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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 23486

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Dr. Carlos Alberto Silva

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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
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Dr. Enrico Tomelleri

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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

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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
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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

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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

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Published Papers (9 papers)

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Research

24 pages, 24203 KiB

Open AccessArticle

Tree-Level Chinese Fir Detection Using UAV RGB Imagery and YOLO-DCAM

by Jiansen Wang, Huaiqing Zhang, Yang Liu, Huacong Zhang and Dongping Zheng

Remote Sens. 2024, 16(2), 335; https://doi.org/10.3390/rs16020335 - 14 Jan 2024

Cited by 5 | Viewed by 2162

Abstract

Achieving the accurate and efficient monitoring of forests at the tree level can provide detailed information for precise and scientific forest management. However, the detection of individual trees under planted forests characterized by dense distribution, serious overlap, and complicated background information is still a challenge. A new deep learning network, YOLO-DCAM, has been developed to effectively promote individual tree detection amidst complex scenes. The YOLO-DCAM is constructed by leveraging the YOLOv5 network as the basis and further enhancing the network’s capability of extracting features by reasonably incorporating deformable convolutional layers into the backbone. Additionally, an efficient multi-scale attention module is integrated into the neck to enable the network to prioritize the tree crown features and reduce the interference of background information. The combination of these two modules can greatly enhance detection performance. The YOLO-DCAM achieved an impressive performance for the detection of Chinese fir instances within a comprehensive dataset comprising 978 images across four typical planted forest scenes, with model evaluation metrics of precision (96.1%), recall (93.0%), F1-score (94.5%), and [email protected] (97.3%), respectively. The comparative test showed that YOLO-DCAM has a good balance between model accuracy and efficiency compared with YOLOv5 and advanced detection models. Specifically, the precision increased by 2.6%, recall increased by 1.6%, F1-score increased by 2.1%, and [email protected] increased by 1.4% compared to YOLOv5. Across three supplementary plots, YOLO-DCAM consistently demonstrates strong robustness. These results illustrate the effectiveness of YOLO-DCAM for detecting individual trees in complex plantation environments. This study can serve as a reference for utilizing UAV-based RGB imagery to precisely detect individual trees, offering valuable implications for forest practical applications. 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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20 pages, 9784 KiB

Open AccessArticle

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

by 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

Cited by 2 | Viewed by 1149

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

by Selena Georgiou, Edward T. A. Mitchard, Bart Crezee, Greta C. Dargie, Dylan M. Young, Antonio J. Jovani-Sancho, Benjamin Kitambo, Fabrice Papa, Yannick E. Bocko, Pierre Bola, Dafydd E. Crabtree, Ovide B. Emba, Corneille E. N. Ewango, Nicholas T. Girkin, Suspense A. Ifo, Joseph T. Kanyama, Yeto Emmanuel Wenina Mampouya, Mackline Mbemba, Jean-Bosco N. Ndjango, Paul. I. Palmer, Sofie Sjögersten and Simon L. Lewis Show full author list Hide full author list

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

Cited by 2 | Viewed by 4721

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

by Tian Zhou, Radhika Ravi, Yi-Chun Lin, Raja Manish, Songlin Fei and Ayman Habib

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

Cited by 5 | Viewed by 1840

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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25 pages, 6175 KiB

Open AccessArticle

A Novel Forest EcoSpatial Network for Carbon Stocking Using Complex Network Theory in the Yellow River Basin

by Huiqing Zhang, Simei Lin, Qiang Yu, Ge Gao, Chenglong Xu and Huaguo Huang

Remote Sens. 2023, 15(10), 2612; https://doi.org/10.3390/rs15102612 - 17 May 2023

Cited by 7 | Viewed by 1838

Abstract

The Yellow River Basin serves as a crucial ecological barrier in China, emphasizing the importance of accurately examining the spatial distribution of forest carbon stocks and enhancing carbon sequestration in order to attain “carbon peaking and carbon neutrality”. Forest patches have complex interactions that impact ecosystem services. To our knowledge, very few studies have explored the connection between these interactions and carbon stock. This study addressed this gap by utilizing complex network theory to establish a forest ecospatial network (ForEcoNet) in the Yellow River Basin in which forest patches are represented as nodes (sources) and their interactions as edges (corridors). Our objective was to optimize the ForEcoNet’s structure and enhance forest carbon stocks. First, we employed downscaling technology to allocate the forest carbon stocks of the 69 cities in the study area to grid cells, generating a spatial distribution map of forest carbon density in the Yellow River Basin. Next, we conducted morphological spatial pattern analysis (MSPA) and used the minimum cumulative resistance model (MCR) to extract the ForEcoNet in the basin. Finally, we proposed optimization of the ForEcoNet based on the coupling coordination between the node carbon stock and topological structure. The results showed that: (1) the forest carbon stocks of the upper, middle, and lower reaches accounted for 42.35%, 54.28%, and 3.37% of the total, respectively, (2) the ForEcoNet exhibited characteristics of both a random network and a scale-free network and demonstrated poor network stability, and (3) through the introduction of 51 sources and 46 corridors, we optimized the network and significantly improved its robustness. These findings provide scientific recommendations for the optimization of forest allocation in the Yellow River Basin and achieving the goal of increasing the forest carbon stock. 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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24 pages, 15975 KiB

Open AccessArticle

Research on the Relationship between the Structure of Forest and Grass Ecological Spaces and Ecological Service Capacity: A Case Study of the Wuding River Basin

by Yufan Zeng, Qiang Yu, Xiaoci Wang, Jun Ma, Chenglong Xu, Shi Qiu, Wei Liu and Fei Wang

Remote Sens. 2023, 15(9), 2456; https://doi.org/10.3390/rs15092456 - 7 May 2023

Cited by 4 | Viewed by 2237

Abstract

In recent years, the accelerated pace of urbanization has increased patch fragmentation, which has had a certain impact on the structure and ecological environment of forest–grass ecological networks, and certain protection measures have been taken in various regions. Therefore, studying the spatiotemporal changes and correlations of ecological service functions and forest–grass ecological networks can help to better grasp the changes in landscape ecological structure and function. This paper takes the Wuding River Basin as the research area and uses the windbreak and sand fixation service capacity index, soil conservation capacity, and net primary productivity (NPP) to evaluate the ecological service capacity of the research area from the three dimensions of windbreak and sand fixation, soil conservation, and carbon sequestration. The Regional Sustainability and Environment Index (RSEI) is used to extract ecological source areas, and GIS spatial analysis and the minimum cumulative resistance (MCR) model are used to extract potential ecological corridors. Referring to complex network theory, topology metrics such as degree distribution and clustering coefficient are calculated, and their correlation with ecological service capacity is explored. The results show that the overall ecological service capacity of sand fixation, soil fixation, and carbon sequestration in the research area in 2020 has increased compared to 2000, and the ecological flow at the northern and northwest boundaries of the river basin has been enhanced, but there are still shortcomings such as fragmented ecological nodes, a low degree of clustering, and poor connectivity. In terms of the correlation between topology indicators and ecological service functions, the windbreak and sand fixation service capacity index have the strongest correlation with clustering and the largest grasp, while the correlation between soil conservation capacity and eigencentrality is the strongest and has the largest grasp. The correlation between NPP and other indicators is not obvious, and its correlation with eccentricity and eigencentrality is relatively large. 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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23 pages, 78726 KiB

Open AccessArticle

Spatio-Temporal Dynamic Characteristics of Carbon Use Efficiency in a Virgin Forest Area of Southeast Tibet

by Ziyan Yang, Qiang Yu, Ziyu Yang, Anchen Peng, Yufan Zeng, Wei Liu, Jikai Zhao and Di Yang

Remote Sens. 2023, 15(9), 2382; https://doi.org/10.3390/rs15092382 - 1 May 2023

Cited by 9 | Viewed by 2613

Abstract

The sequestration of carbon in forests plays a crucial role in mitigating global climate change and achieving carbon neutrality goals. Carbon use efficiency (CUE) is an essential metric used to evaluate the carbon sequestration capacity and efficiency of Vegetation. Previous studies have emphasized the importance of assessing CUE at specific regions and times to better understand its spatiotemporal variations. The southeastern region of Tibet in the Qinghai-Tibet Plateau is recognized as one of the most biodiverse areas in China and globally, characterized by diverse vegetation types ranging from subtropical to temperate. In this study, we focused on Nyingchi, which is the largest virgin forest area in southeast Tibet, to explore the spatial-temporal dynamic characteristics of regional CUE based on MODIS remote sensing products. The following results were obtained: (1) On a monthly scale, regional CUE exhibits significant seasonal variations, with varying patterns among different vegetation types. Specifically, the fluctuation of CUE is the lowest in high-altitude forest areas and the greatest in grasslands and barrens. On an annual scale, forests exhibit higher fluctuations than areas with sparse vegetation and the overall volatility of CUE increased over the past 11 years. (2) There are regional differences in the trend of CUE changes, with a substantial downward trend in the Himalayan region and a significant upward trend in the residual branches of the Gangdise Mountains. More than 75% of the regions exhibit no persistent trend in CUE changes. (3) Vegetation type is the main determinant of the range and characteristics of vegetation CUE changes, while the geographical location and climatic conditions affect the variation pattern. CUE in the southern and northern regions of Nyingchi at 28.5°N exhibits different responses to temperature and precipitation changes, with temperature having a more significant impact on CUE. 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

by Thomas Mathes, Dominik Seidel, Karl-Heinz Häberle, Hans Pretzsch and Peter Annighöfer

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

Cited by 14 | Viewed by 3411

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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20 pages, 10842 KiB

Open AccessArticle

Study and Prediction of Surface Deformation Characteristics of Different Vegetation Types in the Permafrost Zone of Linzhi, Tibet

by Xiaoci Wang, Qiang Yu, Jun Ma, Linzhe Yang, Wei Liu and Jianzheng Li

Remote Sens. 2022, 14(18), 4684; https://doi.org/10.3390/rs14184684 - 19 Sep 2022

Cited by 3 | Viewed by 2445

Abstract

Permafrost and alpine vegetation are widely distributed in Tibet, which is a sensitive area for global climate change. In this study, we inverted the surface deformation from 22 May 2018 to 9 October 2021 in a rectangular area within the city of Linzhi, Tibet, using the Sentinel1-A data and two time-series interferometric system aperture radar (InSAR) techniques. Then, the significant features of surface deformation were analyzed separately according to different vegetation types. Finally, multiple machine learning methods were used to predict future surface deformation, and the results were compared to obtain the model with the highest prediction accuracy. This study aims to provide a scientific reference and decision basis for global ecological security and sustainable development. The results showed that the surface deformation rate in the study area was basically between ±10 mm/a, and the cumulative surface deformation was basically between ±35 mm. The surface deformation of grassland, meadow, coniferous forest, and alpine vegetation were all significantly correlated with NDVI, and the effect of alpine vegetation, coniferous forest, and grassland on permafrost was stronger than that of the meadow. The prediction accuracy of the Holt–Winters model was higher than that of Holt′s model and the ARIMA model; it was expected that the ground surface would keep rising in the next two months, and the ground surface deformation of alpine vegetation and the coniferous forest was relatively small. The above studies indicated that the surface deformation in the Tibetan permafrost region was relatively stable under the conditions of alpine vegetation and coniferous forest. Future-related ecological construction needs to pay more attention to permafrost areas under grassland and meadow conditions, which are prone to surface deformation and affect the stability of ecosystems. 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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