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13 pages, 4376 KiB

Open AccessArticle

Under-Canopy UAV Laser Scanning Providing Canopy Height and Stem Volume Accurately

by Juha Hyyppä, Xiaowei Yu, Teemu Hakala, Harri Kaartinen, Antero Kukko, Heikki Hyyti, Jesse Muhojoki and Eric Hyyppä

Forests 2021, 12(7), 856; https://doi.org/10.3390/f12070856 - 29 Jun 2021

Cited by 12 | Viewed by 3781

Abstract

The automation of forest field reference data collection has been an intensive research objective for laser scanning scientists ever since the invention of terrestrial laser scanning more than two decades ago. In this study, we demonstrated that an under-canopy UAV laser scanning system [...] Read more.

The automation of forest field reference data collection has been an intensive research objective for laser scanning scientists ever since the invention of terrestrial laser scanning more than two decades ago. In this study, we demonstrated that an under-canopy UAV laser scanning system utilizing a rotating laser scanner can alone provide accurate estimates of canopy height and stem volume for the majority of trees in a boreal forest. We mounted a rotating laser scanner based on a Velodyne VLP-16 sensor onboard a manually piloted UAV. The UAV was commanded with the help of a live video feed from the onboard camera. Since the system was based on a rotating laser scanner providing varying view angles, all important elements such as treetops, branches, trunks, and ground could be recorded with laser hits. In an experiment including two different forest structures, namely sparse and obstructed canopy, we showed that our system can measure the heights of individual trees with a bias of −20 cm and a standard error of 40 cm in the sparse forest and with a bias of −65 cm and a standard error of 1 m in the obstructed forest. The accuracy of the obtained tree height estimates was equivalent to airborne above-canopy UAV surveys conducted in similar forest conditions or even at the same sites. The higher underestimation and higher inaccuracy in the obstructed site can be attributed to three trees with a height exceeding 25 m and the reduced point density of these tree tops due to occlusion and the limited ranging capacity of the scanner. Additionally, we used our system to estimate the stem volumes of individual trees with a standard error at the level of 10%. This level of error is equivalent to the error obtained when merging above-canopy UAV laser scanner data with terrestrial point cloud data. The results show that we do not necessarily need a combination of terrestrial point clouds and point clouds collected using above-canopy UAV systems in order to accurately estimate the heights and the volumes of individual trees in reference data collection. Full article

(This article belongs to the Special Issue Forestry Applications of Unmanned Aerial Vehicles (UAVs) 2020)

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17 pages, 6079 KiB

Open AccessArticle

Tree Height Measurements in Degraded Tropical Forests Based on UAV-LiDAR Data of Different Point Cloud Densities: A Case Study on Dacrydium pierrei in China

by Xi Peng, Anjiu Zhao, Yongfu Chen, Qiao Chen and Haodong Liu

Forests 2021, 12(3), 328; https://doi.org/10.3390/f12030328 - 11 Mar 2021

Cited by 16 | Viewed by 2907

Abstract

Tropical forest degradation is a major contributor to greenhouse gas emissions. Tree height can be used as an important predictor of forest growth, and yield models can provide basic data for forest degradation assessments. As an important parameter of unmanned aerial vehicle-light detection [...] Read more.

Tropical forest degradation is a major contributor to greenhouse gas emissions. Tree height can be used as an important predictor of forest growth, and yield models can provide basic data for forest degradation assessments. As an important parameter of unmanned aerial vehicle-light detection and ranging (UAV-LiDAR), it is not clear how the point cloud density affects the extraction accuracy of tree height in degraded tropical rain forests. To solve this problem, we collected UAV-LiDAR data at a flight altitude of 150 m, and then resampled the UAV-LiDAR data obtained according to the point cloud density percentage resampling method and obtained UAV-LiDAR data for five different point cloud densities, namely, 12, 17, 28, 64, and 108 points/m². On the basis of the resampled LiDAR data, we generated a canopy height model (CHM) to extract the height of Dacrydium pierrei (D. pierrei). The results show that (1) With the increase in the point cloud density, the accuracy of tree height extraction gradually increased, with a maximum accuracy at 108 points/m² (root mean squared error (RMSE)% = 22.78%, bias% = 14.86%). The accuracy (RMSE%) increased by 6.92% as the point cloud density increased from 12 points/m² to 17 points/m², but only increased by 0.99% as the point cloud density increased from 17 points/m² to 108 points/m², indicating that 17 points/m² is a critical point for tree height extraction of D. pierrei. (2) Compared with the results from broad-leaved forests, the accuracy of D. pierrei height extraction from coniferous forest was higher. With the increase in point cloud density, the difference in the accuracy of D. pierrei height between two stands gradually increased. When the point cloud density was 108 points/m², the differences in RMSE% and bas% were 3.55% and 6.22%, respectively. When the point cloud density was 12 points/m², the differences in RMSE% and bias% were 2.71% and 4.69%, respectively. Our research identified the lowest LiDAR data point cloud density required to ensure a certain accuracy in tree height extraction, which will help scholars formulate UAV-LiDAR forest resource survey plans. Full article

(This article belongs to the Special Issue Forestry Applications of Unmanned Aerial Vehicles (UAVs) 2020)

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14 pages, 3187 KiB

Open AccessEditor’s ChoiceArticle

Influence of Agisoft Metashape Parameters on UAS Structure from Motion Individual Tree Detection from Canopy Height Models

by Wade T. Tinkham and Neal C. Swayze

Forests 2021, 12(2), 250; https://doi.org/10.3390/f12020250 - 22 Feb 2021

Cited by 53 | Viewed by 9534

Abstract

Applications of unmanned aerial systems for forest monitoring are increasing and drive a need to understand how image processing workflows impact end-user products’ accuracy from tree detection methods. Increasing image overlap and making acquisitions at lower altitudes improve how structure from motion point [...] Read more.

Applications of unmanned aerial systems for forest monitoring are increasing and drive a need to understand how image processing workflows impact end-user products’ accuracy from tree detection methods. Increasing image overlap and making acquisitions at lower altitudes improve how structure from motion point clouds represents forest canopies. However, only limited testing has evaluated how image resolution and point cloud filtering impact the detection of individual tree locations and heights. We evaluate how Agisoft Metashape’s build dense cloud Quality (image resolution) and depth map filter settings influence tree detection from canopy height models in ponderosa pine forests. Finer resolution imagery with minimal filtering provided the best visual representation of vegetation detail for trees of all sizes. These same settings maximized tree detection F-score at >0.72 for overstory (>7 m tall) and >0.60 for understory trees. Additionally, overstory tree height bias and precision improve as image resolution becomes finer. Overstory and understory tree detection in open-canopy conifer systems might be optimized using the finest resolution imagery that computer hardware enables, while applying minimal point cloud filtering. The extended processing time and data storage demands of high-resolution imagery must be balanced against small reductions in tree detection performance when down-scaling image resolution to allow the processing of greater data extents. Full article

(This article belongs to the Special Issue Forestry Applications of Unmanned Aerial Vehicles (UAVs) 2020)

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13 pages, 3916 KiB

Open AccessArticle

Using GatorEye UAV-Borne LiDAR to Quantify the Spatial and Temporal Effects of a Prescribed Fire on Understory Height and Biomass in a Pine Savanna

by Maryada Shrestha, Eben N. Broadbent and Jason G. Vogel

Forests 2021, 12(1), 38; https://doi.org/10.3390/f12010038 - 30 Dec 2020

Cited by 7 | Viewed by 2391

Abstract

In the pine savannas of the southeastern United States, prescribed fire is commonly used to manipulate understory structure and composition. Understory characteristics have traditionally been monitored with field sampling; however, remote sensing could provide rapid, spatially explicit monitoring of understory dynamics. We contrasted [...] Read more.

In the pine savannas of the southeastern United States, prescribed fire is commonly used to manipulate understory structure and composition. Understory characteristics have traditionally been monitored with field sampling; however, remote sensing could provide rapid, spatially explicit monitoring of understory dynamics. We contrasted pre- vs. post-fire understory characteristics collected with fixed area plots with estimates from high-density LiDAR point clouds collected using the unmanned aerial vehicle (UAV)-borne GatorEye system. Measuring within 1 × 1 m field plots (n = 20), we found average understory height ranged from 0.17–1.26 m and biomass from 0.26–4.86 Mg C ha⁻¹ before the fire (May 2018), and five months after the fire (November 2018), height ranged from 0.11–1.09 m and biomass from 0.04–3.03 Mg C ha⁻¹. Understory heights estimated with LiDAR were significantly correlated with plot height measurements (R² = 0.576, p ≤ 0.001). Understory biomass was correlated with in situ heights (R² = 0.579, p ≤ 0.001) and LiDAR heights (R² = 0.507, p ≤ 0.001). The biomass estimates made with either height measurement did not differ for the measurement plots (p = 0.263). However, for the larger research area, the understory biomass estimated with the LiDAR indicated a smaller difference after the burn (~12.7% biomass reduction) than observed with in situ measurements (~16% biomass reduction). The two approaches likely differed because the research area’s spatial variability was not captured by the in-situ measurements (0.2% of the research area measured) versus the wall-to-wall coverage provided by LiDAR. The additional benefit of having spatially explicit measurements with LiDAR, and its ease of use, make it a promising tool for land managers wanting greater spatial and temporal resolution in tracking understory biomass and its response to prescribed fire. Full article

(This article belongs to the Special Issue Forestry Applications of Unmanned Aerial Vehicles (UAVs) 2020)

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22 pages, 13738 KiB

Open AccessArticle

Discriminant Analysis of the Damage Degree Caused by Pine Shoot Beetle to Yunnan Pine Using UAV-Based Hyperspectral Images

by Mengying Liu, Zhonghe Zhang, Xuelian Liu, Jun Yao, Ting Du, Yunqiang Ma and Lei Shi

Forests 2020, 11(12), 1258; https://doi.org/10.3390/f11121258 - 26 Nov 2020

Cited by 9 | Viewed by 2496

Abstract

Due to the increased frequency and intensity of forest damage caused by diseases and pests, effective methods are needed to accurately monitor the damage degree. Unmanned aerial vehicle (UAV)-based hyperspectral imaging is an effective technique for forest health surveying and monitoring. In this [...] Read more.

Due to the increased frequency and intensity of forest damage caused by diseases and pests, effective methods are needed to accurately monitor the damage degree. Unmanned aerial vehicle (UAV)-based hyperspectral imaging is an effective technique for forest health surveying and monitoring. In this study, a framework is proposed for identifying the severity of damage caused by Tomicus spp. (the pine shoot beetle, PSB) to Yunnan pine (Pinus yunnanensis Franch) using UAV-based hyperspectral images. Four sample plots were set up in Shilin, Yunnan Province, China. A total of 80 trees were investigated, and their hyperspectral data were recorded. The spectral data were subjected to a one-way ANOVA. Two sensitive bands and one sensitive parameter were selected using Pearson correlation analysis and stepwise discriminant analysis to establish a diagnostic model of the damage degree. A discriminant rule was established to identify the degree of damage based on the median value between different degrees of damage. The diagnostic model with R₆₉₀ and R₇₉₈ as variables had the highest accuracy (R² = 0.854, RMSE = 0.427), and the test accuracy of the discriminant rule was 87.50%. The results are important for forest damage caused by the PSB. Full article

(This article belongs to the Special Issue Forestry Applications of Unmanned Aerial Vehicles (UAVs) 2020)

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18 pages, 2348 KiB

Open AccessArticle

Estimation of Genetic Parameters and Selection of Superior Genotypes in a 12-Year-Old Clonal Norway Spruce Field Trial after Phenotypic Assessment Using a UAV

by Mateusz Liziniewicz, Liviu Theodor Ene, Johan Malm, Jens Lindberg, Andreas Helmersson and Bo Karlsson

Forests 2020, 11(9), 992; https://doi.org/10.3390/f11090992 - 15 Sep 2020

Cited by 5 | Viewed by 2654

Abstract

Height is a key trait in the indices applied when selecting genotypes for use in both tree breeding populations and production populations in seed orchards. Thus, measurement of tree height is an important activity in the Swedish Norway spruce breeding program. However, traditional [...] Read more.

Height is a key trait in the indices applied when selecting genotypes for use in both tree breeding populations and production populations in seed orchards. Thus, measurement of tree height is an important activity in the Swedish Norway spruce breeding program. However, traditional measurement techniques are time-consuming, expensive, and often involve work in bad weather, so automatization of the data acquisition would be beneficial. Possibilities for such automatization have been opened by advances in unmanned aerial vehicle (UAV) technology. Therefore, to test its applicability in breeding programs, images acquired by a consumer-level UAV (DJI Phantom 4 Pro V2.0) system were used to predict the height and breast height diameter of Norway spruce trees in a 12-year-old genetic field trial established with 2.0 × 2.0 m initial spacing. The tree heights were also measured in the field using an ultrasonic system. Three additive regression models with different numbers of predictor variables were used to estimate heights of individual trees. On stand level, the average height estimate derived from UAV data was 2% higher than the field-measured average. The estimation of family means was very accurate, but the genotype-level accuracy, which is crucial for selection in the Norway spruce breeding program, was not high enough. There was just ca. 60% matching of genotypes in groups selected using actual and estimated heights. In addition, heritability values calculated from the predicted values were underestimated and overestimated for height and diameter, respectively, with deviations from measurement-based estimates ranging between −19% and +12%. However, the use of more sophisticated UAV and camera equipment could significantly improve the results and enable automatic individual tree detection. Full article

(This article belongs to the Special Issue Forestry Applications of Unmanned Aerial Vehicles (UAVs) 2020)

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21 pages, 3819 KiB

Open AccessArticle

Estimating Individual Conifer Seedling Height Using Drone-Based Image Point Clouds

by Guillermo Castilla, Michelle Filiatrault, Gregory J. McDermid and Michael Gartrell

Forests 2020, 11(9), 924; https://doi.org/10.3390/f11090924 - 24 Aug 2020

Cited by 15 | Viewed by 4108

Abstract

Research Highlights: This is the most comprehensive analysis to date of the accuracy of height estimates for individual conifer seedlings derived from drone-based image point clouds (DIPCs). We provide insights into the effects on accuracy of ground sampling distance (GSD), phenology, ground determination [...] Read more.

Research Highlights: This is the most comprehensive analysis to date of the accuracy of height estimates for individual conifer seedlings derived from drone-based image point clouds (DIPCs). We provide insights into the effects on accuracy of ground sampling distance (GSD), phenology, ground determination method, seedling size, and more. Background and Objectives: Regeneration success in disturbed forests involves costly ground surveys of tree seedlings exceeding a minimum height. Here we assess the accuracy with which conifer seedling height can be estimated using drones, and how height errors translate into counting errors in stocking surveys. Materials and Methods: We compared height estimates derived from DIPCs of different GSD (0.35 cm, 0.75 cm, and 3 cm), phenological state (leaf-on and leaf-off), and ground determination method (based on either the DIPC itself or an ancillary digital terrain model). Each set of height estimates came from data acquired in up to three linear disturbances in the boreal forest of Alberta, Canada, and included 22 to 189 surveyed seedlings, which were split into two height strata to assess two survey scenarios. Results: The best result (root mean square error (RMSE) = 24 cm; bias = −11 cm; R² = 0.63; n = 48) was achieved for seedlings >30 cm with 0.35 cm GSD in leaf-off conditions and ground elevation from the DIPC. The second-best result had the same GSD and ground method but was leaf-on and not significantly different from the first. Results for seedlings ≤30 cm were unreliable (nil R²). Height estimates derived from manual softcopy interpretation were similar to the corresponding DIPC results. Height estimation errors hardly affected seedling counting errors (best balance was 8% omission and 6% commission). Accuracy and correlation were stronger at finer GSDs and improved with seedling size. Conclusions: Millimetric (GSD <1 cm) DIPC can be used for estimating the height of individual conifer seedlings taller than 30 cm. Full article

(This article belongs to the Special Issue Forestry Applications of Unmanned Aerial Vehicles (UAVs) 2020)

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18 pages, 5677 KiB

Open AccessArticle

Unmanned Aerial System and Machine Learning Techniques Help to Detect Dead Woody Components in a Tropical Dry Forest

by Carlos Campos-Vargas, Arturo Sanchez-Azofeifa, Kati Laakso and Philip Marzahn

Forests 2020, 11(8), 827; https://doi.org/10.3390/f11080827 - 29 Jul 2020

Cited by 6 | Viewed by 2777

Abstract

Background and Objectives: Increased frequency and intensity of drought events are predicted to occur throughout the world because of climate change. These extreme climate events result in higher tree mortality and fraction of dead woody components, phenomena that are currently being reported worldwide [...] Read more.

Background and Objectives: Increased frequency and intensity of drought events are predicted to occur throughout the world because of climate change. These extreme climate events result in higher tree mortality and fraction of dead woody components, phenomena that are currently being reported worldwide as critical indicators of the impacts of climate change on forest diversity and function. In this paper, we assess the accuracy and processing times of ten machine learning (ML) techniques, applied to multispectral unmanned aerial vehicle (UAV) data to detect dead canopy woody components. Materials and Methods: This work was conducted on five secondary dry forest plots located at the Santa Rosa National Park Environmental Monitoring Super Site, Costa Rica. Results: The coverage of dead woody components at the selected secondary dry forest plots was estimated to range from 4.8% to 16.1%, with no differences between the successional stages. Of the ten ML techniques, the support vector machine with radial kernel (SVMR) and random forests (RF) provided the highest accuracies (0.982 vs. 0.98, respectively). Of these two ML algorithms, the processing time of SVMR was longer than the processing time of RF (8735.64 s vs. 989 s). Conclusions: Our results demonstrate that it is feasible to detect and quantify dead woody components, such as dead stands and fallen trees, using a combination of high-resolution UAV data and ML algorithms. Using this technology, accuracy values higher than 95% were achieved. However, it is important to account for a series of factors, such as the optimization of the tuning parameters of the ML algorithms, the environmental conditions and the time of the UAV data acquisition. Full article

(This article belongs to the Special Issue Forestry Applications of Unmanned Aerial Vehicles (UAVs) 2020)

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16 pages, 7536 KiB

Open AccessArticle

Spatial Estimation of the Latent Heat Flux in a Tropical Dry Forest by Using Unmanned Aerial Vehicles

by Philip Marzahn, Linda Flade and Arturo Sanchez-Azofeifa

Forests 2020, 11(6), 604; https://doi.org/10.3390/f11060604 - 26 May 2020

Cited by 13 | Viewed by 3201

Abstract

In this paper, we address the retrieval of spatially distributed latent heat flux (

λ

E) over a tropical dry forest using multi-spectral and thermal unmanned aerial vehicle (UAV) imagery. The study was carried out in the Santa Rosa National Park Environmental Monitoring [...] Read more.

In this paper, we address the retrieval of spatially distributed latent heat flux (

λ

E) over a tropical dry forest using multi-spectral and thermal unmanned aerial vehicle (UAV) imagery. The study was carried out in the Santa Rosa National Park Environmental Monitoring Super-Site, Costa Rica, in June 2016. The triangle method was used to derive

λ

E from the UAV imagery and the results were compared to

λ

E measurements of an eddy covariance system within the coincident eddy flux tower footprint. The tower footprint was derived using a two-dimensional parameterization model for flux footprint prediction. The comparisons with the flux tower measurements showed a mean relative difference of 10.98% with a slight overestimation of the UAV-based flux retrievals by nearly 7.7 Wm

^{- 2}

. The results are in good agreement with satellite-based retrievals, as provided by the literature, for which the triangle method was initially developed and mostly used so far. This study proved to be a promising approach for transferring the triangle method to UAV imagery in ecosystems such as tropical dry forests. With the presented approach, new details in spatially distributed latent heat flux estimates at ultra-high resolution are now possible, thereby potentially closing the gap in spatial resolution between satellites and flux towers. Even more, it allows tracing the latent heat flux from single trees at leaf level. Besides, this approach also opens new perspectives for the monitoring of latent heat fluxes in tropical dry forests. Full article

(This article belongs to the Special Issue Forestry Applications of Unmanned Aerial Vehicles (UAVs) 2020)

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Review

Jump to: Research

41 pages, 1032 KiB

Open AccessReview

Recent Advances in Unmanned Aerial Vehicles Forest Remote Sensing—A Systematic Review. Part II: Research Applications

by Riccardo Dainelli, Piero Toscano, Salvatore Filippo Di Gennaro and Alessandro Matese

Forests 2021, 12(4), 397; https://doi.org/10.3390/f12040397 - 27 Mar 2021

Cited by 72 | Viewed by 9632

Abstract

Forest sustainable management aims to maintain the income of woody goods for companies, together with preserving non-productive functions as a benefit for the community. Due to the progress in platforms and sensors and the opening of the dedicated market, unmanned aerial vehicle–remote sensing [...] Read more.

Forest sustainable management aims to maintain the income of woody goods for companies, together with preserving non-productive functions as a benefit for the community. Due to the progress in platforms and sensors and the opening of the dedicated market, unmanned aerial vehicle–remote sensing (UAV–RS) is improving its key role in the forestry sector as a tool for sustainable management. The use of UAV (Unmanned Aerial Vehicle) in precision forestry has exponentially increased in recent years, as demonstrated by more than 600 references published from 2018 until mid-2020 that were found in the Web of Science database by searching for “UAV” + “forest”. This result is even more surprising when compared with similar research for “UAV” + “agriculture”, from which emerge about 470 references. This shows how UAV–RS research forestry is gaining increasing popularity. In Part II of this review, analyzing the main findings of the reviewed papers (227), numerous strengths emerge concerning research technical issues. UAV–RS is fully applicated for obtaining accurate information from practical parameters (height, diameter at breast height (DBH), and biomass). Research effectiveness and soundness demonstrate that UAV–RS is now ready to be applied in a real management context. Some critical issues and barriers in transferring research products are also evident, namely, (1) hyperspectral sensors are poorly used, and their novel applications should be based on the capability of acquiring tree spectral signature especially for pest and diseases detection, (2) automatic processes for image analysis are poorly flexible or based on proprietary software at the expense of flexible and open-source tools that can foster researcher activities and support technology transfer among all forestry stakeholders, and (3) a clear lack exist in sensors and platforms interoperability for large-scale applications and for enabling data interoperability. Full article

(This article belongs to the Special Issue Forestry Applications of Unmanned Aerial Vehicles (UAVs) 2020)

27 pages, 3712 KiB

Open AccessReview

Recent Advances in Unmanned Aerial Vehicle Forest Remote Sensing—A Systematic Review. Part I: A General Framework

by Riccardo Dainelli, Piero Toscano, Salvatore Filippo Di Gennaro and Alessandro Matese

Forests 2021, 12(3), 327; https://doi.org/10.3390/f12030327 - 11 Mar 2021

Cited by 71 | Viewed by 9443

Abstract

Natural, semi-natural, and planted forests are a key asset worldwide, providing a broad range of positive externalities. For sustainable forest planning and management, remote sensing (RS) platforms are rapidly going mainstream. In a framework where scientific production is growing exponentially, a systematic analysis [...] Read more.

Natural, semi-natural, and planted forests are a key asset worldwide, providing a broad range of positive externalities. For sustainable forest planning and management, remote sensing (RS) platforms are rapidly going mainstream. In a framework where scientific production is growing exponentially, a systematic analysis of unmanned aerial vehicle (UAV)-based forestry research papers is of paramount importance to understand trends, overlaps and gaps. The present review is organized into two parts (Part I and Part II). Part II inspects specific technical issues regarding the application of UAV-RS in forestry, together with the pros and cons of different UAV solutions and activities where additional effort is needed, such as the technology transfer. Part I systematically analyzes and discusses general aspects of applying UAV in natural, semi-natural and artificial forestry ecosystems in the recent peer-reviewed literature (2018–mid-2020). The specific goals are threefold: (i) create a carefully selected bibliographic dataset that other researchers can draw on for their scientific works; (ii) analyze general and recent trends in RS forest monitoring (iii) reveal gaps in the general research framework where an additional activity is needed. Through double-step filtering of research items found in the Web of Science search engine, the study gathers and analyzes a comprehensive dataset (226 articles). Papers have been categorized into six main topics, and the relevant information has been subsequently extracted. The strong points emerging from this study concern the wide range of topics in the forestry sector and in particular the retrieval of tree inventory parameters often through Digital Aerial Photogrammetry (DAP), RGB sensors, and machine learning techniques. Nevertheless, challenges still exist regarding the promotion of UAV-RS in specific parts of the world, mostly in the tropical and equatorial forests. Much additional research is required for the full exploitation of hyperspectral sensors and for planning long-term monitoring. Full article

(This article belongs to the Special Issue Forestry Applications of Unmanned Aerial Vehicles (UAVs) 2020)

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