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.     

基于TM遥感影像的森林类型分类方法比较

以吉林省汪清林业局为例,基于Landsat5-TM影像,充分利用遥感影像光谱信息,分别采用动态聚类法和组合监督分类法对该林区的森林类型进行分类,并对分类结果的精度进行比较分析.研究结果表明,利用组合监督分类的精度比动态聚类法分类的精度要高,总体分类精度高11％,其中针叶林、阔叶林、混交林和其他用地的分类精度分别高8％、11％、17％和10％.     

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.     

Using multitemporal satellite imagery to characterize forest wildlife habitat: The case of ruffed grouse

Like many similar forest species, ruffed grouse (Bonasa umbellus; hereafter grouse) populations in the central and southern Appalachians (CSA) are strongly affected by forest composition at the landscape scale. Because these populations are in decline, managers require accurate forest maps to understand how stand level characteristics affect the survival and reproductive potentials of individual birds to design management strategies that improve grouse abundance. However, traditional mapping techniques are often labor-intensive and cost-prohibitive. We used a normalized difference vegetation index (NDVI) from each of 8 Landsat images and the digital elevation model (DEM)-derived variables of elevation and aspect in discriminant analyses to classify 7 study areas to 3 overstory classes (evergreen, hardwoods, and oak) and distinguish evergreen and deciduous understories in the CSA, 2000–2002. Overall accuracy was 82.08%, varying from 83.59% for oak to 79.79% for hardwoods overstories. Periods with large phenological differences among classes, particularly early and late spring, were most useful for discriminating overstory vegetation types. Alternatively, winter NDVI in combination with elevation was critical for differentiating evergreen and deciduous understories. Multitemporal image sets used in concert with DEMs provided a cost-effective alternative to hyperspectral sensors for improving wildlife habitat classification accuracy with Landsat imagery. This allowed for enhanced understanding of grouse-habitat relationships and habitat affects on grouse populations that allowed for improved management. With the incorporation of simple adjustments for local forest plant species phenology into the model, it may be used to better classify wildlife habitat of similar species in areas with comparable forest communities and topography. Multitemporal images can also be used to differentiate grassland communities, monitor wetlands, and serve as baseline data for detecting changes in land use over longer temporal scales, making their use in forest wildlife habitat studies cost-justifiable.     

森林面积方法研究

本文通过对遥感图像的处理和光谱特征信息的分析,应用ETM 影像数据和地面调查数据,研究了遥感数据处理技术在植被信息提取中的应用,尝试对高山峡谷区的森林资源调查提出较为完善的计算机图像处理技术和自动分类方法。对道孚县台站林场和麻孜林场地类的分类结果表明:运用TM453波段融合能够达到较好的图像增强效果;运用无监督分类方法提取森林面积能达到较高的分类精度;NDVI比RVI更能突出植被信息和消除山体阴影的影响。     

基于BP神经网络的针阔混交林TM遥感图像自动分类技术研究

在对标准BP神经网络试验分析的基础上，通过输入矢量归一化处理、主成分分析、增加验证集、改进训练学习算法、扩大隐层和输出层规模等措施，对BP神经网络自动分类系统进行改进；利用改进后的BP系统对吉林省汪清林业局的典型针阔混交林TM遥感图像进行辩识、分类试验研究。结果表明：改进后的BP网络分类系统自动分类精度提高了19．14％，比传统无监督自动分类精度提高8．55％，达到了区分森林类型的分类要求。研究还显示了该改进系统应用于针阔混交林TM遥感图像自动分类识别的精度随网络规模增大而提高。     

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.     

基于CART决策树方法的遥感影像分类

以云南省香格里拉县为研究区域,构建一种基于CART遥感影像的决策树分类方法.对遥感影像采用主成分提取、植被信息提取、纹理信息提取等方法,并结合试验区主要地物类型训练样本,采用Landsat 5 TM影像数据、DEM数据以及遥感处理软件ENVI为平台进行影像分类,并将结果与最大似然分类结果作比较.结果表明,基于CART遥感影像决策树分类精度优于最大似然分类,有较好的分类效果.     

基于植被指数的城市绿化覆盖面积提取技术研究——以山东省泰安市为例

绿化覆盖面积是评价园林城市绿地质量的基础数据,本文针对城市绿化覆盖面积的概念,在对SPOT5多光谱影像进行光谱分析的基础上,构建了差和比指数和加和指数两模型,两模型作为新的波段,结合植被分区特点参与分类,提取城市绿化覆盖面积。通过精度评价,可以确定该技术能达到较高的精度水平。     

Forest cover changes in the Oregon Coast Range from 1939 to 1993

Understanding the shifts over time in the distribution and amount of forest vegetation types in relation to forest management and environmental conditions is critical for many policy and ecological questions. Our objective was to assess the influences of ownership and environment on changes in forest vegetation from post-settlement historical to recent times in the central Coast Range of Oregon. We evaluated land cover types on 1475 20 m plots, using scanned, geo-referenced historical (1939) and recent (1993) aerial photos. The amount of older conifer cover declined by 63% relative to its former amount, from 36 to 13% of the landscape, during the 54-year period. Dominant ownership of older conifer stands shifted from industrial private to Forest Service lands. Younger conifer stands showed the greatest expansion in cover, increasing more than two-fold, from 21 to 44% of the landscape. Shrub and hardwood cover declined by 16%, from 31 to 25% of the landscape. Shrubs and hardwoods occurred at lower slope positions and closer to streams at the end of the period than at the beginning. Ownership was not an important determinant of the presence of large and very large conifer cover or shrub and hardwood cover in 1939, but was a very important factor affecting the presence of these cover types in 1993. Landscape transitional pathways were distributed among many types and no single transitional pathway was dominant. Even the most stable cover types (hardwood trees and herbs) had low absolute stability, with over 65% of their plots changing to another cover type by 1993. Our research indicates that the importance of ownership as a factor affecting the type of vegetation cover present has increased greatly during this time, whereas the relative influence of environment has lessened considerably. Land owners in the Oregon Coast Range have altered the cover and distribution of vegetation in diverse ways, changing the landscape to one dominated by young conifers, shifting the distribution of younger successional shrubs and hardwoods toward streams, and restricting the location of older coniferous stands to particular ownerships and site types.     

连云港花果山高分辨率森林火险等级三维可视化短期预报研究

以连云港花果山为研究区,将火险等级预报因子归纳为森林火险区划、天气、火源等3个类别。其中火险区划根据植被类型、植被特征、地形因子获取;天气因子通过24h天气预报获取;火源危险等级由景区内的道路进行缓冲区分析获取。利用3类火险因子,通过加权叠置法建立火险等级短期预报模型,进行火险等级预报;利用DEM和SPOT5影像,通过GIS三维可视化技术,对预报结果进行三维可视化显示。通过研究实现了火险等级短期预报,将火险等级落实到小班,并将火险等级分布信息以三维可视化方式显示,为研究区森林火险实时监测提供了基础平台,为山区林火科学化管理提供技术支持。     

Estimating above-ground biomass by fusion of LiDAR and multispectral data in subtropical woody plant communities in topographically complex terrain in North-eastern Australia

We investigated a strategy to improve predicting capacity of plot-scale above-ground biomass （AGB） by fusion of LiDAR and Land- sat5 TM derived biophysical variables for subtropical rainforest and eucalypts dominated forest in topographically complex landscapes in North-eastern Australia. Investigation was carried out in two study areas separately and in combination. From each plot of both study areas, LiDAR derived structural parameters of vegetation and reflectance of all Landsat bands, vegetation indices were employed. The regression analysis was carded out separately for LiDAR and Landsat derived variables indi- vidually and in combination. Strong relationships were found with LiDAR alone for eucalypts dominated forest and combined sites compared to the accuracy of AGB estimates by Landsat data. Fusing LiDAR with Landsat5 TM derived variables increased overall performance for the eucalypt forest and combined sites data by describing extra variation （3% for eucalypt forest and 2% combined sites） of field estimated plot-scale above-ground biomass. In contrast, separate LiDAR and imagery data, andfusion of LiDAR and Landsat data performed poorly across structurally complex closed canopy subtropical minforest. These findings reinforced that obtaining accurate estimates of above ground biomass using remotely sensed data is a function of the complexity of horizontal and vertical structural diversity of vegetation.     

永顺万坪乡植被生态系统模型研究

通过对永顺万坪乡30个不同类型群落的实地调查,结合TWINSPAN来进行梯度分析和分类,识别了7种天然林植被类型:楠竹(Phyllostachys heterocycla'Pubescens')林,落叶阔叶林,常绿落叶阔叶林,常绿阔叶林,针叶林,针、阔混交林和灌木林;从而制作出万坪乡天然林植被生态系统模型,揭示其演替规律,预测天然林群落的演替发展方向,以便于更好地对万坪乡天然林进行生态系统模式的经营、管理与保护。     

Analysis of forest cover change at Khadimnagar National Park,Sylhet, Bangladesh,using Landsat TM and GIS data

We mapped the forest cover of Khadimnagar National Park （KNP） of Sylhet Forest Division and estimated forest change over a period of 22 years （1988-2010） using Landsat TM images and other GIS data. Supervised classification and Normalized Difference Vegetation Index （NDVI） image classification approaches were applied to the images to produce three cover classes, viz. dense forest, medium dense forest, and bare land. The change map was produced by differencing classified imageries of 1988 and 2010 as before image and after image, respectively, in ERDAS IMAGINE. Error matrix and kappa statistics were used to assess the accuracy of the produced maps. Overall map accuracies resulting from supervised classification of 1988 and 2010 imageries were 84.6% （Kappa 0.75） and 87.5% （Kappa 0.80）, respec- tively. Forest cover statistics resulting from supervised classification showed that dense forest and bare land declined from 526 ha （67%） to 417 ha （59%） and 105 ha （13%） to 8 ha （1%）, respectively, whereas medium dense forest increased from 155 ha （20%） to 317 ha （40%）. Forest cover change statistics derived from NDVI classification showed that dense forest declined from 525 ha （67%） to 421 ha （54%） while medium dense forest increased from 253 ha （32%） to 356 ha （45%）. Both supervised and NDVI classification approaches showed similar trends of forest change, i.e. decrease of dense forest and increase of medium dense forest, which indicates dense forest has been converted to medium dense forest. Area of bare land was unchanged. Illicit felling, encroachment, and settlement near forests caused the dense forest decline while short and long rotation plantations raised in various years caused the increase in area of medium dense forest. Protective measures should be undertaken to check further degradation of forest at KNP.     

Characterizing woodland caribou habitat in sub-boreal and boreal forests

Woodland caribou (Rangifer tarandus caribou) are sensitive to changes in understory vegetation resulting from forest harvesting and are, therefore, of special concern for foresters and habitat biologists. Effective management of this species requires reliable habitat inventories which, because of the large heterogeneous areas over which caribou range, can be costly. We used Landsat Thematic Mapper (TM) imagery and digital elevation data to identify 23 vegetative cover types across the 5100 km² range of the Wolverine caribou herd of northcentral British Columbia, Canada. The classification was augmented with available geographical information system (GIS) data for a total of 27 cover types. We achieved an overall accuracy of 76.7% based on known ground samples; however, accuracy varied according to cover type. Considering the size of the study area, the procedure we employed was relatively cost effective and efficient. We discuss the advantages of such an approach for wildlife-habitat studies reliant on large-scale vegetation maps.     

基于SVM方法的长白山森林植被信息提取的研究

本文以Landsat TM影像数据为基础,采用基于支持向量机分类方法对长白山地区大荒沟林场进行森林植被信息提取,并与传统的最大似然法分类进行对比。结果表明,基于支持向量机方法的森林信息提取精度,Kappa值分别为0.981 0、0.971 6、0.975 3,均超过了最大似然法(MLC)的提取精度和Kappa值0.963 4。该方法有很好的操作性和实用性,准确度满足了林业规划设计的基础数据材料精度要求。     

Forest type identification by random forest classification combined with SPOT and multitemporal SAR data

We developed a forest type classification technology for the Daxing'an Mountains of northeast China using multisource remote sensing data.A SPOT-5 image and two temporal images of RADARSAT-2 full-polarization SAR were used to identify forest types in the Pangu Forest Farm of the Daxing'an Mountains.Forest types were identified using random forest(RF) classification with the following data combination types: SPOT-5 alone,SPOT-5 and SAR images in August or November,and SPOT-5 and two temporal SAR images.We identified many forest types using a combination of multitemporal SAR and SPOT-5 images,including Betula platyphylla,Larix gmelinii,Pinus sylvestris and Picea koraiensis forests.The accuracy of classification exceeded 88% and improved by 12% when compared to the classification results obtained using SPOT data alone.RF classification using a combination of multisource remote sensing data improved classification accuracy compared to that achieved using single-source remote sensing data.     

卫星遥感数据在森林采伐监测中的应用

分别采用SPOT5、TM5影像前后两期多光谱遥感影像的波谱特征变化,检测森林资源变化信息,确定变化类型,以计算机自动识别对森林资源变化(减少)的区域(伐区)进行信息提取,并在此基础上进行室内人工预判读;结合采伐证、伐区作业设计、二类调查材料,进行补充判读,得出森林采伐图斑。古丈TM5(30 m分辨率)的面积正判率为96.3%;古丈SPOT5(10 m分辨率)的面积正判率为96.9%。实证分析表明,使用中、高分辨率卫星遥感数据能对森林采伐进行监测,结合辅助材料后能显著提高森林采伐监测精度。     

基于影像区域合并分割技术的植被覆盖分级研究--以三峡库区小江流域为例

以影像区域合并分割技术为基础,综合运用相关GIS和RS软件,提出了植被覆盖分级研究的新方法.以三峡库区小江流域为例,以2002年的TM图像为信息源,结合研究区的地形图、GPS野外采样数据、植被指数和DEM等,对研究区的植被覆盖进行了分级研究.研究表明,三峡库区小江流域的植被覆盖总体上分布不均,但与流域区位及海拔高度呈现一定的规律性空间分布格局.这一研究对确定水土流失强度等级具有十分重要的意义,也为其它的遥感影像分析应用提供参考.     

TM影像在热带林林地资源调查中的应用

利用TM影像对滇西南7个地（州）27个县（市）开展了调查，并按2级分类的要求进行目视解译，其结果为研究区热带林覆盖率为46.09%，郁闭林面积占研究区总面积的10.46%，破坏林面积占总面积的35.63%，常绿阔叶林面积占总面积的32.61%，热带雨林和季雨林面积占总面积的0.20%，判读精度81.53%。表明TM影像能满足热带林宏观林地资源调查的需求，既快速，又省力、省钱，具有一定的实用性。