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Remote Sensing
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Remote Sensing Models of Forest Structure, Composition, and Function
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Remote Sensing Models of Forest Structure, Composition, 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 (20 May 2022) | Viewed by 13022

Special Issue Editors

Dr. Nikolay S. Strigul

E-Mail Website
Guest Editor
Department of Mathematics and Statistics, College of Arts and Sciences, Washington State University Vancouver, Vancouver, WA 98686, USA
Interests: forest modeling; remote sensing; photogrammetry; 3-D modeling of vegetation; individual-based models; stochastic modeling
Special Issues, Collections and Topics in MDPI journals
Dr. Adam Erickson

E-Mail Website
Guest Editor
Biospheric Sciences Laboratory, Code 618, NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA
Interests: terrestrial biosphere modeling; machine/deep learning; imaging spectroscopy; LiDAR; photogrammetric computer vision; UAS

Special Issue Information

Dear Colleagues,

In this Special Issue, we provide a platform for a collegial exchange of ideas and a broad discussion on the role of remote sensing in next-generation models of forests. Empirical, process, and/or stochastic models have long been used to model forest structure, composition, and/or function, yet a number of vegetation properties and processes remain challenging to simulate. These include detailed spatiotemporal patterns of canopy competition, leaf pigment concentrations, photosynthetic rates, phenological cycles, CO2, CH4, and BVOC emissions, mortality, regeneration, decomposition, canopy radiative transfer, and the spread of fire, pests, and pathogens. These and many other dynamics are linked to forest genetics through long-timescale phylogeographic processes and short-timescale adaptive gene expression. Current individual-based models of forests are poised to grow in geometric/structural and biochemical realism, necessitating efficient model approximations and/or statistical emulators for global modeling. Recent advances in deep learning for remote sensing provide a new opportunity to develop new spatiotemporal models. Machine learning models based on remote sensing may capture forest properties and processes, and can be embedded in existing models, ideally, without prohibitive parameterization requirements. These and other approaches such as radiative transfer model emulation and/or inversion may be applied to a variety of remote sensing data sources, including passive optical multi-spectral, hyperspectral, and high-resolution structure-from-motion (SfM) data, and active LiDAR, SAR, and GNSS-R data, as well as PhenoCam near-sensing observations, FLUXNET tower measurements, and the TRY database for mapping traits to species locations. We especially invite papers demonstrating state-of-the-art techniques for learning models of forest properties and processes directly from these and other data sources in order to address shortcomings in the representation of forests in existing models of the terrestrial biosphere.

Dr. Nikolay Strigul
Dr. Adam Erickson
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.

Keywords

  • Data-driven vegetation models
  • Remote sensing of vegetation
  • Machine/deep learning
  • Convolutional neural networks
  • Generative adversarial networks
  • Radiative transfer model inversion
  • Near sensing of vegetation

Published Papers (4 papers)

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Research

18 pages, 9530 KiB  
Article
Using Airborne LiDAR to Map Red Alder in the Sappho Long-Term Ecosystem Productivity Study
by Ally Kruper, Robert J. McGaughey, Sarah Crumrine, Bernard T. Bormann, Keven Bennett and Courtney R. Bobsin
Remote Sens. 2022, 14(7), 1591; https://doi.org/10.3390/rs14071591 - 25 Mar 2022
Cited by 1 | Viewed by 1919
Abstract
A fundamental question of forestry is that of species composition: which species are present, and which are not. However, traditional forest measurements needed to map species over large areas can be both time consuming and costly. In this study, we combined airborne light [...] Read more.
A fundamental question of forestry is that of species composition: which species are present, and which are not. However, traditional forest measurements needed to map species over large areas can be both time consuming and costly. In this study, we combined airborne light detection and ranging (LiDAR) data with extensive field data from the Long-Term Ecosystem Productivity study located near Sappho, Washington, USA to increase the accuracy of our GIS data and to differentiate between red alder (Alnus rubra Bong.) and other dominant tree species. We adjusted plot and tree locations using LiDAR canopy height models (CHMs) by manually matching tree canopies on the CHMs with tree stem maps based on field data. We then used the adjusted tree locations and metrics computed from LiDAR point cloud data to create a classification model to identify and map red alder. The manual matching of field stem maps to CHMs improved tree locations, allowing us to create model training data. These data were used to create a random forest model that discriminated between red alder and conifer species with an accuracy of 96%. Our findings highlight the potential of LiDAR to improve coordinates of individual trees as well as discriminate between selected coniferous and deciduous tree species using LiDAR data collected in leaf-off conditions in Pacific Northwest ecosystems. Full article
(This article belongs to the Special Issue Remote Sensing Models of Forest Structure, Composition, and Function)
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19 pages, 3917 KiB  
Article
Tree Crowns Cause Border Effects in Area-Based Biomass Estimations from Remote Sensing
by Nikolai Knapp, Andreas Huth and Rico Fischer
Remote Sens. 2021, 13(8), 1592; https://doi.org/10.3390/rs13081592 - 20 Apr 2021
Cited by 14 | Viewed by 2976
Abstract
The estimation of forest biomass by remote sensing is constrained by different uncertainties. An important source of uncertainty is the border effect, as tree crowns are not constrained by plot borders. Lidar remote sensing systems record the canopy height within a certain area, [...] Read more.
The estimation of forest biomass by remote sensing is constrained by different uncertainties. An important source of uncertainty is the border effect, as tree crowns are not constrained by plot borders. Lidar remote sensing systems record the canopy height within a certain area, while the ground-truth is commonly the aboveground biomass of inventory trees geolocated at their stem positions. Hence, tree crowns reaching out of or into the observed area are contributing to the uncertainty in canopy-height–based biomass estimation. In this study, forest inventory data and simulations of a tropical rainforest’s canopy were used to quantify the amount of incoming and outgoing canopy volume and surface at different plot sizes (10, 20, 50, and 100 m). This was performed with a bottom-up approach entirely based on forest inventory data and allometric relationships, from which idealized lidar canopy heights were simulated by representing the forest canopy as a 3D voxel space. In this voxel space, the position of each voxel is known, and it is also known to which tree each voxel belongs and where the stem of this tree is located. This knowledge was used to analyze the role of incoming and outgoing crowns. The contribution of the border effects to the biomass estimation uncertainty was quantified for the case of small-footprint lidar (a simulated canopy height model, CHM) and large-footprint lidar (simulated waveforms with footprint sizes of 23 and 65 m, corresponding to the GEDI and ICESat GLAS sensors). A strong effect of spatial scale was found: e.g., for 20-m plots, on average, 16% of the CHM surface belonged to trees located outside of the plots, while for 100-m plots this incoming CHM fraction was only 3%. The border effects accounted for 40% of the biomass estimation uncertainty at the 20-m scale, but had no contribution at the 100-m scale. For GEDI- and GLAS-based biomass estimates, the contributions of border effects were 23% and 6%, respectively. This study presents a novel approach for disentangling the sources of uncertainty in the remote sensing of forest structures using virtual canopy modeling. Full article
(This article belongs to the Special Issue Remote Sensing Models of Forest Structure, Composition, and Function)
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24 pages, 5065 KiB  
Article
Comparison of Machine Learning Methods for Mapping the Stand Characteristics of Temperate Forests Using Multi-Spectral Sentinel-2 Data
by Kourosh Ahmadi, Bahareh Kalantar, Vahideh Saeidi, Elaheh K. G. Harandi, Saeid Janizadeh and Naonori Ueda
Remote Sens. 2020, 12(18), 3019; https://doi.org/10.3390/rs12183019 - 16 Sep 2020
Cited by 36 | Viewed by 3845
Abstract
The estimation and mapping of forest stand characteristics are vital because this information is necessary for sustainable forest management. The present study considers the use of a Bayesian additive regression trees (BART) algorithm as a non-parametric classifier using Sentinel-2A data and topographic variables [...] Read more.
The estimation and mapping of forest stand characteristics are vital because this information is necessary for sustainable forest management. The present study considers the use of a Bayesian additive regression trees (BART) algorithm as a non-parametric classifier using Sentinel-2A data and topographic variables to estimate the forest stand characteristics, namely the basal area (m2/ha), stem volume (m3/ha), and stem density (number/ha). These results were compared with those of three other popular machine learning (ML) algorithms, such as generalised linear model (GLM), K-nearest neighbours (KNN), and support vector machine (SVM). A feature selection was done on 28 variables including the multi-spectral bands on Sentinel-2 satellite, related vegetation indices, and ancillary data (elevation, slope, and topographic solar-radiation index derived from digital elevation model (DEM)) and then the most insignificant variables were removed from the datasets by recursive feature elimination (RFE). The study area was a mountainous forest with high biodiversity and an elevation gradient from 26 to 1636 m. An inventory dataset of 1200 sample plots was provided for training and testing the algorithms, and the predictors were fed into the ML models to compute and predict the forest stand characteristics. The accuracies and certainties of the ML models were assessed by their root mean square error (RMSE), mean absolute error (MAE), and R-squared (R2) values. The results demonstrated that BART generated the best basal area and stem volume predictions, followed by GLM, SVM, and KNN. The best RMSE values for both basal area (8.12 m2/ha) and stem volume (29.28 m3/ha) estimation were obtained by BART. Thus, the ability of the BART model for forestry application was established. On the other hand, KNN exhibited the highest RMSE values for all stand variable predictions, thereby exhibiting the least accuracy for this specific application. Moreover, the effectiveness of the narrow Sentinel-2 bands around the red edge and elevation was highlighted for predicting the forest stand characteristics. Therefore, we concluded that the combination of the Sentinel-2 products and topographic variables derived from the PALSAR data used in this study improved the estimation of the forest attributes in temperate forests. Full article
(This article belongs to the Special Issue Remote Sensing Models of Forest Structure, Composition, and Function)
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15 pages, 5753 KiB  
Article
Individual Tree Crown Delineation from UAS Imagery Based on Region Growing and Growth Space Considerations
by Jianyu Gu, Heather Grybas and Russell G. Congalton
Remote Sens. 2020, 12(15), 2363; https://doi.org/10.3390/rs12152363 - 23 Jul 2020
Cited by 16 | Viewed by 3191
Abstract
The development of unmanned aerial systems (UAS) equipped with various sensors (e.g., Lidar, multispectral sensors, and/or cameras) has provided the capability to “see” the individual trees in a forest. Individual tree crowns (ITCs) are the building blocks of precision forestry, because this knowledge [...] Read more.
The development of unmanned aerial systems (UAS) equipped with various sensors (e.g., Lidar, multispectral sensors, and/or cameras) has provided the capability to “see” the individual trees in a forest. Individual tree crowns (ITCs) are the building blocks of precision forestry, because this knowledge allows users to analyze, model and manage the forest at the individual tree level by combing multiple data sources (e.g., remote sensing data and field surveys). Trees in the forest compete with other vegetation, especially neighboring trees, for limited resources to grow into the available horizontal and vertical space. Based on this assumption, this research developed a new region growing method that began with treetops as the initial seeds, and then segmented the ITCs, considering its growth space between the tree and its neighbors. The growth space was allocated by Euclidian distance and adjusted based on the crown size. Results showed that the over-segmentation accuracy (Oa), under-segmentation (Ua), and quality rate (QR) reached 0.784, 0.766, and 0.382, respectively, if the treetops were detected from a variable window filter based on an allometric equation for crown width. The Oa, Ua, and QR increased to 0.811, 0.853, and 0.296, respectively, when the treetops were manually adjusted. Treetop detection accuracy has a great impact on ITCs delineation accuracy. The uncertainties and limitations within this research including the interpretation error and accuracy measures were also analyzed and discussed, and a unified framework assessing the segmentation accuracy was highly suggested. Full article
(This article belongs to the Special Issue Remote Sensing Models of Forest Structure, Composition, and Function)
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