Canopy Area Research Articles

An analysis of the effects of water regime on grapevine canopy status using a UAV and a mobile robot

In this paper, we propose a novel approach for analyzing the effects of water regime on grapevine canopy status using robotics as an aid for monitoring and mapping. Data from an unmanned aerial vehicle (UAV) and a ground mobile robot are used to obtain multispectral images and multiple vegetation indexes, and the 3D reconstruction of the canopy, respectively. Unlike previous works, sixty vegetation indexes are computed precisely by using the projected area of the vineyard point cloud as a mask. Extensive experimental tests on repeated plots of Pinot gris vines show that the GDVI, PVI, and TGI vegetation indexes are positively correlated with the water potential: GDVI (R2=0.90 and 0.57 for the stem and pre-dawn water potential, respectively), PVI (R2=0.90 and 0.57), TGI (R2=0.87 and 0.77). Furthermore, the canopy volume and the canopy area projected on the ground are impacted by the water status, as well as stem and pre-dawn water potential measurements. The results obtained in this work demonstrate the feasibility of the proposed approach and the potential of robotic technologies, supporting precision viticulture.

Photovoltaic potential prediction and techno-economic analysis of China railway stations

As an infrastructure, the railway stations' roof and platform canopy have considerable space potential for deploying photovoltaic power generation systems. In order to study the feasibility of installing PV systems in railway stations, this paper analyzes the PV potential and techno-economic characteristics of China's high-grade railroad stations by combining a three-dimensional digital earth system (LSV) and PV plant calculation methods. The results show that the total installed PV capacity of Chinese high-grade railway stations, which are mainly used for passenger transportation, can reach 820 MW, and the total annual PV power generation capacity can reach 1111GWh. The self-sufficiency rate of each station is affected by the scale of the station building, the number of canopies on the passenger platform and the architectural design factors. The smaller the scale of the station building, the larger the area of the platform canopy, the smaller the overlap between the station building and the station canopy, the easier the railway station to achieve self-sustaining. The payback period is the time it takes for the total return on PV power generation to equal the investment in PV construction. 63% of the stations can realize full payback of their investment within 4–7 years under the operation strategy of full self-consumption of power generation. In addition the photovoltaic systems in railroad stations can generate considerable ecological benefits, reducing carbon dioxide emissions by 820,000 tons per year, which is about the carbon emissions of 304,000 cars in one year.

Evaluation of Coffee Plants Transplanted to an Area with Surface and Deep Liming Based on Multispectral Indices Acquired Using Unmanned Aerial Vehicles

The use of new technologies to monitor and evaluate the management of coffee crops allowed for a significant increase in productivity. Precision coffee farming has leveraged the development of this commodity by using remote sensing and Unmanned Aerial Vehicles (UAVs). However, the success of coffee farming in the country also resulted from management practices, including liming management in the soils. This study aimed to evaluate the response of coffee seedlings transplanted to areas subjected to deep liming in comparison to conventional (surface) liming, using vegetation indices (VIs) generated by multispectral images acquired using UAVs. The study area was overflown bimonthly by UAVs to measure the plant height, crown diameter, and chlorophyll content in the field. The VIs were generated and compared with the data measured in the field using linear time graphs and a correlation analysis. Linear regression was performed to predict the biophysical parameters as a function of the VIs. A significant difference was found only in the chlorophyll content. Most indices were correlated with the biophysical parameters, particularly the green chlorophyll index (GCI) and the canopy area calculated via vectorization. Therefore, UAVs proved to be effective coffee monitoring tools and can be recommended for coffee producers.

Measuring the spatiotemporal variability in snow depth in subarctic environments using UASs – Part 2: Snow processes and snow–canopy interactions

Abstract. Detailed information on seasonal snow cover and depth is essential to the understanding of snow processes, to operational forecasting, and as input for hydrological models. Recent advances in uncrewed or unmanned aircraft systems (UASs) and structure from motion (SfM) techniques have enabled low-cost monitoring of spatial snow depth distribution in resolutions of up to a few centimeters. Here, we study the spatiotemporal variability in snow depth and interactions between snow and vegetation in different subarctic landscapes consisting of a mosaic of conifer forest, mixed forest, transitional woodland/shrub, and peatland areas. To determine the spatiotemporal variability in snow depth, we used high-resolution (50 cm) snow depth maps generated from repeated UAS–SfM surveys in the winter of 2018/2019 and a snow-free bare-ground survey after snowmelt. Due to poor subcanopy penetration with the UAS–SfM method, tree masks were utilized to remove canopy areas and the area (36 cm) immediately next to the canopy before analysis. Snow depth maps were compared to the in situ snow course and a single-point continuous ultrasonic snow depth measurement. Based on the results, the difference between the UAS–SfM survey median snow depth and single-point measurement increased for all land cover types during the snow season, from +5 cm at the beginning of the accumulation to −16 cm in coniferous forests and −32 cm in peatland during the melt period. This highlights the poor representation of point measurements in selected locations even on the subcatchment scale. The high-resolution snow depth maps agreed well with the snow course measurement, but the spatial extent and resolution of maps were substantially higher. The snow depth range (5th–95th percentiles) within different land cover types increased from 17 to 42 cm in peatlands and from 33 to 49 cm in the coniferous forest from the beginning of the snow accumulation to the melt period. Both the median snow depth and its range were found to increase with canopy density; this increase was greatest in the conifer forest area, followed by mixed forest, transitional woodland/shrub, and open peatlands. Using the high-spatial-resolution data, we found a systematic increase (2–20 cm) and then a decline in snow depth near the canopy with increasing distance (from 1 to 2.5 m) of the peak value through the snow season. This study highlights the applicability of the UAS–SfM in high-resolution monitoring of snow depth in multiple land cover types and snow–vegetation interactions in subarctic and remote areas where field data are not available or where the available data are collected using classic point measurements or snow courses.

Multi-scale biomass estimation of an alpine desert shrub based on relative cover: A case study of Asterothamnus centraliasiaticus in the Qaidam Desert on the Qinghai–Tibet Plateau

Biomass estimates and the allocation characteristics of shrubs are essential indicators for studying the structure, function, and material and energy flows of alpine desert ecosystems. However, conventional destructive biomass estimation methods are unsuitable for large-scale application in alpine desert areas that are characterized by sparse vegetation and comprise extremely fragile ecosystems. In this study, we propose a multi-scale method for estimating the biomass of alpine desert shrubs based on relative cover and verified its accuracy, feasibility, and efficiency using data obtained from 100 individual plants and 27 quadrat plots of Asterothamnus centraliasiaticus, an important species growing in the Qaidam Desert on the Qinghai–Tibet Plateau. Having gained this information, we developed models to estimate the new branch (NBB), aboveground (AGB), and belowground (BGB) biomasses of A. centraliasiaticus at individual, quadrat, and regional scales. The modelling results revealed that an allometric relationship model (power law function) could be used to accurately represent the synergistic relationship between plant morphological variables and biomass increases at all assessed scales. Moreover, we established that at the individual scale, estimation models (NBB = 0.3015 × C1.0610 and AGB = 1.0046 × C1.4239) incorporating canopy area (C) as a parameter can better reflect the cumulative characteristics of the new branch and aboveground biomasses of A. centraliasiaticus, respectively, whereas canopy volume (CH, resembles a cylinder) is the most appropriate parameter for the model (BGB = 0.3262 × CH0.8822) used to estimate belowground biomass. In addition, at the sample scale, the total canopy cover (TC) of A. centraliasiaticus in the sample was established to be an appropriate parameter for estimating total biomass (TNBB = 0.0042 × TC1.4647, TAGB = 0.0033 × TC1.6308, and TBGB = 0.0110 × TC1.2554). Furthermore, at the regional scale, we found that incorporating relative canopy cover (RC) in models was effective for estimating the biomass density of A. centraliasiaticus (DNBB = 0.0012 × RC1.4647, DAGB = 0.0014 × RC1.6308, and DBGB = 0.0020 × RC1.2554) and can be combined with regional area estimates to determine the total biomass of A. centraliasiaticus at any spatial scale. On the basis of our findings, we established that the biomass estimation model for alpine desert shrubs, incorporating the parameter relative cover, can effectively solve the longstanding problem of scale conversion and facilitates the rapid and accurate estimation of the biomass components of alpine desert shrubs. In future studies, biomass could be efficiently estimated by combining models based on the relative cover data of shrub communities in relevant areas obtained using aerial photography and remote sensing. The results of this study have important implications for estimating the biomass of alpine desert shrubs, and will accordingly make a valuable contribution to the management of alpine desert ecosystems.

Assessing canopy rainfall partitioning by Mediterranean dryland shrubs under extreme rainfall

AbstractIntense rain events have become more frequent in some regions due to climate change, and this trend is particularly concerning in dryland regions where the ecological and geomorphological impacts of rainfall are intimately tied to its intensity. The interception of rainfall by vegetation is a critical process in the water balance of drylands; thus, this study estimated the canopy interception capacity and interception rates as well as stemflow of three typical Mediterranean shrub species (Rosmarinus officinalis, Thymus vulgaris, and Macrochloa tenacissima) of three size classes in Spain under a simulated extreme rainfall rate (~8 mm min−1, historical return period of >100 years). Given that these plants' canopy structures markedly differ from taller woody plants (i.e., trees), a novel method was developed to assess the stemflow fraction. Results showed significant differences in interception amount, rates, and storage capacity among the shrub species, with variations in plant morphology, such as shrub height and canopy diameter, being the key factors determining interception capacity. R. officinalis had the highest interception fraction per unit canopy area, or ‘specific interception,’ (18.4%). In contrast, the lowest specific interception fraction was measured for M. tenacissima (6.5%). Thymus vulgaris was characterized by the highest stemflow yields per unit canopy area (4.85 mm) and fraction (up to 29.6% of rainfall), which was the lowest for M. tenacissima (1.09 mm, ~1%–4%). Strong linear correlations were found between canopy interception and shrub canopy diameter (|r| > −0.51, max = −0.90), when observations were grouped for size class. These linear correlations between shrub morphology and partitioning enabled multiple‐regression linear models to be developed that predicted canopy interception and stemflow with good accuracy (r2 > 0.64, with a maximum of 0.82) from shrub height, canopy diameter, dry biomass, size class, and species. Despite these measurements being conducted under one extreme storm depth and intensity, the results provide: (i) values of rainfall partitioning for important shrub species in Mediterranean dryland environments; and (ii) a simple but reliable model that may be further developed (e.g., embedding variable rainfall values as weather input or incorporating other morphological parameters) and may be integrated into complex hydrological models.

Initial Growth of Gmelina arborea and Efficacy of RGB Image to Capture Canopy Area in a Large Range of Stockings

At present, there is a high demand for carbon (C) sequestration alternatives; thus, understanding tree growth and the efficacy of remote sensing techniques to capture forest plantation ecophysiology is crucial. This study evaluated the effect of contrasting stockings of Gmelina arborea on its initial growth and aboveground Carbon stock, and the efficacy of aerial images obtained using drones to capture the crown cover at different stockings. The results indicated that denser stockings showed greater tree heights and stem diameter increments, contrary to traditional measurements. The C storage capacity of Gmelina arborea was promising, with an aboveground estimated C stock of about 13 Mg ha−1 in 9 months, making it a valuable and promising species for CO2 sequestration under the context of climate change. The use of simple Red-Green-Blue (RGB) cameras and drones to detect and estimate crown areas in young plantations was mainly viable within the commercial range of stockings (500–2000 trees ha−1), and can be used as a powerful tool to better understand tree initial growth. The results showed effective discrimination without weeds independently of the stocking level; however, when weeds were present, the effectiveness decreased. This research provides valuable insights into forest management and improves the understanding of the silviculture behavior of a potential native species for reforestation in the tropics.

Effects of composition and structure variables of urban trees in the reduction of heat islands; case study, Temuco city, Chile

The Urban Heat Island (UHI) effect is a growing problem that has a critical impact on human thermal comfort and public health. Some strategies to reduce the UHI effect are increasing the amount of urban forest. This research aims at assessing the effect of the composition and structure of urban trees, as part of cities's Green Infrastructure (GI), on the UHI phenomenon in the south of Chile. Linear regression analysis and non-parametrical statistical tests were used to assess the relationship. Micro-level features analysis demonstrates that shape and quality of trees have significant effect on temperature oscillations (p-value = 0.003 and 0.072, respectively). Better quality and larger area of canopy favour the refrigeration process. Besides, there are differences between native and introduced tree species behaviour, but they do not result significant. Composition and structure variables of the GI are relevant for dealing with the UHI phenomenon. Then, a proper management of the urban GI would ensure better and more sustainable planning of cities.

Assessment of wetland landscape changes based on landscape metrics and trophic state index (case study: Anzali International Wetland).

Wetlands play an important role in conserving biodiversity, the hydrosphere's equilibrium, and the maintenance of daily livelihood; therefore, the trophic process hastens the succession procedure in them, resulting in structural changes in the landscape. The study aimed to monitor and investigate the impact of the trophic procedure on landscape structural changes in Anzali Wetland, specifically domains related to vegetation canopy and water bodies, over 24 years. The TSI (trophic state index) of the Anzali Wetland, a vital habitat in the south of the Caspian Sea, was estimated by using the Carlson TSI for 1994, 2002, 2014, and 2018. Based on satellite data for these years, the structural landscape changes were also measured using metrics such as the number of patches (NumP), class area (CA), mean patch size (MPS), and mean shape index (MSI) of the measured patch using in Patch Analyst. The Spearman rank correlation coefficient was then used to calculate the correlation between the two variables of trophic index modifications and landscape metrics. Results showed that the TSI of the wetland touched 59.51 in 1994 and then reached 65.10 in 2018. Its water body area, which was 5283.90 ha in 1994, decreased to 4183.92 ha in 2018, indicating the greatest decrease in the area from 2002 to 2018. In addition, the maximum area of vegetation canopy in 2018 was 11696.31 ha. The trophic exhibited a positive correlation of 0.8 with the area of the vegetation canopy and a positive correlation of 0.4 with the NumP of the vegetation canopy. It also had an inverse correlation of -0.4 with the area and NumP of the water body. Based on the study findings, changes in the trophic level of Anzali Wetland can be regarded as a direct factor influencing the vegetation canopy and water body.

Optimal tree architecture for high‐yield yellowhorn (Xanthoceras sorbifolium) management

AbstractTree architectural attributes demonstrate a significant association with fruit yield. Yellowhorn is the future bioenergy tree in China; however, the species suffers from high reproductive energy and exceedingly low reproductive output. To optimize yellowhorn management and pinpoint priority trees featuring optimal architecture, we employed machine learning modeling to develop high fruit yielding predictive models using five yield indicators (dependent variables: FrW, SeW, ShW, FrW, and SeN) and five tree characteristics (independent variables: CA, TH, DGL, HLC, and MBN) of yellowhorn. Results showed that trees characterized by a substantial canopy area (>1.70 m2) and a large diameter at ground level (>3.71 cm) have been found to yield a higher fruit production. However, increased tree height does not invariably correlate with an elevated yield. Effective selection of high‐yielding individuals can be accomplished by restricting tree height within the range of 192–232.4 cm. This approach emphasizes the importance of integrating considerations of tree architecture into forestry management practices. Such integration can bolster productivity, thereby contributing to both the sustainability and economic viability of yellowhorn forests.

Vision-based normalized canopy area estimation for variable nitrogen application in apple orchards

Normalized canopy area is a measure of spatial canopy fill or canopy coverage in a tree. It is an important factor for growers to consider in making various farming decisions, such as timing and amount of fertilization, pesticide application, and irrigation, as it gives a sense of the overall canopy growth. This study focused on estimating the normalized canopy area of apple trees using a ground-based stereo-vision system in a commercial orchard under natural lighting conditions. A color, depth, and height threshold, followed by a point density-based outlier removal was applied to segment the target tree canopies. The segmented canopy area was compared with the area estimated with manual segmentation, which showed an F1 score of 0.78. The overall growth of the tree canopies was then represented using normalized canopy area, which was calculated as a fraction of pixels occupied by the tree foliage over the total possible area a tree could occupy for a given tree spacing. It was found that the normalized canopy area of individual trees was highly correlated with the surface volume of the canopy (r = 0.7), which is a traditionally used parameter to assess or represent canopy growth. The estimated normalized canopy area was then compared against experts' nitrogen recommendation for the sample trees, which was based on their visual assessment of the vigor/growth of the trees. Four individual experts (one apple farmer, one orchard manager, and two horticulturists with research and outreach experience) were asked to provide their independent nitrogen recommendation and an average recommendation level was calculated. The comparison showed an overall correlation coefficient of -0.5, indicating an opposite relationship (higher the canopy density, lower the need for nitrogen and vice versa) between the normalized canopy area and the average of the experts' nitrogen recommendation. When the relationship between the normalized area and nitrogen recommendation level was assessed for individual experts, the correlation coefficient was estimated to be -0.86, -0.84, -0.96, and -0.78, respectively, for Expert1, Expert2, Expert3, and Expert4 respectively. The results from this study identified a critical measure for assessing nitrogen needs at individual tree levels in apple orchards and provides an automated tool to estimate the measure. The outcomes of this study could be used as a part of an integrated decision support system for orchard operations (e.g., fertilization or irrigation) that may include additional canopy characteristics such as spectral signature, trunk diameter, and rate of change of leaf color in the fall.

Multi-Parameter Health Assessment of Jujube Trees Based on Unmanned Aerial Vehicle Hyperspectral Remote Sensing

To address the current difficult problem of scientifically assessing the health status of date palm trees due to a single parameter for date palm health assessment, an imperfect index system, and low precision. In this paper, using jujube trees in 224 regiment of the 14th division of Xinjiang Production and Construction Corps “Kunyu city” as the research object, we carried out the inversion study of various physicochemical parameters of jujube trees (canopy chlorophyll content, leaf area index (LAI), tree height, canopy area) using the unmanned aerial vehicle (UAV) hyperspectral imagery of jujube trees during the period of fruit expansion, and put forward a model for assessing the health of jujube trees based on multiple physicochemical parameters. First, we calculated six spectral indices for inversion of chlorophyll content and four spectral index for inversion of LAI, analyzed the spectral index with high correlation with chlorophyll content and LAI of jujube trees canopy, and constructed the inversion models of chlorophyll content and LAI. Second, the Mask R-CNN model was used to achieve jujube trees’ canopy segmentation and area extraction, and the segmented canopy was matched with the Canopy Height Model (CHM) for jujube trees’ height extraction. Finally, based on the four physicochemical parameters of inversion, we construct four jujube trees’ health assessment models, namely, Partial Least Squares Regression Analysis (PLSR), Random Forest (RF), Support Vector Machines (SVM), and Decision Tree (DT). The results showed that the R2 of the PLSR tree health assessment model constructed based on the multi-physical and chemical parameters of chlorophyll content, LAI, tree height, and canopy area was 0.853, and the RMSE was 0.3. Compared with the jujube trees’ health assessment models constructed by RF, SVM, and DT, the R2 increased by 0.127, 0.386, and 0.165, and the RMSE decreased by 0.04, 0.175, and 0.063, respectively. This paper can achieve rapid and accurate inversion of multi-physical and chemical parameters of jujube trees with the help of UAV hyperspectral images, and the PLSR model constructed based on multi-physical and chemical parameters can accurately assess the health status of jujube trees and provide a reference for a scientific and reasonable assessment of jujube trees’ health.

The role of LAI and leaf chlorophyll on NDVI estimated by UAV in grapevine canopies

Despite the wide use of the Normalized Difference Vegetation Index (NDVI) in precision viticulture, there are no studies aimed at discriminating the contribution of plant biomass from that of leaf chlorophyll on canopy NDVI. Leaf area index (LAI) and leaf chlorophyll (Chl) were concomitantly monitored by ground measurements and projected canopy area (PCA), canopy volume (CV) and NDVI by high resolution UAV multispectral images in fully productive “Sangiovese” grapevines either grown in containers or in the field and subjected to different irrigation regimes over two consecutive years. NDVI values calculated from only vine canopy pixels (NDVIUAV) and NDVI obtained from mixed ground-canopy pixels (simulated Satellite NDVI, NDVISAT) were both evaluated as potential predictor of LAI and leaf Chl concentration.The seasonal patterns of LAI and leaf Chl concentration were affected by irrigation, showing differences depending on field vs container-grown conditions. In situations where a decoupling between LAI and leaf Chl occurred, NDVIUAV and NDVISAT showed different responses: NDVIUAV patterns strictly followed the leaf Chl ones, whereas NDVISAT was more affected by LAI. The coefficient of determination between NDVIUAV and leaf Chl ranged between 0.51 and 0.78, that between NDVIUAV and leaf Chl from 0.01 to 0.76, depending on the irrigation-growing conditions combination. NDVISAT was a better predictor of LAI (R2=0.69) than NDVIUAV (R2=0.42). In field-grown vines the relationships between NDVI (both UAV and SAT) and LAI was stronger than in potted ones. The relationships between LAI and PCA (R2=0.44) or LAI and canopy volume (R2=0.77) were both significant. The results allowed to confirm the two main hypotheses behind this experiment: i) leaf Chl concentration had a greater impact than LAI on NDVI values obtained from vine canopy pixels (NDVIUAV), whereas NDVISAT was more affected by LAI; ii) The canopy volume from UAV images was a better predictor of LAI than NDVI and the resulting relationship showed a better temporal stability.

Development of an IOT based Solenoid Controlled Pressure Regulation System for Precision Sprayer

Abstract: In recent years, Precision Agriculture has embarked a promising role in augmenting agricultural production and various farm mechanization. The present research was conducted to design and develop a Precision Spraying System (PSS) that is controlled by an Automatic Control System (ACS). PSS was equipped with micro-controller (NodeMCU), an analog pressure regulator, solenoid valve, diaphragm pump coupled with 12V DC motor; operated on a Iot platform BLYNK. A 3D printed full cone nozzle (0.2 mm orifice dia., cone angle of 95°) was designed to achieve desired spraying efficacy as per the canopy area and the need of the crop cultivated. Integration of PSS with ACS, intrigues a proper regulation on the valves (24V, 7.92W), the pump speed, pressure head and flow rates. The spray patternator test was conducted to evaluate the Delay Time Losses (DTL), application losses, effective spraying height and pressure. The test was conducted for 4 sets of increasing pressure (5.46, 6.24, 6.86 and 7.80 bar) and 3 sets of decreasing nozzle heights (890, 760 and 620 mm). The results revealed that the maximum average discharge collected from the Patternator tray was 0.45L/min (at pressure 7.80 bar; height 620 mm) and the minimum average discharge collected was 0.26 L/min (at pressure 5.46 bar; height 890 mm). The spray angle was found to vary from 72 to 82° and the percent (%) loss in discharge was estimated to maximum of 49.02% (at height 890 mm) and the minimum value of 11.76% (at height of 620 mm) suggesting possible wind drift with increasing nozzle height. Thus, the designed PSS is propitious to forefend all the ill effects of conventional spraying and it significantly reduces the DTL to 0.5 sec

PROPAGATION OF Cnidoscolus aconitifolius USING STEM CUTTINGS AT DIFFERENT MATURITY STAGES AND GROWING MEDIA

Chaya leaves (Cnidoscolus aconitifolius (Mill.) I.M. Johnst.) are leafy vegetables that contain many vitamins and minerals. Information about the intensification of chaya cultivation is still rare. Chaya propagated by stem cuttings. The maturity level of stem cuttings and planting media were expected to affect the success of plant propagation. This study aims to determine the maturity level of cuttings material that can grow well and deter-mine the type of media that can support the growth of chaya propagation. This research has been carried out from July to October 2022 in an off-campus experimental outdoor facility located in Inderalaya, South Suma-tra. This study was conducted using a randomized block design with 2 factors. The first factor was the level of maturity of stem cuttings material (upper, middle, and basal), and the second factor was the type of growing medium (control, chicken manure, and cow manure). The results showed that the use of cuttings from the mid-dle and basal has a better percentage of growth ability than the upper cuttings. Chaya plants used basal stem cuttings exhibited slower shoot grow but produced higher number of leaves, greater leaf area, canopy area, and biomass. The use of cow manure in a mixture of growing medium gave the best results on growth of chaya plants.

Effects of UAV flight height on biomass estimation of desert shrub communities

Accurate estimation of desert vegetation biomass is crucial for monitoring changes in carbon stocks and productivity status. Unmanned aerial vehicle (UAV) remote sensing allows large-scale biomass surveys at the individual or patch scale. However, since desert shrubs are short and sparse, the UAV-based techniques do not always accurately capture biomass-related indicators at any flight height. This study investigated the effects of flight height on above-ground biomass (AGB) estimation using UAV images of typical shrub communities (Reaumuria soongarica) captured at different heights (i.e., 30 m, 50 m, 70 m, 90 m, 110 m, 130 m, and 150 m) in desert-grassland ecosystems. Several structural indicators associated with shrub allometric growth were extracted for AGB modeling, including canopy area (horizontal properties), canopy height (vertical properties), and canopy volume. Results revealed that the values of canopy height and volume decreased with increasing flight height, which made the poor performance of AGB models based on these indicators worse. For example, the variance explained (VE) of the models based on the mean canopy height decreased from about 62% to -137%, while the root mean square error (RMSE) increased from about 39 g to 92 g. In contrast, the canopy area was less affected by flight height, maintaining stable AGB models with VE around 72% and RMSE at 33 g. Adjusting the coefficients of linear models based on canopy height and volume with flight height significantly improved their predictive performance, with VE between 54% and 77% and RMSE between 30 g and 43 g for the optimized models based on mean canopy height. Furthermore, a higher flight height (e.g., 90–110 m) could be chosen to enhance operational efficiency while ensuring the accuracy of biomass observation. Our study offers valuable insights and guidance for vegetation surveys and research in desert-grassland ecosystems.

Individual Tree AGB Estimation of Malania oleifera Based on UAV-RGB Imagery and Mask R-CNN

Forest aboveground biomass (AGB) is an important research topic in the field of forestry, with implications for carbon cycles and carbon sinks. Malania oleifera Chun et S. K. Lee (M. oleifera) is a valuable plant species that is listed on the National Second-Class Protected Plant checklist and has received global attention for its conservation and resource utilization. To obtain accurate AGB of individual M. oleifera trees in a fast, low-finance-cost and low-labor-cost way, this study first attempted to estimate individual M. oleifera tree AGB by combining the centimeter-level resolution RGB imagery derived from unmanned aerial vehicles (UAVs) and the deep learning model of Mask R-CNN. Firstly, canopy area (CA) was obtained from the 3.5 cm high-resolution UAV-RGB imagery using the Mask R-CNN; secondly, to establish an allometric growth model between the diameter at breast height (DBH) and CA, the correlation analysis of both was conducted; thirdly, the AGB estimation method of individual M. oleifera trees was presented based on an empirical equation. The study showed that: (1) The deep learning model of Mask R-CNN achieved an average segmentation accuracy of 90% in the mixed forests to the extraction of the canopy of M. oleifera trees from UAV-RGB imagery. (2) The correlation between the extracted CA and field-measured DBH reached an R2 of 0.755 (n = 96). (3) The t-test method was used to verify the predicted and observed values of the CA-DBH model presented in this study, and the difference in deviation was not significant (p > 0.05). (4) AGB of individual M. oleifera was estimated for the first time. This study provides a reference method for the estimation of individual tree AGB of M. oleifera based on centimeter-level resolution UAV-RGB images and the Mask R-CNN deep learning.

From root to shoot; Quantifying nematode tolerance in Arabidopsis thaliana by high-throughput phenotyping of plant development.

Nematode migration, feeding site formation, withdrawal of plant assimilates, and activation of plant defence responses have a significant impact on plant growth and development. Plants display intraspecific variation in tolerance limits for root-feeding nematodes. Although disease tolerance has been recognised as a distinct trait in biotic interactions of mainly crops, we lack mechanistic insights. Progress is hampered by difficulties in quantification and laborious screening methods. We turned to the model plant Arabidopsis thaliana, since it offers extensive resources to study the molecular and cellular mechanisms underlying nematode-plant interactions. Through imaging of tolerance-related parameters the green canopy area was identified as an accessible and robust measure for assessing damage due to cyst nematode infection. Subsequently, a high-throughput phenotyping platform simultaneously measuring the green canopy area growth of 960 A. thaliana plants was developed. This platform can accurately measure cyst- and root-knot nematode tolerance limits in A. thaliana through classical modelling of tolerance limits. Furthermore, real-time monitoring provided data for a novel view of tolerance, identifying a compensatory growth response. These findings show that our phenotyping platform will enable further studies into a mechanistic understanding of tolerance to below-ground biotic stress.

Effects of a range of phosphorus levels on yield, phosphorus efficiency, and canopy sunlight interception in direct‐seeded winter rapeseed

AbstractPhosphorus (P) fertilization is a crucial practice for winter rapeseed (Brassica napus L.) production. With the shift to a direct seeding cropping system, the P fertilizer supply should be re‐evaluated for the requirement of direct‐seeded winter rapeseed in southwest China. A two‐factor field experiment was conducted in 2017–18 and 2018–19 in the region. The experiment aimed to investigate the responses of a wide range of P levels (0, 45, 90, 135, and 180 kg P ha−1) on yield, P‐efficiency, and canopy sunlight interception of direct‐seeded winter rapeseed. The results demonstrated that winter rapeseed yield was enhanced as the P fertilizer rate increased from 0 to 90 kg P ha−1, and excessive P fertilizer supply did not significantly contribute to higher grain yield at high P levels. Rapeseed produced 7.1–10.1 kg seed ha−1 to supply 1 kg P ha−1 average across different five P levels from 0 to 180 kg P ha−1. Phosphorus efficiency significantly firstly increased (0–90 kg P ha−1) and then progressively decreased with the rise of P levels (around 120 kg P ha−1). Greater P fertilizer rates were absorbed by the stem and pericarp while playing a negligible role in boosting winter rapeseed yield. When the target yield of 3000 kg ha−1 was set, the corresponding winter rapeseed canopy area index should be 1.42–2.71 to facilitate yield formation depending on the growth stages. Meanwhile, it intercepted 30.8%–88.7% of sunlight by the rapeseed canopy. A reduced P‐fertilizer level of 90–120 kg P ha−1 was recommended to obtain target yield with less environmental risks in direct‐seeded winter rapeseed planting regions.

Catching up with the trees: empirical advancements to improve herbaceous representation in models.

This article is a Commentary on Curasi et al. (2023), 239: 562–575.