Some applications of humidity profiles estimated from INSAT infra red digital cloud imagery data

Moisture profiles have been estimated over the region bounded by the latitudes 40°N and 40°S and longitudes 30°E to 130°E using INSAT digital infra red cloud imagery data. The representativeness of these profiles in representing moisture field associated with the development and movement of synoptic scale systems during the period September 15th, 1996 to March 31st, 1997 has been examined. It has been shown that the changes in the moisture field associated with the withdrawal of the southwest and northeast monsoons from the Indian sub-continent, development and movement of synoptic scale sytems (depressions, tropical cyclones and waves in easterlies) and equatorial troughs in the Indian Ocean could be clearly seen in humidity profiles. The initial development of tropical systems is first seen in the humidity field in the upper troposphere. These profiles could be used in monitoring the initial development and subsequent movement of tropical systems. Further the data on moisture distribution from the data gap regions of the Indian Ocean could be used as an additional source of moisture in numerical analysis and prognosis.     

长江中游城市群空间结构的多分形特征

在城市群越来越演化为多尺度、多区域复杂系统背景下,有必要引入多分形理论与方法研究其空间结构。本文基于2018年NPP-VIIRS夜间灯光数据,计算长江中游城市群整体及其局部的多分维谱,根据谱线分析不同尺度下长江中游城市空间结构的多分形特征。结果显示：① 长江中游城市群夜间灯光容量维在整体和局部都出现双标度现象。② q < -5.5时,整体广义关联维谱线突破理论上限2,在q > 0时,武汉城市圈和环长株潭城市群的分维显著较高。③ 整体的局部分维谱和武汉城市圈、环长株潭城市群、环鄱阳湖城市群、宜荆荆城市群局部分维谱表现为单峰偏右。根据上述结果,得到和验证了以下结论：① 长江中游城市群区域一体化程度较低。② 长江中游城市群不同层级和区域的空间结构差异显著,呈现出多尺度复杂特征。③ 长江中游城市群在不同尺度中均倾向于中心集聚式发展。研究揭示多分形模型能够从尺度依赖视角有效揭示巨型城市群空间结构的复杂性及其背后的问题,具有很好的理论探索和实践分析前景。     

Estimating agricultural production in marginal and food insecure areas in Kenya using very high resolution remotely sensed imagery

Regularly monitoring the amount of food produced in food insecure, isolated, subsistence farming areas can be used to help identify households or communities who may be in need of additional food resources. Measuring seasonal food production in developing countries, particularly at a sub-national level, is complicated by lack of data. In this study we use high resolution remotely sensed data to calculate cultivated area in two different growing areas, during two different seasons in Kenya. The results of the research support the usefulness of this approach for agricultural monitoring in the developing world and suggest that monitoring cultivated area requires attention to the specific growing characteristics of an area.     

Integrating environmental variables and WorldView-2 image data to improve the prediction and mapping of Thaumastocoris peregrinus (bronze bug) damage in plantation forests

This study integrated environmental variables together with high spectral resolution WorldView-2 imagery to detect and map Thaumastocoris peregrinus damage in Eucalypt plantation forests in KwaZulu-Natal, South Africa. The WorldView-2 bands, vegetation indices and environmental variables were entered separately into PLS regression models to predict T. peregrinus damage. The datasets were then integrated to test the collective strength in predicting T. peregrinus damage. Important variables were identified by variable importance (VIP) scores and were re-entered into a PLS regression model. The VIP model was then extrapolated to map the severity of damage and predicted T. peregrinus damage with an R² value of 0.71 and a RMSE of 3.26% on an independent test dataset. The red edge and near-infrared bands of the WorldView-2 sensor together with the temperature dataset were identified as important variables in predicting T. peregrinus damage. The results indicate the potential of integrating WorldView-2 data and environmental variables to improve the mapping and monitoring of insect outbreaks in plantation forests. The result is critical for plantation health monitoring using a new sensor which contains important vegetation wavelengths.     

Assessing the performance of two unsupervised dimensionality reduction techniques on hyperspectral APEX data for high resolution urban land-cover mapping

Despite the high richness of information content provided by airborne hyperspectral data, detailed urban land-cover mapping is still a challenging task. An important topic in hyperspectral remote sensing is the issue of high dimensionality, which is commonly addressed by dimensionality reduction techniques. While many studies focus on methodological developments in data reduction, less attention is paid to the assessment of the proposed methods in detailed urban hyperspectral land-cover mapping, using state-of-the-art image classification approaches. In this study we evaluate the potential of two unsupervised data reduction techniques, the Autoassociative Neural Network (AANN) and the BandClust method – the first a transformation based approach, the second a feature-selection based approach – for mapping of urban land cover at a high level of thematic detail, using an APEX 288-band hyperspectral dataset. Both methods were tested in combination with four state-of-the-art machine learning classifiers: Random Forest (RF), AdaBoost (ADB), the multiple layer perceptron (MLP), and support vector machines (SVM). When used in combination with a strong learner (MLP, SVM) BandClust produces classification accuracies similar to or higher than obtained with the full dataset, demonstrating the method’s capability of preserving critical spectral information, required for the classifier to successfully distinguish between the 22 urban land-cover classes defined in this study. In the AANN data reduction process, on the other hand, important spectral information seems to be compromised or lost, resulting in lower accuracies for three of the four classifiers tested. Detailed analysis of accuracies at class level confirms the superiority of the SVM/Bandclust combination for accurate urban land-cover mapping using a reduced hyperspectral dataset. This study also demonstrates the potential of the new APEX sensor data for detailed mapping of land cover in spatially and spectrally complex urban areas.     

Automatic registration of optical imagery with 3D LiDAR data using statistical similarity

The development of robust and accurate methods for automatic registration of optical imagery and 3D LiDAR data continues to be a challenge for a variety of applications in photogrammetry, computer vision and remote sensing. This paper proposes a new approach for the registration of optical imagery with LiDAR data based on the theory of Mutual Information (MI), which exploits the statistical dependency between same- and multi-modal datasets to achieve accurate registration. The MI-based similarity measures quantify dependencies between aerial imagery, and both LiDAR intensity data and 3D point cloud data. The needs for specific physical feature correspondences, which are not always attainable in the registration of imagery with 3D point clouds, are avoided. Current methods for registering 2D imagery to 3D point clouds are first reviewed, after which the mutual MI approach is presented. Particular attention is given to adoption of the Normalised Combined Mutual Information (NCMI) approach as a means to produce a similarity measure that exploits the inherently registered LiDAR intensity and point cloud data so as to improve the robustness of registration between optical imagery and LiDAR data. The effectiveness of local versus global similarity measures is also investigated, as are the transformation models involved in the registration process. An experimental program conducted to evaluate MI-based methods for registering aerial imagery to LiDAR data is reported and the results obtained in two areas with differing terrain and land cover, and with aerial imagery of different resolution and LiDAR data with different point density are discussed. These results demonstrate the potential of the MI and especially the CMI methods for registration of imagery and 3D point clouds, and they highlight the feasibility and robustness of the presented MI-based approach to automated registration of multi-sensor, multi-temporal and multi-resolution remote sensing data for a wide range of applications.     

宽角相机低空航测的精度分析

首先从理论上对低空航测精度与相机像场角的关系进行了定量分析,得出低空航测应尽可能使用宽角相机的结论;接着指明了单镜头相机扩大像场角的局限和现有市场上的组合宽角相机因重量过大而不适用于低空轻荷载无人机的不足,阐述了作者研制的具有自检校自稳定功能的组合宽角低空轻小型相机的特点,尤其是实现组合成像静态误差和动态误差自检校的技术原理;针对大比例尺测图的实践,提出通过宽角相机大重叠航空摄影提高低空航测精度的技术建议;最后用典型工程生产数据验证了上述理论分析的正确性。     

基于定向片模型的SPOT-5遥感影像自检校光束法平差

本文通过建立内方位自检校模型,利用定向片模型对外方位元素进行内插建模,建立了基于定向片模型的自检校光束法平差,研究不同控制点数量和不同定向片间隔对定位精度的影响,并利用河南嵩山地区SPOT-5遥感影像数据对模型进行了实验验证和分析,结果表明,平面精度可以达到10.06m,高程精度可以达到8.84m.     

植被覆盖区高光谱遥感影像上蚀变岩与蚀变矿物信息的提取

在高光谱遥感找矿应用中,从影像上提取的蚀变信息经常包含了很多对找矿无用的信息。为了避免这些无用信息,本研究选取植被覆盖的云南普朗斑岩铜矿区作为研究区,使用该区高光谱遥感数据分别提取蚀变岩和蚀变矿物的信息,分析它们对找矿的有用性。提取蚀变矿物信息使用的是从影像上提取的矿物端元参考光谱,光谱匹配采用的是光谱角方法;提取蚀变岩信息使用的是野外蚀变岩参考光谱,光谱匹配不仅考虑光谱的整体形态,而且考虑吸收谷位置形态。在蚀变岩识别图上,4个典型蚀变岩点的野外验证表明:识别结果与实地地物基本一致。但是,在蚀变矿物信息图中,这4个圈定点无任何信息。这表明:这些点的蚀变岩信息较其表面蚀变矿物信息更丰富,因而能被有效提取出。通过比较蚀变岩、蚀变矿物的信息分布图,发现:一些粘土矿物(比如硬石膏和高岭石)的大量信息分布在沉积岩区,因而可能是假的蚀变信息。这表明:通过蚀变岩光谱获取的蚀变信息比通过蚀变矿物光谱获取的蚀变信息更可靠。在蚀变岩信息提取中,比较吸收谷位置识别前后的结果表明:考虑光谱整体和局部形态比仅考虑整体形态的识别结果具有更高的可靠性。     

顾及空间相关性的遥感影像信息量的度量方法

提出了一种结合信息论与地学统计法的遥感影像信息量计算的方法。此方法基于遥感影像加性噪声模型和互信息上界的计算原理,同时考虑了噪声和空间相关性等影响遥感影像信息量的因素,适于计算具有稳健空间相关性、不同地物类型的光学影像的信息含量。利用Landsat TM影像子集,分别计算了城市、农田、山地3种不同地物类型的影像信息量。结果表明,城市含有最大的信息量,同时影像信息量与影像方差呈对数正相关关系。