Special Issue "Remote Sensing in the Age of Electronic Ecology"

A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: closed (30 June 2019).

Special Issue Editors

Dr. Aditya Singh
Guest Editor
Department of Agricultural and Biological Engineering, University of Florida, Gainesville,USA
Interests: imaging spectroscopy; canopy traits; land cover change; biodiversity
Dr. Zoltan Szantoi
Guest Editor
European Commission Joint Research Centre, Copernicus Global Land Service, Italy
Interests: object-based image analysis; machine learning; hyperspectral and multispectral image analysis; land cover mapping; change detection; big data analysis
Special Issues and Collections in MDPI journals
Dr. Peter Wolter
Guest Editor
Associate Professor, Department of Natural Resource Ecology and Management, Iowa State University, 2310 Pammel Dr. Ames, IA 50011, USA
Interests: forest ecosystem monitoring; remote sensing; forest structure; forest fire
Special Issues and Collections in MDPI journals
Dr. Philip Townsend
Guest Editor
Professor, Department of Forest and Wildlife Ecology, University of Wisconsin-Madison. 1630 Linden Dr. Madison, WI 53706, USA
Interests: forest ecology; remote sensing; imaging spectroscopy; foliar biochemistry; plant metabolism and function

Special Issue Information

Dear Colleagues,

Recent advances in sensor miniaturization have expanded our ability of collecting high-quality environmental data in unprecedented amounts. For example, the merging of data from field-based sensor networks has enabled the translation of ideas, such as the farm-of-the-future into large-scale agricultural operation management systems. Not surprisingly, the development of such prototypes has further spawned the rapid development, commercialization, and deployment of sensors that have the ability to monitor multiple indicators of plant health and performance. We now have the ability to monitor disparate ecophysiological traits, including canopy chemistry and microclimate, soil health and nutrient status, and leaf wetness and soil moisture content on a near real-time basis.

Indeed, we are at a juncture where data streams from sensor networks can be collected into a focused ‘Internet of plants’ similar to the overarching ecosystem of the Internet of Things (IoT). Concurrent advances in remote sensing sensor systems such as micro-hyperspectral, LiDAR, and thermal imagers mounted on small unmanned aircraft systems (sUASs) continue to help bridge gaps between field observations and satellite sensor data. At landscape scales as well, moderate resolution data from spaceborne systems is increasingly being made freely available from the ESA Copernicus and NASA Landsat Data Continuity Missions at 5-day revisit cycles with expanded radiometric characteristics (i.e., narrow red, red-edge bands). Availability of these data are a further boost to the possibility of scaling critical observations of plant health and performance from field-based sensors or sUASs to entire landscapes.

While these developments have immensely expanded avenues of ecological and agricultural research into many novel arenas, scaling observations from individual plants or plots to entire fields or ecosystems remain an active field of research. This Special Issue will focus on recent advances in remote sensing technologies and applications that cover these upcoming avenues.

We solicit reviews and original manuscripts on topics ranging from the use of remote sensing techniques for assessment of vegetation health and productivity, the development of novel algorithms and techniques for fusing data from visual, multispectral, spectroscopic, thermal and LiDAR imagery, and the development of near-real-time integrated observation and monitoring frameworks for large interconnected systems. In addition to manuscripts demonstrating the use of such systems in operational research, we encourage submissions of proof-of-concept papers on the integrated use of remotely sensed imagery with data streams from novel environmental sensor systems.

Dr. Aditya Singh
Dr. Zoltan Szantoi
Dr. Peter Wolter
Dr. Philip Townsend
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2200 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.


  • remote sensing
  • sensor networks
  • UAS
  • data fusion
  • scaling
  • hyperspectral
  • LiDAR
  • thermal

Published Papers (1 paper)

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Open AccessArticle
Applications of High-Resolution Imaging for Open Field Container Nursery Counting
Remote Sens. 2018, 10(12), 2018; https://doi.org/10.3390/rs10122018 - 12 Dec 2018
Cited by 1
Frequent inventory data of container nurseries is needed by growers to ensure proper management and marketing strategies. In this paper, inventory data are estimated from aerial images. Since there are thousands of nursery species, it is difficult to find a generic classification algorithm [...] Read more.
Frequent inventory data of container nurseries is needed by growers to ensure proper management and marketing strategies. In this paper, inventory data are estimated from aerial images. Since there are thousands of nursery species, it is difficult to find a generic classification algorithm for all cases. In this paper, the development of classification methods was confined to three representative categories: green foliage, yellow foliage, and flowering plants. Vegetation index thresholding and the support vector machine (SVM) were used for classification. Classification accuracies greater than 97% were obtained for each case. Based on the classification results, an algorithm based on canopy area mapping was built for counting. The effects of flight altitude, container spacing, and ground cover type were evaluated. Results showed that container spacing and interaction of container spacing with ground cover type have a significant effect on counting accuracy. To mimic the practical shipping and moving process, incomplete blocks with different voids were created. Results showed that the more plants removed from the block, the higher the accuracy. The developed algorithm was tested on irregular- or regular-shaped plants and plants with and without flowers to test the stability of the algorithm, and accuracies greater than 94% were obtained. Full article
(This article belongs to the Special Issue Remote Sensing in the Age of Electronic Ecology)
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