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Remote Sensing for Forest Morphological and Physiological Traits Monitoring

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

Deadline for manuscript submissions: 26 May 2024 | Viewed by 6118

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Dr. Jiaojiao Tian

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Remote Sensing Technology Institute, German Aerospace Center (DLR), Muenchener Strasse 20, 82234 Wessling, Germany
Interests: forest remote sensing building extraction; 2D/3D change detection; data fusion; time-series image analysis; semantic 3D point cloud segmentation; computer vision; 3D reconstruction
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Prof. Dr. Clement Atzberger

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Guest Editor

Institute of Geomatics, University of Natural Resources and Life Sciences, 1090 Vienna, Austria
Interests: remote sensing of vegetation with focus on time series analysis and use of physically based radiative transfer models for mapping biochemical and biophysical traits
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Dr. Jochem Verrelst

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Guest Editor

Laboratory for Earth Observation, Image Processing Laboratory - Scientific Park, University of Valencia, C/ Catedrático José Beltrán, 2, 46980 Paterna, Valencia, Spain
Interests: imaging spectroscopy; vegetation properties retrieval; FLEX; vegetation fluorescence; optical remote sensing; radiative transfer models; retrieval methods
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Prof. Dr. Jie Shan

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Guest Editor

School of Civil Engineering, Purdue University, West Lafayette, IN, USA
Interests: aerospace lidar mapping; 3D reconstruction; geospatial data mining
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Prof. Dr. Thomas Knoke

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Department of Life Sciences System, Technical University of Munich, 85354 Freising, Germany
Interests: forest management with special focus on risk modeling and diversification strategy planning; methodologies drawn from decision theory, operations research and modern finance theory and applied to forest science issues and land use problems in general

Special Issue Information

Dear Colleagues,

The assessment of forest morphological and physiological traits is of direct importance to forest functional monitoring. Remote sensing can serve as a nondestructive alternative to more robust morphological and physiological trait monitoring methods as compared to traditional forest field survey methods. Recent advances in remote sensing create opportunities to provide a realistic and efficient way to quantify plant traits at a large scale with a higher temporal and spatial resolution. In addition to highly accurate airborne light detection and ranging (LiDAR), spaceborne LiDAR, high-resolution multi-/hyperspectral data, and InSAR data are being introduced to this field, providing new opportunities for large-scale monitoring.

This Special Issue invites papers highlighting cutting-edge research in forest morphological and physiological trait monitoring using remote sensing techniques, including advances in the remote sensing sensors and methodologies used to measure the key morphological (e.g., height and canopy structure) and physiological parameters (e.g., chlorophyll, carotenoids, nitrogen, and water content), and the relating applications with regard to forest dynamics.

Suggested themes include, but are not limited to:

Morphological trait monitoring;
Physiological trait monitoring;
Single-tree crown segmentation;
Biomass estimation;
Forest functional traits;
3D modeling;
Machine learning in forest remote sensing;
Forest structure analyses;
Forest gap detection;
Biodiversity;
Benchmark dataset.

Dr. Jiaojiao Tian
Prof. Dr. Clement Atzberger
Dr. Jochem Verrelst
Prof. Dr. Jie Shan
Prof. Dr. Thomas Knoke
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.

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20 pages, 9862 KiB

Open AccessArticle

Tree Species Classification Based on Upper Crown Morphology Captured by Uncrewed Aircraft System Lidar Data

by Robert J. McGaughey, Ally Kruper, Courtney R. Bobsin and Bernard T. Bormann

Remote Sens. 2024, 16(4), 603; https://doi.org/10.3390/rs16040603 - 06 Feb 2024

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Abstract

The application of lidar data to assist with forest inventory is common around the world. However, the determination of tree species is still somewhat elusive. Lidar data collected using UAS (uncrewed aircraft systems) platforms offer high density point cloud data for areas from a few to several hundred hectares. General point cloud metrics computed using these data captured differences in the crown structure that proved useful for species classification. For our study, we manually adjusted plot and tree locations to align field trees and UAS lidar point data and computed common descriptive metrics using a small cylindrical sample of points designed to capture the top three meters and leader of each tree. These metrics were used to train a random forest classifier to differentiate between two conifer species, Douglas fir and western hemlock, common in the Pacific Northwest region of the United States. Our UAS lidar data had a single swath pulse density of 90 pulses/m² and an aggregate pulse density of 556 pulses/m². We trained classification models using both height and intensity metrics, height metrics alone, intensity metrics alone, and a small subset of five metrics, and achieved overall accuracies of 91.8%, 88.7%, 78.6%, and 91.5%, respectively. Overall, we showed that UAS lidar data captured morphological differences between the upper crowns of our two target species and produced a classification model that could be applied over large areas. Full article

(This article belongs to the Special Issue Remote Sensing for Forest Morphological and Physiological Traits Monitoring)

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33 pages, 56873 KiB

Open AccessEditor’s ChoiceArticle

An Open Benchmark Dataset for Forest Characterization from Sentinel-1 and -2 Time Series

by Sarah Hauser, Michael Ruhhammer, Andreas Schmitt and Peter Krzystek

Remote Sens. 2024, 16(3), 488; https://doi.org/10.3390/rs16030488 - 26 Jan 2024

Viewed by 1259

Abstract

Earth observation satellites offer vast opportunities for quantifying landscapes and regional land cover composition and changes. The integration of artificial intelligence in remote sensing is essential for monitoring significant land cover types like forests, demanding a substantial volume of labeled data for effective AI model development and validation. The Wald5Dplus project introduces a distinctive open benchmark dataset for mid-European forests, labeling Sentinel-1/2 time series using data from airborne laser scanning and multi-spectral imagery. The freely accessible satellite images are fused in polarimetric, spectral, and temporal domains, resulting in analysis-ready data cubes with 512 channels per year on a 10 m UTM grid. The dataset encompasses labels, including tree count, crown area, tree types (deciduous, coniferous, dead), mean crown volume, base height, tree height, and forested area proportion per pixel. The labels are based on an individual tree characterization from high-resolution airborne LiDAR data using a specialized segmentation algorithm. Covering three test sites (Bavarian Forest National Park, Steigerwald, and Kranzberg Forest) and encompassing around six million trees, it generates over two million labeled samples. Comprehensive validation, including metrics like mean absolute error, median deviation, and standard deviation, in the random forest regression confirms the high quality of this dataset, which is made freely available. Full article

(This article belongs to the Special Issue Remote Sensing for Forest Morphological and Physiological Traits Monitoring)

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13 pages, 4116 KiB

Open AccessArticle

Terrestrial vs. UAV-Based Remote Measurements in Log Volume Estimation

by Andreja Đuka, Ivica Papa, Mihael Lovrinčević, Zoran Bumber, Tomislav Poršinsky and Kristijan Tomljanović

Remote Sens. 2023, 15(21), 5143; https://doi.org/10.3390/rs15215143 - 27 Oct 2023

Viewed by 735

Abstract

This study compared oak butt-log volume estimations gained through terrestrial measurements in the forest stand with a remote approach using an unmanned aerial system (UAS) and photogrammetric post-processing. Terrestrial measurements were conducted in the lowland part of Croatia after a completed motor–manual final felling of a 140-year-old even-aged oak stand. Butt-logs’ volumes were estimated with four methods: the sectioning method and Huber’s, Smailan’s and Riecke–Newton’s methods. Measuring diameters and lengths and estimating volumes remotely were based on orthophotos using four different software: ArcGIS, QGIS, AutoCAD and Pix4D. Riecke–Newton’s method for volume estimation had the smallest relative bias of +1.74%, while for Huber’s method it was −8.07% and with Smailan’s method it was +21.23%. Log volume estimations gained remotely via ArcGIS and QGIS were, in the case of Huber’s method, at +3.63% relative bias, and in the case of Riecke–Newton’s method at +1.39% relative bias. Volume estimation using the sectioning method resulted in a total of 51.334 m³ for the whole sample, while the sectioning method performed with the help of AutoCAD resulted in 55.151 m³, i.e., +7.43% relative bias. Volume estimation of thirty oak butt-logs given by Pix4D software (version 4.8.4) resulted in +9.34% relative bias (56.134 m³). Comparing terrestrial measurements and the volume estimations based on them to those gained remotely showed a very high correlation in all cases. This study showed that using a UAS for log volume estimation surveys has the potential for broader use, especially after final felling in even-aged forests where the remaining trees in the stand would not block photogrammetric analysis. Full article

(This article belongs to the Special Issue Remote Sensing for Forest Morphological and Physiological Traits Monitoring)

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23 pages, 9006 KiB

Open AccessEditor’s ChoiceArticle

Terrestrial Laser Scanning for Non-Destructive Estimation of Aboveground Biomass in Short-Rotation Poplar Coppices

by María Menéndez-Miguélez, Guillermo Madrigal, Hortensia Sixto, Nerea Oliveira and Rafael Calama

Remote Sens. 2023, 15(7), 1942; https://doi.org/10.3390/rs15071942 - 05 Apr 2023

Cited by 2 | Viewed by 1568

Abstract

Poplar plantations in high-density and short-rotation coppices (SRC) are a suitable way for the fast production of wood that can be transformed into bioproducts or bioenergy. Optimal management of these coppices requires accurate assessment of the total standing biomass. However, traditional field inventory is a challenging task, given the existence of multiple shoots, the difficulty of identifying terminal shoots, and the extreme high density. As an alternative, in this work, we propose to develop individual stool and plot biomass models using metrics derived from terrestrial laser scanning (TLS) as predictors. To this aim, we used data from a SRC poplar plantation, including nine plots and 154 individual stools. Every plot was scanned from different positions, and individual stools were felled, weighed, and dried to compute aboveground biomass (AGB). Individual stools were segmented from the cloud point, and different TLS metrics at stool and plot level were derived following processes of bounding box, slicing, and voxelization. These metrics were then used, either alone or combined with field-measured metrics, to fit biomass models. Our results indicate that at individual-stool level, the biomass models combining TLS metrics and easy to measure in field metrics (stool diameter) perform similarly to the traditional allometric models based on field inventories, while at plot scales, TLS-derived models show superiority over traditional models. Our proposed methodology permits accurate and non-destructive estimates of the biomass fixed in SRC plantations. Full article

(This article belongs to the Special Issue Remote Sensing for Forest Morphological and Physiological Traits Monitoring)

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11 pages, 2832 KiB

Open AccessTechnical Note

Large-Scale Controls on the Leaf Economic Spectrum of the Overstory Tree Species Metrosideros polymorpha

by Megan M. Seeley and Gregory P. Asner

Remote Sens. 2023, 15(19), 4707; https://doi.org/10.3390/rs15194707 - 26 Sep 2023

Viewed by 701

Abstract

The role of intraspecific trait variation in functional ecology has gained traction in recent years as many papers have observed its importance in driving community diversity and ecology. Yet much of the work in this field relies on field-based trait surveys. Here, we used continuous canopy trait information derived from remote sensing data of a highly polymorphic tree species, Metrosideros polymorpha, to quantify environmental controls on intraspecific trait variation. M. polymorpha, an endemic, keystone tree species in Hawai’i, varies morphologically, chemically, and genetically across broad elevation and soil substrate age gradients, making it an ideal model organism to explore large-scale environmental drivers of intraspecific trait variation. M. polymorpha canopy reflectance (visible to shortwave infrared; 380–2510 nm) and light detection and ranging (LiDAR) data collected by the Global Airborne Observatory were modeled to canopy trait estimates of leaf mass per area, chlorophyll a and b, carotenoids, total carbon, nitrogen, phosphorus, phenols, cellulose, and top of canopy height using previously developed leaf chemometric equations. We explored how these derived traits varied across environmental gradients by extracting elevation, slope, aspect, precipitation, and soil substrate age data at canopy locations. We then obtained the feature importance values of the environmental factors in predicting each leaf trait by training random forest models to predict leaf traits individually. Of these environmental factors, elevation was the most important predictor for all canopy traits. Elevation not only affected canopy traits directly but also indirectly by influencing the relationships between soil substrate age and canopy traits as well as between nitrogen and other traits, as indicated by the change in slope between the variables at different elevation ranges. In conclusion, intraspecific variation in M. polymorpha traits derived from remote sensing adheres to known leaf economic spectrum (LES) patterns as well as interspecific LES traits previously mapped using imaging spectroscopy. Full article

(This article belongs to the Special Issue Remote Sensing for Forest Morphological and Physiological Traits Monitoring)

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