Canopy Structure Research Articles

Integrative hyperspectral, transcriptomic, and metabolomic analysis reveals the mechanism of tea plants in response to sooty mold disease.

Sooty mold (SM), caused by Cladosporium species, is a pervasive threat to tea plant health, affecting both canopy structure and crop yield. Despite its significance, understanding the complex interplay between defense genes and metabolites in tea plants across various SM-infected canopy layers remains limited. Our study employed hyperspectral imaging, transcriptomic profiling, and metabolomic analysis to decipher the intricate mechanisms underlying the tea plant's response to SM infection. Our hyperspectral imaging identified three critical wavelengths (552, 673, and 800nm) inflection points associated with varying degrees of SM infection. This non-invasive method allows for the precise assessment of disease progression. Concurrently, transcriptome analysis revealed a wealth of differentially expressed genes (DEGs) enriched in metabolic pathways, secondary metabolite biosynthesis, and plant-pathogen interactions. Cluster analysis highlighted an intensified immune response in A2 and A3 samples. A comprehensive metabolomic profile identified 733 co-changed metabolites in SM-infected leaves, with alcohols, lipids (free fatty acids), hydrocarbons, and amino acids significantly accumulating in A1, while flavonoids were predominantly upregulated in A2 and A3. Weighted Gene Co-Expression Network Analysis (WGCNA) uncovered five hub genes (Dormancy-associated protein, Serine/threonine-protein phosphatase, ABC transporter, and some uncharacterized proteins) and two hub metabolites (D-Mannitol and 17-Hydroxylinolenic Acid) that exhibit significant relationships with DEGs and metabolites. Further co-expression analysis indicated that tea plants mainly employed genes and metabolites related to the biosynthesis of secondary metabolites, plant hormone signal transduction, and plant-pathogen interaction to combat SM. This study establishes a foundation for understanding the immune mechanisms of tea plants across different canopy layers in response to SM infection. It not only sheds light on the complex defense strategies employed by tea plants but also identifies candidate genes and metabolites crucial for enhancing tea plant breeding and resistance to SM.

Internal physiological drivers of leaf development in trees: Understanding the relationship between non‐structural carbohydrates and leaf phenology

Abstract Plant phenology is crucial for understanding plant growth and climate feedback. It affects canopy structure, surface albedo, and carbon and water fluxes. While the influence of environmental factors on phenology is well‐documented, the role of plant intrinsic factors, particularly internal physiological processes and their interaction with external conditions, has received less attention. Non‐structural carbohydrates (NSC), which include sugars and starch essential for growth, metabolism and osmotic regulation, serve as indicators of carbon availability in plants. NSC levels reflect the carbon balance between photosynthesis (source activity) and the demands of growth and respiration (sink activity), making them key physiological traits that potentially influence phenology during critical periods such as spring leaf‐out and autumn leaf senescence. However, the connections between NSC concentrations in various organs and phenological events are poorly understood. This review synthesizes current research on the relationship between leaf phenology and NSC dynamics. We qualitatively delineate seasonal NSC variations in deciduous and evergreen trees and propose testable hypotheses about how NSC may interact with phenological stages such as bud break and leaf senescence. We also discuss how seasonal variations in NSC levels, align with existing conceptual models of carbon allocation. Accurate characterization and simulation of NSC dynamics are crucial and should be incorporated into carbon allocation models. By comparing and reviewing the development of carbon allocation models, we highlight the shortcomings in current methodologies and recommend directions to address these gaps in future research. Understanding the relationship between NSC, source–sink relationships, and leaf phenology poses challenges due to the difficulty of characterizing NSC dynamics with high temporal resolution. We advocate for a multi‐scale approach that combines various methods, which include deepening our mechanistic understanding through manipulative experiments, integrating carbon sink and source data from multiple observational networks with carbon allocation models to better characterize the NSC dynamics, and quantifying the spatial pattern and temporal trends of the NSC‐phenology relationship using remote sensing and modelling. This will enhance our comprehension of how NSC dynamics impact leaf phenology across different scales and environments. Read the free Plain Language Summary for this article on the Journal blog.

Evaluation of the impact of wood forest management operations on Amazon nut trees in forest concession areas in the Amazon

The Amazon rainforest holds has immense biodiversity and offers various possibilities for use. Moreover, there is an increasing need for development models that reconcile the rational use of forest resources with socio-economic development. Multiple-use forest management emerges as a legal mechanism for the sustainable use of forest resources. In this context, a forest use system that appears suitable for multiple-use management is one where timber is selectively harvested from forests that are also sources of Brazil nuts. However, the effects of logging management on forests with a high density of Brazil nut trees needs to be studied, as the damage resulting from logging operations on Bertholletia excelsa Bonpl. trees may impact the long-term maintenance of these nut stands. This study aimed to assess the impact of logging operations for timber production on Brazil nut trees to evaluate the compatibility of logging management with the management of native nut stands in a forest concession area in the Amazon. The study was conducted in two National Forests (Jamari and Jacundá) in the state of Rondônia. The damage from cutting and logging operations on the canopy structure of Brazil nut trees was quantified and assessed using photogrammetry with remotely piloted aircraft. The Jamari National Forest showed 4.3% canopy damage to Brazil nut trees, while the Jacundá National Forest showed 3.2% damage. The results indicated that the damage to the canopies of Brazil nut trees due to timber extraction was minimal, suggesting potential compatibility between logging management and the management of native nut stands. However, further studies are needed, especially those focused on planning the collection of Brazil nuts, since access to the nut stands is essential for effectively structuring management, ensuring that the full production potential is sustainably managed and generates income for the extractivists.

Cultivar mixtures of maize enhance grain yield and nitrogen use efficiency by promoting canopy photosynthetically active radiation and root growth

Cultivar mixtures increases crop diversification and grain yield stability. It is a major challenge to achieve high grain yield and nitrogen use efficiency with environmentally friendly practices. However, it is currently unclear whether the cultivar mixtures of maize can improve nitrogen use efficiency. A two-year field experiment was conducted using two maize cultivars with different roots angles and leaf angles planted in monoculture or in mixtures under four nitrogen levels N0 (0 kg N ha-1), N140 (140 kg N ha-1), N280 (280 kg N ha-1) and N340 (340kg N ha-1). Cultivar mixtures significantly increased light interception of middle canopy, dry matter accumulation and total roots length under N0, N140, and N280 conditions. Light interception of middle canopy positively related to dry matter accumulation and thus increased grain yield. And light interception of whole canopy positively related to total lateral root length, while the increased total lateral root length of outer nodal roots significantly improved nitrogen accumulation and nitrogen use efficiency. Thus, cultivar mixtures promoted an optimal canopy structure and good root growth, then improved grain yield and nitrogen use efficiency. These findings could deepen our understanding of the facilitating effect of canopy structure and root traits of cultivar mixtures on the collaborative promotion of grain yield and nitrogen use efficiency.

Canopy structure and herbage intake rate of three tropical forage grasses cultivated as pure or mixed stands

Context Using forage grass species with complementary growth and resource-use strategies to enhance forage plant diversity in pastures may be an alternative to traditional monocultures in tropical regions. Aims This study aimed to determine whether a mixture of three perennial tropical forage grasses (Andropogon gayanus cv. Planaltina, Panicum maximum cv. Massai, and Brachiaria brizantha cv. BRS Piatã) could be an alternative to enhance herbage intake rates relative to their respective monocultures. Methods The treatments corresponded to three perennial tropical forage grasses cultivated as monocultures and as a mixture composed of all three species. Defoliation management corresponded to a pre-cutting height of 35 cm and post-cutting height of 17.5 cm. Key results The botanical composition of the mixture was dynamic throughout the experimental period, with variations in the proportion of species across seasons. The upper half of the canopy predominantly comprised leaves for all treatments. The canopy structure of the mixture allowed for greater herbage intake rates than monocultures during winter/early spring. Andropogon gambagrass showed lower herbage intake rates than the other treatments. Conclusions The findings of this study indicate that it is possible to combine tropical forage grass species without compromising canopy structure and grazing animal responses, compared with single-species grass pastures. Implications The selection of forage species for mixed pastures should consider their phenological cycle, growth, and resource-use strategies to achieve temporal complementarity and provide an optimal grazing environment for animals throughout the year.

Tree Species Classification by Multi-Season Collected UAV Imagery in a Mixed Cool-Temperate Mountain Forest

Effective forest management necessitates spatially explicit information about tree species composition. This information supports the safeguarding of native species, sustainable timber harvesting practices, precise mapping of wildlife habitats, and identification of invasive species. Tree species identification and geo-location by machine learning classification of UAV aerial imagery offer an alternative to tedious ground surveys. However, the timing (season) of the aerial surveys, input variables considered for classification, and the model type affect the classification accuracy. This work evaluates how the seasons and input variables considered in the species classification model affect the accuracy of species classification in a temperate broadleaf and mixed forest. Among the considered models, a Random Forest (RF) classifier demonstrated the highest performance, attaining an overall accuracy of 83.98% and a kappa coefficient of 0.80. Simultaneously using input data from summer, winter, autumn, and spring seasons improved tree species classification accuracy by 14–18% from classifications made using only single-season input data. Models that included vegetation indices, image texture, and elevation data obtained the highest accuracy. These results strengthen the case for using multi-seasonal data for species classification in temperate broadleaf and mixed forests since seasonal differences in the characteristics of species (e.g., leaf color, canopy structure) improve the ability to discern species.

Planting Density and Sowing Date Strongly Influence Canopy Characteristics and Seed Yield of Soybean in Southern Xinjiang

Southern Xinjiang is an important soybean production region in China. However, the short growing season and the cultivation of winter crops (such as wheat) in the region limit the expansion of soybean planting areas. An increased planting density can compensate for the loss in yield due to delayed sowing. To identify the quantitative relationship between increased density and delayed days, a two-year field experiment was conducted at the Tarim University Agronomy Experiment Station. Two sowing dates (April 7 (S1) and May 7 (S2)) and three planting densities of 206,800 plants·ha−1 (D1), 308,600 plants·ha−1 (D2), and 510,200 plants·ha−1 (D3) were used to compare various plant growth parameters and canopy characteristics. Late sowing and a high planting density significantly increased the plant height (S2 was 37.3% higher than S1, and D3 was 17.6% and 8.8% higher than D1 and D2), main stem internode, petiole length, and the mean tilt angle of the leaves (S2 was 22.5% higher than S1, and D3 was 11.7% higher than D2) but reduced the stem diameter (D3 was 28.6% and 12.5% lower than D1 and D2), branch number (S2 was 26.7% lower than S1, and D2 was 75% lower than D1), canopy light transmittance (S2 was 49.2% lower than S1, and D3 was 36.7% and 20.8% lower than D1 and D2), photosynthetic rate, and dry matter. The highest yield was achieved at S1D1, but the lowest yield was found for S2D1. Overall, the results suggest that earlier sowing and a lower planting density contribute to achieving an optimum canopy structure and higher yield. Our conclusions provide a reference for soybean production in southern Xinjiang.

A gamma rays-induced novel fertile subsessile leaf mutant in cowpea [Vigna unguiculata (L.) Walp

Protein-rich legumes contribute to nutritional security by complementing cereals-based diets rich in carbohydrates. In cowpea, an arid legume with a narrow genetic base, we attempted to induce ideotype mutations using gamma rays for augmenting productivity and discovered a novel fertile, subsessile leaf mutant ‘PLM211’. Despite the mutant and the parent exhibiting similar individual leaf area and node number, the rudimentary petioles, shortened rachis, little to no branching, and consequently fewer leaves greatly influenced the canopy structure of the mutant. Histological sections of the mutant’s 0.55- 1.01 cm short petioles showed reduced cell size and number as opposed to the parent’s 12.64-14.55 cm long petioles. The potential for uncovering the underlying genes associated with petiole length, leaf morphogenesis, and branching highlights the significance of the mutant as a genetic treasure, opening up avenues for manipulating plant architecture, especially in legumes.

Canopy structure regulates autumn phenology by mediating the microclimate in temperate forests

Canopy structure regulates autumn phenology by mediating the microclimate in temperate forests

Excellent Canopy Structure in Soybeans Can Improve Their Photosynthetic Performance and Increase Yield

In the maize-soybean intercropping system, varying degrees of maize leaf shading are an important factor that reduces the uniformity of light penetration within the soybean canopy, altering the soybean canopy structure. Quantitative analysis of the relationship between the soybean canopy structure and canopy photosynthesis helps with breeding shade-tolerant soybean varieties for intercropping systems. This study examined the canopy structure and photosynthesis of intercropped soybeans during the shading stress period (28 days before the corn harvest), the high light adaptation period (15 days after the corn harvest), and the recovery period (35 and 55 days after the corn harvest), using a field high-throughput phenotyping platform and a plant gas exchange testing system (CAPTS). Additionally, indoor shading experiments were conducted for validation. The results indicate that shade-tolerant soybean varieties (STV varieties) have significantly higher yields than shade-sensitive soybean varieties (SSV varieties). This is attributable to the STV varieties having a larger top area, lateral width, and lateral external rectangular area. Compared to the SSV varieties, the four top areas of the STV varieties are, on average, 52.09%, 72.05%, and 61.37% higher during the shading stress, high light adaptation, and recovery periods, respectively. Furthermore, the average maximum growth rates (GRs) for the side mean width (SMW) and side rectangle area (SRA) of the STV varieties are 62.92% and 22.13% in the field, and 83.36% and 55.53% in the indoor environment, respectively. This results in a lower canopy overlap in STV varieties, leading to a more uniform light distribution within the canopy, which is reflected in higher photosynthetic rates (Pn), apparent quantum efficiency, and whole-leaf photosynthetic potential (WLPP) for the STV varieties, thereby enhancing their adaptability to shading stress. Above-ground dry matter accumulation was higher in STV varieties, with more assimilates stored in the source and sink, promoting assimilate accumulation in the grains. These results provide new insights into how the superior canopy structure and photosynthesis of shade-tolerant soybean varieties contribute to increased yield.

Simulating High-Resolution Sun-Induced Chlorophyll Fluorescence Image of Three-Dimensional Canopy Based on Photon Mapping

The remote sensing of sun-induced chlorophyll fluorescence (SIF) is an emerging technique with immense potential for terrestrial vegetation sciences. However, the interpretation of fluorescence data is often hindered by the complexity of observed land surfaces. Therefore, advanced remote sensing models, particularly physically based simulations, are critical to accurately interpret SIF data. In this work, we propose a three-dimensional (3D) radiative transfer model that employs the Monte Carlo ray-tracing technique to simulate the excitation and transport of SIF within plant canopies. This physically based approach can quantify the various radiative processes contributing to the observed SIF signal with high fidelity. The model’s performance is rigorously evaluated by comparing the simulated SIF spectra and angular distributions to field measurements, as well as conducting systematic comparisons with an established radiative transfer model. The results demonstrate the proposed model’s ability to reliably reproduce the key spectral and angular characteristics of SIF, with the coefficient of determination (R2) exceeding 0.98 and root mean square error (RMSE) being less than 0.08 mW m−2 sr−1 nm−1 for both the red and far-red fluorescence peaks. Furthermore, the model’s versatile representation of canopy structures, enabled by the decoupling of radiation and geometry, is applied to study the impact of 3D structure on SIF patterns. This capability makes the proposed model a highly attractive tool for investigating SIF distributions in realistic, heterogeneous canopy environments.

Avian diversity across guilds in North America versus vegetation structure as measured by the Global Ecosystem Dynamics Investigation (GEDI)

Avian diversity, a key indicator of ecosystem health, is closely related to canopy structure. Most avian diversity models are based on either optical remote sensing or airborne lidar data, but the latter is limited to small study areas. The launch of the Global Ecosystem Dynamics Investigation (GEDI) instrument in 2018 has opened new avenues for exploring the influence of vegetation structure on avian diversity. To examine how direct measurements of canopy structural characteristics explain bird diversity across North America, we analyzed 18 GEDI metrics from 2019 to 2022, along with corresponding Breeding Bird Survey (BBS) counts and AVONET morphological data, analyzing effects across broad regions and at varying spatial extents. We grouped 440 bird species into 20 ecological guilds under six guild categories and employed random forest algorithms to model avian diversity across eight spatial extents (1, 2, 3, 4, 5, 10, 20, and 39.2 km). The models predicted six diversity indices, including species richness (sRich), functional richness (fRich), evenness (fEve), dispersion (fDis), divergence (fDiv), and redundancy (fRed) across eight spatial extents. The best-predicted guilds varied for each diversity index. The most accurate models were sRich (pseudo-R2 = 0.71, RMSE = 4.28) and fRed (pseudo-R2 = 0.60, RMSE = 0.13) for forest specialists guilds; fRich (pseudo-R2 = 0.55, RMSE = 0.18) for urban guilds; fEve (pseudo-R2 = 0.28, RMSE = 0.08) for insectivore guilds; and fDiv (pseudo-R2 = 0.38, RMSE = 0.12) and fDis (pseudo-R2 = 0.53, RMSE = 0.87) for short distance migrants guilds. Our results highlight the critical role of canopy structure, including its horizontal and vertical distribution and variation, in predicting avian diversity, as measured by the mean number of detected modes (num_detectedmodes), the standard deviation of foliage height diversity (FHD), num_detectedmodes, canopy cover, and plant area index (PAI) across the spatial extents centered on BBS routes. Therefore, we recommend incorporating the GEDI metrics into avian diversity modeling and mapping across North America, thereby potentially enhancing bird habitat management and conservation efforts.

A Wind Tunnel Test for the Effect of Seed Tree Arrangement on Wake Wind Speed

Changes in canopy structures caused by harvesting and regeneration practices can significantly alter the wind environment. Therefore, it is essential to understand the wind patterns influenced by seed tree arrangements for predicting seed dispersal by winds and ensuring the success of natural regeneration. This study aimed to identify how wind speed responds to seed tree arrangement designs with differing horizontal distances, vertical positions, and free-stream wind speeds. A wind tunnel test was conducted using pine saplings for a scale model of various seed tree arrangements, and the change in wake speed was tracked. The wake’s relative wind speed averaged 71%, ranging from 3.5% to 108.5%, depending on the seed tree arrangement, distance from saplings, and vertical position. It peaked within the patch of three seed trees compared to other arrangements and at the top canopy layer. The empirical function effectively described the wind speed reduction and recovery with distance from saplings. For instance, the minimum wind speed was reached at 0.6–2.2 times the canopy height, and a wind speed reduction of over 20% of the free-stream wind speed was maintained at a 1.6–7.6 canopy height. A negative relationship between the seed tree leaf area and the relative wind speed was observed only at the top canopy layer. This study presents empirical evidence on the patterns of wake winds induced by different types of heterogeneous canopy structures.

Long-term alien marsh grass in China brings high carbon uptake capacity but cannot sustain high-temperature weather

Coastal wetlands, crucial in the global carbon cycle, face increasing challenges brought by extreme climate events, particularly high temperatures above plant tolerance thresholds. These conditions often exert great impact on plant, thereby potentially reducing overall ecosystem productivity. However, it has been observed that alien species, typically exhibiting higher productivity compared to native plant. Would plant invasion offset the loss of productivity caused by high-temperature events at ecosystem scale? In this study, we utilized data from 2020 to 2023 in China's Yangtze Estuary to investigate the responses of Spartina alterniflora (alien) and Phragmites australis (native) to high-temperature stress. Our results demonstrate that though the alien vegetation exhibits higher productivity before high temperature events, it experiences significant declines during high temperatures. In average, net ecosystem productivity (NEP) and gross primary productivity (GPP) of alien plant drops by 21.03% overall, with a notable 29.59% reduction during Neap tide. In contrast, native vegetation maintains a more stable productivity profile under the same conditions. Spring tide alleviate the negative impact of high temperatures on the alien vegetation, exhibiting a distinct environmental buffering effect. Photosynthetic photon flux density emerged as a crucial factor driving productivity, yet its effectiveness was moderated by aerodynamic conductance for heat transfer (Ga_h). Through the application of the Michaelis-Menten model, we confirmed that both species maintain similar maximum light utilization efficiencies, yet native vegetation demonstrates greater resilience to thermal stress. Additionally, we observed a 33.82% overestimation in productivity by the vegetation photosynthesis model (VPM) under high temperatures, emphasizing the need to refine how Ga_h impacts are integrated, particularly when comparing the resilience of native and alien species. We emphasize necessity of incorporating canopy structure factors into ecological models and underscore the importance of maintaining tidal dynamics for coastal wetland management.

Exploring the potential of forest snow modeling at the tree and snowpack layer scale

Abstract. Boreal and sub-alpine forests host seasonal snow for multiple months per year; however, snow regimes in these environments are rapidly changing due to rising temperatures and forest disturbances. Accurate prediction of forest snow dynamics, relevant for ecohydrology, biogeochemistry, cryosphere, and climate sciences, requires process-based models. While snow schemes that track the microstructure of individual snow layers have been proposed for avalanche research, so far, tree-scale processes resolving canopy representations only exist in a few snow-hydrological models. A framework that enables layer- and microstructure-resolving forest snow simulations at the meter scale is lacking to date. To fill this research gap, this study introduces the forest snow modeling framework FSMCRO, which combines two detailed, state-of-the art model components: the canopy representation from the Flexible Snow Model (FSM2) and the snowpack representation of the Crocus ensemble model system (ESCROC). We apply FSMCRO to discontinuous forests at boreal and sub-alpine sites to showcase how tree-scale forest snow processes affect layer-scale snowpack properties. Simulations at contrasting locations reveal marked differences in stratigraphy throughout the winter. These arise due to different prevailing processes at under-canopy versus gap locations and due to variability in snow metamorphism dictated by a spatially variable snowpack energy balance. Ensemble simulations allow us to assess the robustness and uncertainties of simulated stratigraphy. Spatially explicit simulations unravel the dependencies of snowpack properties on canopy structure at a previously unfeasible level of detail. Our findings thus demonstrate how hyper-resolution forest snow simulations can complement observational approaches to improve our understanding of forest snow dynamics, highlighting the potential of such models as research tools in interdisciplinary studies.

A LiDAR‐driven pruning algorithm to delineate canopy drainage areas of stemflow and throughfall drip points

Abstract Precipitation channelled down tree stems (stemflow) or into drip points of ‘throughfall’ beneath trees results in spatially concentrated inputs of water and chemicals to the ground. Currently, these flows are poorly characterised due to uncertainties about which branches redirect rainfall to stemflow or throughfall drip points. We introduce a graph theoretic algorithm that ‘prunes’ quantitative structural models of trees (derived from terrestrial LiDAR) to identify branches contributing to stemflow and those contributing to throughfall drip points. To demonstrate the method's utility, we analysed two trees with similar canopy sizes but contrasting canopy architecture and rainfall partitioning behaviours. For both trees, the branch ‘watershed’ area contributing to stemflow (under conditions assumed to represent moderate precipitation intensity) was found to be only half of the total ground area covered by the canopy. The study also revealed significant variations between trees in the number and median contribution areas of modelled throughfall drip points (69 vs. 94 drip points tree−1, with contributing projected areas of 28.6 vs. 7.8 m2 tree−1, respectively). Branch diameter, surface area, volumes and woody area index of components contributing to stemflow and throughfall drip points may play a role in the trees' differing rainfall partitioning behaviours. Our pruning algorithm, enabled by the proliferation of LiDAR observations of canopy structure, promises to enhance studies of canopy hydrology. It offers a novel approach to refine our understanding of how trees interact with rainfall, thereby broadening the utility of existing LiDAR data in environmental research.

Influence of Vegetation Phenology on the Temporal Effect of Crop Fractional Vegetation Cover Derived from Moderate-Resolution Imaging Spectroradiometer Nadir Bidirectional Reflectance Distribution Function–Adjusted Reflectance

Moderate-Resolution Imaging Spectroradiometer (MODIS) Nadir Bidirectional Reflectance Distribution Function (BRDF)-Adjusted Reflectance (NBAR) products are being increasingly used for the quantitative remote sensing of vegetation. However, the assumption underlying the MODIS NBAR product’s inversion model—that surface anisotropy remains unchanged over the 16-day retrieval period—may be unreliable, especially since the canopy structure of vegetation undergoes stark changes at the start of season (SOS) and the end of season (EOS). Therefore, to investigate the MODIS NBAR product’s temporal effect on the quantitative remote sensing of crops at different stages of the growing seasons, this study selected typical phenological parameters, namely SOS, EOS, and the intervening stable growth of season (SGOS). The PROBA-V bioGEOphysical product Version 3 (GEOV3) Fractional Vegetation Cover (FVC) served as verification data, and the Pearson correlation coefficient (PCC) was used to compare and analyze the retrieval accuracy of FVC derived from the MODIS NBAR product and MODIS Surface Reflectance product. The Anisotropic Flat Index (AFX) was further employed to explore the influence of vegetation type and mixed pixel distribution characteristics on the BRDF shape under different stages of the growing seasons and different FVC; that was then combined with an NDVI spatial distribution map to assess the feasibility of using the reflectance of other characteristic directions besides NBAR for FVC correction. The results revealed the following: (1) Generally, at the SOSs and EOSs, the differences in PCCs before vs. after the NBAR correction mainly ranged from 0 to 0.1. This implies that the accuracy of FVC derived from MODIS NBAR is lower than that derived from MODIS Surface Reflectance. Conversely, during the SGOSs, the differences in PCCs before vs. after the NBAR correction ranged between –0.2 and 0, suggesting the accuracy of FVC derived from MODIS NBAR surpasses that derived from MODIS Surface Reflectance. (2) As vegetation phenology shifts, the ensuing differences in NDVI patterning and AFX can offer auxiliary information for enhanced vegetation classification and interpretation of mixed pixel distribution characteristics, which, when combined with NDVI at characteristic directional reflectance, could enable the accurate retrieval of FVC. Our results provide data support for the BRDF correction timescale effect of various stages of the growing seasons, highlighting the potential importance of considering how they differentially influence the temporal effect of NBAR corrections prior to monitoring vegetation when using the MODIS NBAR product.

Canopy height uniformity: a new 3D phenotypic indicator linking individual plant to canopy

Canopy height uniformity (CHU) is a key indicator linking individual plants to populations. Determining CHU by the manual measurement of the height of individual plants is inefficient and subjective, making meeting the demand for a high-throughput assessment of CHU in high-yield crop management and variety selection challenging. Therefore, a high-throughput CHU estimation approach using unmanned aerial vehicle (UAV) light detection and ranging (LiDAR) data is proposed. First, individual plant point clouds were segmented by incorporating planting density, and the CHU was estimated based on the extracted plant heights (PHs). The CHU was then applied to quantify the effects of different cropping practices on maize canopy structure. In addition, 18 canopy structural parameters (SPs) were extracted from the canopy point cloud, and aboveground biomass (AGB) was estimated by combining these SPs with Pelican Optimization Algorithm (POA) machine learning models (POA-MLs). Finally, as an indicator of individual variability within the canopy, CHU was integrated into the training process to evaluate the accuracy of AGB estimation for different models and datasets. The results showed that the plant height extracted by the canopy population-individual plant segmentation was accurate, with R2 ranging from 0.85 to 0.93. The CHU was able to accurately quantify the effects of different cropping practices on canopy structure. An increase in applied nitrogen fertilizer and irrigation could significantly contribute to an increase in CHU and the formation of a clean and homogeneous canopy structure. Meanwhile, marginal effects can be accurately quantified through PHs estimation to further quantify the intra-canopy differences. In addition, the accuracy of the AGB estimation can be effectively improved by merging SPs, PH, and CHU. In this study, we demonstrated the efficacy of CHU as a phenotypic indicator for representing differences in canopy structure, thus providing practical phenotypic identification information for breeders and field managers.

Modeling forest canopy structure and developing a stand health index using satellite remote sensing

Biotic and abiotic disturbances modify tree structure and degrade stand health. Accurate geospatial data on stand structure is important for monitoring tree growth, forest health, progression and severity of diseases and pests, and estimating resilience to climate stress. The live crown ratio (LCR) of trees serves as a key health indicator but has been understudied at the landscape level using remote sensing data. This study generated the leaf area index (LAI) and a novel spatial layer of LCR at site and landscape scales using a combination of satellite data and ground observations. We conducted field surveys to collect plot-level (10 m × 10 m) data in four eastern white pine (EWP; Pinus strobus L.)-dominated sites in the state of Maine, USA. The plot-level data were used to develop regression models for LAI and LCR estimation using microwave (Sentinel-1) and optical (Sentinel-2) remote sensing data and applying the Random Forest (RF) and Support Vector Machine (SVM) machine learning algorithms. The RF model showed higher prediction accuracy than the SVM model at the site level. Moreover, the prediction accuracy at the site and landscape levels were comparable for LAI (R2 > 0.76) and LCR (R2 > 0.71) using the RF model. Furthermore, the predicted LAI and LCR were integrated with canopy height and stand density to develop a novel health index map for EWP. The resulting health index map successfully delineated patches representing various health categories. Forestry practitioners and decision-makers can use the derived health index map and intermediate spatial data layers (LAI and LCR) to guide stand management. The developed framework can potentially be applied to other coniferous and broadleaved species for remote sensing-based LCR estimation and forest health assessment upon further studies and verification.

Architecture Pruning Technique (Pollarding) of Moringa (Moringa oleifera)

Moringa is a fast-growing, deciduous tree that can reach a height of 10–12 m and is not traditionally pollarded in agriculture production. A field experiment was conducted between May 2023 and Jun 2024 in MARDI, Serdang to determine the effect of pollarding at different levels of pruning heights on vegetative growth and yield of Moringa. The trial was laid down in a randomized complete block design (RCBD) with four replications and five plants per plot and the data was analyzed using ANOVA in SAS (SAS 9.4) software. Differences between means were compared using Duncan’s Multiple Range Test (DMRT) at P≤0.05. Five months after being cultivated on field condition, trees were pruned pollarding at four levels: 30, 60, 90 cm and control (unpruned). Subsequently, the growth performance was evaluated by collecting data on plant height, diameter of the main stem, length of the secondary branch, number of budbursts, diameter of the secondary branch and leave area. Meanwhile, the yield components were recorded based on the fresh weight and dry weight of the leaves. Results showed that different levels of pruning heights significantly affected overall dependent variables. Unpruned trees produced an optimal response for all dependent variables, but it is not suggested due to the trees growing erratically, challenging to harvest, and having an uneven canopy structure. Meanwhile, for the yield components' pruning at 60 and 90 cm from the ground level was comparable and did not differ significantly from unpruned trees. Therefore, pruning at 60 cm was suggested as an architecture pruning technique for moringa production cultivated on mineral soil under tropical climates. Pollarding can be a way to improve the productivity of agricultural produce, but further evaluation of yield trends must be conducted to ensure the consistency of production.