重庆市城市用地现状遥感调查研究

此文主要介绍利用航空彩红外像片对重庆市城市用地现状进行调查研究的过程,详细论述了进行城市用地类型解译的方法、解译标志与影像特征,基本查明了重庆市城市用地面积和现状分布,在此基础上分析讨论了重庆市城市用地现状的特点,指出了城市用地存在的主要问题。     

岳阳市城区重点污染源遥感调查

作者利用彩红外航空像片对岳阳市城区重点污染源(如:烟、气、水体污染、固体垃圾等)进行了较详细的遥感解译,对解译成果及主要污染源和污染状况进行分析,圈定了城区固体废弃物堆放场分布类型,并进行了面积估算。     

超小比例尺彩红外高空摄影应用探讨

本文介绍了国内外超小比例尺彩红外高空摄影像片应用现状,并对它的光谱分辨率、空间分辨率、综合分辨率、专题表达能力、成本等进行了较深入的分析,以探讨其应用于环境与资源调查的技术经济性能。研究表明,超小比例尺彩红外高空摄影像片的应用有着广阔的前景。     

黑白航空像片在盐城市绿化现状调查中的应用

本文介绍了采用航空遥感技术对盐城市建成区1988年绿化现状进行的调查研究。基本方法是:黑白航空像片判读一绿化填图和抽样调查校正一再判读一面积测量及综合分析。通过这次调查,编制了1:4000绿化分类分布图,测算了绿化覆盖面积和绿化覆盖率,调查了骨干树种和常绿树与落叶树之比,并提出了提高盐城市绿化程度的建议。     

彩色红外航空像片反演地物波谱的研究

本文通过对彩色红外航空摄影的物理机制的研究,揭示了其影像光谱密度的指数变换与地物光谱反射比之间的线性关系,给出了实用的由彩色红外航空像片反演地物波谱的统计方法并作了相应实验,结果表明,用国产彩色红外航空像片HCJ-2可以反演6个以上波段的地物波谱。由于本统计方法易于使整幅航空像片的反演在计算机上实现,故它基本上具有实用、深入和高效的特点。     

遥感技术在甘肃北山地质调查和找矿中的应用

应用陆地卫星TM、MSS图像、航空彩红外像片、红外细分光谱扫描数字图像等多种遥感图像,结合多源信息复合处理技术,在甘肃安西北部地区（北山南带西段）开展了1:5万遥感地质调查和以金、铀为主的遥感找矿预测。通过工作,在合理应用综合遥感技术方法、基础地质研究、金铀矿产预测,以及1:5万遥感地质成图方法研究等方面均取得了重大进展。三年中,11人共编测了1:5万遥感地质图和1:5万遥感成矿预测图各21幅,解译发现了近千条蚀变岩带,圈出了67个金矿找矿靶区和铀成矿远景区,为开辟找矿新区提供了大量新的重要依据。     

森林航空摄影比例尺和森林判读像片放大倍数的研究

为了确定森林航空摄影的最佳比例尺和波段以及判读像片的放大倍数,我们在广东省南昆山林业遥感试验场,测定了地物光谱,拍摄了不同比例尺的多光谱片和彩色红外片,进行了内业判读、野外检测和综合分析。本文讨论了航空摄影机、胶片、地物光谱反射系数和摄影位移等因素对像片分辨力的影响,用经验关系式估算了南昆山像片的影像分辨力,阐述了内业判读和实地检测的情况。试验表明,在所试验的波长附围内绿波段表达森林影像的效果最佳,用于森林判读的影像分辨力宜放大到3.5线/毫米,若用新型航空摄影机和高分辨力胶片,则用于森林资源调查分类判读的像片比例尺可缩小到1:4.8万。     

基于微机的彩色航空遥感正射影像图制作技术

本文运用影像匹配、不波多分辨率分析理论和色度学原理，以实际的彩红外航空像片为处理对象，实现了大区域彩色航空遥感正射影像图制作技术。并在微机上形成了相应实用系统，该系统速度快、精度高、镶嵌后的彩色影像图信息丰富、色调和谐一致，是一种适用方便、功能齐全的正射影像图制作系统。     

二滩、官地库区土地覆盖类型图制作方法研究

利用航空彩红外正射影像镶嵌图,用光谱角分类与目视解译相结合的方法,对二滩、官地库区进行土地利用调查工作,取得了较好的效果。     

京西地区多种遥感图像地质解译效果评价

笔者以京西王平村地区作为评价区,对多种遥感图像在地质上的解像能力进行了比较和评述。指出了彩红外像片,侧视雷达像片和陆地卫星图像的应用条件,并分析了各种图像在解译中的优势和不足。     

航空遥感与地理信息系统相结合在杭州西湖风景区用地调查中的应用

为了满足城市发展对用地资料的需要，采用彩红外航空遥感和地理信息系统相结合的方法，对杭州西湖风景区的用地情况进行调查，文中介绍这次工作的技术路线、航空遥感资料判读与用地图编制、资料建库与应用等方面的情况。     

应用彩色红外航空像片进行城市居民生活条件调查和分析

本文介绍了应用彩红外航片对城市居民生活条件进行调查和分析,其基本方法是:首先通过直接和间接判读标志,对居民物质生活条件和精神生活条件进行解译,然后从方便度和舒适度两项评价指标入手,选取八项评价因子,进行定量的综合评价和空间分析,最后提出改善实验区居民生活条件的建议。     

激光雷达森林参数反演研究进展

激光雷达通过发射激光能量和接收返回信号的方式,来获取高精度的森林空间结构和林下地形信息。全波形激光雷达通过记录返回信号的全部能量,得到亚米级植被垂直剖面;离散回波激光雷达记录的单个或多个回波,表示来自不同冠层的回波信号。星载激光雷达一般采用全波形或光子计数激光剖面系统,仅能获取卫星轨道下方的单波束或多波束数据,用于区域/全球范围的森林垂直结构及变化观测。机载激光雷达多采用离散回波或全波形激光扫描系统,能够获取飞行轨迹下方特定视场范围内的扫描数据,用于林分/区域范围的森林结构观测。地基激光雷达多采用离散回波激光扫描系统,获取以测站为中心的球形空间内扫描数据,用于单木/样地范围的森林结构观测。激光雷达单木因子估测方法可分为CHM单木法、NPC单木法和体元单木法3类。CHM单木法通过局部最大值识别树冠顶点,采用区域生长或图像分割算法识别树冠边界或树冠主方向,NPC单木法一般通过空间聚类或形态学算法识别单木,体元单木法在3维体元空间采用区域生长或空间聚类算法识别树冠。根据激光雷达冠层高度分布可以估测林分因子,冠层高度分布特征来自于离散点云或全波形。多时相激光雷达可用于森林生长量、生物量变化等监测,以及森林采伐、灾害等引起的结构变化监测。随着激光雷达技术的发展,它将在森林调查、生态环境建模等生产与科学研究领域中得到更为广泛的应用。     

彩色红外片的“二色性”及其改进

针对彩红片的“二色性”,改进了国产彩红片的加工工艺,获得色彩鲜艳、层次丰富的彩红片,明显提高了航片信息量和解释能力,它对各种专业遥感均有普遍意义。国家遥感中心资料服务部秦友奎、赵继成、郭志杰、李小军等协助有关工作,谨此致谢。     

Automatic Tree Identification and Diameter Estimation Using Single Scan Terrestrial Laser Scanner Data in Central Indian Forests

Forest inventory parameters, primarily tree diameter and height, are required for several management and planning activities. Currently, Terrestrial Laser Scanning (TLS) is a promising technology in automated measurements of tree parameters using dense 3D point clouds. In comparison with conventional manual field inventory methods, TLS systems would supplement field data with detailed and relatively higher degree of accurate measurements and increased measurement frequency. Although, multiple scans from TLS captures more area, they are resource and time consuming to ensure proper co-registration between the scans. On the other hand, Single scans provide a fast and recording of the data but are often affected by occlusions between the trees. The current study evaluates potential of single scan TLS data to (1) develop an automatic method for tree stem identification and diameter estimation (diameter at breast height—DBH) using random sample consensus (RANSAC) based circle fitting algorithm, (2) validate using field based measurements to derive accuracy estimates and (3) assess the influence of distance to scanner on detection and measurement accuracies. Tree detection and diameter measurements were validated for 5 circular plots of 20 m radius using single scans in dry deciduous forests of Betul, Madhya Pradesh. An overall tree detection accuracy of 85 and 70% was observed in the scanner range of 15 and 20 m respectively. The tree detection accuracies decreased with increased distance to the scanner due to the decrease in visible area. Also, estimated stem diameter using TLS was found to be in agreement with the field measured diameter (R² = 0.97). The RMSE of estimated DBH was found to be 3.5 cm (relative RMSE ~20%) over 202 trees detected over 5 plots. Results suggest that single scan approach suffices the cause of accuracy, reducing uncertainty and adds to increased sampling frequency in forest inventory and also implies that TLS has a seemingly high potential in forest management.     

Comparison of sampling methods for inventorying the stand volume using satellite remote sensing

Remote sensing is being increasingly used for forest resource inventory as it saves time and the cost. Aerial photographs and satellite images have been effectively utilized for forest inventory all over the world. This study highlights the application of IRS LISS-III imagery for inventorying the stand volume in Lachchhiwala Forest Range of Siwaliks. The satellite image was visually interpreted for forest type and density stratification. Both random as well as stratified random sampling techniques were used to see their impact on the volume estimates. Field sampling was done in the plots of 0.1 ha size. The total growing stock in all types of forests in the study area was estimated to be 1.87 mill.m³, of which Sal Forest accounted for 1.32 mill.m³, Sal Mixed Forest for 0.09 mill.m³, Mixed Sal Forest for 0.08 mill.m³, Miscellaneous Forest for 0.06 mill.m³ and Forest Plantations for 0.02 mill.m³. The results were compared with an independent field-based inventory carried out by forest department. The two sampling methods were compared by ratioing of the mean of variance (gain in precision) and it was found that the timber volume estimates using stratified random sampling technique were 15 per cent more accurate than simple random sampling. The satellite image-based inventory using stratified random sampling was found to have about 90 per cent correspondence with the inventory done by the Forest Department.     

A bottom-up approach to segment individual deciduous trees using leaf-off lidar point cloud data

Light Detection and Ranging (Lidar) can generate three-dimensional (3D) point cloud which can be used to characterize horizontal and vertical forest structure, so it has become a popular tool for forest research. Recently, various methods based on top-down scheme have been developed to segment individual tree from lidar data. Some of these methods, such as the one developed by Li et al. (2012), can obtain the accuracy up to 90% when applied in coniferous forests. However, the accuracy will decrease when they are applied in deciduous forest because the interlacing tree branches can increase the difficulty to determine the tree top. In order to solve challenges of the tree segmentation in deciduous forests, we develop a new bottom-up method based on the intensity and 3D structure of leaf-off lidar point cloud data in this study. We applied our algorithm to segment trees in a forest at the Shavers Creek Watershed in Pennsylvania. Three indices were used to assess the accuracy of our method: recall, precision and F-score. The results show that the algorithm can detect 84% of the tree (recall), 97% of the segmented trees are correct (precision) and the overall F-score is 90%. The result implies that our method has good potential for segmenting individual trees in deciduous broadleaf forest.     

Forest inventories by LiDAR data: A comparison of single tree segmentation and metric-based methods for inventories of a heterogeneous temperate forest

Inventories of temperate forests of Central Europe mainly rely on terrestrial measurements. Rapid alterations of forests by disturbances and multilayer silvicultural systems increasingly challenge the use of conventional plot based inventories, particularly in protected areas. Airborne LiDAR offers an alternative or supplement to conventional inventories, but despite the possibility of obtaining such remote sensing data, its operational use for broader areas in Central Europe remains experimental. We evaluated two methods of forest inventory that use LiDAR data at the landscape level: the single tree segment-based method and an area-based method. We compared a set of structural forest attributes modeled by these methods with a conventional forest inventory of the highly heterogeneous forest of the Bavarian Forest National Park (Germany), which partially includes stands affected by severe natural disturbances. Area-based models were accurate for all structural attributes, with cross-validated average root mean squared error ranging from ∼3.4 to ∼13.4 in the best modeling case. The coefficients of variation for the mapped area-based estimations were mostly minor. The area-based estimations were varied but highly correlated (Pearson’s correlations between ∼ 0.56 and 0.85) with single tree segmentation estimations; undetected trees in the single tree segmentat-based method were the main sources of inconsistency. The single tree segment-based method was highly correlated (∼ 0.54 to 0.90) with data from ground-based forest inventories. The single tree-based algorithm delivered highly reliable estimates for a set of forest structural attributes that are of interest in forest inventories at the landscape scale. We recommend LiDAR forest inventories at the landscape scale in both heterogeneous commercial forests and large protected areas in the central European temperate sites.     

大兴安岭林区无人机可见光影像散发枯立木识别算法

枯立木识别对森林资源管理,生物多样性保护,以及森林碳储量变化评估具有重要价值。无人机高分辨率影像为枯立木调查提供了较为便捷的方式。现有枯立木识别算法多依靠拥有红边、近红外波段的多光谱影像来实现。相比于多光谱相机,消费级无人机通常搭载的是用于获取可见光(RGB)影像的普通数码相机,较少的波段信息为基于RGB影像的枯立木自动化精准识别带来很大的挑战。现有利用无人机可见光影像进行枯立木高精度识别多依赖于人工目视解译,自动化识别程度较低,且缺乏单木尺度的研究;此外,现有研究多集中在强扰动(如病虫害)引起的群发枯立木上,而对森林自然演替过程中产生的散发枯立木关注较少。为此,本研究提出了利用无人机可见光影像进行单木尺度的散发枯立木高精度自动化识别算法。在已有单木分割算法的基础上,发展了基于红绿波段比值(RGI)和蓝绿波段比值(BGI)光谱指数迭代统计分析的枯立木树冠自动化检测算法,提出了基于数字表面模型纹理特征的森林掩膜自动提取方法,实现了单木尺度的散发枯立木自动识别。经过实地调查和目视解译的枯立木参考数据的验证,结果表明枯立木查全率和精确率均接近95%,单木树冠分割结果中的欠分割和错分割是枯立木识别误差的主要来源,提高单木树冠提取精度是进一步完善单木尺度枯立木识别的关键。