Canopy Surface Research Articles

Estimating canopy surface height of wheat and corn crops in reclaimed cropland using multispectral images from a small unmanned aircraft system

Crop height is useful to monitor the effects of cropland reclamation. Unmanned aircraft system (UAS)-based observation through digital cameras has exhibited significant potential as a cost-effective way to predict crop height. However, the performance of UAS-based multispectral images has not been adequately analyzed in the context of estimating crop height. Therefore, our study investigates the performance of UAS-based multispectral images in terms of modeling and extracting crop height by considering a region of reclaimed cropland as an example. A framework is developed to model and extract the height of wheat and corn plants, the point cloud of varying densities is generated using the images, and geometric and physical outliers are removed from the point cloud. The flight altitude, threshold of extraction, and format of the data are examined to accurately describe and verify the canopy structure. The results indicate that UAS-based multispectral images can be used to estimate crop height. The relative accuracy of the estimated height of corn (<6 % ) was higher than that of wheat (>9 % ), but the opposite result was obtained in terms of absolute accuracy. The estimated crop height was significantly correlated with the measured above-ground biomass and can be used to monitor typical reclaimed cropland.

A New Field Instrument for Leaf Volatiles Reveals an Unexpected Vertical Profile of Isoprenoid Emission Capacities in a Tropical Forest

Both plant physiology and atmospheric chemistry are substantially altered by the emission of volatile isoprenoids (VI), such as isoprene and monoterpenes, from plant leaves. Yet, since gaining scientific attention in the 1950’s, empirical research on leaf VI has been largely confined to laboratory experiments and atmospheric observations. Here, we introduce a new field instrument designed to bridge the scales from leaf to atmosphere, by enabling precision VI detection in real time from plants in their natural ecological setting. With a field campaign in the Brazilian Amazon, we reveal an unexpected distribution of leaf emission capacities (EC) across the vertical axis of the forest canopy, with EC peaking in the mid-canopy instead of the sun-exposed canopy surface, and moderately high emissions occurring in understory specialist species. Compared to the simple interpretation that VI protect leaves from heat stress at the hot canopy surface, our results encourage a more nuanced view of the adaptive role of VI in plants. We infer that forest emissions to the atmosphere depend on the dynamic microenvironments imposed by canopy structure, and not simply on canopy surface conditions. We provide a new emissions inventory from 52 tropical tree species, revealing moderate consistency in EC within taxonomic groups. We highlight priorities in leaf volatiles research that require field-portable detection systems. Our self-contained, portable instrument provides real-time detection and live measurement feedback with precision and detection limits better than 0.5 nmolVI m–2leaf s–1. We call the instrument ‘PORCO’ based on the gas detection method: photoionization of organic compounds. We provide a thorough validation of PORCO and demonstrate its capacity to detect ecologically driven variation in leaf emission rates and thus accelerate a nascent field of science: the ecology and ecophysiology of plant volatiles.

TheLNet270v1 - A Novel Deep-Network Architecture for the Automatic Classification of Thermal Images for Greenhouse Plants.

The real challenge for separating leaf pixels from background pixels in thermal images is associated with various factors such as the amount of emitted and reflected thermal radiation from the targeted plant, absorption of reflected radiation by the humidity of the greenhouse, and the outside environment. We proposed TheLNet270v1 (thermal leaf network with 270 layers version 1) to recover the leaf canopy from its background in real time with higher accuracy than previous systems. The proposed network had an accuracy of 91% (mean boundary F1 score or BF score) to distinguish canopy pixels from background pixels and then segment the image into two classes: leaf and background. We evaluated the classification (segment) performance by using more than 13,766 images and obtained 95.75% training and 95.23% validation accuracies without overfitting issues. This research aimed to develop a deep learning technique for the automatic segmentation of thermal images to continuously monitor the canopy surface temperature inside a greenhouse.

Comparison of an improved Penman-Monteith model and SWH model for estimating evapotranspiration in a meadow wetland in a semiarid region

Accurately estimating evapotranspiration (ET) for meadow wetland ecosystems is of great significance for water management in semiarid regions. Taking a meadow wetland ecosystem in the Horqin Sandy Land as an example, this study coupled the Ball-Berry canopy stomatal conductance model to Penman-Monteith (P-M) model and Shuttleworth-Wallace (S-W) model, and the improved P-M model and SWH model were calibrated and validated by long-term (2013–2018 growing seasons) eddy covariance (EC) measurements. The results indicated that the improved P-M model and SWH model performed well either at half-hourly or daily timescales, with high coefficient of determination (R2) and index of agreement (IA) and low root mean square error (RMSE). Generally, the SWH model performed better than the improved P-M model, especially under the low leaf area index (LAI) conditions. In addition, in the growing seasons from 2013 to 2018, the mean ET measured by the EC system was 2.78 mm/d, which was approximately equal to the mean modeled ET from the SWH model (2.75 mm/d) and slightly higher than the mean modeled ET from the improved P-M model (2.34 mm/d). The improved P-M model and SWH model were highly sensitive to the parameter in estimating canopy surface resistance and to vapor pressure deficit (VPD) in meteorological variables.

Anthropogenic pressure on mangrove ecosystems: Quantification and source identification of surficial and trapped debris

The mangroves are well known for their ecological services and livelihood support to humankind. The mangrove forest is experiencing extreme pressure due to anthropogenic activities, mainly the debris pollution posing great harm to the mangrove ecosystems. The abundance, sources, and composition of surficial and trapped debris items in the six contiguous mangrove regions of Mumbai were studied by the belt-transect and quadrats method. A total number of 3526 surficial debris items (368 kg) were collected from twenty belt transects. The estimated mean surficial debris was 8.8 ± 3.4 pieces/m2 with a weight of 920 ± 317 g/m2. The mean trapped debris was 35 ± 10 pieces/tree and 2514 ± 758 g/tree. Plastic (62.4%) includes carry bags and food wrappers mainly. Shoreline/recreational activity-based debris (38.9%) and other items (32.7%) contributed significantly to the total debris pollution. The study provides evidence that the mangrove ecosystem acts as a natural filter and trap for coastal water debris. The trapped debris is a potential risk to the mangroves due to the barrier created on the canopy surface for the incident solar radiation utilized for photosynthesis. By conducting the cleaning programs in the mangrove vegetation stretches, the ecological disturbances to the mangrove ecosystems can be minimized. Further, the regular removal of trapped debris will complement coastal pollution management. The data generated from this study will help the policymakers and resource managers about the effective control and management of debris pollution in the mangroves region.

The cropping system matters – Contrasting responses of winter faba bean (Vicia faba L.) genotypes to drought stress

Intercropping of legumes and cereals provides many ecological advantages and contributes to a sustainable agriculture. These agricultural systems face ongoing shifts in precipitation patterns and seasonal drought. Although the effect of drought stress on legumes has been frequently studied, knowledge about water deficits influencing legumes under different cropping systems is still limited. Therefore, we investigated the impact of water deficit and re-irrigation on two winter faba bean genotypes (S_004 and S_062) and winter wheat (var. Genius) in pure and intercropped stands under greenhouse conditions. Various physiological and biochemical parameters, such as canopy surface temperature, leaf relative water content and proline content, were collected at three time points (beginning of water deficit, end of water deficit, after re-irrigation). In addition, water use efficiency (WUE) was analyzed at the end of the experiment. The overall drought stress tolerance was determined as conceptual analysis of all measured parameters. Water deficit significantly affected WUE, surface temperature and proline content of both winter faba bean genotypes. Interestingly, intercropping with wheat resulted in an overall high drought tolerance of genotype S_004, while genotype S_062 had a high drought tolerance in pure stands. Under water deficit, pure stands of S_062 substantially increased WUE by 30.5%. Intercropping of genotype S_004 increased the dry matter per plant by 31.7% compared to pure stands under water deficit. Contrary, intercropping of genotype S_062 did not improve the dry matter production. Our findings indicate that genotype S_004 benefits from resource complementarity in intercropping systems with wheat, whereas S_062 is better suitable for pure stands due to competitive effects. Thus, our study highlights that the drought tolerance of winter faba bean genotypes depends on the cropping system, leading to a demand for drought-adapted cultivars specifically selected for intercropping.

Wind Tunnel Test of Wind Load on a Typical Cross Line High-Speed Railway Station

In order to study the influence of the layout of station buildings on the wind field characteristic, wind tunnel tests of wind load on the main roof and platform canopy of the Jinan West Railway Station are presented in this paper. Four 1:250-scale rigid models of the station with different characteristics were made. Not only the wind loading of the first phase station, but also the interference effect of the extension of the second phase station, the longitudinal gap of the platform canopy and the blocking rate of the interchange corridor on wind loading of the station were studied. The results show that the station roof, especially the overhanging eave mainly subjected to wind suction. Although the longitudinal gap of the platform canopy increases the turbulence intensity, the wind pressure on the upper and lower surface of the platform canopy tend to weaken each other. The extension of the second phase station will change the wind load characteristic of the first phase station roof and platform canopy obviously. Under specific wind angle, wind loading on large area of the platform canopy will change from wind suction to wind pressure. With the increase of blocking rate of the interchange corridor, both the wind pressure and wind suction of the platform canopy will be intensified.

Impact of steep slope management system and row orientation on canopy microclimate. Comparing terraces to downslope vineyards

Historically and under cooler climate conditions, steep slope vineyards yielded best quality wines and highest reputation, due to their distinctive microclimate, especially during ripening period. Nevertheless, steep slope vineyard sites primarily suffer from reduced competitiveness leading to abandonment, thus a loss of valuable vineyard sites. The aim of this work was to investigate differences in microclimatic conditions between different steep slope vineyard management systems and row orientations. Records of inner canopy microclimatic parameters were taken over two consecutive vegetation periods including seven vineyard pairs. A Bayesian mixed effect model was used to properly account for the complexity of the conducted experiment. Additionally, irradiation and canopy surface temperature data was compared. Grapevines planted downslope (control) exhibited a more even light distribution on canopy sides. Contrarily, at terraced vineyard sites canopy sides showed big differences regarding light interception, also affecting diurnal canopy surface temperature. Differences in N-S/E-W row orientation comparison were more pronounced compared to vineyard pairs aligned NE-SW/NW-SE. Night temperatures were slightly higher in terraced vineyards, while daily mean and maximum temperature and temperature amplitude were higher in vineyard rows planted in line of the greatest slope. While a treatment effect was not always clearly visible, an impact of row orientation on temperature microclimate was observed. Differences between treatments may become more pronounced under projected climate change conditions with consequences on physiological processes, thus grapevine performance influencing berry composition.

Spatio-Temporal Estimation of Biomass Growth in Rice Using Canopy Surface Model from Unmanned Aerial Vehicle Images

The awareness of spatial and temporal variations in site-specific crop parameters, such as aboveground biomass (total dry weight: (TDW), plant length (PL) and leaf area index (LAI), help in formulating appropriate management decisions. However, conventional monitoring methods rely on time-consuming manual field operations. In this study, the feasibility of using an unmanned aerial vehicle (UAV)-based remote sensing approach for monitoring growth in rice was evaluated using a digital surface model (DSM). Approximately 160 images of paddy fields were captured during each UAV survey campaign over two vegetation seasons. The canopy surface model (CSM) was developed based on the differences observed between each DSM and the first DSM after transplanting. Mean canopy height (CH) was used as a variable for the estimation models of LAI and TDW. The mean CSM of the mesh covering several hills was sufficient to explain the PL (R2 = 0.947). TDW and LAI prediction accuracy of the model were high (relative RMSE of 20.8% and 28.7%, and RMSE of 0.76 m2 m−2 and 141.4 g m−2, respectively) in the rice varieties studied (R2 = 0.937 (Basmati370), 0.837 (Nipponbare and IR64) for TDW, and 0.894 (Basmati370), 0.866 (Nipponbare and IR64) for LAI). The results of this study support the assertion of the benefits of DSM-derived CH for predicting biomass development. In addition, LAI and TDW could be estimated temporally and spatially using the UAV-based CSM, which is not easily affected by weather conditions.

On the phenology of protists: recurrent patterns reveal seasonal variation of protistan (Rhizaria: Cercozoa and Endomyxa) communities in tree canopies.

ABSTRACTTree canopies are colonized by billions of highly specialized microorganisms that are well adapted to the highly variable microclimatic conditions, caused by diurnal fluctuations and seasonal changes. In this study, we investigated seasonality patterns of protists in the tree canopies of a temperate floodplain forest via high-throughput sequencing with group-specific primers for the phyla Cercozoa and Endomyxa. We observed consistent seasonality, and identified divergent spring and autumn taxa. Tree crowns were characterized by a dominance of bacterivores and omnivores, while eukaryvores gained a distinctly larger share in litter and soil communities on the ground. In the canopy seasonality was largest among communities detected on the foliar surface: In spring, higher variance within alpha diversity of foliar samples indicated greater heterogeneity during initial colonization. However, communities underwent compositional changes during the aging of leaves in autumn, highly reflecting recurring phenological changes during protistan colonization. Surprisingly, endomyxan root pathogens appeared to be exceptionally abundant across tree canopies during autumn, demonstrating a potential role of the canopy surface as a physical filter for air-dispersed propagules. Overall, about 80% of detected OTUs could not be assigned to known species—representing dozens of microeukaryotic taxa whose canopy inhabitants are waiting to be discovered.

Comparison and Evaluation of Different Pit-Filling Methods for Generating High Resolution Canopy Height Model Using UAV Laser Scanning Data

As a common form of light detection and ranging (LiDAR) in forestry applications, the canopy height model (CHM) provides the elevation distribution of aboveground vegetation. A CHM is traditionally generated by interpolating all the first LiDAR echoes. However, the first echo cannot accurately represent the canopy surface, and the resulting large amount of noise (data pits) also reduce the CHM quality. Although previous studies concentrate on many pit-filling methods, the applicability of these methods in high-resolution unmanned aerial vehicle laser scanning (UAVLS)-derived CHMs has not been revealed. This study selected eight widely used, recently developed, representative pit-filling methods, namely first-echo interpolation, smooth filtering (mean, medium and Gaussian), highest point interpolation, pit-free algorithm, spike-free algorithm and graph-based progressive morphological filtering (GPMF). A comprehensive evaluation framework was implemented, including a quantitative evaluation using simulation data and an additional application evaluation using UAVLS data. The results indicated that the spike-free algorithm and GPMF had excellent visual performances and were closest to the real canopy surface (root mean square error (RMSE) of simulated data were 0.1578 m and 0.1093 m, respectively; RMSE of UAVLS data were 0.3179 m and 0.4379 m, respectively). Compared with the first-echo method, the accuracies of the spike-free algorithm and GPMF improved by approximately 23% and 22%, respectively. The pit-free algorithm and highest point interpolation method also have advantages in high-resolution CHM generation. The global smooth filter method based on the first-echo CHM reduced the average canopy height by approximately 7.73%. Coniferous forests require more pit-filling than broad-leaved forests and mixed forests. Although the results of individual tree applications indicated that there was no significant difference between these methods except the median filter method, pit-filling is still of great significance for generating high-resolution CHMs. This study provides guidance for using high-resolution UAVLS in forestry applications.

A Sentinel-2 Image-Based Irrigation Advisory Service: Cases for Tea Plantations

In this study, we aim to develop an inexpensive site-specific irrigation advisory service for resolving disadvantages related to using immobile soil moisture sensors and to the differences in irrigation needs of different tea plantations affected by variabilities in cultivars, plant ages, soil heterogeneity, and management practices. In the paper, we present methodologies to retrieve two biophysical variables, surface soil water content and canopy water content of tea trees from Sentinel-2 (S2) (European Space Agency, Paris, France) images and consider their association with crop water availability status to be used for making decisions to send an alert level. Precipitation records are used as auxiliary information to assist in determining or modifying the alert level. Once the site-specific alert level for each target plantation is determined, it is sent to the corresponding farmer through text messaging. All the processes that make up the service, from downloading an S2 image from the web to alert level text messaging, are automated and can be completed before 7:30 a.m. the next day after an S2 image was taken. Therefore, the service is operated cyclically, and corresponds to the five-day revisit period of S2, but one day behind the S2 image acquisition date. However, it should be noted that the amount of irrigation water required for each site-specific plantation has not yet been estimated because of the complexities involved. Instead, a single irrigation rate (300 t ha−1) per irrigation event is recommended. The service is now available to over 20 tea plantations in the Mingjian Township, the largest tea producing region in Taiwan, free of charge since September 2020. This operational application is expected to save expenditures on buying irrigation water and induce deeper root systems by decreasing the frequency of insufficient irrigation commonly employed by local farmers.

Understanding Growth Dynamics and Yield Prediction of Sorghum Using High Temporal Resolution UAV Imagery Time Series and Machine Learning

Unmanned aerial vehicles (UAV) carrying multispectral cameras are increasingly being used for high-throughput phenotyping (HTP) of above-ground traits of crops to study genetic diversity, resource use efficiency and responses to abiotic or biotic stresses. There is significant unexplored potential for repeated data collection through a field season to reveal information on the rates of growth and provide predictions of the final yield. Generating such information early in the season would create opportunities for more efficient in-depth phenotyping and germplasm selection. This study tested the use of high-resolution time-series imagery (5 or 10 sampling dates) to understand the relationships between growth dynamics, temporal resolution and end-of-season above-ground biomass (AGB) in 869 diverse accessions of highly productive (mean AGB = 23.4 Mg/Ha), photoperiod sensitive sorghum. Canopy surface height (CSM), ground cover (GC), and five common spectral indices were considered as features of the crop phenotype. Spline curve fitting was used to integrate data from single flights into continuous time courses. Random Forest was used to predict end-of-season AGB from aerial imagery, and to identify the most informative variables driving predictions. Improved prediction of end-of-season AGB (RMSE reduction of 0.24 Mg/Ha) was achieved earlier in the growing season (10 to 20 days) by leveraging early- and mid-season measurement of the rate of change of geometric and spectral features. Early in the season, dynamic traits describing the rates of change of CSM and GC predicted end-of-season AGB best. Late in the season, CSM on a given date was the most influential predictor of end-of-season AGB. The power to predict end-of-season AGB was greatest at 50 days after planting, accounting for 63% of variance across this very diverse germplasm collection with modest error (RMSE 1.8 Mg/ha). End-of-season AGB could be predicted equally well when spline fitting was performed on data collected from five flights versus 10 flights over the growing season. This demonstrates a more valuable and efficient approach to using UAVs for HTP, while also proposing strategies to add further value.

Mapping Very-High-Resolution Evapotranspiration from Unmanned Aerial Vehicle (UAV) Imagery

There is a growing concern about water scarcity and the associated decline in Australia’s agricultural production. Efficient water use as a natural resource requires more precise and adequate monitoring of crop water use and irrigation scheduling. Therefore, accurate estimations of evapotranspiration (ET) at proper spatial–temporal scales are critical to understand the crop water demand and uptake and to enable optimal irrigation scheduling. Remote sensing (RS)-based ET estimation has been adopted as a method for large-scale applications when the detailed spatial representation of ET is required. This research aimed to estimate instantaneous ET using very-high-resolution (VHR) multispectral and thermal imagery (GSD &lt; 8 cm) collected using a single flight of a UAV over a high-density peach orchard with a discontinuous canopy. The energy balance component estimation was based on the high-resolution mapping of evapotranspiration (HRMET) model. A tree-by-tree ET map was produced using the canopy surface temperature and the leaf area index (LAI) resampled at the corresponding scale via a systematic feature segmentation method based on pure canopy extraction. Results showed a strong linear relationship between the estimated ET and the leaf transpiration (n = 42) measured using a gas exchange sensor, with a coefficient of determination (R2) of 0.89. Daily ET (5.5 mm d−1) derived from the instantaneous ET map was comparable with daily crop ET (6.4 mm d−1) determined by the meteorological approach over the study site. The proposed approach has important implications for mapping tree-by-tree ET over horticultural fields using VHR imagery.

An Evaluation of the Effects of UAS Flight Parameters on Digital Aerial Photogrammetry Processing and Dense-Cloud Production Quality in a Scots Pine Forest

The application of unmanned aerial systems (UAS) in forest research includes a wide range of equipment, systems, and flight settings, creating a need for enhancing data acquisition efficiency and quality. Thus, we assessed the effects of flying altitude and lateral and longitudinal overlaps on digital aerial photogrammetry (DAP) processing and the ability of its products to provide point clouds for forestry inventory. For this, we used 18 combinations of flight settings for data acquisition, and a nationwide airborne laser scanning (ALS) dataset as reference data. Linear regression was applied for modeling DAP quality indicators and model fitting quality as the function of flight settings; equivalence tests compared DAP- and ALS-products. Most of DAP-Digital Terrain Models (DTM) showed a moderate to high agreement (R2 &gt; 0.70) when fitted to ALS-based models; nine models had a regression slope within the 1% region of equivalence. The best DAP-Canopy Height Model (CHM) was generated using ALS-DTM with an R2 = 0.42 when compared with ALS-CHM, indicating reduced similarity. Altogether, our results suggest that the optimal combination of flight settings should include a 90% lateral overlap, a 70% longitudinal overlap, and a minimum altitude of 120 m above ground level, independent of the availability of an ALS-derived DTM for height normalization. We also provided insights into the effects of flight settings on DAP outputs for future applications in similar forest stands, emphasizing the benefits of overlaps for comprehensive scene reconstruction and altitude for canopy surface detection.

Estimation of Surface Canopy Water in Pacific Northwest Forests by Fusing Radar, Lidar, and Meteorological Data

Surface Canopy Water (SCW) is the intercepted rain water that resides within the tree canopy and plays a significant role in the hydrological cycle. Challenges arise in measuring SCW in remote areas using traditional ground-based techniques. Remote sensing in the radio spectrum has the potential to overcome the challenges where traditional modelling approaches face difficulties. In this study, we aim at estimating the SCW by fusing information extracted from the radar imagery acquired with the Sentinel-1 constellation, aerial laser scanning, and meteorological data. To describe the change of radar backscatter with moisture, we focused on six forest stands in the H.J. Andrews experimental forest in central Oregon, as well as four clear cut areas and one golf course, over the summers of 2015–2017. We found significant relationships when we executed the analysis on radar images in which individual tree crowns were delineated from lidar, as opposed to SCW estimated from individual pixel backscatter. Significant differences occur in the mean backscatter between radar images taken during rain vs. dry periods (no rain for &gt;1 h), but these effects only last for roughly 30 min after the end of a rain event. We developed a predictive model for SCW using the radar images acquired at dawn, and proved the capability of space-based radar systems to provide information for estimation of the canopy moisture under conditions of fresh rainfall during the dry season.

Biodiversity facets affect community surface temperature via 3D canopy structure in grassland communities

AbstractCanopy structure is an important driver of the energy budget of grassland ecosystem and is, at the same time, altered by plant diversity. Diverse plant communities typically have taller and more densely packed canopies than less diverse communities. With this, they absorb more radiation, have a higher transpiring leaf surface and are better coupled to the atmosphere which leads to cooler canopy surfaces. However, whether plant diversity generally translates into a cooling potential remains unclear and lacks empirical evidence. Here, we assessed how functional identity, functional diversity and species richness of grassland communities in the Jena Experiment predict the mean and variation of plant surface temperature mediated via effects of canopy structure.Using terrestrial laser scanning, we estimated canopy structure describing metrics of vertical structure (mean height, LAI), the distribution (evenness) and the highest allocation (centre of gravity) of biomass along height strata. As metrics of horizontal structure, we considered community stand gaps, canopy surface variation and emergent flowers. We measured surface temperature with a thermal camera. We used SEM models to predict biodiversity effects on the surface temperature during two seasonal peaks of biomass.Before the first cut in May, herb‐dominated communities directly promoted lower leaf surface temperatures. However, communities with lower centre of gravity (mostly herb dominated) also increased canopy surface temperatures compared with grass‐dominated communities with higher biomass stored in the top canopy. Grass‐dominated communities showed a smaller variation of surface temperatures, which was also positively affected by species richness via an increase in mean height. In August, mean surface temperature decreased with increasing community CC and LAI. The variation of surface temperature was greater in herb‐dominated than in grass‐dominated communities and increased with plant species richness (direct effects).Synthesis. The mean and variation of canopy surface temperature were driven by differences in functional group composition (herbs‐ vs. grass dominance) and to a lesser extent by plant diversity. These effects were partly mediated the metrics of canopy structure but also by direct effects unrelated to the structural metrics considered.

Application of Remote Sensing Techniques to Discriminate the Effect of Different Soil Management Treatments over Rainfed Vineyards in Chianti Terroir

The work aimed to discriminate among different soil management treatments in terms of beneficial effects by high-resolution thermal and spectral vegetation imagery using an unmanned aerial vehicle and open-source GIS software. Five soil management treatments were applied in two organic vineyards (cv. Sangiovese) from Chianti Classico terroir (Tuscany, Italy) during two experimental years. The treatments tested consisted of conventional tillage, spontaneous vegetation, pigeon bean (Vicia faba var. minor Beck) incorporated in spring, mixture of barley (Hordeum vulgare L.) and clover (Trifolium squarrosum L.) incorporated or left as dead mulch in late spring. The images acquired remotely were analyzed through map-algebra and map-statistics in QGIS and correlated with field ecophysiological measurements. The surface temperature, crop water stress index (CWSI) and normalized difference vegetation index (NDVI) of each vine row under treatments were compared based on frequency distribution functions and statistics descriptors of position. The spectral vegetation and thermal-based indices were significantly correlated with the respective leaf area index (R2 = 0.89) and stem water potential measurements (R2 = 0.59), and thus are an expression of the crop vigor and water status. The gravel and active limestone soil components determined the spatial variability of vine biophysical (e.g., canopy vigor) and physiological characteristics (e.g., vine chlorophyll content) in both farms. The vine canopy surface temperature, and CWSI were lower on the spontaneous and pigeon bean treatments in both farms, thus evidencing less physiological stress on the vine rows derived from the cover crop residual effect. In conclusion, the proposed methodology showed the capacity to discriminate across soil management practices and map the spatial variability within vineyards. The methodology could serve as a simple and non-invasive tool for precision soil management in rainfed vineyards to guide producers on using the most efficient and profitable practice.

Estimating Canopy Density Parameters Time-Series for Winter Wheat Using UAS Mounted LiDAR

Monitoring of canopy density with related metrics such as leaf area index (LAI) makes a significant contribution to understanding and predicting processes in the soil–plant–atmosphere system and to indicating crop health and potential yield for farm management. Remote sensing methods using optical sensors that rely on spectral reflectance to calculate LAI have become more mainstream due to easy entry and availability. Methods with vegetation indices (VI) based on multispectral reflectance data essentially measure the green area index (GAI) or response to chlorophyll content of the canopy surface and not the entire aboveground biomass that may be present from non-green elements that are key to fully assessing the carbon budget. Methods with light detection and ranging (LiDAR) have started to emerge using gap fraction (GF) to estimate the plant area index (PAI) based on canopy density. These LiDAR methods have the main advantage of being sensitive to both green and non-green plant elements. They have primarily been applied to forest cover with manned airborne LiDAR systems (ALS) and have yet to be used extensively with crops such as winter wheat using LiDAR on unmanned aircraft systems (UAS). This study contributes to a better understanding of the potential of LiDAR as a tool to estimate canopy structure in precision farming. The LiDAR method proved to have a high to moderate correlation in spatial variation to the multispectral method. The LiDAR-derived PAI values closely resemble the SunScan Ceptometer GAI ground measurements taken early in the growing season before major stages of senescence. Later in the growing season, when the canopy density was at its highest, a possible overestimation may have occurred. This was most likely due to the chosen flight parameters not providing the best depictions of canopy density with consideration of the LiDAR’s perspective, as the ground-based destructive measurements provided lower values of PAI. Additionally, a distinction between total LiDAR-derived PAI, multispectral-derived GAI, and brown area index (BAI) is made to show how the active and passive optical sensor methods used in this study can complement each other throughout the growing season.

Feasibility of using an energy balance to measure evapotranspiration in green stormwater infrastructure.

Effective green stormwater infrastructure (GSI) design requires comprehensive quantification of the volume of water that can be treated or removed over a given time period. It is recognized that evapotranspiration (ET) can be a substantial pathway for stormwater volume reduction in bioretention systems. However, measuring ET is often difficult and expensive, such as with lysimeters or a mass balance approach. This research focused on a new technique for quantifying ET in bioretention systems by exploring an approach using thermal imaging to calculate ET by measuring the flux of energy at the canopy surface. This thermal imaging approach was compared to ET measurements given by a traditional mass balance approach. The experimental setup had three benchtop scale vegetated lysimeters planted with Switchgrass. Time lapse thermal images of the Switchgrass plants were taken at 10 second intervals and paired with meteorological data. The data were used in an energy balance to estimate the mass of water lost from the lysimeter plant/soil system. That mass was compared to the change in weight measured by weighing the lysimeter before and after the data collection period. For comparison, reference ET was also calculated for the vegetated systems using three common reference ET equations. The uncalibrated energy balance equation developed here estimated an averaged ET over 12 data collection days within 1 mm of the mass balance measured ET. These findings demonstrate the feasibility of using a thermal image energy balance technique to estimate ET.