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UAS-Based Lidar and Imagery Data for Forest

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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 (20 February 2024) | Viewed by 18411

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Dr. Francesca Giannetti

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GEOLAB - Laboratory of Forest Geomatics, Università degli Studi di Firenze, Florence, Italy
Interests: forests; mapping natural resources; forest management; remote sensing to map forest resources; UAV forest applications
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Arturo Sanchez-Azofeifa

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Guest Editor

Earth and Atmospheric Sciences Department, University of Alberta, Edmonton, AB T6G 2R3, Canada
Interests: multi and hyper-spectral remote sensing; ecosystem succession; time series trend-analysis; geostatistics; spatial modeling; wireless sensor networks
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Special Issue Information

Dear Colleagues,

Unmanned aerial vehicle (UAV) applications are rapidly expanding and revolutionizing remote sensing for forest monitoring. UAV platforms provide a unique opportunity to acquire high-resolution 3D and 2D data using LiDAR or digital structure-from-motion photogrammetry. These data can improve the efficiency of forest inventories for small-scale forest management and large-scale forest inventories when the data are linked, for example, with satellite imagery. In addition, thanks to their ability to capture very fine resolution information on the forest structure, UAVs are increasingly used to map or estimate not only classical forest inventory variables (e.g., growing stock volume, tree species composition, and biomass) but also biodiversity indicators (e.g., microhabitat, canopy cover, and gaps), impact on soils by forest operations (e.g., soils displacement), and forest disturbances monitoring (e.g., fire and windstorm damage). Nonetheless, it is not yet clear which UAV applications are cost-effective and accurate in forest applications. The lack of such information is currently hindering the extensive operational use of UAVs in the forest sector. The Special Issue examines the potential of using UAV-based LiDAR and UAV imagery data in forest applications to map and estimate forest variables at stand level and/or tree level. Research papers that focus on both forest metrics and methodological development are welcome. This Special Issue aims to collect new application and innovative data elaboration methodologies that use UAV-based LiDAR and UAV imagery data in research applications, focusing on:

forest inventory;
forest management;
forest canopy height and attributes;
biomass estimation;
forest disturbances;
forest biodiversity indicators;
canopy gaps;
soils displacement after forest operations.

Dr. Francesca Giannetti
Prof. Dr. Arturo Sanchez-Azofeifa
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (7 papers)

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22 pages, 6511 KiB

Open AccessArticle

Studying Tropical Dry Forests Secondary Succession (2005–2021) Using Two Different LiDAR Systems

by Chenzherui Liu, Arturo Sanchez-Azofeifa and Connor Bax

Remote Sens. 2023, 15(19), 4677; https://doi.org/10.3390/rs15194677 - 24 Sep 2023

Cited by 1 | Viewed by 1183

Abstract

Chronosequence changes among Tropical Dry Forests (TDFs) are essential for understanding this unique ecosystem, which is characterized by its seasonality (wet and dry) and a high diversity of deciduous trees and shrubs. From 2005 to 2021, we used two different airborne LiDAR systems to quantify structural changes in the forest at Santa Rosa National Park. Line- and shape-based waveform metrics were used to record the overall changes in the TDF structure. Based on a 16-year growth analysis, notable variations in height-related profiles were observed, particularly for RH50, RH100, and waveform-produced canopy heights. The results showed that Cy and RG have increased since the forests have been growing, whereas Cx has decreased. The decrease in Cx is because ground returns are lower when the canopy density i and canopy height increase. A positive relationship was observed between Cy and CH, RG, and RH100, particularly for the wet season data collected in 2021. These findings provide important insights into the growth dynamics of TDFs in Santa Rosa National Park and could inform future conservation efforts. Full article

(This article belongs to the Special Issue UAS-Based Lidar and Imagery Data for Forest)

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19 pages, 67087 KiB

Open AccessArticle

Estimating Fractional Vegetation Cover Changes in Desert Regions Using RGB Data

by Lu Xie, Xiang Meng, Xiaodi Zhao, Liyong Fu, Ram P. Sharma and Hua Sun

Remote Sens. 2022, 14(15), 3833; https://doi.org/10.3390/rs14153833 - 08 Aug 2022

Cited by 8 | Viewed by 2502

Abstract

Fractional vegetation cover (FVC) is an important indicator of ecosystem changes. Both satellite remote sensing and ground measurements are common methods for estimating FVC. However, desert vegetation grows sparsely and scantly and spreads widely in desert regions, making it challenging to accurately estimate its vegetation cover using satellite data. In this study, we used RGB images from two periods: images from 2006 captured with a small, light manned aircraft with a resolution of 0.1 m and images from 2019 captured with an unmanned aerial vehicle (UAV) with a resolution of 0.02 m. Three pixel-based machine learning algorithms, namely gradient enhancement decision tree (GBDT), k-nearest neighbor (KNN) and random forest (RF), were used to classify the main vegetation (woody and grass species) and calculate the coverage. An independent data set was used to evaluate the accuracy of the algorithms. Overall accuracies of GBDT, KNN and RF for 2006 image classification were 0.9140, 0.9190 and 0.9478, respectively, with RF achieving the best classification results. Overall accuracies of GBDT, KNN and RF for 2019 images were 0.8466, 0.8627 and 0.8569, respectively, with the KNN algorithm achieving the best results for vegetation cover classification. The vegetation coverage in the study area changed significantly from 2006 to 2019, with an increase in grass coverage from 15.47 ± 1.49% to 27.90 ± 2.79%. The results show that RGB images are suitable for mapping FVC. Determining the best spatial resolution for different vegetation features may make estimation of desert vegetation coverage more accurate. Vegetation cover changes are also important in terms of understanding the evolution of desert ecosystems. Full article

(This article belongs to the Special Issue UAS-Based Lidar and Imagery Data for Forest)

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16 pages, 1055 KiB

Open AccessArticle

UAV-Based Hyperspectral Imagery for Detection of Root, Butt, and Stem Rot in Norway Spruce

by Benjamin Allen, Michele Dalponte, Hans Ole Ørka, Erik Næsset, Stefano Puliti, Rasmus Astrup and Terje Gobakken

Remote Sens. 2022, 14(15), 3830; https://doi.org/10.3390/rs14153830 - 08 Aug 2022

Cited by 3 | Viewed by 2128

Abstract

Numerous species of pathogenic wood decay fungi, including members of the genera Heterobasidion and Armillaria, exist in forests in the northern hemisphere. Detection of these fungi through field surveys is often difficult due to a lack of visual symptoms and is cost-prohibitive for most applications. Remotely sensed data can offer a lower-cost alternative for collecting information about vegetation health. This study used hyperspectral imagery collected from unmanned aerial vehicles (UAVs) to detect the presence of wood decay in Norway spruce (Picea abies L. Karst) at two sites in Norway. UAV-based sensors were tested as they offer flexibility and potential cost advantages for small landowners. Ground reference data regarding pathogenic wood decay were collected by harvest machine operators and field crews after harvest. Support vector machines were used to classify the presence of root, butt, and stem rot infection. Classification accuracies as high as 76% with a kappa value of 0.24 were obtained with 490-band hyperspectral imagery, while 29-band imagery provided a lower classification accuracy (~60%, kappa = 0.13). Full article

(This article belongs to the Special Issue UAS-Based Lidar and Imagery Data for Forest)

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31 pages, 11421 KiB

Open AccessArticle

Comparative Evaluation of a Newly Developed Trunk-Based Tree Detection/Localization Strategy on Leaf-Off LiDAR Point Clouds with Varying Characteristics

by Tian Zhou, Renato César dos Santos, Jidong Liu, Yi-Chun Lin, William Changhao Fei, Songlin Fei and Ayman Habib

Remote Sens. 2022, 14(15), 3738; https://doi.org/10.3390/rs14153738 - 04 Aug 2022

Cited by 3 | Viewed by 2128

Abstract

LiDAR data acquired by various platforms provide unprecedented data for forest inventory and management. Among its applications, individual tree detection and segmentation are critical and prerequisite steps for deriving forest structural metrics, especially at the stand level. Although there are various tree detection and localization approaches, a comparative analysis of their performance on LiDAR data with different characteristics remains to be explored. In this study, a new trunk-based tree detection and localization approach (namely, height-difference-based) is proposed and compared to two state-of-the-art strategies—DBSCAN-based and height/density-based approaches. Leaf-off LiDAR data from two unmanned aerial vehicles (UAVs) and Geiger mode system with different point densities, geometric accuracies, and environmental complexities were used to evaluate the performance of these approaches in a forest plantation. The results from the UAV datasets suggest that DBSCAN-based and height/density-based approaches perform well in tree detection (F1 score > 0.99) and localization (with an accuracy of 0.1 m for point clouds with high geometric accuracy) after fine-tuning the model thresholds; however, the processing time of the latter is much shorter. Even though our new height-difference-based approach introduces more false positives, it obtains a high tree detection rate from UAV datasets without fine-tuning model thresholds. However, due to the limitations of the algorithm, the tree localization accuracy is worse than that of the other two approaches. On the other hand, the results from the Geiger mode dataset with low point density show that the performance of all approaches dramatically deteriorates. Among them, the proposed height-difference-based approach results in the greatest number of true positives and highest F1 score, making it the most suitable approach for low-density point clouds without the need for parameter/threshold fine-tuning. Full article

(This article belongs to the Special Issue UAS-Based Lidar and Imagery Data for Forest)

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25 pages, 6179 KiB

Open AccessArticle

Integrating UAV-SfM and Airborne Lidar Point Cloud Data to Plantation Forest Feature Extraction

by Tatsuki Yoshii, Naoto Matsumura and Chinsu Lin

Remote Sens. 2022, 14(7), 1713; https://doi.org/10.3390/rs14071713 - 01 Apr 2022

Cited by 7 | Viewed by 3164

Abstract

A low-cost but accurate remote-sensing-based forest-monitoring tool is necessary for regularly inventorying tree-level parameters and stand-level attributes to achieve sustainable management of timber production forests. Lidar technology is precise for multi-temporal data collection but expensive. A low-cost UAV-based optical sensing method is an economical and flexible alternative for collecting high-resolution images for generating point cloud data and orthophotos for mapping but lacks height accuracy. This study proposes a protocol of integrating a UAV equipped without an RTK instrument and airborne lidar sensors (ALS) for characterizing tree parameters and stand attributes for use in plantation forest management. The proposed method primarily relies on the ALS-based digital elevation model data (ALS-DEM), UAV-based structure-from-motion technique generated digital surface model data (UAV-SfM-DSM), and their derivative canopy height model data (UAV-SfM-CHM). Following traditional forest inventory approaches, a few middle-aged and mature stands of Hinoki cypress (Chamaecyparis obtusa) plantation forests were used to investigate the performance of characterizing forest parameters via the canopy height model. Results show that the proposed method can improve UAV-SfM point cloud referencing transformation accuracy. With the derived CHM data, this method can estimate tree height with an RMSE ranging from 0.43 m to 1.65 m, equivalent to a PRMSE of 2.40–7.84%. The tree height estimates between UAV-based and ALS-based approaches are highly correlated (R² = 0.98, p < 0.0001), similarly, the height annual growth rate (HAGR) is also significantly correlated (R² = 0.78, p < 0.0001). The percentage HAGR of Hinoki trees behaves as an exponential decay function of the tree height over an 8-year management period. The stand-level parameters stand density, stand volume stocks, stand basal area, and relative spacing are with an error rate of less than 20% for both UAV-based and ALS-based approaches. Intensive management with regular thinning helps the plantation forests retain a clear crown shape feature, therefore, benefitting tree segmentation for deriving tree parameters and stand attributes. Full article

(This article belongs to the Special Issue UAS-Based Lidar and Imagery Data for Forest)

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18 pages, 16237 KiB

Open AccessArticle

Integrating Spectral and Textural Information for Monitoring the Growth of Pear Trees Using Optical Images from the UAV Platform

by Yahui Guo, Shouzhi Chen, Zhaofei Wu, Shuxin Wang, Christopher Robin Bryant, Jayavelu Senthilnath, Mario Cunha and Yongshuo H. Fu

Remote Sens. 2021, 13(9), 1795; https://doi.org/10.3390/rs13091795 - 05 May 2021

Cited by 19 | Viewed by 3126

Abstract

With the recent developments of unmanned aerial vehicle (UAV) remote sensing, it is possible to monitor the growth condition of trees with the high temporal and spatial resolutions of data. In this study, the daily high-throughput RGB images of pear trees were captured from a UAV platform. A new index was generated by integrating the spectral and textural information using the improved adaptive feature weighting method (IAFWM). The inter-relationships of the air climatic variables and the soil’s physical properties (temperature, humidity and conductivity) were firstly assessed using principal component analysis (PCA). The climatic variables were selected to independently build a linear regression model with the new index when the cumulative variance explained reached 99.53%. The coefficient of determination (R²) of humidity (R² = 0.120, p = 0.205) using linear regression analysis was the dominating influencing factor for the growth of the pear trees, among the air climatic variables tested. The humidity (%) in 40 cm depth of soil (R² = 0.642, p < 0.001) using a linear regression coefficient was the largest among climatic variables in the soil. The impact of climatic variables on the soil was commonly greater than those in the air, and the R² grew larger with the increasing depth of soil. The effects of the fluctuation of the soil-climatic variables on the pear trees’ growth could be detected using the sliding window method (SWM), and the maximum absolute value of coefficients with the corresponding day of year (DOY) of air temperature, soil temperature, soil humidity, and soil conductivity were confirmed as 221, 227, 228, and 226 (DOY), respectively. Thus, the impact of the fluctuation of climatic variables on the growth of pear trees can last 14, 8, 7, and 9 days, respectively. Therefore, it is highly recommended that the adoption of the integrated new index to explore the long-time impact of climate on pears growth be undertaken. Full article

(This article belongs to the Special Issue UAS-Based Lidar and Imagery Data for Forest)

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15 pages, 6026 KiB

Open AccessTechnical Note

Predicting Tree Mortality Using Spectral Indices Derived from Multispectral UAV Imagery

by Kai O. Bergmüller and Mark C. Vanderwel

Remote Sens. 2022, 14(9), 2195; https://doi.org/10.3390/rs14092195 - 04 May 2022

Cited by 11 | Viewed by 2557

Abstract

Past research has shown that remotely sensed spectral information can be used to predict tree health and vitality. Recent developments in unmanned aerial vehicles (UAVs) have now made it possible to derive such information at the tree and stand scale from high-resolution imagery. We used visible and multispectral bands from UAV imagery to calculate a set of spectral indices for 52,845 individual tree crowns within 38 forest stands in western Canada. We then used those indices to predict the mortality of these canopy trees over the following year. We evaluated whether including multispectral indices leads to more accurate predictions than indices derived from visible wavelengths alone and how the performance varies among three different tree species (Picea glauca, Pinus contorta, Populus tremuloides). Our results show that spectral information can be effectively used to predict tree mortality, with a random forest model producing a mean area under the receiver operating characteristic curve (AUC) of 89.8% and a balanced accuracy of 83.3%. The exclusion of multispectral indices worsened the model performance, but only slightly (AUC = 87.9%, balanced accuracy = 81.8%). We found variation in model performance among species, with higher accuracy for the broadleaf species (balanced accuracy = 85.2%) than the two conifer species (balanced accuracy = 73.3% and 77.8%). However, all models overpredicted tree mortality by a major degree, which limits the use for tree mortality predictions on an individual level. Further improvements such as long-term monitoring, the use of hyperspectral data and cost-sensitive learning algorithms, and training the model with a larger and more balanced data set are necessary. Nevertheless, our results demonstrate that imagery from UAVs has strong potential for predicting annual mortality for individual canopy trees. Full article

(This article belongs to the Special Issue UAS-Based Lidar and Imagery Data for Forest)

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