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1.
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. 相似文献
2.
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. 相似文献
3.
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 总被引：3，自引：0，他引：3
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。 相似文献
4.
[目的]研究不同立地质量对杉木生物量遥感估测精度的影响,为进一步提高和完善森林生物量遥感监测体系提供一种新的思路和方法。[方法]以2007年建德市森林资源二类调查数据和TM影像为研究材料,采用蓄积量—生物量换算因子连续函数法计算杉木林生物量和地位级法评价立地质量等级,比较杉木立地质量好、中等、差和不分地位等级4种生物量遥感估测模型,并进行精度检验。[结果]表明:(1)以TM遥感影像主成分分析中第一主成分为自变量的模型拟合效果最好,决定系数R2均在0.69以上,最高0.855。(2)利用预留独立样本对模型精度进行验证,不分地位级总体估测精度为87.78%,分立地质量等级好、中、差3种类型总体估测精度分别为97.37%、95.82%、98.23%。分不同立地质量类型可以提高杉木生物量遥感估测精度。[结论]研究结果为森林生物量遥感估测提供一种改进的思路,且为提高森林生物量和碳储量遥感估测精度提供一种参考方法。 相似文献
5.
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. 相似文献
6.
7.
基于GIS和RS的广州市森林植被分类研究 总被引：10，自引：0，他引：10
首先对森林植被类型分类方法的研究概况进行了综述，提出采用植被指数进行植被分类，根据广州市中心城区植被冠层反射率数据，通过对多种植被指数的计算和分析，得出植被指数和植被类型的关系。进而对TM影像数据处理。得出广州市中心城区森林植被分类情况。 相似文献
8.
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（ＥＲＳ－１ＳＡＲ、ＴＭ３、ＴＭ４和ＴＭ７）与森林参数（包括平均树高、胸径、枝下高、冠层厚度和郁闭度）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影像的影响大于对所用ＴＭ波段的影响，说明ＥＲＳ－１ＳＡＲ资料在森林应用中，对森林参数乃至于森林蓄积量的估测是有潜力的。 相似文献
9.
Johannes Breidenbach Sonia M. Ortiz Manfred Reich 《European Journal of Forest Research》2010,129(5):813-823
Several TerraSAR-X satellite images acquired in high resolution spotlight mode with different polarisations for two study
sites in southern Germany were used to distinguish forest from other land cover classes (agriculture, built-up, water bodies)
using logistic regression models. In general, we observed that the mean and particularly the standard deviation of the backscatter
were viable measures to discriminate land cover classes. Both measures were lowest for water bodies and highest for built-up
areas, with agricultural areas and forest in intermediate positions. Trees outside forest were not differentiable from forest
with the applied method. The HH-polarised image was better suited for a classification of built-up areas, whereas the VV-polarised
image was more appropriate when classifying agricultural areas. Consequently, the combination of the two differently polarised
images leads to a significantly better model. Since forests in one study area were generally found on steeper slopes in comparison
to other land cover classes, the inclusion of terrain slope further improved the classification, which resulted in an overall
accuracy of 92–95%. Systematic differences in the parameter values of the explanatory variables for one class between the
study areas may be caused by differing class probabilities. Thus, transferring the model of one study area to the image of
another resulted in a 7–9% loss of accuracy. 相似文献
10.
林分蓄积量估测是林业遥感的重要研究领域,由于云雾天气和光谱饱和现象等因素限制了光学遥感影像估测林分蓄积量的精度。合成孔径雷达(SAR)具有穿透性强、受云雾影响小等特点,弥补了光学遥感的不足。以江西省龙南县的针叶林为研究对象,结合Landsat 8与PALSAR-2双极化SAR影像数据,在遥感数据预处理基础上,提取了光谱信息、植被指数、纹理信息和后向散射系数等共245个遥感因子。基于Pearson相关系数法和多元逐步回归法,筛选出65个遥感因子参与林分蓄积量估测。以林分郁闭度作为分层因子,分别采用线性、KNN、支持向量机(SVM)、多重感知机(MLP)和随机森林(RF)5种模型估测林分蓄积量,并对估测结果进行精度检验。实验结果表明:1)相比单独使用Landsat 8的光谱和纹理信息,基于郁闭度分级并融合PALSAR-2的后向散射信息明显提高了蓄积量的反演精度;2)对于低郁闭度林分,线性模型精度最高(rRMSE=21.16%),中郁闭度林分,多重感知机模型估测效果最好(rRMSE=30.61%),高郁闭度林分,多重感知机模型估测效果最好(rRMSE=27.53%)。在结合PALSAR-2的后向散射系数的基础上,郁闭度分层能有效改善中高蓄积量区域的反演精度。 相似文献
11.
【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。 相似文献
12.
基于BP神经网络的森林植被遥感分类研究 总被引：12，自引：4，他引：8
如何解决多类别图像的识别并满足一定的精度,是遥感图像研究中的一个关键问题,具有十分重要的意义。本文使用Landsat7ETM+遥感数据和森林资源分布图等地理辅助数据,用BP神经网络方法对森林植被进行了分类,并与最大似然法的分类结果进行精度比较分析。结果表明地理辅助数据参与的BP神经网络用于森林植被遥感图像分类其效果是较好的,是一种有效的图像分类方法。 相似文献
13.
借助遥感技术提高国家森林资源连续清查效率具有重要的意义。利用最大似然法、BP神经网络和最近邻算法3种不同的分类方法对诸暨市森林资源进行监测,并将分类结果与二类森林资源调查数据作对比。结果表明,3种分类方法都能较高精度地监测诸暨市不同森林类型总面积,精度在77.53%～83.18%之间;但是在乡镇尺度上,3种分类方法的精度都不理想,总相对均方根误差分别为41.83%,44.91%和44.18%。除灌木林外,以上3种分类方法在精度上没有显著差异(P>0.5)。今后研究应从多源遥感影像融合等技术上提高像元尺度上的分类精度。 相似文献
14.
采用Worldview-2八波段影像作为数据源,选取东洞庭湖湿地核心区域作为研究区,进行了Worldview-2八波段特征分析、构建改进遥感指数、采用改进遥感指数阈值分层分类的策略对湿地区域进行信息提取。研究结果表明,基于Worldview-2八波段影像改进指数的湿地类型分类总精度达到了92.24%,Kappa系数为0.902,比原始遥感指数的分类精度提高了8.18%,特别是对草滩地和泥滩地的区分有了较大的提高,是有效、准确提取湿地类型的技术方法。 相似文献
15.
The feasibility of ERS SAR Tandem data for mapping forest and non-forest cover in China was evaluated over Zengcheng County in the South China. An accuracy of 75% has been achieved. Then, the MACFERST (Mapping China Forest with ERS SAR Tandem data) project started by the Ministry of Science and Technology (MOST) of China and the European Space Agency (ESA) in 1999. The generation of a large-scale forest map requires solving problems such as the georeferencing and mosaicking of very long image strips covering huge regions and the adaptation of the classification algorithm to account for radiometric differences. Approaches to solve these problems and the classification results in the Northeastern of China will be presented in this paper. 相似文献
16.
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％. 相似文献
17.
松毛虫早期灾害点遥感监测研究初报 总被引：7，自引：1，他引：7
松毛虫早期灾害点遥感监测研究初报武红敢，黄建文，乔彦友（中国林业科学研究院资源信息所北京１００００１）严小君，陈林洪（浙江省江山市森林防疫站江山３２４１００）关键词松毛虫害，ＴＭ数据，影像，监测松毛虫为我国最为严重的森林害虫［１］。几十年来，各级政府... 相似文献
18.
三江源自然保护区土地利用遥感分类方法研究 总被引：1，自引：0，他引：1
以三江源区域索加曲麻河自然保护区为例,基于TM影像进行数据变换和波段运算后获取的特征指数,采用决策树方法,探讨了高寒区域土地利用遥感分类方法。然后通过与传统的最大似然法监督分类所得到的结果进行对比,结果表明:利用基于指数的决策树分类方法对高寒区域土地利用/土地覆盖类型进行遥感分类,较传统的最大似然法监督分类总体精度提高15.48%,总体kappa系数提高0.174 1;滩地、沼泽、高覆盖度草地、低覆盖度草地、裸岩石砾地等地类的用户精度提高较大,分别提高28.13%,25.00%,17.86%,17.86%和12.50%。低、中、高3种覆盖度草地,裸岩石砾地的生产者精度也有较大幅度的提升。表明基于指数的决策树分类方法是高寒区域土地利用遥感分类的一种有效手段。 相似文献
19.
Jonas Bohlin Jörgen Wallerman Johan E. S. Fransson 《Scandinavian Journal of Forest Research》2016,31(5):517-525
Discrimination of deciduous trees using spectral information from aerial images has only been partly successfully due to the complexity of the reflectance at different view angles, times of acquisition, phenology of the trees and inter-tree radiance. Therefore, the objective was to evaluate the accuracy of estimating the proportion of deciduous stem volume (P) utilizing change detection between canopy height models (CHMs) generated by digital photogrammetry from leaf-on and leaf-off aerial images instead of using spectral information. The study was conducted at a hemi-boreal study area in Sweden. Using aerial images from three seasons, CHMs with a resolution of approximately 0.5?m were generated using semi-global matching. For training plots, metrics describing the change between leaf-on and leaf-off conditions were calculated and used to model the continuous variable P, using the Random Forest approach. Validated at sub-stands, the estimation accuracy of P in terms of root mean square error and bias was found to be 18% and ?6%, respectively. The overall classification accuracy, using four equally wide classes, was 83% with a kappa value of 0.68. The validation plots in classes of high proportion of coniferous or deciduous stem volume were well classified, whereas the mixed forest classes showed lower classification accuracies. 相似文献
20.
We evaluated the influence of texture information from remote sensed data on the accuracy of forest type classification at
different spatial resolutions. We used 4-m spatial resolution imagery to create five different sets of imagery with lower
spatial resolutions down to 30 m. We classified forest type using spectral information alone, texture information alone, and
spectral and texture information combined at each spatial resolution, and compared the classification accuracy at each resolution.
The classification and regression tree method was used for classification. The accuracy of all three tests decreased slightly
with lower spatial resolution. The accuracy with the combined data was generally higher than with either the spectral or texture
information alone. At most resolutions, the lowest accuracy was with texture information alone. However, there was no clear
difference in accuracy between the combined data and spectral data alone at 25- and 30-m spatial resolution. These results
indicate that adding texture information to spatial information improves the accuracy of forest type classification from very
high resolution (4-m spatial resolution) to medium resolution imagery (20-m spatial resolution), but this accuracy improvement
does not appear to hold for relatively coarse resolution imagery (25- to 30-m spatial resolution). 相似文献