Satellite and UAV Platforms, Remote Sensing for Geographic Information Systems
Department of Geographical and Historical Studies, University of Eastern Finland, Yliopistokatu 7, P.O. Box 111, 80101 Joensuu, Finland
Sensors 2022, 22(12), 4564; https://doi.org/10.3390/s22124564
Submission received: 14 June 2022
/
Accepted: 15 June 2022
/
Published: 17 June 2022
(This article belongs to the Special Issue Satellite and UAV Platforms, Remote Sensing for Geographic Information Systems)
Satellite and UAV (unmanned aerial vehicle) imagery has become an important source of data for Geographic Information Systems (GISs). Enormous volumes of remote sensing and GIS data are nowadays produced, stored and analyzed in high-capacity network-based geoinformatics systems. Satellite imagery in a wide range of spatial, spectral, and temporal resolutions provides the scientific community with rapidly available global data to be used as an integral part of spatial data structures and analyses. Remote sensing platforms, such as Modis and Landsat, have a unique historical record of providing tens of years of uninterrupted global data, provided by repositories such as NASA and ESA.
For local applications, the rapid evolution of unmanned aerial vehicles and lightweight sensors has provided the scientific community with a tool for acquiring data of extremely high resolution, covering areas that vary from several hectares to hundreds of square kilometers in size. These data can be used for precision farming, forestry, and environmental monitoring.
The present Special Issue on the integration of UAV and satellite imagery with GIS contains ten articles, which can be divided into three parts: UAV applications, satellite remote sensing and methodological work using RS data.
Three articles deal with pure UAV applications, two applying UAV for agricultural crop monitoring [1,2] and one paper [3] using GIS and computer vision to analyze UAV orthomosaics. The planning of high-altitude long-endurance pseudo-satellite missions is dealt with in this paper [4].
The integration of satellite RS data in GIS systems for vegetation monitoring is used in three papers: one paper dealing with winter stress on arctic understory vegetation [5], one on the application of Copernicus (CMEMS GlobColour-Merged CHL-OC5 Satellite Observations) satellite-derived data concerning the aquatic environment [6] and one on the application of MODIS NDVI data and GIS to assess the effect of wildlife upon tropical savannah vegetation [7].
The problem of mosaicking multiple high-resolution orthoimages (UAV or satellite) is dealt with in paper [8], introducing a novel method based upon the D-LinkNet Neural Network.
Precise satellite telemetry data can be used to monitor the minute deformations occurring in the Earth’s crust. The paper by Jagoda et al. describes the use of high-precision laser altimetry data to assess the deformation of the Earth’s crust caused by external planetary bodies (tidal forces) [9]. The last paper uses data derived from global navigation satellite systems (GNSS) to monitor the crustal deformation of the Earth [10].
Funding
This research received no external funding.
Conflicts of Interest
The author declare no conflict of interest.
References
- Cheng, Z.; Meng, J.; Shang, J.; Liu, J.; Huang, J.; Qiao, Y.; Qian, B.; Jing, Q.; Dong, T.; Yu, L. Generating Time-Series LAI Estimates of Maize Using Combined Methods Based on Multispectral UAV Observations and WOFOST Model. Sensors 2020, 20, 6006. [Google Scholar] [CrossRef] [PubMed]
- Qi, G.; Zhao, G.; Xi, X. Soil Salinity Inversion of Winter Wheat Areas Based on Satellite-Unmanned Aerial Vehicle-Ground Collaborative System in Coastal of the Yellow River Delta. Sensors 2020, 20, 6521. [Google Scholar] [CrossRef] [PubMed]
- Kentsch, S.; Cabezas, M.; Tomhave, L.; Groß, J.; Burkhard, B.; Lopez Caceres, M.; Waki, K.; Diez, Y. Analysis of UAV-Acquired Wetland Orthomosaics Using GIS, Computer Vision, Computational Topology and Deep Learning. Sensors 2021, 21, 471. [Google Scholar] [CrossRef] [PubMed]
- Kiam, J.; Besada-Portas, E.; Schulte, A. Hierarchical Mission Planning with a GA-Optimizer for Unmanned High Altitude Pseudo-Satellites. Sensors 2021, 21, 1630. [Google Scholar] [CrossRef] [PubMed]
- Ritz, E.; Bjerke, J.; Tømmervik, H. Monitoring Winter Stress Vulnerability of High-Latitude Understory Vegetation Using Intraspecific Trait Variability and Remote Sensing Approaches. Sensors 2020, 20, 2102. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Gbagir, A.; Colpaert, A. Assessing the Trend of the Trophic State of Lake Ladoga Based on Multi-Year (1997–2019) CMEMS GlobColour-Merged CHL-OC5 Satellite Observations. Sensors 2020, 20, 6881. [Google Scholar] [CrossRef] [PubMed]
- Gbagir, A.; Sikopo, C.; Matengu, K.; Colpaert, A. Assessing the Impact of Wildlife on Vegetation Cover Change, Northeast Namibia, Based on MODIS Satellite Imagery (2002–2021). Sensors 2022, 22, 4006. [Google Scholar] [CrossRef] [PubMed]
- Yuan, S.; Yang, K.; Li, X.; Cai, H. Automatic Seamline Determination for Urban Image Mosaicking Based on Road Probability Map from the D-LinkNet Neural Network. Sensors 2020, 20, 1832. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Jagoda, M.; Rutkowska, M.; Lejba, P.; Katzer, J.; Obuchovski, R.; Šlikas, D. Satellite Laser Ranging for Retrieval of the Local Values of the Love h2 and Shida l2 Numbers for the Australian ILRS Stations. Sensors 2020, 20, 6851. [Google Scholar] [CrossRef] [PubMed]
- Jagoda, M. Determination of Motion Parameters of Selected Major Tectonic Plates Based on GNSS Station Positions and Velocities in the ITRF2014. Sensors 2021, 21, 5342. [Google Scholar] [CrossRef] [PubMed]
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2022 by the author. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
MDPI and ACS Style
Colpaert, A. Satellite and UAV Platforms, Remote Sensing for Geographic Information Systems. Sensors 2022, 22, 4564. https://doi.org/10.3390/s22124564
AMA Style
Colpaert A. Satellite and UAV Platforms, Remote Sensing for Geographic Information Systems. Sensors. 2022; 22(12):4564. https://doi.org/10.3390/s22124564
Chicago/Turabian StyleColpaert, Alfred. 2022. "Satellite and UAV Platforms, Remote Sensing for Geographic Information Systems" Sensors 22, no. 12: 4564. https://doi.org/10.3390/s22124564
APA StyleColpaert, A. (2022). Satellite and UAV Platforms, Remote Sensing for Geographic Information Systems. Sensors, 22(12), 4564. https://doi.org/10.3390/s22124564
Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.
