Quantifying the potential of evidence-based planting-pattern for reducing the outdoor thermal stress from a bio-meteorological perspective in tropical conditions of Indian cities.

The impact of declined natural greenery and increased built surfaces exacerbates heat stress in urban areas causing limited usage of outdoor spaces. Greenery strategies such as trees are capable of mitigating outdoor thermal stress gain because of their phytological properties. While urban greenery guidelines have suggested the ad-hoc procedure of tree planting-schemes based on aesthetic-value, soil-water preservation etc., understanding of their morphological character help in regulating extreme thermal condition. Hence, this study aims to investigate the most efficient planting pattern based on canopies densities and trees clusters for reducing the outdoor thermal stress from bio-meteorological perspective.It initiates with the measurement of the site's morphological and meteorological attributes in existing commercial market of Bhopal City which has a humid sub-tropical climate (Aw, Koppen climate categorization). Furthermore, it leads to the development of 4-different iterated clusters incorporating moderate to high-density canopies and their overlaps pattern to estimate reduction potential in outdoors using field surveys and validated simulation model. The reduction potential in terms of magnitude and duration of thermal stress is quantified across 3-thermal variables i.e., air temperature, mean radiant temperature and universal thermal climate index. Results indicate highly-dense canopies are more effective in reducing greater magnitude of thermal stress along longer duration. Also overlapped planting pattern within the same canopy density does not make significant difference in stress reduction as compared to the changing the densities. This study will help planners and landscape architects to adopt evidence-based planting-pattern strategies for improving outdoor microclimate.

Twenty years of irrigation acclimation is driven by denser canopies and not by plasticity in twig- and needle-level hydraulics in a Pinus sylvestris forest.

Climate change is predicted to increase atmospheric vapor pressure deficit, exacerbating soil drought, and thus enhancing tree evaporative demand and mortality. Yet, few studies have addressed the longer-term drought acclimation strategy of trees, particularly the importance of morphological versus hydraulic plasticity. Using a long-term (20 years) irrigation experiment in a natural forest, we investigated the acclimation of Scots pine (Pinus sylvestris) morpho-anatomical traits (stomatal anatomy and crown density) and hydraulic traits (leaf water potential, vulnerability to cavitation (Ψ50), specific hydraulic conductivity (Ks), and tree water deficit) to prolonged changes in soil moisture. We found that low water availability reduced twig water potential and increased tree water deficit during the growing season. Still, the trees showed limited adjustments in most branch-level hydraulic traits (Ψ50 and Ks) and needle anatomy. In contrast, trees acclimated to prolonged irrigation by increasing their crown density and hence the canopy water demand. This study demonstrates that despite substantial canopy adjustments, P. sylvestris may be vulnerable to extreme droughts because of limited adjustment potential in its hydraulic system. While sparser canopies reduce water demand, such shifts take decades to occur under chronic water deficits and might not mitigate short-term extreme drought events.

Microplastic retention in marine vegetation canopies under breaking irregular waves.

The present study provides indications and underlying drivers of wave-induced transport and retention potential of microplastic particles (MP) in marine vegetation canopies having different densities. The anthropogenic occurrence of MP in coastal waters is well documented in the recent literature. It is acknowledged that coastal vegetation can serve as a sink for MP due to its energy dissipating features, which can mimic a novel ecosystem service. While the transport behavior of MP in vegetation has previously been investigated to some extent for stationary flow conditions, fundamental investigations for unsteady surf zone flow conditions under irregular waves are still lacking. Herein, we demonstrate by means of hydraulic model tests that a vegetation's retention potential of MP in waves increases with the vegetation shoot density, the MP settling velocity and decreasing wave energy. It is found that particles migrating by traction (predominantly in contact with the bed) are trapped in the wake regions around a canopy, whereas suspended particles are able to pass vegetated areas more easily. Very dense canopies can also promote the passage of MP with diameters larger than the plant spacing, as the canopies then show characteristics of a solid sill and avoid particle penetration. The particle migration ability through a marine vegetation canopy is quantified, and the key drivers are described by an empirical expression based on the particle settling velocity, the canopy length and density. The findings of this study may contribute to improved prediction and assessment of MP accumulation hotspots in vegetated coastal areas and, thus, may help in tracing MP sinks. Such knowledge can be considered a prerequisite to develope methods or new technologies to recover plastic pollutants and rehabilitate valuable coastal environments.

Response of needle photosynthetic and anatomical characteristics of naturally regenerated Pinus koraiensis seedlings to different canopy densities.

We took 5-year-old Pinus koraiensis seedlings under natural secondary forests with canopy densities of 0.2-0.3, 0.4-0.6, and 0.7-0.9 at Laoshan Plantation Experimental Station in Maoershan Experimental Forest Farm of Northeast Forestry University as monitor object, and P. koraiensis seedlings under full-light environment as control (CK), to investigate the photosynthetic characteristics and the anatomical structure of P. koraiensis needles in response to the changes of canopy densities. The results showed that the height and diameter of P. koraiensis seedlings tended to decrease while specific leaf area increased with the increases of canopy densities. The total biomass of P. koraiensis seedlings under different canopy densities ranked in an order of 0.4-0.6＞CK＞0.7-0.9＞0.2-0.3. Photosynthetically active radiation (PAR) was significantly and positively correlated with leaf biomass, stem biomass, and root biomass. The net photosynthetic rate, transpiration rate, and intercellular CO2 concentration of P. koraiensis seedlings showed a decreasing trend with the increases of canopy densities, while the stomatal conductance showed an increasing trend. Net photosynthetic rate and chlorophyll a/b showed a significant positive correlation with PAR. Stomatal density showed a gradual decreasing trend with the increases of canopy densities, and the needle cross-sectional area, mesophyll tissue area, xylem area, and phloem area of P. koraiensis seedlings under canopy density 0.4-0.6 were significantly higher than those in other treatments. P. koraiensis seedlings with stronger photosynthetic abilities and higher needle anatomy parameters under canopy density 0.4-0.6, and were able to maintain strong competitiveness in this habitat. Those results indicated that 5-year-old P. koraiensis seedlings need certain shading environment.

Combining Landsat time series and GEDI data for improved characterization of fuel types and canopy metrics in wildfire simulation.

Wildfires in the Chornobyl Exclusion Zone (CEZ) and other radioactively contaminated areas threaten human health and well-being with the potential to resuspend radionuclides. Wildfire behavior simulation is a necessary tool to examine the efficiency of fuel treatments in the CEZ, but it requires systematically updated maps of fuel types and canopy metrics. The objective of this study was to demonstrate an effective approach for mapping fuel types, canopy height (CH), and canopy cover (CC) in territories contaminated by radionuclides using Landsat time series (LTS) and Global Ecosystem Dynamics Investigation (GEDI) LiDAR observations. We combined LTS and GEDI data to map fuel types and canopy metrics used in wildfire simulations within the CEZ. Our classification model showed an adequate overall accuracy (75%) in mapping land covers and associated fuel types. The phenology metrics extracted from LTS reliably distinguished spectrally similar vegetation types (such as grasslands and croplands) which exhibit different flammability through the year. We also predicted a suite of relative heights metrics and CC at Landsat 30-m pixel level (R2 = 0.23-0.26) using the nearest neighbor technique. The imputed maps adequately captured the dynamics of CH and CC in the CEZ after recent large wildfires occurred in 2015, 2020, and 2022. Thus, we illustrate a LTS processing approach to produce wall-to-wall maps of canopy characteristics that are important for wildfire simulations. We conclude that continuous updating of land cover and canopy fuel data is crucial to ensure relevant fire management of radioactively contaminated landscapes and support local decision-making.

[Effects of canopy density on understory plant diversity in Robinia pseudoacacia plantations on the Loess Plateau of China]. / é»„åœŸé«˜åŽŸåˆºæ§äººå·¥æž—éƒé—­åº¦å¯¹æž—ä¸‹æ¤ç‰©å¤šæ ·æ€§ç‰¹å¾çš„å½±å“.

To solve the problems of understory plant shortage and biodiversity reduction caused by high tree density of Robinia pseudoacacia plantations on the Loess Plateau in northwest China, we analyzed the data collected by field survey and from literatures. We used the upper boundary line method to examine the impacts of canopy density on understory plant diversity. Based on field survey at the Guanshan Forest Farm of Jingchuan County of Gansu Pro-vince, the species number of understory plants in R. pseudoacacia plantations was higher than that in natural grassland (91 vs. 78). The dominant species varied with canopy density, which was completely different from that of natural grassland. A comprehensive analysis of both literature data and field survey data showed that, when mean annual precipitation (MAP) <550 mm or >550 mm, the increases in canopy density first led to a stable understory plant coverage and then a sharp or slight decrease, and understory plant biomass showed a sharp and continuous decrease or a slight increase and then decrease. Understory plant species richness and other diversity indices (Shannon, Simpson, and Pielou indices) first increase and then decrease, with bigger variation range under lower MAP. In all, the characteristics (coverage, biomass and species diversity, etc.) of understory plant community in R. pseudoacacia plantations were strongly affected by canopy density, with higher sensitivity under lower MAP. There was a general threshold range of canopy density of 0.45-0.6. When canopy density was above or below this thre-shold range, it would lead to a rapid decrease of the most characteristics of understory plant community. Therefore, keeping canopy density within the rational range of 0.45-0.6 in the management of R. pseudoacacia plantations is the key to make all the above-mentioned understory plant characteristics at relatively high level.

Adaptiveness of RGB-image derived algorithms in the measurement of fractional vegetation coverage.

BACKGROUND: Fractional vegetation coverage (FVC) is a crucial parameter in determining vegetation structure. Automatic measurement of FVC using digital images captured by mobile smart devices is a potential direction for future research on field survey methods in plant ecology, and this algorithm is crucial for accurate FVC measurement. However, there is a lack of insight into the influence of illumination on the accuracy of FVC measurements. Therefore, the main objective of this research is to assess the adaptiveness and performance of different algorithms under varying light conditions for FVC measurements and to deepen our understanding of the influence of illumination on FVC measurement. METHODS AND RESULTS: Based on a literature survey, we selected four algorithms that have been reported to have high accuracy in automatic FVC measurements. The first algorithm (Fun01) identifies green plants based on the combination of [Formula: see text], [Formula: see text], and [Formula: see text] ([Formula: see text], [Formula: see text], and [Formula: see text] are the actual pixel digital numbers from the images based on each RGB channel, [Formula: see text] is the abbreviation of the Excess Green index), the second algorithm (Fun02) is a decision tree that uses color properties to discriminate plants from the background, the third algorithm (Fun03) uses [Formula: see text] ([Formula: see text] is the abbreviation of the Excess Red index) to recognize plants in the image, and the fourth algorithm (Fun04) uses [Formula: see text] and [Formula: see text] to separate the plants from the background. [Formula: see text] is an algorithm used to determine a threshold to transform the image into a binary image for the vegetation and background. We measured the FVC of several surveyed quadrats using these four algorithms under three scenarios, namely overcast sky, solar forenoon, and solar noon. FVC values obtained using the Photoshop-assisted manual identification method were used as a reference to assess the accuracy of the four algorithms selected. Results indicate that under the overcast sky scenario, Fun01 was more accurate than the other algorithms and the MAPE (mean absolute percentage error), BIAS, relBIAS (relative BIAS), RMSE (root mean square error), and relRMSE (relative RMSE) are 8.68%, 1.3, 3.97, 3.13, and 12.33%, respectively. Under the scenario of the solar forenoon, Fun02 (decision tree) was more accurate than other algorithms, and the MAPE, BIAS, relBIAS, RMSE, and relRMSE are 22.70%, - 2.86, - 7.70, 5.00, and 41.23%. Under the solar noon scenario, Fun02 was also more accurate than the other algorithms, and the MAPE, BIAS, relBIAS, RMSE, and relRMSE are 20.60%, - 6.39, - 20.67, 7.30, and 24.49%, respectively. CONCLUSIONS: Given that each algorithm has its own optimal application scenario, among the four algorithms selected, Fun01 (the combination of [Formula: see text], [Formula: see text], and [Formula: see text]) can be recommended for measuring FVC on cloudy days. Fun02 (decision tree) is more suitable for measuring the FVC on sunny days. However, it considerably underestimates the FVC in most cases. We expect the findings of this study to serve as a useful reference for automatic vegetation cover measurements.

Can Different Forest Structures Lead to Different Levels of Therapeutic Effects? A Systematic Review and Meta-Analysis.

In recent decades, forests have expanded from natural resources for conservation and production to health-promoting resources. With the growing body of evidence supporting the therapeutic effects of forests, the number of investigations on the relationship between forest characteristics and therapeutic effects have increased. However, quantitative synthesis of primary studies has rarely been conducted due to a limited number of health studies including forest description and high heterogeneity of forest variables. In this study, we conducted a systematic review and meta-analysis to evaluate the relationship between the forest structure and the therapeutic effect. We systematically searched the studies examining the therapeutic effects of forests with different stand density and canopy density and synthesized the results. As a result of subgroup analysis, we found that stand density modifies the therapeutic effects. Emotional and cognitive restoration showed greatest improvement in low-density forests with a stand density of less than 500/ha and the therapeutic effects diminish as the stand density increases. The impact of canopy density was not found due to a lack of studies reporting canopy density. Although some limitations remain, the findings in this study have great significance in providing the basis for establishing management strategies of forests for therapeutic use.

Functional dynamics of vegetated model patches: The minimum patch size effect for canopy restoration.

For the past two centuries coastal zones have been suffering seagrass loss resulting in a network of vegetated patches which are barely interconnected and which may compromise the ecological services provided by the canopy. To optimize management efforts for successful restoration strategies, questions need to be addressed about what appropriate canopy architectural considerations are required under certain hydrodynamic conditions. In this study, a set of laboratory experiments were conducted in which hydrodynamic conditions, plant densities and vegetated patch lengths were varied to determine minimum patch lengths for successful management strategies. Based on the TKE production, this study finds two possible canopy behaviours of seagrasses under oscillating flows: one where plants do not interact with the flow and the other where they interact with waves and produce TKE. A threshold from the first to second behaviour occurs for [Formula: see text] = 2, where CD is the drag of the vegetated patch, n is the number of stems per m2, d is the stem diameter and ϕ is the solid plant fraction. Therefore, high canopy densities, large patches of vegetation or moderate wave velocities will produce plant-wave interaction, whereas low canopy densities, small vegetation patches or slow wave velocities will produce a behaviour akin to the non-vegetated cases.

Opportunities and Possibilities of Developing an Advanced Precision Spraying System for Tree Fruits.

Reducing risk from pesticide applications has been gaining serious attention in the last few decades due to the significant damage to human health, environment, and ecosystems. Pesticide applications are an essential part of current agriculture, enhancing cultivated crop productivity and quality and preventing losses of up to 45% of the world food supply. However, inappropriate and excessive use of pesticides is a major rising concern. Precision spraying addresses these concerns by precisely and efficiently applying pesticides to the target area and substantially reducing pesticide usage while maintaining efficacy at preventing crop losses. This review provides a systematic summary of current technologies used for precision spraying in tree fruits and highlights their potential, briefly discusses factors affecting spraying parameters, and concludes with possible solutions to reduce excessive agrochemical uses. We conclude there is a critical need for appropriate sensing techniques that can accurately detect the target. In addition, air jet velocity, travel speed, wind speed and direction, droplet size, and canopy characteristics need to be considered for successful droplet deposition by the spraying system. Assessment of terrain is important when field elevation has significant variability. Control of airflow during spraying is another important parameter that needs to be considered. Incorporation of these variables in precision spraying systems will optimize spray decisions and help reduce excessive agrochemical applications.

Artificial selection for grain yield has increased net CO2 exchange of the ear leaf in maize crops.

Identifying the physiological traits indirectly selected during the search for high-yielding maize hybrids is useful for guiding further improvements. To investigate such traits, in this study we focused on the critical period of kernel formation because kernel number is the main yield component affected by breeding. Our results show that breeding has increased the number of florets per ear and ear growth rate but not the vegetative shoot growth rate, suggesting localised effects around the ear. Consistent with this possibility, breeding has increased the net CO2 exchange of the ear leaf in field-grown crops grown at high population densities. This response is largely accounted for by increased light interception (which increases photosynthesis) and by reduced rates of respiration of the ear leaf in modern hybrids compared to older ones. Modern hybrids show increased ear-leaf area per unit leaf dry matter (specific leaf area), which accounts for the reduced respiratory load per unit leaf area. These observations are consistent with a model where the improved ear leaf CO2 exchange helps the additional florets produced by modern hybrids to survive the critical period of high susceptibility to stress and hence to produce kernels.

Predicting the deforestation probability using the binary logistic regression, random forest, ensemble rotational forest, REPTree: A case study at the Gumani River Basin, India.

Rapid population growth and its corresponding effects like the expansion of human settlement, increasing agricultural land, and industry lead to the loss of forest area in most parts of the world especially in such highly populated nations like India. Forest canopy density (FCD) is a useful measure to assess the forest cover change in its own as numerous works of forest change have been done using only FCD with the help of remote sensing and GIS. The coupling of binary logistic regression (BLR), random forest (RF), ensemble of rotational forest and reduced error pruning trees (RTF-REPTree) with FCD makes it more convenient to find out the deforestation probability. Advanced vegetation index (AVI), bare soil index (BSI), shadow index (SI), and scaled vegetation density (VD) derived from Landsat imageries are the main input parameters to identify the FCD. After preparing the FCDs of 1990, 2000, 2010 and 2017 the deforestation map of the study area was prepared and considered as dependent parameter for deforestation probability modelling. On the other hand, twelve deforestation determining factors were used to delineate the deforestation probability with the help of BLR, RF and RTF-REPTree models. These deforestation probability models were validated through area under curve (AUC), receiver operating characteristics (ROC), efficiency, true skill statistics (TSS) and Kappa co-efficient. The validation result shows that all the models like BLR (AUC = 0.874), RF (AUC = 0.886) and RTF-REPTree (AUC = 0.919) have good capability of assessing the deforestation probability but among them, RTF-REPTree has the highest accuracy level. The result also shows that low canopy density area i.e. not under the dense forest cover has increased by 9.26% from 1990 to 2017. Besides, nearly 30% of the forested land is under high to very high deforestation probable zone, which needs to be protected with immediate measures.

Seasonal drivers of understorey temperature buffering in temperate deciduous forests across Europe.

AIM: Forest understorey microclimates are often buffered against extreme heat or cold, with important implications for the organisms living in these environments. We quantified seasonal effects of understorey microclimate predictors describing canopy structure, canopy composition and topography (i.e., local factors) and the forest patch size and distance to the coast (i.e., landscape factors). LOCATION: Temperate forests in Europe. TIME PERIOD: 2017-2018. MAJOR TAXA STUDIED: Woody plants. METHODS: We combined data from a microclimate sensor network with weather-station records to calculate the difference, or offset, between temperatures measured inside and outside forests. We used regression analysis to study the effects of local and landscape factors on the seasonal offset of minimum, mean and maximum temperatures. RESULTS: The maximum temperature during the summer was on average cooler by 2.1 °C inside than outside forests, and the minimum temperatures during the winter and spring were 0.4 and 0.9 °C warmer. The local canopy cover was a strong nonlinear driver of the maximum temperature offset during summer, and we found increased cooling beneath tree species that cast the deepest shade. Seasonal offsets of minimum temperature were mainly regulated by landscape and topographic features, such as the distance to the coast and topographic position. MAIN CONCLUSIONS: Forest organisms experience less severe temperature extremes than suggested by currently available macroclimate data; therefore, climate-species relationships and the responses of species to anthropogenic global warming cannot be modelled accurately in forests using macroclimate data alone. Changes in canopy cover and composition will strongly modulate the warming of maximum temperatures in forest understories, with important implications for understanding the responses of forest biodiversity and functioning to the combined threats of land-use change and climate change. Our predictive models are generally applicable across lowland temperate deciduous forests, providing ecologically important microclimate data for forest understories.

Downregulation of the CpSRP43 gene expression confers a truncated light-harvesting antenna (TLA) and enhances biomass and leaf-to-stem ratio in Nicotiana tabacum canopies.

MAIN CONCLUSION: Downregulation in the expression of the signal recognition particle 43 (SRP43) gene in tobacco conferred a truncated photosynthetic light-harvesting antenna (TLA property), and resulted in plants with a greater leaf-to-stem ratio, improved photosynthetic productivity and canopy biomass accumulation under high-density cultivation conditions. Evolution of sizable arrays of light-harvesting antennae in all photosynthetic systems confers a survival advantage for the organism in the wild, where sunlight is often the growth-limiting factor. In crop monocultures, however, this property is strongly counterproductive, when growth takes place under direct and excess sunlight. The large arrays of light-harvesting antennae in crop plants cause the surface of the canopies to over-absorb solar irradiance, far in excess of what is needed to saturate photosynthesis and forcing them to engage in wasteful dissipation of the excess energy. Evidence in this work showed that downregulation by RNA-interference approaches of the Nicotiana tabacum signal recognition particle 43 (SRP43), a nuclear gene encoding a chloroplast-localized component of the photosynthetic light-harvesting assembly pathway, caused a decrease in the light-harvesting antenna size of the photosystems, a corresponding increase in the photosynthetic productivity of chlorophyll in the leaves, and improved tobacco plant canopy biomass accumulation under high-density cultivation conditions. Importantly, the resulting TLA transgenic plants had a substantially greater leaf-to-stem biomass ratio, compared to those of the wild type, grown under identical agronomic conditions. The results are discussed in terms of the potential benefit that could accrue to agriculture upon application of the TLA-technology to crop plants, entailing higher density planting with plants having a greater biomass and leaf-to-stem ratio, translating into greater crop yields per plant with canopies in a novel agronomic configuration.

Vegetation carbon stocks driven by canopy density and forest age in subtropical forest ecosystems.

Subtropical forests play an important role in global carbon cycle and in mitigating climate change. Knowledge on the abiotic and biotic driving factors that affect vegetation carbon stocks in subtropical forest ecosystems is needed to take full advantage of the carbon sequestration potential. We used a large-scale database from national forest continuous inventory in Zhejiang Province, and combined the Random Forest analysis (RF) and structural equation modeling (SEM) to quantify the contribution of biotic and abiotic driving factors on vegetation carbon stocks, and to evaluate the direct and indirect effects of the main driving factors. The RF model explained 50% of the variation in vegetation carbon stocks; canopy density accounted for 17.9%, and forest age accounted for 7.0%. Moreover, the SEM explained 52% of the variation in vegetation carbon stocks; the value of standardized total effects of canopy density and forest age were 0.469 and 0.327, respectively, suggesting that they were the most crucial driving factors of vegetation carbon stocks. Since the forests in our study were relatively young, the forests had a large potential for carbon sequestration. Overall, our study provided new insights into the sensitivity and potential response of subtropical forest ecosystems carbon cycle to climate change.

Photosynthetic antenna engineering to improve crop yields.

MAIN CONCLUSION: Evidence shows that decreasing the light-harvesting antenna size of the photosystems in tobacco helps to increase the photosynthetic productivity and plant canopy biomass accumulation under high-density cultivation conditions. Decreasing, or truncating, the chlorophyll antenna size of the photosystems can theoretically improve photosynthetic solar energy conversion efficiency and productivity in mass cultures of algae or plants by up to threefold. A Truncated Light-harvesting chlorophyll Antenna size (TLA), in all classes of photosynthetic organisms, would help to alleviate excess absorption of sunlight and the ensuing wasteful non-photochemical dissipation of excitation energy. Thus, solar-to-biomass energy conversion efficiency and photosynthetic productivity in high-density cultures can be increased. Applicability of the TLA concept was previously shown in green microalgae and cyanobacteria, but it has not yet been demonstrated in crop plants. In this work, the TLA concept was applied in high-density tobacco canopies. The work showed a 25% improvement in stem and leaf biomass accumulation for the TLA tobacco canopies over that measured with their wild-type counterparts grown under the same ambient conditions. Distinct canopy appearance differences are described between the TLA and wild type tobacco plants. Findings are discussed in terms of concept application to crop plants, leading to significant improvements in agronomy, agricultural productivity, and application of photosynthesis for the generation of commodity products in crop leaves.

[Effects of canopy density on the functional group of soil macro fauna in Pinus massoniana plantations]. / 马尾松人工林郁闭度对大型土壤动物功能群的影响.

In order to understand the effects of canopy density on the functional group characteristics of soil macrofauna in Pinus massoniana plantations, we divided the captured soil fauna into five types including xylophages, predators, saprophages, omnivores and fungal feeders. The results showed that 1) Saprozoic feeders had the highest percentage of total individuals, and the omnivores and xylophages occupied higher percentages of total taxa. 2) The individual and group number of the predators, and the group number of xylophages did not change significantly under 0.5-0.6 and then decreased significantly under 0.6-0.9 canopy density. 3) With the increasing canopy density, the individual an dgroup number of predators in litter layer decreased significantly, the saprozoic individual number in 5-10 cm soil layer represented irregular trends. The individual number of xylophage increased with the depth of soil, and the group number in litter layer, the individual and group number in 5-10 cm soil layer decreased significantly. 4) Pielou evenness of xylophage had no significant changes with the canopy density, all the other diversity index of xylophage and saprophage were various with the increasing canopy density. The predatory Simpson index was stable under 0.5-0.8, and then decreased significantly under 0.8-0.9 canopy density. 5) The CCA (canonical correlation analysis) indicated that soil bulk density and moisture content were the main environmental factors affecting functional groups of soil macro fauna. Moisture content greatly impacted on the number of saprophagous individuals. But xylophage and predators were mostly affected by soil bulk density, and the predatory Simpson index was mainly affected by soil pH value and total phosphorus. Our research indicated that the structure of soil macro faunal functional group under 0.7 canopy density was comparatively stable, which would facilitate the maintenance of soil fertility and ecological function in Pinus massoniana plantation.

A Canopy Density Model for Planar Orchard Target Detection Based on Ultrasonic Sensors.

Orchard target-oriented variable rate spraying is an effective method to reduce pesticide drift and excessive residues. To accomplish this task, the orchard targets' characteristic information is needed to control liquid flow rate and airflow rate. One of the most important characteristics is the canopy density. In order to establish the canopy density model for a planar orchard target which is indispensable for canopy density calculation, a target density detection testing system was developed based on an ultrasonic sensor. A time-domain energy analysis method was employed to analyze the ultrasonic signal. Orthogonal regression central composite experiments were designed and conducted using man-made canopies of known density with three or four layers of leaves. Two model equations were obtained, of which the model for the canopies with four layers was found to be the most reliable. A verification test was conducted with different layers at the same density values and detecting distances. The test results showed that the relative errors of model density values and actual values of five, four, three and two layers of leaves were acceptable, while the maximum relative errors were 17.68%, 25.64%, 21.33% and 29.92%, respectively. It also suggested the model equation with four layers had a good applicability with different layers which increased with adjacent layers.

MECS-VINE®: A New Proximal Sensor for Segmented Mapping of Vigor and Yield Parameters on Vineyard Rows.

Ground-based proximal sensing of vineyard features is gaining interest due to its ability to serve in even quite small plots with the advantage of being conducted concurrently with normal vineyard practices (i.e., spraying, pruning or soil tilling) with no dependence upon weather conditions, external services or law-imposed limitations. The purpose of the present work was to test performance of the new terrestrial multi-sensor MECS-VINE® in terms of reliability and degree of correlation with several canopy growth and yield parameters in the grapevine. MECS-VINE®, once conveniently positioned in front of the tractor, can provide simultaneous assessment of growth features and microclimate of specific canopy sections of the two adjacent row sides. MECS-VINE® integrates a series of microclimate sensors (air relative humidity, air and surface temperature) with two (left and right) matrix-based optical RGB imaging sensors and a related algorithm, termed Canoyct). MECS-VINE® was run five times along the season in a mature cv. Barbera vineyard and a Canopy Index (CI, pure number varying from 0 to 1000), calculated through its built-in algorithm, validated vs. canopy structure parameters (i.e., leaf layer number, fractions of canopy gaps and interior leaves) derived from point quadrat analysis. Results showed that CI was highly correlated vs. any canopy parameter at any date, although the closest relationships were found for CI vs. fraction of canopy gaps (R² = 0.97) and leaf layer number (R² = 0.97) for data pooled over 24 test vines. While correlations against canopy light interception and total lateral leaf area were still unsatisfactory, a good correlation was found vs. cluster and berry weight (R² = 0.76 and 0.71, respectively) suggesting a good potential also for yield estimates. Besides the quite satisfactory calibration provided, main improvements of MECS-VINE® usage versus other current equipment are: (i) MECS-VINE® delivers a segmented evaluation of the canopy up to 15 different sectors, therefore allowing to differentiate canopy structure and density at specific and crucial canopy segments (i.e., basal part where clusters are located) and (ii) the sensor is optimized to work at any time of the day with any weather condition without the need of any supplemental lighting system.

Georeferenced LiDAR 3D vine plantation map generation.

The use of electronic devices for canopy characterization has recently been widely discussed. Among such devices, LiDAR sensors appear to be the most accurate and precise. Information obtained with LiDAR sensors during reading while driving a tractor along a crop row can be managed and transformed into canopy density maps by evaluating the frequency of LiDAR returns. This paper describes a proposed methodology to obtain a georeferenced canopy map by combining the information obtained with LiDAR with that generated using a GPS receiver installed on top of a tractor. Data regarding the velocity of LiDAR measurements and UTM coordinates of each measured point on the canopy were obtained by applying the proposed transformation process. The process allows overlap of the canopy density map generated with the image of the intended measured area using Google Earth(®), providing accurate information about the canopy distribution and/or location of damage along the rows. This methodology was applied and tested on different vine varieties and crop stages in two important vine production areas in Spain. The results indicate that the georeferenced information obtained with LiDAR sensors appears to be an interesting tool with the potential to improve crop management processes.