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Special Issue "Remote Sensing to Assess Canopy Structure and Function"

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Forest Remote Sensing".

Deadline for manuscript submissions: closed (28 September 2018)

Special Issue Editor

Guest Editor
Dr. Geoffrey Parker

Smithsonian Environmental Research Center, the USA
Website | E-Mail
Phone: 443-482-2210
Interests: canopy structure; comparative research; ecosystem processes; forest ecology; landscape scales; remote sensing

Special Issue Information

Dear Colleagues,

The canopy is a fundamental component of vegetation.  The structure of the canopy has a critical role in the many functional properties of vegetation, for example, interior complexity, habitat quality and microclimate; vegetation type, stage, spatial organization and disturbance regime; ecosystem processes involving energy, water and carbon.  Structure not only constrains and indicates functions but also is often easier to measure than function.  Understanding the links between structure and function can be critical for scaling and modelling.  From a remote sensing perspective, the outer canopy is the part of vegetation primarily observed.  Here, we define canopy structure as the arrangement of the aboveground components of vegetation in time and space. 

We invite researchers to submit articles describing new methods, findings and insights for a Special Issue on Remote Sensing of Canopy Structure and Function.  The submissions can be based on various platforms (drone, airborne or satellite), sensors (LIDAR, RADAR, spectral, digital image aggregations), and structural attributes of interest (height, total surface area, cover, texture, spatial arrangement).  We suspect most reports will focus on forests; studies on other sorts of vegetation are appreciated. 

Especially welcome are the following: 1.) analyses based on the fusion of qualitatively different sensors, especially when co-located (for example, LIDAR-hyperspectral systems such as the NASA G-LiHT or the NEON AOP)—how do structural and reflective properties interact?  2.) studies of structure combining both remotely sensed information and ground observations—are these viewpoints complementary? 3.) investigations combining data of different inherent spatial scales—how can these be integrated?  4.) considerations of canopy regions not readily perceived remotely—what can be learned about canopy interior structure?  5.) examination of structural variation in time—how can we distinguish and quantify changes?  We particularly encourage submissions that identify and explore a mechanistic basis of the connection between important canopy structural features and the performance of the remote sensor. 

Dr. Geoffrey Parker
Guest Editor

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 monthly 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 1800 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.

Keywords

  • canopy
  • dynamics
  • function
  • fusion
  • mechanism
  • structure

Published Papers (3 papers)

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Research

Open AccessArticle Structural and Spectral Analysis of Cereal Canopy Reflectance and Reflectance Anisotropy
Remote Sens. 2018, 10(11), 1767; https://doi.org/10.3390/rs10111767
Received: 14 September 2018 / Revised: 21 October 2018 / Accepted: 31 October 2018 / Published: 8 November 2018
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Abstract
The monitoring of agricultural areas is one of the most important topics for remote sensing data analysis, especially to assist food security in the future. To improve the quality and quantify uncertainties, it is of high relevance to understand the spectral reflectivity regarding
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The monitoring of agricultural areas is one of the most important topics for remote sensing data analysis, especially to assist food security in the future. To improve the quality and quantify uncertainties, it is of high relevance to understand the spectral reflectivity regarding the structural and spectral properties of the canopy. The importance of understanding the influence of plant and canopy structure is well established, but, due to the difficulty of acquiring reflectance data from numerous differently structured canopies, there is still a need to study the structural and spectral dependencies affecting top-of-canopy reflectance and reflectance anisotropy. This paper presents a detailed study dealing with two fundamental issues: (1) the influence of plant and canopy architecture changes due to crop phenology on nadir acquired cereal top-of-canopy reflectance, and (2) the anisotropic reflectance of cereal top-of-canopy reflectance and its inter-annual variations as affected by varying contents of biochemical constituents and changes on canopy structure across green phenological stages between tillering and inflorescence emergence. All of the investigations are based on HySimCaR, a computer-based approach using 3D canopy models and Monte Carlo ray tracing (drat). The achieved results show that the canopy architecture significantly influences top-of-canopy reflectance and the bidirectional reflectance function (BRDF) in the VNIR (visible and near infrared), and SWIR (shortwave infrared) wavelength ranges. In summary, it can be said that the larger the fraction of the radiation reflected by the plants, the stronger is the influence of the canopy structure on the reflectance signal. A significant finding for the anisotropic reflectance is that the relative row orientation of the cereal canopies is mapped in the 3D-shape of the BRDF. Summarised, this study provides fundamental knowledge for improving the retrieval of biophysical vegetation parameters of agricultural areas for current and upcoming sensors with large FOV (field of view) with respect to the quantification of uncertainties. Full article
(This article belongs to the Special Issue Remote Sensing to Assess Canopy Structure and Function)
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Open AccessArticle Filtering Stems and Branches from Terrestrial Laser Scanning Point Clouds Using Deep 3-D Fully Convolutional Networks
Remote Sens. 2018, 10(8), 1215; https://doi.org/10.3390/rs10081215
Received: 7 June 2018 / Revised: 24 July 2018 / Accepted: 29 July 2018 / Published: 2 August 2018
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Abstract
Terrestrial laser scanning (TLS) can produce precise and detailed point clouds of forest environment, thus enabling quantitative structure modeling (QSM) for accurate tree morphology and wood volume allocation. Applying QSM to plot-scale wood delineation is highly dependent on wood visibility from forest scans.
[...] Read more.
Terrestrial laser scanning (TLS) can produce precise and detailed point clouds of forest environment, thus enabling quantitative structure modeling (QSM) for accurate tree morphology and wood volume allocation. Applying QSM to plot-scale wood delineation is highly dependent on wood visibility from forest scans. A common problem is to filter wood point from noisy leafy points in the crowns and understory. This study proposed a deep 3-D fully convolution network (FCN) to filter both stem and branch points from complex plot scans. To train the 3-D FCN, reference stem and branch points were delineated semi-automatically for 14 sampled areas and three common species. Among seven testing areas, agreements between reference and model prediction, measured by intersection over union (IoU) and overall accuracy (OA), were 0.89 (stem IoU), 0.54 (branch IoU), 0.79 (mean IoU), and 0.94 (OA). Wood filtering results were further incorporated to a plot-scale QSM to extract individual tree forms, isolated wood, and understory wood from three plot scans with visual assessment. The wood filtering experiment provides evidence that deep learning is a powerful tool in 3-D point cloud processing and parsing. Full article
(This article belongs to the Special Issue Remote Sensing to Assess Canopy Structure and Function)
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Open AccessArticle Terrestrial Laser Scanning to Detect Liana Impact on Forest Structure
Remote Sens. 2018, 10(6), 810; https://doi.org/10.3390/rs10060810
Received: 22 March 2018 / Revised: 7 May 2018 / Accepted: 17 May 2018 / Published: 23 May 2018
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Abstract
Tropical forests are currently experiencing large-scale structural changes, including an increase in liana abundance and biomass. Higher liana abundance results in reduced tree growth and increased tree mortality, possibly playing an important role in the global carbon cycle. Despite the large amount of
[...] Read more.
Tropical forests are currently experiencing large-scale structural changes, including an increase in liana abundance and biomass. Higher liana abundance results in reduced tree growth and increased tree mortality, possibly playing an important role in the global carbon cycle. Despite the large amount of data currently available on lianas, there are not many quantitative studies on the influence of lianas on the vertical structure of the forest. We study the potential of terrestrial laser scanning (TLS) in detecting and quantifying changes in forest structure after liana cutting using a small scale removal experiment in two plots (removal plot and non-manipulated control plot) in a secondary forest in Panama. We assess the structural changes by comparing the vertical plant profiles and Canopy Height Models (CHMs) between pre-cut and post-cut scans in the removal plot. We show that TLS is able to detect the local structural changes in all the vertical strata of the plot caused by liana removal. Our study demonstrates the reproducibility of the TLS derived metrics for the same location confirming the applicability of TLS for continuous monitoring of liana removal plots to study the long-term impacts of lianas on forest structure. We therefore recommend to use TLS when implementing new large scale liana removal experiments, as the impact of lianas on forest structure will determine the aboveground competition for light between trees and lianas, which has important implications for the global carbon cycle. Full article
(This article belongs to the Special Issue Remote Sensing to Assess Canopy Structure and Function)
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