共查询到20条相似文献，搜索用时 359 毫秒
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D. Gómez-Candón F. López-Granados J. J. Caballero-Novella M. Gómez-Casero M. Jurado-Expósito L. García-Torres 《Precision Agriculture》2011,12(6):876-891
The aim of this paper is to assess co-registration errors in remote imagery through the AUGEO system, which consists of geo-referenced coloured tarps acting as terrestrial targets (TT), captured in the imagery and semi-automatically recognised by AUGEO2.0® software. This works as an add-on of ENVI® for image co-registration. To validate AUGEO, TT were placed in the ground, and remote images from satellite Quick Bird (QB), airplanes and unmanned aerial vehicles (UAV) were taken at several locations in Andalusia (southern Spain) in 2008 and 2009. Any geo-referencing system tested showed some error in comparison with the Differential Global Positioning System (DGPS)-geo-referenced verification targets. Generally, the AUGEO system provided higher geo-referencing accuracy than the other systems tried. The root mean square errors (RMSE) from the panchromatic and multi-spectral QB images were around 8 and 9 m, respectively and, once co-registered by AUGEO, they were about 1.5 and 2.5 m, for the same images. Overlapping the QB-AUGEO-geo-referenced image and the National Geographic Information System (NGIS) produced a RMSE of 6.5 m, which is hardly acceptable for precision agriculture. The AUGEO system efficiently geo-referenced farm airborne images with a mean accuracy of about 0.5–1.5 m, and the UAV images showed a mean accuracy of 1.0–4.0 m. The geo-referencing accuracy of an image refers to its consistency despite changes in its spatial resolution. A higher number of TT used in the geo-referencing process leads to a lower obtained RMSE. For example, for an image of 80 ha, about 10 and 17 TT were needed to get a RMSE less than about 2 and 1 m. Similarly, with the same number of TT, accuracy was higher for smaller plots as compared to larger plots. Precision agriculture requires high spatial resolution images (i.e., <1.5 m pixel?1), accurately geo-referenced (errors <1–2 m). With the current DGPS technology, satellite and airplane images hardly meet this geo-referencing requirement; consequently, additional co-registration effort is needed. This can be achieved using geo-referenced TT and AUGEO, mainly in areas where no notable hard points are available. 相似文献
2.
A flexible unmanned aerial vehicle for precision agriculture 总被引：1，自引：0，他引：1
Jacopo Primicerio Salvatore Filippo Di Gennaro Edoardo Fiorillo Lorenzo Genesio Emanuele Lugato Alessandro Matese Francesco Primo Vaccari 《Precision Agriculture》2012,13(4):517-523
An unmanned aerial vehicle (??VIPtero??) was assembled and tested with the aim of developing a flexible and powerful tool for site-specific vineyard management. The system comprised a six-rotor aerial platform capable of flying autonomously to a predetermined point in space, and of a pitch and roll compensated multi-spectral camera for vegetation canopy reflectance recording. Before the flight campaign, the camera accuracy was evaluated against high resolution ground-based measurements, made with a field spectrometer. Then, ??VIPtero?? performed the flight in an experimental vineyard in Central Italy, acquiring 63 multi-spectral images during 10?min of flight completed almost autonomously. Images were analysed and classified vigour maps were produced based on normalized difference vegetation index. The resulting vigour maps showed clearly crop heterogeneity conditions, in good agreement with ground-based observations. The system provided very promising results that encourage its development as a tool for precision agriculture application in small crops. 相似文献
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Fahad S. Khan Qamar U. Zaman Young K. Chang Aitazaz A. Farooque Arnold W. Schumann Ali Madani 《Precision Agriculture》2016,17(2):155-167
Wild blueberry (Vaccinium angustifolium Ait.) fields in the north east Canada are naturally grown in a course textured thin layer of soil and below this layer is a soilless layer of gravel. The root zone depth of this crop varies from 10 to 15 cm. Investigating the depth to the gravel layer below the course textured soil is advantageous, as it affects the water holding capacity of the root zone. Water and nutrient management are the two primary determinants of crop yield and the amount of leaching. The objective of this study was to estimate the depth to the gravel layer using DualEM-2 instrument. A C++ program written in Visual Studio 2010 was used to develop mathematical models for estimating the depth to the gravel layer from the outputs of DualEM-2 sensor. Two wild blueberry fields were selected in central Nova Scotia, Canada to evaluate the performance of DualEM-2 instrument in estimating the rootzone depth above the gravel layer. The mid points of squares created by grid lines were used as the sampling points at each experimental site. The actual depth to the interface was measured manually at selected grid points (n = 50). The apparent ground conductivity (ECa) values of DualEM-2 were recorded and the depth to the interface was estimated for the same sampling points within the selected fields. The fruit yield samples were also collected from the same grid points to identify the impact of the depth to the gravel layer on crop yield. After calibrations, comprehensive surveys were conducted and the actual and estimated depths to the interface were established. The interpolated maps of fruit yield, and the actual (zin) and estimated (\( {\text{z}}_{\text{in}}^{*} \)) depths to the interface were created in ArcGIS 10 software. Results indicated that the zin was significantly correlated with \( {\text{z}}_{\text{in}}^{*} \) for the North River (R 2 = 0.73; RMSE = 0.27 m) and the Carmel (R 2 = 0.45; RMSE = 0.20 m) sites. Results revealed that the areas with shallow depth to the gravel layer were low yielding, indicating that the variation in the depth to the gravel layer can have an impact on crop productivity. Non-destructive estimations of the depth to the gravel layer can be used to develop erosion control strategies, which will result in an increased crop production. 相似文献
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Assessing the accuracy of mosaics from unmanned aerial vehicle (UAV) imagery for precision agriculture purposes in wheat 总被引：1，自引：0，他引：1
High spatial resolution images taken by unmanned aerial vehicles (UAVs) have been shown to have the potential for monitoring agronomic and environmental variables. However, it is necessary to capture a large number of overlapped images that must be mosaicked together to produce a single and accurate ortho-image (also called an ortho-mosaicked image) representing the entire area of work. Thus, ground control points (GCPs) must be acquired to ensure the accuracy of the mosaicking process. UAV ortho-mosaics are becoming an important tool for early site-specific weed management (ESSWM), as the discrimination of small plants (crop and weeds) at early growth stages is subject to serious limitations using other types of remote platforms with coarse spatial resolutions, such as satellite or conventional aerial platforms. Small changes in flight altitude are crucial for low-altitude image acquisition because these variations can cause important differences in the spatial resolution of the ortho-images. Furthermore, a decrease of flying altitude reduces the area covered by each single overlapped image, which implies an increase of both the sequence of images and the complexity of the image mosaicking procedure to obtain an ortho-image covering the whole study area. This study was carried out in two wheat fields naturally infested by broad-leaved and grass weeds at a very early phenological stage. The geometric accuracy differences and crop line alignment among ortho-mosaics created from UAV image series were investigated while taking into account three different flight altitudes (30, 60 and 100 m) and a number of GCPs (from 11 to 45). The results did not show relevant differences in geo-referencing accuracy on the interval of altitudes studied. Similarly, the increase of the number of GCPs did not imply a relevant increase of geo-referencing accuracy. Therefore, the most important parameter to consider when choosing the flying altitude is the ortho-image spatial resolution required rather than the geo-referencing accuracy. Regarding the crop mis-alignment, the results showed that the overall errors were less than twice the spatial resolution, which did not break the crop line continuity at the studied spatial resolutions (pixels from 7.4 to 24.7 mm for 30, 60 and 100 m flying altitudes respectively) on the studied crop (early wheat). The results lead to the conclusion that a UAV flying at a range of 30 to 100 m altitude and using a moderate number of GCPs is able to generate ultra-high spatial resolution ortho-imagesortho-images with the geo-referencing accuracy required to map small weeds in wheat at a very early phenological stage. This is an ambitious agronomic objective that is being studied in a wide research program whose global aim is to create broad-leaved and grass weed maps in wheat crops for an effective ESSWM. 相似文献
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ETM+遥感影像融合方法的土地覆盖分类精度的研究 总被引：1，自引：1，他引：0
采用小波变换、小波和PCA相结合、小波和HIS相结合等遥感影像融合方法对Landsat7 ETM+多光谱与全色影像进行融合和土地覆盖分类研究,并结合影像的光谱统计参数和融合影像分类精度对这些方法进行评价,结果发现这3种融合方法均能不同程度地提高影像的分类精度。其中,第3种方法所得融合影像与原多光谱影像的相关系数最大,均方差、相关系数和信息熵最大,影像所含信息量最多,光谱特性保持较好;清晰度较高,在空间细节信息的表现能力上较优,所以融合影像的分类精度最高。因此,小波和HIS相结合的融合方法更适合ETM+融合影像的土地覆盖分类研究。 相似文献
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遥感影像分辨率的高低直接影响着森林植被监测的精度、成本和效率,故选择适合森林植被监测的影像最佳分辨率具有重要的应用价值。针对森林植被监测影像最佳分辨率选择方法及结果缺乏的问题,从林业实际应用出发,提出了基于1个步长的变异函数分析空间变异并综合考虑监测精度、成本和效率来确定森林植被监测影像最佳分辨率方法。基于最新的国产高分二号（GF-2）全色影像,利用1个步长的变异函数对湖南常宁洋泉镇林区3种典型分布类型森林植被进行拟合分析,初步确定适合森林植被监测的影像最低分辨率。然后对重采样形成的不同尺度多光谱影像分别进行监督分类,并对结果进行定量定性分析,结合影像成本和数据处理时间,找到适合不同类型森林植被监测的影像最佳分辨率。研究表明:不同分布类型的森林植被,适合遥感监测的影像最佳分辨率不同:①小冠幅森林植被3.2 m;②大冠幅森林植被16.0 m;③混合冠幅森林植被8.0 m。该森林植被遥感监测影像最佳分辨率确定方法和结果可为其他区域森林植被遥感监测影像最佳分辨率确定提供借鉴。 相似文献
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遥感影像融合与分类在城市边缘带扩展监测中应用研究 总被引：2，自引：0，他引：2
探讨了TM30m分辨率波段与SPOT10m分辨率全色波段通过融合来提高城市扩展动态监测精度的方法和应用潜力。首先采用IHS变换完成TM的多光谱波段与SPOT全色波段融合，增强变化信息在光谱和几何特征上的表征，然后采用最大似然分类方法对融合图像进行分类。实验结果表明光谱与纹理特征组合在用户精度上比单纯光谱、纹理特征分类分别提高21．87％和10．22％；在生产者精度上比各自分别提高8．4％和17．88％；Kappa系数分别提高0．10和0．21。通过高几何分辨率图像与多光谱波段融合方法可以，增强变化信息，纹理特征参与变化信息提取可以提高变化类型的分类精度。 相似文献
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为检测高分辨率遥感影像不同波段纹理特征对于森林蓄积量估算精度的影响,以湖北省荆门市京山县太子山林场马尾松纯林为对象,基于灰度共生矩阵的方法分别提取高分辨率遥感影像Worldview-2 红光、绿光、蓝光、近红外波段和全色波段的纹理特征,利用随机森林算法,分别建立野外样地蓄积量与纹理参数的模型。结果表明,全色波段对马尾松森林的精度最高（R2=0.86,RMSE=47.37 m3·hm-2）,其次是绿色波段（R2=0.85,RMSE=50.82 m3·hm-2）和近红外波段（R2=0.85,RMSE=46.85 m3·hm-2）,蓝色波段（R2=0.68,RMSE=60.72 m3·hm-2）和红色波段（R2=0.69,RMSE=56.27 m3·hm-2）的精度最低;窗口大小对模型精度影响较小,全色波段的R2取值在0.82~0.86,RMSE取值在47.66~51.99 m3·hm-2,多光谱波段的R2取值在0.88~0.89;蓝色和红色波段的非相似度（DIS）的估算模型精度相对较高,绿色波段的对比度（CON）（R2=0.87,RMSE=46.21 m3·hm-2）估算精度最高,红色波段的非相似度（R2=0.68,RMSE=58.30 m3·hm-2）估算精度较高,近红外波段的角二阶矩阵（ASM）（R2=0.68,RMSE=60.30 m3·hm-2）精度最高,全色波段的对比度、相关性、熵、变化量模型精度较高,R2为0.85。利用高分辨率遥感影像纹理特征估算森林参数时需综合考虑不同波段的纹理特征对模型的贡献。 相似文献
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【目的】本文使用分位数回归和分位数组合对枝下高进行建模和预测,为单木枝下高模型的构建提供新的思路和方法。【方法】利用大兴安岭新林区4个林场的兴安落叶松天然林实测数据,采用非线性回归构建枝下高基础和广义模型并分别扩展到分位数回归。使用三分位数组合(τ=0.1,0.5,0.9)、五分位数组合(τ=0.1,0.3,0.5,0.7,0.9)、九分位数组合(τ=0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9)和4种抽样设计(抽最大树、抽最小树、抽平均木、随机抽取)进行预测,比较不同分位数组合的预测效果并分析不同抽样设计对预测精度的影响。同时使用双重交叉检验对非线性回归、最优位数回归和最优分位数组合进行比较。模型拟合和检验的评价指标主要包括平均绝对误差(MAE)、均方根误差(RMSE)、相对误差(MPE)和调整确定系数(R2adj)。【结果】(1)无论是非线性回归还是分位数回归,广义模型的拟合MAE较基础模型可降低6%~12%,RMSE可降低6%~10%,检验效果也优于基础模型。枝下高与胸径呈负相关、与样地优势高和每公顷断面积呈正相关。(2)中位数回归在所有分位数中拟合能力最好,且效果与非线性回归相似。分位数回归可以描述枝下高的分布。(3)3种分位数组合都可以对枝下高模型进行预测且效果相差不大,三分位数组合就可以满足枝下高的预测精度。中位数回归的交叉检验结果与非线性回归相似,三分位数组合的预测能力最优,MAE和MPE较非线性回归和中位数回归分别下降了20%和4%左右,R2adj提高了16%左右。(4)基础和广义分位数组合的最优抽样设计分别为抽平均木5株和抽大树7株。【结论】本研究基于三分位数组合(τ=0.1,0.5,0.9)的枝下高模型可以提高预测精度,具体应用基础和广义分位数组合模型的最优抽样设计分别为抽平均木5株和抽大树7株。综合预测精度和调查成本的考虑,在实践中应用分位数组合时,推荐在样地中抽取5株平均木对枝下高进行预测。 相似文献
11.
卫星遥感技术在引大秦王川灌区土地利用调查的应用 总被引：1，自引：0，他引：1
苗春 《甘肃农业大学学报》2001,36(4):447-451
越来越多的土地利用调查研究不仅要求多光谱分辨率,还要求更高空间分辨率。利用SPOT和TM(ETM+)的融合等卫星遥感技术对引大秦王川灌区土地利用进行调查,实现了土地利用调查的快速准确处理,可明显提高土地利用调查的工作效率而且降低了成本,保证了调查质量和科学准确性。 相似文献
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受稀疏植被与明亮土壤背景影响,干旱地区植被覆盖精确遥感估测难度较大。以Hyperion影像为数据源,选取甘肃省民勤绿洲-荒漠过渡带为研究区,系统比较了利用不同类型高光谱及多光谱植被指数估测干旱地区稀疏植被覆盖度的能力,以期确定干旱地区稀疏植被覆盖度估测的最佳植被指数。不同植被指数估测稀疏植被覆盖度的能力利用线性回归R2及留一交叉验证的均方根误差进行比较,结果表明:高光谱植被指数估测稀疏植被覆盖度的能力显著优于相应的多光谱植被指数,抗大气植被指数(ARVI)及抗土壤和大气植被指数(SARVI)表现明显优于归一化植被指数(NDVI)与土壤调节植被指数(SAVI),其中以基于833.3nm/640.5nm波段组合的ARVI表现最佳,R2可达0.7294,均方根误差(RMSE)仅为5.5488。 相似文献
14.
Biomass monitoring is one of the main pillars of precision farm management as it involves deeper knowledge about pest and weed status, soil quality, water stress, and yield prediction, among others. This research focuses on estimating crop biomass from high-resolution red, green, blue imaging obtained with an unmanned aerial vehicle. Onion, as one of the most cultivated vegetables, was studied for two seasons under non-controlled conditions in two commercial plots. Green canopy cover, crop height, and canopy volume (Vcanopy) were the predictor variables extracted from the geomatic products. Strong relationships were found between Vcanopy and dry leaf biomass and dry bulb biomass. Adjusted coefficient of determination (\({\text{R}}_{\text{adj}}^2\)) values were 0.76 and 0.95, respectively. Nevertheless, crop management practices and leaf depletion at vegetative stages significantly affect the accuracy of the canopy model. These results suggested that obtaining biomass using aerial images are a good alternative to other sensors and platforms as they have high spatial and temporal resolution to perform high-quality biomass monitoring. 相似文献
15.
M. L. Guillen-Climent Pablo J. Zarco-Tejada J. A. J. Berni P. R. J. North F. J. Villalobos 《Precision Agriculture》2012,13(4):473-500
This study was conducted to model the fraction of intercepted photosynthetically active radiation (fIPAR) in heterogeneous row-structured orchards, and to develop methodologies for accurate mapping of the instantaneous fIPAR at field scale using remote sensing imagery. The generation of high-resolution maps delineating the spatial variation of the radiation interception is critical for precision agriculture purposes such as adjusting management actions and harvesting in homogeneous within-field areas. Scaling-up and model inversion methods were investigated to estimate fIPAR using the 3D radiative transfer model, Forest Light Interaction Model (FLIGHT). The model was tested against airborne and field measurements of canopy reflectance and fIPAR acquired on two commercial peach and citrus orchards, where study plots showing a gradient in the canopy structure were selected. High-resolution airborne multi-spectral imagery was acquired at 10?nm bandwidth and 150?mm spatial resolution using a miniaturized multi-spectral camera on board an unmanned aerial vehicle (UAV). In addition, simulations of the land surface bidirectional reflectance were conducted to understand the relationships between canopy architecture and fIPAR. Input parameters used for the canopy model, such as the leaf and soil optical properties, canopy architecture, and sun geometry were studied in order to assess the effect of these inputs on canopy reflectance, vegetation indices and fIPAR. The 3D canopy model approach used to simulate the discontinuous row-tree canopies yielded spectral RMSE values below 0.03 (visible region) and below 0.05 (near-infrared) when compared against airborne canopy reflectance imagery acquired over the sites under study. The FLIGHT model assessment conducted for fIPAR estimation against field measurements yielded RMSE values below 0.08. The simulations conducted suggested the usefulness of these modeling methods in heterogeneous row-structured orchards, and the high sensitivity of the normalized difference vegetation index and fIPAR to background, row orientation, percentage cover and sun geometry. Mapping fIPAR from high-resolution airborne imagery through scaling-up and model inversion methods conducted with the 3D model yielded RMSE error values below 0.09 for the scaling-up approach, and below 0.10 for the model inversion conducted with a look-up table. The generation of intercepted radiation maps in row-structured tree orchards is demonstrated to be feasible using a miniaturized multi-spectral camera on board UAV platforms for precision agriculture purposes. 相似文献
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应用遥感技术估算生物多样性是森林生态学的研究热点和难点。为探索高分辨率卫星数据在估算森林植被多样性上的应用潜力,以吉林蛟河市阔叶红松林大样地数据为支撑,提取了红松树冠的空间分布格局,并估算了乔木物种多样性。首先,基于冬季GeoEye-1影像提取红松树冠分布图,与样地每木调查数据吻合较好。然后,使用景观生态学和地统计学指数,分析红松种群空间分布规律,发现了最大自相关尺度30~40 m。最后,选择拥有红边波段的秋季RapidEye卫星图像,建立遥感影像5 m光谱值和30 m窗口大小纹理参数与生物多样性指数的多元线性回归模型。结果表明,红边信息并未显著改善多光谱数据估算植被生物多样性的能力,但所获大区域制图结果与林分龄级吻合较好。 相似文献
17.
基于无人机多光谱遥感的冬小麦叶面积指数反演 总被引：6，自引：1，他引：5
以获取的冬小麦无人机多光谱影像为数据源,充分利用多光谱传感器的红边通道对传统植被指数进行改进,通过灰色关联度分析后基于多个植被指数建模的方法对冬小麦的叶面积指数(leaf area index,LAI)进行反演精度对比。结果显示:使用基于多植被指数的随机森林(RF)比赤池信息量准则-偏最小二乘法(AIC-PLS)反演精度高。得到的LAI反演值和真实值之间的R~2=0.822,RMSE=1.218。研究证明通过随机森林预测具有更好的拟合效果,对冬小麦的LAI反演有较好的适用性。 相似文献
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利用高光谱成像技术对泾源黄牛肉色度的PLSR预测模型进行构建。通过可见近红外高光谱成像系统获取牛肉样本的高光谱图像,提取感兴趣区域(ROI)的光谱信息并计算平均光谱,采用蒙特卡洛法剔除异常样本后进行样本集划分,并对划分后的样本数据进行预处理。其中,亮度(L*)经Deresolve法预处理的模型结果最好,其$R_{C}^{2}$为0.979 0,预测集相关系数$R_{P}^{2}$为0.976 6;红度(a*)经卷积平滑法预处理的模型结果最好,其$R_{C}^{2}$和$R_{P}^{2}$分别为0.807 0、0.915 5;黄度(b*)经卷积平滑法预处理的模型结果最好,其$R_{C}^{2}$和$R_{P}^{2}$分别为0.931 1、0.950 6。分别利用竞争性自适应重加权法(CARS)、连续投影算法(SPA)和无信息变量消除算法(UVE)进行特征波长提取,建立基于特征波段的偏最小二乘回归(PLSR)模型,进而优选出最佳预测模型,结合视觉的空间深度、立体程度,对样本的形态和色觉感知进行提取与辨别。结果表明,利用高光谱成像技术所构建的色度PLSR模型均是可行的,研究结果为牛肉品质在线快速检测提供了理论依据。 相似文献
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Francisca López-Granados Jorge Torres-Sánchez Angélica Serrano-Pérez Ana I. de Castro Fco.-Javier Mesas-Carrascosa José-Manuel Peña 《Precision Agriculture》2016,17(2):183-199
Site-specific weed management is defined as the application of customised control treatments only where weeds are located within the crop-field by using adequate herbicide according to weed emergence. The aim of the study was to generate georeferenced weed seedling infestation maps in two sunflower fields by analysing overlapping aerial images of the visible and near-infrared spectrum (using visible or multi-spectral cameras) collected by an unmanned aerial vehicle (UAV) flying at 30 and 60 m altitudes. The main tasks focused on the configuration and evaluation of the UAV and its sensors for image acquisition and ortho-mosaicking, as well as the development of an automatic and robust image analysis procedure for weed seedling mapping used to design a site-specific weed management program. The control strategy was based on seven weed thresholds with 2.5 steps of increasing ratio from 0 % (herbicide must be applied just when there is presence or absence of weed) to 15 % (herbicide applied when weed coverage >15 %). As a first step of the imagery analysis, sunflower rows were correctly matched to the ortho-mosaicked imagery, which allowed accurate image analysis using object-based image analysis [object-based-image-analysis (OBIA) methods]. The OBIA algorithm developed for weed seedling mapping with ortho-mosaicked imagery successfully classified the sunflower-rows with 100 % accuracy in both fields for all flight altitudes and camera types, indicating the computational and analytical robustness of OBIA. Regarding weed discrimination, high accuracies were observed using the multi-spectral camera at any flight altitude, with the highest (approximately 100 %) being those recorded for the 15 % weed threshold, although satisfactory results from 2.5 to 5 % thresholds were also observed, with accuracies higher than 85 % for both field 1 and field 2. The lowest accuracies (ranging from 50 to 60 %) were achieved with the visible camera at all flight altitudes and 0 % weed threshold. Herbicide savings were relevant in both fields, although they were higher in field 2 due to less weed infestation. These herbicide savings varied according to the different scenarios studied. For example, in field 2 and at 30 m flight altitude and using the multi-spectral camera, a range of 23–3 % of the field (i.e., 77 and 97 % of area) could be treated for 0–15 % weed thresholds. The OBIA procedure computed multiple data which permitted calculation of herbicide requirements for timely and site-specific post-emergence weed seedling management. 相似文献