遥感技术在“6·17”丹巴堵江泥石流灾害链灾区应急救援抢险决策中的应用     

唐尧  王立娟  赵娟  张成信 《中国地质调查》2020,7(5):114-122

2020年6月17日,四川丹巴县半扇门镇梅龙沟发生泥石流,阻断小金川河,形成堰塞湖,造成重大财产损失与人员伤亡。为掌握第一手灾情,辅助开展应急救援,以国产高分二号卫星数据为主要数据源,结合无人机航空摄影数据和灾后现场调查等资料,开展了“6·17”丹巴堵江泥石流灾害链灾区应急调查与分析。研究认为: 灾区影响人口约6.5万人; 国网丹巴县供电公司应急救援队与小金县联通通信应急抢险救援队距离最近,半扇门中学等5所学校可作为临时安置避难场所备选; 受影响矿山企业6家、重要水库1座、水电站1座; 泥石流沟总体呈NW—SE向展布,掩没面积约14.27万m²; 堰塞湖湖面面积约1.03 km²,淹没区约49.8万m²,路基路面全毁路段约2.1 km,下游疑似灾害隐患点9处,因灾受损民居25处、桥梁5处; 优选了2条灾后通达性较好的通往泥石流重灾区的救援生命线。借助遥感技术深入分析泥石流灾害链影响人口等灾情先期研判、灾情灾损应急调查及应急救援生命通道优选,对我国西南山区类似条件地质灾害隐患点的应急抢险救援、防灾减灾工作具有重要的指导意义。  相似文献   

基于散射计风场数据的台风强度诊断方法——以海洋二号卫星数据为例   总被引：2，自引：2，他引：0  

杨典  宋清涛  蒋兴伟  刘宇昕  刘圆 《海洋学报》2019,41(1):151-159

本文提出了一种基于散射计风场数据的台风定强方法。该方法定义了台风所在海区的一个圆形区域上的风速平均值来对台风进行定强，并使用海洋二号卫星散射计历史数据对该方法进行了验证。验证结果表明，该台风定强方法可以克服散射计反演台风风速过低的问题。与台风强度监测的历史记录比较的结果显示，在台风强度低于"强台风"这一强度等级时，该方法能够有效估算台风强度。  相似文献   

Subsurface flow measurements using passive flux meters in variably-saturated cold-regions landscapes     

Élise G. Devoie  Ryan F. Connon  James R. Craig  William L. Quinton 《水文研究》2020,34(23):4541-4546

To date, passive flux meters have predominantly been applied in temperate environments for tracking the movement of contaminants in groundwater. This study applies these instruments to reduce uncertainty in (typically instantaneous) flux measurements made in a low-gradient, wetland dominated, discontinuous permafrost environment. This method supports improved estimation of unsaturated and over-winter subsurface flows which are very difficult to quantify using hydraulic gradient-based approaches. Improved subsurface flow estimates can play a key role in understanding the water budget of this landscape.  相似文献   

国产高分辨率遥感影像农村公路核查方法及其应用     

蔡红玥  袁胜古  阳柯  王芳  盛光晓 《测绘通报》2020,(3):91-95

传统的农村公路核查需要人工实地抽查或通过GNSS设备进行信息采集验核，存在成本高、效率低等问题。遥感影像具有成像范围广、时效性高、成本低、能客观反映现实情况等优点。相比于传统方法，将遥感影像引入农村公路核查，能客观、准确、高效地对农村公路相关信息进行核查。本文基于国产高分辨率遥感影像，结合农村公路遥感核查业务，采用遥感影像道路提取算法，设计并实现了一种农村公路核查方法。将本方法应用于某中部省份农村公路遥感核查业务，实际应用表明该方法能有效提高现有农村公路遥感核查的工作效率。  相似文献   

高分三号SAR影像双阈值变化检测   总被引：1，自引：1，他引：0  

崔斌  张永红  闫利  魏钜杰 《遥感学报》2020,24(1):1-10

双阈值合成孔径雷达SAR(Synthetic Aperture Radar)变化检测算法具有在发现变化区域的同时还能确定地表发生后向散射变化类型的优点。针对广义高斯双阈值最小误差法D-GKIT(Dual Generalized Kittler and Illingworth Thresholding)在进行阈值选取时直方图中不同类别像素灰度级重叠严重时,分割结果容易在尖峰单侧选取出双阈值而导致无法正确分割差异图的问题,本文提出一种结合归一化最大类间方差和广义高斯最小误差法GKIT(Generalized Kittler and Illingworth Thresholding)的双阈值SAR变化检测方法。首先,提出以归一化最大类间方差值作为灰度级重叠程度的判别参数,确定阈值的选取顺序及两个候选区间;然后,利用GKIT在候选区间内进行分割,获取单侧阈值及非变化类拟合函数;最后,提出利用非变化类拟合函数更新后的直方图作为另一侧阈值选取基础进行分割,得到对应分割阈值。以宁波地区高分三号(GF-3)SAR卫星影像作为试验研究数据,结果表明:本文方法能较好地解决灰度级重叠时D-GKIT无法进行正确分割的问题,具有良好的变化检测效果和更强的鲁棒性且达到了利用研究区数据验证利用GF-3号SAR卫星影像进行变化检测研究可行性的目的。  相似文献   

波段选择协同表达高光谱异常探测算法     

朱德辉  杜博  张良培 《遥感学报》2020,24(4):427-438

高光谱遥感影像具有光谱分辨率极高的特点,承载了大量可区分不同类型地物的诊断性光谱信息以及区分亚类相似地物之间细微差别的光谱信息,在目标探测领域具有独特的优势。与此同时,高光谱遥感影像也带来了数据维数高、邻近波段之间存在大量冗余信息的问题,高维度的数据结构往往使得高光谱影像异常目标类和背景类之间的可分性降低。为了缓解上述问题,本文提出了一种基于波段选择的协同表达高光谱异常探测算法。首先,使用最优聚类框架对高光谱波段进行选择,获得一组波段子集来表示原有的全部波段,使得高光谱影像异常目标类与背景类之间的可分性增强。然后使用协同表达对影像上的像元进行重建,由于异常目标类和背景类之间的可分性增强,对异常目标像元进行协同表达时将会得到更大的残差,异常目标像元的输出值增大,可以更好地实现异常目标和背景类的分离。本文使用了3组高光谱影像数据进行异常目标探测实验,实验结果表明,该方法与其他现有高光谱异常目标探测算法对比,曲线下面积AUC(Area Under Curve)值更高,可以更好地实现异常目标与背景分离,能够更有效地对高光谱影像进行异常目标探测。  相似文献   

内点最大化与冗余点控制的无人机遥感图像配准     

余蕊  陈玮扬  杨扬  杨昆  罗毅 《遥感学报》2020,24(11):1325-1341

利用小型无人机进行遥感图像配准在自然灾害损害评估、环境监测和目标检测与追踪等领域发挥着至关重要的作用，但小型无人机的图像采集过程容易受风速/风向、复杂地形、电池容量、飞行姿态、飞行高度等自然或人为因素的影响。这些问题通常会导致捕捉到的场景重叠率低与图像非刚性畸变，在特征点提取过程中产生大量冗余点，增加了图像配准的难度。本文提出一种基于特征点的小型无人机图像配准方法，该方法的核心思想是在配准过程中识别冗余点，同时最大化可用内点数量。所识别的冗余点当作控制点，用于控制网格代图像的运动。最后通过最大化内点和合理移动控制点来恢复图像变换。本文使用50对小型无人机图像进行特征匹配和图像配准的实验，其中平均配准精度可达80.38%，并且本文方法在所有的情况下都优于5种当前流行算法。  相似文献   

A new concept for estimating the influence of vegetation on throughfall kinetic energy using aerial laser scanning     

Johannes Antenor Senn  Fabian Ewald Fassnacht  Jana Eichel  Steffen Seitz  Sebastian Schmidtlein 《地球表面变化过程与地形》2020,45(7):1487-1498

Soil loss caused by erosion has enormous economic and social impacts. Splash effects of rainfall are an important driver of erosion processes; however, effects of vegetation on splash erosion are still not fully understood. Splash erosion processes under vegetation are investigated by means of throughfall kinetic energy (TKE). Previous studies on TKE utilized a heterogeneous set of plant and canopy parameters to assess vegetation's influence on erosion by rain splash but remained on individual plant- or plot-levels. In the present study we developed a method for the area-wide estimation of the influence of vegetation on TKE using remote sensing methods. In a literature review we identified key vegetation variables influencing splash erosion and developed a conceptual model to describe the interaction of vegetation and raindrops. Our model considers both amplifying and protecting effect of vegetation layers according to their height above the ground and aggregates them into a new indicator: the Vegetation Splash Factor (VSF). It is based on the proportional contribution of drips per layer, which can be calculated via the vegetation cover profile from airborne LiDAR datasets. In a case study, we calculated the VSF using a LiDAR dataset for La Campana National Park in central Chile. The studied catchment comprises a heterogeneous mosaic of vegetation layer combinations and types and is hence well suited to test the approach. We calculated a VSF map showing the relation between vegetation structure and its expected influence on TKE. Mean VSF was 1.42, indicating amplifying overall effect of vegetation on TKE that was present in 81% of the area. Values below 1 indicating a protective effect were calculated for 19% of the area. For future work, we recommend refining the weighting factor by calibration to local conditions using field-reference data and comparing the VSF with TKE field measurements. © 2020 The Authors. Earth Surface Processes and Landforms published by John Wiley & Sons Ltd  相似文献   

Monitoring soil surface roughness under growing winter wheat with low-altitude UAV sensing: Potential and limitations     

Nils Onnen  Anette Eltner  Goswin Heckrath  Kristof Van Oost 《地球表面变化过程与地形》2020,45(14):3747-3759

Soil surface roughness (SSR) is an important factor in controlling sediment and runoff generation, influencing directly a wide spectrum of erosion parameters. SSR is highly variable in time and space under natural conditions, and characterizing SSR to improve the parameterization of hydrological and erosion models has proved challenging. Our study uses recent technological and algorithmic developments in capturing and processing close aerial sensing data to evaluate how high-resolution imagery can assist the temporally and spatially explicit monitoring of SSR. We evaluated the evolution of SSR under natural rainfall and growing vegetation conditions on two arable fields in Denmark. Unmanned aerial vehicle (UAV) photogrammetry was used to monitor small field plots over 7 months after seeding of winter wheat following conventional and reduced tillage treatments. Field campaigns were conducted at least once a month from October until April, resulting in nine time steps of data acquisition. Structure from motion photogrammetry was used to derive high-resolution point clouds with an average ground sampling distance of 2.7 mm and a mean ground control point accuracy of 1.8 mm. A comprehensive workflow was developed to process the point clouds, including the detection of vegetation and the removal of vegetation-induced point cloud noise. Rasterized and filtered point clouds were then used to determine SSR geostatistically as the standard deviation of height, applying different kernel sizes and using semivariograms. The results showed an influence of kernel size on roughness, with a value range of 0.2–1 cm of average height deviation during the monitoring period. Semivariograms showed a measurable decrease in sill variance and an increase in range over time. This research demonstrated multiple challenges to measuring SSR with UAV under natural conditions with increasing vegetation cover. The proposed workflow represents a step forward in tackling those challenges and provides a knowledge base for future research. © 2020 John Wiley & Sons, Ltd.  相似文献   

Inter-comparison of remote sensing platforms for height estimation of mango and avocado tree crowns     

《International Journal of Applied Earth Observation and Geoinformation》2020

To support the adoption of precision agricultural practices in horticultural tree crops, prior research has investigated the relationship between crop vigour (height, canopy density, health) as measured by remote sensing technologies, to fruit quality, yield and pruning requirements. However, few studies have compared the accuracy of different remote sensing technologies for the estimation of tree height. In this study, we evaluated the accuracy, flexibility, aerial coverage and limitations of five techniques to measure the height of two types of horticultural tree crops, mango and avocado trees. Canopy height estimates from Terrestrial Laser Scanning (TLS) were used as a reference dataset against height estimates from Airborne Laser Scanning (ALS) data, WorldView-3 (WV-3) stereo imagery, Unmanned Aerial Vehicle (UAV) based RGB and multi-spectral imagery, and field measurements. Overall, imagery obtained from the UAV platform were found to provide tree height measurement comparable to that from the TLS (R² = 0.89, RMSE = 0.19 m and rRMSE = 5.37 % for mango trees; R² = 0.81, RMSE = 0.42 m and rRMSE = 4.75 % for avocado trees), although coverage area is limited to 1–10 km² due to battery life and line-of-sight flight regulations. The ALS data also achieved reasonable accuracy for both mango and avocado trees (R² = 0.67, RMSE = 0.24 m and rRMSE = 7.39 % for mango trees; R² = 0.63, RMSE = 0.43 m and rRMSE = 5.04 % for avocado trees), providing both optimal point density and flight altitude, and therefore offers an effective platform for large areas (10 km²–100 km²). However, cost and availability of ALS data is a consideration. WV-3 stereo imagery produced the lowest accuracies for both tree crops (R² = 0.50, RMSE = 0.84 m and rRMSE = 32.64 % for mango trees; R² = 0.45, RMSE = 0.74 m and rRMSE = 8.51 % for avocado trees) when compared to other remote sensing platforms, but may still present a viable option due to cost and commercial availability when large area coverage is required. This research provides industries and growers with valuable information on how to select the most appropriate approach and the optimal parameters for each remote sensing platform to assess canopy height for mango and avocado trees.  相似文献