A Study of Dynamic Visualization of Discrete Spatial-temporal Data on WebGIS Platform

doi:10.6046/gtzyyg.2012.02.26

Download: PDF(1781 KB) HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks

Abstract Most earth observation and monitoring data are discrete spatial - temporal data of massive volumes, and the efficient management, analysis and visualization of these data for decision making support constitute a very challenging task in information system construction. This study combines the latest techniques, such as OpenGL, Ajax and JavaScript on a WebGIS platform to flexibly manage and display various discrete data with fast integrated dynamic mode. The technology developed can be widely applied to monitoring various spatial-temporal discrete data such as environmental pollution diffusion data and making forewarning so as to support emergency decision-making.

Keywords remote sensing Landsat TM enhanced index-based built-up index(EIBI) normalized difference built-up index (NDBI) bare soil index (BSI)

TP 79

Issue Date: 03 June 2012

	Service

	E-mail this article
	E-mail Alert
	RSS
	Articles by authors

	WU Zhi-jie
	ZHAO Shu-he

Cite this article:

WU Zhi-jie,ZHAO Shu-he. A Study of Dynamic Visualization of Discrete Spatial-temporal Data on WebGIS Platform[J]. REMOTE SENSING FOR LAND & RESOURCES, 2012, 24(2): 143-147.

URL:

https://www.gtzyyg.com/EN/10.6046/gtzyyg.2012.02.26 OR https://www.gtzyyg.com/EN/Y2012/V24/I2/143

[1] 李卫东,单新建,张国宏.ArcIMS添加服务器端动态图层技术的实现[J].微计算机信息,2008,24(3):148-150.
[2] 周筠珺,李展,瞿婞,等.基于Google Earth的气象多参数综合显示系统[J].地理空间信息,2010,8(4):16-19.
[3] 李旭文,黎刚,繆蓓蓓.Google Earth和ArcGIS9.2软件在太湖水污染及蓝藻监测数据展现中的应用[J].国土资源遥感,2008(1):97-99.
[4] 刘兆平,杨进,武炜.地球物理数据网格化方法的选取[J].物探与化探,2010,34(1):93-97.
[5] 郭良辉,孟小红,郭志宏,等.地球物理不规则分布数据的空间网格化法[J].物探与化探,2005,29(5):438-442.
[6] Lo S H.Delaunay Triangulation of Non-convex Planar Domains[J].International Journal for Numerical Methods in Engineering,1989,28(11):2695-2707.
[7] Macedonia G,PareschiM T. An Algorithm for the Triangulation of Arbitrarily Distributed Points: Applications to Volume Estimate and Terrain Fitting[J]. Computers & Geosciences,1991,17(7):859-874.
[8] 姚燕,朱江,薛蕾.WebGIS在气象通信信息系统中的应用与研究[J].计算机工程,2008,34(10):271-273.

[1]	LI Weiguang, HOU Meiting. A review of reconstruction methods for remote-sensing-based time series data of vegetation and some examples[J]. Remote Sensing for Natural Resources, 2022, 34(1): 1-9.
[2]	DING Bo, LI Wei, HU Ke. Inversion of total suspended matter concentration in Maowei Sea and its estuary, Southwest China using contemporaneous optical data and GF SAR data[J]. Remote Sensing for Natural Resources, 2022, 34(1): 10-17.
[3]	GAO Qi, WANG Yuzhen, FENG Chunhui, MA Ziqiang, LIU Weiyang, PENG Jie, JI Yanzhen. Remote sensing inversion of desert soil moisture based on improved spectral indices[J]. Remote Sensing for Natural Resources, 2022, 34(1): 142-150.
[4]	ZHANG Qinrui, ZHAO Liangjun, LIN Guojun, WAN Honglin. Ecological environment assessment of three-river confluence in Yibin City using improved remote sensing ecological index[J]. Remote Sensing for Natural Resources, 2022, 34(1): 230-237.
[5]	HE Peng, TONG Liqiang, GUO Zhaocheng, TU Jienan, WANG Genhou. A study on hidden risks of glacial lake outburst floods based on relief amplitude: A case study of eastern Shishapangma[J]. Remote Sensing for Natural Resources, 2022, 34(1): 257-264.
[6]	LIU Wen, WANG Meng, SONG Ban, YU Tianbin, HUANG Xichao, JIANG Yu, SUN Yujiang. Surveys and chain structure study of potential hazards of ice avalanches based on optical remote sensing technology: A case study of southeast Tibet[J]. Remote Sensing for Natural Resources, 2022, 34(1): 265-276.
[7]	WANG Qian, REN Guangli. Application of hyperspectral remote sensing data-based anomaly extraction in copper-gold prospecting in the Solake area in the Altyn metallogenic belt, Xinjiang[J]. Remote Sensing for Natural Resources, 2022, 34(1): 277-285.
[8]	LYU Pin, XIONG Liyuan, XU Zhengqiang, ZHOU Xuecheng. FME-based method for attribute consistency checking of vector data of mines obtained from remote sensing monitoring[J]. Remote Sensing for Natural Resources, 2022, 34(1): 293-298.
[9]	ZHANG Daming, ZHANG Xueyong, LI Lu, LIU Huayong. Remote sensing image segmentation based on Parzen window density estimation of super-pixels[J]. Remote Sensing for Natural Resources, 2022, 34(1): 53-60.
[10]	XUE Bai, WANG Yizhe, LIU Shuhan, YUE Mingyu, WANG Yiying, ZHAO Shihu. Change detection of high-resolution remote sensing images based on Siamese network[J]. Remote Sensing for Natural Resources, 2022, 34(1): 61-66.
[11]	SONG Renbo, ZHU Yuxin, GUO Renjie, ZHAO Pengfei, ZHAO Kexin, ZHU Jie, CHEN Ying. A method for 3D modeling of urban buildings based on multi-source data integration[J]. Remote Sensing for Natural Resources, 2022, 34(1): 93-105.
[12]	AI Lu, SUN Shuyi, LI Shuguang, MA Hongzhang. Research progress on the cooperative inversion of soil moisture using optical and SAR remote sensing[J]. Remote Sensing for Natural Resources, 2021, 33(4): 10-18.
[13]	LI Teya, SONG Yan, YU Xinli, ZHOU Yuanxiu. Monthly production estimation model for steel companies based on inversion of satellite thermal infrared temperature[J]. Remote Sensing for Natural Resources, 2021, 33(4): 121-129.
[14]	LIU Bailu, GUAN Lei. An improved method for thermal stress detection of coral bleaching in the South China Sea[J]. Remote Sensing for Natural Resources, 2021, 33(4): 136-142.
[15]	WU Fang, JIN Dingjian, ZHANG Zonggui, JI Xinyang, LI Tianqi, GAO Yu. A preliminary study on land-sea integrated topographic surveying based on CZMIL bathymetric technique[J]. Remote Sensing for Natural Resources, 2021, 33(4): 173-180.