Vol. 8 No. 2 (2019)

Open Access

Original Research Articles

Article ID: 883

Discussion on Remote Sensing Big Data to Promote the Development of Smart City

by Ying Jiang, Jian Yin, Libin Zha

Remote Sensing, Vol.8, No.2, 2020; 511 Views, 25 PDF Downloads

With the development and application of Internet technology, cloud computing, big data technology, Internet of things technology and other new generation information technology, smart city has gradually become the focus of global urban development. Remote sensing technology big data is the combination of remote sensing technology and big data technology. Remote sensing technology has the characteristics of long-distance, non-contact detection and wide coverage. And the data information collected by remote sensing equipment is analyzed by using big data technology to improve the application value of remote sensing technology. This paper first describes the characteristics of remote sensing big data and the connotation of smart city, and that the remote sensing big data technology can promote the intelligent supervision of urban pollution, urban planning, urban traffic intelligent response, and construction more reasonable and humanized, then it can help realize the development of urban traffic intelligent.

Open Access

Original Research Articles

Article ID: 1094

A DroneGo Disaster Relief Response System

by Ge Zhang, Kangli Ma, Chang Liu

Remote Sensing, Vol.8, No.2, 2020; 172 Views, 8 PDF Downloads

To support the Puerto Rico hurricane disaster scenario, we develop a DroneGo disaster response system by establishing the following models. First, we establish a location analysis model for ISO containers based on the coverage of video reconnaissance and the priority comparison between the two required missions–medical supply delivery and video reconnaissance. According to the locations of 11 harbors in Puerto Rico, we select three suitable harbors to position three cargo containers called CON 1, 2 and 3 to conduct the missions. Second, we build two packing configuration models to design the packing configuration for containers. In one model, we recommend a drone fleet for CON 1 and 3 according to reconnaissance conditions, and then put drones into containers in order. In another model for CON 2, we determine the type of drones according to the medical supply demands of hospitals. For both models, the number of drones of each type is determined by the enumeration method and the packing placement is determined by the greedy algorithm. The algorithms are coded in Visual C++ and MATLAB. The computational results show that the space utilizations for the three containers are all above 80.8%. Third, we design a drone flight plan model based on graph theory. According to the time and space constraints of drones, we devise flight plans as well as delivery routes and schedule. The computational results show that the coverage of video reconnaissance is up to 70.1%. Finally, we carry out the error and sensitivity analysis, discuss the strengths and weaknesses of our models, and design the future work. In addition, a two-page memo that summarizes our modeling results, conclusions, and recommendations is given at the end of the paper.

Open Access

Original Research Articles

Article ID: 1097

Summary of Agricultural Application of Radar Remote Sensing

by Sicheng Li

Remote Sensing, Vol.8, No.2, 2020; 255 Views, 15 PDF Downloads

Radar remote sensing has the ability of all-day and all-weather monitoring, has certain penetration ability to vegetation, and is sensitive to the shape, structure and dielectric constant of vegetation scatterers. These characteristics make it have great potential in agricultural application. Firstly, this paper introduces the application fields of radar remote sensing in agriculture, and summarizes the current research literature in many fields, such as crop identification and classification, farmland soil moisture inversion, crop growth monitoring and so on. Then, the application status and research achievements of radar scatterometer and various SAR features (including SAR backscattering features, polarization features, interference features and tomography features) in various fields of agriculture are described respectively. Finally, the problems and reasons existing in the current research are summarized according to the agricultural application requirements and the development of SAR technology, and the future development is prospected.