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Special Issue "Editorial Board Members’ Collection Series: Forest Environment Monitoring Based on Multi-Source Remote Sensing Data"

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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 (30 November 2023) | Viewed by 6409

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Special Issue Editors

Dr. Carlos Alberto Silva

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Guest Editor

Forest Biometrics and Remote Sensing Lab (Silva Lab), School of Forest, Fisheriers and Geomatics Science, University of Florida, P.O. Box 110410, Gainesville, FL 32611, USA
Interests: lidar remote sensing (ALS, TLS, UAV-lidar, GEDI); tropical forest structure and ecology; industrial forest plantations; algorithms and tools development; data integration and change detection
Special Issues, Collections and Topics in MDPI journals

Dr. Enrico Tomelleri

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Guest Editor

Faculty of Science and Technology, Free University of Bozen/Bolzano, 39100 Bozen-Bolzano, Italy
Interests: biogeochemistry; forest ecology; remote sensing; proximal sensing; UAVs; spectrometry

Prof. Dr. Dominik Seidel

E-Mail Website
Guest Editor

Department for Spatial Structures and Digitization of Forests, University of Goettingen, 37077 Goettingen, Germany
Interests: forest structure; tree architecture; structural complexity; LiDAR; structure from motion; structure-function-relationships
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Forests play an important role in climatic environments, ecosystem diversity, and the production of wood products, among others. At present, the technology used in forest assessment and forest management that utilizess multi-source remote-sensing data is becoming more and more complex, but due to the complexity of actual forest environments, some technologies still face technical difficulties in the production processes, and continuous innovation and breakthroughs are required.

Lidar technology has a wide range of applications in forestry and forest ecology, and new spaceborne lidars (GEDI and ICESat-2) can perform detailed measurements of vegetation vertical structures. Lidar and small hyperspectral sensors carried by drones offer more detailed datasets to researchers and play an important role in the calibration and validation of forest monitoring. The rapidly growing commercial imaging industry is also deploying constellations of small satellites, changing the way Earth is observed, with multi-platform sensing enabling near real-time, high spatial resolution, multispectral, hyperspectral and polarization interferometric SAR (PolInSAR) of the world's forests. Synthetic aperture radar remote-sensing technology also provides new methods, concepts and applications for forest biomass assessment and forest mapping.

These technologies have already had a significant impact on forest monitoring, but we hope that these multi-source remote-sensing technologies and data can be further mined and applied to forest remote sensing. In this Special Issue, we welcome a variety of new studies that use multi-source remote-sensing techniques for forest monitoring and that focus on the following topics:

LiDAR point cloud processing in forests;
SAR imaging for forest applications;
Multi-platform LiDAR data fusion for tree modeling and 3D reconstruction;
GEDI and ICESat-2 missions for forest inventory and monitoring;
Tree species detection and individual tree detection;
Application of new remote-sensing techniques to estimate forest aboveground biomass carbon storage and soil carbon storage;
Integration of multi-temporal or multi-sensor data to detect dynamic changes in and distrubances of forest resources.

Dr. Carlos Alberto Silva
Dr. Enrico Tomelleri
Prof. Dr. Dominik Seidel
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

forest monitoring
forest ecosystem structure, composition, and dynamics
aboveground biomass
multi-sensor fusion
lidar remote sensing
polarimetric interferometric SAR
hyperspectral imagery
aerial photogrammetry
multispectral optical remote sensing

Published Papers (4 papers)

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20 pages, 9784 KiB

Open AccessArticle

Forest Height Inversion by Combining Single-Baseline TanDEM-X InSAR Data with External DTM Data

Wenjie He

Jianjun Zhu

Juan M. Lopez-Sanchez

Cristina Gómez

Haiqiang Fu

and

Qinghua Xie

Remote Sens. 2023, 15(23), 5517; https://doi.org/10.3390/rs15235517 - 27 Nov 2023

Viewed by 234

Abstract

Forest canopy height estimation is essential for forest management and biomass estimation. In this study, we aimed to evaluate the capacity of TanDEM-X interferometric synthetic aperture radar (InSAR) data to estimate canopy height with the assistance of an external digital terrain model (DTM). A ground-to-volume ratio estimation model was proposed so that the canopy height could be precisely estimated from the random-volume-over-ground (RVoG) model. We also refined the RVoG inversion process with the relationship between the estimated penetration depth (PD) and the phase center height (PCH). The proposed method was tested by TanDEM-X InSAR data acquired over relatively homogenous coniferous forests (Teruel test site) and coniferous as well as broadleaved forests (La Rioja test site) in Spain. Comparing the TanDEM-X-derived height with the LiDAR-derived height at plots of size 50 m × 50 m, the root-mean-square error (RMSE) was 1.71 m (R² = 0.88) in coniferous forests of Teruel and 1.97 m (R² = 0.90) in La Rioja. To demonstrate the advantage of the proposed method, existing methods based on ignoring ground scattering contribution, fixing extinction, and assisting with simulated spaceborne LiDAR data were compared. The impacts of penetration and terrain slope on the RVoG inversion were also evaluated. The results show that when a DTM is available, the proposed method has the optimal performance on forest height estimation. Full article

(This article belongs to the Special Issue Editorial Board Members’ Collection Series: Forest Environment Monitoring Based on Multi-Source Remote Sensing Data)

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38 pages, 97200 KiB

Open AccessArticle

Mapping Water Levels across a Region of the Cuvette Centrale Peatland Complex

Remote Sens. 2023, 15(12), 3099; https://doi.org/10.3390/rs15123099 - 13 Jun 2023

Viewed by 2813

Abstract

Inundation dynamics are the primary control on greenhouse gas emissions from peatlands. Situated in the central Congo Basin, the Cuvette Centrale is the largest tropical peatland complex. However, our knowledge of the spatial and temporal variations in its water levels is limited. By addressing this gap, we can quantify the relationship between the Cuvette Centrale’s water levels and greenhouse gas emissions, and further provide a baseline from which deviations caused by climate or land-use change can be observed, and their impacts understood. We present here a novel approach that combines satellite-derived rainfall, evapotranspiration and L-band Synthetic Aperture Radar (SAR) data to estimate spatial and temporal changes in water level across a sub-region of the Cuvette Centrale. Our key outputs are a map showing the spatial distribution of rainfed and flood-prone locations and a daily, 100 m resolution map of peatland water levels. This map is validated using satellite altimetry data and in situ water table data from water loggers. We determine that 50% of peatlands within our study area are largely rainfed, and a further 22.5% are somewhat rainfed, receiving hydrological input mostly from rainfall (directly and via surface/sub-surface inputs in sloped areas). The remaining 27.5% of peatlands are mainly situated in riverine floodplain areas to the east of the Congo River and between the Ubangui and Congo rivers. The mean amplitude of the water level across our study area and over a 20-month period is 22.8 ± 10.1 cm to 1 standard deviation. Maximum temporal variations in water levels occur in the riverine floodplain areas and in the inter-fluvial region between the Ubangui and Congo rivers. Our results show that spatial and temporal changes in water levels can be successfully mapped over tropical peatlands using the pattern of net water input (rainfall minus evapotranspiration, not accounting for run-off) and L-band SAR data. Full article

(This article belongs to the Special Issue Editorial Board Members’ Collection Series: Forest Environment Monitoring Based on Multi-Source Remote Sensing Data)

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28 pages, 30401 KiB

Open AccessArticle

In Situ Calibration and Trajectory Enhancement of UAV and Backpack LiDAR Systems for Fine-Resolution Forest Inventory

and

Remote Sens. 2023, 15(11), 2799; https://doi.org/10.3390/rs15112799 - 28 May 2023

Viewed by 1014

Abstract

Forest inventory has been relying on labor-intensive manual measurements. Using remote sensing modalities for forest inventory has gained increasing attention in the last few decades. However, tools for deriving accurate tree-level metrics are limited. This paper investigates the feasibility of using LiDAR units onboard uncrewed aerial vehicle (UAV) and Backpack mobile mapping systems (MMSs) equipped with an integrated Global Navigation Satellite System/Inertial Navigation System (GNSS/INS) to provide high-quality point clouds for accurate, fine-resolution forest inventory. To improve the quality of the acquired point clouds, a system-driven strategy for mounting parameters estimation and trajectory enhancement using terrain patches and tree trunks is proposed. By minimizing observed discrepancies among conjugate features captured at different timestamps from multiple tracks by single/multiple systems, while considering the absolute and relative positional/rotational information provided by the GNSS/INS trajectory, system calibration parameters and trajectory information can be refined. Furthermore, some forest inventory metrics, such as tree trunk radius and orientation, are derived in the process. To evaluate the performance of the proposed strategy, three UAV and two Backpack datasets covering young and mature plantations were used in this study. Through sequential system calibration and trajectory enhancement, the spatial accuracy of the UAV point clouds improved from 20 cm to 5 cm. For the Backpack datasets, when the initial trajectory was of reasonable quality, conducting trajectory enhancement significantly improved the relative alignment of the point cloud from 30 cm to 3 cm, and an absolute accuracy at the 10 cm level can be achieved. For a lower-quality trajectory, the initial 1 m misalignment of the Backpack point cloud was reduced to 6 cm through trajectory enhancement. However, to derive products with accurate absolute accuracy, UAV point cloud is required as a reference in the trajectory enhancement process of the Backpack dataset. Full article

(This article belongs to the Special Issue Editorial Board Members’ Collection Series: Forest Environment Monitoring Based on Multi-Source Remote Sensing Data)

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21 pages, 3058 KiB

Open AccessArticle

What Are We Missing? Occlusion in Laser Scanning Point Clouds and Its Impact on the Detection of Single-Tree Morphologies and Stand Structural Variables

and

Remote Sens. 2023, 15(2), 450; https://doi.org/10.3390/rs15020450 - 12 Jan 2023

Cited by 4 | Viewed by 1688

Abstract

Laser scanning has revolutionized the ability to quantify single-tree morphologies and stand structural variables. In this study, we address the issue of occlusion when scanning a spruce (Picea abies (L.) H.Karst.) and beech (Fagus sylvatica L.) forest with a mobile laser scanner by making use of a unique study site setup. We scanned forest stands (1) from the ground only and (2) from the ground and from above by using a crane. We also examined the occlusion effect by scanning in the summer (leaf-on) and in the winter (leaf-off). Especially at the canopy level of the forest stands, occlusion was very pronounced, and we were able to quantify its impact in more detail. Occlusion was not as noticeable as expected for crown-related variables but, on average, resulted in smaller values for tree height in particular. Between the species, the total tree height underestimation for spruce was more pronounced than that for beech. At the stand level, significant information was lost in the canopy area when scanning from the ground alone. This information shortage is reflected in the relative point counts, the Clark–Evans index and the box dimension. Increasing the voxel size can compensate for this loss of information but comes with the trade-off of losing details in the point clouds. From our analysis, we conclude that the voxelization of point clouds prior to the extraction of stand or tree measurements with a voxel size of at least 20 cm is appropriate to reduce occlusion effects while still providing a high level of detail. Full article

(This article belongs to the Special Issue Editorial Board Members’ Collection Series: Forest Environment Monitoring Based on Multi-Source Remote Sensing Data)

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