A novel terminal-processing application for utilizing satellite imagery in Mobile GIS

The good network conditions are of great importance in most current mobile GIS systems for the application of satellite images, as most image processing and analyses are conducted on the server side, whereas the mobile device needs to frequently sent the requests and receive the outcomes of the server through the Internet. However, because of the poor network condition in isolated workplace of fieldworkers, it is difficult for them to have enough interactions with the server side efficiently. Therefore, a new terminal-processing prototype application, RSTools, for handling satellite images on the client side is designed to reduce dependence on good networks. The prototype allows users to conduct real-time operations, covering band math, color composite and rule-based image classifications for multi-source satellite images such as Landsat 8, WorldView2 and GaoFen-1. Compared to traditional remote sensing applications, this application is efficient at reducing not only the interactions times but also the time spent in obtaining the results of image processing in real-time, especially when the data transmission time is considered. As such, if the network condition is poor, the application will be a better choice to efficiently obtain results of satellite images in real-time.

[1]  David M. Aanensen,et al.  EpiCollect: Linking Smartphones to Web Applications for Epidemiology, Ecology and Community Data Collection , 2009, PloS one.

[2]  Youhei Kawamura,et al.  Using GIS to develop a mobile communications network for disaster-damaged areas , 2014, Int. J. Digit. Earth.

[3]  Hardy Pundt,et al.  Domain ontologies for data sharing-an example from environmental monitoring using field GIS , 2002 .

[4]  Douglas C. Schmidt,et al.  R&D challenges and solutions for mobile cyber-physical applications and supporting Internet services , 2010, Journal of Internet Services and Applications.

[5]  Jing Sun,et al.  Distributed and hierarchical object-based image analysis for damage assessment: a case study of 2008 Wenchuan earthquake, China , 2016 .

[6]  Lorena Montoya,et al.  Geo-data acquisition through mobile GIS and digital video: an urban disaster management perspective , 2003, Environ. Model. Softw..

[7]  Xiaoli Wang,et al.  An integrated system based on wireless communication technology and mobile GIS , 2005, Proceedings. 2005 IEEE International Geoscience and Remote Sensing Symposium, 2005. IGARSS '05..

[8]  You-yi Jiang,et al.  Development of mobile GIS system for forest resources second-class inventory , 2011, Journal of Forestry Research.

[9]  B. Gao NDWI—A normalized difference water index for remote sensing of vegetation liquid water from space , 1996 .

[10]  Hao Gong,et al.  An Internet-Based GIS Platform Providing Data for Visualization and Spatial Analysis of Urbanization in Major Asian and African Cities , 2017, ISPRS Int. J. Geo Inf..

[11]  Jianyu Chen,et al.  Automatic extraction of yardangs using Landsat 8 and UAV images: A case study in the Qaidam Basin, China , 2018, Aeolian Research.

[12]  Shian-Shyong Tseng,et al.  Discovering Traffic Bottlenecks in an Urban Network by Spatiotemporal Data Mining on Location-Based Services , 2011, IEEE Transactions on Intelligent Transportation Systems.

[13]  Ming-Hsiang Tsou,et al.  Integrated Mobile GIS and Wireless Internet Map Servers for Environmental Monitoring and Management , 2004 .

[14]  Jianping Wu,et al.  Design and Implementation of a Mobile GIS for Field Data Collection , 2009, 2009 WRI World Congress on Computer Science and Information Engineering.

[15]  Karel Charvat,et al.  Mobile GIS support for remote sensing data interpretation. , 2002 .

[16]  Delu Pan,et al.  Land-Cover Reconstruction and Change Analysis Using Multisource Remotely Sensed Imageries in Zhoushan Islands since 1970 , 2014 .

[17]  M. Tsou,et al.  A Web-Based Java Framework for Cross-Platform Mobile GIS and Remote Sensing Applications , 2005 .

[18]  Felipe Gonzalez,et al.  Autonomous UAV with vision based on-board decision making for remote sensing and precision agriculture , 2017, 2017 IEEE Aerospace Conference.

[19]  Ming-Hsiang Tsou Integrating Web-based GIS and image processing tools for environmental monitoring and natural resource management , 2004, J. Geogr. Syst..

[20]  John Trinder,et al.  Automatic super-resolution shoreline change monitoring using Landsat archival data: a case study at Narrabeen–Collaroy Beach, Australia , 2017 .

[21]  Kumbesan Sandrasegaran,et al.  Multi-Level Indoor Navigation Ontology for High Assurance Location-Based Services , 2017, 2017 IEEE 18th International Symposium on High Assurance Systems Engineering (HASE).

[22]  Lorenzo Bruzzone,et al.  Automatic Spectral Rule-Based Preliminary Mapping of Calibrated Landsat TM and ETM+ Images , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[23]  M. Tsou,et al.  Web-based Remote Sensing Applications and Java Tools for Environmental Monitoring , 2003 .

[24]  Matthew F. McCabe,et al.  High-resolution sensing for precision agriculture: from Earth-observing satellites to unmanned aerial vehicles , 2016, Remote Sensing.

[25]  P. Dong,et al.  Efficient rendering of natural hazards data in mobile GIS , 2016 .

[26]  Erik Persson,et al.  Design, Implementation and Evaluation of a Mobile GIS Solution for a Land Registration Project in Lesotho , 2013 .

[27]  Delu Pan,et al.  Using long time series of Landsat data to monitor impervious surface dynamics: a case study in the Zhoushan Islands , 2013 .

[28]  Ali Asghar Alesheikh,et al.  Developing a Mobile GIS for Field Geospatial Data Acquisition , 2008 .

[29]  Ming-Hsiang Tsou,et al.  Increasing Spatial Awareness by Integrating Internet Geographic Information Services (GIServices) with Real Time Wireless Mobile GIS Applications , 2010, Int. J. Strateg. Inf. Technol. Appl..

[30]  R. Tateishi,et al.  Relationships between percent vegetation cover and vegetation indices , 1998 .

[31]  Jianyu Chen,et al.  Structural Analysis of the Hero Range in the Qaidam Basin, Northwestern China, Using Integrated UAV, Terrestrial LiDAR, Landsat 8, and 3-D Seismic Data , 2015, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[32]  Jay Gao,et al.  Use of normalized difference built-up index in automatically mapping urban areas from TM imagery , 2003 .