whole of West Africa for the 1991–1992 dry season. The West African region includes ecosystems ranging from dry Sahelian grasslands to moist tropical forests. Furthermore, these ecosystems show considerable seasonal variability. Existing methods were found to be inadequate for fire detection for the whole region becauseof the spatial and temporal heterogeneity of the region's environments. A number of changes were made to the established methods and the new fire detection procedure was applied to the time-series. Field verification and interpretation of the results in the context of the main ecological divisionsof the regionshowed the new method to give good results for all ecosystems throughout the season. Finally, interpretation of fire counts using a Geographical Information System illustrates how such data can improve our knowledge of fire activity at national and sub-continental scales.     

Assessment of forest fire danger conditions in southern Spain from NOAA images and meteorological indices

Traditionally, the estimation of fire danger is performed from meteorological danger indices that are computed for single locations, where the weather stations are located. Frequently, these locations are far from forested areas, and there is a need to spatially interpolate danger variables. Methods for spatial interpolation are always prone to error, especially for those variables that show a greater spatial variability (wind, mainly). Satellite images may be considered a good alternative for interpolation of danger values, since they perform a spatially exhaustive observation of the territory. This paper analyses the spatial distribution of the Canadian Drought Code (DC), part of the Canadian Forest Fire Weather Index System (CFFWIS), in the region of Andaluc{@a (south Spain) following two procedures. First, maps of DC values were obtained from spatial interpolation of a network of 30 weather stations using the squared inverse distance algorithm. These results were compared with interpolation based on linear regression analysis, using National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) derived bands as independent variables. The most significant variables found for these empirical fittings were relative greenness, the ratio of Normalized Difference Vegetation Index (NDVI) and surface temperature, and a temporal variable, which accounts for the variations in day length throughout the fire season. After several empirical fittings were obtained, the most precise estimation was found after adjusting the coefficients to the time period considered.     

Fuzzy expert systems and GIS for cholera health risk prediction in southern Africa

Cholera (Vibrio cholerae) is endemic in southern Africa and frequently breaks out in epidemics along the eastern seaboard. Extensive resources are directed at combating cholera yet it remains a significant problem. Limited resources could better be directed to prevent outbreaks if it were possible to assess the risk of an outbreak in space and time. The CSIR in South Africa is investigating technologies to predict health risk in line with national priorities. This paper describes an early warning GIS prototype tool aimed at identifying favourable preconditions for cholera outbreaks. These preconditions were defined using an expert system approach. The variables thus identified were input into a spatial fuzzy logic model that outputs risks. The model is based on the assumption that endemic reservoirs of cholera occur and that environmental conditions, especially algal blooms, trigger Vibrio growth in the natural environment. If the preconditions are met, the subsequent spread of cholera depends mainly on socio-economic factors such as human behaviour and access to safe water supply and sanitation. This paper focuses on the environmental preconditions. The methodology described relies on capturing expert knowledge and historic data that integrate climatic and biophysical parameters with epidemiological data to produce a fuzzy surface of cholera outbreak risk potential.     

Modeling fuels and fire effects in 3D: Model description and applications

Scientists and managers critically need ways to assess how fuel treatments alter fire behavior, yet few tools currently exist for this purpose. We present a spatially-explicit-fuel-modeling system, FuelManager, which models fuels, vegetation growth, fire behavior (using a physics-based model, FIRETEC), and fire effects. FuelManager's flexible approach facilitates modeling fuels across a wide range of detail. Large trees or shrubs with specific coordinates are modeled as individual “Plants”, while understory plants are modeled as collections of plants called “LayerSets”. Both Plants and LayerSets contain various fuel particles (leaves, needles, twigs) with various properties including shape, size and surface area to volume ratio. A wide range of vegetation and treatments can be modeled, analyzed quantitatively and visualized in a 3D viewer. We describe the modeling approach and demonstrate fuel modeling at different levels of detail, fuel treatment and fire effects capabilities. Detailed model equations are provided in the Appendices.     

A typical pattern of vegetation conditions in southern Africa during El Nino years detected from AVHRR data using three-channel numerical index

The 1997-98 El Nino is one of the strongest events of the last 50 years. Its absolute magnitude, areal extent, and rapid development have raised a serious concern among decision makers because of its possible impact on global ecosystems. In this study, El Nino consequences for land ecosystem were examined using the new AVHRR-based three-channel index (VT), widely used for monitoring drought around the world. The VT index was used to identify a typical pattern of vegetation conditions in southern Africa during the recent El Nino years. Features and trends of the 1997-98 El Nino including its intensity, extent, and impact on vegetation are also discussed.     

Characterization of head modulation during touchdown process using magnetic spacing sensitivity analysis

Microsystem Technologies - Recent growth in the cloud storage industry has created a massive demand for higher capacity hard disk drives (HDD). A sub-nanometer head media spacing (HMS) remains the...     

JPEG2000中重要性编码及上下文建模的改进

众所周知,基于上下文建模的算术编码是JPEG2000标准中的关键技术,而对于重要性编码,JPEG2000中采用的上下文模型是码块内的8邻域系数,由于其根据经验将所有上下文分成9类,且所有比特平面都使用这套固定的上下文分类方案。因此针对这种上下文分类方案的不足,对JPEG2000中重要性编码及其上下文建模方式进行了改进,即首先建立比原来的3×3更大的上下文模型;然后提出了一种基于重要上下文的扫描方式;最后基于新的扫描方式根据最小码长的准则进行上下文量化的优化,考虑到不同子扫描之间的统计差异,对不同的扫描子过程使用不同的上下文模型方案。实验结果表明,与JPEG2000的无损压缩相比,新的重要性编码算法的无损压缩结果(平均比特率)较JPEG2000提高了1.312%。     

Comparison of TAMSAT and CPC rainfall estimates with raingauges,for southern Africa

Two different TAMSAT (Tropical Applications of Meteorological Satellites) methods of rainfall estimation were developed for northern and southern Africa, based on Meteosat images. These two methods were used to make rainfall estimates for the southern rainy season from October 1995 to April 1996. Estimates produced by both TAMSAT methods and estimates produced by the CPC (Climate Prediction Center) method were then compared with kriged data from over 800 raingauges in southern Africa. This shows that operational TAMSAT estimates are better over plateau regions, with 59% of estimates within one standard error (s.e.) of the kriged rainfall. Over mountainous regions the CPC approach is generally better, although all methods underestimate and give only 40% of estimates within 1 s.e. The two TAMSAT methods show little difference across a whole season, but when looked at in detail the northern method gives unsatisfactory calibrations. The CPC method does have significant overall improvements by building in real-time raingauge data, but only where sufficient raingauges are available.     

Assessment of fire severity and species diversity in the southern Appalachians using Landsat TM and ETM+ imagery

Relatively little is known about the disturbance ecology of large wildfires in the southern Appalachians. The occurrence of a 4000-ha wildfire in the Linville Gorge Wilderness area in western North Carolina has provided a rare opportunity to study a large fire with a range of severities. The objectives of this study were to 1) assess the potential for using multi-temporal Landsat imagery to map fire severity in the southern Appalachians, 2) examine the influences of topography and forest community type on the spatial pattern of fire severity; and 3) examine the relationship between predicted fire severity and changes in species richness. A non-linear regression equation predicted a field-based composite burn index (CBI) as a function of change in the Normalized Burn Ratio (dNBR) with an R² of 0.71. Fire severity was highest on drier landforms located on upper hillslopes, ridges, and on southwest aspects, and was higher in pine communities than in other forest types. Predicted CBI was positively correlated with changes in species richness and with the post-fire cover of pine seedlings (Pinus virginiana, P. rigida, and P. pungens), suggesting that burn severity maps can be used to predict community-level fire effects across large landscapes. Despite the relatively large size of this fire for the southern Appalachians, severity was strongly linked to topographic variability and pre-fire vegetation, and spatial variation in fire severity was correlated with changes in species richness. Thus, the Linville Gorge fire appears to have generally reinforced the ecological constraints imposed by underlying environmental gradients.     

Interpretation of Nimbus-7 37 GHz microwave brightness temperature data in semi-arid southern Africa

Abstract

Monthly 37 GHz microwave polarization difference temperatures (MPDT) derived from the Nimbus-7 scanning multichannel microwave radiometer (SMMR) for southern Africa from 1979 to 1985 are compared with rainfall and Advanced Very High Resolution Radiometer (AVHRR) normalized difference vegetation index (NDVI) data. MPDT rose sharply during a drought episode which occurred within the period included in the data. The rise was seen not only in the growing season, but also in the dry season MPDT when no actively photosynthetic, water-containing leaves are present. The results suggest that scattering of the emitted microwave radiation by dead and living vegetation is a more important factor than has previously been recognized. The sensitivity of MPDT to small quantities of dry vegetation encourages the hope that standing dead vegetation and plant litter may be remotely sensed. In the absence of vegetation, rough terrain reduced the MPDT whereas a damp surface increased it.     

Estimating fire severity and carbon emissions over Australian tropical savannahs based on passive microwave satellite observations

ABSTRACT

We investigated the use of a recently developed satellite-based vegetation optical depth (VOD) data set to estimate fire severity and carbon emission over Australian tropical savannahs. VOD is sensitive to the dynamics of all aboveground vegetation and available nearly every two days. For areas burned during 2003–2010, we calculated the VOD change (ΔVOD) pre- and post-fire and the associated loss in the above ground biomass carbon. ΔVOD agreed well with the Normalized Burn Ratio change (ΔNBR) which is the metric used to estimate fire severity and carbon loss compared well with modelled emissions from the Global Fire Emissions Database (GFED). We found that the ΔVOD and ΔNBR are generally linearly related. The Pearson correlation coefficients (r) between VOD- and GFED-based fire carbon emissions for monthly and annual total estimates are very high, 0.92 and 0.96, respectively. A key feature of fire carbon emissions is the strong inter-annual variation, ranging from 21.1 Mt in 2010 to 84.3 Mt in 2004. This study demonstrates that a reasonable estimate of fire severity and carbon emissions can be achieved in a timely manner based on multiple satellite observations over Australian tropical savannahs, which can be complementary to the currently used approaches.     

A statistical study of NDVI sensitivity to seasonal and interannual rainfall variations in Southern Africa

The relation between rainfall and the Normalized Difference Vegetation Index (NDVI) in Africa south of 15 S (1983-1988) is studied. For 115 1 by 1 grid-points, the spatial distribution of annual NDVI and rainfall means is highly comparable. Both parameters have overall decreasing values from Mozambique to South-Western Africa. The strongest correlations occur when NDVI monthly values are compared with the bimonthly preceding rainfall amounts, attesting a time response of one to two months. At these time and space scales, NDVI does not appear to be sensitive to the seasonal and interannual rainfall variations in the Namib desert, South Namibia and western Cape Province. Along the Indian Ocean coast, it is weakly sensitive to the seasonal cycle only. It becomes largely sensitive to the seasonal cycle in Zimbabwe, and in South-Western Zambia. A high sensitivity to the interannual rainfall variability is only observed on the Southern African Plateau, around the Kalahari basin. Multivariate analyses show that geographical conditions of seasonal and interannual rainfall-NDVI associations strongly differ. While a sensitivity to seasonal rainfall is observed in areas where mean annual rainfall varies from 300 to 900mm and where the contrast between wet and dry seasons is strong, the sensitivity to interannual rainfall anomalies is observed only for relative dry areas, where mean annual rainfall varies from 300 to 500mm. In both cases, the relation is observed whatever the soil type or vegetation formation.