Canopy Area Research Articles

Using UAV RGB Images for Assessing Tree Species Diversity in Elevation Gradient of Zao Mountains

Vegetation biodiversity in mountainous regions is controlled by altitudinal gradients and their corresponding microclimate. Higher temperatures, shorter snow cover periods, and high variability in the precipitation regime might lead to changes in vegetation distribution in mountains all over the world. In this study, we evaluate vegetation distribution along an altitudinal gradient (1334–1667 m.a.s.l.) in the Zao Mountains, northeastern Japan, by means of alpha diversity indices, including species richness, the Shannon index, and the Simpson index. In order to assess vegetation species and their characteristics along the mountain slope selected, fourteen 50 m × 50 m plots were selected at different altitudes and scanned with RGB cameras attached to Unmanned Aerial Vehicles (UAVs). Image analysis revealed the presence of 12 dominant tree and shrub species of which the number of individuals and heights were validated with fieldwork ground truth data. The results showed a significant variability in species richness along the altitudinal gradient. Species richness ranged from 7 to 11 out of a total of 12 species. Notably, species such as Fagus crenata, despite their low individual numbers, dominated the canopy area. In contrast, shrub species like Quercus crispula and Acer tschonoskii had high individual numbers but covered smaller canopy areas. Tree height correlated well with canopy areas, both representing tree size, which has a strong relationship with species diversity indices. Species such as F. crenata, Q. crispula, Cornus controversa, and others have an established range of altitudinal distribution. At high altitudes (1524–1653 m), the average shrubs’ height is less than 4 m, and the presence of Abies mariesii is negligible because of high mortality rates caused by a severe bark beetle attack. These results highlight the complex interactions between species abundance, canopy area, and altitude, providing valuable insights into vegetation distribution in mountainous regions. However, species diversity indices vary slightly and show some unusually low values without a clear pattern. Overall, these indices are higher at lower altitudes, peak at mid-elevations, and decrease at higher elevations in the study area. Vegetation diversity indices did not show a clear downward trend with altitude but depicted a vegetation composition at different altitudes as controlled by their surrounding environment. Finally, UAVs showed their significant potential for conducting large-scale vegetation surveys reliably and in a short time, with low costs and low manpower.

Impact of organic mulch and exposure to shallow groundwater levels on Cnidoscolus aconitifolius in a tropical wetland, South Sumatra, Indonesia

Agricultural land in Indonesia is decreasing due to its conversion for various non-agricultural interests, which are economically more profitable. The remaining land available for agricultural activities is a suboptimal wetland. The reduction in cultivable areas has led to the evaluation of alternative crops in suboptimal land conditions. Chaya (Cnidoscolus aconitifolius) is a fast-growing perennial plant, its leaves are edible and rich in vitamins, minerals, and dietary fiber. However, the chaya plant has not been intensively tested for its adaptation to shallow groundwater tables in tropical lowlands. This study aimed to assess chaya’s adaptability to shallow groundwater table conditions and the benefits of using organic mulch. This research consisted of two separate parts, one part was related to organic mulch benefits (planting media without watering, with organic mulch but not watered, and with organic mulch and watering), while the other one was related to shallow groundwater table tolerance. The study followed a randomized complete block design consisting of three replications. Results showed that chaya plants negatively responded to the shallow groundwater table conditions. Growth retardation due to shallow groundwater levels is reflected in the averages of leaf length, leaf width, leaf area, canopy diameter, and canopy area; fresh and dry weight of stem, petiole, and leaf blade; development of roots; SPAD value at 7 weeks after transplanting and it was based on visual appearance. However, despite the increase in substrate humidity due to the application of organic mulch, the effects were not significant on most of the measured morphological traits. In conclusion, chaya plants are not able to adapt to shallow groundwater levels and do not require organic mulch on the soil surface.

Factors and effects of inter-individual variability in silver birch phenology using dense LiDAR time-series

Comprehending and quantifying local variability in plant phenology, alongside its impacts on tree growth, is challenging due to spatially and temporally heterogeneous environmental factors that interact to affect phenological events and periods. Although previous studies have focused on the climatic factors driving phenological events at the stand level, the influences of competition, neighborhood characteristics, species richness, and water availability on plant phenology remain unclear. In the present paper, we used hourly terrestrial LiDAR surveys performed between April 2020 and April 2021 to investigate the influence of local factors (neighborhood and topography) on the phenology of silver birch trees (Betula pendula Roth.). We also examined how phenological events affect growth in tree height and crown area. All response and explanatory variables were estimated using LiDAR time-series data and a field survey campaign. Our findings demonstrate a high within-species variability in plant phenology that is controlled by biotic and abiotic characteristics of the ecosystem. We found that between tree variation in leaf burst, completion of the leaf growth and length of canopy growth period were affected by tree size, neighborhood species richness, level of suppression and competition, which potentially indicate plant responses to light availability. The beginning of senescence, completion of leaf drop, and length of autumn phenology were mostly affected by topographic water index, which reflects water availability and can be linked to nutrient availability and exposure to wind. Furthermore, we demonstrated that an earlier leaf burst, and delayed beginning of senescence were associated with larger absolute growth in canopy area but found no clear relationship between height growth and phenology. Our study highlights the capability of dense, high-quality LiDAR time-series to detect significant phenological variation among individuals of the same species within the same locality, demonstrating the potential of LiDAR time-series as a tool for understanding phenology and its impacts.

A Sustainable Agri-Photovoltaic Greenhouse for Lettuce Production in Qatar

Qatar identified that food supply security, including self-sufficiency in vegetable production and increasing sustainable renewable energy generation, is important for increasing economic and environmental resiliency. Very favorable solar energy resources in Qatar suggest opportunities to simultaneously meet this goal by integrating solar energy generation and food production. This study examines the feasibility of developing a sustainable agri-photovoltaic (APV) greenhouse design. A comprehensive greenhouse with solar energy generation included is developed for year-round operation in Lusail, Qatar. The performance of the system is predicted by integrating meteorological data and MATLAB simulations of system components. Important design considerations included optimizing solar energy generation by fixed solar photovoltaic panels placed on the maximum available surface area of the greenhouse canopy, while balancing crop insolation and energy needs for greenhouse HVAC systems. Electrical energy is also stored in an industrial battery. Results suggest the APV greenhouse is technically and economically viable and that it could provide benefits, including enhancing food security, promoting renewable energy, and contributing to sustainable food and energy production in Qatar.

Exploring the wild almond, Prunus arabica (Olivier), as a genetic source for almond breeding

During the process of almond (Prunus dulcis) domestication, essential traits, which gave plants the plasticity for facing unstable environmental conditions, were lost. In general, the domestication process often narrows the natural genetic diversity. Modern selections (i.e., breeding programs) dramatically accelerated this genetic bottleneck trend to a few successful almond cultivars, which are presently the founders of most commercial cultivars worldwide. The concept of utilizing wild species as a source for important traits and for the enrichment of the gene pool was deeply discussed in previous studies. However, in almonds and other Prunus species, deliberate utilization of wild species as a genetic resource for breeding programs is quite rare. To address these significant challenges, we generated an interspecific F1 population between the Israeli almond cultivar Um el Fahem (UEF) and a specimen of a local wild almond species, Prunus arabica (P. arabica), originating from the Judea desert. This interspecific F1 population possesses high phenotypic variability, and sixteen segregating traits were phenotyped. Among the segregating traits, we were able to genetically associate six agriculturally important traits, such as leaf chlorophyll content (LCC), flower size, and fruit size. The alleles for Self-Compatibility (SC) and kernel bitterness were previously mapped in almond and were reexamined on the background of the distinctive wild genetic material of P. arabica. Finally, phenotypic interactions between traits were suggested, such as rootstock perimeter and canopy area that were positively correlated with total yield in the F1 population. This study is a first step towards developing a well-characterized almond interspecies genetic population. The availability of such a genetic tool with detailed phenotypic analysis is crucial to address and explore the profound influence of almond wild species in Prunus genetic research and breeding. By using the interspecific population as the infrastructure, we show the advantages and importance of utilizing wild relatives.

Distribution and prediction of biocrusts under the canopy of typical vascular plants on the Loess Plateau, Northwest China.

Vascular plants exert significant effects on micro-environment, thereby affecting the distribution of biological soil crusts (biocrusts). The relationship between vascular plants and the spatial distribution characteristics of biocrusts is largely unknown. We investigated the distribution characteristics of biocrusts under the canopy of vascular plants in the water-wind erosion crisscross area of the Loess Plateau, where larger areas of biocrusts had been formed since the implantation of "Grain for Green" project. We analyzed the relationship between the canopy characteristics of different vascular plants and the spatial distribution of biocrusts using correlation analysis and random forest importance ranking methods, and further constructed a predictive model for the area of biocrusts under the canopy of vascular plants. The results showed that: 1) Cyanobacteria crust was the predominant biocrusts, followed by moss crust. 2) The canopy of vascular plants affected the spatial distribution of biocrusts, with notable differences in distribution pattern across different directions under the canopy of vascular plants. Biocrusts were primarily distributed in the 270°-315° and 315°-360° directions, while was less frequent in the 90°-135° and 135°-180° directions. 3) Radially, the coverage of biocrusts gradually increased from the root-base to the edge of the canopy of vascular plants. 4) The coverage of biocrusts under canopy was significantly related to the characteristics of vascular plants, including canopy area, long crown width, short crown width, litter area and plant height. 5) The relative importance of canopy area, long crown width, and short crown width to the biocrusts under the canopy was 13.7%, 12.1%, and 11.9%, respectively, while the relative importance of plant height and species type was relatively low, being 6.7% and 4.4%, respectively. 6) Results of the random forest model demonstrated strong predictive performance for biocrusts distribution based on canopy characteristics of vascular plants, with a prediction accuracy of 0.59 (R2) and a root mean square error of 1.2 m2. This model could be applied to predict and estimate the area of biocrusts under the canopy of vascular plants. This study provided a theoretical basis for in-depth understanding of the relationship between vascular plants and biocrusts in semi-arid climate regions, as well as for predicting the spatial distribution of biocrusts.

Natural forest colonisation as a management strategy to restore soil functioning of abandoned agricultural lands

Abstract Land abandonment promotes forest expansion into abandoned agricultural lands in Europe. This process leads to changes that affect several ecosystem services, but it is considered a low‐cost strategy to restore soil functions in past agricultural lands. Soil microbial communities play a key role in restoring functions. However, the relationship among forest expansion, microbial communities, and soil functioning is unclear. In this study, we used a Juniperus thurifera expansion gradient on abandoned agricultural lands in Alto Tajo National Park (Spain) to discover the changes elicited by the soil microbial communities and their functions along this gradient considering two microhabitats, under the canopy and open areas. Specifically, our objectives were (i) to analyse how soil properties (organic matter and pH), microbial communities (using PLFAs‐NFLAs) and enzymatic activities (related to C, N and P cycling) varied along the forest expansion gradient and between microhabitats and (ii) to decipher the pathways by which soil properties control the carbon and nutrient cycling in soils. The forest expansion gradient had a direct negative effect on phosphatase activity. The microhabitat showed a positive direct effect on organic matter content, pH, actinomycetes and arbuscular mycorrhizal fungi biomass and on soil C and P cycling. Moreover, the biomass of gram‐positive bacteria determined the biomass of other microbial groups. Synthesis and Applications: Though its effectiveness is variable, passive restoration can be more effective than active restoration. Our research indicates that passive tree colonisation of past agricultural land is enough to achieve soil functionality similar to a mature forest for most variables studied. However, some variables would need more time to reach mature forest levels, such as total microbial biomass and organic matter content. Therefore, to support ecosystem recovery, the management of this applied forest ecology strategy requires continuous monitoring of newly established trees and soil to elucidate the time needed to achieve mature soil properties.

Study on Population and Roosting Ecology of Bats in and Around Chikkamagaluru

Bats spend over half of their lives in their roost environment, so these eventually play a prominent role in their ecology and evolution. Roosts are vital to the bats as it is plays where they actively get involved in mating, rearing offspring’s and facilitate the social living. With this background, the present study has been planned to evaluate the ecobiology of the bats in the vicinity of Chikkamagaluru. It is situated roughly in the south western part of Karnataka exists in mid-Western Ghats. The population dynamics of bat roosts were studied by emergence Count and direct roost count method. The roosting trees were identified by using standard botanical taxonomic keys. Bats roosted much more in trees belonging to Fabaceae family (1,527) followed by Myrtaceae family (1,180), Moraceae family (519), Bignoniaceae family (253), Sapindiaceae family (202), Proteaceae family (103), and very less population found in Poaceae family (21). Bat roosted much more in Pipal tree (40.72%), Red buckeye (26.03%), Banyan tree (19.97%), Rubber tree (6.18%), Avenue tree (3.22%), Bamboo (2.7%) and very less population in Tamarind tree (1.15%) in rural side of the study area. Where as in the city premises bats roosted much more in Nilgiri tree (38.19%), followed by Blue jacaranda tree (29.72%), White locust tree (25.26%) and very less roosting of bats in Silver tree (6.81%).In the present survey the population of bats and hosting trees varies from one locality to another locality. This is because of variation in vegetation, environmental condition and more or less adoptability of food and water and the less or more predators. The present study showed that bats preferred to roost in taller and larger trees having greater canopy areas as roosting sites, because these trees could provide more space for their large colonies.

Assessing the relationship between urban park spatial features and physical activity levels in Residents: A spatial analysis Utilizing drone remote sensing

The park environment is crucial for promoting physical activity (PA). While numerous studies show that park environments influence PA behavior, inconsistencies remain, likely due to varing research methods and parks types. This study employs a fixed spatial grid method to systematically sample four representative parks in Tianjin, China. High-precision orthophoto map (DOM) data from drones provided detailed environmental attributes (like tree canopy area, lawn area, and paved area) and PA characteristics (number of participants, intensity, diversity). The results show: 1) Cluster analysis grouped 1839 park grids into 12 environmental attribute integrations, each correlating with different PA characteristics. “Tree-lined jogging corridors” and “Large sports field areas” exhibit the highest PA intensity, while “Entrance plazas”, “Central plazas,” and “Open sports spaces” have the highest number of participants and PA diversity. 2) Correlation analysis shows that various environmental attributes, including Lawn Area, and Paved Area, are significantly correlated with PA characteristics. 3）Random Forest analysis indicates the key attributes are the paved area for the number of PA participants and PA diversity, and specialized sports facilities area for PA intensity. These findings support urban green space planning and highlight the importance of better park environments for public health.

Measuring ornamental tree canopy attributes for precision spraying using drone technology and self-supervised segmentation

Tree canopy attributes or characteristics, such as canopy volume and density, are important parameters for calculating the precise quantity of agrochemicals required in each tree. Assessing the agrochemical needs in a nursery can help growers effectively estimate expenses, mitigate the risks of overuse or underuse, enhance resource utilization, and promote sustainable agricultural practices. This study aimed to develop an unmanned aerial vehicle (UAV)-based system to measure tree canopy attributes like tree height, tree count, canopy area and canopy volume using an image processing and self-supervised zero-shot segmentation approach. A high-resolution red-green-blue (RGB) sensor mounted on a drone captured three aerial image datasets, D1 and D2 from the first experimental plot and D3 from the second experimental plot. The acquired aerial images were stitched together and processed to generate a digital surface model (DSM) and a digital terrain model (DTM). A self-supervised zero-shot segmentation model, Segment Anything Model (SAM), was applied for tree segmentation and counting. The tree canopy area was calculated by segmenting individual trees and applying a conversion factor to convert pixelwise areas into square meters. Height calculation was done using elevation data of each pixel from DSM-DTM imagery within SAM-derived bounding box coordinates, and the highest pixel was considered as the height of the tree. The drone-calculated heights of 24 randomly selected trees were compared with manually measured heights in all datasets. The results showed an average absolute error of 2.43% in D1, 7.09% in D2 and 12.58% in D3. The Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) were noted to be 0.09 m and 0.23 m in D1, 0.13 m and 0.25 m in D2, and 0.16 m and 0.21 m in D3, respectively. In D3, manual area and volume calculations were performed for validation. The RMSE and MAE for area calculation were calculated as 0.18 m2 and 0.15 m2, respectively, and the RMSE and MAE for volume measurements were calculated as 0.33 m3 and 0.26 m3 respectively, which indicated the level of agreement between manual and drone measurements. Using canopy volume information, calculated by summing pixel heights within each tree’s bounding box and multiplying by the ground sample distance (GSD) of 0.025 m per pixel, the average quantity of agrochemicals needed for a Maple tree was estimated at 0.20 L. These results underscore the high potential of this method for accurate canopy characteristic calculations and precision spray applications in the ornamental industry.

Nutrient Stress Symptom Detection in Cucumber Seedlings Using Segmented Regression and a Mask Region-Based Convolutional Neural Network Model

The health monitoring of vegetable and fruit plants, especially during the critical seedling growth stage, is essential to protect them from various environmental stresses and prevent yield loss. Different environmental stresses may cause similar symptoms, making visual inspection alone unreliable and potentially leading to an incorrect diagnosis and delayed corrective actions. This study aimed to address these challenges by proposing a segmented regression model and a Mask R-CNN model for detecting the initiation time and symptoms of nutrient stress in cucumber seedlings within a controlled environment. Nutrient stress was induced by applying two different treatments: an indicative nutrient deficiency with an electrical conductivity (EC) of 0 dSm−1, and excess nutrients with a high-concentration nutrient solution and an EC of 6 dSm−1. Images of the seedlings were collected using an automatic image acquisition system two weeks after germination. The early initiation of nutrient stress was detected using a segmented regression analysis, which analyzed morphological and textural features extracted from the images. For the Mask R-CNN model, 800 seedling images were annotated based on the segmented regression analysis results. Nutrient-stressed seedlings were identified from the initiation day to 4.2 days after treatment application. The Mask R-CNN model, implemented using ResNet-101 for feature extraction, leveraged transfer learning to train the network with a smaller dataset, thereby reducing the processing time. This study identifies the top projected canopy area (TPCA), energy, entropy, and homogeneity as prospective indicators of nutritional deficits in cucumber seedlings. The results from the Mask R-CNN model are promising, with the best-fit image achieving an F1 score of 93.4%, a precision of 93%, and a recall of 94%. These findings demonstrate the effectiveness of the integrated statistical and machine learning (ML) methods for the early and accurate diagnosis of nutrient stress. The use of segmented regression for initial detection, followed by the Mask R-CNN for precise identification, emphasizes the potential of this approach to enhance agricultural practices. By facilitating the early detection and accurate diagnosis of nutrient stress, this approach allows for quicker and more precise treatments, which improve crop health and productivity. Future research could expand this methodology to other crop types and field conditions to enhance image processing techniques, and researchers may also integrate real-time monitoring systems.

Urban green tree morphology and the problem of minimizing runoff. A case study in Ly Thuong Kiet Street, Hue City, Vietnam.

Abstract Sustainable urban drainage by green infrastructure is emphasized. Trees can change the three processes of interception, evapotranspiration, and infiltration of the hydrological cycle, so they are considered one of the effective solutions to reduce urban flooding. The article applies Minnesota’s Minimum Impact Design Standards (MIDS) Calculator method to compare the ability to reduce surface runoff of urban trees on Ly Thuong Kiet Street before 2009 and 2022. The calculation results show that before 2009 it was 3,406m3; after expanding the road and replacing the type of greenery, it was only 1,570m3 in 2022, a decrease of 2.16 times compared to before 2009. The factors that create the difference are canopy area (CP), leaf area index (LAI), tree species, and soil volume for each tree. The decrease in this ability is due to the greenery replaced after 2010 being unsuitable for local characteristics, so the trees are poorly developed or dead.

Effect of cutting age and auxin concentration on growth of pinto peanut (Arachis pintoi)

Pinto peanuts are known as versatile cover crops for soil conservation, biomulch, and ornamental plants. These plants rarely produce seeds, so they are commonly propagated vegetatively, resulting in low efficiency of seedling production and availability of cuttings. The aim of this study was to determine the effect of cutting age and auxin application on the growth of pinto peanut. This experiment used a complete randomized block factorial design. The first factor was the age of the cuttings (stolons) with three levels (young, intermediate, old). Each cutting had three nodes, with young cuttings measured from the tip, intermediate cuttings measured from the fourth node, and old cuttings measured from the seventh node. The second factor was the concentration of the commercial auxin product ROOTONE F (0 ppm, 500 ppm, 1000 ppm, 1500 ppm, 2000 ppm). The results showed that the difference in cutting age significantly affected the number of leaves (at 10 and 50 days after planting), plant length (at 10 to 50 days after planting), number of branches (at 30 to 50 days after planting), and canopy area. Differences in auxin concentration only significantly affected plant length from 10 to 30 days after planting. The interaction between cutting age and auxin concentration was significant only for the number of leaves at 10 to 20 days after planting and plant length at 50 days after planting. The highest values for the observed parameters were consistently obtained by intermediate cuttings, which are therefore recommended as planting material for ornamental peanut cuttings.

Soil Fungal Function Centralization Enhances the Decomposition of Fine Roots at Canopy Gap Borders

Canopy gaps can result in abiotic heterogeneities and diverse niches from gap borders to centers, potentially affecting fine root decompositions mediated by soil fungal communities. Despite extensive discussions on the relationship between soil fungi and fine root decomposition, the mechanism by which gap locations regulate fine root decomposition through the soil fungal community remains elusive. Here, we conducted an in situ field decomposition experiment of Chinese Toon (Toona senensis) fine roots in a low-efficiency weeping cypress (Cupressus funebris) plantation forest across three microhabitats: gap centers, gap borders, and closed canopy areas. Soil fungal communities were determined using internal transcribed spacer (ITS) sequencing after two years of field incubation. Results showed that soil properties and nutrient content in residual roots varied across the three microhabitats, with the gap borders exhibiting the highest decomposition rates. While fungal α-diversity remained relatively consistent, taxonomic compositions differed significantly. Decomposition rates did not show significant correlations with soil properties, observed fungal ASVs, or overall community composition. However, they positively correlated with the relative abundance of saprotrophic Sordariomycetes, which in turn positively correlated with soil total nitrogen (with a highest correlation), peaking at the gap borders. Overall community variations were primarily driven by soil temperature and magnesium content in residual roots. Further analysis revealed high fungal community similarities and low dispersal limitations between the gap borders and closed canopy areas, with more phylogenetically clustered communities at the borders. These results demonstrate that the gap borders possess a high decomposition rate, likely due to the centralization of functions driven by soil fungi such as saprotrophs existing in the “microbial seed bank” or migrating from closed canopy areas. These findings highlight the key role of soil fungi, especially saprotrophic fungi, in fine root decomposition at the gap borders, stressing the importance of soil fungi-driven mechanisms in nutrient cycling, and also informing sustainable forest management practices.

Detection of Individual Corn Crop and Canopy Delineation from Unmanned Aerial Vehicle Imagery

Precise monitoring of individual crop growth and health status is crucial for precision agriculture practices. However, traditional inspection methods are time-consuming, labor-intensive, prone to human error, and may not provide the comprehensive coverage required for the detailed analysis of crop variability across an entire field. This research addresses the need for efficient and high-resolution crop monitoring by leveraging Unmanned Aerial Vehicle (UAV) imagery and advanced computational techniques. The primary goal was to develop a methodology for the precise identification, extraction, and monitoring of individual corn crops throughout their growth cycle. This involved integrating UAV-derived data with image processing, computational geometry, and machine learning techniques. Bi-weekly UAV imagery was captured at altitudes of 40 m and 70 m from 30 April to 11 August, covering the entire growth cycle of the corn crop from planting to harvest. A time-series Canopy Height Model (CHM) was generated by analyzing the differences between the Digital Terrain Model (DTM) and the Digital Surface Model (DSM) derived from the UAV data. To ensure the accuracy of the elevation data, the DSM was validated against Ground Control Points (GCPs), adhering to standard practices in remote sensing data verification. Local spatial analysis and image processing techniques were employed to determine the local maximum height of each crop. Subsequently, a Voronoi data model was developed to delineate individual crop canopies, successfully identifying 13,000 out of 13,050 corn crops in the study area. To enhance accuracy in canopy size delineation, vegetation indices were incorporated into the Voronoi model segmentation, refining the initial canopy area estimates by eliminating interference from soil and shadows. The proposed methodology enables the precise estimation and monitoring of crop canopy size, height, biomass reduction, lodging, and stunted growth over time by incorporating advanced image processing techniques and integrating metrics for quantitative assessment of fields. Additionally, machine learning models were employed to determine relationships between the canopy sizes, crop height, and normalized difference vegetation index, with Polynomial Regression recording an R-squared of 11% compared to other models. This work contributes to the scientific community by demonstrating the potential of integrating UAV technology, computational geometry, and machine learning for accurate and efficient crop monitoring at the individual plant level.

Mapping Characteristics in Vaccinium uliginosum Populations Predicted Using Filtered Machine Learning Modeling

Bog bilberry (Vaccinium uliginosum L.) is considered a highly valued non-wood forest product (NWFP) species with edible and medicinal uses in East Asia. It grows in the northeastern forests of China, where stand attributes and structure jointly determine its population characteristics and individuals’ growth. Mapping the regional distributions of its population characteristics can be beneficial in the management of its natural resources, and this mapping should be predicted using machine learning modeling to obtain accurate results. In this study, a total of 60 stands were randomly chosen and screened to investigate natural bog bilberry populations in the eastern mountains of Heilongjiang and Jilin provinces in northeastern China. Individual height, canopy cover area, and fresh weight all increased in stands at higher latitudes, and shoot height was also higher in the eastern stands. The rootstock grove density showed a polynomial quadratic distribution pattern along increasing topographical gradients, resulting in a minimum density of 0.43–0.52 groves m−2 in stands in the southern part (44.3016° N, 129.4558° E) of Heilongjiang. Multivariate linear regression indicated that the bog bilberry density was depressed by host forest tree species diversity; this was assessed using both the Simpson and Shannon–Wiener indices, which also showed polynomial quadratic distribution patterns (with a modeling minimum of 0.27 and a maximum of 1.21, respectively) in response to the increase in latitude. Structural equation models identified positive contributions of tree diameter at breast height and latitude to shoot height and a negative contribution of longitude to the bog bilberry canopy area. Random forest modeling indicated that dense populations with heavy individuals were distributed in eastern Heilongjiang, and large-canopy individuals were distributed in Mudanjiang and Tonghua. In conclusion, bog bilberry populations showed better attributes in northeastern stands where host forest trees had low species diversity, but the dominant species had strong trunks.

Design and Experiment of Ordinary Tea Profiling Harvesting Device Based on Light Detection and Ranging Perception

Due to the complex shape of the tea tree canopy and the large undulation of a tea garden terrain, the quality of fresh tea leaves harvested by existing tea harvesting machines is poor. This study proposed a tea canopy surface profiling method based on 2D LiDAR perception and investigated the extraction and fitting methods of canopy point clouds. Meanwhile, a tea profiling harvester prototype was developed and field tests were conducted. The tea profiling harvesting device adopted a scheme of sectional arrangement of multiple groups of profiling tea harvesting units, and each unit sensed the height information of its own bottom canopy area through 2D LiDAR. A cross-platform communication network was established, enabling point cloud fitting of tea plant surfaces and accurate estimation of cutter profiling height through the RANSAC algorithm. Additionally, a sensing control system with multiple execution units was developed using rapid control prototype technology. The results of field tests showed that the bud leaf integrity rate was 84.64%, the impurity rate was 5.94%, the missing collection rate was 0.30%, and the missing harvesting rate was 0.68%. Furthermore, 89.57% of the harvested tea could be processed into commercial tea, with 88.34% consisting of young tea shoots with one bud and three leaves or fewer. All of these results demonstrated that the proposed device effectively meets the technical standards for machine-harvested tea and the requirements of standard tea processing techniques. Moreover, compared to other commercial tea harvesters, the proposed tea profiling harvesting device demonstrated improved performance in harvesting fresh tea leaves.

Land use and oriental pied-hornbill occurrence in Singapore

Oriental Pied-Hornbills (Anthracoceros albirostris) disappeared from Singapore in the 1800s but were returned in the 2000s through a combination of active reintroduction and recolonization. Since then, they have multiplied and even thrived in Singapore, spreading throughout much of the country. Singapore is extremely urban and densely populated, and here we examine where hornbills have been seen as a function of land-use. We combine citizen science data from eBird and iNaturalist with GIS information to model hornbill occurrence. We find that vegetation with no canopy, marshy habitat, and built-up areas have strong positive effects on the probability of hornbill sightings. We interpret our results with respect to larger regional patterns.

The Effect of Irrigation on the Vineyard Canopy and Individual Leaf Morphology Evaluated with Proximal Sensing, Colorimetry, and Traditional Morphometry

The high number of grapevine (Vitis vinifera L.) cultivars grown world-wide are described and identified according to detailed morphological and morphometric descriptor lists. The grapevine leaf is of utmost importance in characterization, despite its traits being very sensitive to environmental factors. In this study, the effect of irrigation/drought stress on the individual leaf morphology and morphometry of the ‘Hárslevelű’ grapevine (Vitis vinifera L.) cultivar was examined. To verify the effect of the applied irrigation methods (drip and subsoil irrigation) on the plant’s water status, water potential measurements were carried out during the 2022 season. The effect of the applied treatments on the vegetative growth was evaluated according to point quadrat and a multichannel LiDAR analysis in order to describe the width of the canopy area, row volume, and area coverage index. The individual leaf morphology was assessed via traditional morphometry and colorimetry. Our results showed that rainfed plants had a significantly lower stem ψ compared to the drip- and subsoil-irrigated plants at all examined dates. The point quadrat results indicate that the leaf layer number was significantly (p < 0.05) influenced by the position, while the treatment showed no effect on the leaf layer number. The leaf colorimetry showed a difference among the samples, as significant alterations were found in 28 out of the 32 examined color properties. Within the traditional morphometric analysis, 54 traits were evaluated, and 14 of the traits were significantly altered due to the different water management systems.

Mangrove Cover and Extent of Protection Influence Lateral Erosion Control at Hybrid Mangrove Living Shorelines

Erosion poses a significant threat to coastal and estuarine environments worldwide and is further exacerbated by anthropogenic activities and increasing coastal hazards. While conventional engineered structures, such as seawalls and revetments, are commonly employed to protect shorelines from wave impact and erosion, they can also cause detrimental environmental effects. By creating/restoring coastal habitats with engineered structures, hybrid living shorelines offer coastal protection and other co-benefits. Using aerial imagery, we studied the rates of shoreline change before and after living shoreline installation, and between living shorelines and adjacent bare shorelines in three estuaries in New South Wales, Australia. Mangroves had established behind most rock fillets and displayed a trend of increasing canopy cover with fillet age. In the first 3 years since installation, the rates of lateral shoreline change reduced from − 0.20, − 0.16, and − 0.10 m/year to − 0.03, − 0.01, and 0.06 m/year in living shorelines in Hunter, Manning, and Richmond Rivers, respectively. However, when compared to control shorelines, the effectiveness in reducing erosion varied among living shorelines with mean effect sizes of 0.04, − 0.28, and 1.74 across the three estuaries. A more positive rate of shoreline change was associated with an increasing percentage of mangrove canopy area and an increasing length of protected shoreline at wide channels. While hybrid mangrove living shorelines are a promising solution for mitigating erosion and creating habitats at an estuary-wide scale, they may also contribute to downdrift erosion, emphasising the importance of considering site-specific hydrogeomorphology and sediment movement when installing living shorelines.