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Keywords = canopy storage capacity

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23 pages, 2695 KiB  
Article
Estimation of Subtropical Forest Aboveground Biomass Using Active and Passive Sentinel Data with Canopy Height
by Yi Wu, Yu Chen, Chunhong Tian, Ting Yun and Mingyang Li
Remote Sens. 2025, 17(14), 2509; https://doi.org/10.3390/rs17142509 - 18 Jul 2025
Viewed by 356
Abstract
Forest biomass is closely related to carbon sequestration capacity and can reflect the level of forest management. This study utilizes four machine learning algorithms, namely Multivariate Stepwise Regression (MSR), K-Nearest Neighbors (k-NN), Artificial Neural Network (ANN), and Random Forest (RF), to estimate forest [...] Read more.
Forest biomass is closely related to carbon sequestration capacity and can reflect the level of forest management. This study utilizes four machine learning algorithms, namely Multivariate Stepwise Regression (MSR), K-Nearest Neighbors (k-NN), Artificial Neural Network (ANN), and Random Forest (RF), to estimate forest aboveground biomass (AGB) in Chenzhou City, Hunan Province, China. In addition, a canopy height model, constructed from a digital surface model (DSM) derived from Sentinel-1 Interferometric Synthetic Aperture Radar (InSAR) and an ICESat-2-corrected SRTM DEM, is incorporated to quantify its impact on the accuracy of AGB estimation. The results indicate the following: (1) The incorporation of multi-source remote sensing data significantly improves the accuracy of AGB estimation, among which the RF model performs the best (R2 = 0.69, RMSE = 24.26 t·ha−1) compared with the single-source model. (2) The canopy height model (CHM) obtained from InSAR-LiDAR effectively alleviates the signal saturation effect of optical and SAR data in high-biomass areas (>200 t·ha−1). When FCH is added to the RF model combined with multi-source remote sensing data, the R2 of the AGB estimation model is improved to 0.74. (3) In 2018, AGB in Chenzhou City shows clear spatial heterogeneity, with a mean of 51.87 t·ha−1. Biomass increases from the western hilly part (32.15–68.43 t·ha−1) to the eastern mountainous area (89.72–256.41 t·ha−1), peaking in Dongjiang Lake National Forest Park (256.41 t·ha−1). This study proposes a comprehensive feature integration framework that combines red-edge spectral indices for capturing vegetation physiological status, SAR-derived texture metrics for assessing canopy structural heterogeneity, and canopy height metrics to characterize forest three-dimensional structure. This integrated approach enables the robust and accurate monitoring of carbon storage in subtropical forests. Full article
(This article belongs to the Collection Feature Paper Special Issue on Forest Remote Sensing)
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21 pages, 10337 KiB  
Article
Study on Forest Growing Stock Volume in Kunming City Considering the Relationship Between Stand Density and Allometry
by Jing Zhang, Cheng Wang, Jinliang Wang, Xiang Huang, Zilin Zhou, Zetong Zhou and Feng Cheng
Forests 2025, 16(6), 891; https://doi.org/10.3390/f16060891 - 25 May 2025
Viewed by 503
Abstract
Forest growing stock volume (GSV) is a fundamental indicator for assessing the status of forest resources. It reflects forest carbon storage levels and serves as a key metric for evaluating the carbon sequestration capacity of forest ecosystems, thereby playing a crucial role in [...] Read more.
Forest growing stock volume (GSV) is a fundamental indicator for assessing the status of forest resources. It reflects forest carbon storage levels and serves as a key metric for evaluating the carbon sequestration capacity of forest ecosystems, thereby playing a crucial role in supporting national “dual-carbon” objectives. Traditional allometric models typically estimate GSV using tree species, diameter at breast height (DBH), and canopy height. However, at larger spatial scales, these models often neglect stand density, resulting in substantial estimation errors in regions characterized by significant density variability. To enhance the accuracy of large-scale GSV estimation, this study incorporates high-resolution, spatially continuous forest structural parameters—including dominant tree species, stand density, canopy height, and DBH—extracted through the synergistic utilization of active (e.g., Sentinel-1 SAR, ICESat-2 photon data) and passive (e.g., Landsat-8 OLI, Sentinel-2 MSI) multi-source remote sensing data. Within an allometric modeling framework, stand density is introduced as an additional explanatory variable. Subsequently, GSV is modeled in a stratified manner according to tree species across distinct ecological zones within Kunming City. The results indicate that: (1) the total estimated GSV of Kunming City in 2020, based on remote sensing imagery and second-class forest inventory data collected in the same year, was 1.01 × 108 m3, which closely aligns with contemporaneous statistical records. The model yielded an R2 of 0.727, an RMSE of 537.566 m3, and a MAE of 239.767 m3, indicating a high level of overall accuracy when validated against official ground-based inventory plots organized by provincial and municipal forestry authorities; (2) the incorporation of the dynamic stand density parameter significantly improved model performance, which elevated R2 from 0.565 to 0.727 and significantly reduced RMSE. This result confirms that stand density is a critical explanatory factor; and (3) GSV exhibited pronounced spatial heterogeneity across both tree species and administrative regions, underscoring the spatial structural variability of forests within the study area. Full article
(This article belongs to the Section Forest Inventory, Modeling and Remote Sensing)
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32 pages, 9739 KiB  
Article
Estimating Spatiotemporal Dynamics of Carbon Storage in Roinia pseudoacacia Plantations in the Caijiachuan Watershed Using Sample Plots and Uncrewed Aerial Vehicle-Borne Laser Scanning Data
by Yawei Hu, Ruoxiu Sun, Miaomiao He, Jiongchang Zhao, Yang Li, Shengze Huang and Jianjun Zhang
Remote Sens. 2025, 17(8), 1365; https://doi.org/10.3390/rs17081365 - 11 Apr 2025
Cited by 1 | Viewed by 425
Abstract
Forest ecosystems play a pivotal role in the global carbon cycle and climate change mitigation. Forest aboveground biomass (AGB), a critical indicator of carbon storage and sequestration capacity, has garnered significant attention in ecological research. Recently, uncrewed aerial vehicle-borne laser scanning (ULS) technology [...] Read more.
Forest ecosystems play a pivotal role in the global carbon cycle and climate change mitigation. Forest aboveground biomass (AGB), a critical indicator of carbon storage and sequestration capacity, has garnered significant attention in ecological research. Recently, uncrewed aerial vehicle-borne laser scanning (ULS) technology has emerged as a promising tool for rapidly acquiring three-dimensional spatial information on AGB and vegetation carbon storage. This study evaluates the applicability and accuracy of UAV-LiDAR technology in estimating the spatiotemporal dynamics of AGB and vegetation carbon storage in Robinia pseudoacacia (R. pseudoacacia) plantations in the gully regions of the Loess Plateau, China. At the sample plot scale, optimal parameters for individual tree segmentation (ITS) based on the canopy height model (CHM) were determined, and segmentation accuracy was validated. The results showed root mean square error (RMSE) values of 13.17 trees (25.16%) for tree count, 0.40 m (3.57%) for average tree height (AH), and 320.88 kg (16.94%) for AGB. The regression model, which links sample plot AGB with AH and tree count, generated AGB estimates that closely matched the observed AGB values. At the watershed scale, ULS data were used to estimate the AGB and vegetation carbon storage of R. pseudoacacia plantations in the Caijiachuan watershed. The analysis revealed a total of 68,992 trees, with a total carbon storage of 2890.34 Mg and a carbon density of 62.46 Mg ha−1. Low-density forest areas (<1500 trees ha−1) dominated the landscape, accounting for 94.38% of the tree count, 82.62% of the area, and 92.46% of the carbon storage. Analysis of tree-ring data revealed significant variation in the onset of growth decline across different density classes of plantations aged 0–30 years, with higher-density stands exhibiting delayed growth decline compared to lower-density stands. Compared to traditional methods based on diameter at breast height (DBH), carbon storage assessments demonstrated superior accuracy and scientific validity. This study underscores the feasibility and potential of ULS technology for AGB and carbon storage estimation in regions with complex terrain, such as the Loess Plateau. It highlights the importance of accounting for topographic factors to enhance estimation accuracy. The findings provide valuable data support for density management and high-quality development of R. pseudoacacia plantations in the Caijiachuan watershed and present an efficient approach for precise forest carbon sink accounting. Full article
(This article belongs to the Special Issue Biomass Remote Sensing in Forest Landscapes II)
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20 pages, 4918 KiB  
Article
Mapping Individual Tree- and Plot-Level Biomass Using Handheld Mobile Laser Scanning in Complex Subtropical Secondary and Old-Growth Forests
by Nelson Pak Lun Mak, Tin Yan Siu, Ying Ki Law, He Zhang, Shaoti Sui, Fung Ting Yip, Ying Sim Ng, Yuhao Ye, Tsz Chun Cheung, Ka Cheong Wa, Lap Hang Chan, Kwok Yin So, Billy Chi Hang Hau, Calvin Ka Fai Lee and Jin Wu
Remote Sens. 2025, 17(8), 1354; https://doi.org/10.3390/rs17081354 - 10 Apr 2025
Viewed by 1928
Abstract
Forests are invaluable natural resources that provide essential ecosystem services, and their carbon storage capacity is critical for climate mitigation efforts. Quantifying this capacity would require accurate estimation of forest structural attributes for deriving their aboveground biomass (AGB). Traditional field measurements, while precise, [...] Read more.
Forests are invaluable natural resources that provide essential ecosystem services, and their carbon storage capacity is critical for climate mitigation efforts. Quantifying this capacity would require accurate estimation of forest structural attributes for deriving their aboveground biomass (AGB). Traditional field measurements, while precise, are labor-intensive and often spatially limited. Handheld Mobile Laser Scanning (HMLS) offers a rapid alternative for building forest inventories; however, its effectiveness and accuracy in diverse subtropical forests with complex canopy structure remain under-investigated. In this study, we employed both HMLS and traditional surveys within structurally complex subtropical forest plots, including old-growth forests (Fung Shui Woods) and secondary forests. These forests are characterized by dense understories with abundant shrubs and lianas, as well as high stem density, which pose challenges in Light Detection and Ranging (LiDAR) point cloud data processing. We assessed tree detection rates and extracted tree attributes, including diameter at breast height (DBH) and canopy height. Additionally, we compared tree-level and plot-level AGB estimates using allometric equations. Our findings indicate that HMLS successfully detected over 90% of trees in both forest types and precisely measured DBH (R2 > 0.96), although tree height detection exhibited relatively higher uncertainty (R2 > 0.35). The AGB estimates derived from HMLS were comparable to those obtained from traditional field measurements. By producing highly accurate estimates of tree attributes, HMLS demonstrates its potential as an effective and non-destructive method for rapid forest inventory and AGB estimation in subtropical forests, making it a competitive option for aiding carbon storage estimations in complex forest environments. Full article
(This article belongs to the Special Issue Forest Biomass/Carbon Monitoring towards Carbon Neutrality)
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12 pages, 1273 KiB  
Article
Leaf Water Storage Capacity Among Eight US Hardwood Tree Species: Differences in Seasonality and Methodology
by Natasha Scavotto, Courtney M. Siegert, Heather D. Alexander and J. Morgan Varner
Hydrology 2025, 12(2), 40; https://doi.org/10.3390/hydrology12020040 - 18 Feb 2025
Viewed by 718
Abstract
Canopy hydrology and forest water inputs are directly linked to the physical properties of tree crowns (e.g., foliar and woody surfaces), which determine a tree’s capacity to intercept and retain incident rainfall. The changing forest structure, notably the decline of oak’s (Quercus [...] Read more.
Canopy hydrology and forest water inputs are directly linked to the physical properties of tree crowns (e.g., foliar and woody surfaces), which determine a tree’s capacity to intercept and retain incident rainfall. The changing forest structure, notably the decline of oak’s (Quercus) dominance and encroachment of non-oak species in much of the upland hardwood forests of the eastern United States, challenges our understanding of how species-level traits scale up to control the forest hydrologic budget. The objective of this study was to determine how the leaf water storage capacity varies across species and canopy layers, and how these relationships change throughout the growing season. We measured the leaf water storage capacity of overstory and midstory trees of native deciduous oaks (Q. alba, Q. falcata, Q. stellata) and non-oak species (Carya tomentosa, Acer rubrum, Ulmus alata, Liquidambar styraciflua, Nyssa sylvatica) using two methods (water displacement and rainfall simulation). Overstory Q. alba leaves retained 0.5 times less water per unit leaf area than other overstory species (p < 0.001) in the early growing season, while in the late growing season, C. tomentosa leaves had the lowest storage capacity (p = 0.024). Quercus falcata leaves displayed a minimal change in storage between seasons, while Q. alba and Q. stellata leaves had higher water storage in the late growing season. Midstory U. alata leaves had 3.5 times higher water storage capacity in the early growing season compared to all the other species (p < 0.001), but this difference diminished in the late growing season. Furthermore, the water storage capacities from the simulated rainfall experiments were up to two times higher than those in the water displacement experiments, particularly during the early growing season. These results underscore the complexity of leaf water storage dynamics, the methodology, and the implications for forest hydrology and species interactions. Broader efforts to understand species-level controls on canopy water portioning through leaf and other crown characteristics are necessary. Full article
(This article belongs to the Section Ecohydrology)
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20 pages, 2381 KiB  
Article
Impact of Loblolly Pine (Pinus taeda L.) Plantation Management on Biomass, Carbon Sequestration Rates and Storage
by Farzam Tavankar, Rodolfo Picchio, Mehrdad Nikooy, Behroz Karamdost Marian, Rachele Venanzi and Angela Lo Monaco
Sustainability 2025, 17(3), 888; https://doi.org/10.3390/su17030888 - 22 Jan 2025
Cited by 1 | Viewed by 1192
Abstract
Loblolly pine plantations have long been cultivated primarily for timber production due to their rapid growth and economic value. However, these forests are now increasingly acknowledged for their important role in mitigating climate change. Their dense canopies and fast growth rates enable them [...] Read more.
Loblolly pine plantations have long been cultivated primarily for timber production due to their rapid growth and economic value. However, these forests are now increasingly acknowledged for their important role in mitigating climate change. Their dense canopies and fast growth rates enable them to absorb and store substantial amounts of atmospheric carbon dioxide. By integrating sustainable management practices, these plantations can maximize both timber yield and carbon sequestration, contributing to global efforts to reduce greenhouse gas emissions. Balancing timber production with vital ecosystem services, such as carbon storage, demands carefully tailored management strategies. This study examined how the timing of thinning—specifically early thinning at 17 years and late thinning at 32 years—impacts biomass accumulation, carbon storage capacity, and carbon sequestration rates in loblolly pine plantations located in northern Iran. Two thinning intensities were evaluated: normal thinning (removal of 15% basal area) and heavy thinning (removal of 35% basal area). The results demonstrated that thinning significantly improved biomass, sequestration rates and carbon storage compared to unthinned stands. Early thinning proved more effective than late thinning in enhancing these metrics. Additionally, heavy thinning had a greater impact than normal thinning on increasing biomass, carbon storage, and sequestration rates. In early heavy-thinned stands, carbon storage reached 95.8 Mg C/ha, which was 63.0% higher than the 58.8 Mg C/ha observed in unthinned 32-year-old stands. In comparison, early normal thinning increased carbon storage by 41.3%. In late heavy-thinned stands, carbon storage reached 199.4 Mg C/ha, which was 29.0% higher than in unthinned stands of the same age (154.6 Mg C/ha at 52 years). In contrast, late normal thinning increased carbon storage by 13.3%. Similarly, carbon sequestration rates in unthinned stands were 1.84 Mg C/ha/yr at 32 years and 2.97 Mg C/ha/yr at 52 years. In comparison, 32-year-old stands subjected to normal and heavy thinning had sequestration rates of 2.60 and 2.99 Mg C/ha/yr, respectively, while 54-year-old normally and heavily thinned stands reached 3.37 and 3.83 Mg C/ha/yr, respectively. The highest carbon storage was concentrated in the stems for 52–58% of the total. Greater thinning intensity increased the proportion of carbon stored in stems while decreasing the contribution from foliage. These results indicate that heavy early thinning is the most effective strategy for maximizing both timber production and carbon sequestration in loblolly pine plantations. Full article
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19 pages, 5152 KiB  
Article
Assessment of Water Retention and Absorption of Organic Mulch Under Simulated Rainfall for Soil and Water Conservation
by Iug Lopes, João L. M. P. de Lima, Abelardo A. A. Montenegro and Ailton Alves de Carvalho
Soil Syst. 2025, 9(1), 4; https://doi.org/10.3390/soilsystems9010004 - 10 Jan 2025
Viewed by 1570
Abstract
The use of organic mulch as a natural practice to enhance water retention and absorption is underexplored, highlighting the need for a deeper understanding of its effectiveness under varying conditions. The aim of this study was to investigate the process of interception, retention, [...] Read more.
The use of organic mulch as a natural practice to enhance water retention and absorption is underexplored, highlighting the need for a deeper understanding of its effectiveness under varying conditions. The aim of this study was to investigate the process of interception, retention, and absorption of rainwater by different types, sizes, and densities of some organic mulch covers. Six organic mulches of various sizes were used, all largely available in the Brazilian semiarid: coconut leaf (cc), cashew leaf (ca), elephant grass (el), corn leaf (co), Brachiaria grass (br), and sugar cane leaf (su), under simulated rainfall conditions. The experimental scheme consisted of a factorial of six types of mulches, three sizes (50, 100, and 200 mm), and four densities (1, 2, 4, and 8 t ha−1). Water adsorption and retention curves were constructed, and the interception capacity of different vegetation materials was estimated. Analysis of variance, Tukey Test, Regression polynomial, and Principal Components Analysis were applied. It was observed that increasing density systematically led to an increase in water retention and absorption. For 8 t ha−1 the values were 11 to 23% for water retention and 7 to 16% for water absorption of the gross rainfall depth. When comparing 8 t ha−1 and 2 t ha−1 densities, rainfall retention and absorption increased more than 100%. Higher values were obtained for cashew and Brachiaria grass, improving water retention and cashew leaves for absorption. Coconut leaves promoted only 83% retention and 67% water absorption, when compared to the cashew leaf and Brachiaria grass. Full article
(This article belongs to the Special Issue Land Use and Management on Soil Properties and Processes)
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21 pages, 7764 KiB  
Article
Atmospheric Boundary Layer Stability in Urban Beijing: Insights from Meteorological Tower and Doppler Wind Lidar
by Linlin Wang, Bingcheng Wan, Yuanjian Yang, Sihui Fan, Yi Jing, Xueling Cheng, Zhiqiu Gao, Shiguang Miao and Han Zou
Remote Sens. 2024, 16(22), 4246; https://doi.org/10.3390/rs16224246 - 14 Nov 2024
Cited by 2 | Viewed by 1302
Abstract
The limited understanding of the structure of the urban surface atmospheric boundary layer can be attributed to its inherent complexity, as well as a deficiency in comprehensive measurements. We analyzed one year of meteorological data and Doppler wind lidar measurements in Beijing to [...] Read more.
The limited understanding of the structure of the urban surface atmospheric boundary layer can be attributed to its inherent complexity, as well as a deficiency in comprehensive measurements. We analyzed one year of meteorological data and Doppler wind lidar measurements in Beijing to explore how atmospheric stability is influenced by wind speed, radiation, turbulence, and pollution levels. Results indicate that the predominant state of the urban boundary layers in Beijing is an active condition (characterized by strong unstable and unstable stability regimes) throughout the day, attributed to the significant heat storage capacity of the urban canopy. Strong stable regimes are more frequently observed during winter and autumn, peaking during transitions from night to day. Furthermore, both strong unstable and strong stable regimes occur under very weak wind conditions (indicating weak dynamic instability), with strong instability associated with high net radiation levels while strong stability correlates with low net radiation conditions (indicative of robust thermal stability). The unstable regime manifests under strong winds (reflecting strong dynamic instability) alongside moderate net radiation environments, characterized by elevated values of turbulence kinetic energy and urban boundary height, highlighting the critical role of mechanical turbulence generation during periods of high wind activity. Additionally, six instances of pronounced stable conditions observed during daytime can be partially attributed to low radiation coupled with high pollutant concentrations near the surface, resulting from prolonged temperature inversions due to intense radiative cooling effects and weak dynamic forcing. Our findings presented herein are expected to have urban boundary layer climate and environment implications for other cities with high pollution and dense urban infrastructure all over the world. Full article
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19 pages, 2603 KiB  
Article
Carbon Storage and Sequestration Analysis by Urban Park Grid Using i-Tree Eco and Drone-Based Modeling
by Juhyeon Kim, Youngeun Kang, Dongwoo Kim, Seungwoo Son and Eujin Julia Kim
Forests 2024, 15(4), 683; https://doi.org/10.3390/f15040683 - 10 Apr 2024
Cited by 11 | Viewed by 5299
Abstract
Urban areas play a crucial role in carbon absorption, while also producing a considerable amount of carbon emissions. However, there has been a lack of research that has systematically examined the carbon storage and sequestration in green spaces located within urban environments, at [...] Read more.
Urban areas play a crucial role in carbon absorption, while also producing a considerable amount of carbon emissions. However, there has been a lack of research that has systematically examined the carbon storage and sequestration in green spaces located within urban environments, at a spatial scale. This study analyzes carbon storage and sequestration in Yurim Park, Daejeon, South Korea on a grid basis to fill the research gap. The research compares the variation in sequestration capacity across different grids and provides insights into the development of sustainable urban parks in urban planning. The classification of grids is based on specific site characteristics, such as land cover, tree distribution, type, and density. This results in a total of seven distinct types. The study employs a combination of the I-tree eco model, drone-based modeling, and on-site surveys to estimate carbon storage and sequestration in urban parks. The results show that the average carbon storage per unit area in the entire park was 15.3 tons of carbon per hectare, ranging from a minimum of 5.0 to a maximum of 21.4 tons per hectare. For the planted area, the average carbon storage was 8.6 tons per hectare. Grids with green areas dominated by broad-leaved trees and closed canopy cover had the highest carbon sequestration and storage values. The planting area ratio and the type of trees planted were found to directly influence the carbon sequestration capacity per unit area of urban parks. This study stands out from previous research by conducting a detailed area-based comparison and analysis of carbon sequestration capacity in urban parks using sophisticated measurement techniques. The findings offer direct insights into strategies and policies for securing future urban carbon sinks and can be of practical use in this regard. Full article
(This article belongs to the Section Urban Forestry)
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20 pages, 5309 KiB  
Article
A Theoretical Nonlinear Regression Model of Rainfall Surface Flow Accumulation and Basin Features in Park-Scale Urban Green Spaces Based on LiDAR Data
by Hengshuo Huang, Yuan Tian, Mengjia Wei, Xiaoli Jia, Peng Wang, Aidan C. Ackerman, Siddharth G. Chatterjee, Yang Liu and Guohang Tian
Water 2023, 15(13), 2442; https://doi.org/10.3390/w15132442 - 2 Jul 2023
Cited by 2 | Viewed by 2056
Abstract
Green infrastructure is imperative for efficiently mitigating flood disasters in urban areas. However, inadequate green space planning under rapid urbanization is a critical issue faced by most Chinese cities. Aimed at theoretically understanding the rainwater storage capacity and improvement potential of urban green [...] Read more.
Green infrastructure is imperative for efficiently mitigating flood disasters in urban areas. However, inadequate green space planning under rapid urbanization is a critical issue faced by most Chinese cities. Aimed at theoretically understanding the rainwater storage capacity and improvement potential of urban green spaces, a synthetic simulation model was developed to quantify rainfall surface flow accumulation (FA) based on the morphological factors of a flow basin: the area, circumference, maximum basin length, and stream length sum. This model consisted of applying the Urban Forest Effects-Hydrology model (UFORE-Hydro) to simulate the actual precipitation-to-surface runoff ratio through a procedure involving canopy interception, soil infiltration, and evaporation; additionally, a relatively accurate multiple flow direction-maximum downslope (MFD-md) algorithm was applied to distribute the surface flow in a highly realistic manner, and a self-built “extraction algorithm” extracted the surface runoff corresponding to each studied basin alongside four fundamental morphological parameters. The various nonlinear regression functions were assessed from both univariable and multivariable perspectives. We determined that the Gompertz function was optimal for predicting the theoretical quantification of surface FA according to the morphological features of any given basin. This article provides parametric vertical design guidance for improving the rainwater storage capacities of urban green spaces. Full article
(This article belongs to the Section Urban Water Management)
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14 pages, 1956 KiB  
Article
Determinants of Aboveground Carbon Storage of Woody Vegetation in an Urban–Rural Transect in Shanghai, China
by Yanyan Wei, Chi-Yung Jim, Jun Gao and Min Zhao
Sustainability 2023, 15(11), 8574; https://doi.org/10.3390/su15118574 - 25 May 2023
Cited by 6 | Viewed by 1904
Abstract
Carbon storage of urban woody vegetation is crucial for climate change mitigation. Biomass structure and species composition have been shown to be important determinants of carbon storage in woody vegetation. In this study, allometric equations were used to estimate the aboveground carbon storage [...] Read more.
Carbon storage of urban woody vegetation is crucial for climate change mitigation. Biomass structure and species composition have been shown to be important determinants of carbon storage in woody vegetation. In this study, allometric equations were used to estimate the aboveground carbon storage of urban woody vegetation along an urban–rural transect in Shanghai. A random forest model was developed to evaluate the importance scores and influence of species diversity, canopy cover, species evenness, and tree density on aboveground carbon storage. The results showed that tree density, canopy cover, species diversity, species evenness, and aboveground carbon storage of urban woody vegetation vary with the degree of urbanization and urban–rural environment. In addition, the Bayesian optimization algorithm optimized the random forest model parameters to enhance model accuracy, and good modeling results were demonstrated in the study. The R2 was at 0.61 in the testing phase and 0.78 in the training phase. The root mean square errors (RMSEs) were 0.84 Mg/ha of carbon in the testing phase and 0.57 Mg/ha in the training phase, which is indicative of a low error of the optimized model. Tree species diversity, canopy cover, species evenness, and tree density were found to correlate with aboveground carbon storage. Tree density was the most important contributor, followed by species diversity and canopy cover, and species evenness was the least effective for aboveground carbon storage. Meanwhile, the results of the partial dependence analysis indicated the combination of factors most conducive to aboveground carbon storage at a tree density of 2200 trees/ha, canopy cover of 50%, species diversity of 1.2, and species evenness of 0.8 in the transect. The findings provided practical recommendations for urban forest managers to adjust the structure and composition of woody vegetation to increase carbon storage capacity and reduce greenhouse gas emissions. Full article
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16 pages, 2829 KiB  
Article
Beech Leaf Disease Severity Affects Ectomycorrhizal Colonization and Fungal Taxa Composition
by Claudia Bashian-Victoroff, Alexis Brown, Andrew L. Loyd, Sarah R. Carrino-Kyker and David J. Burke
J. Fungi 2023, 9(4), 497; https://doi.org/10.3390/jof9040497 - 21 Apr 2023
Cited by 5 | Viewed by 3782
Abstract
Beech leaf disease (BLD) is an emerging forest infestation affecting beech trees (Fagus spp.) in the midwestern and northeastern United States and southeastern Canada. BLD is attributed to the newly recognized nematode Litylenchus crenatae subsp. mccannii. First described in Lake County, [...] Read more.
Beech leaf disease (BLD) is an emerging forest infestation affecting beech trees (Fagus spp.) in the midwestern and northeastern United States and southeastern Canada. BLD is attributed to the newly recognized nematode Litylenchus crenatae subsp. mccannii. First described in Lake County, Ohio, BLD leads to the disfigurement of leaves, canopy loss, and eventual tree mortality. Canopy loss limits photosynthetic capacity, likely impacting tree allocation to belowground carbon storage. Ectomycorrhizal fungi are root symbionts, which rely on the photosynthesis of autotrophs for nutrition and growth. Because BLD limits tree photosynthetic capacity, ECM fungi may receive less carbohydrates when associating with severely affected trees compared with trees without BLD symptoms. We sampled root fragments from cultivated F. grandifolia sourced from two provenances (Michigan and Maine) at two timepoints (fall 2020 and spring 2021) to test whether BLD symptom severity alters colonization by ectomycorrhizal fungi and fungal community composition. The studied trees are part of a long-term beech bark disease resistance plantation at the Holden Arboretum. We sampled from replicates across three levels of BLD symptom severity and compared fungal colonization via visual scoring of ectomycorrhizal root tip abundance. Effects of BLD on fungal communities were determined through high-throughput sequencing. We found that ectomycorrhizal root tip abundance was significantly reduced on the roots of individuals of the poor canopy condition resulting from BLD, but only in the fall 2020 collection. We found significantly more ectomycorrhizal root tips from root fragments collected in fall 2020 than in spring 2021, suggesting a seasonal effect. Community composition of ectomycorrhizal fungi was not impacted by tree condition but did vary between provenances. We found significant species level responses of ectomycorrhizal fungi between levels of both provenance and tree condition. Of the taxa analyzed, two zOTUs had significantly lower abundance in high-symptomatology trees compared with low-symptomatology trees. These results provide the first indication of a belowground effect of BLD on ectomycorrhizal fungi and contribute further evidence to the role of these root symbionts in studies of tree disease and forest pathology. Full article
(This article belongs to the Special Issue Friends of Plants: Mycorrhizal Fungi)
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23 pages, 7429 KiB  
Article
Water Retention Evaluation of Slab Trench on Rocky Desertification Slope in a Karst Area of Southwest China
by Shiya Liu, Cheng Zhou, Shan Gao, Qiming Zhong, Lijuan Fan, Qi Luo, Qun Chen, Zechang Zhou and Xunhong Zhu
Water 2023, 15(8), 1576; https://doi.org/10.3390/w15081576 - 18 Apr 2023
Cited by 1 | Viewed by 2378
Abstract
Soil erosion and water loss are serious problems on the rocky desertification slopes in the karst dynamic system of southwest China. The lack of soil and shortage of water restrict the ecological restoration of the regional slopes and utility of water resources. Therefore, [...] Read more.
Soil erosion and water loss are serious problems on the rocky desertification slopes in the karst dynamic system of southwest China. The lack of soil and shortage of water restrict the ecological restoration of the regional slopes and utility of water resources. Therefore, a new slab trench capable of storing soil and water in layers on rocky desertification slopes is introduced in this paper to promote vegetation restoration. To explore the water-storing and -holding capacity of the new type of vegetated slab trench, five groups of model experiments were carried out on the vegetated slab trench under different rainfall intensities and different numbers of plants. Under rainfall and then following dry conditions, the effects of rainfall intensity and the number of plants on the water-storing and -holding capacity of vegetated slab trench models were compared and analyzed. Water-storing and -holding capacity was further explored in three groups of models with single planting or combinations of plants including water stored only in succulent root plant, only in succulent stem plant, or in mixed plants. The test results show that the new type of vegetated slab trench can effectively help to store and hold water. In the rainfall period, due to the runoff of the rainfall not being considered, the greater the rainfall intensity, the higher the water storage efficiency; the more vegetation implanted, the greater the blocking effect of the plant canopy during falling rainwater, and the more reduction is induced on the water storage efficiency of the vegetated slab trench. In the following dry period, both the succulent root plant and succulent stem plant have strong water storage capacity, but the succulent root plant has a stronger capacity for water storage. The growth status of the mixed plants was better than that of single planting, which may be due to the water complementarities between the succulent root plant and succulent stem plant in a mixed planting manner. This study is important for solving the problem of soil erosion and water loss in rocky desertification slopes, and it helps to restore the ecological environment of the area. Full article
(This article belongs to the Special Issue Karst Dynamic System and Its Water Resources Environmental Effects)
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23 pages, 3340 KiB  
Review
Remote Sensing Grassland Productivity Attributes: A Systematic Review
by Tsitsi Bangira, Onisimo Mutanga, Mbulisi Sibanda, Timothy Dube and Tafadzwanashe Mabhaudhi
Remote Sens. 2023, 15(8), 2043; https://doi.org/10.3390/rs15082043 - 12 Apr 2023
Cited by 7 | Viewed by 4374
Abstract
A third of the land on the Earth is composed of grasslands, mainly used for forage. Much effort is being conducted to develop tools to estimate grassland productivity (GP) at different extents, concentrating on spatial and seasonal variability pertaining to climate change. GP [...] Read more.
A third of the land on the Earth is composed of grasslands, mainly used for forage. Much effort is being conducted to develop tools to estimate grassland productivity (GP) at different extents, concentrating on spatial and seasonal variability pertaining to climate change. GP is a reliable indicator of how well an ecosystem works because of its close connection to the ecological system equilibrium. The most commonly used proxies of GP in ecological studies are aboveground biomass (AGB), leaf area index (LAI), canopy storage capacity (CSC), and chlorophyll and nitrogen content. Grassland science gains much information from the capacity of remote sensing (RS) techniques to calculate GP proxies. An overview of the studies on RS-based GP prediction techniques and a discussion of current matters determining GP monitoring are critical for improving future GP prediction performance. A systematic review of articles published between 1970 and October 2021 (203 peer-reviewed articles from Web of Science, Scopus, and DirectScience databases) showed a trend in the choice of the sensors, and the approaches to use are largely dependent on the extent of monitoring and assessment. Notably, all the reviewed articles demonstrate the growing demand for high-resolution sensors, such as hyperspectral scanners and computationally efficient image-processing techniques for the high prediction accuracy of GP at various scales of application. Further research is required to attract the synthesis of optical and radar data, multi-sensor data, and the selection of appropriate techniques for GP prediction at different scales. Mastering and listing major uncertainties associated with different algorithms for the GP prediction and pledging to reduce these errors are critical. Full article
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24 pages, 4228 KiB  
Article
Inter-Seasonal Estimation of Grass Water Content Indicators Using Multisource Remotely Sensed Data Metrics and the Cloud-Computing Google Earth Engine Platform
by Anita Masenyama, Onisimo Mutanga, Timothy Dube, Mbulisi Sibanda, Omosalewa Odebiri and Tafadzwanashe Mabhaudhi
Appl. Sci. 2023, 13(5), 3117; https://doi.org/10.3390/app13053117 - 28 Feb 2023
Cited by 5 | Viewed by 3909
Abstract
Indicators of grass water content (GWC) have a significant impact on eco-hydrological processes such as evapotranspiration and rainfall interception. Several site-specific factors such as seasonal precipitation, temperature, and topographic variations cause soil and ground moisture content variations, which have significant impacts on GWC. [...] Read more.
Indicators of grass water content (GWC) have a significant impact on eco-hydrological processes such as evapotranspiration and rainfall interception. Several site-specific factors such as seasonal precipitation, temperature, and topographic variations cause soil and ground moisture content variations, which have significant impacts on GWC. Estimating GWC using multisource data may provide robust and accurate predictions, making it a useful tool for plant water quantification and management at various landscape scales. In this study, Sentinel-2 MSI bands, spectral derivatives combined with topographic and climatic variables, were used to estimate leaf area index (LAI), canopy storage capacity (CSC), canopy water content (CWC) and equivalent water thickness (EWT) as indicators of GWC within the communal grasslands in Vulindlela across wet and dry seasons based on single-year data. The results illustrate that the use of combined spectral and topo-climatic variables, coupled with random forest (RF) in the Google Earth Engine (GEE), improved the prediction accuracies of GWC variables across wet and dry seasons. LAI was optimally estimated in the wet season with an RMSE of 0.03 m−2 and R2 of 0.83, comparable to the dry season results, which exhibited an RMSE of 0.04 m−2 and R2 of 0.90. Similarly, CSC was estimated with high accuracy in the wet season (RMSE = 0.01 mm and R2 = 0.86) when compared to the RMSE of 0.03 mm and R2 of 0.93 obtained in the dry season. Meanwhile, for CWC, the wet season results show an RMSE of 19.42 g/m−2 and R2 of 0.76, which were lower than the accuracy of RMSE = 1.35 g/m−2 and R2 = 0.87 obtained in the dry season. Finally, EWT was best estimated in the dry season, yielding a model accuracy of RMSE = 2.01 g/m−2 and R2 = 0.91 as compared to the wet season (RMSE = 10.75 g/m−2 and R2 = 0.65). CSC was best optimally predicted amongst all GWC variables in both seasons. The optimal variables for estimating these GWC variables included the red-edge, near-infrared region (NIR) and short-wave infrared region (SWIR) bands and spectral derivatives, as well as environmental variables such as rainfall and temperature across both seasons. The use of multisource data improved the prediction accuracies for GWC indicators across both seasons. Such information is crucial for rangeland managers in understanding GWC variations across different seasons as well as different ecological gradients. Full article
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