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

Deadline for manuscript submissions: closed (1 May 2023) | Viewed by 21409

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Special Issue Editors

Dr. Chao Ding

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Guest Editor

Center for Territorial Spatial Planning and Real Estate Studies, Beijing Normal University, Zhuhai 519087, China
Interests: vegetation phenology; change detection; land degradation; satellite time series reconstruction; land cover mapping
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Kai Yan

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Guest Editor

School of Land Science and Technology, China University of Geosciences (Beijing), Beijing 100083, China
Interests: vegetation parameters production; radiative transfer modeling; leaf area index (LAI); fraction of absorbed photosynthetically active radiation (fPAR); vegetation dynamics
Special Issues, Collections and Topics in MDPI journals

Dr. Jianbo Qi

E-Mail Website
Guest Editor

State Forestry and Grassland Administration Key Laboratory of Forest Resources and Environmental Management, Beijing Forestry University, Beijing 100083, China
Interests: vegetation remote sensing; radiative transfer modeling; LiDAR remote sensing of vegetation

Special Issue Information

Dear Colleagues,

Vegetation structural properties have substantial impacts on ecosystem processes and are essential for the evaluation of ecosystem functions and services, for example, climate regulation, carbon dynamics and habitat provision. Remote sensing is playing an indispensable role in observing vegetation structural parameters (e.g., leaf area index, fractional vegetation cover, canopy height, growing stock volume, aboveground biomass, carbon storage) across spatial scales. In recent decades, progresses in the estimation of vegetation structural parameters and coarse resolution products have greatly promoted our knowledge of large-scale ecosystem dynamics under the forces of climate and human activities. However, uncertainties in the remotely sensed vegetation structural parameters and findings relying on them remain due to various biotic and abiotic factors, such as missing data, sensor degradation, complex terrain and vegetation structure, etc. Recent developments in optical, SAR, and LiDAR sensors onboard satellite and unmanned aerial vehicle platforms and deep learning algorithms are expected to improve the estimation of vegetation structural parameters and/or the explanations of the uncertainties. We invite colleagues to share their new insights and findings on the estimations of vegetation structural parameters from various remote sensing data sources in this Special Issue. Novel methods using remotely sensed vegetation structural parameters for ecosystem monitoring and evaluation, or new results, which have strong implications for regional ecosystem management, based on the estimated vegetation structural parameters are also welcome.

Dr. Chao Ding
Prof. Dr. Kai Yan
Dr. Jianbo Qi
Guest Editors

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Keywords

vegetation structural properties
optical remote sensing
lidar
SAR
estimation algorithms
canopy radiative transfer
deep learning
product validation
ecosystem dynamics

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Published Papers (8 papers)

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Editorial

Jump to: Research

2 pages, 611 KiB

Open AccessEditorial

New Insights into Remote Sensing of Vegetation Structural Parameters

by Kai Yan, Chao Ding and Jianbo Qi

Forests 2024, 15(9), 1555; https://doi.org/10.3390/f15091555 - 4 Sep 2024

Viewed by 662

Abstract

The accurate and efficient estimation of vegetation structural parameters from remote sensing is a pivotal subject within the field of remote sensing [...] Full article

(This article belongs to the Special Issue New Insights into Remote Sensing of Vegetation Structural Parameters)

Research

Jump to: Editorial

19 pages, 5733 KiB

Open AccessArticle

Evaluation of NASA’s GEDI Lidar Observations for Estimating Biomass in Temperate and Tropical Forests

by Mei Sun, Lei Cui, Jongmin Park, Mariano García, Yuyu Zhou, Carlos Alberto Silva, Long He, Hu Zhang and Kaiguang Zhao

Forests 2022, 13(10), 1686; https://doi.org/10.3390/f13101686 - 13 Oct 2022

Cited by 18 | Viewed by 4050

Abstract

Accurate estimation of forest aboveground biomass (AGB) is vital for informing ecosystem and carbon management. The Global Ecosystem Dynamics Investigation (GEDI) instrument—a new-generation spaceborne lidar system from NASA—provides the first global coverage of high-resolution 3D altimetry data aimed specifically for mapping Earth’s forests, but its performance is yet to be tested for large parts of the world. Here, our goal is to evaluate the accuracies of GEDI in measuring terrain, forest vertical structures, and AGB in reference to independent airborne lidar data over temperate and tropical forests in North America. We compared GEDI-derived elevations and canopy heights (e.g., relative height percentiles such as RH50 and RH100) with those from the Shuttle Radar Topography Mission (SRTM) or from two airborne lidar systems: the Laser Vegetation Imaging Sensor (LVIS) and Goddard’s Lidar, Hyperspectral and Thermal system (G-LiHT). We also estimated GEDI’s geolocation errors by matching GEDI waveforms and G-LiHT pseudo-waveforms. We assessed the predictive power of GEDI metrics in estimating AGB using Random Forests regression. Results showed that GEDI-derived ground elevations correlated strongly those from LVIS, G-LiHT, and LVIS (R² > 0.91), but with nonnegligible RMSEs of 5.7 m (G-LiHT), 3.1 m (LVIS), and 10.9 m (SRTM). GEDI canopy heights had poorer correlation with LVIS (e.g., R² = 0.44 for RH100) than with G-LiHT (e.g., R² = 0.60 for RH100). The estimated horizontal geolocation errors of GEDI footprints averaged 6.5 meters, comparable to the nominal accuracy of 9 m. Correction for the locational errors improved the correlation of GEDI vs G-LiHT canopy heights significantly, on average by 53% (e.g., R² from 0.57 to 0.82 for RH50). GEDI canopy metrics were useful for predicting AGB (R² = 0.82 and RMSE = 19.1 Mg/Ha), with the maximum canopy height RH100 being the most useful predictor. Our results highlight the importance of accommodating or correcting for GEDI geolocation errors for estimating forest characteristics and provide empirical evidence on the utility of GEDI for monitoring global biomass dynamics from space. Full article

(This article belongs to the Special Issue New Insights into Remote Sensing of Vegetation Structural Parameters)

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20 pages, 2452 KiB

Open AccessArticle

Laser Caliper Reliability in Upper-Stem Diameter Measurements by Multiple Users

by Cornel Cristian Tereşneu, Ciprian Tudor and Maria Magdalena Vasilescu

Forests 2022, 13(9), 1522; https://doi.org/10.3390/f13091522 - 19 Sep 2022

Cited by 3 | Viewed by 1737

Abstract

Considering the uncertainty of upper-stem diameter measurements and the fact that there are few studies on the accuracy of diameters using the Mantax Black caliper with Gator Eyes (Haglöf, Långsele, Sweden), the aim of this research is to check laser caliper reliability in upper-stem diameter measurements. The study was conducted in Parc Aventura Braşov (Romania), where a target tree was marked with visible signs at 1 m, 3 m, 5 m, 7 m, 9 m, and 13 m above the ground, and the diameters of the six sections were measured using a conventional caliper and climbing equipment. Later on, 14 forest mensurationists used a laser caliper to measure the diameters of the marked sections 13 m away from the tree, maintaining the direction of measurement. Each user performed repeated independent measurements of the upper-stem diameters, resulting in 14 data sets with 10 values for every section and a total number of 840 observations. Applying ANOVA for all the sections, we found that there are significant differences between the data sets collected by many users, and the pairwise t-test and the Benjamini-Hochberg method showed significant differences. Taking into account the analysis of the individual errors in measuring the upper-stem diameters using a laser caliper, we were able to identify the data sets affected by abnormal errors. By measuring the diameters along the stem up to 13 m above the ground using a laser caliper, one out of 2.4 measurements up to one out of approximately 1.5 was determined with an error below 2 cm. At heights above 5 m, a maximum of one out of five measurements was affected by errors above 4 cm. In addition, it was noted that there is generally a tendency to underestimate the upper-stem diameter and volume estimate when the laser caliper is used for the measurements. The absolute mean error varied between 1.46 cm and 2.52 cm along the stem and the root mean squared error varied between 1.84 cm and 3.04 cm. Nevertheless, general uncertainty about this subject remains, because if we measure upper-stem diameters without contact with the trunk, we will never know whether a single reading shows a negligible error to be used for calibrating taper equations or for increasing volume estimation accuracy. Consequently, we recommend that when used for this purpose, diameters should be measured several times, by experienced users who have proven their skill in measurements that yield smaller errors. Full article

(This article belongs to the Special Issue New Insights into Remote Sensing of Vegetation Structural Parameters)

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14 pages, 5764 KiB

Open AccessArticle

Satellite-Observed Spatio-Temporal Variation in Spring Leaf Phenology of Subtropical Forests across the Nanling Mountains in Southern China over 1999–2019

by Chao Ding, Wenjiang Huang, Yuanyuan Meng and Biyao Zhang

Forests 2022, 13(9), 1486; https://doi.org/10.3390/f13091486 - 14 Sep 2022

Cited by 5 | Viewed by 1775

Abstract

Knowledge of spatio-temporal variation in vegetation phenology is essential for understanding environmental change in mountainous regions. In recent decades, satellite remote sensing has contributed to the understanding of vegetation phenology across the globe. However, vegetation phenology in subtropical mountains remains poorly understood, despite their important ecosystem functions and services. Here, we aim to characterize the spatio-temporal pattern of the start of the growing season (SOS), a typical spring leaf phenological metric, in subtropical forests across the Nanling Mountains (108–116° E, 24–27° N) in southern China. SOS was estimated from time series of GEOV2 leaf area index (LAI) data at 1 km spatial resolution during the period 1999–2019. We observed a slightly earlier regional mean SOS in the southern of the region (24–25° N) than those in the central and northern regions. We also observed spatially varying elevation gradients of the SOS. The SOS showed a change slope of −0.2 days/year (p = 0.21) at the regional scale over 1999–2019. In addition, approximately 22% of the analyzed forested pixels experienced a significantly earlier SOS (p < 0.1). Partial correlation analysis revealed that preseason air temperature was the most responsible climate factor controlling interannual variation in SOS for this region. Furthermore, impacts of air temperature on the SOS vary with forest types, with mixed forests showing a stronger correlation between the SOS and air temperature in spring and weaker in winter than those of evergreen broadleaf forests and open forests. This suggests the complication of the role of air temperature in regulating spring leaf phenology in subtropical forests. Full article

(This article belongs to the Special Issue New Insights into Remote Sensing of Vegetation Structural Parameters)

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19 pages, 2924 KiB

Open AccessArticle

Crown Structure Metrics to Generalize Aboveground Biomass Estimation Model Using Airborne Laser Scanning Data in National Park of Hainan Tropical Rainforest, China

by Chenyun Li, Zhexiu Yu, Shaojie Wang, Fayun Wu, Kunjian Wen, Jianbo Qi and Huaguo Huang

Forests 2022, 13(7), 1142; https://doi.org/10.3390/f13071142 - 20 Jul 2022

Cited by 7 | Viewed by 2418

Abstract

Forest aboveground biomass (AGB) is an important indicator for characterizing forest ecosystem structures and functions. Therefore, how to effectively investigate forest AGB is a vital mission. Airborne laser scanning (ALS) has been demonstrated as an effective way to support investigation and operational applications among a wide range of applications in the forest inventory. Moreover, three-dimensional structure information relating to AGB can be acquired by airborne laser scanning. Many studies estimated AGB from variables that were extracted from point cloud data, but few of them took full advantage of variables related to tree crowns to estimate the AGB. In this study, the main objective was to evaluate and compare the capabilities of different metrics derived from point clouds obtained from ALS. Particularly, individual tree-based alpha-shape, along with other traditional and commonly used plot-level height and intensity metrics, have been used from airborne laser scanning data. We took the random forest and multiple stepwise linear regression to estimate the AGB. By comparing AGB estimates with field measurements, our results showed that the best approach is mixed metrics, and the best estimation model is random forest (R² = 0.713, RMSE = 21.064 t/ha, MAE = 15.445 t/ha), which indicates that alpha-shape may be a good alternative method to improve AGB estimation accuracy. This method provides an effective solution for estimating aboveground biomass from airborne laser scanning. Full article

(This article belongs to the Special Issue New Insights into Remote Sensing of Vegetation Structural Parameters)

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22 pages, 5458 KiB

Open AccessArticle

Long-Term Effects of Fire Severity and Climatic Factors on Post-Forest-Fire Vegetation Recovery

by Bin Hao, Xu Xu, Fei Wu and Lei Tan

Forests 2022, 13(6), 883; https://doi.org/10.3390/f13060883 - 6 Jun 2022

Cited by 19 | Viewed by 4197

Abstract

As a major disturbance to forest ecosystems, wildfires pose a serious threat to the ecological environment. Monitoring post-fire vegetation recovery is critical to quantifying the effects of wildfire on ecosystems and conducting forest resource management. Most previous studies have analyzed short-term (less than five years) post-fire recovery and limited the driving factors to temperature and precipitation. The lack of long-term and multi-faceted observational analyses has limited our understanding of the long-term effects of fire on vegetation recovery. This study utilized multi-source remote sensing data for a long time series analysis of post-fire vegetation recovery in China based on Google Earth Engine (GEE) cloud computing platform. Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Normalized Burn Ratio (NBR), and Normalized Difference Moisture Index (NDMI) were selected to quantify the low, moderate, and high severity of burned areas. Ridge Regression Model (RRM) was used to analyze the relationship between 15 driving factors and the vegetation regeneration process. The results show that it took at least 7–10 years for the vegetation index to recover to the pre-fire level after a forest fire. The recovery rate of high severity combustion areas was the fastest within the first two years. From the results of Ridge Regression, it came out that the overall fitting degree of the model with NDVI as the dependent variable was superior than that with EVI. The four variables of temperature, precipitation, soil temperature, and soil moisture were able to explain the change in more detail in vegetation indices. Our study enriches the research cases of global forest fires and vegetation recovery, provides a scientific basis for the sustainable development of forest ecosystems in China, and provides insight into environmental issues and resource management. Full article

(This article belongs to the Special Issue New Insights into Remote Sensing of Vegetation Structural Parameters)

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14 pages, 2873 KiB

Open AccessArticle

Using a Vegetation Index-Based Mixture Model to Estimate Fractional Vegetation Cover Products by Jointly Using Multiple Satellite Data: Method and Feasibility Analysis

by Wanjuan Song, Tian Zhao, Xihan Mu, Bo Zhong, Jing Zhao, Guangjian Yan, Li Wang and Zheng Niu

Forests 2022, 13(5), 691; https://doi.org/10.3390/f13050691 - 29 Apr 2022

Cited by 5 | Viewed by 2326

Abstract

Remote sensing fractional vegetation cover (FVC) requires both finer-resolution and high-frequency in climate and ecosystem research. The increasing availability of finer-resolution (≤ 30 m) remote sensing data makes this possible. However, data from different satellites have large differences in spatial resolution, spectral response function, and so on, making joint use difficult. Herein, we showed that the vegetation index (VI)-based mixture model with the appropriate VI values of pure vegetation (V_v) and bare soil (V_s) from the MODIS BRDF product via the multi-angle VI method (MultiVI) was feasible to estimate FVC with multiple satellite data. Analyses of the spatial resolution and spectral response function differences for MODIS and other satellites including Landsat 8, Chinese GF 1, and ZY 3 predicted that (1) the effect of V_v and V_s downscaling on FVC estimation uncertainty varied from satellite to satellite due to the positioning differences, and (2) after spectral normalization, the uncertainty (RMSDs) for FVC estimation decreased by ~2.6% compared with the results without spectral normalization. FVC estimation across multiple satellite data will help to improve the spatiotemporal resolution of FVC products, which is an important development for numerous biophysical applications. Herein, we proved that the VI-based mixture model with V_v and V_s from MultiVI is a strong candidate. Full article

(This article belongs to the Special Issue New Insights into Remote Sensing of Vegetation Structural Parameters)

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19 pages, 3743 KiB

Open AccessArticle

Revisiting the Performance of the Kernel-Driven BRDF Model Using Filtered High-Quality POLDER Observations

by Hanliang Li, Kai Yan, Si Gao, Wanjuan Song and Xihan Mu

Forests 2022, 13(3), 435; https://doi.org/10.3390/f13030435 - 10 Mar 2022

Cited by 7 | Viewed by 2726

Abstract

The Bidirectional Reflectance Distribution Function (BRDF) is usually used to describe the reflectance anisotropy of a non-Lambertian surface and estimate surface parameters. Among the BRDF models, the kernel-driven models have been extensively used due to their simple form and powerful fitting ability, and their reliability has been validated in some studies. However, existing validation efforts used in situ measurements or limited satellite data, which may be subject to inadequate observational conditions or quality uncertainties. A recently released high-quality BRDF database from Polarization and Directionality of the Earth’s Reflectances (POLDER) provides an opportunity to revisit the performance of the kernel-driven models. Therefore, in order to evaluate the fitting ability of the kernel-driven models under different observational conditions and explore their application direction in the future, we use the filtered high-quality BRDF database to evaluate the fitting ability of the kernel-driven model represented by the RossThick-LiSparseR (RTLSR) kernels in this paper. The results show that the RTLSR model performs well, which shows small fitting residuals under most observational conditions. However, the applicability of the RTLSR model performed differently across land cover types; the RTLSR model exhibited larger fitting residuals, especially over non-vegetated surfaces. Under different sun-sensor geometries, the fitting residuals show a strong positive correlation with the Solar Zenith Angle. The above two factors cause the RTLSR model to exhibit a poorer fitting ability at high latitudes. As an exploration, we designed a model combination strategy that combines the advantages of different models and achieved a better performance at high latitudes. We believe that this study provides a better understanding of the RTLSR model. Full article

(This article belongs to the Special Issue New Insights into Remote Sensing of Vegetation Structural Parameters)

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