Journal of Sensors

Journal of Sensors / 2018 / Article
Special Issue

Remote Sensing of Sustainable Ecosystems

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Editorial | Open Access

Volume 2018 |Article ID 9683415 | https://doi.org/10.1155/2018/9683415

Yichun Xie, Zongyao Sha, Victor Mesev, "Remote Sensing of Sustainable Ecosystems", Journal of Sensors, vol. 2018, Article ID 9683415, 2 pages, 2018. https://doi.org/10.1155/2018/9683415

Remote Sensing of Sustainable Ecosystems

Received21 Oct 2018
Accepted22 Oct 2018
Published13 Nov 2018

1. Introduction

Ecosystems, containing biotic and abiotic elements, are complex communities that impact all aspects of the environment. Their equilibrium and sustainability are paramount to their functionality, health, and survival. Remote sensing and other advanced geospatial data acquisition and processing techniques are critical for the measurement of sustainable ecosystems. This special issue includes the latest advancements in remote sensor systems and computing platforms that have made it possible to collect data on ecosystems quickly and routinely. In particular, there is an increasing volume of multispectral and hyperspectral data from unmanned aerial vehicles (UAV), airborne and satellite sensors. They provide rich information for mapping, monitoring, and analyzing a wide range of ecological applications. Big datasets from various remotely sensed platforms are essential for understanding the science behind ecosystem functions and thus provide critical insights on how ecosystems are sustainable.

2. Remote Sensing of Sustainable Ecosystems

This special issue was proposed as a follow-up to GSES (Geoinformatics in Sustainable Ecosystem and Society) conference at Wuhan University, China, in September 2017. The forum covered diverse topics centering on advances in earth observation, geospatial analysis, and technologies and their applications in natural resource management and sustainable society. We received 18 papers submitted to this special issue, 6 of which were accepted and published after peer review (one-third acceptance rate). What binds the papers are themes for spatial observation (using ground-based in-situ sensors or moving sensors) for acquiring information on key ecosystem elements and innovative data assimilation strategies to improve our understanding of the interactions of those elements within or between ecosystems. The 6 accepted papers focus on the design of systematic data acquisition frames, the approaches for processing and extracting ecosystem-related datasets, and the models for understanding the science behind some ecosystems. In summary, the topics illustrated by the published papers include the following: (i)Advanced image processing and geostatistical techniques for analyzing and classifying hyperspectral data(ii)Calibrating unmanned aerial vehicles using hyperspectral and digital surface models(iii)Accessing unlimited cloud computing resources for storing and transforming multisource data(iv)Big data analytics based on spatial modeling and machine learning to understand the science for promoting sustainable ecosystem services(v)Measuring leaf dust retention across visible and infrared wavelengths(vi)Application of Voronoi diagrams for simulation(vii)Mapping rare and protected ecosystem service capacities more accurately

Conflicts of Interest

Based on my best knowledge, I and my co-guest editors of this special issue have no conflict of interest.

Acknowledgments

We would like to thank all the authors for their valuable contributions in this special issue as well as all the reviewers whose constructive suggestions helped to improve the quality of the papers and the publication of this special issue. We also thank Wuhan University, China, and Eastern Michigan University, USA, for co-organizing the International Conference on Geoinformatics in Sustainable Ecosystem and Society (GSES-2017) which provided the source for most of the submitted papers to this special issue.

Yichun Xie
Zongyao Sha
Victor Mesev

Copyright © 2018 Yichun Xie et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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