ERS—1SAR影像森林应用研究初探

本研究利用１９９２年成像的覆盖山东省烟台地区的ＥＲＳ－１ＳＡＲ和ＴＭ资料开展工作。研究中首先将这两种遥感资料进行了几何精纠正和与地形图的配准，尔后，用典型相关分析法对遥感资料与森林参数间进行了相关分析研究，结果表明，Ｃ波段的ＥＲＳ－１ＳＡＲ资料与森林冠层厚度和平均树高相关程度较高；森林参数综合因子对ＥＲＳ－１ＳＡＲ影响大于对所用ＴＭ波段的影响，说明ＥＲＳ－１ＳＡＲ资料在森林应用中，对森林参数乃至于     

基于ERDAS的森林资源遥感信息初步研究

以图像处理系统ＥＲＤＡＳ为基础，以ＴＭ卫星遥感图像和地面森林资源调查数据为材料，讨论了ＥＲ－ＤＡＳ计算机图像处理的有关过程，研究了如何把遥感图像和森林资源调查数据相结合并从中提取相关信息的方法，建立了遥感图像与ＧＩＳ资源数据库之间的对应关系。结果表明，该研究方法正确可行，输出图像精度高，可为森林资源管理工作提供重要的遥感基础信息。     

应用多重极化L—波段合成孔径雷达数据估测森林生物量,林冠结构和树…

本文报道了应用多重极化Ｌ－波段合成孔径雷达（ＳＡＲ）数据估测森林生物量，冠层结构和树种组成的主要研究成果。ＳＡＲ数据与树高，胸径，植株密度，方位和样地几何形状等因素呈显著相关（Ｐ＝０．０５或Ｐ＝０．０１），特别能反映硬阔叶林分子其它森林树种组间林冠特征差异（包括分枝形态，冠重量和面积），主要树种组内，树种组间与林结构的变化（包括树高，植株胸径和密度）以及一些低生物量和部分疏林地与潮湿土壤及枯枝落叶     

多指标分层抽样调查中样本层权的确定

多指标分层抽样调查中样本层权的确定赵建丛牛玉刚（河北农业大学，保定０７１０００关键词多指标分层抽样；样本层权；加权平均法中图分类号Ｓ７１１ＤＥＴＥＲＭＩＮＡＴＩＯＮＯＦＳＡＭＰＬＥＬＡＹＥＲ＇ＳＷＥＩＧＨＴＳＩＮＭＵＬＴＩ－ＩＮＤＥＸＬＡＹＥＲＥＤＳ...     

应用多重极化L─波段合成孔径雷达数据估测森林生物量、林冠结构和树种组成（上）

本文报道了应用多重极化Ｌ－波段合成孔径雷达（ＳＡＲ）数据估测森林生物量、冠层结构和树种组成的主要研究成果。ＳＡＲ数据与树高、胸径、植株密度、方位和样地几何形状等因素呈显著相关（Ｐ＝０．０５或Ｐ＝０．０１），特别能反映硬阔叶林分子其它森林树种组间林冠特征差异（包括分枝形态、树冠重量和面积）、主要树种组内、树种组间与林分结构的变化（包括树高、植株胸径和密度）以及一些低生物量和部分疏林地与潮湿土壤及枯枝落叶层的分布和裸露程度。     

京津冀刺槐人工林直径,树高及蓄积量模型

京津冀刺槐人工林直径、树高及蓄积量模型张春生刘忠柱李新玫刘丽华（平泉县林业局，０６７５００）（衡水市林业局，０５３０００）关键词刺槐；数学模型；逐步变量选择法中图分类号Ｓ７５８．５ＭＡＴＨＥＭＡＴＩＣＡＬＭＯＤＥＬＳＦＯＲＤＩＡＭＥＴＥＲ，ＴＲＥＥＨ...     

树皮厚度、树皮材积与直径和树高相关关系的研究

树皮厚度、树皮材积与直径和树高相关关系的研究陈东来，秦淑英（河北林学院林学系保定０７１０００）关键词树皮厚度，树皮材积，直径，树高，线性相关中图分类号Ｓ７５８．１ＳＴＵＤＩＥＳＯＮＣＯＲＲＥＬＡＴＩ０ＮＯＦＴＨＩＣＫＮＥＳＳＡＮＤＶＯＬＵＭＥ０ＦＴＲ...     

石灰氮打破葡萄休眠试验

石灰氮打破葡萄休眠试验冯爱华，伊承继（秦皇岛市林业局西山园艺场，秦皇岛０６６１００）（北戴河区农委，秦皇岛０６６１００）关键词石灰氮，休眠，葡萄中图分类号Ｓ６６３．１ＧＲＡＰＥＤＯＲＭＡＮＣＹ－ＢＲＥＡＫＩＮＧＴＥＳＴＷＩＴＨＬＩＭＥ－ＮＩＴＲＯＧＥ...     

土壤微团聚体与土壤的能量变化

土壤微团聚体与土壤的能量变化郭素萍（河北林学院林经系保定０７１０００）关键词土壤微团聚体，土壤肥力，土壤粘粒，腐殖质，能量中图分类号Ｓ７１４．１ＳＯＩＬＭＩＣＲＯ－ＡＧＧＲＥＧＡＴＥＡＮＤＳＯＩＬＥＮＥＲＧＹＧｕｏＳｕｐｉｎｇ（ＨｅｂｅｉＦｏｒｅｓｔ...     

ASARDL项目正式运行

ＡＳＡＲＤＬ项目正式运行“退化土地利用及其社会经济手段研究ＡＬＴＥＲＮＡＴＩＶＥＳＯＣＩＯＥＣＯＮＯＭＩＣＡＰＰＲＯＡＣＨＥＳＴＯＲＥ－ＣＬＡＩＭＩＮＧＤＥＧＲＡＤＥＤＬＡＮＤＳ”（简称ＡＳＡＲＤＬ）项目经近两年的准备，已于１９９５年１０月１日正式运...     

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TM影像和统计分析的林分参数估测研究

林业研究中的主要兴趣点之一在于通过经验或半经验模型建立林分参数与遥感影像数据间的相互关系来估测林分参数.基于覆盖美国佛罗里达州东北Duval县的遥感数据和两块样地清查数据,论文探讨了所选林分参数与TM影像光谱DN值间的相关性.相关性分析结果表明,单波段或植被指数对林分参数的解释能力低于50%,为此构建了林分参数与影像多波段间多元回归模型来估测林分参数.预测结果通过另一组数据验证,除林分密度外,其它参数估测可信度达75%以上.论文最后探讨了预测模型不足和需改进的地方,并指出该研究有助于更好地理解影像光谱值和林分参数间的关系.图1表2参9.     

Estimation of forest parameters based on TM imagery and statistical analysis

One of the primary forestry research interests lies in estimating forest stand parameters by applying empirical or semi-empirical model to establish the relationship between the forest stand parameters and remote sensing data. Using remote sensing image and the inventory data from 2 compartments in northeast Florida, U.S.A., this paper explored the correlation between forest stand parameters and Landsat TM spectral digital number （DN） value. Results showed that less than 50% of the total variance could be explained by linear regression models with only either a single band or such vegetation indices as vegetation index （VI） or normalized difference vegetation index （NDVI） as predicators. In consequence, multi-linear regression models which synthesized more predicators were introduced to estimate forest parameters. Regression results were tested in terms of the other group of data, and verification showed a better capability of explaining over 75% variance except for forest density. The weakness and further improvement of prediction models were also discussed in the article. This paper is expected to provide a better understanding of the relationship between TM spectral and forest characteristics     

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.     

崂山林场林分生物量遥感反演估算研究

以Landsat TM影像和高分一号影像为数据源,结合外业实测数据,利用遥感影像和实测数据建立崂山林场生物量多元线性反演模型,比较分析不同数据源下反演出的模型精度,估测了崂山林场森林生物量。研究发现,利TM遥感影像作为数据源的崂山林场森林生物量反演模型平均精度为77.12%。高分一号遥感数据反演的生物量模型平均反演精度达到80.75%,高于TM数据源下的生物量反演模型精度。分别根据TM遥感影像和高分一号遥感影像林分生物量估测模型,估测的崂山林场2009年的林分生物量为401185.62t,2013年的林分生物量为402485.44t。     

Detection of Windthrow in Mountainous Regions with Different Remote Sensing Data and Classification Methods

After a disastrous storm event, quick and reliable information on the extent of forest damage is required. This study evaluated different remote sensing data and methods to detect windthrown forests in mountainous regions as an alternative to the manual analysis of aerial images or terrestrial methods. To this end, both optical satellite sensors (Landsat-7, Spot-4 and Ikonos) and synthetic aperture radar (SAR) data at various frequencies (X-, L-, P- and C-band) were evaluated, and classifications of the windthrown forests were performed. This study was designed to state the advantages and disadvantages of the investigated data and methods. Classification results were compared with aerial images which were interpreted manually on a stereoscopic base. The study showed that the manual interpretation of Ikonos data revealed the most accurate results, followed by an automatic classification of Spot-4 data. Except for ERS-1/2 data, which are too inaccurate in mountainous regions, and SAR P-band data, all sensors and methods investigated have different advantages, so the choice of a specific sensor and method will depend on the question being answered.     

多极化星载SAR森林覆盖变化检测方法

【目的】利用多极化星载SAR数据,分析后向散射强度比值影像的概率密度分布特征,融合后向散射强度信息和影像空间上下文信息,提出一种具有较高检测正确率及较低虚警率和漏警率的森林覆盖变化检测方法,为多极化SAR卫星数据的业务化应用提供技术支撑。【方法】将"2期分别分类森林覆盖变化检测法"(CBFC)与"贝叶斯最大期望-马尔科夫随机场(EM-MRF)变化检测法"相结合,首先采用阈值分割法分别对2期多极化SAR影像进行森林-非森林分类得到初始森林覆盖变化图,然后以初始森林覆盖变化图作为训练数据对多极化比值影像进行Fisher特征变换和EM-MRF分类处理,2个时相的HH、HV极化比值影像经Fisher特征变换转化为一个综合差异影像,输入EM-MRF进行迭代分类得到森林覆盖变化检测结果。以黑龙江省逊克县为试验区,以2期ALOSPALSAR双极化数据为SAR遥感数据,以对2期Landsat-5影像、高空间分辨率遥感影像进行目视解译得到的森林覆盖变化图为参考,对本研究提出方法的有效性与CBFC方法及直接用CBFC提取的森林覆盖变化检测图掩膜EM-MRF地表覆盖变化检测图方法(CBFC-EM-MRF)进行比较评价。【结果】通过Fisher特征变换得到的差异影像可有效增强森林覆盖变化、未变化类别的对比度;CBFC通过阈值分割法进行森林-非森林分类,提取的森林覆盖变化图中出现很多面积很小的虚警检测,漏警率也很高,而本研究提出方法通过MRF加入影像空间上下文信息,提高了检测结果的空间连贯性,森林覆盖变化检测虚警率为1.58%,漏警率为11.87%,正确率为98.36%,检测效果和精度明显优于CBFC和CBFC-EM-MRF。【结论】多极化星载SAR森林覆盖变化检测方法具有收敛性好、检测结果可信度高、需要用户交互较少等特点,对我国高分三号及未来其他多极化SAR卫星的森林资源监测业务应用具有重要参考价值。     

基于Landsat长时间序列的森林扰动参数提取与树高估算

【目的】研究基于遥感影像的森林扰动信息定量提取及其对树高估算的影响,为遥感反演森林参数(树高、生物量)提供参考和借鉴。【方法】选取黑龙江省凉水国家级自然保护区为研究区,以1984—2006年33期Landsat TM/ETM+多光谱遥感影像为数据源,对其进行缨帽变换提取缨帽角(TCA)和缨帽距离(TCD)2个扰动监测指数,采用时间轨迹分析方法(LandTrendr)对TCA与TCD指数进行时间序列重构,分别提取扰动发生的前一年(DBYEA)、扰动发生前的光谱值(DBVAL)、扰动持续时间(DDUR)、扰动量级(DMAG)、扰动后开始修复的时间(RBYEAR)、扰动后开始修复的光谱值(RBVAL)、修复量级(RMAG)和修复持续时间(RDUR)8个时间序列扰动参数。基于单时相Landsat影像光谱信息与单时相Landsat影像光谱信息+森林扰动参数2组变量分别采用随机森林(RF)算法估算树高。【结果】采用单时相Landsat影像光谱信息结合基于TCA和TCD提取的16个时间序列扰动参数建立的树高反演模型预估精度比采用单时相Landsat影像光谱信息建立的树高反演模型预估精度提高6.34%,均方根误差(RMSE)降低0.50 m。树高反演模型中基于TCA提取的时间序列扰动参数变量重要性高于基于TCD提取的时间序列扰动参数变量重要性。【结论】基于LandTrendr提取的森林时间序列扰动参数能够增强反射率与树高之间的相关性,提高遥感树高模型的反演精度,基于TCA提取的森林时间序列扰动参数对树高的解释能力高于基于TCD提取的森林时间序列扰动参数。     

三种回归分析方法在Landsat-5影像森林碳密度反演中的比较

随着遥感技术的快速发展,基于遥感影像和地面样地的方法成为目前森林碳密度精确估算的主要手段,然而没有找到具有普适性的建模因子和最佳的森林碳密度估算模型。鉴于此,本文通过分析研究区地面固定样地碳密度与Landsat-5影像及其衍生波段的相关性,筛选出估算森林碳密度的敏感因子。采用三种回归分析方法(逐步回归、偏最小二乘回归及非线性回归)分别建立森林碳密度的最优遥感估算模型。结果表明:1参与建模的遥感因子中,1/TM3与森林碳密度的相关性最大,敏感性最高;2三种回归分析方法建立的预测模型中,以4个遥感因子建立的非线性回归模型预测精度最高,预测值与实测值得决定系数R2为0.74;3通过测算,研究区平均森林碳密度为14.36 t/hm2,变化范围介于0.00~38.28 t/hm2之间。研究表明非线性回归在区域森林碳密度反演方面具有一定的潜力。     

基于CASA模型的人工植被生物量的估算及特征分析

利用改进的CASA模型,结合Landsat TM遥感影像及气象数据,估算榆林飞播林2010年7月的植被净初级生产力。通过实测植被生物量,验证CASA模型在研究区的估算结果。结果表明:CASA模型适用于榆林飞播林植被净初级生产力估算;CASA模型估算的不同地区NPP区别明显,榆林市横山县与榆阳区交界处的植被NPP值最高,其值介233.21～414.15gC/m2之间;榆林飞播林生态系统属于较低生产力的生态系统;沙柳的NPP水平最高,以柠条+沙柳+沙蒿为播种模式的人工林地生物量最高;除沙柳、花棒和沙蒿外,其他飞播植物生物量与含水量无明显的相关性;不同飞播年代的同种植被生物量与含水量、土壤养分以及气候等因素之间具有密切的相关性。