新一代数字地球平台与“数字中国”技术体系架构探讨

本文系统分析和总结了Google Earth的四大特点,及为地球科学研究、"数字地球"和"数字中国"的发展所带来的影响和启迪,提出了数字地球平台(DEP)的概念,并针对"数字中国"的建设,提出了建立"数字中国"的数据交换标准(DCML),提出了全新的以地学信息浏览器/空间信息服务器(G/S)结构为主的下一代数字地球、数字中国的技术体系架构,进一步对"数字中国"技术体系架构应遵循的原则进行了论述,对"数字中国"建设和应用服务进行了展望。     

多种对地观测技术及Google Earth技术应用于土地利用/覆被变化建库

为克服传统生态环境监测周期长,可靠性差,精度低的局限性,提出利用多种对地观测技术及Google Earth 技术进行土地利用/覆被变化监测方法.即利用新近获取的遥感影像为信息源,以原有的遥感影像解译数据为基础,对新遥感影像进行土地利用/覆被变化解译,修改旧遥感解译数据库;利用Google Earth上具有的高分辨率遥感影像,在室内对解译过程中的不确定地物进行补判;利用手持GPS接收机在野外对解译结果进行核查,并更新数据库;利用GIS空间分析功能,提取出高精度土地利用/覆被变化信息.实验选取近年旱涝灾害严重的四川作为研究对象,研究结果表明利用多种对地观测技术及Google Earth技术可以有效的对土地利用/覆被变化进行动态监测,并对下一步研究方向进行了探讨.     

Google Earth as a data source for investigating river forms and processes: Discriminating river types using form-based process indicators

Google Earth provides potential for exploiting an enormous reservoir of freely-available remotely sensed data to support river science and management. In this paper, we consider how the platform can support investigation of river physical forms and processes by developing an empirically-based reach-scale classification of semi-natural European single thread to transitional rivers. Using strict reach and image selection criteria, we identified 194 reaches of 68 rivers for analysis. Measurements of channel dimensions and counts of in-channel and floodplain features, standardised for reach length and channel width where necessary, were used to derive a series of geomorphologically-relevant process indicators. A suite of multivariate analyses were then applied to this data set, resulting in the discrimination of five river types: laterally stable, laterally active sinuous-meandering; transitional (near-braided); bedrock; and cascade/step dominated. The results of the classification were tested by examining the characteristics and distribution of the river classes in relation to known independent controls of river form including reach-scale energy and valley confinement conditions. Our results show that if methods of data extraction are carefully developed, physically meaningful river reach discrimination can be achieved using Google Earth. Although there are limits to the types of information that can be extracted such that field investigations cannot always be avoided, there is enormous potential to mine Google Earth across different space and time scales, supporting the assembly of large, reliable data sets relevant to river forms and processes in a very cost-effective way. © 2019 John Wiley & Sons, Ltd.     

基于GEE平台的广州市主城区不透水面时间序列提取

不透水面作为城市化水平以及城市环境的重要评价指标,其提取已经是当下的研究热点。与单时相影像相比,时间序列制图能够获取其准确的变化趋势,对于监测城市的快速发展具有重要意义。本文以广州市主城区为研究区,以Google Earth Engine平台为基础,利用2000-2017年的Landsat TOA影像计算BCI和NDVI,并通过自适应迭代法确定它们的阈值,从而提取初始的不透水面,然后进行时间一致性检验,使不透水面时间序列更加合理。研究结果表明:①BCI与NDVI的结合以及时间一致性检验能够提高不透水面的提取质量;②本文中不透水面提取的平均总体精度为90.4%,平均Kappa系数为0.812;③在2000-2017年广州市主城区不透水面面积增加近一倍,但增速有所放缓。④新增的不透水面主要集中在原本相对落后的主城区外围;⑤高程、道路密度和购物场所密度等是影响广州市主城区不透水面扩张的主要因素。     

Identifying and forecasting potential biophysical risk areas within a tropical mangrove ecosystem using multi-sensor data

Mangroves are one of the most productive ecosystems known for provisioning of various ecosystem goods and services. They help in sequestering large amounts of carbon, protecting coastline against erosion, and reducing impacts of natural disasters such as hurricanes. Bhitarkanika Wildlife Sanctuary in Odisha harbors the second largest mangrove ecosystem in India. This study used Terra, Landsat and Sentinel-1 satellite data for spatio-temporal monitoring of mangrove forest within Bhitarkanika Wildlife Sanctuary between 2000 and 2016. Three biophysical parameters were used to assess mangrove ecosystem health: leaf chlorophyll (CHL), Leaf Area Index (LAI), and Gross Primary Productivity (GPP). A long-term analysis of meteorological data such as precipitation and temperature was performed to determine an association between these parameters and mangrove biophysical characteristics. The correlation between meteorological parameters and mangrove biophysical characteristics enabled forecasting of mangrove health and productivity for year 2050 by incorporating IPCC projected climate data. A historical analysis of land cover maps was also performed using Landsat 5 and 8 data to determine changes in mangrove area estimates in years 1995, 2004 and 2017. There was a decrease in dense mangrove extent with an increase in open mangroves and agricultural area. Despite conservation efforts, the current extent of dense mangrove is projected to decrease up to 10% by the year 2050. All three biophysical characteristics including GPP, LAI and CHL, are projected to experience a net decrease of 7.7%, 20.83% and 25.96% respectively by 2050 compared to the mean annual value in 2016. This study will help the Forest Department, Government of Odisha in managing and taking appropriate decisions for conserving and sustaining the remaining mangrove forest under the changing climate and developmental activities.     

A method for monthly mapping of wet and dry snow using Sentinel-1 and MODIS: Application to a Himalayan river basin

Satellite Remote Sensing, with both optical and SAR instruments, can provide distributed observations of snow cover over extended and inaccessible areas. Both instruments are complementary, but there have been limited attempts at combining their measurements. We describe a novel approach to produce monthly maps of dry and wet snow areas through application of data fusion techniques to MODIS fractional snow cover and Sentinel-1 wet snow mask, facilitated by Google Earth Engine. The method is demonstrated in a 55,000 km² river basin in the Indian Himalayan region over a period of ∼2.5 years, although it can be applied to any areas of the world where Sentinel-1 data are routinely available. The typical underestimation of wet snow area by SAR is corrected using a digital elevation model to estimate the average melting altitude. We also present an empirical model to derive the fractional cover of wet snow from Sentinel-1. Finally, we demonstrate that Sentinel-1 effectively complements MODIS as it highlights a snowmelt phase which occurs with a decrease in snow depth but no/little decrease in snowpack area. Further developments are now needed to incorporate these high resolution observations of snow areas as inputs to hydrological models for better runoff analysis and improved management of water resources and flood risk.     

Primitives as building blocks for constructing land cover maps

Land cover maps play an integral role in environmental management. However, countries and institutes encounter many challenges with producing timely, efficient, and temporally harmonized updates to their land cover maps. To address these issues we present a modular Regional Land Cover Monitoring System (RLCMS) architecture that is easily customized to create land cover products using primitive map layers. Primitive map layers are a suite of biophysical and end member maps, with land cover primitives representing the raw information needed to make decisions in a dichotomous key for land cover classification. We present best practices to create and assemble primitives from optical satellite using computing technologies, decision tree logic and Monte Carlo simulations to integrate their uncertainties. The concept is presented in the context of a regional land cover map based on a shared regional typology with 18 land cover classes agreed on by stakeholders from Cambodia, Laos PDR, Myanmar, Thailand, and Vietnam. We created annual map and uncertainty layers for the period 2000–2017. We found an overall accuracy of 94% when taking uncertainties into account. RLCMS produces consistent time series products using free long term historical Landsat and MODIS data. The customizable architecture can include a variety of sensors and machine learning algorithms to create primitives and the best suited smoothing can be applied on a primitive level. The system is transferable to all regions around the globe because of its use of publicly available global data (Landsat and MODIS) and easily adaptable architecture that allows for the incorporation of a customizable assembly logic to map different land cover typologies based on the user's landscape monitoring objectives     

基于Google Earth的1∶50000地质地理底图更新

数字地理底图是地质环境调查不可缺少的组成部分,但现有数据大多现势性较差,如何更新值得探讨。文中提出利用Google Earth进行地理底图更新的方法,并介绍了更新的步骤和精度,该方法简单快捷、可操作性较强,具有一定的实用价值。     

基于SketchUp和ArcGIS的城市三维可视化研究

城市信息三维可视化是构建数字城市的前提和基础,而二、三维GIS平台的一致性使数字城市应用系统具有更广泛的应用前景。对各种三维可视化软件进行分析,并以Google SketchUp和ArcGIS为例,对城市信息三维可视化以及对应的空间分析进行探讨,并研究其相应的三维建模流程与相关技术问题。     

从Google Maps看我国全球化地理信息服务面临的挑战和对策

以Google Maps为代表的地图服务的推出预示着全球化地理信息服务的开始。IT领域竞争市场的形成、高分辨率卫星影像的应用及Web地理信息服务开发平台的实现是全球化地理信息服务的主要趋势。面对全球化地理信息服务发展机遇,我国面临地理数据库建设、地理信息市场社会化与地理信息区域均衡发展的严峻挑战。在分析我国地理信息服务面临挑战的基础上,从数字化建设、地理信息服务市场培育、卫星遥感技术商业化发展3方面提出我国地理信息服务发展的对策与建议。