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Forest Monitoring in a Multi-Sensor Approach

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

Deadline for manuscript submissions: closed (31 March 2022) | Viewed by 40469

Special Issue Editors

Department of Cartographic Engineering, Faculty of Agriculture anf Forest Engineering, Universidad de Leon, 24401 Ponferrada, Spain
Interests: natural resources monitoring; remote sensing; geoinformatics
Special Issues, Collections and Topics in MDPI journals
Department of Computing and Control Engineering, Faculty of Technical Sciences, University of Novi Sad, 21000 Novi Sad, Serbia
Interests: geoinformatics; spatial databases; GeoAI; remote sensing; data analytics; big data; water resources monitoring
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Sustainable planning and management of forest ecosystems requires understanding forest resources and their dynamics, for economic and environment purposes, especially in a climate change scenario. Using remote sensing in a multisensor approach is a powerful tool to provide critical information at different scales to monitor and manage commercial and noncommercial forests, as well as for establishing forest policies and planning.

With this Special Issue, we compile research papers which use data from different sensors, platforms (satellite, airplane, unmanned aerial vehicle (UAVs)), 2D or 3D data, images or point clouds, optical or SAR/LiDAR data, and different spectral resolutions, to address various aspects of forest monitoring: forest structure characterization, biomass/carbon sequestration estimations, fire extension and severity mapping, ecosystem recovery/degradation, forest health monitoring, invasive species mapping, early warning systems, and applications at various spatial or temporal scales. Review contributions are welcomed, as well as papers describing new sensors/techniques.

Dr. Flor Alvarez-Taboada
Dr. Miro Govedarica
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.

Keywords

  • Multisensor, multitemporal, multiresolution data
  • Afforestation monitoring
  • Forest biophysical variables
  • Time series
  • Forest health early warning systems
  • Pest and diseases monitoring
  • Wildfire mapping and post-fire effects monitoring
  • Forest structure characterization
  • Airborne Lidar (ALS), terrestrial Lidar (TLS) and digital aerial photogrammetry (DAP) point clouds
  • Precision forestry
  • Ecosystem services monitoring
  • Tree identification
  • GPR

Published Papers (11 papers)

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Research

32 pages, 23250 KiB  
Article
Integration of VIIRS Observations with GEDI-Lidar Measurements to Monitor Forest Structure Dynamics from 2013 to 2020 across the Conterminous United States
by Khaldoun Rishmawi, Chengquan Huang, Karen Schleeweis and Xiwu Zhan
Remote Sens. 2022, 14(10), 2320; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14102320 - 11 May 2022
Cited by 8 | Viewed by 2377
Abstract
Consistent and spatially explicit periodic monitoring of forest structure is essential for estimating forest-related carbon emissions, analyzing forest degradation, and supporting sustainable forest management policies. To date, few products are available that allow for continental to global operational monitoring of changes in canopy [...] Read more.
Consistent and spatially explicit periodic monitoring of forest structure is essential for estimating forest-related carbon emissions, analyzing forest degradation, and supporting sustainable forest management policies. To date, few products are available that allow for continental to global operational monitoring of changes in canopy structure. In this study, we explored the synergy between the NASA’s spaceborne Global Ecosystem Dynamics Investigation (GEDI) waveform LiDAR and the Visible Infrared Imaging Radiometer Suite (VIIRS) data to produce spatially explicit and consistent annual maps of canopy height (CH), percent canopy cover (PCC), plant area index (PAI), and foliage height diversity (FHD) across the conterminous United States (CONUS) at a 1-km resolution for 2013–2020. The accuracies of the annual maps were assessed using forest structure attribute derived from airborne laser scanning (ALS) data acquired between 2013 and 2020 for the 48 National Ecological Observatory Network (NEON) field sites distributed across the CONUS. The root mean square error (RMSE) values of the annual canopy height maps as compared with the ALS reference data varied from a minimum of 3.31-m for 2020 to a maximum of 4.19-m for 2017. Similarly, the RMSE values for PCC ranged between 8% (2020) and 11% (all other years). Qualitative evaluations of the annual maps using time series of very high-resolution images further suggested that the VIIRS-derived products could capture both large and “more” subtle changes in forest structure associated with partial harvesting, wind damage, wildfires, and other environmental stresses. The methods developed in this study are expected to enable multi-decadal analysis of forest structure and its dynamics using consistent satellite observations from moderate resolution sensors such as VIIRS onboard JPSS satellites. Full article
(This article belongs to the Special Issue Forest Monitoring in a Multi-Sensor Approach)
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25 pages, 13133 KiB  
Article
Improving Heterogeneous Forest Height Maps by Integrating GEDI-Based Forest Height Information in a Multi-Sensor Mapping Process
by David Morin, Milena Planells, Nicolas Baghdadi, Alexandre Bouvet, Ibrahim Fayad, Thuy Le Toan, Stéphane Mermoz and Ludovic Villard
Remote Sens. 2022, 14(9), 2079; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14092079 - 26 Apr 2022
Cited by 13 | Viewed by 3249
Abstract
Forests are one of the key elements in ecological transition policies in Europe. Sustainable forest management is needed in order to optimise wood harvesting, while preserving carbon storage, biodiversity and other ecological functions. Forest managers and public bodies need improved and cost-effective forest [...] Read more.
Forests are one of the key elements in ecological transition policies in Europe. Sustainable forest management is needed in order to optimise wood harvesting, while preserving carbon storage, biodiversity and other ecological functions. Forest managers and public bodies need improved and cost-effective forest monitoring tools. Research studies have been carried out to assess the use of optical and radar images for producing forest height or biomass maps. The main limitations are the quantity, quality and representativeness of the reference data for model training. The Global Ecosystem Dynamics Investigation (GEDI) mission (full waveform LiDAR on board the International Space Station) has provided an unprecedented number of forest canopy height samples from 2019. These samples could be used to improve reference datasets. This paper aims to present and validate a method for estimating forest dominant height from open access optical and radar satellite images (Sentinel-1, Sentinel-2 and ALOS-2 PALSAR-2), and then to assess the use of GEDI samples to replace field height measurements in model calibration. Our approach combines satellite image features and dominant height measurements, or GEDI metrics, in a Support Vector Machine regression algorithm, with a feature selection process. The method is tested on mixed uneven-aged broadleaved and coniferous forests in France. Using dominant height measurements for model training, the cross-validation shows 7.3 to 11.6% relative Root Mean Square Error (RMSE) depending on the forest class. When using GEDI height metrics instead of field measurements for model training, errors increase to 12.8–16.7% relative RMSE. This level of error remains satisfactory; the use of GEDI could allow the production of dominant height maps on large areas with better sample representativeness. Future work will focus on confirming these results on new study sites, improving the filtering and processing of GEDI data, and producing height maps at regional or national scale. The resulting maps will help forest managers and public bodies to optimise forest resource inventories, as well as allow scientists to integrate these cartographic data into climate models. Full article
(This article belongs to the Special Issue Forest Monitoring in a Multi-Sensor Approach)
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20 pages, 3640 KiB  
Article
Single Tree Classification Using Multi-Temporal ALS Data and CIR Imagery in Mixed Old-Growth Forest in Poland
by Agnieszka Kamińska, Maciej Lisiewicz and Krzysztof Stereńczak
Remote Sens. 2021, 13(24), 5101; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13245101 - 15 Dec 2021
Cited by 8 | Viewed by 3339
Abstract
Tree species classification is important for a variety of environmental applications, including biodiversity monitoring, wildfire risk assessment, ecosystem services assessment, and sustainable forest management. In this study we used a fusion of three remote sensing (RM) datasets including ALS (leaf-on and leaf-off) and [...] Read more.
Tree species classification is important for a variety of environmental applications, including biodiversity monitoring, wildfire risk assessment, ecosystem services assessment, and sustainable forest management. In this study we used a fusion of three remote sensing (RM) datasets including ALS (leaf-on and leaf-off) and colour-infrared (CIR) imagery (leaf-on), to classify different coniferous and deciduous tree species, including dead class, in a mixed temperate forest in Poland. We used intensity and structural variables from the ALS data and spectral information derived from aerial imagery for the classification procedure. Additionally, we tested the differences in classification accuracy of all the variants included in the data integration. The random forest classifier was used in the study. The highest accuracies were obtained for classification based on both point clouds and including image spectral information. The mean values for overall accuracy and kappa were 84.3% and 0.82, respectively. Analysis of the leaf-on and leaf-off alone is not sufficient to identify individual tree species due to their different discriminatory power. Leaf-on and leaf-off ALS point cloud features alone gave the lowest accuracies of 72% ≤ OA ≤ 74% and 0.67 ≤ κ ≤ 0.70. Classification based on both point clouds was found to give satisfactory and comparable results to classification based on combined information from all three sources (83% ≤ OA ≤ 84% and 0.81 ≤ κ ≤ 0.82). The classification accuracy varied between species. The classification results for coniferous trees were always better than for deciduous trees independent of the datasets. In the classification based on both point clouds (leaf-on and leaf-off), the intensity features seemed to be more important than the other groups of variables, especially the coefficient of variation, skewness, and percentiles. The NDVI was the most important CIR-based feature. Full article
(This article belongs to the Special Issue Forest Monitoring in a Multi-Sensor Approach)
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21 pages, 20868 KiB  
Article
Detection of Bark Beetle Disturbance at Tree Level Using UAS Multispectral Imagery and Deep Learning
by Robert Minařík, Jakub Langhammer and Theodora Lendzioch
Remote Sens. 2021, 13(23), 4768; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13234768 - 24 Nov 2021
Cited by 18 | Viewed by 2955
Abstract
This study aimed to examine the potential of convolutional neural networks (CNNs) for the detection of individual trees infested by bark beetles in a multispectral high-resolution dataset acquired by an unmanned aerial system (UAS). We compared the performance of three CNN architectures and [...] Read more.
This study aimed to examine the potential of convolutional neural networks (CNNs) for the detection of individual trees infested by bark beetles in a multispectral high-resolution dataset acquired by an unmanned aerial system (UAS). We compared the performance of three CNN architectures and the random forest (RF) model to classify the trees into four categories: pines, sbbd (longer infested trees when needles turn yellow), sbbg (trees under green attack) and non-infested trees (sh). The best performance was achieved by the Nez4c3b CNN (kappa 0.80) and Safaugu4c3b CNN (kappa 0.76) using only RGB bands. The main misclassifications were between sbbd and sbbg because of the similar spectral responses. Merging sbbd and sbbg into a more general class of infested trees made the selection of model type less important. All tested model types, including RF, were able to detect infested trees with an F-score of the class over 0.90. Nevertheless, the best overall metrics were achieved again by the Safaugu3c3b model (kappa 0.92) and Nez3cb model (kappa 0.87) using only RGB bands. The performance of both models is comparable, but the Nez model has a higher learning rate for this task. Based on our findings, we conclude that the Nez and Safaugu CNN models are superior to the RF models and transfer learning models for the identification of infested trees and for distinguishing between different infestation stages. Therefore, these models can be used not only for basic identification of infested trees but also for monitoring the development of bark beetle disturbance. Full article
(This article belongs to the Special Issue Forest Monitoring in a Multi-Sensor Approach)
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32 pages, 6414 KiB  
Article
Multi-Temporal Sentinel-2 Data Analysis for Smallholding Forest Cut Control
by Alberto López-Amoedo, Xana Álvarez, Henrique Lorenzo and Juan Luis Rodríguez
Remote Sens. 2021, 13(15), 2983; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13152983 - 29 Jul 2021
Cited by 6 | Viewed by 2380
Abstract
Land fragmentation and small plots are the main features of the rural environment of Galicia (NW Spain). Smallholding limits land use management, representing a drawback in local forest planning. This study analyzes the potential use of multitemporal Sentinel-2 images to detect and control [...] Read more.
Land fragmentation and small plots are the main features of the rural environment of Galicia (NW Spain). Smallholding limits land use management, representing a drawback in local forest planning. This study analyzes the potential use of multitemporal Sentinel-2 images to detect and control forest cuts in very small pine and eucalyptus plots located in southern Galicia. The proposed approach is based on the analysis of Sentinel-2 NDVI time series in 4231 plots smaller than 3 ha (average 0.46 ha). The methodology allowed us to detect cuts, allocate cut dates and quantify plot areas due to different cutting cycles in an uneven-aged stand. An accuracy of approximately 95% was achieved when the whole plot was cut, with an 81% accuracy for partial cuts. The main difficulty in detecting and dating cuts was related to cloud cover, which affected the multitemporal analysis. In conclusion, the proposed methodology provides an accurate estimation of cutting date and area, helping to improve the monitoring system in sustainable forest certifications to ensure compliance with forest management plans. Full article
(This article belongs to the Special Issue Forest Monitoring in a Multi-Sensor Approach)
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19 pages, 1866 KiB  
Article
Classification of Nemoral Forests with Fusion of Multi-Temporal Sentinel-1 and 2 Data
by Kristian Skau Bjerreskov, Thomas Nord-Larsen and Rasmus Fensholt
Remote Sens. 2021, 13(5), 950; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13050950 - 03 Mar 2021
Cited by 29 | Viewed by 4099
Abstract
Mapping forest extent and forest cover classification are important for the assessment of forest resources in socio-economic as well as ecological terms. Novel developments in the availability of remotely sensed data, computational resources, and advances in areas of statistical learning have enabled the [...] Read more.
Mapping forest extent and forest cover classification are important for the assessment of forest resources in socio-economic as well as ecological terms. Novel developments in the availability of remotely sensed data, computational resources, and advances in areas of statistical learning have enabled the fusion of multi-sensor data, often yielding superior classification results. Most former studies of nemoral forests fusing multi-sensor and multi-temporal data have been limited in spatial extent and typically to a simple classification of landscapes into major land cover classes. We hypothesize that multi-temporal, multi-sensor data will have a specific strength in the further classification of nemoral forest landscapes owing to the distinct seasonal patterns in the phenology of broadleaves. This study aimed to classify the Danish landscape into forest/non-forest and further into forest types (broadleaved/coniferous) and species groups, using a cloud-based approach based on multi-temporal Sentinel 1 and 2 data and a random forest classifier trained with National Forest Inventory (NFI) data. Mapping of non-forest and forest resulted in producer accuracies of 99% and 90%, respectively. The mapping of forest types (broadleaf and conifer) within the forested area resulted in producer accuracies of 95% for conifer and 96% for broadleaf forest. Tree species groups were classified with producer accuracies ranging 34–74%. Species groups with coniferous species were the least confused, whereas the broadleaf groups, especially Quercus species, had higher error rates. The results are applied in Danish national accounting of greenhouse gas emissions from forests, resource assessment, and assessment of forest biodiversity potentials. Full article
(This article belongs to the Special Issue Forest Monitoring in a Multi-Sensor Approach)
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25 pages, 8447 KiB  
Article
Mapping Species at an Individual-Tree Scale in a Temperate Forest, Using Sentinel-2 Images, Airborne Laser Scanning Data, and Random Forest Classification
by Veerle Plakman, Thomas Janssen, Nienke Brouwer and Sander Veraverbeke
Remote Sens. 2020, 12(22), 3710; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12223710 - 12 Nov 2020
Cited by 25 | Viewed by 5362
Abstract
Detailed information about tree species composition is critical to forest managers and ecologists. In this study, we used Sentinel-2 imagery in combination with a canopy height model (CHM) derived from airborne laser scanning (ALS) to map individual tree crowns and identify them to [...] Read more.
Detailed information about tree species composition is critical to forest managers and ecologists. In this study, we used Sentinel-2 imagery in combination with a canopy height model (CHM) derived from airborne laser scanning (ALS) to map individual tree crowns and identify them to species level. Our study area covered 140 km2 of a mainly mixed temperate forest in the Veluwe area in The Netherlands. Ground truth data on tree species were acquired for 2460 trees. Tree crowns were automatically delineated from the CHM model. We identified the delineated tree crowns to species and phylum level (angiosperm vs. gymnosperm) using a random forest (RF) classification. The RF model used multitemporal spectral variables from Sentinel-2 and crown structural variables from the CHM and was validated using an independent dataset. Different combinations of variables were tested. After feature reduction from 25 to 15 features, the RF model identified tree crowns with an overall accuracy of 78.5% (Kappa value 0.75) for tree species and 84.5% (Kappa value 0.73) for tree phyla whilst using the combination of all variables. Adding crown structural and multitemporal spectral information improved the RF classification compared to using only a Sentinel image from one season as input data. The producer’s accuracies varied between 43.8% for Norway spruce (Picea abies) to 95.3% for Douglas fir (Pseudotsuga menziesii). The RF model was extrapolated to generate a tree species map over a study area (140 km2). The map showed high abundances of common oak (Quercus robur; 35.5%) and Scots pine (Pinus sylvestris; 22.8%) and low abundances of Norway spruce (Picea abies; 1.7%) and Douglas fir (Pseudotsuga menziesii; 2.8%). Our results indicate a high potential for individual tree classification based on Sentinel-2 imagery and automatically derived tree crowns from canopy height models. Full article
(This article belongs to the Special Issue Forest Monitoring in a Multi-Sensor Approach)
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16 pages, 2616 KiB  
Article
The Best of Both Worlds? Integrating Sentinel-2 Images and airborne LiDAR to Characterize Forest Regeneration
by Stéphanie Landry, Martin-Hugues St-Laurent, Gaetan Pelletier and Marc-André Villard
Remote Sens. 2020, 12(15), 2440; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12152440 - 29 Jul 2020
Cited by 3 | Viewed by 2397
Abstract
Sustainable forest management relies on practices ensuring vigorous post-harvest regeneration. Data on regeneration structure and composition are often collected through intensive field surveys. Remote sensing technologies (e.g., Light Detection and Ranging (LiDAR), satellite imagery) can cover a much larger spatial extent, but their [...] Read more.
Sustainable forest management relies on practices ensuring vigorous post-harvest regeneration. Data on regeneration structure and composition are often collected through intensive field surveys. Remote sensing technologies (e.g., Light Detection and Ranging (LiDAR), satellite imagery) can cover a much larger spatial extent, but their ability to estimate regeneration characteristics is often challenged by the obstruction associated with canopy foliage. Here, we determined whether the integration of LiDAR and Sentinel-2 images can increase the accuracy of sapling density estimates and whether this accuracy decreased with canopy cover in the Acadian forest of New Brunswick, Canada. Using random forest regression, we compared the accuracy of three models (LiDAR and Sentinel-2 images alone or combined) to estimate sapling density for two species groups: saplings of all species or commercial species only. The integration of both sensors did not increase the accuracy of sapling density estimates, nor did it reduce the negative influence of canopy cover for either species group compared to LiDAR, but it increased the accuracy by approximately 15% relative to Sentinel-2 images. Under very high canopy cover, the accuracy of density estimates for all species combined was significantly lower with Sentinel-2 images only. We recommend using LiDAR and high-resolution satellite images acquired in the fall to obtain more accurate estimates of sapling density. Full article
(This article belongs to the Special Issue Forest Monitoring in a Multi-Sensor Approach)
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21 pages, 9341 KiB  
Article
Detection of Very Small Tree Plantations and Tree-Level Characterization Using Open-Access Remote-Sensing Databases
by Laura Alonso, Juan Picos, Guillermo Bastos and Julia Armesto
Remote Sens. 2020, 12(14), 2276; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12142276 - 15 Jul 2020
Cited by 4 | Viewed by 3485
Abstract
Highly fragmented land property hinders the planning and management of single species tree plantations. In such situations, acquiring information about the available resources is challenging. This study aims to propose a method to locate and characterize tree plantations in these cases. Galicia (Northwest [...] Read more.
Highly fragmented land property hinders the planning and management of single species tree plantations. In such situations, acquiring information about the available resources is challenging. This study aims to propose a method to locate and characterize tree plantations in these cases. Galicia (Northwest of Spain) is an area where property is extremely divided into small parcels. European chestnut (Castanea sativa) plantations are an important source of income there; however, it is often difficult to obtain information about them due to their small size and scattered distribution. Therefore, we selected a Galician region with a high presence of chestnut plantations as a case study area in order to locate and characterize small plantations using open-access data. First, we detected the location of chestnut plantations applying a supervised classification for a combination of: Sentinel-2 images and the open-access low-density Light Detection and Ranging (LiDAR) point clouds, obtained from the untapped open-access LiDAR Spanish national database. Three classification algorithms were used: Random Forest (RF), Support Vector Machine (SVM), and XGBoost. We later characterized the plots at the tree-level using the LiDAR point-cloud. We detected individual trees and obtained their height applying a local maxima algorithm to a point-cloud-derived Canopy Height Model (CHM). We also calculated the crown surface of each tree by applying a method based on two-dimensional (2D) tree shape reconstruction and canopy segmentation to a projection of the LiDAR point cloud. Chestnut plantations were detected with an overall accuracy of 81.5%. Individual trees were identified with a detection rate of 96%. The coefficient of determination R2 value for tree height estimation was 0.83, while for the crown surface calculation it was 0.74. The accuracy achieved with these open-access databases makes the proposed procedure suitable for acquiring knowledge about the location and state of chestnut plantations as well as for monitoring their evolution. Full article
(This article belongs to the Special Issue Forest Monitoring in a Multi-Sensor Approach)
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23 pages, 9095 KiB  
Article
Integration of Multi-Sensor Data to Estimate Plot-Level Stem Volume Using Machine Learning Algorithms–Case Study of Evergreen Conifer Planted Forests in Japan
by Kotaro Iizuka, Yuichi S. Hayakawa, Takuro Ogura, Yasutaka Nakata, Yoshiko Kosugi and Taichiro Yonehara
Remote Sens. 2020, 12(10), 1649; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12101649 - 21 May 2020
Cited by 10 | Viewed by 4687
Abstract
The development of new methods for estimating precise forest structure parameters is essential for the quantitative evaluation of forest resources. Conventional use of satellite image data, increasing use of terrestrial laser scanning (TLS), and emerging trends in the use of unmanned aerial systems [...] Read more.
The development of new methods for estimating precise forest structure parameters is essential for the quantitative evaluation of forest resources. Conventional use of satellite image data, increasing use of terrestrial laser scanning (TLS), and emerging trends in the use of unmanned aerial systems (UASs) highlight the importance of modern technologies in the realm of forest observation. Each technology has different advantages, and this work seeks to incorporate multiple satellite, TLS- and UAS-based remote sensing data sets to improve the ability to estimate forest structure parameters. In this paper, two regression analysis approaches are considered for the estimation: random forest regression (RFR) and support vector regression (SVR). To collect the dependent variable, in situ measurements of individual tree parameters (tree height and diameter at breast height (DBH)) were taken in a Japanese cypress forest using the nondestructive TLS method, which scans the forest to obtain dense and accurate point clouds under the tree canopy. Based on the TLS data, the stem volume was then computed and treated as ground truth information. Topographic and UAS information was then used to calculate various remotely sensed explanatory variables, such as canopy size, canopy cover, and tree height. Canopy cover and canopy shapes were computed via the orthoimages derived from the UAS and watershed segmentation method, respectively. Tree height was computed by combining the digital surface model (DSM) from the UAS and the digital terrain model (DTM) from the TLS data. Topographic variables were computed from the DTM. The backscattering intensity in the satellite imagery was obtained based on L-band (Advanced Land Observing Satellite-2 (ALOS-2) Phased Array type L-band Synthetic Aperture Radar-2 (PALSAR-2)) and C-band (Sentinel-1) synthetic aperture radar (SAR). All satellite (10–25 m resolution), TLS (3.4 mm resolution) and UAS (2.3–4.6 cm resolution) data were then combined, and RFR and SVR were trained; the resulting predictive powers were then compared. The RFR method yielded fitting R2 up to 0.665 and RMSE up to 66.87 m3/ha (rRMSE = 11.95%) depending on the input variables (best result with canopy height, canopy size, canopy cover, and Sentinel-1 data), and the SVR method showed fitting R2 up to 0.519 and RMSE up to 80.12 m3/ha (rRMSE = 12.67%). The RFR outperformed the SVR method, which could delineate the relationship between the variables for better model accuracy. This work has demonstrated that incorporating various remote sensing data to satellite data, especially adding finer resolution data, can provide good estimates of forest parameters at a plot level (10 by 10 m), potentially allowing advancements in precision forestry. Full article
(This article belongs to the Special Issue Forest Monitoring in a Multi-Sensor Approach)
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19 pages, 8542 KiB  
Article
Analyzing the Angle Effect of Leaf Reflectance Measured by Indoor Hyperspectral Light Detection and Ranging (LiDAR)
by Peilun Hu, Huaguo Huang, Yuwei Chen, Jianbo Qi, Wei Li, Changhui Jiang, Haohao Wu, Wenxin Tian and Juha Hyyppä
Remote Sens. 2020, 12(6), 919; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12060919 - 12 Mar 2020
Cited by 14 | Viewed by 4104
Abstract
Hyperspectral light detection and ranging (LiDAR) (HSL) combines the characteristics of hyperspectral imaging and LiDAR techniques into a single instrument without any data registration. It provides more information than hyperspectral imaging or LiDAR alone in the extraction of vegetation physiological and biochemical parameters. [...] Read more.
Hyperspectral light detection and ranging (LiDAR) (HSL) combines the characteristics of hyperspectral imaging and LiDAR techniques into a single instrument without any data registration. It provides more information than hyperspectral imaging or LiDAR alone in the extraction of vegetation physiological and biochemical parameters. However, the laser pulse intensity is affected by the incident angle, and its effect on HSL has not yet been fully explored. It is important for employing HSL to investigate vegetation properties. The aim of this paper is to study the incident angle effect of leaf reflectance with HSL and build a model about this impact. In this paper, we studied the angle effect of leaf reflectance from indoor HSL measurements of individual leaves from four typical tree species in Beijing. We observed that (a) the increasing of incident angle decreases the leaf reflectance; (b) the leaf spectrum observed by HSL from 650 to 1000 nm with 10 nm spectral resolution (36 channels) are consistent with those that measured by Analytica Spectra Devices (ASD) spectrometer (R2 = 0.9472 ~ 0.9897); (c) the specular reflection is significant in the red bands, and clear non-Lambertian characteristics are observed. In the near-infrared, there is little specular reflection, but it follows the Lambert-scattering law. We divided the whole band (650–1000 nm) into six bands and established an empirical model to correct the influence of angle effect on the reflectance of the leaf for HSL applications. In the future, the calibration of HSL measurements applied for other targets will be studied by rigorous experiments and modelling. Full article
(This article belongs to the Special Issue Forest Monitoring in a Multi-Sensor Approach)
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