A comparison of Landsat TM and MODIS vegetation indices for estimating forage phenology in desert bighorn sheep (Ovis canadensis nelsoni) habitat in the Sonoran Desert,USA

Sonoran Desert bighorn sheep (Ovis canadensis mexicana) occupy rugged upland areas that experience irregular periods of vegetation growth associated with precipitation events. These episodic and often spatially limited events provide important forage and preformed water resources that may be important drivers of animal movement and habitat use. Habitat-use models that incorporate forage phenology would broaden our understanding of desert bighorn ecology and have considerable potential to inform conservation efforts for the species. Field-based methods are of limited utility to characterize vegetation phenology across large areas. Vegetation indices (VI) derived from satellite imagery are a viable alternative, but may be confounded by areas of high relief and shadow effects that can degrade VI values. The varying spatial and temporal resolutions of readily available satellite sensors, such as the Landsat thematic mapper (TM) and moderate-resolution imaging spectrometer (MODIS), present additional challenges. In this study, we sought to minimize degrading effects of terrain on TM- and MODIS-based estimates of vegetation phenology. We compared effects of high topographic relief on time series MODIS- and TM-based VI such as the normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI) using VI departures from average (DA) in shaded and unshaded areas. Sun elevation angle negatively impacted TM-derived NDVI and EVI values in areas of steep terrain. In contrast, MODIS-derived NDVI values were insensitive to sun elevation and terrain effects, whereas MODIS-derived EVI was degraded in areas of steep terrain. Time series MODIS NDVI and EVI DA values differed significantly during months of low sun elevation angle. Average MODIS EVI departure values were ≥20% lower than NDVI under these conditions, confounding time series estimates of plant phenology. Our best results were obtained from MODIS 16-day composited NDVI. These remote-sensing-based VI estimates of seasonal plant phenology and productivity can be used to inform models of habitat use and movements of desert bighorn over large areas.     

Terrain Modelling from Feature Primitives

We introduce a compact hierarchical procedural model that combines feature‐based primitives to describe complex terrains with varying level of detail. Our model is inspired by skeletal implicit surfaces and defines the terrain elevation function by using a construction tree. Leaves represent terrain features and they are generic parametrized skeletal primitives, such as mountains, ridges, valleys, rivers, lakes or roads. Inner nodes combine the leaves and subtrees by carving, blending or warping operators. The elevation of the terrain at a given point is evaluated by traversing the tree and by combining the contributions of the primitives. The definition of the tree leaves and operators guarantees that the resulting elevation function is Lipschitz, which speeds up the sphere tracing used to render the terrain. Our model is compact and allows for the creation of large terrains with a high level o detail using a reduced set of primitives. We show the creation of different kinds of landscapes and demonstrate that our model allows to efficiently control the shape and distribution of landform features.     

Feature based terrain generation using diffusion equation

This paper presents a diffusion method for generating terrains from a set of parameterized curves that characterize the landform features such as ridge lines, riverbeds or cliffs. Our approach provides the user with an intuitive vector‐based feature‐oriented control over the terrain. Different types of constraints (such as elevation, slope angle and roughness) can be attached to the curves so as to define the shape of the terrain. The terrain is generated from the curve representation by using an efficient multigrid diffusion algorithm. The algorithm can be efficiently implemented on the GPU, which allows the user to interactively create a vast variety of landscapes.     

Estimating surface solar radiation over complex terrain using moderate‐resolution satellite sensor data

It is well known that taking into account the influence of complex terrain is essential when using high‐resolution satellite remotely sensed data to estimate surface net solar radiation. This paper investigates whether this is also the case when using moderate‐resolution satellite remotely sensed data, such as Moderate Resolution Imaging Spectroradiometer (MODIS) and Advanced Very High Resolution Radiometer (AVHRR). Firstly, topographic data from a gridded digital elevation model, field measurements from the Tibetan Plateau, and results from the atmospheric 6S model are used to estimate surface incoming solar radiation over complex terrain. The associated error caused by not taking into account terrain complexity is then calculated, and the relative radiation error is estimated by standardizing the error. The results show that the standard deviation of the relative radiation error depends on the solar zenith angle, standard deviation of the height, and resolution of the digital elevation model (or resolution of the satellite sensor data). A single regression equation describes the change in the standard deviation of the relative radiation error with solar zenith angle, standard deviation of height, and resolution of the digital elevation model. This demonstrates that it is necessary to consider terrain complexity when using moderate‐resolution remotely sensed data.     

基于迁移学习的低空摄影测量滑坡方量估算方法

针对现有方法难以准确地估算山体滑坡体积的问题,引入人工智能算法,提出耦合迁移学习与微分算法的低空摄影测量山体滑坡方量估算方法。首先,利用SfM与SGM密集匹配等算法从低空无人机立体影像中解算出高精度三维密集点云,结合可见光植被指数和双边滤波算法从密集点云中剥离出目标区地面点云;然后,构建深度神经网络插值模型来表征二维坐标与高程之间的非线性映射关系,并基于参数共享的迁移学习来自适应优化深度神经网络以实现滑坡目标区高程值预测,进而重构滑坡区域的数字地表模型;最后,基于目标区滑坡前后数字地表模型高程差值和微分算法实现山体滑坡方量估算。实验结果表明,该方法平均相对误差为2.7%,相比常用的方法,显著提高了滑坡方量估计精度,并能适应不同地形条件下滑坡方量估算。     

茂密植被区域LiDAR点云数据滤波方法研究

点云数据的滤波和分类是激光雷达数据应用处理重要环节,是当前研究的热点问题。本文针对茂密植被区域点云数据的特点,提出了以移动窗口和坡度算法为基础的改进的点云数据滤波算法。试验结果表明,改进的滤波算法对地形变化复杂、植被郁闭度较高覆盖、地面激光脚点比少的点云数据有良好的效果。     

GEDI地面高程和森林冠层高度的精度评价与影响分析

精确地提取地面高程和植被冠层高度,对于地形地貌、生态学等方面的研究具有重要意义。2018年12月发射的新一代全球生态系统动力学调查雷达（GEDI）为地面高程和植被冠层高度大范围精确提取提供了前所未有的机会。研究旨在利用机载激光雷达数据验证GEDI提取的地面高程和冠层高度精度,并探讨地理定位误差、地形坡度、坡向、植被覆盖度、方位角、采集时间、光束类型和不同森林类型因素对其精度的影响。结果表明：通过校正GEDI数据地理定位误差,可以明显提高其提取的地面高程和冠层高度精度;影响冠层高度提取精度最主要的因素是植被覆盖度,其次是坡度;影响地面高程提取精度的主要因素为坡向、坡度。植被覆盖度大于25%时,数据精度更高;坡度为0°—5°的缓坡地区地面高程和冠层高度精度最高。该研究结果将为GEDI数据筛选与应用提供依据。     

基于优化的电势理论规划三维飞行路径

利用优化的电势理论进行飞行器三维航迹规划。对电势理论进行了优化,使其既能回避雷达、火力威胁,又能有效地回避地形威胁,使规划出的三维航迹具有一定的实用性。根据突防任务的需要,确定地形威胁与雷达、火力威胁的权重,并将模拟地形的高程数据与雷达、火力威胁按各自的权重叠加得到综合威胁电场;通过限定位于起始点和目标点之间的搜索范围,并对搜索条件进行改进,保证飞行路径最终能收敛于目标点;最后,用坡度限制平滑算法、曲率限制平滑算法对航迹进行法向加速度和曲率限制使其符合飞行器机动性能和可飞性要求。仿真结果表明,优化的电势理论可以进一步考虑地形威胁,而且在能够考虑目标点附近的各种威胁,提高了该方法的实用性,还能缩短航线规划的时间。     

VARIANT: A System for Terrain Modeling at Variable Resolution

We describe VARIANT (VAriable Resolution Interactive ANalysis of Terrain), an extensible system for processing and visualizing terrains represented through Triangulated Irregular Networks (TINs), featuring the accuracy of the representation, possibly variable over the terrain domain, as a further parameter in computation.VARIANT is based on a multiresolution terrain model, which we developed in our earlier research. Its architecture is made of a kernel, which provides primitive operations for building and querying the multiresolution model; and of application programs, which access a terrain model based on the primitives in the kernel.VARIANT directly supports basic queries (e.g., windowing, buffering, computation of elevation at a given point, or along a given line) as well as high-level operations (e.g., fly-over visualization, contour map extraction, viewshed analysis). However, the true power of VARIANT lies in the possibility of extending it with new applications that can exploit its multiresolution features in a transparent way.     

Geomorphometric processing of digital elevation models

The general system of geomorphometry is composed of elevation, derivatives of elevation at a point, and moments of the distribution of elevation over some area. All of the point measures in this system can be obtained by computer processing of a digital elevation model (DEM), and they can be used as input to the analysis and classification of terrain. A suite of FORTRAN programs implementing this system for dense grid DEMs has been designed and used in various operating environments. Attention has been given to the methods used in the approximation of terrain concepts such as slope and relief. An area of high relief in subarctic Canada is used to illustrate the discussion.     

Evaluating error associated with lidar-derived DEM interpolation

Light detection and ranging (lidar) technology is capable of precisely measuring a variety of vegetation metrics, the estimates of which are usually based on relative heights above a digital elevation model (DEM). As a result, the development of these elevation models is a critical step when processing lidar observations. A number of different algorithms exist to interpolate lidar ground hits into a terrain surface. We tested seven interpolation routines, using small footprint lidar data, collected over a range of vegetation classes on Vancouver Island, British Columbia, Canada. The lidar data were randomly subsetted into a prediction dataset and a validation dataset. A suite of DEMs were then generated using linear, quintic, natural neighbour, regularized spline, spline with tension, a finite difference approach (ANUDEM), and inverse distance weighted interpolation routines, at spatial resolutions of 0.5, 1.0 and 1.5 m. In order to examine the effects of terrain and ground cover on interpolation accuracies, the study area was stratified by terrain slope, vegetation structural class, lidar ground return density, and normalized difference vegetation indices (NDVI) derived from Quickbird and Landsat7 ETM+ imagery. The root mean square (RMS) and mean absolute errors of the residuals between the surfaces and the validation points indicated that the 0.5 m DEMs were the most accurate. Of the tested approaches, the regularized spline and IDW algorithms produced the most extreme outliers, sometimes in excess of ±6 m in sloping terrain. Overall, the natural neighbour algorithm provided the best results with a minimum of effort. Finally, a method to create prediction uncertainty maps using classification and regression tree (CART) analysis is proposed.     

Visibility analysis on a massively data-parallel computer

Visibility analysis algorithms use digital elevation models (DEMs), which represent terrain topography, to determine visibility at each point on the terrain from a given location in space. This analysis can be computationally very demanding, particularly when manipulating high resolution DEMs accurately at interactive response rates. Massively data-parallel computers offer high computing capabilities and are very well-suited to handling and processing large regular spatial data structures. In the paper, the authors present a new scanline-based data-parallel algorithm for visibility analysis. Results from an implementation onto a MasPar massively data-parallel SIMD computer are also presented.     

等高线图的高度恢复算法研究

用于等高线图到数字高度图（Digital Elevation Map)转换的转换算法是很多应用都需要的算法，为了提高这一转换的精度与效率，一个相应的算法－区域内插法被特别提出来，该算法利用了等高线图固有的特性，即“图象被等高线分割成多个区域，每个区域内的边界只有两个等高线值”的特性，该算法可以在计算机上快速实现，经过与现有的象限搜索法的实验比较，由于它更好地利用了等高线图结构上的特点，致使其在提高速度的同时，精度上较象限搜索法也有很大的提高，因而具有很强的实用价值。     

Large-scale mapping of the riverbanks,mud flats and salt marshes of the Scheldt basin,using airborne imaging spectroscopy and LiDAR

For maintaining the tidal waterways in the Scheldt basin, including the rivers Rupel and Durme and a large part of the Nete catchment, and for ecological monitoring of the mud flats, salt marshes and riverbank vegetation, the Flemish government needs detailed maps of these rivers and their bank structures. These maps indicate not only vegetation types, plant associations and sediment types but also hard structures, such as quays, locks, sluices and roads. Different remote sensing techniques were used to collect the data necessary to produce the required detailed maps. During the months of July and August 2007 an airborne flight campaign took place to collect hyperspectral and LiDAR data of the Scheldt basin and the Nete catchments. These rivers have a total length of about 240 km. The Airborne Imaging Spectrometer for Applications (AISA) Eagle sensor acquired hyperspectral data in 32 spectral bands covering the visible/near-infrared (VIS/NIR) part of the electromagnetic spectrum with a ground resolution of 1 m. A multiple binary classification algorithm based on Fisher's linear discriminant analysis (LDA) was used to map the salt marshes and riverbank vegetation. Ground truth information, that is vegetation and sediment types, together with their geographical locations collected around the time of the flight campaign, was used to train the classifier in the later classification step. Laser scanning was performed using the Riegl LMS-Q560. The LiDAR dataset obtained had a resolution of at least 1 point per m² and was used to produce a digital elevation model (DEM) that contains all elements of the terrain. From this DEM a digital terrain model (DTM) was derived by applying appropriate filtering techniques. The elevation models were used primarily to derive information on the height, slope and aspect of the banks and dikes, but they also served as expert knowledge in the classification of the mud flats and bank vegetation.

Overall, this work illustrates how airborne hyperspectral and LiDAR data can be used to derive highly detailed maps of the sediments, vegetation and hard structures along tidal rivers in large river basins. It also shows how large datasets can be handled in an expert system, in combination with different classification techniques, to produce the required result and accuracy.     

一种新的三维地景库的建模方法

针对传统的模拟器计算机成像系统之不足,提出了1种新的视景系统的建模方法,即基于矢量数字地图的三维地景生成算法。该算法以矢量地图为约束,各种地表纹理作为输入样图,用矢量地图中的高程信息构建三维地形网格,并根据矢量数字地图中图元之间的不同属性映射相应的纹理,得到了与矢量数字地图相一致的三维地景。与传统的地景库生成算法相比,新算法生成的三维地景更加真实精确;解决了以往地景库一经生成就无法更改的问题;具有一定的预测和重现场景的功能;缩短了地景库的开发周期,降低了开发成本。同时,运用了基于视点的地景数据动态调度策略,可实现真实场景的实时绘制。     

Retrieving vegetation height of forests and woodlands over mountainous areas in the Pacific Coast region using satellite laser altimetry

The challenge to retrieve canopy height from large-footprint satellite lidar waveforms over mountainous areas is formidable given the complex interaction of terrain and vegetation. This study explores the potential of GLAS (Geoscience Laser Altimeter System) for retrieving maximum canopy height over mountainous areas in the Pacific Coast region, including two conifers sites of tall and closed canopy and one broadleaf woodland site of shorter and sparse canopy. Both direct methods and statistical models are developed and tested using spatially extensive coincident airborne lidar data. The major findings include: 1) the direct methods tend to overestimate the canopy height and are complicated by the identification of waveform signal start and terrain ground elevation, 2) the exploratory data analysis indicates that the edge-extent linear regression models have better generalizability than the edge-extent nonlinear models at the inter-site level, 3) the inter-site level test with mixed-effects models reveals that the edge-extent linear models have statistically-justified generalizability between the two conifer sites but not between the conifer and woodland sites, 4) the intra-site level test indicates that the edge-extent linear models have statistically-justified generalizability across different vegetation community types within any given site; this, combined with 3), unveils that the statistical modeling of maximum canopy height over large areas with edge-extent linear models only need to consider broad vegetation differences (such as woodlands versus conifer forests instead of different vegetation communities within woodlands or conifer forests), and 5) the simulations indicate that the errors and uncertainty in canopy height estimation can be significantly reduced by decreasing the footprint size. It is recommended that the footprint size of the next-generation satellite lidar systems be at least 10 m or so if we want to achieve meter-level accuracy of maximum canopy height estimation using direct and statistical methods.     

Terrain topographic inversion using single-pass polarimetric SAR image data

1IntroductionFullypolarimetricSARimagerytechnologyisoneofthemostimportantadvance-mentsforspace-borneremotesensing.Ithasbeenextensivelyappliedtoterrainsurfaceclassification.The22-D(Dimensional)complexscatteringamplitudefunctionsFpq(p,q=v,h),and44-DrealMuellermatrixMij(i,j=1,…,4)canbemeasured[1].Co-polarizedorcross-polarizedbackscatteringsignatureisthefunctionoftheincidencewavewiththeellipticityanglecandorientationangley.Recently,twoflightsofpo-larimetricSARimagedatahavebeenutilizedtogene…     

Photorealistic terrain imaging and flight simulation

Photorealistic terrain visualization results from combining two data sets. The first contains information about terrain color (texture), usually from a vertical view angle, such as an aerial or satellite image. The second data set contains information about terrain topography, in the form of elevation samples. This data set is also known as a digital terrain model, or DTM. We can reconstruct the 3D terrain from the DTM using various methods. The conventional approach triangulates the terrain into a continuous surface consisting of relatively large planar facets. An alternative approach uses a regular array of atomic values called voxels to represent the terrain. After terrain surface reconstruction, we render the oblique perspective terrain image by a process called phototexturing-the mapping of the corresponding texture onto this surface. Before doing this, we must register the texture and DTM so that the overlay is accurate. This corrects geometric distortions in the data sets, which originate in measurement device or sensor inaccuracies. We obtain the final image by projecting the colored surface onto a viewing plane, incorporating hidden surface elimination     

Assessment of the impacts of surface topography, off-nadir pointing and vegetation structure on vegetation lidar waveforms using an extended geometric optical and radiative transfer model

In order to prioritize the measurement requirements and accuracies of the two new lidar missions, a physical model is required for a fundamental understanding of the impact of surface topography, footprint size and off-nadir pointing on vegetation lidar waveforms and vegetation height retrieval. In this study, we extended a well developed Geometric Optical and Radiative Transfer (GORT) vegetation lidar model to take into account for the impacts of surface topography and off-nadir pointing on vegetation lidar waveforms and vegetation height retrieval and applied this extended model to assess the aforementioned impacts on vegetation lidar waveforms and height retrieval.Model simulation shows that surface topography and off-nadir pointing angle stretch waveforms and the stretching effect magnifies with footprint size, slope and off-nadir pointing angle. For an off-nadir pointing laser penetrating vegetation over a slope terrain, the waveform is either stretched or compressed based on the relative angle. The stretching effect also results in a disappearing ground peak return when slope or off-nadir pointing angle is larger than the “critical slope angle”, which is closely related to various vegetation structures and footprint size. Model simulation indicates that waveform shapes are affected by surface topography, off-nadir pointing angle and vegetation structure and it is difficult to remove topography effects from waveform extent based only on the shapes of waveform without knowing any surface topography information.Height error without correction of surface topography and off-nadir pointing angle is the smallest when the laser beams at the toward-slope direction and the largest from the opposite direction. Further simulation reveals within 20° of slope and off-nadir pointing angle, given the canopy height as roughly 25 m and the footprint size as 25 m, the error for vegetation height (RH100) ranges from − 2 m to greater than 12 m, and the error for the height at the medium energy return (RH50) from − 1 m to 4 m. The RH100 error caused by unknown surface topography and without correction of off-nadir pointing effect can be explained by an analytical formula as a function of vegetation height, surface topography, off-nadir pointing angle and footprint size as a first order approximation. RH50 is not much affected by topography, off-nadir pointing and footprint size. This forward model simulation can provide scientific guidance on prioritizing future lidar mission measurement requirements and accuracies.     

数字高程模型中求趋势的一种方法：——无编最？…

在地理信息系统（ＧＩＳ）的数字高程模型中,经常需要求一些地形的趋势。本文给出一个求趋势的方法－－无偏最优求趋势法。根据某点周围若干个信息点上的地形高程数据估计出该点处的趋势值,而且这种估计 在满足线性、无偏、最小估计方差条件下求得的,这就是无偏最优求趋势法。它比经常用来求解势的方法－趋势分析方法,有许多优点。趋势分析方法只是无偏最优求趋势法的一个特例。