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Remote Sensing Applications in Forests Inventory and Management

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A special issue of Forests (ISSN 1999-4907). This special issue belongs to the section "Forest Inventory, Modeling and Remote Sensing".

Deadline for manuscript submissions: closed (20 July 2021) | Viewed by 51246

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Special Issue Editor

Prof. Dr. John Couture

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Guest Editor

Departments of Entomology and Forestry and Natural Resources and Purdue Center for Plant Biology, Purdue University, West Lafayette, IN 47907, USA
Interests: landscape ecology; environmental change; plant and insect ecology; spectroscopy; remote sensing; forestry and natural resources

Special Issue Information

Dear Colleagues,

Managed forest systems contribute substantially to local, national, and global economies. Forests worldwide are a substantial contributor of terrestrial biomass productivity and carbon storage and also provide a number of ecosystem services that benefit human welfare, including climate regulation, generating forest products, fostering wildlife resources and conservation values, increasing education and scientific services, and maintaining biodiversity. Forest systems face logistical issues with traditional monitoring approaches considering the scale and size of plantations, highlighting the need to identify other approaches to help to manage forest systems. The aim of this Special Issue is to highlight novel technological approaches using remote sensing products or products with remote sensing capabilities in forest management. The scope of this Special Issue includes a broad collection of research that uses remote sensing technologies to advance forest management, and the Special Issue will include the most recent advancements in the use of imagery and analyses to assess forest inventory and management. We are soliciting papers that address current and relevant issues in monitoring forest inventory and management that use remote sensing or remote sensing based products.

Prof. Dr. John Couture
Guest Editor

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. Forests is an international peer-reviewed open access monthly 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 2600 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.

Keywords

forest management
forest inventory
forests pests
hyperspectral data
LiDAR
photogrammetry
remote sensing
RGB imagery
satellite imagery
UAV imagery

Published Papers (14 papers)

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Research

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27 pages, 43715 KiB

Open AccessArticle

Simplifying UAV-Based Photogrammetry in Forestry: How to Generate Accurate Digital Terrain Model and Assess Flight Mission Settings

by Facundo Pessacg, Francisco Gómez-Fernández, Matías Nitsche, Nicolás Chamo, Sebastián Torrella, Rubén Ginzburg and Pablo De Cristóforis

Forests 2022, 13(2), 173; https://0-doi-org.brum.beds.ac.uk/10.3390/f13020173 - 24 Jan 2022

Cited by 5 | Viewed by 3843

Abstract

In forestry, aerial photogrammetry by means of Unmanned Aerial Systems (UAS) could bridge the gap between detailed fieldwork and broad-range satellite imagery-based analysis. However, optical sensors are only poorly capable of penetrating the tree canopy, causing raw image-based point clouds unable to reliably collect and classify ground points in woodlands, which is essential for further data processing. In this work, we propose a novel method to overcome this issue and generate accurate a Digital Terrain Model (DTM) in forested environments by processing the point cloud. We also developed a highly realistic custom simulator that allows controlled experimentation with repeatability guaranteed. With this tool, we performed an exhaustive evaluation of the survey and sensor settings and their impact on the 3D reconstruction. Overall, we found that a high frontal overlap (95%), a nadir camera angle (90°), and low flight altitudes (less than 100 m) results in the best configuration for forest environments. We validated the presented method for DTM generation in a simulated and real-world survey missions with both fixed-wing and multicopter UAS, showing how the problem of structural forest parameters estimation can be better addressed. Finally, we applied our method for automatic detection of selective logging. Full article

(This article belongs to the Special Issue Remote Sensing Applications in Forests Inventory and Management)

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19 pages, 3929 KiB

Open AccessArticle

Early Monitoring of Health Status of Plantation-Grown Eucalyptus pellita at Large Spatial Scale via Visible Spectrum Imaging of Canopy Foliage Using Unmanned Aerial Vehicles

by Megat Najib Megat Mohamed Nazir, Razak Terhem, Ahmad R. Norhisham, Sheriza Mohd Razali and Roger Meder

Forests 2021, 12(10), 1393; https://0-doi-org.brum.beds.ac.uk/10.3390/f12101393 - 13 Oct 2021

Cited by 11 | Viewed by 3215

Abstract

Eucalyptus is a diverse genus from which several species are often deployed for commercial industrial tree plantation due to their desirable wood properties for utilization in both solid wood and fiber products, as well as their growth and productivity in many environments. In this study, a method for monitoring the health status of a 22.78 ha Eucalyptus pellita plantation stand was developed using the red-green-blue channels captured using an unmanned aerial vehicle. The ortho-image was generated, and visual atmospheric resistance index (VARI) indices were developed. Herein, four classification levels of pest and disease were generated using the VARI-green algorithm. The range of normalized VARI-green indices was between −2.0 and 2.0. The results identified seven dead trees (VARI-green index −2 to 0), five trees that were severely infected (VARI-green index 0 to 0.05), 967 trees that were mildly infected (VARI-green index 0.06 to 0.16), and 10,090 trees that were considered healthy (VARI-green index 0.17 to 2.00). The VARI-green indices were verified by manual ground-truthing and by comparison with normalized difference vegetation index which showed a mean correlation of 0.73. This study has shown practical application of aerial survey of a large-scale operational area of industrial tree plantation via low-cost UAV and RGB camera, to analyze VARI-green images in the detection of pest and disease. Full article

(This article belongs to the Special Issue Remote Sensing Applications in Forests Inventory and Management)

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14 pages, 5488 KiB

Open AccessArticle

Different Temporal Stability and Responses to Droughts between Needleleaf Forests and Broadleaf Forests in North China during 2001–2018

by Xiran Li, Muxing Liu, Olivia L. Hajek and Guodong Yin

Forests 2021, 12(10), 1331; https://0-doi-org.brum.beds.ac.uk/10.3390/f12101331 - 29 Sep 2021

Cited by 2 | Viewed by 1616

Abstract

Droughts can affect the physiological activity of trees, damage tissues, and even trigger mortality, yet the response of different forest types to drought at the decadal time scale remains uncertain. In this study, we used two remote sensing-based vegetation products, the MODIS enhanced vegetation index (EVI) and MODIS gross primary productivity (GPP), to explore the temporal stability of deciduous needleleaf forests (DNFs) and deciduous broadleaf forests (DBFs) in droughts and their legacy effects in North China from 2001 to 2018. The results of both products showed that the temporal stability of DBFs was consistently much higher than that of DNFs, even though the DBFs experienced extreme droughts and the DNFs did not. The DBFs also exhibited similar patterns in their legacy effects from droughts, with these effects extending up to 4 years after the droughts. These results indicate that DBFs have been better acclimated to drought events in North China. Furthermore, the results suggest that the GPP was more sensitive to water variability than EVI. These findings will be helpful for forest modeling, management, and conservation. Full article

(This article belongs to the Special Issue Remote Sensing Applications in Forests Inventory and Management)

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32 pages, 9550 KiB

Open AccessArticle

Integrated Evaluation of Vegetation Drought Stress through Satellite Remote Sensing

by Daniela Avetisyan, Denitsa Borisova and Emiliya Velizarova

Forests 2021, 12(8), 974; https://0-doi-org.brum.beds.ac.uk/10.3390/f12080974 - 22 Jul 2021

Cited by 15 | Viewed by 2736

Abstract

In the coming decades, Bulgaria is expected to be affected by higher air temperatures and decreased precipitation, which will significantly increase the risk of droughts, forest ecosystem degradation and loss of ecosystem services (ES). Drought in terrestrial ecosystems is characterized by reduced water storage in soil and vegetation, affecting the function of landscapes and the ES they provide. An interdisciplinary assessment is required for an accurate evaluation of drought impact. In this study, we introduce an innovative, experimental methodology, incorporating remote sensing methods and a system approach to evaluate vegetation drought stress in complex systems (landscapes and ecosystems) which are influenced by various factors. The elevation and land cover type are key climate-forming factors which significantly impact the ecosystem’s and vegetation’s response to drought. Their influence cannot be sufficiently gauged by a traditional remote sensing-based drought index. Therefore, based on differences between the spectral reflectance of the individual natural land cover types, in a near-optimal vegetation state and divided by elevation, we assigned coefficients for normalization. The coefficients for normalization by elevation and land cover type were introduced in order to facilitate the comparison of the drought stress effect on the ecosystems throughout a heterogeneous territory. The obtained drought coefficient (DC) shows patterns of temporal, spatial, and interspecific differences on the response of vegetation to drought stress. The accuracy of the methodology is examined by field measurements of spectral reflectance, statistical analysis and validation methods using spectral reflectance profiles. Full article

(This article belongs to the Special Issue Remote Sensing Applications in Forests Inventory and Management)

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14 pages, 3432 KiB

Open AccessArticle

Accuracy Assessment of Total Stem Volume Using Close-Range Sensing: Advances in Precision Forestry

by Dimitrios Panagiotidis and Azadeh Abdollahnejad

Forests 2021, 12(6), 717; https://0-doi-org.brum.beds.ac.uk/10.3390/f12060717 - 31 May 2021

Cited by 6 | Viewed by 2202

Abstract

Accurate collection of dendrometric information is essential for improving decision confidence and supporting potential advances in forest management planning (FMP). Total stem volume is an important forest inventory parameter that requires high accuracy. Terrestrial laser scanning (TLS) has emerged as one of the most promising tools for automatically measuring total stem height and diameter at breast height (DBH) with very high detail. This study compares the accuracy of different methods for extracting the total stem height and DBH to estimate total stem volume from TLS data. Our results show that estimates of stem volume using the random sample consensus (RANSAC) and convex hull and H_TSP methods are more accurate (bias = 0.004 for RANSAC and bias = 0.009 for convex hull and H_TSP) than those using the circle fitting method (bias = 0.046). Furthermore, the RANSAC method had the best performance with the lowest bias and the highest percentage of accuracy (78.89%). The results of this study provide insight into the performance and accuracy of the tested methods for tree-level stem volume estimation, and allow for the further development of improved methods for point-cloud-based data collection with the goal of supporting potential advances in precision forestry. Full article

(This article belongs to the Special Issue Remote Sensing Applications in Forests Inventory and Management)

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18 pages, 5218 KiB

Open AccessEditor’s ChoiceArticle

sUAS for 3D Tree Surveying: Comparative Experiments on a Closed-Canopy Earthen Dam

by Cuizhen Wang, Grayson Morgan and Michael E. Hodgson

Forests 2021, 12(6), 659; https://0-doi-org.brum.beds.ac.uk/10.3390/f12060659 - 22 May 2021

Cited by 9 | Viewed by 2331

Abstract

Defined as “personal remote sensing”, small unmanned aircraft systems (sUAS) have been increasingly utilized for landscape mapping. This study tests a sUAS procedure of 3D tree surveying of a closed-canopy woodland on an earthen dam. Three DJI drones—Mavic Pro, Phantom 4 Pro, and M100/RedEdge-M assembly—were used to collect imagery in six missions in 2019–2020. A canopy height model was built from the sUAS-extracted point cloud and LiDAR bare earth surface. Treetops were delineated in a variable-sized local maxima filter, and tree crowns were outlined via inverted watershed segmentation. The outputs include a tree inventory that contains 238 to 284 trees (location, tree height, crown polygon), varying among missions. The comparative analysis revealed that the M100/RedEdge-M at a higher flight altitude achieved the best performance in tree height measurement (RMSE = 1 m). However, despite lower accuracy, the Phantom 4 Pro is recommended as an optimal drone for operational tree surveying because of its low cost and easy deployment. This study reveals that sUAS have good potential for operational deployment to assess tree overgrowth toward dam remediation solutions. With 3D imaging, sUAS remote sensing can be counted as a reliable, consumer-oriented tool for monitoring our ever-changing environment. Full article

(This article belongs to the Special Issue Remote Sensing Applications in Forests Inventory and Management)

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26 pages, 11440 KiB

Open AccessArticle

Mapping Floods in Lowland Forest Using Sentinel-1 and Sentinel-2 Data and an Object-Based Approach

by Mateo Gašparović and Damir Klobučar

Forests 2021, 12(5), 553; https://0-doi-org.brum.beds.ac.uk/10.3390/f12050553 - 28 Apr 2021

Cited by 24 | Viewed by 4927

Abstract

The impact of floods on forests is immediate, so it is necessary to quickly define the boundaries of flooded areas. Determining the extent of flooding in situ has shortcomings due to the possible limited spatial and temporal resolutions of data and the cost of data collection. Therefore, this research focused on flood mapping using geospatial data and remote sensing. The research area is located in the central part of the Republic of Croatia, an environmentally diverse area of lowland forests of the Sava River and its tributaries. Flood mapping was performed by merging Sentinel-1 (S1) and Sentinel-2 (S2) mission data and applying object-based image analysis (OBIA). For this purpose, synthetic aperture radar (SAR) data (GRD processing level) were primarily used during the flood period due to the possibility of all-day imaging in all weather conditions and flood detection under the density of canopy. The pre-flood S2 imagery, a summer acquisition, was used as a source of additional spectral data. Geographical information system (GIS) layers—a multisource forest inventory, habitat map, and flood hazard map—were used as additional sources of information in assessing the accuracy of and interpreting the obtained results. The spectral signature, geometric and textural features, and vegetation indices were applied in the OBIA process. The result of the work was a developed methodological framework with a high accuracy and speed of production. The overall accuracy of the classification is 94.94%. Based on the conducted research, the usefulness of the C band of the S1 in flood mapping in lowland forests in the leaf-off season was determined. The paper presents previous research and describes the SAR parameters and characteristics of floodplain forest with a significant impact on the accuracy of classification. Full article

(This article belongs to the Special Issue Remote Sensing Applications in Forests Inventory and Management)

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22 pages, 3174 KiB

Open AccessArticle

Combination of Feature Selection and CatBoost for Prediction: The First Application to the Estimation of Aboveground Biomass

by Mi Luo, Yifu Wang, Yunhong Xie, Lai Zhou, Jingjing Qiao, Siyu Qiu and Yujun Sun

Forests 2021, 12(2), 216; https://0-doi-org.brum.beds.ac.uk/10.3390/f12020216 - 13 Feb 2021

Cited by 92 | Viewed by 7356

Abstract

Increasing numbers of explanatory variables tend to result in information redundancy and “dimensional disaster” in the quantitative remote sensing of forest aboveground biomass (AGB). Feature selection of model factors is an effective method for improving the accuracy of AGB estimates. Machine learning algorithms are also widely used in AGB estimation, although little research has addressed the use of the categorical boosting algorithm (CatBoost) for AGB estimation. Both feature selection and regression for AGB estimation models are typically performed with the same machine learning algorithm, but there is no evidence to suggest that this is the best method. Therefore, the present study focuses on evaluating the performance of the CatBoost algorithm for AGB estimation and comparing the performance of different combinations of feature selection methods and machine learning algorithms. AGB estimation models of four forest types were developed based on Landsat OLI data using three feature selection methods (recursive feature elimination (RFE), variable selection using random forests (VSURF), and least absolute shrinkage and selection operator (LASSO)) and three machine learning algorithms (random forest regression (RFR), extreme gradient boosting (XGBoost), and categorical boosting (CatBoost)). Feature selection had a significant influence on AGB estimation. RFE preserved the most informative features for AGB estimation and was superior to VSURF and LASSO. In addition, CatBoost improved the accuracy of the AGB estimation models compared with RFR and XGBoost. AGB estimation models using RFE for feature selection and CatBoost as the regression algorithm achieved the highest accuracy, with root mean square errors (RMSEs) of 26.54 Mg/ha for coniferous forest, 24.67 Mg/ha for broad-leaved forest, 22.62 Mg/ha for mixed forests, and 25.77 Mg/ha for all forests. The combination of RFE and CatBoost had better performance than the VSURF–RFR combination in which random forests were used for both feature selection and regression, indicating that feature selection and regression performed by a single machine learning algorithm may not always ensure optimal AGB estimation. It is promising to extending the application of new machine learning algorithms and feature selection methods to improve the accuracy of AGB estimates. Full article

(This article belongs to the Special Issue Remote Sensing Applications in Forests Inventory and Management)

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19 pages, 43126 KiB

Open AccessArticle

Using Sentinel-2 Images to Map the Populus euphratica Distribution Based on the Spectral Difference Acquired at the Key Phenological Stage

by Hao Li, Qingdong Shi, Yanbo Wan, Haobo Shi and Bilal Imin

Forests 2021, 12(2), 147; https://0-doi-org.brum.beds.ac.uk/10.3390/f12020147 - 27 Jan 2021

Cited by 11 | Viewed by 2015

Abstract

Populus euphratica is an important tree species in desert ecosystems. The protection and restoration of natural Populus euphratica forests requires accurate positioning information. The use of Sentinel-2 images to map the Populus euphratica distribution at a large scale faces challenges associated with discriminating between Populus euphratica and Tamarix chinensis. To address this problem, this study selected the Daliyabuyi Oasis in the hinterland of the Taklimakan Desert as the study site and sought to distinguish Populus euphratica from Tamarix chinensis. First, we determined the peak spectral difference period (optimal time window) between Populus euphratica and Tamarix chinensis within monthly Sentinel-2 time-series images. Then, an appropriate vegetation index was selected to represent the spectral difference between Populus euphratica and Tamarix chinensis within the key phenological stage. Finally, the maximum entropy method was used to automatically determine the threshold to map the Populus euphratica distribution. The results indicated that the period from 22 April to 1 May was the optimal time window for mapping the Populus euphratica distribution in the Daliyabuyi Oasis. The combination of the inverted red-edge chlorophyll index (IRECI) and the maximum entropy method can effectively distinguish Populus euphratica from Tamarix chinensis. The user’s accuracy of the Populus euphratica distribution extraction from single-data Sentinel-2 images acquired within the optimal time window was 0.83, the producer’s accuracy was 0.72, and the F1-score was 0.77. This study verified the feasibility of mapping Populus euphratica distribution based on Sentinel-2 images, and analyzed the validity of exploiting spectral differences within the key phenological stage from a single-data image to distinguish between the two species. The results can be used to extract the distribution of Populus euphratica and serve as an auxiliary variable for other plant classification methods, providing a reference for the extraction and classification of desert plants. Full article

(This article belongs to the Special Issue Remote Sensing Applications in Forests Inventory and Management)

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17 pages, 7505 KiB

Open AccessArticle

Using U-Net-Like Deep Convolutional Neural Networks for Precise Tree Recognition in Very High Resolution RGB (Red, Green, Blue) Satellite Images

by Kirill A. Korznikov, Dmitry E. Kislov, Jan Altman, Jiří Doležal, Anna S. Vozmishcheva and Pavel V. Krestov

Forests 2021, 12(1), 66; https://0-doi-org.brum.beds.ac.uk/10.3390/f12010066 - 08 Jan 2021

Cited by 32 | Viewed by 4788

Abstract

Very high resolution satellite imageries provide an excellent foundation for precise mapping of plant communities and even single plants. We aim to perform individual tree recognition on the basis of very high resolution RGB (red, green, blue) satellite images using deep learning approaches for northern temperate mixed forests in the Primorsky Region of the Russian Far East. We used a pansharpened satellite RGB image by GeoEye-1 with a spatial resolution of 0.46 m/pixel, obtained in late April 2019. We parametrized the standard U-Net convolutional neural network (CNN) and trained it in manually delineated satellite images to solve the satellite image segmentation problem. For comparison purposes, we also applied standard pixel-based classification algorithms, such as random forest, k-nearest neighbor classifier, naive Bayes classifier, and quadratic discrimination. Pattern-specific features based on grey level co-occurrence matrices (GLCM) were computed to improve the recognition ability of standard machine learning methods. The U-Net-like CNN allowed us to obtain precise recognition of Mongolian poplar (Populus suaveolens Fisch. ex Loudon s.l.) and evergreen coniferous trees (Abies holophylla Maxim., Pinus koraiensis Siebold & Zucc.). We were able to distinguish species belonging to either poplar or coniferous groups but were unable to separate species within the same group (i.e. A. holophylla and P. koraiensis were not distinguishable). The accuracy of recognition was estimated by several metrics and exceeded values obtained for standard machine learning approaches. In contrast to pixel-based recognition algorithms, the U-Net-like CNN does not lead to an increase in false-positive decisions when facing green-colored objects that are similar to trees. By means of U-Net-like CNN, we obtained a mean accuracy score of up to 0.96 in our computational experiments. The U-Net-like CNN recognizes tree crowns not as a set of pixels with known RGB intensities but as spatial objects with a specific geometry and pattern. This CNN’s specific feature excludes misclassifications related to objects of similar colors as objects of interest. We highlight that utilization of satellite images obtained within the suitable phenological season is of high importance for successful tree recognition. The suitability of the phenological season is conceptualized as a group of conditions providing highlighting objects of interest over other components of vegetation cover. In our case, the use of satellite images captured in mid-spring allowed us to recognize evergreen fir and pine trees as the first class of objects (“conifers”) and poplars as the second class, which were in a leafless state among other deciduous tree species. Full article

(This article belongs to the Special Issue Remote Sensing Applications in Forests Inventory and Management)

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21 pages, 6114 KiB

Open AccessArticle

Feature-Level Fusion between Gaofen-5 and Sentinel-1A Data for Tea Plantation Mapping

by Yujia Chen and Shufang Tian

Forests 2020, 11(12), 1357; https://0-doi-org.brum.beds.ac.uk/10.3390/f11121357 - 18 Dec 2020

Cited by 6 | Viewed by 1935

Abstract

The accurate mapping of tea plantations is significant for government decision-making and environmental protection of tea-producing regions. Hyperspectral and Synthetic Aperture Radar (SAR) data have recently been widely used in land cover classification, but effective integration of these data for tea plantation mapping requires further study. This study developed a new feature-level image fusion method called LPPSubFus that combines locality preserving projection and subspace fusion (SubFus) to map tea plantations. Based on hyperspectral and SAR data, we first extracted spectral indexes, textures, and backscattering information. Second, this study applied LPPSubFus to tea plantation mapping with different classification algorithms. Finally, we compared the performance of LPPSubFus, SubFus, and pixel-level image fusion in tea plantation mapping. Feature-level image fusion performed better than pixel-level image fusion. An improvement of about 3% was achieved using feature-level image fusion compared to hyperspectral data alone. Regarding feature-level image fusion, LPPSubFus improved the overall accuracy by more than 3% compared to SubFus. In particular, LPPSubFus using neural network algorithms achieved the highest overall accuracy (95%) and over 90% producer and user accuracy for tea plantations and forests. In addition, LPPSubFus was more compatible with different classification algorithms than SubFus. Based on these findings, it is concluded that LPPSubFus has better and more stable performance in tea plantation mapping than pixel-level image fusion and SubFus. This study demonstrates the potential of integrating hyperspectral and SAR data via LPPSubFus for mapping tea plantations. Our work offers a promising tea plantation mapping method and contributes to the understanding of hyperspectral and SAR data fusion. Full article

(This article belongs to the Special Issue Remote Sensing Applications in Forests Inventory and Management)

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21 pages, 3088 KiB

Open AccessArticle

Application of LiDAR Data for the Modeling of Solar Radiation in Forest Artificial Gaps—A Case Study

by Leszek Bolibok and Michał Brach

Forests 2020, 11(8), 821; https://0-doi-org.brum.beds.ac.uk/10.3390/f11080821 - 28 Jul 2020

Cited by 2 | Viewed by 2232

Abstract

Artificial canopy gaps (forest openings) are frequently used as an element of regeneration cutting. The development of regeneration in gaps can be controlled by selecting a relevant size and shape for the gap, which will regulate the radiation microclimate inside it. Based on the size and shape of a gap computer models can assess where solar radiation is decreased or eliminated by the surrounding canopy. The accuracy of such models to a large extent depends on how the modeled shape of a gap matches the actual shape of the gap. The aim of this study was to compare the results of modeling solar radiation availability by applying Solar Radiation Tools (SRT) that use a different digital surface model (DSM) for a description of the shape of a studied gap, with the results of the analysis of 27 hemispherical photographs. The three-dimensional gap shape was approximated with the use of simple geometrical prisms or airborne laser scanning (LiDAR) data. The impact of two variations of exposure (automatic and manual underexposure) and two variations of automatic thresholding on the congruence of SRT and Gap Light Analyzer (GLA) results were studied. Taking into account information on differences in height between trees surrounding the gap enhanced the results of modeling. The best results were obtained when the boundary of the gap base estimated from LiDAR was expanded in all directions by a value close to a mean radius of the crowns of surrounding trees. Modeling of radiation conditions on the gap floor based on LiDAR data by an SRT program is efficient and more time effective than taking hemispherical photographs. The proposed solution can be successfully applied as a trustworthy source of information about light conditions in gaps, which is needed for management decision-making in silviculture. Full article

(This article belongs to the Special Issue Remote Sensing Applications in Forests Inventory and Management)

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Review

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19 pages, 3719 KiB

Open AccessReview

A Systematic Review of Best Practices for UAS Data Collection in Forestry-Related Applications

by Connor Cromwell, Jesse Giampaolo, Joseph Hupy, Zachary Miller and Aishwarya Chandrasekaran

Forests 2021, 12(7), 957; https://0-doi-org.brum.beds.ac.uk/10.3390/f12070957 - 20 Jul 2021

Cited by 12 | Viewed by 2688

Abstract

Recent advancements in unmanned aerial systems and GPS technology, allowing for centimeter precision without ground-based surveys, have been groundbreaking for applications in the field of forestry. As this technology becomes integrated into forest management approaches, it is important to consider the implementation of proper safety and data collection strategies. The creation of such documentation is beneficial, because it allows for those aspiring to create a UAS program to learn from others’ experiences, without bearing the consequences of past blunders associated with the development of these practices. When establishing a UAS program, it is pertinent to deeply research the necessary equipment, create documentation that establishes operational norms, and develop standards for in-field operations. Regarding multispectral vs. RGB sensor payloads, the sensor selection should be based upon what type of information is desired from the imagery acquired. It is also important to consider the methods for obtaining the most precise geolocation linked to the aerial imagery collected by the sensor. While selecting the proper UAS platform and sensor are key to establishing a UAS operation, other logistical strategies, such as flight crew training and operational planning, are equally important. Following the acquisition of proper equipment, further preparations must be made in order to ensure safe and efficient operations. The creation of crew resource management and safety management system documentation is an integral part of any successful UAS program. Standard operating procedure documents for individual tasks and undertakings are also a necessity. Standardized practices for the scheduling, communication, and management of the UAS fleet must also be formulated. Once field operations are set in motion, the continuous improvement of the documentation and best practices is paramount. Full article

(This article belongs to the Special Issue Remote Sensing Applications in Forests Inventory and Management)

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19 pages, 2368 KiB

Open AccessReview

LiDAR Applications to Estimate Forest Biomass at Individual Tree Scale: Opportunities, Challenges and Future Perspectives

by Dandan Xu, Haobin Wang, Weixin Xu, Zhaoqing Luan and Xia Xu

Forests 2021, 12(5), 550; https://0-doi-org.brum.beds.ac.uk/10.3390/f12050550 - 28 Apr 2021

Cited by 58 | Viewed by 7648

Abstract

Accurate forest biomass estimation at the individual tree scale is the foundation of timber industry and forest management. It plays an important role in explaining ecological issues and small-scale processes. Remotely sensed images, across a range of spatial and temporal resolutions, with their advantages of non-destructive monitoring, are widely applied in forest biomass monitoring at global, ecoregion or community scales. However, the development of remote sensing applications for forest biomass at the individual tree scale has been relatively slow due to the constraints of spatial resolution and evaluation accuracy of remotely sensed data. With the improvements in platforms and spatial resolutions, as well as the development of remote sensing techniques, the potential for forest biomass estimation at the single tree level has been demonstrated. However, a comprehensive review of remote sensing of forest biomass scaled at individual trees has not been done. This review highlights the theoretical bases, challenges and future perspectives for Light Detection and Ranging (LiDAR) applications of individual trees scaled to whole forests. We summarize research on estimating individual tree volume and aboveground biomass (AGB) using Terrestrial Laser Scanning (TLS), Airborne Laser Scanning (ALS), Unmanned Aerial Vehicle Laser Scanning (UAV-LS) and Mobile Laser Scanning (MLS, including Vehicle-borne Laser Scanning (VLS) and Backpack Laser Scanning (BLS)) data. Full article

(This article belongs to the Special Issue Remote Sensing Applications in Forests Inventory and Management)

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