Predicting species-specific basal areas in urban forests using airborne laser scanning and existing stand register data

The aim of this work was to examine how well species-specific stand attributes can be predicted using a combination of airborne laser scanning (ALS) and existing stand register data in urban forests. In this context, the ability of three data combinations: ALS data and stand register data, ALS data and digital aerial images and all of these combined, was tested in the prediction of species-specific basal areas. We divided tree species into seven and three different tree species strata and applied two prediction methods: (1) regression method, in which the predicted total basal area was divided into tree species based on tree species proportions from stand register data, and (2) the nearest neighbour (NN) method, in which tree species proportions were used as predictor variables for species-specific basal areas. Prediction models were built based on training data of 205 field plots, and the accuracy of the models was tested based on validation data of 52 forests stands. Our results showed that species-specific predictions of seven tree species were more accurate when tree species proportions from stand register data were used in the prediction. Both the regression and the NN method provided reasonable accuracy. This study showed that tree species information from existing stand register data could be used as an alternative for aerial images in ALS-based forests inventories. The use of ALS data together with stand register data and small field data could also be economically beneficial in an inventory of urban forests.     

高精度DEM支持下的多时期航片杉木人工林树高生长监测

【目的】集成多时期航片数据和由机载激光雷达数据获取的密集林区数字高程模型,估测多时期杉木人工林冠层高度,并对其生长情况进行定量监测,为多时期航片监测森林生长趋势和评价林地生产力提供可能。【方法】首先基于分类后的激光雷达点云数据获得林下高精度数字高程模型和森林数字表面模型,利用航片数据构建立体像对,通过自动立体匹配算法生成森林冠层的摄影测量数字表面模型,然后借助数字高程模型将2种数字表面模型进行高度归一化,提取研究区多时期森林冠层高度。利用1996、2004年历史航片和2014年数字航片以及激光雷达数据,构建18年内皖南杉木人工林3期森林冠层高度,并对其精度进行分析。【结果】1)由2014年数字航片和激光雷达数据获取的森林冠层高度的R^2为0. 52,RMSE为1. 79 m; 2)由2014年数字航片处理得到的森林冠层高度与对应样地实测上层木的平均高验证精度较高,平均绝对误差1. 59 m,平均相对误差15%,最大绝对误差3. 45 m,最大相对误差30. 80%,测量精度85. 00%; 3)由1996、2004、2014年航片得到3期杉木人工林冠层高度,其增长趋势与树高生长曲线预测趋势一致。【结论】在多山复杂地形条件下,利用航片可准确定量反映山脊向阳面的森林冠层高度变化,但对于山谷阴影处,则会出现冠层高度被低估情况,利用多期航片结合高精度DEM数据可定量反映上层木的冠层高度变化。     

Comparing biophysical forest characteristics estimated from photogrammetric matching of aerial images and airborne laser scanning data

Recent development in aerial digital cameras and software facilitate the photogrammetric point cloud as a new data source in forest management planning. A total of 151 field training plots were distributed systematically within three predefined strata in a 852.6 ha study area located in the boreal forest in southeastern Norway. Stratum-specific regression models were fitted for six studied biophysical forest characteristics. The explanatory variables were various canopy height and canopy density metrics derived by means of photogrammetric matching of aerial images and small-footprint laser scanning. The ground sampling distance was 17 cm for the images and the airborne laser scanning (ALS) pulse density was 7.4 points m^–2. Resampled images were assessed to mimic acquisitions at higher flying altitudes. The digital terrain model derived from the ALS data was used to represent the ground surface. The results were evaluated using 63 independent test stands. When estimating height in young forest and mature forest on poor sites, the root mean square error (RMSE) values were slightly better using data from image matching compared to ALS. However, for all other combinations of biophysical forest characteristics and strata, better results were obtained using ALS data. In general, the best results were found using the highest image resolution.     

低空遥感影像的测绘性能研究

低空遥感是一种新型的对地观测技术,在国土监测、城市规划、精准农业、防灾救灾、军事侦查等领域具有重要作用和广泛的应用前景。以山区、丘陵和平原三种不同地形为研究对象,分析低空影像的测绘精度和误差成因,结果表明:①丘陵和平原平坦地形低空影像的测绘精度可满足一般测绘的要求;山区低空影像的测绘精度较低,可作为背景信息与其它测绘数据结合使用。②低空影像的测绘精度与设备性能有较大关系,其校正精度取决于内、外元素测量仪器的精度和传感器的光学性能。③随着机载惯性系统MU(Inertial Measuring Unit)和高精度定位终端的小型化和性能提高,低空遥感技术将成为中小区域精准调查的重要技术手段。     

Forest variable estimation using photogrammetric matching of digital aerial images in combination with a high-resolution DEM

Abstract

The rapid development in aerial digital cameras in combination with the increased availability of high-resolution Digital Elevation Models (DEMs) provides a renaissance for photogrammetry in forest management planning. Tree height, stem volume, and basal area were estimated for forest stands using canopy height, density, and texture metrics derived from photogrammetric matching of digital aerial images and a high-resolution DEM. The study was conducted at a coniferous hemi-boreal site in southern Sweden. Three different data-sets of digital aerial images were used to test the effects of flight altitude and stereo overlap on an area-based estimation of forest variables. Metrics were calculated for 344 field plots (10 m radius) from point cloud data and used in regression analysis. Stand level accuracy was evaluated using leave-one-out cross validation of 24 stands. For these stands the tree height ranged from 4.8 to 26.9 m (17.8 m mean), stem volume 13.3 to 455 m³ ha^?1 (250 m³ ha^?1 mean), and basal area from 4.1 to 42.9 m² ha^?1 (27.1 m² ha^?1 mean) with mean stand size of 2.8 ha. The results showed small differences in estimation accuracy of forest variables between the data-sets. The data-set of digital aerial images corresponding to the standard acquisition of the Swedish National Land Survey (Lantmäteriet), showed Root Mean Square Errors (in percent of the surveyed stand mean) of 8.8% for tree height, 13.1% for stem volume and 14.9% for basal area. The results imply that photogrammetric matching of digital aerial images has significant potential for operational use in forestry.     

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.     

北京山区森林健康的多尺度评价

北京山区森林是北京市重要的绿色屏障,其健康状况事关首都生态安全等重大问题。基于森林资源二类调查数据和样地调查数据等,利用多层模糊综合评价法,从林分、景观和区域多尺度对北京山区森林健康状况进行耦合评价。结果表明:在区域尺度上,北京山区森林整体处于亚健康状态;在景观尺度上,18类森林景观要素类型中,健康的森林4类,亚健康的12类,不健康的2类;在林分尺度上,30164个森林经营小班中,亚健康的最多,数量为12226个,面积比例42.21%;不健康和疾病的数量为9534个,面积比例27.57%。因此,北京山区森林急需进行健康经营和管理以提高健康等级。     

Process-based approach to automated classification of forest structures using medium format digital aerial photos and ancillary GIS information

The methods of forest inventory data acquisition, based on the analysis of remotely sensed images have been well tested and implemented during the last decade. The predominant visual interpretation and pixel-based automated techniques are now being gradually replaced by the object-based image classification at multiple levels. This paper describes an experiment using medium-format digital aerial imagery for the purpose of automated updating of the existing GIS forest management database (LHPO). The method emphasises the pre-processing phase, where various image transforms and additional channels i.e. spectral ratios and vegetation indices (NDVI), low-pass filters, Sobel edge and GLCM (grey level co-occurrence matrix) texture measures are derived from the original dataset. The layer stack is then transferred into the object-oriented classification environment together with the existing thematic vector layer, and analysed on three hierarchical object levels. The classification involves the recognition of the successional stage of forest compartments and the estimation of tree species composition in terms of area coverage. In addition, age information on the GIS forestry management map can be updated and the spatial distribution of classes corrected using the multi-scale object relations of the former analysis. The advances of the automated procedure based on sequential processing of image objects are partially covered. Moreover, aspects of utilisation of the medium-format colour infra-red images (CIR) as an alternative to traditional aerial photos and very high resolution (VHR) satellite data, were considered.     

北京山区林业社区节能灶炕改造效果分析

文章以实地调查为根据,把节能灶炕与普通土炕进行了对比分析,指出节能灶炕具有调整能源结构、实现节能减排、改善生活环境和降低生活成本的功能和优点。节能灶炕俗称"吊炕",是在旧式炕灶基础上改进研制而成的。该研究通过对北京山区密云县花园村应用和未应用吊炕农户的调查分析,对吊炕的生态和经济效果进行了量化,从而为该技术的大面积推广应用提供了数据支持。       

Satellite-based stand-wise forest cover type mapping using a spatially adaptive classification approach

Due to high variation in forest communities, forest structure and the fragmentation of the forested area in Central Europe, satellite-based forest inventory methods have to meet particularly high-quality requirements. This study presents an innovative method to combine official forest inventory information at stand level with multidate satellite imagery using a spatially adaptive classification approach for producing wall-to-wall forest cover maps of important tree species and management classes across multiple ownership regions in a heterogeneous low mountain range in Germany. The classification approach was applied to a 5,200-km² area (about 2,080?km² of forest land, mostly mixed forests) located in the Eifel mountain range in Central Europe. In comparison with conventional classifiers, our results demonstrate a significant increase in classification accuracy in the order of 12%. The method was tested with ASTER images but holds the potential to be used for regular state forest inventories based on standard and novel earth observation data supplied for instance from the SPOT-5 and RapidEye sensors.     

Estimating aboveground biomass of Pinus densata-dominated forests using Landsat time series and permanent sample plot data

Southwest China is one of three major forest regions in China and plays an important role in carbon sequestration.Accurate estimations of changes in aboveground biomass are critical for understanding forest carbon cycling and promoting climate change mitigation.Southwest China is characterized by complex topographic features and forest canopy structures,complicating methods for mapping aboveground biomass and its dynamics.The integration of continuous Landsat images and national forest inventory data provides an alternative approach to develop a long-term monitoring program of forest aboveground biomass dynamics.This study explores the development of a methodological framework using historical national forest inventory plot data and Landsat TM timeseries images.This method was formulated by comparing two parametric methods:Linear Regression for Multiple Independent Variables(MLR),and Partial Least Square Regression(PLSR);and two nonparametric methods:Random Forest(RF)and Gradient Boost Regression Tree(GBRT)based on the state of forest aboveground biomass and change models.The methodological framework mapped Pinus densata aboveground biomass and its changes over time in Shangri-la,Yunnan,China.Landsat images and national forest inventory data were acquired for 1987,1992,1997,2002 and 2007.The results show that:(1)correlation and homogeneity texture measures were able to characterize forest canopy structures,aboveground biomass and its dynamics;(2)GBRT and RF predicted Pinus densata aboveground biomass and its changes better than PLSR and MLR;(3)GBRT was the most reliable approach in the estimation of aboveground biomass and its changes;and,(4)the aboveground biomass change models showed a promising improvement of prediction accuracy.This study indicates that the combination of GBRT state and change models developed using temporal Landsat and national forest inventory data provides the potential for developing a methodological framework for the long-term mapping and monitoring program of forest aboveground biomass and its changes in Southwest China.     

太白山林木真菌病害的垂直分布

我们对秦岭主峰太白山的林木真菌病害的分布规律进行了多年的探讨,结果表明,太白山林木真菌病害可划分为四个垂直分布带。即低山、低中山阔叶林叶点霉病和白粉病分布带;中山、高中山阔叶林锈病和立木腐朽分布带;中高山针叶林散斑病分布带;高山灌丛漆斑病分布带。     

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。     

辽东山区现有次生林结构类型的数量分类

应用模糊聚类方法，对辽东山区现有次生林结构类型进行了数量分类，共分为4类，即柞木林、阔叶混交林、硬阔叶林和杨桦林，并对每一类型的林分的经营措施作了初步的探讨。     

菜阳河自然保护区森林生态系统服务功能价值评估

为研究中国云南亚热带南部季风常绿阔叶林生态系统的服务功能,选择菜阳河自然保护区,应用市场价值、影子价格和机会成本法等方法,对保护区内的优势地带性植被(季风常绿阔叶林),以及另外2种植被类型(季节雨林和山地雨林)的生态系统服务功能的经济价值进行了估算。结果表明:涵养水源价值为4 919.56万元/a,固持土壤的价值为9 631.76万元/a,固定CO2的价值为1 365.05万元/a,净化空气的价值为2 504.92万元/a。4项合计的总价值平均每年为1.842 1亿元。     

Effect of aerial insecticide spraying on pine wilt disease in central Japan

To clarify the effect of aerial insecticide spraying on pine wilt disease, pine wilt disease impacts was surveyed in pine stands and the damage evaluated using a logistic model. Eleven quadrats were established randomly in stands with or without aerial spraying. Aerial spraying alone did not entirely prevent the disease. Average annual mortality was lower in stands with aerial spraying than in stands without aerial spraying. Aerial spraying slowed the progress of pine wilt disease, as measured by the disease progress index. The biomass of live pine trees decreased with increased damage in stands without aerial spraying, whereas biomass was maintained in stands with aerial spraying. However, the relative basal area showed that succession of pine forests to forests dominated by other species was accelerated even in stands with aerial insecticide spraying.     

遥感数字图像的无缝镶嵌

在遥感图像的应用中,当研究区处于几幅图像的交界处或研究区较大需多幅图像才能覆盖时,常常需要把覆盖研究区的那些图像进行配准并把这些图像镶嵌起来,才能更好地统一处理、解译分析和研究.以航空像片为例,利用图像处理软件ERMAPPER 6.2对图像进行数字镶嵌并作无缝接边处理.结果表明:这样,大大改善了镶嵌后图像的质量,解决了手工镶嵌图像中接缝偏移、色调不均匀、地质界线延伸、影像错位等许多问题 (大幅图像尤其明显),为后期的图像处理创造良好的基础.目前出现的米级高分辨率的卫星图像同本次采用的航空像片在波段和分辨率上具有相似性,因此本研究对这些高分辨率卫星图像的镶嵌同样适用.     

基于线性谱聚类的林地图像中枯死树监测

【目的】将基于线性谱聚类超像素的方法应用在森林病虫害防治领域,可智能监测无人机森林虫害图像中的枯死树,为森林有害生物的监测工作提供技术支撑。【方法】分别以湖北省受松材线虫与辽宁省受红脂大小蠹侵害的松林无人机图像为试验数据,首先使用线性谱聚类超像素分割算法将图像划分为多个超像素;然后基于枯死树木的颜色特征,初步提取可能为枯死树的超像素区域;最后基于枯死树木与其他干扰地物具有不同的纹理特征,计算超像素的区域密度和缝隙量,利用支持向量机对初步提取的超像素进行分类,从而检测出图像中的枯死树。【结果】基于线性谱聚类超像素和支持向量机的枯死树监测方法可有效排除与枯死树木颜色相近的其他干扰地物,较准确地提取出枯死树木。使用该方法与基于植被颜色指数的阈值分割方法、基于简单线性迭代聚类超像素和随机森林的方法,对35幅受灾松林无人机图像进行试验,并选用交并比、虚警率和漏检率3个评价指标对3种方法进行定量对比分析。结果表明,基于线性谱聚类超像素的方法监测出的枯死树区域最精确,其监测结果与人工检测结果的交并比均值大于58%,且虚警率和漏检率均优于另外2种方法。【结论】基于线性谱聚类超像素的枯死树监测方法能实现松林中枯死树的快速、准确检测及定位。     

徐州山地森林生态质量评价研究

利用徐州山地森林标准地调查数据,从森林结构与功能和森林健康状况2个方面来构建森林生态质量评价指标体系,采用层次分析法、等级评分法、加权综合指数法来评价徐州山地森林生态质量状况。结果表明：无论是森林结构与功能还是健康状况,森林生态质量都是天然林好于混交林,混交林好于人工林;徐州山地森林生态质量等级好、中、差所占的比重大小悬殊,其中质量等级为差的所占比重最大,说明了徐州山地提高森林生态质量还有很大的潜力。     

广东竹林自然区划续报

论述广东3条气候带的竹类植物分布情况,并提供广东竹类植物名录。