基于TM和ASTER的植被指数建模性能对比

在对TM、ASTER数据进行多种植被指数生成的基础上,结合研究区48个杨树样地的测树因子数据,分别就杨树单位面积蓄积量、杨树平均高和杨树年龄进行了遥感统计建模研究,通过植被指数及其变形式共65个特征对比结果表明,由ASTER数据派生的植被指数中RVI3(0.5)对于上述3个测树因子的估测性能最好,由TM数据导出的植被指数中则是DVI2对上述3个测树因子回归效果最好,从建模性能上分析,ASTER数据生成的植被指数要优于TM数据生成的植被指数。     

Forest mapping:a comparison between hyperspectral and multispectral images and technologies

Mapping forests is an important process in managing natural resources.At present,due to spectral resolution limitations,multispectral images do not give a complete separation between different forest species.In contrast,advances in remote sensing technologies have provided hyperspectral tools and images as a solution for the determination of species.In this study,spectral signatures for stone pine(Pinus pinea L.) forests were collected using an advanced spectroradiometer "ASD FieldSpec 4 Hi-Res" with an accuracy of 1 nm.These spectral signatures are used to compare between different multispectral and hyperspectral satellite images.The comparison is based on processing satellite images: hyperspectral Hyperion,hyperspectral CHRIS-Proba,Advanced Land Imager(ALI),and Landsat 8.Enhancement and classification methods for hyperspectral and multispectral images are investigated and analyzed.In addition,a well-known hyperspectral image classification algorithm,spectral angle mapper(SAM),has been improved to perform the classification process efficiently based on collected spectral signatures.The results show that the modified SAM is 9% more accurate than the conventional SAM.In addition,experiments indicate that the CHRIS-Proba image is more accurate than Landsat 8(overall accuracy 82%,precision 93%,and Kappa coefficient 0.43 compared to 60,67%,and 0.035,respectively).Similarly,Hyperion is better than ALI in mapping stone pine(overall accuracy 92%,precision 97%,and Kappa coefficient 0.74 compared to 52,56%,and -0.032,respectively).     

Forest fuel mapping and evaluation of LANDFIRE fuel maps in Boulder County,Colorado, USA

A key challenge in modern wildfire mitigation and forest management is accurate mapping of forest fuels in order to determine spatial fire hazard, plan mitigation efforts, and manage active fires. This study quantified forest fuels of the montane zone of Boulder County, CO, USA in an effort to aid wildfire mitigation planning and provide a metric by which LANDFIRE national fuel maps may be compared. Using data from 196 randomly stratified field plots, pre-existing vegetation maps, and derived variables, predictive classification and regression tree models were created for four fuel parameters necessary for spatial fire simulation with FARSITE (surface fuel model, canopy bulk density, canopy base height, and stand height). These predictive models accounted for 56–62% of the variability in forest fuels and produced fuel maps that predicted 91.4% and 88.2% of the burned area of two historic fires simulated in the FARSITE model. Simulations of areas burned based on LANDFIRE national fuel maps were less accurate, burning 77.7% and 40.3% of the historic fire areas. Our results indicate that fuel mapping efforts that utilize local area information and biotic as well as abiotic predictors will more accurately simulate fire spread rates and reflect the inherent variability of forested environments than do current LANDFIRE data products.     

Sensitivity analysis of fire behavior modeling with LIDAR-derived surface fuel maps

Each year, wildland fires burn millions of hectares of forest worldwide. Fire managers need to provide effective methods for mapping fire fuels accurately. Fuel distribution is very important for predicting fire behavior. The overall aim of this project is to model fire behavior using FARSITE (Fire Area Simulator) and investigate differences in modeling outputs using fuel model maps, which differ in accuracy, in east Texas. This simulator model requires as input spatial data themes such as elevation, slope, aspect, surface fuel model, and canopy cover along with separate weather and wind data. Seven fuel models, including grass, brush, and timber models, are identified in the study area. To perform modeling sensitivity analysis, two different fuel model maps were used, one obtained by classifying a QuickBird image and the other obtained by classifying a LIDAR (LIght Detection and Ranging) and QuickBird fused data set. Our previous investigations showed that LIDAR improves the accuracy of fuel mapping by at least 13%. According to our new results, LIDAR-derived variables also provides more detailed information about characteristics of fire. This study will show the importance of using accurate maps of fuel models derived using new LIDAR remote sensing techniques.     

北京市森林可燃物分类研究

利用北京市土地利用图和Landsat TM影像,采用监督分类方法对可燃物进行分类,获得可燃物分类图,并对各类可燃物的特征进行详细描述.结果表明:可燃物可分6类,即O- 1草地(中低盖度)、O- 2草地(高盖度)、S- 1灌丛、S- 2幼林、C- 1针叶林、M- 1针阔混交林和B- 1阔叶林.由于TM卫星影像分辨率的限制,很难分别出林分垂直结构的差异.这一分类结果,可以满足森林火险等级预报的需要,但对于火行为预报,还需要根据林分燃烧性、林分垂直结构及可燃物分布状态进一步分类.对当前的可燃物分类方法与制图途径以及未来的发展趋势进行讨论.     

Comparative analysis of multi-criteria probabilistic FR and AHP models for forest fire risk(FFR) mapping in Melghat Tiger Reserve(MTR) forest

A comparative study of Frequency Ratio(FR)and Analytic Hierarchy Process(AHP)models are performed for forest fire risk(FFR)mapping in Melghat Tiger Reserve forest,central India.Identification of FFR depends on various hydrometeorological parameters altitude,slope,aspect,topographic position index,normalized differential vegetation index,rainfall,air temperature,land surface temperature,wind speed,distance to settlements,and distance by road are integrated using a GIS platform.The results from FR and AHP show similar trends.The FR model was significantly higher accurate(overall accuracy of 81.3%,kappa statistic 0.78)than the AHP model(overall accuracy 79.3%,kappa statistic 0.75).The FR model total forest fire risk areas were classified into five classes:very low(7.1%),low(22.2%),moderate(32.3%),high(26.9%),and very high(11.5%).The AHP fire risk classes were very low(6.7%),low(21.7%),moderate(34.0%),high(26.7%),and very high(10.9%).Sensitivity analyses were performed for AHP and FR models.The results of the two different models are compared and justified concerning the forest fire sample points(Forest Survey of India)and burn images(2010-2016).These results help in designing more effective fire management plans to improve the allocation of resources across a landscape framework.     

基于ASD与Hyperion高光谱数据的主要针叶树种分类研究

以云南省香格里拉市为研究区,对ASD光谱仪实测的4种针叶树种光谱数据采用包络线去除法、光谱一阶微分法和光谱二阶微分法3种波段选择方法得到Hyperion高光谱影像数据的分类特征波段,采用最大似然法、支持向量机2种分类方法对所选的特征波段开展树种识别分类,对原始影像采用光谱角填图分类方法作对比实验。结果表明,基于ASD数据的光谱一阶波段选择方案的支持向量机分类方法精度最高,总体分类精度为81.95%,Kappa系数为0.725 1。采用ASD实测光谱数据能有效指导Hyperion进行树种分类,基于数据尺度和换算方式,一阶微分更适合特征波段选择;与传统的数理统计分类方法和光谱特征分类方法相比,基于机器学习的方法如支持向量机等在高光谱遥感分类中具有更大的应用潜力。     

基于INFORM模型与GEE的根河火烧迹地冠层含水量反演研究

[目的]提出一种结合辐射传输模型与遥感云平台反演火烧迹地冠层含水量(Canopy water content,CWC)的新方法,弥补目前对火烧迹地恢复阶段植被含水量的监测,为定量监测植被水分与火灾预警提供理论参考.[方法]以内蒙古自治区根河市火烧迹地为研究对象,基于INFORM辐射传输模型,使用查找表的方法反演森林冠层...     

森林火灾损失分类方法和评估指标评述

构建合理的损失分类方法才能采用科学的评估指标进行森林火灾损失评估。本文在研究国内外关于森林火灾损失分类评估体系的基础上,结合相关学科的知识,引入＂系统＂的概念,重新提出森林火灾损失的术语和具体的损失分类方法。新的损失分类方法体系共三级,其中一级分类为固有性系统损失、注入补偿性系统损失和效应性系统损失;二级分类为固有生物量损失、固有非生物量损失、趋利注入性损失、避害补偿性损失、社会效益损失和生态效益损失。新的术语和分类方法着力对森林火灾重要损失做全面统计,避免漏估和重复估计,为日后科学、合理和有效的森林火灾损失评估提供基础依据。     

Chapter 10. Mapping Ecological Attributes Using an Integrated Vegetation Classification System Approach

Summary

Land managers need vegetation maps to inventory, monitor, and manage ecological resources across multiple spatial and temporal scales. Current vegetation maps usually only describe one vegetation characteristic, such as cover types, across the landscape. Although these maps provide important information for land management, they often fall short of addressing key issues like forest health and ecosystem management. In this paper we present an integrated approach where three different vegetation classifications are used in concert to spatially characterize many ecological attributes such as snag densities, insect susceptibility, and fire behavior across the landscape. Two examples from the Pacific Northwest are used to illustrate how this approach can be used to describe fuel characteristics and resource hazard across multiple scales.     

Estimation of fire severity by use of Landsat TM images and its relevance to vegetation and topography in the 2000 Samcheok forest fire

The severity of the 2000 Samcheok forest fire was classified by using Landsat TM images, and the effects of vegetation structures and topographic conditions on fire severity were analyzed. The estimated normalized difference vegetation index differences between the pre and post-fire Landsat TM images were used as the criteria in determining the levels of fire severity–low, moderate, and extreme. According to the results from fire severity estimation, of the 10,600 ha forest stands, 28% was severely damaged by crown fires, 38% was moderately damaged, and the remaining 34% was damaged slightly by surface fires. The overall accuracy of the fire severity classification was 83% (Kappa coefficient = 0.76). The results of χ ²-tests showed that fire severity differed significantly with the vegetation and topographic conditions as follows. The coniferous stands, compared with the mixed and broad-leaved, were more vulnerable to fire damage; the higher the slope of fire sites, the greater the fire damage; the south was the most vulnerable aspect; fire severity of coniferous forest stands increased with increasing elevation. However, in the study area it was found that fire severity of broad-leaved forest stands were negatively related to the elevation of the corresponding fire sites and affected more by vegetation conditions rather than by topographic conditions.     

Classifying coastal resources by integrating optical and radar imagery and color infrared photography

A progressive classification of a marsh and forest system using Landsat Thematic Mapper (TM), color infrared (CIR) photograph, and ERS-1 synthetic aperture radar (SAR) data improved classification accuracy when compared to classification using solely TM reflective band data. The classification resulted in a detailed identification of differences within a nearly monotypic black needlerush marsh. Accuracy percentages of these classes were surprisingly high given the complexities of classification. The detailed classification resulted in a more accurate portrayal of the marsh transgressive sequence than was obtainable with TM data alone. Individual sensor contribution to the improved classification was compared to that using only the six reflective TM bands. Individually, the green reflective CIR and SAR data identified broad categories of water, marsh, and forest. In combination with TM, SAR and the green CIR band each improved overall accuracy by about 3% and 15% respectively. The SAR data improved the TM classification accuracy mostly in the marsh classes. The green CIR data also improved the marsh classification accuracy and accuracies in some water classes. The final combination of all sensor data improved almost all class accuracies from 2% to 70% with an overall improvement of about 20% over TM data alone. Not only was the identification of vegetation types improved, but the spatial detail of the classification approached 10 m in some areas.     

Assessing fire risk using Monte Carlo simulations of fire spread

Understanding the spatial pattern of fire is essential for Mediterranean vegetation management. Fire-risk maps are typically constructed at coarse resolutions using vegetation maps with limited capacity for prescribing prevention activities. This paper describes and evaluates a novel approach for fire risk assessment that may produce a decision support system for actual fire management at fine scales. FARSITE, a two-dimensional fire growth and behavior model was activated, using ArcView VBA code, to generate Monte Carlo simulations of fire spread. The study area was 300 km² of Mt. Carmel, Israel. FARSITE fuel models were adjusted for Mediterranean conditions. The simulation session consisted of 500 runs. For each simulation run, a calendar date, fire length, ignition location, climatic data and other parameters were selected randomly from known distributions of these parameters. Distance from road served as a proxy for the probability of ignition. The resulting 500 maps of fire distribution (the entire area burnt in a specific fire) were overlaid to produce a map of ‘hotspots’ and ‘cold spots’ of fire frequency. The results revealed a clear pattern of fires, with high frequency areas concentrated in the northwestern part. The spatial pattern of the fire frequency map bears partial resemblance to the fuel map, but seems to be affected by several other factors as well, including the location of urban areas, microclimate, topography and the distribution of ignition locations (which is affected by road pattern). These results demonstrate the complexities of fire behavior, showing a very clear pattern of risk level even at fine scales, where neighboring areas have different risk levels due to combinations of vegetation cover, topography, microclimate and other factors.     

Image-based reflectance conversion of ASTER and IKONOS imagery as precursor to structural assessment of plantation forests in KwaZulu-Natal,South Africa

Reflectance-converted imagery is a requirement for establishing temporally robust remote sensing algorithms, given the reduction of time-specific atmospheric effects. Thus, in this study image-based atmospheric correction methods for ASTER and IKONOS imagery for retrieving surface reflectance of plantation forests in KwaZulu-Natal, South Africa were evaluated. This effort formed part of a larger initiative that focused on retrieval of forest structural attributes from resultant reflectance imagery. Atmospheric correction methods in this study included the apparent reflectance model (AR), dark object subtraction model (DOS), and the cosine approximation model (COST). Spectral signatures derived from different image-based models for ASTER and IKONOS were inspected visually as first departure. This was followed by comparison of the total accuracy and Kappa index computed from supervised classification of images that were derived from different image-based atmospheric correction of ASTER and IKONOS imagery. The classification accuracy of DOS images derived from ASTER and IKONOS imagery exhibited percentages of 93.3% and 94.7%, respectively. Classification accuracies for images from AR and COST, on the other hand, resulted in lower accuracy values of 87.9% and 83.6% for ASTER and 90.5% and 92.8% for IKONOS, respectively. We concluded that the image-based DOS model was better suited to atmospheric correction for ASTER and IKONOS imagery in this study area and for the purpose of forest structural assessment. This has important implications for the operational use of similar imagery types for forest inventory approaches.     

应用卫星遥感影像分析厦门市地表植被变化

以厦门市地表植被变化为研究对象 ,以 1 996-2 0 0 0年为时间跨度 ,在计算机的支持下 ,利用LandsatTM卫星遥感影像数据进行处理分析 ,计算影像的植被指数并进行多波段彩色合成 ,分析地表植被变化和特征 ,提取变化分布地点及类型 ,形成专题图件 ,为厦门市森林生态网络建设提供依据。     

Airborne small-footprint discrete-return LiDAR data in the assessment of boreal mire surface patterns, vegetation, and habitats

Boreal mires encompass high diversity in species and habitats, many of which are endangered. In Finland, extensive use of peatlands has resulted in habitat fragmentation. A need for accurate and cost-efficient vegetation mapping and monitoring of habitat changes exists in mire conservation, restoration and peatland forestry. LiDAR is an emerging and excellent tool for probing the geometry of vegetation and terrain. Modern systems measure the backscattered signal accurately and also provide radiometric information. Experiments were carried out in a complex minerotrophic–ombrotrophic eccentric raised bog in southern Finland (61°47′N, 24.18′E). First, we tested discrete-return LiDAR for the modeling of mire surface patterns and the detection of hummocks and hollows, as well as the effect of mire plants on the Z accuracy of the surface echoes. Secondly, the response of different mire vegetation samples in LiDAR intensity was examined. Thirdly, we tested area-based geometric and radiometric features in supervised classification of mire habitats to discover the meaningful variables. The vertical accuracy of LiDAR in mire surface modeling was high: 0.05–0.10 m. A binary hummock-hollow model that was estimated from a LiDAR-based elevation model matched flawlessly in aerial images and had moderate explanatory power in habitat classification trials. The intensity of LiDAR in open-mire vegetation was mainly influenced by the surface moisture, and separation of vegetation classes spanning from ombrotrophic to mesotrophic vegetation proved to be difficult. Area-based features that characterize the height distribution of LiDAR points in the canopy were the strongest explanatory variables in the classification of 21 diverse mire site types. Actual qualifying differences in the ground flora were unattainable in the LiDAR data, which resulted in inferior accuracy in the characterization of ecohydrological conditions and nutrient level of open mires and sparsely forested wet sites. Mire habitat classification accuracy with LiDAR surpassed earlier results with optical data. The results suggested that LiDAR constitutes an efficient aid for monitoring applications. We propose the co-use of images and LiDAR for enhanced mapping of open mires and tree species. In situ calibration and validation procedures are required until invariant geometric and radiometric features are discovered.     

南宁市兴宁区植被覆盖度变化分析

基于2008年ALOS和2010年"天绘一号"两期南宁市兴宁区遥感数据,应用NDVI像元二分方法得出研究区域的植被覆盖度情况,并与2010年林地利用类型图进行叠加对比分析。结果表明:南宁市兴宁区2010年植被覆盖度与2008年相比,中高植被覆盖度的面积增加,低覆盖度的面积减少;林地的植被覆盖度面积整体由高向低变化,由于林地面积的确定,林地对研究区域植被覆盖度的提高影响不明显。     

新西兰森林火险等级系统及预测应用

新西兰是森林火灾多发的国家,由于近年来气候变化导致新西兰火灾季节延长且火灾频发。探究新西兰火险等级体系的火天气指数和火行为分析模型,可以用于模拟历史、当前和未来的火险。文中对新西兰火险等级、火行为分析模型以及用于监测当前火险的火天气系统进行了探讨,认为开展相关火行为模拟研究是上述3种管理工具的研发基础,并且可以确保火险信息的有效性和准确性。不同植被的火行为是林火管理系统的重要输入因子,当前的天气、火行为和火险由新西兰国家乡村消防局进行每日更新并通过网络发布给全国的防火机构和公众,用于中、短期防火管理计划的实施。这些火行为模型已经成为新西兰很多火管理决策支持工具的基础。新西兰气象局把火天气指标系统和他们的预测模型结合起来,产生每小时的预测图用于短期计划和应急管理。借鉴新西兰的森林火险等级系统可以为我国构建完备的国家火险等级预报系统提供技术参考,而且探讨世界各国火险等级体系和火险模型可以促进我国与世界各国森林火险信息平台共享,提高预测和应对重特大森林火灾的能力,减少森林资源的损失,助力我国“双碳”目标的实现。     

Integration of LiDAR and QuickBird imagery for mapping riparian biophysical parameters and land cover types in Australian tropical savannas

Riparian zones are exposed to increasing pressures because of disturbance from agricultural and urban expansion and overgrazing. Accurate and cost-effective mapping of riparian environments is important for baseline inventories and monitoring and managing their functions associated with water quality, biodiversity, and wildlife habitats. In this study, we integrate remotely sensed light detection and ranging (LiDAR) data and high spatial resolution satellite imagery (QuickBird-2) to estimate riparian biophysical parameters and land cover types in the Fitzroy catchment in Queensland, Australia. An object based image analysis (OBIA) was adopted for the study. A digital terrain model (DTM), a tree canopy model (TCM) and a plant projective cover (PPC) map were first derived from the LiDAR data. A map of the streambed was then produced using the DTM information. Finally, all the LiDAR-derived biophysical map products and the QuickBird image bands were combined in an OBIA to (1) map the following land cover types: riparian vegetation, streambed, bare ground, woodlands and rangelands; (2) determine the distribution of overhang vegetation within the streambed; and (3) measure the width of both the riparian zone and the streambed. The combined use of both datasets allowed accurate land cover mapping, with an overall accuracy of 85.6%. The estimated widths of the riparian zone and the streambed showed strong correlation with the actual field measurements (r = 0.82 and 0.98 respectively). Our results show that the combined use of LiDAR and high spatial resolution imagery can potentially be used for the assessment of the riparian condition in a tropical savanna woodland riparian environment. This work also shows the capacity of OBIA to assist in the assessment of the composition of the riparian environment from multiple image datasets.     

Satellite-based stand-wise forest cover type mapping using a spatially adaptive classification approach

Due to high variation in forest communities, forest structure and the fragmentation of the forested area in Central Europe, satellite-based forest inventory methods have to meet particularly high-quality requirements. This study presents an innovative method to combine official forest inventory information at stand level with multidate satellite imagery using a spatially adaptive classification approach for producing wall-to-wall forest cover maps of important tree species and management classes across multiple ownership regions in a heterogeneous low mountain range in Germany. The classification approach was applied to a 5,200-km² area (about 2,080?km² of forest land, mostly mixed forests) located in the Eifel mountain range in Central Europe. In comparison with conventional classifiers, our results demonstrate a significant increase in classification accuracy in the order of 12%. The method was tested with ASTER images but holds the potential to be used for regular state forest inventories based on standard and novel earth observation data supplied for instance from the SPOT-5 and RapidEye sensors.