Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
8.3
4.2
Journals
Remote Sensing
Special Issues
Remote Sensing of Forest Carbon
remotesensing-logo
Submit to Remote Sensing Review for Remote Sensing Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Remote Sensing of Forest Carbon

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

Deadline for manuscript submissions: closed (30 November 2022) | Viewed by 12665

Special Issue Editors

Dr. Daniel Hayes

E-Mail Website
Guest Editor
School of Forest Resources, University of Maine, Orono, ME 04469, USA
Interests: ecological modelling; carbon cycle science; remote sensing; arctic system science
Special Issues, Collections and Topics in MDPI journals
Dr. Chad Babcock

E-Mail Website
Guest Editor
Department of Forest Resources, University of Minnesota, Minneapolis, MN 55455, USA
Interests: remote sensing; forest inventory; geospatial analysis; Bayesian statistics

Special Issue Information

Dear Colleagues, 

Forests have an essential function in the global climate system, in large part through their uptake and storage of carbon, which slows the build-up of anthropogenic greenhouse gases in the atmosphere. Managing forests to enhance carbon stores as a strategy for mitigating future climate change requires accurate and reliable information for monitoring, reporting, and verification at the spatial and temporal scales that policy and decisions are made. The availability of new, state-of-the-art remote sensing systems is revolutionizing the way in which forests are measured and studied—from individual trees to continental extents. The current challenge is to integrate these multi-modal, multi-resolution, and spatio-temporally variable remote sensing data sets within inventory and modeling frameworks for the diagnosis of forest carbon stocks, attribution of their dynamics, and prediction of their future functioning.

This Special Issue invites papers highlighting cutting-edge research in the remote sensing of forest carbon, including advances in the data and methodologies used to measure the key biophysical parameters required for its estimation. For example, optical and hyperspectral data distinguish forest tree species composition, ranging technologies such as radar and lidar make detailed measurements of three-dimensional forest structure, and thermal sensing provides insight into forest ecosystem function. Remote sensors are collecting these data from the full range of terrestrial, UAS, airborne, and spaceborne platforms. We look to include papers in this Special Issue that describe emerging methods such as machine learning in the estimation and scaling of forest carbon attributes, as well as time-series algorithms leveraging the historical satellite record to provide perspectives on forest carbon dynamics. Studies demonstrating the remote sensing retrieval and integration of key forest parameters for initializing, calibrating, and/or validating mechanistic carbon cycle and land surface models are also encouraged. 

Dr. Daniel Hayes
Dr. Chad Babcock
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 submissions that pass pre-check are 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 2700 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.

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue polices can be found here.

Published Papers (2 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

27 pages, 24113 KiB  
Article
Synthesizing Disparate LiDAR and Satellite Datasets through Deep Learning to Generate Wall-to-Wall Regional Inventories for the Complex, Mixed-Species Forests of the Eastern United States
by Elias Ayrey, Daniel J. Hayes, John B. Kilbride, Shawn Fraver, John A. Kershaw, Jr., Bruce D. Cook and Aaron R. Weiskittel
Remote Sens. 2021, 13(24), 5113; https://doi.org/10.3390/rs13245113 - 16 Dec 2021
Cited by 8 | Viewed by 4511
Abstract
Light detection and ranging (LiDAR) has become a commonly-used tool for generating remotely-sensed forest inventories. However, LiDAR-derived forest inventories have remained uncommon at a regional scale due to varying parameters among LiDAR data acquisitions and the availability of sufficient calibration data. Here, we [...] Read more.
Light detection and ranging (LiDAR) has become a commonly-used tool for generating remotely-sensed forest inventories. However, LiDAR-derived forest inventories have remained uncommon at a regional scale due to varying parameters among LiDAR data acquisitions and the availability of sufficient calibration data. Here, we present a model using a 3-D convolutional neural network (CNN), a form of deep learning capable of scanning a LiDAR point cloud, combined with coincident satellite data (spectral, phenology, and disturbance history). We compared this approach to traditional modeling used for making forest predictions from LiDAR data (height metrics and random forest) and found that the CNN had consistently lower uncertainty. We then applied the CNN to public data over six New England states in the USA, generating maps of 14 forest attributes at a 10 m resolution over 85% of the region. Aboveground biomass estimates produced a root mean square error of 36 Mg ha−1 (44%) and were within the 97.5% confidence of independent county-level estimates for 33 of 38 or 86.8% of the counties examined. CNN predictions for stem density and percentage of conifer attributes were moderately successful, while predictions for detailed species groupings were less successful. The approach shows promise for improving the prediction of forest attributes from regional LiDAR data and for combining disparate LiDAR datasets into a common framework for large-scale estimation. Full article
(This article belongs to the Special Issue Remote Sensing of Forest Carbon)
►▼ Show Figures

Figure 1

24 pages, 5872 KiB  
Article
Sensor Agnostic Semantic Segmentation of Structurally Diverse and Complex Forest Point Clouds Using Deep Learning
by Sean Krisanski, Mohammad Sadegh Taskhiri, Susana Gonzalez Aracil, David Herries and Paul Turner
Remote Sens. 2021, 13(8), 1413; https://doi.org/10.3390/rs13081413 - 7 Apr 2021
Cited by 46 | Viewed by 7102
Abstract
Forest inventories play an important role in enabling informed decisions to be made for the management and conservation of forest resources; however, the process of collecting inventory information is laborious. Despite advancements in mapping technologies allowing forests to be digitized in finer granularity [...] Read more.
Forest inventories play an important role in enabling informed decisions to be made for the management and conservation of forest resources; however, the process of collecting inventory information is laborious. Despite advancements in mapping technologies allowing forests to be digitized in finer granularity than ever before, it is still common for forest measurements to be collected using simple tools such as calipers, measuring tapes, and hypsometers. Dense understory vegetation and complex forest structures can present substantial challenges to point cloud processing tools, often leading to erroneous measurements, and making them of less utility in complex forests. To address this challenge, this research demonstrates an effective deep learning approach for semantically segmenting high-resolution forest point clouds from multiple different sensing systems in diverse forest conditions. Seven diverse point cloud datasets were manually segmented to train and evaluate this model, resulting in per-class segmentation accuracies of Terrain: 95.92%, Vegetation: 96.02%, Coarse Woody Debris: 54.98%, and Stem: 96.09%. By exploiting the segmented point cloud, we also present a method of extracting a Digital Terrain Model (DTM) from such segmented point clouds. This approach was applied to a set of six point clouds that were made publicly available as part of a benchmarking study to evaluate the DTM performance. The mean DTM error was 0.04 m relative to the reference with 99.9% completeness. These approaches serve as useful steps toward a fully automated and reliable measurement extraction tool, agnostic to the sensing technology used or the complexity of the forest, provided that the point cloud has sufficient coverage and accuracy. Ongoing work will see these models incorporated into a fully automated forest measurement tool for the extraction of structural metrics for applications in forestry, conservation, and research. Full article
(This article belongs to the Special Issue Remote Sensing of Forest Carbon)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-2
Back to TopTop