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Special Issue "Remote Sensing of Tropical Vegetation"

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Agriculture and Vegetation".

Deadline for manuscript submissions: closed (1 June 2021).

Special Issue Editors

Prof. Jean-François Bastin
E-Mail Website
Guest Editor
TERRA Teaching and Research Centre, Gembloux Agro-Bio Tech, University of Liege, 5030 Gembloux, Belgium
Interests: remote-sensing; forest ecology; global ecosystem ecology; climate change; deep learning; very high resolution
Prof. Sassan Saatchi
E-Mail Website
Guest Editor
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91109, USA
Interests: remote sensing tropical ecology; carbon and water cycling; climate change; machine learning; SAR; LiDAR
Prof. Ben Sparrow
E-Mail Website
Guest Editor
TERN Ecological Surveillance Monitoring, University of Adelaide, Adelaide, Australia
Interests: forest and grassland ecology; ecosystem monitoring; drones

Special Issue Information

Dear Colleagues,

Remote sensing data libraries’ accessibility, diversity, quality, and computing capacity increase every day and provide new opportunities to describe the state and functioning of our planet. Long-term observations of terrestrial ecosystems from air and space have allowed a better understanding of ecosystems dynamics globally in the last several decades. This is particularly important for unmanaged ecosystems across the tropics, where difficulty of access and infrastructure have limited the capacity for ground observations and in-situ data. Tropical ecosystems are under increasing pressure due to the expansion of land use activities and are experiencing severe environmental stress from climate change and variability, threatening their functions and services such as carbon cycling and biodiversity. The use of innovative techniques such as recent advances in machine learning, and remote sensing observations that can quantify the stresses on the physical environment and ecological responses of these ecosystems will provide a better understanding of their role in the Earth system and their resilience to environmental threats.

In this Special Issue of Remote Sensing, we welcome research focusing on spatio-temporal observations of humid tropical and subtropical ecosystems from airborne or spaceborne sensors, with particular attention paid to data processing making use of machine learning approaches such as deep learning. The selection of papers for publication will depend on the quality and rigor of research and results. Specific topics include, but are not limited to:

  • Time series observations of the ecosystem physical environment (structure, functional traits, and biodiversity);
  • Time series observations of the ecosystem function (water, carbon, and energy cycling);
  • Understanding and quantifying anthropogenic stresses and ecosystem responses;
  • Role of disturbance and recovery (deforestation, fire, degradation) on ecosystem structure, ecological functions, and services;
  • Upscaling ground measurements and processes using remote sensing techniques;
  • Data fusion of remote sensing data across the frequency spectrum (microwave to optical) and techniques (active and passive);
  • Applications of machine-learning and multiscale spatio-temporal analysis of remote sensing and ground observations;
  • Next-generation platforms and sensors to address tropical and subtropical ecosystems

Prof. Jean-François Bastin
Prof. Sassan Saatchi
Prof. Ben Sparrow
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 2400 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.


  • ecosystem dynamics
  • long-term monitoring
  • machine learning
  • deep learning
  • remote sensing of vegetation
  • phenology
  • tropical and sub-tropical biome

Published Papers (1 paper)

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Estimating Tree Diameters from an Autonomous Below-Canopy UAV with Mounted LiDAR
Remote Sens. 2021, 13(13), 2576; https://doi.org/10.3390/rs13132576 - 02 Jul 2021
Viewed by 549
Below-canopy UAVs hold promise for automated forest surveys because their sensors can provide detailed information on below-canopy forest structures, especially in dense forests, which may be inaccessible to above-canopy UAVs, aircraft, and satellites. We present an end-to-end autonomous system for estimating tree diameters [...] Read more.
Below-canopy UAVs hold promise for automated forest surveys because their sensors can provide detailed information on below-canopy forest structures, especially in dense forests, which may be inaccessible to above-canopy UAVs, aircraft, and satellites. We present an end-to-end autonomous system for estimating tree diameters using a below-canopy UAV in parklands. We used simultaneous localization and mapping (SLAM) and LiDAR data produced at flight time as inputs to diameter-estimation algorithms in post-processing. The SLAM path was used for initial compilation of horizontal LiDAR scans into a 2D cross-sectional map, and then optimization algorithms aligned the scans for each tree within the 2D map to achieve a precision suitable for diameter measurement. The algorithms successfully identified 12 objects, 11 of which were trees and one a lamppost. For these, the estimated diameters from the autonomous survey were highly correlated with manual ground-truthed diameters (R2=0.92, root mean squared error = 30.6%, bias = 18.4%). Autonomous measurement was most effective for larger trees (>300 mm diameter) within 10 m of the UAV flight path, for medium trees (200–300 mm diameter) within 5 m, and for trees with regular cross sections. We conclude that fully automated below-canopy forest surveys are a promising, but still nascent, technology and suggest directions for future research. Full article
(This article belongs to the Special Issue Remote Sensing of Tropical Vegetation)
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