Gravity gradient modeling using gravity and DEM

A model of the gravity gradient tensor at aircraft altitude is developed from the combination of ground gravity anomaly data and a digital elevation model. The gravity data are processed according to various operational solutions to the boundary-value problem (numerical integration of Stokes’ integral, radial-basis splines, and least-squares collocation). The terrain elevation data are used to reduce free-air anomalies to the geoid and to compute a corresponding indirect effect on the gradients at altitude. We compare the various modeled gradients to airborne gradiometric data and find differences of the order of 10–20 E (SD) for all gradient tensor elements. Our analysis of these differences leads to a conclusion that their source may be primarily measurement error in these particular gradient data. We have thus demonstrated the procedures and the utility of combining ground gravity and elevation data to validate airborne gradiometer systems.     

利用知识图谱的国土资源数据管理与检索研究

针对国土资源不同数据产品间难以进行有效管理与快速应用的问题,研究利用图数据库对GlobaLand30、FROM-GLC10_2017、GLC_FCS30_2020等公开土地覆盖数据集进行语义层面的结构化存储,建立中国国土资源知识图谱。构建以行政区划为单位进行土地覆盖数据产品管理、知识提取以及数据获取与更新的新型应用框架,利用基于图的异常数据检索算法探究不同产品间的一致性,提出了一种基于知识图谱的感兴趣图节点快速检索算法。通过引入知识图谱,形成了具有447 817个节点、447 816条关系,且可动态更新的中国国土资源知识图谱,并发现了在覆盖全国的2 875个行政单元中有92个区域单元的产品数据一致性不足60%,区域产品精度可能存在较大误差。充分利用了多源土地覆盖数据产品间的信息,缩短了数据预处理的时间,为中国国土资源的知识化管理与应用提供了新思路。     

Geothopica and the interactive analysis and visualization of the updated Italian National Geothermal Database

The Italian National Geothermal Database (BDNG), is the largest collection of Italian Geothermal data and was set up in the 1980s. It has since been updated both in terms of content and management tools: information on deep wells and thermal springs (with temperature > 30 °C) are currently organized and stored in a PostgreSQL relational database management system, which guarantees high performance, data security and easy access through different client applications. The BDNG is the core of the Geothopica web site, whose webGIS tool allows different types of user to access geothermal data, to visualize multiple types of datasets, and to perform integrated analyses. The webGIS tool has been recently improved by two specially designed, programmed and implemented visualization tools to display data on well lithology and underground temperatures.This paper describes the contents of the database and its software and data update, as well as the webGIS tool including the new tools for data lithology and temperature visualization. The geoinformation organized in the database and accessible through Geothopica is of use not only for geothermal purposes, but also for any kind of georesource and CO₂ storage project requiring the organization of, and access to, deep underground data. Geothopica also supports project developers, researchers, and decision makers in the assessment, management and sustainable deployment of georesources.     

Digitized global land-sea map and access software

A computer-efficient global data file, which contains digitized information that enables identification of a given latitude/longitude defined point as over land or over water, was generated from a data base which defines the world's shoreline. The method used in the generation of this land-sea boundary data map and its data structure are discussed. The data file was originally generated on a Control Data Corporation(CDC) computer, but it has been transported to other computer systems, includingIBM, DEC/VAX, UNIVAC and Cray computers. The land-sea boundary map also includes information on islands and inland lakes. The resolution of this map is 5′×5′ or an equivalent of9 km square surface blocks at the equator. The software to access this data base is structured to be easily transportable to different computers. This data base was used in the generation of the Seasat Geophysical Data Record(GDR) to identify whether a spaceborne radar altimeter measurement was over-land or over-ocean.     

HJ-1A CCD与TM数据及其估算草地LAI和鲜生物量效果比较分析

基于地面实测和PROSAIL模型模拟数据,研究了新型传感器HJ-1ACCD与TM数据一致性问题,分析了传感器天顶角和光谱相应函数差异的影响,对比两种传感器数据估算草地LAI和鲜生物量的效果,得出以下结论:(1)HJ-1ACCD和TM反射率数据的拟合系数R2在0.7322和0.9205左右,在反射率较小时,两种传感器数据一致性较好;随着反射率增大,HJ-1ACCD数值逐渐高于TM。总体而言,在可见光和近红外波段,两种传感器较为接近,其中红波段最接近。(2)两种传感器的NDVI数据一致性非常高,且受传感器天顶角和光谱响应函数影响作用较小(相对误差约为0.34%—0.53%),而反射率的相对差别在3.34%—9.54%。(3)传感器天顶角较光谱响应函数对反射率影响更大。(4)基于HJ-1ACCD反射率数据估算草地LAI和鲜生物量效果较好,其中以CCD2传感器估算效果最好。     

Forest classification and impact of BIOMASS resolution on forest area and aboveground biomass estimation

The European Space Agency (ESA) is currently implementing the BIOMASS mission as 7th Earth Explorer satellite. BIOMASS will provide for the first time global forest aboveground biomass estimates based on P-band synthetic aperture radar (SAR) imagery. This paper addresses an often overlooked element of the data processing chain required to ensure reliable and accurate forest biomass estimates: accurate identification of forest areas ahead of the inversion of radar data into forest biomass estimates.The use of the P-band data from BIOMASS itself for the classification into forest and non-forest land cover types is assessed in this paper. For airborne data in tropical, hemi-boreal and boreal forests we demonstrate that classification accuracies from 90 up to 97% can be achieved using radar backscatter and phase information. However, spaceborne data will have a lower resolution and higher noise level compared to airborne data and a higher probability of mixed pixels containing multiple land cover types. Therefore, airborne data was reduced to 50 m, 100 m and 200 m resolution. The analysis revealed that about 50–60% of the area within the resolution level must be covered by forest to classify a pixel with higher probability as forest compared to non-forest. This results in forest omission and commission leading to similar forest area estimation over all resolutions. However, the forest omission resulted in a biased underestimated biomass, which was not equaled by the forest commission. The results underline the necessity of a highly accurate pre-classification of SAR data for an accurate unbiased aboveground biomass estimation.     

Multidimensional and quantitative interlinking approach for Linked Geospatial Data

ABSTRACT

Linked Data is known as one of the best solutions for multisource and heterogeneous web data integration and discovery in this era of Big Data. However, data interlinking, which is the most valuable contribution of Linked Data, remains incomplete and inaccurate. This study proposes a multidimensional and quantitative interlinking approach for Linked Data in the geospatial domain. According to the characteristics and roles of geospatial data in data discovery, eight elementary data characteristics are adopted as data interlinking types. These elementary characteristics are further combined to form compound and overall data interlinking types. Each data interlinking type possesses one specific predicate to indicate the actual relationship of Linked Data and uses data similarity to represent the correlation degree quantitatively. Therefore, geospatial data interlinking can be expressed by a directed edge associated with a relation predicate and a similarity value. The approach transforms existing simple and qualitative geospatial data interlinking into complete and quantitative interlinking and promotes the establishment of high-quality and trusted Linked Geospatial Data. The approach is applied to build data intra-links in the Chinese National Earth System Scientific Data Sharing Network (NSTI-GEO) and data -links in NSTI-GEO with the Chinese Meteorological Data Network and National Population and Health Scientific Data Sharing Platform.     

GNSS data management and processing with the GPSTk

We organize complex problems in simple ways using a GNSS data management strategy based on “GNSS Data Structures” (GDS), coupled with the open source “GPS Toolkit” (GPSTk) suite. The code resulting from using the GDS and their associated “processing paradigm” is remarkably compact and easy to follow, yielding better code maintainability. Furthermore, the data abstraction allows flexible handling of concepts beyond mere data encapsulation, including programmable general solvers. An existing GPSTk class can be modified to achieve the goal. We briefly describe the “GDS paradigm” and show how the different GNSS data processing “objects” may be combined in a flexible way to develop data processing strategies such as Precise Point Positioning (PPP) and network-based PPP that computes satellite clock offsets on-the-fly.     

Global geospatial data from Earth observation: status and issues

ABSTRACT

Data covering the whole of the surface of the Earth in a homogeneous and reliable manner has been accumulating over many years. This type of data became available from meteorological satellites from the 1960s and from Earth-observing satellites at a small scale from the early 1970s but has gradually accumulated at larger scales up to the present day when we now have data covering many environmental themes at large scales. These data have been used to generate information which is presented in the form of global data sets. This paper will give a brief introduction to the development of Earth observation and to the organisations and sensors which collect data and produce global geospatial data sets. Means of accessing global data sets will set out the types of data available that will be covered. Digital elevation models are discussed in a separate section because of their importance in georeferencing image data as well as their application to analysis of thematic data. The paper will also examine issues of availability, accuracy, validation and reliability and will look at future challenges.     

Big spatial data for urban and environmental sustainability

ABSTRACT

Eighty percent of big data are associated with spatial information, and thus are Big Spatial Data (BSD). BSD provides new and great opportunities to rework problems in urban and environmental sustainability with advanced BSD analytics. To fully leverage the advantages of BSD, it is integrated with conventional data (e.g. remote sensing images) and improved methods are developed. This paper introduces four case studies: (1) Detection of polycentric urban structures; (2) Evaluation of urban vibrancy; (3) Estimation of population exposure to PM2.5; and (4) Urban land-use classification via deep learning. The results provide evidence that integrated methods can harness the advantages of both traditional data and BSD. Meanwhile, they can also improve the effectiveness of big data itself. Finally, this study makes three key recommendations for the development of BSD with regards to data fusion, data and predicting analytics, and theoretical modeling.     

利用哈尔变换和高斯随机数进行矢量空间数据坐标加密

基于安全保密的考虑,需要对矢量空间数据进行加密,现有做法是对数据文件整体进行加密,会破坏矢量空间数据结构并影响属性数据的查看。提出了一种不改变矢量空间数据结构,仅对坐标数据加密的方法,能够保护数据的安全且矢量数据结构依然保持不变。运用SHA-512加密用户密钥得到哈希密钥,用高斯随机数置乱哈希密钥生成用来加密坐标数据的密钥。首先读取矢量空间数据的顶点序列,并对矢量数据的顶点坐标序列进行哈尔变换,使用上述密钥对哈尔变换后的均值系数和差值系数进行加密,再实施逆哈尔变换得到加密后坐标,使用高斯随机数置乱顶点序列得到加密后的矢量空间数据。实验结果表明,矢量空间数据的坐标被加密,但文件结构及属性数据完全保持不变,且运行效率高;拥有密钥的用户还可以解密坐标,还原出原始矢量空间数据,安全性高。     

融合高时空分辨率数据估算植被净初级生产力

植被净初级生产力NPP(Net Primary Production)遥感估算与分析,有赖于高时空分辨率的遥感数据,但目前中高分辨率的遥感数据受卫星回访周期及天气的影响,在中国南方地区难以获取连续时间序列的数据,从而影响了高精度的区域植被净初级生产力的遥感估算。为此,提出一种基于多源遥感数据时空融合技术与CASA模型估算高时空分辨率NPP的方法。首先,利用多源遥感数据,即Landsat8 OLI数据与MODIS13Q1数据,采用遥感数据时空融合方法,获得了时间序列的Landsat8 OLI融合数据;然后,基于Landsat8 OLI时空融合数据,并采用CASA模型,以长株潭城市群核心区为例,进行区域植被NPP的遥感估算。研究结果表明,基于时间序列Landsat融合数据估算的30m分辨率的NPP具有良好的空间细节信息,且估算值与实测值的相关系数达0.825,与实测NPP数据保持了较好的一致性。     

Blending Aggregation and Selection: Adapting Parallel Coordinates for the Visualization of Large Datasets

Abstract

Many of the traditional data visualization techniques, which proved to be supportive for exploratory analysis of datasets of moderate sizes, fail to fulfil their function when applied to large datasets. There are two approaches to coping with large amounts of data: data selection, when only a portion of data is displayed, and data aggregation, i.e. grouping data items and considering the groups instead of the original data. None of these approaches alone suits the needs of exploratory data analysis, which requires consideration of data on all levels: overall (considering a dataset as a whole), intermediate (viewing and comparing collective characteristics of arbitrary data subsets, or classes), and elementary (accessing individual data items). Therefore, it is necessary to combine these approaches, i.e. build a tool showing the whole set and arbitrarily defined subsets (object classes) in an aggregated way and superimposing this with a representation of arbitrarily selected individual data items.

We have achieved such a combination of approaches by modifying the technique of parallel coordinate plot. These modifications are described and analysed in the paper.     

Mapping mangrove forests using multi-tidal remotely-sensed data and a decision-tree-based procedure

Mangrove forests grow in intertidal zones in tropical and subtropical regions and have suffered a dramatic decline globally over the past few decades. Remote sensing data, collected at various spatial resolutions, provide an effective way to map the spatial distribution of mangrove forests over time. However, the spectral signatures of mangrove forests are significantly affected by tide levels. Therefore, mangrove forests may not be accurately mapped with remote sensing data collected during a single-tidal event, especially if not acquired at low tide. This research reports how a decision-tree −based procedure was developed to map mangrove forests using multi-tidal Landsat 5 Thematic Mapper (TM) data and a Digital Elevation Model (DEM). Three indices, including the Normalized Difference Moisture Index (NDMI), the Normalized Difference Vegetation Index (NDVI) and NDVI_L·NDMI_H (the multiplication of NDVI_L by NDMI_H, L: low tide level, H: high tide level) were used in this algorithm to differentiate mangrove forests from other land-cover and land-use types in Fangchenggang City, China. Additionally, the recent Landsat 8 OLI (Operational Land Imager) data were selected to validate the results and compare if the methodology is reliable. The results demonstrate that short-term multi-tidal remotely-sensed data better represent the unique nearshore coastal wetland habitats of mangrove forests than single-tidal data. Furthermore, multi-tidal remotely-sensed data has led to improved accuracies using two classification approaches: i.e. decision trees and the maximum likelihood classification (MLC). Since mangrove forests are typically found at low elevations, the inclusion of elevation data in the two classification procedures was tested. Given the decision-tree method does not assume strict data distribution parameters, it was able to optimize the application of multi-tidal and elevation data, resulting in higher classification accuracies of mangrove forests. When using multi-source data of differing types and distributions to map mangrove forests, a decision-tree method appears to be superior to traditional statistical classifiers.     

Challenges and strategies for the visual exploration of complex environmental data

ABSTRACT

In this opinion paper, we, a group of scientists from environmental-, geo-, ocean- and information science, argue visual data exploration should become a common analytics approach in Earth system science due to its potential for analysis and interpretation of large and complex spatio-temporal data. We discuss the challenges that appear such as synthesis of heterogeneous data from various sources, reducing the amount of information and facilitating multidisciplinary, collaborative research. We argue that to fully exploit the potential of visual data exploration, several bottlenecks and challenges have to be addressed: providing an efficient data management and an integrated modular workflow, developing and applying suitable visual exploration concepts and methods with the help of effective and tailored tools as well as generating and raising the awareness of visual data exploration and education. We are convinced visual data exploration is worth the effort since it significantly facilitates insight into environmental data and derivation of knowledge from it.     

大数据视角下的地矿工作发展与变革研究

本文提出了地矿大数据应用需要考虑地球系统和社会系统两个方面的内部规律,分析了地矿大数据的三类组成,给出了地矿大数据的四层应用架构;重点就大数据下地矿工作发展和变革提出了四项建议,包括重视数据,转变地矿工作决策模式;收集数据,夯实地矿工作决策基础;分析数据,提高地矿工作科学决策水平;开放数据,提高地矿工作服务能力和水平。     

Fast spherical collocation: theory and examples

 It has long been known that a spherical harmonic analysis of gridded (and noisy) data on a sphere (with uniform error for a fixed latitude) gives rise to simple systems of equations. This idea has been generalized for the method of least-squares collocation, when using an isotropic covariance function or reproducing kernel. The data only need to be at the same altitude and of the same kind for each latitude. This permits, for example, the combination of gravity data at the surface of the Earth and data at satellite altitude, when the orbit is circular. Suppose that data are associated with the points of a grid with N values in latitude and M values in longitude. The latitudes do not need to be spaced uniformly. Also suppose that it is required to determine the spherical harmonic coefficients to a maximal degree and order K. Then the method will require that we solve K systems of equations each having a symmetric positive definite matrix of only N × N. Results of simulation studies using the method are described. Received: 18 October 2001 / Accepted: 4 October 2002 Correspondence to: F. Sansò     

Exploratory Analysis of Spatial Data Using Interactive Maps and Data Mining

We present new methods for analyzing geo-referenced statistical data. These methods combine visualization and direct manipulation techniques of exploratory data analysis and algorithms for data mining. The methods have been implemented by integrating two hitherto separate software tools: Descartes for interactive thematic mapping, and the data mining toolbox Kepler. In using these tools, data analysis may proceed as a steady interaction between visual inspiration and insights gained from mathematical–statistical calculations. After introducing the various components of the methods and tools, the paper guides the reader through in-depth examples of using the tools in the context of analysis of urban demographic data. In particular, it is shown how geography-based classifications of urban districts can be related to available thematic characteristics by applying the data mining algorithms classification tree derivation, attribute weighting, and subgroup discovery.     

Reviewing the discoverability and accessibility to data and information products linked to Essential Climate Variables

ABSTRACT

Essential Climate Variables (ECVs) are geophysical records generated from systematic Earth Observations associated with climate variations, changes, and impacts. ECVs products support the data and information needs of international frameworks and policies such as the work of the United Nations Framework Convention on Climate Change (UNFCCC) and the Intergovernmental Panel on Climate Change (IPCC). We map the main networks and initiatives publishing ECVs, by presenting an overview of existing satellite-based ECVs, their general data creation characteristics, discoverability and accessibility methods from an end-user perspective. We investigate key initiatives providing or coordinating access to ECV data records, such as the Global Climate Observing System (GCOS), the Committee on Earth Observation Satellites (CEOS), the Coordination Group for Meteorological Satellites (CGMS), Joint Working Group on Climate (WGClimate), the Remote Sensing Systems (REMSS), and the European Space Agency Climate Change Initiative (ESA CCI). We find that ECV data discovery and access is difficult and time consuming due to the lack of common data and metadata catalogues. In addition, the selection of fit-for-purpose data records by end-users requires the implementation of interoperable standards and scalable data infrastructures to allow the generation of tailored applications and data-driven information products in support of decision-making processes.     

Analysis of temporal sar and optical data for rice mapping

This study investigates the potential of multi-temporal signature analysis of satellite imagery to map rice area in South 24 Paraganas district of West Bengal. Two optical data (IRS ID LISS III) and three RADARSAT SAR data of different dates were acquired during 2001. Multi-temporal SAR backscatter signatures of different landcovers were incorporated into knowledge based decision rules and kharif landcover map was generated. Based on the spectral variation in signature, the optical data acquired during rabi (January) and summer (March) season were classified using supervised maximum likelihood classifier. A co-incidence matrix was generated using logical approach for a combined “rabi-summer” and “kharif-rabi-summer” landcover mapping. The major landcovers obtained in South 24 Paraganas using remote sensing data are rice, water, aquaculture ponds, homestead, mangrove, and urban area. The classification accuracy of rice area was 98.2% using SAR data. However, while generating combined “kharif-rabi-summer” landcovers, the classification accuracy of rice area was improved from 81.6% (optical data) to 96.6% (combined SAR-Optical). The primary aim of the study is to achieve better accuracy in classifying rice area using the synergy between the two kinds of remotely sensed data.