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Remote Sensing for Forest Morphological and Physiological Traits Monitoring

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

Deadline for manuscript submissions: 15 January 2025 | Viewed by 12904

Special Issue Editors


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Guest Editor
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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Guest Editor
Mantle Labs Ltd., Grünentorgasse 19, 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
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
School of Civil Engineering, Purdue University, West Lafayette, IN 47907, USA
Interests: automated aerospace image and LiDAR mapping; geospatial modeling and analysis; geosocial data mining
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Guest Editor
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

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Published Papers (9 papers)

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Research

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24 pages, 6370 KiB  
Article
Enhanced Impacts of Extreme Weather Events on Forest: The Upper Valtellina (Italy) Case Study
by Blanka Barbagallo, Nicolò Rocca, Lorenzo Cresi, Guglielmina Adele Diolaiuti and Antonella Senese
Remote Sens. 2024, 16(19), 3692; https://doi.org/10.3390/rs16193692 - 3 Oct 2024
Viewed by 460
Abstract
Extreme weather events are increasingly recognized as major stress factors for forest ecosystems, causing both immediate and long-term effects. This study focuses on the impacts experienced by the forests of Valdisotto, Valfurva, and Sondalo (28% of the total area is covered by forests) [...] Read more.
Extreme weather events are increasingly recognized as major stress factors for forest ecosystems, causing both immediate and long-term effects. This study focuses on the impacts experienced by the forests of Valdisotto, Valfurva, and Sondalo (28% of the total area is covered by forests) in Upper Valtellina (Italy) due to the Vaia storm that occurred in October 2018. To define the immediate impacts of Vaia, we assess the economic value of forest ecosystem services (ESs), particularly those provided by timber production and carbon sequestration, pre- and post-Vaia and during the emergency period. We used the market price method to assess the economic values of timber production and carbon sequestration, as these are considered to be marketable goods. Based on data processed from Sentinel-2 satellite images (with a spatial resolution of 10 m), our results show that, despite the reduction in forest area (−2.02%) and timber stock (−2.38%), the economic value of the timber production increased after Vaia due to higher timber prices (i.e., from a total of €124.97 million to €130.72 million). However, considering the whole emergency period (2019–2020), the total losses are equal to €5.10 million for Valdisotto, €0.32 million for Valfurva, and €0.43 million for Sondalo. Instead, an economic loss of 2.88% is experienced for carbon sequestration, with Valdisotto being the more affected municipality (−4.48% of the pre-Vaia economic value). In terms of long-term impacts, we discuss the enhanced impacts due to the spread of the bark beetle Ips typopgraphus. Full article
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19 pages, 4892 KiB  
Article
Comparative Analysis of Machine Learning Techniques and Data Sources for Dead Tree Detection: What Is the Best Way to Go?
by Júlia Matejčíková, Dana Vébrová and Peter Surový
Remote Sens. 2024, 16(16), 3086; https://doi.org/10.3390/rs16163086 - 21 Aug 2024
Viewed by 827
Abstract
In Central Europe, the extent of bark beetle infestation in spruce stands due to prolonged high temperatures and drought has created large areas of dead trees, which are difficult to monitor by ground surveys. Remote sensing is the only possibility for the assessment [...] Read more.
In Central Europe, the extent of bark beetle infestation in spruce stands due to prolonged high temperatures and drought has created large areas of dead trees, which are difficult to monitor by ground surveys. Remote sensing is the only possibility for the assessment of the extent of the dead tree areas. Several options exist for mapping individual dead trees, including different sources and different processing techniques. Satellite images, aerial images, and images from UAVs can be used as sources. Machine and deep learning techniques are included in the processing techniques, although models are often presented without proper realistic validation.This paper compares methods of monitoring dead tree areas using three data sources: multispectral aerial imagery, multispectral PlanetScope satellite imagery, and multispectral Sentinel-2 imagery, as well as two processing methods. The classification methods used are Random Forest (RF) and neural network (NN) in two modalities: pixel- and object-based. In total, 12 combinations are presented. The results were evaluated using two types of reference data: accuracy of model on validation data and accuracy on vector-format semi-automatic classification polygons created by a human evaluator, referred to as real Ground Truth. The aerial imagery was found to have the highest model accuracy, with the CNN model achieving up to 98% with object classification. A higher classification accuracy for satellite imagery was achieved by combining pixel classification and the RF model (87% accuracy for Sentinel-2). For PlanetScope Imagery, the best result was 89%, using a combination of CNN and object-based classifications. A comparison with the Ground Truth showed a decrease in the classification accuracy of the aerial imagery to 89% and the classification accuracy of the satellite imagery to around 70%. In conclusion, aerial imagery is the most effective tool for monitoring bark beetle calamity in terms of precision and accuracy, but satellite imagery has the advantage of fast availability and shorter data processing time, together with larger coverage areas. Full article
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18 pages, 4549 KiB  
Article
Investigating Dual-Source Satellite Image Data and ALS Data for Estimating Aboveground Biomass
by Wen Fan, Jiaojiao Tian, Thomas Knoke, Bisheng Yang, Fuxun Liang and Zhen Dong
Remote Sens. 2024, 16(10), 1804; https://doi.org/10.3390/rs16101804 - 19 May 2024
Viewed by 882
Abstract
Accurate estimation of above-ground biomass (AGB) in forested areas is essential for studying forest ecological functions, surface carbon cycling, and global carbon balance. Over the past decade, models that harness the distinct features of multi-source remote sensing observations for estimating AGB have gained [...] Read more.
Accurate estimation of above-ground biomass (AGB) in forested areas is essential for studying forest ecological functions, surface carbon cycling, and global carbon balance. Over the past decade, models that harness the distinct features of multi-source remote sensing observations for estimating AGB have gained significant popularity. It is worth exploring the differences in model performance by using simple and fused data. Additionally, quantitative estimation of the impact of high-cost laser point clouds on satellite imagery of varying costs remains largely unexplored. To address these challenges, model performance and cost must be considered comprehensively. We propose a comprehensive assessment based on three perspectives (i.e., performance, potential and limitations) for four typical AGB-estimation models. First, different variables are extracted from the multi-source and multi-resolution data. Subsequently, the performance of four regression methods is tested for AGB estimation with diverse indicator combinations. Experimental results prove that the combination of multi-source data provides a highly accurate AGB regression model. The proposed regression and variables rating approaches can flexibly integrate other data sources for modeling. Furthermore, the data cost is discussed against the AGB model performance. Our study demonstrates the potential of using low-cost satellite data to provide a rough AGB estimation for larger areas, which can allow different remote sensing data to meet different needs of forest management decisions. Full article
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20 pages, 9862 KiB  
Article
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 - 6 Feb 2024
Cited by 1 | Viewed by 1434
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 [...] Read more.
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
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33 pages, 56873 KiB  
Article
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 2185
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 [...] Read more.
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
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13 pages, 4116 KiB  
Article
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
Cited by 1 | Viewed by 1136
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 [...] Read more.
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
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23 pages, 9006 KiB  
Article
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 - 5 Apr 2023
Cited by 5 | Viewed by 2126
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 [...] Read more.
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
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12 pages, 19371 KiB  
Technical Note
BAMFORESTS: Bamberg Benchmark Forest Dataset of Individual Tree Crowns in Very-High-Resolution UAV Images
by Jonas Troles, Ute Schmid, Wen Fan and Jiaojiao Tian
Remote Sens. 2024, 16(11), 1935; https://doi.org/10.3390/rs16111935 - 28 May 2024
Cited by 1 | Viewed by 1264
Abstract
The anthropogenic climate crisis results in the gradual loss of tree species in locations where they were previously able to grow. This leads to increasing workloads and requirements for foresters and arborists as they are forced to restructure their forests and city parks. [...] Read more.
The anthropogenic climate crisis results in the gradual loss of tree species in locations where they were previously able to grow. This leads to increasing workloads and requirements for foresters and arborists as they are forced to restructure their forests and city parks. The advancements in computer vision (CV)—especially in supervised deep learning (DL)—can help cope with these new tasks. However, they rely on large, carefully annotated datasets to produce good and generalizable models. This paper presents BAMFORESTS: a dataset with 27,160 individually delineated tree crowns in 105 ha of very-high-resolution UAV imagery gathered with two different sensors from two drones. BAMFORESTS covers four areas of coniferous, mixed, and deciduous forests and city parks. The labels contain instance segmentations of individual trees, and the proposed splits are balanced by tree species and vitality. Furthermore, the dataset contains the corrected digital surface model (DSM), representing tree heights. BAMFORESTS is annotated in the COCO format and is especially suited for training deep neural networks (DNNs) to solve instance segmentation tasks. BAMFORESTS was created in the BaKIM project and is freely available under the CC BY 4.0 license. Full article
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11 pages, 2832 KiB  
Technical 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 1111
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 [...] Read more.
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
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