Modeling potential forest fire danger using modis data

Generation of fire danger maps play a vital role in forest fire management like forest fire research, locating lookout towers, risk assessment and for various other simulation studies. The present study addresses remote sensing and GIS applications in generating fire danger maps for tropical deciduous forests. Fire danger variables such as fuel type, topography, temperature, and relative humidity have been used in modeling fire danger. Information on local climate patterns and past fire records has been used to derive fire frequency map of the study area. Intermediate indices were derived using multiple regressions, where fire frequency data is taken as dependent variable. Results indicate that forests near human settlements are more vulnerable to forest fires.     

Satellite data and geographic information systems for fire and resource management in the Canadian arctic

Abstract

A method of analyzing remotely sensed data, a geographic information system, and an intelligent fire management system have been developed to provide integrated resource data for fire and other resources management. Natural and cultural features were digitized from 1:50,000 topographic maps using a geographic information system (GIS) to cover the 29 communities below the tree line in the western Canadian Arctic. Landsat Thematic Mapper data covering the same area were classified into land cover or fuel types. Detailed information on each fire such as location, area burned, date of discovery, fire number, fire zone, fire class and source of ignition was obtained and added to each map sheet as attribute data. A generalized vegetation cover map using NOAA AVHRR data was also obtained. The Intelligent Fire Management Information System (IFMIS) integrates relational data bases, geographic information display, and expert systems. It also has a spatial analysis procedure for forest fire preparedness planning. Linking the weather to the forest fuels through the Fire Weather Index system (FWI) and the Fire Behaviour Prediction System (FBPS), fire danger and fire behaviour are calculated and displayed, cell‐by‐cell. Values‐at‐risk and fire suppression resources are used in the dispatching and planning component of the system. The planning component allows the user to evaluate the coverage of fire suppression resources under the prevalent forecast fire behaviour conditions. Through the integration of data from the above systems, a set of maps were created which were used to analyze fire behaviour potential, identify fire hazards, and provide a basis for settlement protection strategies within the context of other land use activities such as wildlife harvesting and recreational activities.     

Fire danger assessment in Iran based on geospatial information

Fire danger assessment is a vital issue to alleviate the impacts of wildland fires. In this study, a fire danger assessment system is proposed, which extensively uses geographical databases to characterize the spatial variations of fire danger conditions in Iran. This assessment requires three steps: (i) generation of the required input variables, (ii) methods to integrate those variables for creating synthetic indices and (iii) validation of those indices versus fire occurrence data. This fire danger model is based on previous works but adapted to Iranian conditions. It includes an estimation of the fire ignition potential (both considering human and climatic factors) and fire propagation potential. The former was generated from a logistic regression approach based on a wide range of input variables. The fire propagation probability was estimated from the Flammap fire behavior model. A first stage for validation of our fire danger system was based on comparing the estimated danger values to actual fire occurrence, based on satellite detected active fires and burned areas. The logistic regression model for fire ignition probability estimated 72.7% of true ignitions. Detected hotspots occurred more frequently in areas with higher fire ignition probability (average value: 0.65) than non hotspots (average value: 0.4). Propagation probability showed higher values for areas with higher proportion of burned area (r = 0.68, p < 0.001).     

基于MODIS数据的火险潜在指数（FPI）及其应用研究

死、活可燃物含水率大小决定森林点燃的难易度,是判断林火能否发生、进行林火预报的重要因子。本文应用火险潜在指数（FPI,Fire Potential Index）模型,从这2个方面分析研究可燃物湿度对林火发生的影响。利用MODIS遥感数据提取FPI模型所需因素（气象数据： 相对湿度、温度; 植被数据： 10 h时滞可燃物湿度、归一化水分指数、植被绿度）,并将获得的2004年10月黑龙江省和2008年3月南方几省的气象、植被数据输入FPI模型,得到火险指数和火险等级划分。实践证明,应用该模型能够提高火险在时间和地理分布上的预报能力及预防技术。     

卫星遥感火点监测应用和研究进展

卫星遥感在自然灾害管理和应对中发挥了越来越多的作用。火点遥感在火灾频发的时候也被寄予厚望。梳理了火山、环境、气候、火灾等方面对火点遥感的需求,中国火灾监测的行业用户对时空分辨率需求迫切程度不同;概述了用于火点遥感监测的卫星数据源特点,光谱维度上以热红外敏感波段最重要,卫星轨道特点也是影响火点遥感监测能力重要因素;在分析火点遥感物理原理和方法的基础上,综述了当前火点遥感方法的研究进展及优缺点。按照火灾发生的灾前、灾中、灾后3个阶段,分别讨论了当前火点遥感提供信息支撑的能力。火点遥感用于发现火灾仍需要与地面监测等手段联合使用。     

FY-3D/MERSI-II全球火点监测产品及其应用

FY-3D/MERSI-II全球火点监测产品主要包括全球范围内的火点位置、亚像元火点面积和火点强度等信息,可用于实时监测全球范围的森林草原火灾、秸秆焚烧等生物质燃烧状况。火点判识算法主要根据中红外通道对高温热源的敏感特性,即含有火点的中红外通道像元辐亮度和亮温较远红外通道的辐亮度和亮温偏高,同时较周边非火点的中红外像元偏高,建立合适的阈值可探测含有火点的像元。亚像元火点面积估算主要使用中红外单通道估算,根据亚像元火点面积估算结果对火点强度进行分级,不同的级别表示不同程度的火点辐射强度。基于全球火点自动判识结果,每日生成0.01°分辨率的卫星遥感日全球火点产品,每月生产0.25°×0.25°格点的全球月火点密度图。在利用FY-3D/MERSI-II火点产品开展的全球火点监测应用中,对多起全球重大野火事件进行了监测,为防灾减灾、全球气候变化研究、生态环境保护等方面提供卫星遥感信息支持。     

重力场对弹道导弹自由段落点影响的仿真分析

利用EGM96地球重力场模型,采用数值积分方法分析了重力场J2项、J4项以及扰动重力对弹道导弹自由段落点偏差的影响,讨论了积分步长的选取和重力场模型阶数的选取。仿真计算结果表明,对6000km左右自由段射程的弹道导弹,J2项引起的落点偏差最大达到15km以上;J4项引起的落点偏差最大达到60m左右;扰动重力的影响可达到数百米,为达到米级的落点精度,需考虑到30阶左右的重力场模型。     

Multi‐temporal Mapping of Burned Forest over Canada Using Satellite‐based Change Metrics

Abstract

A procedure for continental‐scale mapping of burned boreal forest at 10‐day intervals was developed for application to coarse resolution satellite imagery. The basis of the technique is a multiple logistic regression model parameterized using 1998 SPOT‐4 VEGETATION clear‐sky composites and training sites selected across Canada. Predictor features consisted of multi‐temporal change metrics based on reflectance and two vegetation indices, which were normalized to the trajectory of background vegetation to account for phenological variation. Spatial‐contextual tests applied to the logistic model output were developed to remove noise and increase the sensitivity of detection. The procedure was applied over Canada for the 1998‐2000 fire seasons and validated using fire surveys and burned area statistics from forest fire management agencies. The area of falsely mapped burns was found to be small (3.5% commission error over Canada), and most burns larger than 10 km2 were accurately detected and mapped (R² = 0.90, P<0.005, n = 91 for burns in two provinces). Canada‐wide satellite burned area was similar, but consistently smaller by comparison to statistics compiled by the Canadian Interagency Forest Fire Centre (by 17% in 1998, 16% in 1999, and 3% in 2000).     

Using cumulative NOAA-AVHRR spectral indices for estimating fire danger codes in northern boreal forests

In Canada, fire danger is rated by the Canadian forest fire danger rating system (CFFDRS). One of its components is the fire weather index (FWI) system, which has among others the drought code (DC). DC is used here as a surrogate of dead forest fuel moisture. DC values were computed from weather data acquired between 1993 and 1999 and compared to 10-day composite NOAA-AVHRR images acquired over Canadian northern boreal forests. They were yearly correlated with single compositing period and cumulative NDVI and surface temperature (ST) NOAA-AVHRR data. Correlations with cumulative spectral variables were stronger than with single compositing period variables and the best correlations occurred for the spring compositing periods (R between 0.57 and 0.80). Spring DC models using both single compositing period and cumulative spectral variables were established. Surface temperature-based indices were more often used in the models than NDVI-based indices. The models were stronger for dry or normal years than for wet years. Limitations and possible improvements of the models are discussed.     

Remote sensing-based assessment of veld fire trends in multiple interwoven land tenure systems in Zimbabwe

The study used Landsat imagery, MODIS fire data and in situ meteorological data to determine emerging fire trends in interwoven multiple tenure systems in Zimbabwe. Remote sensing enabled fire trends to be determined across terrain and official records barriers. The number of fires and area burnt increased from 2001 up to 2009 then fluctuated across tenure systems. Fire events rose from 9 to 80 per year in some of the tenure systems. Complex relationships among number of fires, area burnt and weather variables within and across tenure systems were identified. The fire situation was responsive to intervention; the positive fire trends were reversed from 2009 onwards. Projected trends show that fire events could be reduced to negative values in three systems, while in two they could double by 2026. The veld fire problem could be eliminated if a holistic approach is adopted to tackle it across sectoral and land tenure divides.     

远距离运动车辆快速跟踪与定位系统设计

为快速跟踪及定位远距离的运动车辆,本文设计了利用单目相机与激光望远测距仪的定位系统。此定位系统装备单目相机、望远测距仪与角度传感器,采用LK光流算法实时跟踪既定目标,构建了相机、测距仪、目标的坐标方程式模型,可实时解算100~300 m外运动目标的位置。该定位系统无需在被测运动车辆上安装任何设施,即可对合作或非合作目标实现跟踪定位。本文以汽车作为运动目标,使用高精度的CORS系统进行精度验证,该系统实时跟踪定位的精度可保持在亚米级。     

融合多源时空数据的城市火灾危险性评估

城市层面的火灾风险评估主要包括火灾危险性、危害性及救援能力等方面。本文选取火灾危险性评估进行针对性研究,在大数据思维的指导下,以相关关系代替因果关系,采用多源数据对评估指标权重、分值进行率定,得出福州市城区火灾危险性时空分布图。首先利用高德地图API对消防历史出警记录进行地址解析,将近万条火灾出警地址空间落点,获得福州市历史火灾空间分布;然后综合城市用地性质现状、用地开发性质、人口分布热力图等多源异构数据,探索其与历史火灾空间分布的相关性;最后以福州城区为例,初步实现具有充分数理支撑的火灾危险性评估方法,形成火灾危险性动态评估成果,为城市消防规划等提供支撑和依据。     

基于ArcObjects和GPS的扑火队伍跟踪监控指挥系统的设计与实现

采用ArcGIS组件集合Arc Objects开发技术,通过对系统设计、系统分析和系统实现,以及Arc Objects技术与GPS技术结合的有效性实践探索,构建扑火队伍跟踪监控指挥系统,实现了县级防火指挥中心对扑火队伍实时监控和指挥,以及省、市级防火指挥中心通过互联网对扑火队伍的监控功能。为森林防火决策科学化、指挥快速化、调度实时化提供依据。     

Monitoring Detailed Land‐Cover Changes in Shrubland Habitat Reserves Using Multi‐temporal IKONOS Data

Abstract

The objective of this study was to explore the utility of multi‐temporal, multi‐spectral image data acquired by the IKONOS satellite system for monitoring detailed land cover changes within shrubland habitat reserves. Sub‐pixel accuracy in date‐to‐date registration was achieved, in spite of the irregular relief of the study area and the high spatial resolution of the imagery. Change vector classification enabled features ranging in size from tens of square meters to several hectares to be detected and six general land cover change classes to be identified. Interpretation of the change vector classification product in conjunction with visual inspection of the multi‐temporal imagery enabled identification of specific change types such as: vegetation disturbance and associated increase in soil exposure, shrub removal, urban edge vegetation clearing and fire maintenance, increase in vegetation cover, spread of invasive plant species, fire scars and subsequent recovery, erosional scouring, trail and road development, and expansion of bicycle disturbances.     

Predictive fire occurrence modelling to improve burned area estimation at a regional scale: A case study in East Caprivi,Namibia

Fires threaten human lives, property and natural resources in Southern African savannas. Due to warming climate, fire occurrence may increase and fires become more intense. It is crucial, therefore, to understand the complexity of spatiotemporal and probabilistic characteristics of fires. This study scrutinizes spatiotemporal characteristics of fires and the role played by abiotic, biotic and anthropogenic factors for fire probability modelling in a semiarid Southern African savanna environment. The MODIS fire products: fire hot spots (MOD14A2 and MYD14A2) and burned area product MODIS (MCD45A1), and GIS derived data were used in analysis. Fire hot spots occurrence was first analysed, and spatial autocorrelation for fires investigated, using Moran's I correlograms. Fire probability models were created using generalized linear models (GLMs). Separate models were produced for abiotic, biotic, anthropogenic and combined factors and an autocovariate variable was tested for model improvement. The hierarchical partitioning method was used to determine independent effects of explanatory variables. The discriminating ability of models was evaluated using area under the curve (AUC) from the receiver operating characteristic (ROC) plot. The results showed that 19.2–24.4% of East Caprivi burned when detected using MODIS hot spots fire data and these fires were strongly spatially autocorrelated. Therefore, the autocovariate variable significantly improved fire probability models when added to them. For autologistic models, i.e. models accounting for spatial autocorrelation, discrimination was good to excellent (AUC 0.858–0.942). For models not counting spatial autocorrelation, prediction success was poor to moderate (AUC 0.542–0.745). The results of this study clearly showed that spatial autocorrelation has to be taken in to account in the fire probability model building process when using remotely sensed and GIS derived data. This study also showed that fire probability models accounting for spatial autocorrelation proved to be superior in regional scale burned area estimation when compared with MODIS burned area product (MCD45A1).     

Doppler Surveying with the JMR-1 Receiver

Geoceiver observations of Navy Navigational Satellites are generally reduced by the Method of Independent Point Positioning using the NSWC Precise Ephemeris. With this approach RMS Errors on the order of one meter have been demonstrated for relative geodetic positioning based on reduction of approximately 40 passes per station. Under the same circumstances, but with the Broadcast Ephemeris being used in place of the Precise Ephemeris, accuracies on the order of 3 to 5 meters are normally to be expected. Here, the more rigorous Short Arc Method can be used to significant advantage to overcome the shortcomings of the Broadcast Ephemeris. Results of a recent field test involving a net occupied by four JMR-1 Doppler Receivers show that the Short Arc Method can produce relative accuracies of better than one meter from as few as 25 passes per station when the Broadcast Ephemeris is used.     

新技术在金沙江河道勘测的应用

ADCP等测量新技术用于金沙江河道,取到了宝贵的成果,但GPS在山区使用受到限制,ADCP和测深仪会受含沙量影响不能满足测量需要,以及电波流速仪因电磁辐射干扰阻碍使用等。为此,通过分析仪器原理和环境条件,提出了一些有效的处理办法,并在实践中得到成功运用。     

基于掌上电脑的智能林火标绘仪的设计与实现

本系统是利用掌上电脑的屏幕触控技术,开发智能标绘功能,使防火工作人员在发现着火点的同时依据导航信息快速地在电子地图上标绘火头、火线的位置,撰写火情报告并输出态势图,然后通过无线上网功能,实时将标绘的信息传送给防火指挥中心,为指挥中心及时做出扑火决策提供支持。     

Methods for Mapping and Medium-Range Forecasting of Fire Danger in Forests on the Basis of Weather Conditions

Two Russian researchers outline a method whereby imagery from NOAA-series satellites is used to augment data derived from Russia's network of meteorological stations during extreme fire hazard situations. The focus more specifically is on developing a medium-range forecast of the fire hazard on the basis of repeated imaging and medium-range (10-day, 3-day) weather forecasts, for the purpose of compiling forecast maps of fire danger to support fire detection, prevention, firefighting measures, as well as the timely deployment of personnel and equipment. Translated by Edward Torrey, Alexandria, Virginia, from: Geografiya i prirodnyye resursy, 2002, No. 4, pp. 112-117.     

Semi-automated mapping of burned areas in semi-arid ecosystems using MODIS time-series imagery

Understanding spatial and temporal patterns of burned areas at regional scales, provides a long-term perspective of fire processes and its effects on ecosystems and vegetation recovery patterns, and it is a key factor to design prevention and post-fire restoration plans and strategies. Remote sensing has become the most widely used tool to detect fire affected areas over large tracts of land (e.g., ecosystem, regional and global levels). Standard satellite burned area and active fire products derived from the 500-m Moderate Resolution Imaging Spectroradiometer (MODIS) and the Satellite Pour l’Observation de la Terre (SPOT) are available to this end. However, prior research caution on the use of these global-scale products for regional and sub-regional applications. Consequently, we propose a novel semi-automated algorithm for identification and mapping of burned areas at regional scale. The semi-arid Monte shrublands, a biome covering 240,000 km² in the western part of Argentina, and exposed to seasonal bushfires was selected as the test area. The algorithm uses a set of the normalized burned ratio index products derived from MODIS time series; using a two-phased cycle, it firstly detects potentially burned pixels while keeping a low commission error (false detection of burned areas), and subsequently labels them as seed patches. Region growing image segmentation algorithms are applied to the seed patches in the second-phase, to define the perimeter of fire affected areas while decreasing omission errors (missing real burned areas). Independently-derived Landsat ETM+ burned-area reference data was used for validation purposes. Additionally, the performance of the adaptive algorithm was assessed against standard global fire products derived from MODIS Aqua and Terra satellites, total burned area (MCD45A1), the active fire algorithm (MOD14); and the L3JRC SPOT VEGETATION 1 km GLOBCARBON products. The correlation between the size of burned areas detected by the global fire products and independently-derived Landsat reference data ranged from R² = 0.01–0.28, while our algorithm performed showed a stronger correlation coefficient (R² = 0.96). Our findings confirm prior research calling for caution when using the global fire products locally or regionally.