排序方式： 共有280条查询结果，搜索用时 15 毫秒
11.
基于Google Earth Engine和NDVI时序差异指数的作物种植区提取 总被引：1，自引：0，他引：1
为提高农作物种植信息遥感监测的效率,扩展数据适用范围,本文提出了一种基于时间序列NDVI差异指数的作物种植区提取方法。随着海量遥感与云计算的发展,Google Earth Engine作为一个全球尺度地理空间分析云平台,弥补了单机计算耗时长的不足,为快速遥感分类带来了新机遇。基于Google Earth Engine平台,以河南省开封市杞县为研究区,以2019—2020年杞县地区多时相Sentinel-2影像为数据源,结合物候信息,根据不同作物在时间序列NDVI曲线上的差异构建NDVI时序差异指数,从而提取作物种植区,区分不同作物类型,并与其他方法进行了精度验证和对比。结果表明：① NDVI时序差异指数法以作物物候信息为基础,与GEE高性能的计算能力相结合,形成了作物种植信息快速提取框架,可以方便快捷地进行作物种植区提取,较本地处理具有明显优势;② 杞县冬小麦和大蒜种植区有明显的空间分异性,冬小麦种植区主要集中在研究区西北部以及南部的农村居民点周围,而杞县大蒜则由于产品流通需要,主要集中在研究区中部以及东北部,居民点较为密集,交通便利的城市周边;③ 与时间序列支持向量机法和最大似然法相比较, NDVI时序差异指数进行作物种植区提取的总体精度达到83.72%, Kappa系数为0.67,分别比最大似然法提高了10.02%和0.21,比支持向量机法提高了4.18%和0.09,表明该方法能更高效率,更高精度地提取作物种植信息,实现区域作物种植信息的高效准确监测。总体来看,该方法在一定程度上可拓展遥感数据在农业领域的应用范围,具有推广价值。 相似文献
12.
13.
以甘肃省为例,在基于Google Earth Engine (GEE)平台实现1995年、2000年、2005年、2010年、2015年和2020年土地变化监测的基础上,利用贝叶斯层次时空模型(BHM)分析土地利用程度的时空变化特征。结果表明：① 研究期间内甘肃省土地利用程度呈增长趋势,其中1995―2000年和2010―2015年增长速度较明显;② 土地利用程度空间格局“东高西低”,热点区域主要分布在陇中、陇东和陇南地区;③ 土地利用程度局部变化呈现明显区域差异,整体表现为“东弱西强”,局部变化热点区域主要分布在河西地区;④ 影响土地利用程度变化的主要因素是经济规模和产业结构,其中经济因素影响程度最高。 相似文献
14.
15.
16.
Google Earth作为功能十分强大的虚拟地球软件,已经广泛应用于多个行业。以Google Earth为平台,将通过GPS坐标集成化的地质信息转化为GE支持的KML格式,然后加载到Google Earth。充分利用Google Earth的海量数据管理能力和三维显示能力,提升地质调查工作的效率和成果的质量,更好地为地质调查工作服务。 相似文献
17.
New sources of data such as ‘big data’ and computational analytics have stimulated innovative pedestrian oriented research. Current studies, however, are still limited and subjective with regard to the use of Google Street View and other online sources for environment audits or pedestrian counts because of the manual information extraction and compilation, especially for large areas. This study aims to provide future research an alternative method to conduct large scale data collection more consistently and objectively on pedestrian counts and possibly for environment audits and stimulate discussion of the use of ‘big data’ and recent computational advances for planning and design. We explore and report information needed to automatically download and assemble Google Street View images, as well as other image parameters for a wide range of analysis and visualization, and explore extracting pedestrian count data based on these images using machine vision and learning technology. The reliability tests results based on pedestrian information collected from over 200 street segments in Buffalo, NY, Washington, D.C., and Boston, MA respectively suggested that the image detection method used in this study are capable of determining the presence of pedestrian with a reasonable level of accuracy. The limitation and potential improvement of the proposed method is also discussed. 相似文献
18.
As countries become increasingly urbanized, understanding how urban areas are changing within the landscape becomes increasingly important. Urbanized areas are often the strongest indicators of human interaction with the environment, and understanding how urban areas develop through remotely sensed data allows for more sustainable practices. The Google Earth Engine (GEE) leverages cloud computing services to provide analysis capabilities on over 40 years of Landsat data. As a remote sensing platform, its ability to analyze global data rapidly lends itself to being an invaluable tool for studying the growth of urban areas. Here we present (i) An approach for the automated extraction of urban areas from Landsat imagery using GEE, validated using higher resolution images, (ii) a novel method of validation of the extracted urban extents using changes in the statistical performance of a high resolution population mapping method. Temporally distinct urban extractions were classified from the GEE catalog of Landsat 5 and 7 data over the Indonesian island of Java by using a Normalized Difference Spectral Vector (NDSV) method. Statistical evaluation of all of the tests was performed, and the value of population mapping methods in validating these urban extents was also examined. Results showed that the automated classification from GEE produced accurate urban extent maps, and that the integration of GEE-derived urban extents also improved the quality of the population mapping outputs. 相似文献
19.
20.
Digging into Google Earth: An analysis of “Crisis in Darfur” 总被引：1，自引：0，他引：1
Google publicists have suggested the Crisis in Darfur is an example of the Google Earth software’s “success at tangibly impacting what is happening on the ground.” Yet whether or not Google Earth’s interface, along with a medley of other media representations of the conflict, have impacted events on the ground or led to coherent policies of humanitarian intervention remains open to debate. This article draws upon critical approaches from media studies—namely discourse analysis—to analyze several aspects of the Google Earth/USHMM Crisis in Darfur project. While this project was no doubt developed with the noble intention of generating international awareness about widespread violence that has recently occurred in the Darfur region, it is important to evaluate how representations of global conflicts are changing with uses of new information technologies and whether such representations can actually achieve their desired impacts or effects. The article begins with a discussion of the Crisis in Darfur project’s history, proceeds to analyze some of the press coverage of the project and then moves to a critique of the layer using four categories of analysis: (1) the shifting role of satellite image; (2) the temporality of the interface; (3) the practice of conflict branding; and (4) the practice of “information intervention.” Throughout the article, I explore how the presentation of Darfur-related materials through Google Earth reproduces problematic Western tropes of African tragedy and misses an opportunity to generate public literacy around satellite images. I also consider how humanitarianism is intertwined with digital and disaster capitalism, and suggest that this instance of “information intervention” makes patently clear that high visual capital alone cannot resolve global conflicts. 相似文献