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Vegetation Structure Monitoring with 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 (31 March 2024) | Viewed by 15697

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Dr. Shanshan Wei

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Guest Editor

Centre for Remote Imaging, Sensing and Processing, National University of Singapore, Singapore 119076, Singapore
Interests: remote sensing of vegetation; biophysical/biochemical parameter estimation; radiation transfer model; hyperspectral images; multi-angle remote sensing; very high spatial resolution imagery; unmanned aerial vehicles (UAVs); terrestrial LiDAR

Dr. Tiangang Yin

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Department of Land Surveying and Geo-Informatics, The HongKong Polytechnic University, Hong Kong
Interests: radiative transfer modeling; lidar; forestry; voxelization; ray tracing; biophysical information retrieval

Dr. Hao Tang

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Guest Editor

Department of Geography, National University of Singapore, Singapore 117570, Singapore
Interests: remote sensing; lidar; forestry; vegetation structure; biomass; ecosystem

Prof. Dr. Jean-Philippe Gastellu-Etchegorry

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Guest Editor

CESBIO, Toulouse University, CNES, CNRS, IRD, UT3, Toulouse, France
Interests: 3D radiative transfer modeling; vegetation; hyperspectral; LiDAR; fluorescence; radiative budget
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Special Issue Information

Dear Colleagues,

Vegetation structure (e.g., leaf area index/density, foliar incline angle, fraction of cover, clumping, plant height, biomass) can directly influence photosynthesis, canopy energy balance, and water balance. Thus, vegetation structure monitoring at various scales ranging from individual plant to landscape is crucial to understand ecosystem functioning.

Remote sensing has revolutionized vegetation structure monitoring over large scales thanks to its rapid, cost-effective, and objective quantification. Currently, the vast magnitude of remote sensing data provides an unprecedented opportunity to comprehensively understand vegetation structure and characteristics. These data include low/medium/high/ultra-high spatial resolution multi-spectral image and stereogrametry, hyperspectral imagery, as well as LiDAR and RADAR data with scale ranging from local and regional to global coverage, captured by terrestrial and unmanned aerial vehicles (UAVs), as well as airborne and spaceborne platforms. The development of the remote sensing data is also accompanied by advances of the monitoring methods/technologies, for example, empirical statistical models, 1D/3D radiative transfer models, structure from motion (SFM), deep learning, 3D reconstruction, and cloud computing technology.

This Special Issue will report remote sensing techniques and algorithms developed for vegetation structure monitoring in order to advance our current understanding and inspire the future direction of vegetation structure monitoring using remote sensing data. Potential topics for this Special Issue may include, but are not limited to:

Vegetation structure retrieval from single/multiple remote sensing, including low/medium/high/ultra-high spatial resolution multi-spectral/hyperspectral, LiDAR, RADAR, and other new sensors, based on terrestrial, UAV, airborne, and spaceborne platforms.
Novel data integration/fusion of spectral, stereogrametry, LiDAR, or RADAR data acquired from different platforms.
Radiative transfer model development/improvement considering vegetation structure influence.
Vertical profile/three-dimensional (3D) vegetation structure extraction and modeling.
Qualification of vegetation structure impact on remote sensing signals (e.g., solar-induced chlorophyll fluorescence (SIF) emission, radiative budget, and bidirectional reflectance distribution function (BRDF)).
Vegetation degradation and structure variation monitoring using remote sensing.
Comparison and evaluation of different remote techniques for vegetation structure studies.
New operational vegetation structure product development or the evaluation of uncertainty in current products.

Review articles covering one or more of these topics are also welcome.

Dr. Shanshan Wei
Dr. Tiangang Yin
Dr. Hao Tang
Prof. Dr. Jean-Philippe Gastellu-Etchegorry
Guest Editors

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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.

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Keywords

vegetation structure
multiplatform remote sensing
LiDAR
radiative transfer model
RADAR
biomass
multi-sensor fusion
vegetation dynamics

Published Papers (9 papers)

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Research

22 pages, 7233 KiB

Open AccessArticle

High-Resolution Canopy Height Mapping: Integrating NASA’s Global Ecosystem Dynamics Investigation (GEDI) with Multi-Source Remote Sensing Data

by Cesar Alvites, Hannah O’Sullivan, Saverio Francini, Marco Marchetti, Giovanni Santopuoli, Gherardo Chirici, Bruno Lasserre, Michela Marignani and Erika Bazzato

Remote Sens. 2024, 16(7), 1281; https://doi.org/10.3390/rs16071281 - 05 Apr 2024

Viewed by 822

Abstract

Accurate structural information about forests, including canopy heights and diameters, is crucial for quantifying tree volume, biomass, and carbon stocks, enabling effective forest ecosystem management, particularly in response to changing environmental conditions. Since late 2018, NASA’s Global Ecosystem Dynamics Investigation (GEDI) mission has monitored global canopy structure using a satellite Light Detection and Ranging (LiDAR) instrument. While GEDI has collected billions of LiDAR shots across a near-global range (between 51.6°N and >51.6°S), their spatial distribution remains dispersed, posing challenges for achieving complete forest coverage. This study proposes and evaluates an approach that generates high-resolution canopy height maps by integrating GEDI data with Sentinel-1, Sentinel-2, and topographical ancillary data through three machine learning (ML) algorithms: random forests (RF), gradient tree boost (GB), and classification and regression trees (CART). To achieve this, the secondary aims included the following: (1) to assess the performance of three ML algorithms, RF, GB, and CART, in predicting canopy heights, (2) to evaluate the performance of our canopy height maps using reference canopy height from canopy height models (CHMs), and (3) to compare our canopy height maps with other two existing canopy height maps. RF and GB were the top-performing algorithms, achieving the best 13.32% and 16% root mean squared error for broadleaf and coniferous forests, respectively. Validation of the proposed approach revealed that the 100th and 98th percentile, followed by the average of the 75th, 90th, 95th, and 100th percentiles (AVG), were the most accurate GEDI metrics for predicting real canopy heights. Comparisons between predicted and reference CHMs demonstrated accurate predictions for coniferous stands (R-squared = 0.45, RMSE = 29.16%). Full article

(This article belongs to the Special Issue Vegetation Structure Monitoring with Multi-Source Remote Sensing Data)

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27 pages, 4867 KiB

Open AccessArticle

A Novel Remote Sensing-Based Modeling Approach for Maize Light Extinction Coefficient Determination

by Edson Costa-Filho, José L. Chávez and Huihui Zhang

Remote Sens. 2024, 16(6), 1012; https://doi.org/10.3390/rs16061012 - 13 Mar 2024

Viewed by 631

Abstract

This study focused on developing a novel semi-empirical model for maize’s light extinction coefficient (k_p) by integrating multiple remotely sensed vegetation features from several different remote sensing platforms. The proposed k_p model’s performance was independently evaluated using Campbell’s (1986) original and simplified k_p approaches. The Limited Irrigation Research Farm (LIRF) in Greeley, Colorado, and the Irrigation Innovation Consortium (IIC) in Fort Collins, Colorado, USA, served as experimental sites for developing and evaluating the novel maize k_p model. Data collection involved multiple remote sensing platforms, including Landsat-8, Sentinel-2, Planet CubeSat, a Multispectral Handheld Radiometer, and an unmanned aerial system (UAS). Ground measurements of leaf area index (LAI) and fractional vegetation canopy cover (f_c) were included. The study evaluated the novel k_p model through a comprehensive analysis using statistical error metrics and Sobol global sensitivity indices to assess the performance and sensitivity of the models developed for predicting maize k_p. Results indicated that the novel k_p model showed strong statistical regression fitting results with a coefficient of determination or R² of 0.95. Individual remote sensor analysis confirmed consistent regression calibration results among Landsat-8, Sentinel-2, Planet CubeSat, the MSR, and UAS. A comparison with Campbell’s (1986) k_p models reveals a 44% improvement in accuracy. A global sensitivity analysis identified the role of the normalized difference vegetation index (NDVI) as a critical input variable to predict k_p across sensors, emphasizing the model’s robustness and potential practical environmental applications. Further research should address sensor-specific variations and expand the k_p model’s applicability to a diverse set of environmental and microclimate conditions. Full article

(This article belongs to the Special Issue Vegetation Structure Monitoring with Multi-Source Remote Sensing Data)

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19 pages, 4229 KiB

Open AccessArticle

Integrating Dendrochronological and LiDAR Data to Improve Management of Pinus canariensis Forests under Different Thinning and Climatic Scenarios

by Rafael M. Navarro-Cerrillo, Eva Padrón Cedrés, Antonio M. Cachinero-Vivar, Cristina Valeriano and Jesús Julio Camarero

Remote Sens. 2024, 16(5), 850; https://doi.org/10.3390/rs16050850 - 29 Feb 2024

Viewed by 617

Abstract

Thinning focused on achieving growth and diameter management objectives has typically led to stands with reduced climate sensitivity compared to unthinned stands. We integrated dendrochronological with Airborne Laser Scanner (LiDAR) data and growth models to assess the long-term impact of thinning intensity on Canary pine (Pinus canariensis) radial growth. In 1988, 18 permanent treatment units were established in 73-year-old Canary pine plantations and three thinning treatments were applied (C–control-unthinned; 0% basal area removal; MT–moderate thinning: 10% and 15% basal area removal, and HT–heavy thinning: 46% and 45% basal area removal on the windward and leeward slopes, respectively). Dendrochronological data were measured in 2022 and expressed as basal area increment (BAI). The impact of climate on growth was examined by fitting linear regression models considering two different Representative Concentration Pathway (RCP) climate scenarios, RCP 2.6 and RCP 4.5. Finally, LiDAR data were used for standing segmentation to evaluate changes in overall growth under different climatic scenarios. The LiDAR–stand attributes differed between aspects. The BAI of the most recent 20 years (BAI₂₀) after thinning was significantly higher for the moderate and heavy treatments on the leeward plots (F = 47.31, p < 0.001). On the windward plots, BAI decreased after moderate thinning. Considerable thinning treatments resulted in stronger changes in growth when compared to RCP climatic scenarios. From a silviculture perspective, the mapping of canopy structure and growth response to thinning under different climatic scenarios provides managers with opportunities to conduct thinning strategies for forest adaptation. Combining dendrochronological and LiDAR data at a landscape scale substantially improves the value of the separate datasets as forecasted growth response maps allow improving thinning management plans. Full article

(This article belongs to the Special Issue Vegetation Structure Monitoring with Multi-Source Remote Sensing Data)

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21 pages, 32906 KiB

Open AccessArticle

Retrieval of Three-Dimensional Green Volume in Urban Green Space from Multi-Source Remote Sensing Data

by Zehu Hong, Weiheng Xu, Yun Liu, Leiguang Wang, Guanglong Ou, Ning Lu and Qinling Dai

Remote Sens. 2023, 15(22), 5364; https://doi.org/10.3390/rs15225364 - 15 Nov 2023

Cited by 1 | Viewed by 843

Abstract

Quantification of three-dimensional green volume (3DGV) plays a crucial role in assessing environmental benefits to urban green space (UGS) at a regional level. However, precisely estimating regional 3DGV based on satellite images remains challenging. In this study, we developed a parametric estimation model to retrieve 3DGV in UGS through combining Sentinel-1 and Sentinel-2 images. Firstly, UAV images were used to calculate the referenced 3DGV based on mean of neighboring pixels (MNP) algorithm. Secondly, we applied the canopy height model (CHM) and Leaf Area Index (LAI) derived from Sentinel-1 and Sentinel-2 images to construct estimation models of 3DGV. Then, we compared the accuracy of estimation models to select the optimal model. Finally, the estimated 3DGV maps were generated using the optimal model, and the referenced 3DGV was employed to evaluate the accuracy of maps. Results indicated that the optimal model was the combination of LAI power model and CHM linear model (3DGV = 37.13·LAI^−0.3·CHM + 38.62·LAI^1.8 + 13.8, R² = 0.78, MPE = 8.71%). We validated the optimal model at the study sites and achieved an overall accuracy (OA) of 75.15%; then, this model was used to map 3DGV distribution at the 10 m resolution in Kunming city. These results demonstrated the potential of combining Sentinel-1 and Sentinel-2 images to construct an estimation model for 3DGV retrieval in UGS. Full article

(This article belongs to the Special Issue Vegetation Structure Monitoring with Multi-Source Remote Sensing Data)

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16 pages, 2877 KiB

Open AccessArticle

Tropical Forest Top Height by GEDI: From Sparse Coverage to Continuous Data

by Yen-Nhi Ngo, Dinh Ho Tong Minh, Nicolas Baghdadi and Ibrahim Fayad

Remote Sens. 2023, 15(4), 975; https://doi.org/10.3390/rs15040975 - 10 Feb 2023

Cited by 8 | Viewed by 3056

Abstract

Estimating consistent large-scale tropical forest height using remote sensing is essential for understanding forest-related carbon cycles. The Global Ecosystem Dynamics Investigation (GEDI) light detection and ranging (LiDAR) instrument employed on the International Space Station has collected unique vegetation structure data since April 2019. Our study shows the potential value of using remote-sensing (RS) data (i.e., optical Sentinel-2, radar Sentinel-1, and radar PALSAR-2) to extrapolate GEDI footprint-level forest canopy height model (CHM) measurements. We show that selected RS features can estimate vegetation heights with high precision by analyzing RS data, spaceborne GEDI LiDAR, and airborne LiDAR at four tropical forest sites in South America and Africa. We found that the GEDI relative height (RH) metric is the best at 98% (RH98), filtered by full-power shots with a sensitivity greater than 98%. We found that the optical Sentinel-2 indices are dominant with respect to radar from 77 possible features. We proposed the nine essential optical Sentinel-2 and the radar cross-polarization HV PALSAR-2 features in CHM estimation. Using only ten optimal indices for the regression problems can avoid unimportant features and reduce the computational effort. The predicted CHM was compared to the available airborne LiDAR data, resulting in an error of around 5 m. Finally, we tested cross-validation error values between South America and Africa, including around 40% from validation data in training to obtain a similar performance. We recommend that GEDI data be extracted from all continents to maintain consistent performance on a global scale. Combining GEDI and RS data is a promising method to advance our capability in mapping CHM values. Full article

(This article belongs to the Special Issue Vegetation Structure Monitoring with Multi-Source Remote Sensing Data)

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16 pages, 3673 KiB

Open AccessArticle

Assessing the Vertical Structure of Forests Using Airborne and Spaceborne LiDAR Data in the Austrian Alps

by Manuela Hirschmugl, Florian Lippl and Carina Sobe

Remote Sens. 2023, 15(3), 664; https://doi.org/10.3390/rs15030664 - 22 Jan 2023

Cited by 4 | Viewed by 2828

Abstract

Vertical structure is an important parameter not only for assessment of the naturalness of a forest and several functional parameters, such as biodiversity or protection from avalanches or rockfall, but also for estimating biomass/carbon content. This study analyses the options for assessing vertical forest structure by using airborne (ALS) and spaceborne LiDAR data (GEDI) in a mountainous near-natural forest in the Austrian Alps. Use of the GEDI waveform data (L1B) is still heavily underexploited for vertical forest structure assessments. Two indicators for explaining forest vertical structure are investigated in this study: foliage height diversity (FHD) and number of layers (NoL). For estimation of NoL, two different approaches were tested: break-detection algorithm (BDA) and expert-based assessment (EBA). The results showed that FHD can be used to separate three structural classes; separability is only slightly better for ALS than for GEDI data on a 25 m diameter plot level. For NoL, EBA clearly outperformed BDA in terms of overall accuracy (OA) by almost 20%. A better OA for NoL was achieved using ALS (49.5%) rather than GEDI data (44.2%). In general, OA is limited by difficult terrain and near-natural forests with high vertical structure. The usability of waveform-based structure parameters is, nonetheless, promising and should be further tested on larger areas, including managed forests and simpler stands. Full article

(This article belongs to the Special Issue Vegetation Structure Monitoring with Multi-Source Remote Sensing Data)

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18 pages, 6004 KiB

Open AccessArticle

Fusion of LiDAR and Multispectral Data for Aboveground Biomass Estimation in Mountain Grassland

by Ang Chen, Xing Wang, Min Zhang, Jian Guo, Xiaoyu Xing, Dong Yang, Huilong Zhang, Zhiyan Hou, Ze Jia and Xiuchun Yang

Remote Sens. 2023, 15(2), 405; https://doi.org/10.3390/rs15020405 - 09 Jan 2023

Cited by 3 | Viewed by 1925

Abstract

Grassland aboveground biomass (AGB) is an important indicator for studying the change in grassland ecological quality and carbon cycle. The rapid development of high-resolution remote sensing and unmanned aerial vehicles (UAV) provides a new opportunity for accurate estimation of grassland AGB on the plot scale. In this study, the mountain grassland was taken as the research object. Using UAV Light Detection and Ranging (LiDAR) data and multispectral satellite images, the influence of topographic correction methods on AGB estimation was compared and a series of LiDAR metrics and vegetation indices were extracted. On this basis, a comprehensive indicator, the vegetation index-height-intensity model (VHI), was proposed to estimate AGB quickly. The results show that: (1) Among the four topographic correction methods, the Teillet regression has the best effect, and can effectively improve the accuracy of AGB estimation in mountain grassland. The correlation between corrected ratio vegetation index and AGB was the highest (correlation coefficient: 0.682). (2) Among the height and intensity metrics, median height and max intensity yielded the higher accuracy in estimating AGB, with Root Mean Square Error (RMSE) of 322 g/m² and 333 g/m², respectively. (3) The VHI integrated spectrum and LiDAR information, and its accuracy for AGB estimation for mountain grassland, was obviously better than other indicators, with an RMSE of 272 g/m². We also found that the accuracy of VHI in univariate models was comparable to that of complex multivariate models such as stepwise regression, support vector machine, and random forest. This study provides a new approach for estimating grassland AGB with multi-source data. As a simple and effective indicator, VHI has shown strong application potential for grassland AGB estimating in mountainous areas, and can be further applied to grassland carbon cycle research and fine management. Full article

(This article belongs to the Special Issue Vegetation Structure Monitoring with Multi-Source Remote Sensing Data)

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24 pages, 5513 KiB

Open AccessArticle

Effectiveness of the Reconstructed MODIS Typical-Angle Reflectances on Forest Biomass Estimation

by Lei Cui, Mei Sun, Ziti Jiao, Jongmin Park, Muge Agca, Hu Zhang, Long He, Yiqun Dai, Yadong Dong, Xiaoning Zhang, Yi Lian, Lei Chen and Kaiguang Zhao

Remote Sens. 2022, 14(21), 5475; https://doi.org/10.3390/rs14215475 - 31 Oct 2022

Cited by 4 | Viewed by 1558

Abstract

Multi-angle optical reflectance measurements such as those from the NASA moderate resolution imaging spectroradiometer (MODIS) are sensitive to forest 3D structures, potentially serving as a useful proxy to estimate forest structural variables such as aboveground biomass (AGB)—a potential theoretically recognized but rarely explored. In this paper, we examined the effectiveness of the reconstructed MODIS typical-angle reflectances—reflectances observed from the hotspot, darkspot, and nadir directions—for estimating forest AGB from both theoretical and practical perspectives. To gain theoretical insights, we first tested the sensitivities of typical-angle reflectances to forest AGB through simulations using the 4-scale bidirectional reflectance distribution function (BRDF) model. We then built statistical models to fit the relationship between MODIS multi-angle observations and field-measured deciduous-broadleaf/mixed-temperate forest AGB at five sites in the eastern USA, assisted by a semivariogram analysis to determine the effect of pixel heterogeneity on the MODIS–AGB relationship. We also determined the effects of terrain and season on the predictive relationships. Our results indicated that multi-angle reflectances with fewer visible shadows yielded better AGB estimates (hotspot: R² = 0.63, RMSE = 54.28 Mg/ha; nadir: R² = 0.55, RMSE = 59.95 Mg/ha; darkspot: R² = 0.46, RMSE = 65.66 Mg/ha) after filtering out the effects of complex terrain and pixel heterogeneity; the MODIS typical-angle reflectances in the NIR band were the most sensitive to forest AGB. We also found strong sensitivities of estimated accuracies to MODIS image acquisition dates or season. Overall, our results suggest that the current practice of leveraging only single-angle MODIS data can be a suboptimal strategy for AGB estimation. We advocate the use of MODIS multi-angle reflectances for optical remote sensing of forest AGB or potentially other ecological applications requiring forest structure information. Full article

(This article belongs to the Special Issue Vegetation Structure Monitoring with Multi-Source Remote Sensing Data)

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16 pages, 3251 KiB

Open AccessArticle

Study on the Impact of Spatial Resolution on Fractional Vegetation Cover Extraction with Single-Scene and Time-Series Remote Sensing Data

by Yanfang Wang, Lu Tan, Guangyu Wang, Xinyu Sun and Yannan Xu

Remote Sens. 2022, 14(17), 4165; https://doi.org/10.3390/rs14174165 - 24 Aug 2022

Cited by 8 | Viewed by 1760

Abstract

The spatial resolution of remote sensing images directly affects the accuracy, efficiency, and computational cost of extracting the fractional vegetation cover (FVC). Taking the Liyang woodland region, Jiangsu Province, as the study area, FVCs with varying spatial resolutions were extracted separately from Sentinel-2, Landsat-8, MOD13Q1, and MOD13A1. The variations in FVCs extracted from remote sensing images with varying spatial resolutions were analyzed at one specific time and time series within a year. The results show that (i) the overall mean FVC values of the four spatial resolution images did not differ substantially; however, FVCs with varying spatial resolutions present with a regular pattern of overestimation or underestimation at different vegetation levels. (ii) Taking the 10 m spatial resolution FVC as the reference, the accuracy values of FVC extraction at 30 m, 250 m, and 500 m resolutions were 91.0%, 76.3%, and 76.7%, respectively. The differences in the spatial distribution of FVCs are the most obvious at water–land interfaces and at the edge of each woodland patch. (iii) The highest accuracy of time-series FVC extraction from lower-resolution images is in the range of 0.6~0.7 for FVC. The degree of variation in FVC of time series varying spatial resolutions depends on the season and vegetation cover conditions. In summary, there are considerable differences in the need to monitor high-resolution images depending on the FVC level of the land surface. This study provides a reference for selection and accuracy research of remote sensing images for FVC extraction. Full article

(This article belongs to the Special Issue Vegetation Structure Monitoring with Multi-Source Remote Sensing Data)

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