基于多体接触碰撞的松软地面车轮沉陷仿真

提出了一种大规模多体系统接触碰撞理论与车辆地面力学理论相结合的仿真方法,并基于多体动力学仿真平台开发了车轮土壤相互作用仿真模块。基于该方法建立的土壤动力学模型和车轮土壤接触碰撞模型,进行了车轮沉陷的仿真。根据记录,在普通配置台式计算机上模拟土槽系统30 s的运动,仅需20 min。在相同实验条件下,仿真结果与土槽实验结果趋势一致,仿真曲线与土槽实验曲线吻合良好。由此表明该仿真算法能够满足越野行驶仿真的需要。     

引入地形因子的黑土区大豆干生物量遥感反演模型及验证

为了对田块尺度农作物地上干生物量进行估测,提高大豆地上干生物量反演模型的精度和稳定性,该文获取了研究区地块2016年7、8月份的SPOT-6多光谱数据,并测定不同地形坡位的大豆地上干生物量,以归一化植被指数(normalized difference vegetation index,NDVI)和增强型植被指数(enhanced vegetation index,EVI)为输入量,建立田块尺度大豆地上干生物量一元线性回归模型;加入与地上干生物量相关的地形因子,建立逐步多元回归和神经网络多层感知反演模型.结果表明:1)使用传统的单一植被指数模型预测大豆地上干生物量有可行性,但模型精度和稳定性不高.2)加入地形因子(海拔、坡度、坡向)的神经网络多层感知器模型,有较高的精度和可靠性,模型准确度达到90.4％,验证结果显示预估精度为96.2％.反演结果与地块的地形、地貌、气温和降水特征基本吻合,反映了作物长势的空间分布特征,可以为田块尺度大豆地上干生物量动态监测和精准管理,提供借科学依据.     

Modelling soil erodibility in mountain rangelands of southern Kyrgyzstan

Soil erosion in mountain rangelands in Kyrgyzstan is an emerging problem due to vegetation loss caused by overgrazing. It is further exacerbated by mountain terrain and high precipitation values in Fergana range in the south of Kyrgyzstan. The main objective of this study was to map soil erodibility in the mountainous rangelands of Kyrgyzstan. The results of this effort are expected to contribute to the development of soil erodibility modelling approaches for mountainous areas. In this study, we mapped soil erodibility at two sites, both representing grazing rangelands in the mountains of Kyrgyzstan and having potentially different levels of grazing pressure. We collected a total of 232 soil samples evenly distributed in geographical space and feature space. Then we analyzed the samples in laboratory for grain size distribution and calculated soil erodibility values from these data using the Revised Universal Soil Loss Equation (RUSLE) K-factor formula. After that, we derived different terrain indices and ratios of frequency bands from ASTER GDEM and LANDSAT images to use as auxiliary data because they are among the main soil forming factors and widely used for prediction of various soil properties. Soil erodibility was significantly correlated with channel network base level (geographically extrapolated altitude of water channels), remotely sensed indices of short-wave infrared spectral bands, exposition, and slope degree. We applied multiple regression analysis to predict soil erodibility from spatially explicit terrain and remotely sensed indices. The final soil erodibility model was developed using the spatially explicit predictors and the regression equation and then improved by adding the residuals. The spatial resolution of the model was 30 m, and the estimated mean adjusted coefficient of determination was 0.47. The two sites indicated different estimated and predicted means of soil erodibility values (0.035 and 0.039) with a 0.05 significance level, which is attributed mainly to the considerable difference in elevation.     

基于改进蚁群算法的植保无人机路径规划方法

为了规划出更加高效的植保无人机路径,提出一种基于改进蚁群算法的植保无人机路径规划方法,该方法适用于多个具有复杂多边形边界与内部障碍物的三维作业区域。采用扫描方式生成水平面内的作业路径,经过离散化处理后,在三维地形曲面上插值,获得三维作业路径。在此基础上,建立作业路径生成算法,以三维作业路径总长度尽量短、作业路径数量尽量少为目标,对植保无人机作业航向进行寻优。改进蚁群算法通过附加记录作业路径进入点的机制,实现对三维作业路径的合理排序,生成总长度较短的转移路径。经过算例检验,针对同一作业区域规划出的三维作业路径与水平面内的作业路径的航向角存在较大差异,相差最大为92°,这说明考虑三维地形的必要性。算例中,将改进的蚁群算法与贪婪算法进行了对比,针对一系列相同的作业起点,改进的蚁群算法所得的转移路径总长度均较短,比贪婪算法所得结果缩短3%~28%;在未选定作业起点情况下,改进的蚁群算法与贪婪算法求得的转移路径总长度最小值分别为1661m与1763m,说明改进的蚁群算法具有良好的寻优能力。实例检验情况与算例所得结论基本一致。算例与实例中的作业区域边界与地形复杂,涵盖情况全面,表明本文提出的路径规划方法具有一定实用性。     

数字地形模型在泥石流发生发育特征研究中的应用

 泥石流是发生在山区的一种易对人民的生产、生活条件和生存发展空间造成严重破坏的自然现象,其发生、发展受诸多因素影响,但其中地形地貌条件是影响泥石流发生发育的主要因素。随着计算机技术和3S技术的发展,在GIS平台上,利用地形图、遥感影像等建立数字地形模型（DTM）,模拟地面形状,提取地形信息,对泥石流现象进行研究,将是一种新的思路和方法。本文以MAPGIS地理信息系统为工具,以地形图为数据源,建立DTM,结合土地利用相关资料,分析了山东省下港乡泥石流发生发育原因及特征,为这一研究进行了有益的探索。     

八轮四摆臂无人机动平台越障性能分析与试验

针对南方山区丘陵地带地形复杂、传统农田运输车辆通过性不足的问题,提出并设计了一种具有仿生液压驱动摆臂机构的八轮无人机动平台,其车体姿态可通过四摆臂协同动作进行调节,以适应不同形式地面障碍。越障性能是制约平台通过性的根本因素,建立了无人平台姿态规划模型和关键越障过程动力学模型,得到无人平台在典型垂直障碍的越障性能。为验证理论分析,在ADAMS建立了二次开发仿真平台,并进行了样机动力性试验。研究表明,八轮四摆臂无人机动平台可攀爬高度为轮胎直径1. 13倍的垂直障碍,具有良好的复杂地面环境通过能力,可满足丘陵地带农用运输车辆在复杂农田地形的行走需求。     

秦巴山地景观生态风险的时空分异

[目的] 科学评估秦巴山地景观生态风险,为区域内生态安全建设和可持续发展提供科学依据。[方法] 以秦巴山地2000—2020年3期地表覆被数据为基础,应用空间分析法和地形分布指数法,定量地分析秦巴山地景观生态风险的时空特征,以及其在各地形梯度上的动态变化。[结果] ①秦巴山地景观生态风险整体偏低,空间差异特征显著,且存在着明显的空间集聚效应,呈东西两侧跟中间高,南北低的空间分布格局。②2000—2020年,秦巴山地景观生态风险呈下降趋势,低生态风险区明显扩张,高生态风险区显著缩小。③高生态风险多集中分布于海拔高、地形复杂或海拔低、地形平坦地区,低生态风险在中等地形梯度地区呈优势分布。[结论] 秦巴山地景观生态风险整体呈下降趋势,表明区域内生态环境质量有所提升;秦巴山地景观生态风险的空间分布格局受地形影响较大,且相对稳定。     

复杂地形条件下天然河道水面线计算研究

【目的】解决复杂地形条件下天然河道间断水面线计算问题。【方法】采用预测-校正求解方法,在一维河床冲淤变形计算模型基础上,增加了急流均匀流水深与临界水深判断公式,为解决河道正坡、倒坡交替出现,突扩、突缩频繁发生的复杂地形条件下水面线计算的不连续性问题,并将该方法在黄河羊曲水电站河段进行了实例验证。【结果】建立了解决复杂地形条件下天然河道水面线计算模型,并将该模型进行实例验证,结果表明,用该模型计算的水面线与实测水面线吻合较好,可满足工程设计的精度要求。【结论】将该研究应用于黄河羊曲水电站天然河道的水面线计算,得到了原河道各级流量下的水面线,可供其上、下游水库梯级开发的水能规划部门参考应用。     

Proximal sensor-enhanced soil mapping in complex soil-landscape areas of Brazil

Portable X-ray fluorescence (pXRF) spectrometry and magnetic susceptibility (MS) via magnetometer have been increasingly used with terrain variables for digital soil mapping. However, this methodology is still emerging in many countries with tropical soils. The objective of this study was to use proximal soil sensor data associated with terrain variables at varying spatial resolutions to predict soil classes using the Random Forest (RF) algorithm. The study was conducted on a 316-ha area featuring highly variable soil classes and complex soil-landscape relationships in Minas Gerais State, Brazil. The overall accuracy and Kappa index were evaluated using soils that were classified at 118 sites, with 90 being used for modeling and 28 for validation. Digital elevation models (DEMs) were created at 5-, 10-, 20-, and 30-m resolutions using contour lines from two sources. The resulting DEMs were processed to generate 12 terrain variables. Total Fe, Ti, and SiO₂ contents were obtained using pXRF, with MS determined via a magnetometer. Soil class prediction was performed using the RF algorithm. The quality of the soil maps improved when using only the five most important covariates and combining proximal sensor data with terrain variables at different spatial resolutions. The finest spatial resolution did not always provide the most accurate maps. The high soil complexity in the area prevented highly accurate predictions. The most important variables influencing the soil mapping were MS, Fe, and Ti. Proximal sensor data associated with terrain information were successfully used to map Brazilian soils at variable spatial resolutions.