基于GIS的森林防火地理信息系统建设方案

GIS的森林防火地理信息系统建设方案,以森林资源专题信息数据库、森林防火信息数据库等为森林防火的信息资源,以数字地图检索显示、数据库管理查询、统计报表、热点标绘和显示查询、电子沙盘、情况标绘等主要功能为一体的森林防火管理和指挥决策的地理信息系统。为各级地方政府和防火部门提供一个扑救森林火灾的指挥平台,方便准确及时地了解火场现场的情况,为森林火灾的扑救指挥和预防提供科学的辅助决策。     

基于植被冠层含水量反演数据的森林易燃程度评价

近年来凉山彝族自治州森林火灾频发,且蔓延迅速,扑救难度大。本文选择传统遥感手段,利用遥感卫星和无人机相关数据,以木里藏族自治县森林火灾为背景,明确了可燃物识别及其易燃性的意义;选择地形、地表温度、植被含水情况等因子对目标区域进行评价,并引入生态水(层)概念,将植被生态水作为一种重要的参数因子;分析研究区的自然环境特点,研究森林植被可燃物易燃程度的重要性及其空间分布,并构建一种适用于森林植被茂密地区的可燃物易燃程度评价体系,为降低研究区森林火灾风险提供防控依据。     

机载光学全谱段遥感林火监测

森林火灾是一种危害极大的自然灾害，是森林扰动的主要类型之一，直接影响森林生态系统结构、碳循环甚至全球气候的变化。近年来，航空平台和传感器的技术进步有效地提升了机载遥感系统探测和监测森林火灾的能力，推动了机载遥感在森林可燃物调查及载量评估、火险测报预测、火场态势及火情监测、灾害损失评估以及火烧迹地生态修复治理等方面的应用。本文首先介绍了中国林业科学研究院机载光学全谱段遥感系统CAF-LiTCHy（Chinese Academy of Forestry’s LiDAR，Thermal，CCD and Hyperspectral airborne observation system），描述了激光雷达扫描仪、热红外相机、CCD相机和高光谱传感器等传感器的参数；然后，阐明了集成方案和观测数据的处理方法；最后，以四川省西昌市“3.30森林火灾”作为该系统火后灾情遥感调查和灾情评估应用示例，综合多传感器数据特征，进行森林火烧程度评价，分析该系统采集的正射影像、冠层高度模型、高光谱影像、热红外影像在森林火灾监测评价中的潜力。研究结果表明CAF-LiTCHy机载遥感观测系统能有效获取森林火灾的灾情信息、火场及火环境参数，可为预防、预报预警、扑救指挥、灾害评估和生态修复提供支持。     

近实时中高空间分辨率森林火灾监测系统展望

森林火灾是最为常见的灾害之一,严重危及人类生命安全。及时准确监测森林火灾的发生及火场状况,对应对火灾及减少损失至关重要。当前,森林火灾卫星遥感监测主要以低空间分辨率的卫星遥感为主,空间分辨率过低导致无法探测规模较小火灾及掌握详细火场态势。针对这一问题,结合近些年中高空间分辨率卫星观测、共享及处理能力的发展,本文从森林火灾卫星遥感监测的基本原理、当前可用中高空间分辨率卫星数据及其特点、中高分辨率森林着火区监测算法,以及数据共享与云端存储与计算等4个技术环节,对森林火灾中高分辨率卫星遥感监测当前研究现状与存在问题进行了总结,阐述了近实时中高空间分辨率森林火灾监测系统的可行性。近实时中高空间分辨率森林火灾监测系统可对已有低空间分辨率森林火灾监测体系形成重要补充,依托其空间分辨率的优势有助于及早、准确发现小规模火情,进而为森林火灾的防治与管理提供更好支撑。     

基于Web技术的专题地图制作

数字专题地图制图系统处理的是空间地理对象及专题数据,其数据来源广泛,数据量大,常用数据库进行管理。Web及Internet技术的发展,促进了网络与数据库的结合,使得如何在网络上有效地获取并处理地理数据正变得日趋重要。本文探讨了如何使用VisualC++6.0提供的网络以及数据库编程技术,来实现地理数据的分布式管理以及通过网络进行数据的图形显示和操作。     

结合高分一号和LiDAR的亚热带森林 年度采伐迹地提取

利用高分一号影像结合机载LiDAR数据进行面向对象的亚热带森林年度采伐迹地分类提取。在面向对象的遥感软件Ecognition中,首先利用森林小班数据参与分割,利用小班数据的属性信息确定林地和非林地区域,在林地区域再一次进行多尺度分割,并通过ESP工具确定最佳分割尺度,通过特征表达提取对象的光谱、纹理、形状、冠层高度模型(CHM)等特征信息,通过最小冗余最大相关性(mRMR)特征选择算法提取最优特征子集,且CHM在最优特征子集中。利用随机森林(RF)分类器进行年度森林采伐迹地分类提取。年度采伐迹地提取精度达到了87%,与没有CHM特征参与分类的情况对比,提取精度提高了13%。     

森林防火地理信息系统的设计与开发

将GIS技术应用于森林防火,实现了C/S与B/S相混合的森林防火信息监测模式。以湖北森林防火系统为例,给出了系统软件结构、功能实现及数据库的设计,为实现基于GIS的森林防火系统提供参考。通过系统运行过程中的数据采集、历史数据累积,可以实现森林防火数据的分析、挖掘,为有效防治森林火灾提供重要途径。     

基于遥感数据的森林火灾监测研究概述

研究和总结了遥感数据在森林火灾监测中的应用,研究对象主要是NOAA和MODIS,内容包括遥感监测火灾的原理、基于2种数据的森林火灾监测算法以及目前的研究现状。最终得出结论:MODIS数据在林火监测的准确率和定位精度等方面较NOAA卫星有较大的提高,动态监测具有较好的应用前景。     

基于SuperMap的林火预报与分析系统的设计与实现

森林是人类的重要物质资源,而森林火灾的发生给人民的生命财产带来了重大损失,如何更好地预防森林火灾的发生就显得尤为重要。本文利用SuperMap软件、C#编程语言等最新的GIS技术设计并实现了林火预报与分析系统,该系统实现了二三维地图浏览、数据管理、辅助决策、损失评估等模块,为林业部门的决策提供了有力的科学依据。     

森林火灾三维可视化快速模拟及输出系统设计

以森林火灾为示范,以火灾地区的多元地理信息数据为基础,结合火灾专题数据,采用适用性强的火灾蔓延模型,研发森林火灾的三维仿真模拟可视化示范系统,实现对火灾发生,发展,蔓延情况的实时模拟。利用三维GIS技术对承灾体受灾情况进行逼真表达并输出模拟灾情产品,为相关部门的灾害预警、监测、救援、分析、统计和应急预案制定提供支持。     

Mapping burns and natural reforestation using thematic Mapper data

Remote sensing techniques are specially suitable to detect and to map areas affected by forest fires. In this work, Landsat 5 Thematic Mapper (TM) data has been used to study a number of forest fires that occurred in the province of Valencia (Spain) and to monitor the vegetation regeneration over burnt areas.

A reference area (non‐burnt forest) was established to assess the change produced by fire. The radiance in the thermal band (10.4–12.5 μm) and the normalized difference in reflectance between near 1R (0.76–0.90 μm) and middle IR (2.08–2.35 μm) were the most suitable parameters to map burnt areas. This index can also be used for monitoring vegetation regeneration in burnt areas. About a month after the fire, the burns show temperatures of 5–6 °C higher than those found in the reference area, and the vegetation index shows negative values whereas the reference area values remain positive. The differences between the burns and the reference area for the vegetation index decrease with time as vegetation regenerates.     

Integrated Spatio‐temporal Data Mining for Forest Fire Prediction

Forests play a critical role in sustaining the human environment. Most forest fires not only destroy the natural environment and ecological balance, but also seriously threaten the security of life and property. The early discovery and forecasting of forest fires are both urgent and necessary for forest fire control. This article explores the possible applications of Spatio‐temporal Data Mining for forest fire prevention. The research pays special attention to the spatio‐temporal forecasting of forest fire areas based upon historic observations. An integrated spatio‐temporal forecasting framework – ISTFF – is proposed: it uses a dynamic recurrent neural network for spatial forecasting. The principle and algorithm of ISTFF are presented, and are then illustrated by a case study of forest fire area prediction in Canada. Comparative analysis of ISTFF with other methods shows its high accuracy in short‐term prediction. The effect of spatial correlations on the prediction accuracy of spatial forecasting is also explored.     

Influences of forest roads and their edge effects on the spatial pattern of burn severity

Previous research has shown that forest roads are an important feature in many landscapes and have significant effects on wildfire ignition and cessation. However, forest road effects on burn severity have not been studied at the landscape level. Therefore, the overarching goal of our study is to identify the influences of road edge effects on the spatial patterns of burn severity. We analyzed six fires within the Okanogan–Wenatchee National Forest on the eastern slope of the Cascades mountain range of central Washington.We generated two categories for assessing road variables: (1) Primary Road Effect Zone (area within 150 m of the nearest road) and (2) Secondary Road Effect Zone (area from 150 m to 300 m to the nearest road). A regular sampling grid including one out of every 9 cells was created for each fire.These grids were intersected with burn severity data in the form of the Relative Differenced Normalized Burn Ratio (RdNBR), road distance category, stream distance, elevation, slope, terrain shape index, heat load index, canopy cover, and fuel type. We fit spatial regression models with RdNBR as the dependent variable.We found that high burn severity is less likely to occur in the Primary Road Effect Zone for most fires, although one fire exhibited the opposite relationship. Forest road edge effects were hypothesized to be an important determinant of burn severity because fragmentation created by roads alters the roadside fuel profile and environment and because road corridors create barriers to fire spread. Recognizing roadside effects on burn severity patterns highlights the need for further study of the range of effects that roads have on fuels and the fire environment and the potential for incorporating road effects into landscape-level assessments of fire risk.     

Forest fire susceptibility modeling using hybrid approaches

Forest fires are considered one of the most highly damaging and devastating of natural disasters, causing considerable casualties and financial losses every year. Hence, it is important to produce susceptibility maps for the management of forest fires so as to reduce their harmful effects. The purpose of this study is to map the susceptibility to forest fires over Nowshahr County in Iran, using an integrated approach of index of entropy (IOE) with fuzzy membership value (FMV), frequency ratio (FR), and information value (IV) with a comparison of their precision. The spatial database incorporated the inventory of forest fire and conditioning factors. As a whole, 41 forest fire locations were identified. Out of these, 29 locations (≈70%) were randomly chosen for the forest fire susceptibility modeling (FFSM), and the remaining 12 locations (≈30%) were utilized for the validation of the models. Subsequently, utilizing FMV‐IOE, FR‐IOE, and IV‐IOE models, forest fire susceptibility maps were acquired. Finally, the modeling ability of the models for FFSM was assessed using an area under the receiver operating characteristic (AUROC) curve. The results manifested that the prediction accuracy of the FMV‐IOE model is slightly higher than that of the FR‐IOE and IV‐IOE models. The incorporation of IOE with FMV, FR, and IV models had AUROC values of 0.890, 0.887, and 0.878, respectively. The resulting FFSM can be effective in fire repression resource planning, sustainable development, and primary warning in regions with similar conditions.     

Multi-year MODIS active fire type classification over the Brazilian Tropical Moist Forest Biome

ABSTRACT

The Brazilian Tropical Moist Forest Biome (BTMFB) spans almost 4 million km² and is subject to extensive annual fires that have been categorized into deforestation, maintenance, and forest fire types. Information on fire types is important as they have different atmospheric emissions and ecological impacts. A supervised classification methodology is presented to classify the fire type of MODerate resolution Imaging Spectroradiometer (MODIS) active fire detections using training data defined by consideration of Brazilian government forest monitoring program annual land cover maps, and using predictor variables concerned with fuel flammability, fuel load, fire behavior, fire seasonality, fire annual frequency, proximity to surface transportation, and local temperature. The fire seasonality, local temperature, and fuel flammability were the most influential on the classification. Classified fire type results for all 1.6 million MODIS Terra and Aqua BTMFB active fire detections over eight years (2003–2010) are presented with an overall fire type classification accuracy of 90.9% (kappa 0.824). The fire type user’s and producer’s classification accuracies were respectively 92.4% and 94.4% (maintenance fires), 88.4% and 87.5% (forest fires), and, 88.7% and 75.0% (deforestation fires). The spatial and temporal distribution of the classified fire types are presented and are similar to patterns reported in the available recent literature.     

应用3S技术的森林防火信息系统设计与实现

为了推动森林防火工作的现代化、信息化,以3S技术为核心,基于远程视频监控、网络通讯等技术设计实现了集日常事务管理、林火实时监控、林火预测预报、GPS跟踪定位、灾后损失评估等功能于一体的防火信息系统。该系统在某市的试用中已表现出了较好的使用价值。     

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.     

Forest Fire Burnt Area Assessment in the Biodiversity Rich Regions Using Geospatial Technology: Uttarakhand Forest Fire Event 2016

The hills of Uttarakhand witness forest fire every year during the summer season and the number of these fire events is reported to have increased due to increased anthropogenic disturbances as well as changes in climate. These fires cause significant damage to the natural resources which can be mapped and monitored using satellite images by virtue of its synoptic coverage of the landscape and near real time monitoring. This study presents burnt area assessment caused by the fire episode of April 2016 to the forest vegetation. Digital classification of satellite images was done to extract the burnt area which was found to be 3774.14 km², representing 15.28% of the total forest area of the state. It also gives an account of cumulative progression of forest fire in Uttarakhand using satellite images of three dates viz. 23rd, 27th May and 2nd June, 2016. Results were analyzed at district, administrative and forest division level using overlay analysis. Separate area statistics were given for different categories of biological richness, forest types and protected areas affected by forest fire. The burnt area assessment can be used in mitigation planning to prevent drastic ecological impacts of the forest fire on the landscape.     

An Agent-Based Approach for Regional Forest Fire Detection Using MODIS Data: A Preliminary Study in Iran

Detecting fires, which are at their early stages is the first component of effective fire fighting. To date, several algorithms have been proposed to detect fire spots using remote sensing data. Nevertheless, in order to be able to accurately detect small and cool fires, which are very important at the regional scale, most of these algorithms need to be adjusted and improved. In this paper, an agent-based algorithm is presented for regional forest fire detection using bi-temporal MODIS data. The algorithm is designed to be so self-adaptive and consistent that it could be applied to the different pairs of consecutive images taken by the same satellite platform and at the same daytime. The results clearly show that compared with the MODIS contextual algorithm (version 4), the proposed method is more sensitive to small and cool forest fires in Iran.