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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: closed (15 October 2022) | Viewed by 64253

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Special Issue Editors

Dr. Ana Paula Dalla Corte

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Guest Editor

Forest and Wood Science Center, Department of Forest Sciences, Federal University of Paraná (UFPR), Curitiba, Brazil
Interests: LiDAR remote sensing; digital aerial photogrammetry; tropical and forest plantations; forest inventory and spatial analysis
Special Issues, Collections and Topics in MDPI journals

Dr. Midhun (Mikey) Mohan

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Guest Editor Assistant

Department of Geography, University of California, Berkeley, CA, USA
Interests: drones; LiDAR; satellite remote sensing; tropical forests; forest management and modeling; individual tree detection; forest carbon science; machine learning; biodiversity conservation
Special Issues, Collections and Topics in MDPI journals

Dr. Mikko Vastaranta

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Guest Editor

School of Forest Sciences, University of Eastern Finland, Yliopistokatu 7, P.O.Box 111, FI-80101 Joensuu, Finland
Interests: remote sensing; laser scanning; precision forestry; forest structure
Special Issues, Collections and Topics in MDPI journals

Ms. Shruthi Srinivasan

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Guest Editor Assistant

Texas A&M Forest Service, USA
Interests: UAS; forest mapping and monitoring; LiDAR and remote sensing

Dr. Weijia Li

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Guest Editor

School of Geospatial Engineering and Science, Sun Yat-Sen University, Guangzhou 510080, China
Interests: remote sensing image understanding; computer vision; deep learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Recent advancements in the data processing sphere and introduction of low-cost Unmanned Aerial Vehicles (UAV) have resulted in proliferation of Individual Tree Algorithm (ITD) applications in areas of forestry and conservation in the past decade. Even though, origins of few of the most prominent underlying principles – such as local maxima and watershed algorithm – supporting ITD techniques can be traced back to 1980s, novel, modified and combined versions of these algorithms are still being developed, tested widely and expected to extend further in terms of applicability. Ongoing ITD initiatives within the forest science sector include monitoring, mapping and/or modeling forest growth, deforestation, disturbance, recovery, degradation, species diversity, forest health, aboveground biomass, canopy cover, biodiversity conservation, land use land change, climate change impacts, natural resource assessment, pest detection, among others. Even then, there exists a vast void of unexplored opportunities, unaddressed issues and unfilled gaps – such as constraints arising from dense forest canopy structures, time intensive data analysis paradigms, lack of standardization of UAV remote sensing development platforms, species-specific ITD optimization models and long-term large scale forest inventory databases – that solicit urgent attention.

We, hereby invite authors from the broad forest remote sensing domain to consider this SI as a medium to publish their original and/or review articles that explore the applications of ITD algorithms – which can be based on satellite imagery, drone data, discrete return and full waveform LiDAR (Light Detection and Ranging), hyperspectral data and/or sensor fusion. A few of the potential topics, other than the ones mentioned previously, include:

Tree-level attributes estimation and three-dimensional (3D) canopy structure analysis
Development of novel ITD algorithms and/or comparison and evaluation of existing ones
Compare and contrast performance of various remote sensing techniques in terms of ITD abilities
Data fusion approach for improving tree detection accuracy and characterization
ITD models transferable from one species to another as well as between disparate spatial scales and locations

Dr. Ana Paula Dalla Corte
Mr. Midhun (Mikey) Mohan
Dr. Mikko Vastaranta
Ms. Shruthi Srinivasan
Dr. Weijia Li
Guest Editors

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Keywords

Forest Monitoring
Forest Attributes Mensuration and Modeling
Individual Tree Crown Delineation Methods
Species Identification
Biomass Mapping
3D Point Clouds
Unmanned Aerial Vehicles UAVs
Light Detection and Ranging LiDAR
Multispectral and Hyperspectral Data
Satellite Remote Sensing
Data Fusion
Machine Learning
Deep Learning
Time Series Analysis

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Published Papers (14 papers)

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22 pages, 4369 KiB

Open AccessArticle

Tree Species Classifications of Urban Forests Using UAV-LiDAR Intensity Frequency Data

by Yulin Gong, Xuejian Li, Huaqiang Du, Guomo Zhou, Fangjie Mao, Lv Zhou, Bo Zhang, Jie Xuan and Dien Zhu

Remote Sens. 2023, 15(1), 110; https://doi.org/10.3390/rs15010110 - 25 Dec 2022

Cited by 6 | Viewed by 3267

Abstract

The accurate classification of tree species is essential for the sustainable management of forest resources and the effective monitoring of biodiversity. However, a literature review shows that most of the previous unmanned aerial vehicle (UAV) light detection and ranging (LiDAR)-based studies on fine tree species classification have used only limited intensity features, accurately identifying relatively few tree species. To address this gap, this study proposes developing a new intensity feature—intensity frequency—for the LiDAR-based fine classification of eight tree species. Intensity frequency is defined as the number of times a certain intensity value appears in the individual tree crown (ITC) point cloud. In this study, we use UAV laser scanning to obtain LiDAR data from urban forests. Intensity frequency features are constructed based on the extracted intensity information, and a random forest (RF) model is used to classify eight subtropical forest tree species in southeast China. Based on four-point cloud density sampling schemes of 100%, 80%, 50% and 30%, densities of 230 points/m², 184 points/m², 115 points/m² and 69 points/m² are obtained. These are used to analyze the effect of intensity frequency on tree species classification accuracy under four different point cloud densities. The results are shown as follows. (1) Intensity frequencies of trees are not significantly different for intraspecies (p > 0.05) values and are significantly different for interspecies (p < 0.01) values. (2) The intensity frequency features of LiDAR can be used to classify different tree species with an overall accuracy (OA) of 86.7%. Acer Buergerianum achieves a user accuracy (UA) of over 95% and a producer accuracy (PA) of over 90% for four density conditions. (3) The OA varies slightly under different point cloud densities, but the sum of correct classification trees (SCI) and PA decreases rapidly as the point cloud density decreases, while UA is less affected by density with some stability. (4) The priori feature selected by mean rank (MR) covers the top 10 posterior features selected by RF. These results show that the new intensity frequency feature proposed in this study can be used as a comprehensive and effective intensity feature for the fine classification of tree species. Full article

(This article belongs to the Special Issue Applications of Individual Tree Detection (ITD))

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22 pages, 6920 KiB

Open AccessArticle

Individual Tree Detection in Coal Mine Afforestation Area Based on Improved Faster RCNN in UAV RGB Images

by Meng Luo, Yanan Tian, Shengwei Zhang, Lei Huang, Huiqiang Wang, Zhiqiang Liu and Lin Yang

Remote Sens. 2022, 14(21), 5545; https://doi.org/10.3390/rs14215545 - 3 Nov 2022

Cited by 11 | Viewed by 3230

Abstract

Forests are the most important part of terrestrial ecosystems. In the context of China’s industrialization and urbanization, mining activities have caused huge damage to the forest ecology. In the Ulan Mulun River Basin (Ordos, China), afforestation is standard method for reclamation of coal mine degraded land. In order to understand, manage and utilize forests, it is necessary to collect local mining area’s tree information. This paper proposed an improved Faster R-CNN model to identify individual trees. There were three major improved parts in this model. First, the model applied supervised multi-policy data augmentation (DA) to address the unmanned aerial vehicle (UAV) sample label size imbalance phenomenon. Second, we proposed Dense Enhance Feature Pyramid Network (DE-FPN) to improve the detection accuracy of small sample. Third, we modified the state-of-the-art Alpha Intersection over Union (Alpha-IoU) loss function. In the regression stage, this part effectively improved the bounding box accuracy. Compared with the original model, the improved model had the faster effect and higher accuracy. The result shows that the data augmentation strategy increased AP by 1.26%, DE-FPN increased AP by 2.82%, and the improved Alpha-IoU increased AP by 2.60%. Compared with popular target detection algorithms, our improved Faster R-CNN algorithm had the highest accuracy for tree detection in mining areas. AP was 89.89%. It also had a good generalization, and it can accurately identify trees in a complex background. Our algorithm detected correct trees accounted for 91.61%. In the surrounding area of coal mines, the higher the stand density is, the smaller the remote sensing index value is. Remote sensing indices included Green Leaf Index (GLI), Red Green Blue Vegetation Index (RGBVI), Visible Atmospheric Resistance Index (VARI), and Normalized Green Red Difference Index (NGRDI). In the drone zone, the western area of Bulianta Coal Mine (Area A) had the highest stand density, which was 203.95 trees ha⁻¹. GLI mean value was 0.09, RGBVI mean value was 0.17, VARI mean value was 0.04, and NGRDI mean value was 0.04. The southern area of Bulianta Coal Mine (Area D) was 105.09 trees ha⁻¹ of stand density. Four remote sensing indices were all the highest. GLI mean value was 0.15, RGBVI mean value was 0.43, VARI mean value was 0.12, and NGRDI mean value was 0.09. This study provided a sustainable development theoretical guidance for the Ulan Mulun River Basin. It is crucial information for local ecological environment and economic development. Full article

(This article belongs to the Special Issue Applications of Individual Tree Detection (ITD))

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21 pages, 3405 KiB

Open AccessArticle

Refinement of Individual Tree Detection Results Obtained from Airborne Laser Scanning Data for a Mixed Natural Forest

by Nenad Brodić, Željko Cvijetinović, Milutin Milenković, Jovan Kovačević, Nikola Stančić, Momir Mitrović and Dragan Mihajlović

Remote Sens. 2022, 14(21), 5345; https://doi.org/10.3390/rs14215345 - 25 Oct 2022

Cited by 1 | Viewed by 1825

Abstract

Numerous semi- and fully-automatic algorithms have been developed for individual tree detection from airborne laser-scanning data, but different rates of falsely detected treetops also accompany their results. In this paper, we proposed an approach that includes a machine learning-based refinement step to reduce the number of falsely detected treetops. The approach involves the local maxima filtering and segmentation of the canopy height model to extract different segment-level features used for the classification of treetop candidates. The study was conducted in a mixed temperate forest, predominantly deciduous, with a complex topography and an area size of 0.6 km × 4 km. The classification model’s training was performed by five machine learning approaches: Random Forest (RF), Extreme Gradient Boosting, Artificial Neural Network, the Support Vector Machine, and Logistic Regression. The final classification model with optimal hyperparameters was adopted based on the best-performing classifier (RF). The overall accuracy (OA) and kappa coefficient (κ) obtained from the ten-fold cross validation for the training data were 90.4% and 0.808, respectively. The prediction of the test data resulted in an OA = 89.0% and a κ = 0.757. This indicates that the proposed method could be an adequate solution for the reduction of falsely detected treetops before tree crown segmentation, especially in deciduous forests. Full article

(This article belongs to the Special Issue Applications of Individual Tree Detection (ITD))

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18 pages, 9851 KiB

Open AccessArticle

Semantic Segmentation Guided Coarse-to-Fine Detection of Individual Trees from MLS Point Clouds Based on Treetop Points Extraction and Radius Expansion

by Xiaojuan Ning, Yishu Ma, Yuanyuan Hou, Zhiyong Lv, Haiyan Jin and Yinghui Wang

Remote Sens. 2022, 14(19), 4926; https://doi.org/10.3390/rs14194926 - 1 Oct 2022

Cited by 6 | Viewed by 2062

Abstract

Urban trees are vital elements of outdoor scenes via mobile laser scanning (MLS), accurate individual trees detection from disordered, discrete, and high-density MLS is an important basis for the subsequent analysis of city management and planning. However, trees cannot be easily extracted because of the occlusion with other objects in urban scenes. In this work, we propose a coarse-to-fine individual trees detection method from MLS point cloud data (PCD) based on treetop points extraction and radius expansion. Firstly, an improved semantic segmentation deep network based on PointNet is applied to segment tree points from the scanned urban scene, which combining spatial features and dimensional features. Next, through calculating the local maximum, the candidate treetop points are located. In addition, the optimized treetop points are extracted after the tree point projection plane was filtered to locate the candidate treetop points, and a distance rule is used to eliminate the pseudo treetop points then the optimized treetop points are obtained. Finally, after the initial clustering of treetop points and vertical layering of tree points, a top-down layer-by-layer segmentation based on radius expansion to realize the complete individual extraction of trees. The effectiveness of the proposed method is tested and evaluated on five street scenes in point clouds from Oakland outdoor MLS dataset. Furthermore, the proposed method is compared with two existing individual trees segmentation methods. Overall, the precision, recall, and F-score of instance segmentation are 98.33%, 98.33%, and 98.33%, respectively. The results indicate that our method can extract individual trees effectively and robustly in different complex environments. Full article

(This article belongs to the Special Issue Applications of Individual Tree Detection (ITD))

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19 pages, 3566 KiB

Open AccessArticle

Cross-Comparison of Individual Tree Detection Methods Using Low and High Pulse Density Airborne Laser Scanning Data

by Aaron M. Sparks, Mark V. Corrao and Alistair M. S. Smith

Remote Sens. 2022, 14(14), 3480; https://doi.org/10.3390/rs14143480 - 20 Jul 2022

Cited by 13 | Viewed by 4231

Abstract

Numerous individual tree detection (ITD) methods have been developed for use with airborne laser scanning (ALS) data to provide tree-scale forest inventories across large spatial extents. Despite the growing number of methods, relatively few have been comparatively assessed using a single benchmark forest inventory validation dataset, limiting their operational application. In this study, we assessed seven ITD methods, representing three common approaches (point-cloud-based, raster-based, hybrid), across coniferous forest stands with diverse structure and composition to understand how ITD and height measurement accuracy vary with method, input parameters and data, and stand density. There was little variability in accuracy between the ITD methods where the average F-score and standard deviation (±SD) were 0.47 ± 0.03 using a lower pulse density ALS dataset with an average of 8 pulses per square meter (ppm²) and 0.50 ± 0.02 using a higher pulse density ALS dataset with an average of 22 ppm². Using higher ALS pulse density data produced higher ITD accuracies (F-score increase of 10–13%) in some of the methods versus more modest gains in other methods (F-score increase of 1–3%). Omission errors were strongly related with stand density and largely consisted of suppressed trees underneath the dominant canopy. Simple canopy height model (CHM)-based methods that utilized fixed-size local maximum filters had the lowest omission errors for trees across all canopy positions. ITD accuracy had large intra-method variation depending on input parameters; however, the highest accuracies were obtained when parameters such as search window size and spacing thresholds were equal to or less than the average crown diameter of trees in the study area. All ITD methods produced height measurements for the detected trees that had low RMSE (<1.1 m) and bias (<0.5 m). Overall, the results from this study may help guide end-users with ITD method application and highlight future ITD method improvements. Full article

(This article belongs to the Special Issue Applications of Individual Tree Detection (ITD))

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19 pages, 18950 KiB

Open AccessArticle

Fast Tree Skeleton Extraction Using Voxel Thinning Based on Tree Point Cloud

by Jingqian Sun, Pei Wang, Ronghao Li, Mei Zhou and Yuhan Wu

Remote Sens. 2022, 14(11), 2558; https://doi.org/10.3390/rs14112558 - 26 May 2022

Cited by 8 | Viewed by 3104

Abstract

Tree skeletons play an important role in tree structure analysis and 3D model reconstruction. However, it is a challenge to extract a skeleton from a tree point cloud with complex branches. In this paper, an automatic and fast tree skeleton extraction method (FTSEM) based on voxel thinning is proposed. In this method, a wood–leaf classification algorithm was introduced to filter leaf points for the reduction of the leaf interference on tree skeleton generation, tree voxel thinning was adopted to extract a raw tree skeleton quickly, and a breakpoint connection algorithm was used to improve the skeleton connectivity and completeness. Experiments were carried out in Haidian Park, Beijing, in which 24 trees were scanned and processed to obtain tree skeletons. The graph search algorithm (GSA) was used to extract tree skeletons based on the same datasets. Compared with the GSA method, the FTSEM method obtained more complete tree skeletons. The time cost of the FTSEM method was evaluated using the runtime and time per million points (TPMP). The runtime of FTSEM was from 1.0 s to 13.0 s, and the runtime of GSA was from 6.4 s to 309.3 s. The average value of TPMP was 1.8 s for FTSEM and 22.3 s for GSA, respectively. The experimental results demonstrate that the proposed method is feasible, robust, and fast with good potential for tree skeleton extraction. Full article

(This article belongs to the Special Issue Applications of Individual Tree Detection (ITD))

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22 pages, 4282 KiB

Open AccessArticle

Predicting Individual Tree Diameter of Larch (Larix olgensis) from UAV-LiDAR Data Using Six Different Algorithms

by Yusen Sun, Xingji Jin, Timo Pukkala and Fengri Li

Remote Sens. 2022, 14(5), 1125; https://doi.org/10.3390/rs14051125 - 24 Feb 2022

Cited by 16 | Viewed by 2861

Abstract

Individual tree detection is an increasing trend in LiDAR-based forest inventories. The locations, heights, and crown areas of the detected trees can be estimated rather directly from the LiDAR data by using the LiDAR-based canopy height model and segmentation methods to delineate the tree crowns. However, the most important tree variable is the diameter of the tree stem at the breast height (DBH) which can seldom be interpreted directly from the LiDAR data. Therefore, the use of individually detected trees in forest planning calculations requires predictions for the DBH. This study tested six methods for predicting the DBH from laser scanning data collected by an unmanned aerial vehicle from Larix olgensis plantations located in northeast China. The tested methods were the linear regression model (LM), a linear model with ridge regularization (LMR), support vector regression (SVR), random forest (RF), artificial neural network (ANN), and the k-nearest neighbors (KNN) method. Both tree-level and stand-level metrics derived from the LiDAR point cloud data (for instance percentiles of the height distribution of the echoes) were used as potential predictors of DBH. Compared to the LM, all other methods improved the accuracy of the predictions. On the other hand, all methods tended to underestimate the DBH of the largest trees, which could be due to the inability of the methods to sufficiently describe nonlinear relationships unless different transformations of the LiDAR metrics are used as predictors. The support vector regression was evaluated to be the best method for predicting individual tree diameters from LiDAR data. The benefits of the methods tested in this study can be expected to be the highest in the case of little prior knowledge on the relationships between the predicted variable and predictors, a high number of potential predictors, and strong mutual correlations among the potential predictors. Full article

(This article belongs to the Special Issue Applications of Individual Tree Detection (ITD))

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22 pages, 22567 KiB

Open AccessArticle

Multi-Species Individual Tree Segmentation and Identification Based on Improved Mask R-CNN and UAV Imagery in Mixed Forests

by Chong Zhang, Jiawei Zhou, Huiwen Wang, Tianyi Tan, Mengchen Cui, Zilu Huang, Pei Wang and Li Zhang

Remote Sens. 2022, 14(4), 874; https://doi.org/10.3390/rs14040874 - 11 Feb 2022

Cited by 51 | Viewed by 6951

Abstract

High-resolution UAV imagery paired with a convolutional neural network approach offers significant advantages in accurately measuring forestry ecosystems. Despite numerous studies existing for individual tree crown delineation, species classification, and quantity detection, the comprehensive situation in performing the above tasks simultaneously has rarely been explored, especially in mixed forests. In this study, we propose a new method for individual tree segmentation and identification based on the improved Mask R-CNN. For the optimized network, the fusion type in the feature pyramid network is modified from down-top to top-down to shorten the feature acquisition path among the different levels. Meanwhile, a boundary-weighted loss module is introduced to the cross-entropy loss function L_mask to refine the target loss. All geometric parameters (contour, the center of gravity and area) associated with canopies ultimately are extracted from the mask by a boundary segmentation algorithm. The results showed that F1-score and mAP for coniferous species were higher than 90%, and that of broadleaf species were located between 75–85.44%. The producer’s accuracy of coniferous forests was distributed between 0.8–0.95 and that of broadleaf ranged in 0.87–0.93; user’s accuracy of coniferous was distributed between 0.81–0.84 and that of broadleaf ranged in 0.71–0.76. The total number of trees predicted was 50,041 for the entire study area, with an overall error of 5.11%. The method under study is compared with other networks including U-net and YOLOv3. Results in this study show that the improved Mask R-CNN has more advantages in broadleaf canopy segmentation and number detection. Full article

(This article belongs to the Special Issue Applications of Individual Tree Detection (ITD))

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14 pages, 2158 KiB

Open AccessArticle

UAV-Based LiDAR Scanning for Individual Tree Detection and Height Measurement in Young Forest Permanent Trials

by Francisco Rodríguez-Puerta, Esteban Gómez-García, Saray Martín-García, Fernando Pérez-Rodríguez and Eva Prada

Remote Sens. 2022, 14(1), 170; https://doi.org/10.3390/rs14010170 - 31 Dec 2021

Cited by 12 | Viewed by 5648

Abstract

The installation of research or permanent plots is a very common task in growth and forest yield research. At young ages, tree height is the most commonly measured variable, so the location of individuals is necessary when repeated measures are taken and if spatial analysis is required. Identifying the coordinates of individual trees and re-measuring the height of all trees is difficult and particularly costly (in time and money). The data used comes from three Pinus pinaster Ait. and three Pinus radiata D. Don plantations of 0.8 ha, with an age ranging between 2 and 5 years and mean heights between 1 and 5 m. Five individual tree detection (ITD) methods are evaluated, based on the Canopy Height Model (CHM), where the height of each tree is identified, and its crown is segmented. Three CHM resolutions are used for each method. All algorithms used for individual tree detection (ITD) tend to underestimate the number of trees. The best results are obtained with the R package, ForestTools and rLiDAR. The best CHM resolution for identifying trees was always 10 cm. We did not detect any differences in the relative error (RE) between Pinus pinaster and Pinus radiata. We found a pattern in the ITD depending on the height of the trees to be detected: the accuracy is lower when detecting trees less than 1 m high than when detecting larger trees (RE close to 12% versus 1% for taller trees). Regarding the estimation of tree height, we can conclude that the use of the CHM to estimate height tends to underestimate its value, while the use of the point cloud presents practically unbiased results. The stakeout of forestry research plots and the re-measurement of individual tree heights is an operation that can be performed by UAV-based LiDAR scanning sensors. The individual geolocation of each tree and the measurement of heights versus pole and/or hypsometer measurement is highly accurate and cost-effective, especially when tree height reaches 1–1.5 m. Full article

(This article belongs to the Special Issue Applications of Individual Tree Detection (ITD))

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21 pages, 12221 KiB

Open AccessArticle

Individual Tree Detection and Qualitative Inventory of a Eucalyptus sp. Stand Using UAV Photogrammetry Data

by André Almeida, Fabio Gonçalves, Gilson Silva, Adriano Mendonça, Maria Gonzaga, Jeferson Silva, Rodolfo Souza, Igor Leite, Karina Neves, Marcus Boeno and Braulio Sousa

Remote Sens. 2021, 13(18), 3655; https://doi.org/10.3390/rs13183655 - 13 Sep 2021

Cited by 14 | Viewed by 4241

Abstract

Digital aerial photogrammetry (DAP) data acquired by unmanned aerial vehicles (UAV) have been increasingly used for forest inventory and monitoring. In this study, we evaluated the potential of UAV photogrammetry data to detect individual trees, estimate their heights (ht), and monitor the initial silvicultural quality of a 1.5-year-old Eucalyptus sp. stand in northeastern Brazil. DAP estimates were compared with accurate tree locations obtained with real time kinematic (RTK) positioning and direct height measurements obtained in the field. In addition, we assessed the quality of a DAP-UAV digital terrain model (DTM) derived using an alternative ground classification approach and investigated its performance in the retrieval of individual tree attributes. The DTM built for the stand presented an RMSE of 0.099 m relative to the RTK measurements, showing no bias. The normalized 3D point cloud enabled the identification of over 95% of the stand trees and the estimation of their heights with an RMSE of 0.36 m (11%). However, ht was systematically underestimated, with a bias of 0.22 m (6.7%). A linear regression model, was fitted to estimate tree height from a maximum height metric derived from the point cloud reduced the RMSE by 20%. An assessment of uniformity indices calculated from both field and DAP heights showed no statistical difference. The results suggest that products derived from DAP-UAV may be used to generate accurate DTMs in young Eucalyptus sp. stands, detect individual trees, estimate ht, and determine stand uniformity with the same level of accuracy obtained in traditional forest inventories. Full article

(This article belongs to the Special Issue Applications of Individual Tree Detection (ITD))

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15 pages, 5763 KiB

Open AccessArticle

Towards Amazon Forest Restoration: Automatic Detection of Species from UAV Imagery

by Marks Melo Moura, Luiz Eduardo Soares de Oliveira, Carlos Roberto Sanquetta, Alexis Bastos, Midhun Mohan and Ana Paula Dalla Corte

Remote Sens. 2021, 13(13), 2627; https://doi.org/10.3390/rs13132627 - 4 Jul 2021

Cited by 26 | Viewed by 4387

Abstract

Precise assessments of forest species’ composition help analyze biodiversity patterns, estimate wood stocks, and improve carbon stock estimates. Therefore, the objective of this work was to evaluate the use of high-resolution images obtained from Unmanned Aerial Vehicle (UAV) for the identification of forest species in areas of forest regeneration in the Amazon. For this purpose, convolutional neural networks (CNN) were trained using the Keras–Tensorflow package with the faster_rcnn_inception_v2_pets model. Samples of six forest species were used to train CNN. From these, attempts were made with the number of thresholds, which is the cutoff value of the function; any value below this output is considered 0, and values above are treated as an output 1; that is, values above the value stipulated in the Threshold are considered as identified species. The results showed that the reduction in the threshold decreases the accuracy of identification, as well as the overlap of the polygons of species identification. However, in comparison with the data collected in the field, it was observed that there exists a high correlation between the trees identified by the CNN and those observed in the plots. The statistical metrics used to validate the classification results showed that CNN are able to identify species with accuracy above 90%. Based on our results, which demonstrate good accuracy and precision in the identification of species, we conclude that convolutional neural networks are an effective tool in classifying objects from UAV images. Full article

(This article belongs to the Special Issue Applications of Individual Tree Detection (ITD))

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21 pages, 9217 KiB

Open AccessArticle

Individual Tree Diameter Estimation in Small-Scale Forest Inventory Using UAV Laser Scanning

by Yuanshuo Hao, Faris Rafi Almay Widagdo, Xin Liu, Ying Quan, Lihu Dong and Fengri Li

Remote Sens. 2021, 13(1), 24; https://doi.org/10.3390/rs13010024 - 23 Dec 2020

Cited by 28 | Viewed by 5073

Abstract

Unmanned aerial vehicle laser scanning (UAVLS) systems present a relatively new means of remote sensing and are increasingly applied in the field of forest ecology and management. However, one of the most essential parameters in forest inventory, tree diameter at breast height (DBH), cannot be directly extracted from aerial point cloud data due to the limitations of scanning angle and canopy obstruction. Therefore, in this study DBH-UAVLS point cloud estimation models were established using a generalized nonlinear mixed-effects (NLME) model. The experiments were conducted using Larix olgensis as the subject species, and a total of 8364 correctly delineated trees from UAVLS data within 118 plots across 11 sites were used for DBH modeling. Both tree- and plot-level metrics were obtained using light detection and ranging (LiDAR) and were used as the models’ independent predictors. The results indicated that the addition of site-level random effects significantly improved the model fitting. Compared with nonparametric modeling approaches (random forest and k-nearest neighbors) and uni- or multivariable weighted nonlinear least square regression through leave-one-site-out cross-validation, the NLME model with local calibration achieved the lowest root mean square error (RMSE) values (1.94 cm) and the most stable prediction across different sites. Using the site in a random-effects model improved the transferability of LiDAR-based DBH estimation. The best linear unbiased predictor (BLUP), used to conduct local model calibration, led to an improvement in the models’ performance as the number of field measurements increased. The research provides a baseline for unmanned aerial vehicle (UAV) small-scale forest inventories and might be a reasonable alternative for operational forestry. Full article

(This article belongs to the Special Issue Applications of Individual Tree Detection (ITD))

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20 pages, 28742 KiB

Open AccessEditor’s ChoiceArticle

Individual Tree Attribute Estimation and Uniformity Assessment in Fast-Growing Eucalyptus spp. Forest Plantations Using Lidar and Linear Mixed-Effects Models

by Rodrigo Vieira Leite, Carlos Alberto Silva, Midhun Mohan, Adrián Cardil, Danilo Roberti Alves de Almeida, Samuel de Pádua Chaves e Carvalho, Wan Shafrina Wan Mohd Jaafar, Juan Guerra-Hernández, Aaron Weiskittel, Andrew T. Hudak, Eben N. Broadbent, Gabriel Prata, Ruben Valbuena, Hélio Garcia Leite, Mariana Futia Taquetti, Alvaro Augusto Vieira Soares, Henrique Ferraço Scolforo, Cibele Hummel do Amaral, Ana Paula Dalla Corte and Carine Klauberg

Remote Sens. 2020, 12(21), 3599; https://doi.org/10.3390/rs12213599 - 2 Nov 2020

Cited by 23 | Viewed by 8072

Abstract

Fast-growing Eucalyptus spp. forest plantations and their resultant wood products are economically important and may provide a low-cost means to sequester carbon for greenhouse gas reduction. The development of advanced and optimized frameworks for estimating forest plantation attributes from lidar remote sensing data combined with statistical modeling approaches is a step towards forest inventory operationalization and might improve industry efficiency in monitoring and managing forest resources. In this study, we first developed and tested a framework for modeling individual tree attributes in fast-growing Eucalyptus forest plantation using airborne lidar data and linear mixed-effect models (LME) and assessed the gain in accuracy compared to a conventional linear fixed-effects model (LFE). Second, we evaluated the potential of using the tree-level estimates for determining tree attribute uniformity across different stand ages. In the field, tree measurements, such as tree geolocation, species, genotype, age, height (Ht), and diameter at breast height (dbh) were collected through conventional forest inventory practices, and tree-level aboveground carbon (AGC) was estimated using allometric equations. Individual trees were detected and delineated from lidar-derived canopy height models (CHM), and crown-level metrics (e.g., crown volume and crown projected area) were computed from the lidar 3-D point cloud. Field and lidar-derived crown metrics were combined for ht, dbh, and AGC modeling using an LME. We fitted a varying intercept and slope model, setting species, genotype, and stand (alone and nested) as random effects. For comparison, we also modeled the same attributes using a conventional LFE model. The tree attribute estimates derived from the best LME model were used for assessing forest uniformity at the tree level using the Lorenz curves and Gini coefficient (GC). We successfully detected 96.6% of the trees from the lidar-derived CHM. The best LME model for estimating the tree attributes was composed of the stand as a random effect variable, and canopy height, crown volume, and crown projected area as fixed effects. The %RMSE values for tree-level height, dbh, and AGC were 8.9%, 12.1%, and 23.7% for the LFE model and improved to 7.3%, 7.1%, and 13.6%, respectively, for the LME model. Tree attributes uniformity was assessed with the Lorenz curves and tree-level estimations, especially for the older stands. All stands showed a high level of tree uniformity with GC values approximately 0.2. This study demonstrates that accurate detection of individual trees and their associated crown metrics can be used to estimate Ht, dbh, and AGC stocks as well as forest uniformity in fast-growing Eucalyptus plantations forests using lidar data as inputs to LME models. This further underscores the high potential of our proposed approach to monitor standing stock and growth in Eucalyptus—and similar forest plantations for carbon dynamics and forest product planning. Full article

(This article belongs to the Special Issue Applications of Individual Tree Detection (ITD))

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Remotely Sensed Tree Characterization in Urban Areas: A Review

by Luisa Velasquez-Camacho, Adrián Cardil, Midhun Mohan, Maddi Etxegarai, Gabriel Anzaldi and Sergio de-Miguel

Remote Sens. 2021, 13(23), 4889; https://doi.org/10.3390/rs13234889 - 2 Dec 2021

Cited by 11 | Viewed by 5583

Abstract

Urban trees and forests provide multiple ecosystem services (ES), including temperature regulation, carbon sequestration, and biodiversity. Interest in ES has increased amongst policymakers, scientists, and citizens given the extent and growth of urbanized areas globally. However, the methods and techniques used to properly assess biodiversity and ES provided by vegetation in urban environments, at large scales, are insufficient. Individual tree identification and characterization are some of the most critical issues used to evaluate urban biodiversity and ES, given the complex spatial distribution of vegetation in urban areas and the scarcity or complete lack of systematized urban tree inventories at large scales, e.g., at the regional or national levels. This often limits our knowledge on their contributions toward shaping biodiversity and ES in urban areas worldwide. This paper provides an analysis of the state-of-the-art studies and was carried out based on a systematic review of 48 scientific papers published during the last five years (2016–2020), related to urban tree and greenery characterization, remote sensing techniques for tree identification, processing methods, and data analysis to classify and segment trees. In particular, we focused on urban tree and forest characterization using remotely sensed data and identified frontiers in scientific knowledge that may be expanded with new developments in the near future. We found advantages and limitations associated with both data sources and processing methods, from which we drew recommendations for further development of tree inventory and characterization in urban forestry science. Finally, a critical discussion on the current state of the methods, as well as on the challenges and directions for future research, is presented. Full article

(This article belongs to the Special Issue Applications of Individual Tree Detection (ITD))

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