GF-5卫星高光谱数据大气校正反射率精度评价

反射率是高光谱遥感数据应用的基础,直接关系到高光谱应用效能和质量。目前,对国产GF-5卫星高光谱数据的精确大气校正反射率精度评价方法尚未有全面深入的研究,这严重制约了国产高光谱遥感数据的高质量应用。针对此问题,综合采用6S模型和FLAASH模块,选取了三个实验区的三种典型地物及外业光谱数据,采用三种定量化指标进行大气校正,得出了以下结论:三种地物大气校正反射率与实测反射率曲线特征基本一致,黑土地的大气校正反射率光谱最优,水体由于反射率数值较低,大气校正反射率光谱稍差;可见光近红外波段大气校正效果优于短波红外波段;6S模型大气校正结果略优于FLAASH模块,更适用于GF-5卫星高光谱影像。     

航空高光谱地表氧化铁矿物含量评估技术

针对采用高光谱技术对赤铁矿、针铁矿和褐铁矿等氧化铁矿物含量评估精度不高的问题,提出了一种地表氧化铁矿物含量的航空高光谱定量评估的方法。首先基于大气特征谱线的定标参数进行数据预处理;其次利用连续统去除和二次拟合的方法获取铁离子晶体场转移光谱特征;最后根据地面实测氧化铁含量进行回归分析。应用该方法评估了我国祁连山典型铁矿床区域的地表氧化铁含量,基于研究区的CASI/SASI航空高光谱数据,对地表氧化铁矿物进行了含量评估。研究表明,地表氧化铁航空高光谱定量评估的方法可靠,应用该方法可有效识别出氧化铁蚀变矿物富集区域。     

航空高光谱地表氧化铁矿物含量评估技术

针对采用高光谱技术对赤铁矿、针铁矿和褐铁矿等氧化铁矿物含量评估精度不高的问题,提出了一种地表氧化铁矿物含量的航空高光谱定量评估的方法。首先基于大气特征谱线的定标参数进行数据预处理;其次利用连续统去除和二次拟合的方法获取铁离子晶体场转移光谱特征;最后根据地面实测氧化铁含量进行回归分析。应用该方法评估了我国祁连山典型铁矿床区域的地表氧化铁含量,基于研究区的CASI/SASI航空高光谱数据,对地表氧化铁矿物进行了含量评估。研究表明,地表氧化铁航空高光谱定量评估的方法可靠,应用该方法可有效识别出氧化铁蚀变矿物富集区域。     

Hyperion高光谱遥感数据大气校正方法

由于受到大气的影响,传感器接收到的辐射信息不能真实地反映地表反射光谱信息,因此,从遥感影像中去除大气的影响,即进行大气校正,是高光谱遥感数据处理中极为重要的环节;通过应用大气校正模块FLAASH,研究选择了合适的大气模式、水汽含量、气溶胶模型、波谱分辨率和多散射模型等参数,对内蒙东胜地区Hyperion高光谱遥感影像进行大气校正;比较了校正前后典型地物的光谱曲线,并将它们与实验室典型地物光谱曲线进行对比,大气校正后得到的光谱曲线和实验室得到的光谱曲线具有较好的一致性,达到了去除大气影响的目的,同时校正生成的水汽分布也表明校正效果良好。     

HJ-1A高光谱数据高效大气校正及应用潜力初探

环境与灾害监测预报小卫星于2009年3月30日开始正式交付使用,A星上搭载了我国自主研制的空间调制型干涉高光谱成像仪（HSI）,作为一种新型传感器,HSI数据的应用在我国还处于探索阶段。要充分发挥超光谱数据优势、进行有效的遥感应用,首先需要消除遥感成像过程中的大气影响,获得不同波段的地物真实反射辐射信息。通过使用FLAASH大气辐射传输模型对HSI数据进行大气校正,并与表观反射率进行对比分析,证明了校正后获得的地表光谱反射率的有效性。同时基于校正后得到的光谱反射率图像,进行改良型土壤调整植被指数（MSAVI）与叶面积指数（LAI）的反演,初步展现了HSI数据的实际应用效果。     

高光谱传感器CASI与SASI支持下的水体精准提取

针对水环境监测的实际需求,研究选用具有cm级空间分辨率和nm级光谱分辨率的机载航空高光谱成像仪CASI和SASI数据,在380 ～2450 nm光谱范围内,提出了一种水体精准提取综合模型,建立了一种适用于CASI和SASI数据的水体提取方法体系.有效地解决同谱异物、阴影遮挡和地形起伏等问题,经与七种常规提取方法对比验证,提取精度达到98.41％,k系数达到0.97,在目视效果和制图精度上,都显著高于传统方法,实现了精准提取水体的目标.     

结合光谱响应函数的Landsat-8影像大气校正研究

针对受大气吸收与散射影响,遥感器得到的测量值与目标物的真实值间存在误差,给反演地表反射率/反照率和地表温度等关键参数带来较大误差,影响图像分析精度的问题,该文利用Landsat-8的光谱响应函数,对OLI多光谱数据进行大气辐射校正和反射率反演,对校正前后的地物光谱曲线和归一化植被指数(Normalized Difference Vegtation Index,NDVI)的变化进行了对比。研究表明:OLI大气校正后较好地恢复各类地物光谱的典型特征;大气校正后NDVI增幅明显;类似的基于光谱响应函数的FLAASH大气校正方法可以为其他的高级陆地成像仪等传感器校正提供依据。     

传感器光谱响应差异对NDVI的影响

传感器光谱响应差异是导致不同来源NDVI观测数据之间差异的因素之一,在进行多源遥感数据之间的对比和同化处理时需要对其影响加以分析和校正。基于此,该研究将地物的反射波谱曲线和传感器的光谱响应函数进行卷积,以此来模拟不同传感器在可见光和近红外通道的等效地表反射率并计算相应的NDVI,分别用绝对误差和相对误差两个指标来描述不同传感器观测结果相对于MODIS的差异并建立校正模型。结果表明,光谱响应差异引起的不同传感器观测结果差异可以通过二次多项式模型进行校正,基于相对误差的校正模型比基于绝对误差的校正模型效果更好一些,整体上传感器光谱响应差异对NDVI观测值的影响有限,还需要进一步考虑大气状况及观测几何等其他因素的影响。     

珠海一号欧比特高光谱数据交叉辐射定标初探

搭载于“珠海一号”卫星星座的欧比特高光谱OHS（Orbit Hyper Spectral）传感器,以较高的光谱分辨率和空间分辨率,在近岸及内陆湖泊水色遥感应用方面具有很大潜力。然而OHS缺乏星上定标系统,目前在轨定标采用陆地定标场的资料,其定标结果在水体等低反射率地物误差较大。因此提出一种基于传感器入瞳总辐亮度的交叉辐射定标法,该方法结合QAA（Quasi-Analytical Algorithm）准分析算法和6SV2.1辐射传输模型,利用GOCI（Geostationary Ocean Color Imager）多光谱数据对OHS高光谱数据进行交叉辐射定标。研究结果表明：①GOCI和OHS传感器获取的地物辐射相关性好,在可见光波段范围内,R²均高于0.84;②重新定标后的数据能明显改善不同传感器之间的辐射差异,在可见光波段范围内,定标误差小于9%。实验为高光谱传感器的辐射定标提供了一种新的方法,对建立高光谱定量化、业务化水色遥感处理系统,特别对OHS数据在水域的各种应用具有重要意义。     

Sentinel-2A卫星大气校正方法及校正效果

针对Sentinel-2A卫星大气校正研究的不足,对Sentinel-2A大气校正方法进行介绍,并选取森林、水体、城市建筑物3种地物为研究对象,分析Sentinel-2A单波段通道大气校正前后反射率变化;以Landsat-8、高分一号(GF-1)为辅助数据,从3种传感器大气校正后均质像元反射率曲线、大气校正前后植被指数变化3个方面进行研究。结果表明:1)Sentinel-2A大气校正后,可见光通道反射率变小,波长越长,大气校正效果越不显著;近红外、短波红外反射率增加。2)大气校正后,3种数据源同种地物光谱曲线趋于一致,其中Sentinel-2A水体与植被光谱曲线更能反映地物特征。3)与Landsat-8相比,Sentinel-2A、GF-1WFV1大气校正后林地NDVI明显增大,Sentinel-2A高植被覆盖区增大,低植被覆盖区减小,最能反映植被特征;Sentinel-2ANDWI变化不如Landsat-8NDWI变化显著。     

结合LiDAR单木分割和高光谱特征提取的城市森林树种分类

基于多源遥感数据的城市森林树种分类对城市森林资源调查、森林健康状况评价及科学化管理具有重要意义。以江苏省常熟市虞山国家森林公园内的典型城市森林树种为研究对象,利用同期获取的机载激光雷达(LiDAR)和高光谱数据,针对5个典型城市森林树种进行了树种分类的研究。首先,基于点云距离判断单木分割方法进行单木位置和冠幅提取,并借助实测数据和目视解译结果进行精度验证;然后,在冠幅内提取4组高光谱特征变量,并借助随机森林模型对特征变量进行重要性分析;最后,筛选出重要性高的特征变量进行2个级别的树种分类并借助混淆矩阵进行验证评价。结果表明:基于点云距离判断分割方法的单木位置提取精度较高(探测率为85.7%,准确率为96%,总体精度为90.9%);利用全部特征变量(n=36)对5个树种进行分类,分类的总体精度达到了84%,Kappa系数为0.80;利用优选特征变量(n=9)进行分类,总体精度达83%,Kappa系数为0.79;利用全部特征变量(n=36)对两种森林类型进行分类,分类的总体精度达91.3%,Kappa系数为0.82,其中阔叶树种分类精度为95.6%,针叶树种分类精度为85%;利用优选特征变量(n=9)进行分类,分类的总体精度达90.7%,Kappa系数为0.80,其中阔叶树种分类精度为93.33%,针叶树种分类精度为86.67%。     

Automated tree recognition in old growth conifer stands with high resolution digital imagery

Automated individual tree isolation and species determination with high resolution multispectral imagery is becoming a viable forest survey tool. Application to old growth conifer forests offer unique technical issues including high variability in tree size and dominance, strong tree shading and obscuration, and varying ages and states of health. The capabilities of individual tree analysis are examined with two acquisitions of 70-cm resolution CASI imagery over a hemlock, amabilis fir, and cedar dominated old growth site on the west coast of Canada. Trees were delineated using the valley following approach of the Individual Tree Crown (ITC) software suite, classified according to species (hemlock, amabilis fir, and cedar) using object-based spectral classification and tested on a tree-for-tree basis against data derived from ground plots.Tree-for-tree isolation and species classification accuracy assessment, although often sobering, is important for portraying the overall effectiveness of species composition mapping using single tree approaches. This accuracy considers not only how well each tree is classified, but how well each automated isolation represents a true tree and its species. Omissions and commissions need to be included in overall species accuracy assessment. A structure of rules for defining isolation accuracy is developed and used. An example is given of a new approach to accuracy analysis incorporating both isolation and classification results (automated tree recognition) and the issues this presents.The automated tree isolation performed well on those trees that could be visually identified on the imagery using ground measured stem maps (approximately 50-60% of trees had a good match between manual and automated delineations). There were few omissions. Commission errors, i.e., automated isolations not associated with a delineated ground reference tree, were a problem (25%) usually associated with spurious higher intensity areas within shaded regions, which get confused in the process of trying to isolate shaded trees. Difficulty in classifying species was caused by: variability of the spectral signatures of the old growth trees within the same species, tree health, and trees partly or fully shaded by other trees. To accommodate this variability, several signatures were used to represent each species including shaded trees. Species could not be determined for the shaded cases or for the unhealthy trees and therefore two combined classes, a shaded class and unhealthy class with all species included, were used for further analysis. Species classification accuracy of the trees for which there was a good automated isolation match was 72%, 60%, and 40% for the non-shaded healthy hemlock, balsam, and cedar trees for the 1996 data. Equivalent accuracy for the 1998 imagery was 59% for hemlock, 80% for balsam, with only a few cedar trees being well isolated. If all other matches were considered an error in classification, species classification was poor (approximately 45% for balsam and hemlock, 25% for cedar). However, species classification accuracies incorporating the good isolation matches and trees for which there was a match of an isolations and reference tree but the match was not considered good were moderate (60%, 57%, and 38% for hemlock, balsam, and cedar from the 1996 data; 62%, 61%, and 89%, respectively, for the 1998 imagery).Automated tree isolation and species classification of old growth forests is difficult, but nevertheless in this example useful results were obtained.     

Tree species differentiation using intensity data derived from leaf-on and leaf-off airborne laser scanner data

Tree species identification is important for a variety of natural resource management and monitoring activities including riparian buffer characterization, wildfire risk assessment, biodiversity monitoring, and wildlife habitat assessment. Intensity data recorded for each laser point in a LIDAR system is related to the spectral reflectance of the target material and thus may be useful for differentiating materials and ultimately tree species. The aim of this study is to test if LIDAR intensity data can be used to differentiate tree species. Leaf-off and leaf-on LIDAR data were obtained in the Washington Park Arboretum, Seattle, Washington, USA. Field work was conducted to measure tree locations, tree species and heights, crown base heights, and crown diameters of individual trees for eight broadleaved species and seven coniferous species. LIDAR points from individual trees were identified using the field-measured tree location. Points from adjacent trees within a crown were excluded using a procedure to separate crown overlap. Mean intensity values of laser returns within individual tree crowns were compared between species. We found that the intensity values for different species were related not only to reflective properties of the vegetation, but also to a presence or absence of foliage and the arrangement of foliage and branches within individual tree crowns. The classification results for broadleaved and coniferous species using linear discriminant function with a cross validation suggests that the classification rate was higher using leaf-off data (83.4%) than using leaf-on data (73.1%), with highest (90.6%) when combining these two LIDAR data sets. The result also indicates that different ranges of intensity values between two LIDAR datasets didn't affect the result of discriminant functions. Overall results indicate that some species and species groups can be differentiated using LIDAR intensity data and implies the potential of combining two LIDAR datasets for one study.     

应用属性约简构建含有缺失数据的谱系树

为了解决含有缺失形态学数据谱系树的构建问题,提出了运用属性约简构建谱系树的方法。首先,利用先验知识和较完整的部分物种数据构建初始谱系树;然后,运用属性约简原理获得属性决策组集合的决策点,进而建立先验决策模型;最后,根据先验决策模型确定缺失数据比例较高的物种在初始谱系树中的位置,通过物种嫁接完成谱系演化树的构建。实验结果表明,当单个物种缺失数据比例大于10%时,相比最大简约法在平均准确率方面平均高出10%左右。     

Single tree species classification with a hypothetical multi-spectral satellite

The requirements for high resolution multi-spectral satellite images to be used in single tree species classification for forest inventories are investigated, especially with respect to spatial resolution, sensor noise and geo-registration. In the hypothetical setup, a 3D tree crown map is first obtained from very high resolution panchromatic aerial imagery and subsequently each crown is classified into one of a set of known tree species such that the difference between a model multi-spectral image generated from the 3D crown map and an acquired multi-spectral satellite image of the forested area is minimized. The investigation is conducted partly by generating synthetic data from a 3D crown map from a real mixed forest stand and partly on hypothetical high resolution multi-spectral satellite images obtained from very high resolution colour infrared aerial photographs, allowing different hypothetical spatial resolutions. Conclusions are that until a new generation of even higher resolution satellites becomes available, the most feasible source of remote sensing data for single tree classification will be aerial platforms.     

Mapping forest vegetation using Landsat TM imagery and a canopy reflectance model

Estimates of mean tree size and cover for each forest stand from an invertible forest canopy reflectance model are part of a new forest vegetation mapping system. Image segmentation defines stands which are sorted into general growth forms using per-pixel image classifications. Ecological models based on terrain relations predict species associations for the conifer, hardwood, and brush growth forms. The combination of the model-based estimates of tree size and cover with species associations yields general-purpose vegetation maps useful for a variety of land management needs. Results of timber inventories in the Tahoe and Stanislaus National Forests indicate the vegetation maps form a useful basis for stratification. Patterns in timber volumes for the strata reveal that the cover estimates are more reliable than the tree size estimates. A map accuracy assessment of the Stanislaus National Forest shows high overall map accuracy and also illustrates the problems in estimating tree size.     

Semi-automatic classification of tree species in different forest ecosystems by spectral and geometric variables derived from Airborne Digital Sensor (ADS40) and RC30 data

This study presents an approach for semi-automated classification of tree species in different types of forests using first and second generation ADS40 and RC30 images from two study areas located in the Swiss Alps. In a first step, high-resolution canopy height models (CHMs) were generated from the ADS40 stereo-images. In a second step, multi-resolution image segmentation was applied. Based on image segments seven different tree species for study area 1 and four for study area 2 were classified by multinomial regression models using the geometric and spectral variables derived from the ADS40 and RC30 images. To deal with the large number of explanatory variables and to find redundant variables, model diagnostics and step-wise variable selection were evaluated. Classifications were ten-fold cross-validated for 517 trees that had been visited in field surveys and detected in the ADS40 images. The overall accuracies vary between 0.76 and 0.83 and Cohen's kappa values were between 0.70 and 0.73. Lower accuracies (kappa < 0.5) were obtained for small samples of species such as non-dominant tree species or less vital trees with similar spectral properties. The usage of NIR bands as explanatory variables from RC30 or from the second generation of ADS40 was found to substantially improve the classification results of the dominant tree species. The present study shows the potential and limits of classifying the most frequent tree species in different types of forests, and discusses possible applications in the Swiss National Forest Inventory.     

Single tree detection in heterogeneous boreal forests using airborne laser scanning and area-based stem number estimates

Adaptive single tree detection methods using airborne laser scanning (ALS) data were investigated and validated on 40 large plots sampled from a structurally heterogeneous boreal forest dominated by Norway spruce and Scots pine. Under the working assumption of having uniformly distributed tree locations, area-based stem number estimates were used to guide tree crown delineation from rasterized laser data in two ways: (1) by controlling the amount of smoothing of the canopy height model and (2) by obtaining an appropriate spatial resolution for representing the forest canopy. Single tree crowns were delineated from the canopy height models (CHMs) using a marker-based watershed algorithm, and the delineation results were assessed using a simple tree crown delineation algorithm as a reference method (‘RefMeth’). Using the proposed methods, approximately 46–50% of the total number of trees were detected, while approximately 5–6% false positives were found. The detection rate was, in general, higher for Scots pine than for Norway spruce. The accuracy of individual tree variables (total height and crown width) extracted from the laser data was compared with field-measured data. The individual tree heights were better estimated for deciduous tree species than for the coniferous species Norway spruce and Scots pine. The estimation of crown diameters for Scots pine and deciduous species achieved comparable accuracy, being better than for Norway spruce. The proposed methodology has the potential for easy integration with operational laser scanner-based stand inventories.     

Combining high spatial resolution multi-temporal satellite data with leaf-on LiDAR to enhance tree species discrimination at the crown level

ABSTRACT

The long-standing goal of discriminating tree species at the crown-level from high spatial resolution imagery remains challenging. The aim of this study is to evaluate whether combining (a) high spatial resolution multi-temporal images from different phenological periods (spring, summer and autumn), and (b) leaf-on LiDAR height and intensity data can enhance the ability to discriminate the species of individual tree crowns of red oak (Quercus rubra), sugar maple (Acer saccharum), tulip poplar (Liriodendron tulipifera), and black cherry (Prunus serotina) in the Fernow Experimental Forest, West Virginia, USA. We used RandomForest models to measure a loss of classification accuracy caused by iteratively removing from the classification one or more groups from six groups of variables: spectral reflectance from all multispectral bands in the (1) spring, (2) summer, and (3) autumn images, (4) vegetation indices derived from the three multispectral datasets, (5) canopy height and intensity from the LiDAR imagery, and (6) texture related variables from the panchromatic and LiDAR datasets. We also used ANOVA and decision tree analyses to elucidate how the multispectral and LiDAR datasets combine to help discriminate tree species based on their unique phenological, spectral, textural, and crown architectural traits. From these results, we conclude that combing high spatial resolution multi-temporal satellite data with LiDAR datasets can enhance the ability to discriminate tree species at the crown level.     

基于DenseNet的无人机光学图像树种分类研究

利用无人机航拍获得光学影像数据,结合深度学习理论,建立树种识别模型,以期为大规模树种识别提供一种新的方式。首先以福建安溪县为例,采用无人机获取20 m及40 m高度的航拍影像。其次,以树种为对象,对航拍影像进行分割,获得12种树种影像。最后,结合深度学习理论,采用DenseNet卷积神经网络建立树种识别模型,探讨不同航拍高度以及不同网络深度对树种识别的影响。结果表明:不同航拍高度的树种识别模型,其分类精度均达80%以上,最高精度为87.54%。从航拍影像解析度分析,随着航拍影像解析度的下降,模型识别精度呈现下降趋势,以20 m航拍影像数据建构的树种识别模型,其分类精度高于40 m模型;从模型网络深度分析,随着模型网络层数的增加,模型分类精度出现下降现象,DenseNet121模型分类精度高于DenseNet169模型分类精度。综上所述,基于无人机航拍影像,结合深度卷积神经网络,提出了新的树种识别方式,并以安溪县森林树种识别为例证明了该分类框架的有效性。