松毛虫发生类型区划分与抽样调查技术的研究进展

松毛虫发生类型区划分与抽样调查技术的研究进展薛贤清，赵清山，兰星平（南京林业大学）（林业部森防总站）（贵州省森防站）１９８０年１０月，林业部保护司在原南方森林植物检疫所（江西省弋阳县）召开了全国松毛虫测报工作座谈会，明确规定了开展森林病虫害预测预报是...     

林间招引松毛虫卵期寄生蜂试验初报

林间招引松毛虫卵期寄生蜂试验初报赖杰超，方芳，吴海山，赵瑞兴（阜新县森防检疫站１２３１００）（阜新县森林病虫测报站）（辽宁省森防检疫站）在松毛虫综合防治措施中，保护和利用松毛虫卵期寄生蜂，充分发挥其对松毛虫种群数量的抑制作用，是一项重要的生物防治措施...     

广东省松毛虫测报工作的几点做法

广东省松毛虫测报工作的几点做法舒朝然梁承丰（沈阳林业部森林病虫害防治总站１１００３４）（广东省森林病虫害防治站）广东省松毛虫预测预报工作近年来得到了引人注目的进展，经济效益和生态效益显著，他们的做法是：一、建立健全预测预报网络广东省森防站专门成立了预...     

湿地松对马尾松毛虫抗性程度的初步调查

湿地松对马尾松毛虫抗性程度的初步调查潘玉花（宜昌林业学校４４３１００）关键词湿地松，马尾松毛虫，抗性程度马尾松毛虫（Ｄｅｎｄｒｏｌｉｍｕｓｐｕｎｃｔｅｔｕｓｋａｌｋｅｒ）是我国森林最严重的历史性的大害虫，几乎遍及整个马尾松分布区。北起豫南，南至两广，...     

米螨防治马尾松毛虫的药效分析

米螨防治马尾松毛虫的药效分析梁成杰赵玲（中国林业科学研究院森林保护研究所北京１０００９１）罗基同黄金义（广西壮族自治区森林病虫害防治站南宁５３００２２）关键词马尾松毛虫，米螨，防治，药效分析２４％米螨（Ｔｅｂｕｆｅｎｏｚｉｄｅ）胶悬剂杀虫药剂是美国...     

广西马尾松毛虫发生类型区的划分与应用

广西马尾松毛虫发生类型区的划分与应用庞正轰，杨秀好，文凤芝梁修达（区森防站）（柳州地区森防站）马尾松毛虫是广西最重要的森林大害虫。１９８０年至１９９５年，年均发生面积２０万ｈｍ２以上，直接经济损失５０００万元以上。马尾松毛虫灾害已成为我区巩固造林绿化...     

应用多来宝悬浮剂飞机低容量防治松毛虫试验初报

应用多来宝悬浮剂飞机低容量防治松毛虫试验初报赵瑞兴，赖杰超（辽宁省森防检疫站１１００３６）（阜新县森防检疫站）松毛虫是辽宁省的重要森林害虫，每年发生面积占全省森林病虫害发生总面积的５０％以上，防治经费达数百万元。近十多年来，一直使用菊酯类杀虫剂进行防...     

马尾松毛虫综合治理技术推广应用

马尾松毛虫综合治理技术推广应用＠李瑞龙￥广西玉林地区森防检疫站马屐松毛虫，综合防治马尾松毛虫综合治理技术推广应用李瑞龙（广西玉林地区森防检疫站玉林５３７０００）关键词马屐松毛虫综合防治马尾松毛虫是玉林地区最主要的森林害虫，年发生３～４代，每隔２～３年为一个...     

马尾松毛虫大发生灰色灾变趋势测报方法

马尾松毛虫的猖獗发生可以认为是一个灰色系统过程，为探索松毛虫危害森林的灾变规律，依据瑞金市１９８９－１９９５年大发生面积的时间数据序列，尝试应用灰色系统理论及其ＧＭ（１，１）模型，对马尾松毛虫大发生的灾变趋势，建立预测模型，进行灾变长期趋势预测。     

对松毛虫产业化防治的思考

松毛虫在我国10种森林病虫害排名之首,其危害性较大,全国每年发生面积达2000—4000万亩,重者松叶吃光,树木枯死,造成大面积森林被毁。为了彻底防治松毛虫,国内各级科研机构与防治单位进行了大量的试验示范,获得了很多科研成果和防治松毛虫方法,国家林业局制定了《国家级森林病虫害中心测报点松毛虫监测、预测预报办法》。对测报点监测预测松毛虫提出了规范性的管理办法,为全面科学防治松毛虫提供了较好的基础,有的省、市制订了《松毛虫防治技术方案》,对松毛虫防治作业设计、虫情监测、划类分区、分类实策、防治指标的确定,选择可行的防治方…     

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.     

6种地形校正方法对雷竹林地上生物量遥感估算的影响

结合野外调查数据和Landsat 5 TM影像,分析6种地形校正方法(Teillet -回归,Cosine,C,SCS,SCS+C和Minnaert)对雷竹林地上生物量遥感估算的影响.结果表明:1)除Cosine和SCS方法存在校正过度现象,其余4种地形校正方法均具有良好的校正效果;2)6种地形校正方法均可提高TM4,TM5与地上生物量的相关性,且RVI,NDVI和SAVI这3种植被指数与生物量之间的相关性也得到改善;3)与原始影像相比,6种地形校正后的遥感数据都能不同程度地提高雷竹林生物量估算精度,以Teillet -回归校正后所建雷竹林地上部分生物量估算模型精度最高,相关系数从0.441提高到0.687,RMSE降低17％左右;4)尽管Cosine校正最大程度地提高了TM4,TM5与地上生物量的相关关系,但Cosine方法存在校正过度问题,Cosine校正后雷竹林地上生物量遥感估算模型精度反而略低于Teillet -回归校正;5)虽然地形校正可提高植被指数与雷竹林地上生物量之间的相关性,但所选5种植被指数均未入选雷竹林地上生物量遥感估算模型的变量,这与雷竹林较高的密度有关.     

基于Landsat TM数据估算雷竹林地上生物量

结合Landsat TM遥感数据和雷竹林样地调查数据,采用偏最小二乘回归法(PLS)建立雷竹林地上生物量估算模型,利用该模型估算临安市雷竹林地上部分生物量。结果表明:雷竹单株地上部分生物量与胸径及雷竹林地上部分生物量与株数之间都呈极显著相关(P<0.01);通过PLS-Bootstrap法筛选自变量能够提高模型精度;模型预测的雷竹林地上生物量均方根误差为3.45t·hm-2,满足大范围估算的精度要求;临安市雷竹林地上生物量为13～25t·hm-2,均值为19.52t·hm-2。     

在ERDASIMAGINE中建立邯郸市TM影像土地类型解译标志

为了准确地了解邯郸市的土地利用类型,掌握土地资源的发展动态,在ERDASIMAGINE软件支持下,将Landsat 5号卫星所得的TM4、3、2波段数据组合输出成为标准假彩色影像,对其进行几何校正、重采样、图像裁剪与镶嵌等操作,依据遥感图像特征、地物相关关系、区域特点和全国土地利用分类系统,对处理后所得的邯郸市TM影像建立土地类型解译标志。经过实地验证,其结果判正率比较高,可以满足解译精度的要求,这说明利用遥感技术建立土地利用类型解译标志是可行的。     

基于3S技术的天保区植被变化监测方法研究

以甘肃省清水县天然林保护区植被时空变化为研究对象,以2期Landsat TM遥感影像为主要信息源,以植被面积和植被覆盖度为监测指标,采用最大似然方法进行监督分类获得土地利用分类图和植被面积,采用NDVI像元二分法获得植被覆盖度图。利用空间分析和统计分析方法将土地利用分类图和植被覆盖度图进行空间叠加分析,揭示研究区1996—2004年间土地利用和植被覆盖度的数量变化和空间变化特征,并对天保区与非天保区以及不同天保区内部的差异进行对比分析。研究结果表明：1996年—2004年间甘肃省清水县天然林保护区森林面积和植被覆盖度得到了明显改善,且天保区优于非天保区; 不同类型天保区植被变化状况有差异,3种类型天然林保护区中以重点生态保护区植被改善状况最为显著。     

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.     

Assessing rates of forest change and fragmentation in Alabama,USA, using the vegetation change tracker model

Forest change is of great concern for land use decision makers and conservation communities. Quantitative and spatial forest change information is critical for addressing many pressing issues, including global climate change, carbon budgets, and sustainability. In this study, our analysis focuses on the differences in geospatial patterns and their changes between federal forests and nonfederal forests in Alabama over the time period 1987–2005, by interpreting 163 Landsat Thematic Mapper (TM) scenes using a vegetation change tracker (VCT) model. Our analysis revealed that for the most part of 1990 s and between 2000 and 2005, Alabama lost about 2% of its forest on an annual basis due to disturbances, but much of the losses were balanced by forest regeneration from previous disturbances. The disturbance maps revealed that federal forests were reasonably well protected, with the fragmentation remaining relatively stable over time. In contrast, nonfederal forests, which are predominant in area share (about 95%), were heavily disturbed, clearly demonstrating decreasing levels of fragmentation during the time period 1987–1993 giving way to a subsequent accelerating fragmentation during the time period 1994–2005. Additionally, the identification of the statistical relationships between forest fragmentation status and forest loss rate and forest net change rate in relation to land ownership implied the distinct differences in forest cutting rate and cutting patterns between federal forests and nonfederal forests. The forest spatial change information derived from the model has provided valuable insights regarding regional forest management practices and disturbance regimes, which are closely associated with regional economics and environmental concerns.     

Vegetation inventory of a temperate biosphere reserve in China by image fusion of landsat TM and SPOT HRV

Vegetation cover types on Changbai Mountain, a natural biosphere reserve (2,000 km²) in northeast China, were derived by using multisensor satellite imagery fused with Landsat TM and SPOT HRV-XS. DEM data were used for improving classification accuracy. Cover types were classified into 20 groups. Bands 4 and 5 of Landsat TM image acquired on July 18, 1997, and band 1 of SPOT HRV-XS image acquired on Oct. 19, 1992, were fused to a false color image, and maximum likelihood supervised classification was performed. Data fusion showed high accuracy of identification, compared to individual images. The overall accuracy of classification of individual images by SPOT HRV-XS reached 56%, and TM 66%, while the fused data set provided accuracy of about 78%, which was raised to 81% after recoding by using DEM. There were five vegetation zones on the mountain, from the base to the peak: hardwood forest zone, mixed forest zone, conifer forest zone, birch forest zone, and tundra zone. Spruce-fir dominated conifer forest was the most prevalent (nearly 50%) vegetation type, followed by Korean pine and mixed forest (17%) and larch forest (5%). HRV image taken in leaf-off season is useful for discriminating forest from non-forest, and evergreen forest from hardwood forest, while the summer image (TM) provides detailed information on the difference in similar vegetation types, like hardwood forest with different compositions.     

Characterizing stand-replacing disturbance in western Alberta grizzly bear habitat, using a satellite-derived high temporal and spatial resolution change sequence

Timely and accurate mapping of anthropogenic and natural disturbance patterns can be used to better understand the nature of wildlife habitats, distributions and movements. One common approach to map forest disturbance is by using high spatial resolution satellite imagery, such as Landsat 5 Thematic Mapper (TM) or Landsat 7 Enhanced Thematic Mapper plus (ETM+) imagery acquired at a 30 m spatial resolution. However, the low revisit times of these sensors acts to limit the capability to accurately determine dates for a sequence of disturbance events, especially in regions where cloud contamination is a frequent occurrence. As wildlife habitat use can vary significantly seasonally, annual patterns of disturbance are often insufficient in assessing relationships between disturbance and foraging behaviour or movement patterns.The Spatial Temporal Adaptive Algorithm for mapping Reflectance Change (STAARCH) allows the generation of high-spatial (30 m) and -temporal (weekly or bi-weekly) resolution disturbance sequences using fusion of Landsat TM or ETM+ and Moderate Resolution Imaging Spectroradiometer (MODIS) imagery. The STAARCH algorithm is applied here to generate a disturbance sequence representing stand-replacing events (disturbances over 1 ha in area) for the period 2001-2008, over almost 6 million ha of grizzly bear habitat along the eastern slopes of the Rocky Mountains in Alberta. The STAARCH algorithm incorporates pairs of Landsat images to detect the spatial extent of disturbances; information from the bi-weekly MODIS composites is used in this study to assign a date of disturbance (DoD) to each detected disturbed area. Dates of estimated disturbances with areas over 5 ha are validated by comparison with a yearly Landsat-based change sequence, with producer's accuracies ranging between 15 and 85% (average overall accuracy 62%, kappa statistic of 0.54) depending on the size of the disturbance event. The spatial and temporal patterns of disturbances within the entire region and in smaller subsets, representative of the size of a grizzly bear annual home range, are then explored. Disturbance levels are shown to increase later in the growing season, with most disturbances occurring in late August and September. Individual events are generally small in area (<10 ha) except in the case of wildfires, with, on average, 0.4% of the total area disturbed each year. The application of STAARCH provides unique high temporal and spatial resolution disturbance information over an extensive area, with significant potential for improving understanding of wildlife habitat use.     

Differences in spectral trajectory with stand volume development between Japanese larch and Japanese oak in Hokkaido,Japan

Our objective was to investigate the differences in the spectral trajectory of the LANDSAT Thematic Mapper with stand volume development between Japanese larch (Larix kaempferi) and Japanese oak (Quercus mongolica var. crispula) in the experimental forest of Kyushu University, Hokkaido, Japan. We used nonlinear regression and a categorical variable to investigate the relationship between spectral reflectance factor and stand volume for each forest type. This research showed that the reflectance factor of Japanese larch and Japanese oak reached saturation and that both forest types had different spectral trajectories with stand development. In addition, we found that middle of development stage (stand volume was between 100 and 200 m³/ha) of each forest type had similarity in reflectance factor.