地理国情监测遥感数据源比选方法研究

随着遥感技术的快速发展,遥感新设备、新方法不断出现,遥感数据来源不断扩大、种类不断增加、质量不断提升,遥感数据在地理国情监测中的应用也更加广泛。但由于遥感数据源过于复杂且数据量庞大,地理国情监测数据源的选取成为一个难题。基于地理国情监测的主要目标,选择遥感数据源的主要质量因子,采用一定的赋权方法对其进行评价比较,可实现数据源的最优选取。本文提出了一种结合主观专家评价和客观熵值计算的比选方法,归纳数据源的主要质量因子,构建指标体系,并对各因子赋权计算评价值,实现了对地理国情监测数据源的评价比选,试验证明了本文方法的有效性。     

ZY-102C星数据在矿山遥感监测中的应用研究与分析

利用资源一号(ZY-1)02C卫星(以下简称02C星)全色高分辨率相机(HR)和多光谱相机(MUX)数据,以河北承德多金属矿区和江西寻乌稀土矿区为实验区,开展了1∶5万矿山遥感监测应用研究;分析了应用过程中存在的问题,提出了解决方案和建议,对02C星数据在矿山遥感监测中的规模化应用起到了示范效果。研究结果表明:在数据处理流程上,对空间分辨率为2.36 m的02C星全色HR1与HR2数据需先进行正射纠正再拼接,对空间分辨率为10 m的多光谱数据MUX可采用假彩色合成方法进行模拟真彩色合成;在矿山地物识别上,与SPOT5图像比较,02C星图像可更好地反映出开采面、开采点、中转场和矿山建筑等矿山地物类型,不足之处是纹理信息较平滑、矿山地物的边界相对模糊,因而增加了室内解译的难度;在经济效益上,02C星数据的性价比优势明显。因此,尽管02C星数据处理和解译工作量较大,但其能满足1∶5万矿山遥感监测要求,总体经济效益显著。     

遥感技术在“一张图”实施监督系统中的应用

结合国土空间规划“一张图”实施监督信息系统的开发,详细阐述了卫星遥感技术在规划实施评估、国土空间开发适宜性评价和规划监测预警这几个模块中的应用案例,对应用的内容、方法及效果进行了总结和探讨。分析表明,遥感技术能为国土空间规划“一张图”提供众多的基础数据,对于评估城市发展动态、合理规划城市用地具有非常重要的作用。     

Geospatial technologies for detection and monitoring of Ganoderma basal stem rot infection in oil palm plantations: a review on sensors and techniques

Basal stem rot (BSR) is a type of disease that induces oil palm death within a short span of the appearance of symptoms. BSR early detection would facilitate to curb this by adopting appropriate strategies. In this paper, a systematic review was undertaken to demonstrate the need for authentic health condition monitoring of oil palm plantations. The currently used remotely sensed (RS) techniques for BSR detection and classification were reviewed. Several kinds of RS techniques were exerted for BSR detection and its severity classification up to four levels. It was identified that applied geospatial technologies, including multispectral and hyperspectral remote sensing, terrestrial laser scanning, spatial maps, tomography images, intelligent e-nose and Microfocus X-ray fluorescence, were capable of distinguishing infected oil palms from the non-infected ones. Furthermore, some of them are able to categorize BSR severity level up to four levels as well as of its early detection.     

An efficient geosciences workflow on multi-core processors and GPUs: a case study for aerosol optical depth retrieval from MODIS satellite data

Quantitative remote sensing retrieval algorithms help understanding the dynamic aspects of Digital Earth. However, the Big Data and complex models in Digital Earth pose grand challenges for computation infrastructures. In this article, taking the aerosol optical depth (AOD) retrieval as a study case, we exploit parallel computing methods for high efficient geophysical parameter retrieval. We present an efficient geocomputation workflow for the AOD calculation from the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data. According to their individual potential for parallelization, several procedures were adapted and implemented for a successful parallel execution on multi-core processors and Graphics Processing Units (GPUs). The benchmarks in this paper validate the high parallel performance of the retrieval workflow with speedups of up to 5.x on a multi-core processor with 8 threads and 43.x on a GPU. To specifically address the time-consuming model retrieval part, hybrid parallel patterns which combine the multi-core processor’s and the GPU’s compute power were implemented with static and dynamic workload distributions and evaluated on two systems with different CPU–GPU configurations. It is shown that only the dynamic hybrid implementation leads to a greatly enhanced overall exploitation of the heterogeneous hardware environment in varying circumstances.     

Urbanization between compactness and dispersion: designing a spatial model for measuring 2D binary settlement landscape configurations

Measuring spatial patterns is a crucial task in spatial sciences. Multiple indicators have been developed to measure patterns in a quantitative manner. However, most comparative studies rely on relative comparisons, limiting their explanatory power to specific case studies. Motivated by advancements in earth observation providing unprecedented resolutions of settlement patterns, this paper suggests a measurement technique for spatial patterns to overcome the limits of relative comparisons. We design a model spanning a feature space based on two metrics – largest patch index and number of patches. The feature space is defined as ‘dispersion index’ and covers the entire spectrum of possible two-dimensional binary (settlement) patterns. The model configuration allows for an unambiguous ranking of each possible pattern with respect to spatial dispersion. As spatial resolutions of input data as well as selected areas of interest influence measurement results, we test dependencies within the model. Beyond, common other spatial metrics are selected for testing whether they allow unambiguous rankings. For scenarios, we apply the model to artificially generated patterns representing all possible configurations as well as to real-world settlement classifications differing in growth dynamics and patterns.