基于无人机遥感影像的大豆叶面积指数反演研究

作物叶面积指数的遥感反演是农业定量遥感研究热点之一,利用无人机遥感监测系统获取农作物光谱信息精确反演叶面积指数对精准农业生产与管理意义重大。本研究以山东省嘉祥县一带的大豆种植区为试验区,设计以多旋翼无人机为平台同步搭载Canon Power Shot G16数码相机和ADC-Lite多光谱传感器组成的无人机农情监测系统开展试验,分别获取大豆结荚期和鼓粒期的遥感影像。使用比值植被指数(RVI)、归一化植被指数(NDVI)、土壤调整植被指数(SAVI)、差值植被指数(DVI)、三角植被指数(TVI)5种植被指数,结合田间同步实测叶面积指数(leaf area index,LAI)数据,采用经验模型法分别构建了单变量和多变量LAI反演模型,通过决定系数(R2)、均方根误差(RMSE)和估测精度(EA)3个指标筛选出最佳模型。研究表明,有选择性地分时期进行农作物的叶面积指数反演是必要的,鼓粒期作为2个生育期中大豆LAI反演的最佳时期,其NDVI线性回归模型对大豆LAI的解释能力最强,R2=0.829,RMSE=0.301,反演大豆LAI最准确,EA=85.4%,生成的鼓粒期大豆LAI分布图反映了当地当时大豆真实长势情况。因此,以多旋翼无人机为平台同步搭载高清数码相机和多光谱传感器组成的无人机农情监测系统对研究大豆叶面积指数反演是可行性,可作为指导精准农业研究的一种新方法。     

The Ingrowth Bag Method in Measuring Root Production on Peatland Sites

The aim of this study was to develop a method for segment-based forest inventory and determine whether segment-level inventories can be used in forest management planning. The study area covered 76 ha located in two different aerial photographs in eastern Finland. The study area was segmented into 220 segments with the aid of aerial photographs and the segment-level forest characteristics were assessed in the field using relascope sample plots and a field computer which displayed the aerial photographs, segment borders and surveyor's location on the screen. The segment estimates were calculated as weighted averages of k nearest neighbours (kNN) for the segments and the sample plots. The estimates were tested with a cross-validation technique. The averages and the standard deviations of the spectral values of aerial images extracted for the segments and the sample plots were used in the kNN estimation. The relative root mean square error of the mean volume was 58.1% (bias –6.4%) at the segment level and 57.9% (bias –0.9%) at the sample plot level. The segment-based approach studied here needs further research and improvement before it can be applied to forest management planning.     

Photo interpretation of areas with thin soil cover and bedrock outcrops within forest stands

Data from 16 forest stands were used to assess the accuracy of aerial photo interpretation of areas with thin soil cover and bedrock outcrops. Thin soil cover was defined as soil depth less than 10 cm, including outcrops. The portion of the area with thin soil cover within the stands was interpreted by four experienced persons according to a 4 x 4 Graeco‐Latin square design. IR‐color photographs and panchromatic black‐and‐white photographs at the scales 1:15 000 and 1 :22 000 were used.

The overall differences between photo interpreted portions and the ground reference portions was not significant. According to an analysis of variance, the interpretation results were slightly better for IR‐color film than for the black‐and‐white film (10% level). The effect of scale was far from significant. The effect of stand density was highly significant. Thin soil cover was underestimated in dense stands. In the future, calibration plots with known characteristics should be used in order to achieve successful photo interpretation of areas with thin soil.     

飞播成效影响因素研究

结合我国飞播造林的进程和相关飞播造林数据,得出影响飞播造林成效的因素有以下方面：造林区地类、植被盖度、坡向、树种、降水量、海拔等,重点研究了树种、混播技术对飞播造林成效的影响,为飞播造林的发展提出了合理性的建议.     

飞机超低容量喷洒森得保Ⅱ号防治马尾松毛虫

应用森得保Ⅱ号飞防专用药对马尾松毛虫进行林间防治试验,结果表明,飞机喷洒30g/hm2森得保Ⅱ飞防专用药,10d后,校正死亡率达95.64%。     

Detection of Windthrow in Mountainous Regions with Different Remote Sensing Data and Classification Methods

After a disastrous storm event, quick and reliable information on the extent of forest damage is required. This study evaluated different remote sensing data and methods to detect windthrown forests in mountainous regions as an alternative to the manual analysis of aerial images or terrestrial methods. To this end, both optical satellite sensors (Landsat-7, Spot-4 and Ikonos) and synthetic aperture radar (SAR) data at various frequencies (X-, L-, P- and C-band) were evaluated, and classifications of the windthrown forests were performed. This study was designed to state the advantages and disadvantages of the investigated data and methods. Classification results were compared with aerial images which were interpreted manually on a stereoscopic base. The study showed that the manual interpretation of Ikonos data revealed the most accurate results, followed by an automatic classification of Spot-4 data. Except for ERS-1/2 data, which are too inaccurate in mountainous regions, and SAR P-band data, all sensors and methods investigated have different advantages, so the choice of a specific sensor and method will depend on the question being answered.     

Soil surface infiltration capacity classification based on the bi-directional reflectance distribution function sampled by aerial photographs. The case of vineyards in a Mediterranean area

Spatially distributed hydrological modelling is required to understand and predict erosion, flooding and pollution risks that affect the vine cultivated Mediterranean environment. Previous field studies have demonstrated the dominant influence of soil surface features on overland flow and they therefore constitute an essential input to the hydrological model. In this paper we propose a remote sensing based method to map vineyard soil surface features with a spatial and temporal resolution appropriate for integration into the model. Our goal was to classify each soil surface portion in accordance with a pre-established, field measured infiltration capacity based typology. The radiometric characteristics of the classes of this typology were measured in the field and their Bi-directional Reflectance Distribution Function (BRDF) was modelled. Vineyard sunlit soil surface pixels were automatically extracted from high spatial resolution scanned aerial colour photographs Wassenaar et al., 2001 and Wassenaar et al., 2002. These pixels are radiometrically classified by comparison of their reflectance with BRDF-based reflectance predictions of each soil surface type for the specific illumination and viewing geometry of the pixel.The results show that most hydrological soil surface classes have distinct bi-directional radiometric properties. For one given geometric configuration however, the predicted reflectance ranges of some classes can considerably overlap (tilled soils and stone layers for example), while others can always unambiguously be identified (bare soil crusts, surfaces covered for more than 50% by weed or litter).We conclude that our fuzzy classification approach and the simple radiometric information used, allow us to identify the majority of the hydrological surface types. The method can easily be transposed in time and space. Its performance quite strongly depends on the radiometric and geometric accuracy of the input data. Significant improvements in soil surface classification precision are expected from considering spatial context information and monitoring the soil surface evolution.     

帽儿山林场航空像片数字立体正射影像图制作及应用

以东北林业大学帽儿山试验林场航片和地形图为资料，采用数字微分纠正理论与技术制作1：10000数字立体正射影像，其精度满足林业用图需要。制图过程中，对地形图配准结果和DEM结果进行了精度捡验，并提出了一种对DEM精度进行检验的方法——重合度法。最后就立体正射影像图的应用作了一些探讨。     

无人机遥感技术在水土保持中的应用进展

水土保持是生态文明建设的重要组成部分,传统水土保持工作方法已不能满足新时代水土保持的需求。无人机遥感技术解决了传统水土保持工作中“看不了、看不全、效率低、精度差”等问题。总结了无人机遥感技术在水土保持方案编制、动态监测、监管、规划、验收评估等方面的具体应用。论述了该项技术目前在水土保持工作中面临的问题。     

变色立木的无人机遥感监测技术

笔者简要阐述了国内外应用高空间分辨率遥感数据监测单木级森林病虫灾害的研究现状,提出可服务于松材线虫病死树普查的变色立木低空遥感监测技术要求,给出主要工作流程,建议规范松材线虫病的无人机遥感监测服务市场。