JIA-2018-09

1920 Yanbo Huang et al. Journal of Integrative Agriculture 2018, 17(9): 1915–1931 3.3. Remote sensing data visualization Visualization of remote sensing data and products are critical for users to interpret and analyze. GIS as a platform of remote sensing data visualization has been developing in the last decade in four aspects (Song 2008): • Modularization Modular GIS is organized of components with certain standards and protocols. • Web enabling Web GIS (Fu and Sun 2010) has been developed to publish geospatial data for users to view, query and analyze through Internet. • Miniaturization and mobility Although desktop GIS applications still dominate, mobile GIS clients have been adopted with personal digital assistant (PDA), tablets and smart phones. • Data-based GIS spatial data management has been developed from flat file management, file/database management, to spatial database management. Spatial data management provides the capabilities of massive data management, multi-user co-current operation, data visit permission management, and co-current visit and systematic applicability of database clusters. The integration of remote sensing data with GIS has been developed in the past two decades. Techniques such as machine learning and deep learning offer great potential for better extraction of geographical information from remote sensing data and images. However, issues remain as data organization, algorithm construction and error and uncertainty handling. With the increased volume and complexity of remote sensing data acquired from multiple sensors using multispectral and hyperspectral devices with multi-angle views with the time, new development is needed for visualization tools with spatial, spectral and temporal analysis (Wilkinson 1996; Chen and Zhang 2014). 3.4. Remote sensing data management The generalization, standardization and serialization of remote sensing data and loads are the inevitable trend in future remote sensing technological development. It is the basis for solving the problem of inconsistent remote sensing data. It is also the prerequisite for promoting the application of remote sensing big data. At present, remote sensing satellites are developed and operated by independent institutions or commercial companies; therefore, basically all the satellites have developed their own product system standards, but the lack of a set of a unified product standard system has resulted in misperception of data and hindered development of remote sensing data applications. The technical committee of Geographic Information/ Geomatics of International Organization for Standardization (ISO/TC 211), Defense Geospatial Information Working Group (DGIWC), American National Standards Institute (ANSI), Federal Geographic Data Committee (FGDC), and German Institute for Standardization (DIN) all have established and published standards related to remote sensing data. Examples are <<ISO/TS 19101-2 Geographic information - Reference model - Part 2: Imagery>>, <<ISO/TS 19131 Geographic information - Data product specifications>>, <<ISO/DIS 19144-1 Geographic information - Classification systems - Part 1: Classification system structure>>, <<ISO/ RS 19124 Geographic information - Imagery and gridded data components>> and <<ISO 19115 Geographic information -Metadata>>. However, more standards are needed for consistent applications of remote sensing data frommultiple sources. For geometric information retrieval, MODIS creates a MOD 03 file of geolocation data to store latitude/longitude information corresponding to each pixel in addition to the MOD 02 file of calibrated geolocated radiance. The image of each MODIS scene covers fairly large area. If a specific geographic region is the focus, the data of the whole image to cover a large more area has to be loaded and mapped to the surface of the 3D sphere of the earth. This leads to unnecessary large-volume data handling, difficult data visualization at different levels and scales, and ineffective data transmission. Furthermore, mapping of MODIS images to the sphere in general only uses image four corners as reference points so that mapped images often have relatively large geometric deformation. In order to solve the problem of image mapping on the 3D sphere, the earth surface can be divided into blocks. In each block the pyramid of images is created and the scene can be visualized with image blocks at different resolutions with the altitude of viewing point. Widely used World Wind (NASA), GoogleEarth (Google Inc., Mountain View, CA) and BingMaps (Microsoft) visualize geospatial data through such a method. For example, World Wind, which is more specialized with remote sensing data, expands the 3D sphere into a 2D flat map through Plate Carrée projection (Snyder 1993). Then, the flat map is cut into blocks globally. Map cutting occurs through the division of the map evenly at different levels. The first level has the cutting interval of 36°. The second level has the cutting interval of half of the first level, i.e., 18°. And so on, each follow-up level has the interval of half of the previous level. In this way with the global projection of 3D sphere to 2D flat map, the longitude is as the horizontal axis with the range of –180° to +180° and the latitude is as the vertical axis with the range of