Field-based Dataset Research Articles

Characterisation of Banana Plant Growth Using High-Spatiotemporal-Resolution Multispectral UAV Imagery

The determination of key phenological growth stages of banana plantations, such as flower emergence and plant establishment, is difficult due to the asynchronous growth habit of banana plants. Identifying phenological events assists growers in determining plant maturity, and harvest timing and guides the application of time-specific crop inputs. Currently, phenological monitoring requires repeated manual observations of individual plants’ growth stages, which is highly laborious, time-inefficient, and requires the handling and integration of large field-based data sets. The ability of growers to accurately forecast yield is also compounded by the asynchronous growth of banana plants. Satellite remote sensing has proved effective in monitoring spatial and temporal crop phenology in many broadacre crops. However, for banana crops, very high spatial and temporal resolution imagery is required to enable individual plant level monitoring. Unoccupied aerial vehicle (UAV)-based sensing technologies provide a cost-effective solution, with the potential to derive information on health, yield, and growth in a timely, consistent, and quantifiable manner. Our research explores the ability of UAV-derived data to track temporal phenological changes of individual banana plants from follower establishment to harvest. Individual plant crowns were delineated using object-based image analysis, with calculations of canopy height and canopy area producing strong correlations against corresponding ground-based measures of these parameters (R2 of 0.77 and 0.69 respectively). A temporal profile of canopy reflectance and plant morphology for 15 selected banana plants were derived from UAV-captured multispectral data over 21 UAV campaigns. The temporal profile was validated against ground-based determinations of key phenological growth stages. Derived measures of minimum plant height provided the strongest correlations to plant establishment and harvest, whilst interpolated maxima of normalised difference vegetation index (NDVI) best indicated flower emergence. For pre-harvest yield forecasting, the Enhanced Vegetation Index 2 provided the strongest relationship (R2 = 0.77) from imagery captured near flower emergence. These findings demonstrate that UAV-based multitemporal crop monitoring of individual banana plants can be used to determine key growing stages of banana plants and offer pre-harvest yield forecasts.

Allometric Scaling of Anaerobic Capacity Estimated from a Unique Field-Based Data Set of Fish Swimming.

AbstractLocomotion is a defining characteristic that can dictate many aspects of an organism's life history in the pursuit of maximizing fitness, including escaping predators, capturing prey, and transitioning between habitats. Exhaustive exercise can have negative consequences for both short-term and long-term energetics and life history trade-offs, influencing fish survival and reproduction. Studies of swimming performance and exhaustive exercise in fish are often conducted on individual species, but few multispecies analyses exist and even fewer in field settings. In fish, swimming performance and exercise have historically been studied in the laboratory using swim tunnels, but an increasing body of work in recreational fisheries science provides a novel way to examine swimming capacity and exhaustion. Using fight time, the time it takes for a hooked fish to be landed on rod and reel fishing gear, as an opportunistic proxy for fish exhaustion, a multispecies meta-analysis of data from studies on recreational fisheries was conducted to elucidate the factors that most influence capacity for exhaustive exercise. Data from 39 species of freshwater and marine fish were aggregated, and negative binomial mixed effects models as well as phylogenetic least squares regression were used to identify the factors that most influenced exhaustive exercise in the field. Fish total length, aspect ratio of the caudal fin, and body form were significant factors in explaining the capacity for exhaustive exercise. Large migratory fish with high aspect ratios were able to fight, and therefore exercise, the longest. These results illustrate that body form and physiology are both deeply intertwined to inform function across fish species and point to angling fight time as a useful approximation of fish swimming capabilities that can be further developed for understanding the limits of fish exercise physiology.

Aboveground biomass mapping by integrating ICESat-2, SENTINEL-1, SENTINEL-2, ALOS2/PALSAR2, and topographic information in Mediterranean forests

ABSTRACT The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) provides an extraordinary opportunity to support global large-scale forest carbon mapping, but further research is needed in order to obtain wall-to-wall forest aboveground biomass (AGB) maps with this technology. The effects of vegetation structure on the performance of canopy height and AGB modeling using ICESat-2 photon-counting light detection and ranging (LiDAR) data in Mediterranean forest areas have not been previously studied in the literature. In this study, we combined recent ICESat-2 vegetation (ATL08) data, Airborne Laser Scanning (ALS)- and field-based estimates, and a multi-sensor earth observation composite for extrapolation of AGB estimates and AGB mapping. A diverse gradient of forest Mediterranean ecosystems, distributed over 19,744.15 km2 of forest area in the region of Extremadura (Spain), with different species and structural complexity forming 5 different forest types (3 Quercus spp. dominated and 2 Pinus spp. dominated forests), was used to (i) evaluate the precision of ICESat-2 canopy height estimations, (ii) develop ICESat-2-based AGB models, and (iii) generate a spatially continuous prediction of AGB by using data from the satellite missions Sentinel-1 (S1), Sentinel-2 (S2), Phased Array L-band Synthetic Aperture Radar (ALOS2/PALSAR2), and Shuttle Radar Topography Mission (SRTM). First, ALS- and ICESat-2-derived metrics that best described canopy height (p98 and rh98, respectively) were compared at the ATL08 segment level. Second, ALS-based AGB values were derived at the ATL08 segment scale. Third, ALS-based AGB estimates at the ICESat-2 segment level were used as dependent variables to fit ICESat-2-based AGB models. Fourth, a multi-sensor approach was then implemented to predict ICESat-2-derived AGB, by means of a Random Forest (RF) modeling technique, with predictors retrieved from S1, S2, ALOS2/PALSAR2, and SRTM. Finally, RF was used to generate wall-to-wall AGB maps that were compared with field-, ALS- and ICESat-2-based observations. The agreement between the ALS- and ICESat-2-derived metrics related to the canopy height distribution was higher for Pinus spp. forest than for the Quercus spp-dominated forests. The ICESat-2-based AGB models yielded model efficiency (Mef) values between 0.56 and 0.80, with a RMSE ranging from 7.76 to 17.71 Mg ha−1 and rRMSE from 19.04 to 55.21%. The multi-sensor RF models provided the following results when compared with the ICESat-2- and ALS-based AGB observations: R 2 values of 0.63 and 0.64, and RMSE values of 11.10 Mg ha−1(rRMSE = 28.15%) and 12.28 Mg ha−1 (rRMSE = 31.45%), respectively, and an approximately unbiased result (0.03 Mg ha−1 and 0.09 Mg ha−1). When applied to the field-based validation data set (4th Spanish National Forest Inventory (SNFI-4) plots = 508), the RF-derived AGB model showed a relatively lower predictive capacity (R 2 = 0.45), a higher RMSE value (25.88 Mg ha−1) and slightly biased results (−1.47 Mg ha−1), especially for larger field-derived AGB intervals. The results of this study serve to provide an initial quantitative assessment of the ICESat-2 ATL08 data for large-scale AGB estimation. The findings suggest that a multi-sensor approach may be feasible for extrapolating ICESat-2-derived AGB estimates over areas where field or ALS reference data are not available.

Validation of Remote-Sensing Algorithms for Diffuse Attenuation of Downward Irradiance Using BGC-Argo Floats

Estimates of the diffuse attenuation coefficient (Kd) at two different wavelengths and band-integrated (PAR) were obtained using different published algorithms developed for open ocean waters spanning in type from explicit-empirical, semi-analytical and implicit-empirical and applied to data from spectral radiometers on board six different satellites (MODIS-Aqua, MODIS-Terra, VIIRS–SNPP, VIIRS-JPSS, OLCI-Sentinel 3A and OLCI-Sentinel 3B). The resultant Kds were compared to those inferred from measurements of radiometry from sensors on board autonomous profiling floats (BGC-Argo). Advantages of BGC-Argo measurements compared to ship-based ones include: 1. uniform sampling in time throughout the year, 2. large spatial coverage, and 3. lack of shading by platform. Over 5000 quality-controlled matchups between Kds derived from float and from satellite sensors were found with values ranging from 0.01 to 0.67 m−1. Our results show that although all three algorithm types provided similarly ranging values of Kd to those of the floats, for most sensors, a given algorithm produced statistically different Kd distributions from the two others. Algorithm results diverged the most for low Kd (clearest waters). Algorithm biases were traced to the limitations of the datasets the algorithms were developed and trained with, as well as the neglect of sun angle in some algorithms. This study highlights: 1. the importance of using comprehensive field-based datasets (such as BGC-Argo) for algorithm development, 2. the limitation of using radiative-transfer model simulations only for algorithm development, and 3. the potential for improvement if sun angle is taken into account explicitly to improve empirical Kd algorithms. Recent augmentation of profiling floats with hyper-spectral radiometers should be encouraged as they will provide additional constraints to develop algorithms for upcoming missions such as NASA’s PACE and SBG and ESA’s CHIME, all of which will include a hyper-spectral radiometer.

LWIR hyperspectral image classification based on a temperature-emissivity residual network and conditional random field model

ABSTRACT Long-wave infrared (LWIR) hyperspectral images can provide temperature and emissivity information, and have unique advantages in classification tasks, especially in the fields of minerals, vegetation, and man-made materials. However, LWIR image acquisition is greatly affected by the atmosphere, which results in obvious spectral noise. Meanwhile, field-based and aerial thermal infrared hyperspectral images are faced with problems such as spatial heterogeneity and salt-and-pepper noise, which results in the traditional spectral-based and object-oriented classification methods performing poorly in the classification task. In this paper, using the emissivity spectra and land surface temperature inversed from LWIR hyperspectral images, a classification framework based on a temperature-emissivity residual network and conditional random field model (TERN-CRF) is proposed. In this method, the residual network is designed to extract and fuse in-depth spectral and local spatial features, and the conditional random field (CRF) model incorporates the spatial-contextual information by the use of the temperature information, to improve the problem of voids and isolated regions in the classification map. To validate the performance of the proposed method, three LWIR datasets acquired by the Hyper-Cam sensor were used in the experiments: an aerial dataset and two field-based datasets of minerals and leaves. The experimental classification results obtained using the LWIR datasets confirm the performance of the proposed TERN-CRF classification method, which is shown to be superior to the traditional hyperspectral image classification methods and a CNN-based method. Furthermore, the CRF post-processing part can effectively alleviate the isolated regions in the classification map.

Targeting occupant feedback using digital twins: Adaptive spatial–temporal thermal preference sampling to optimize personal comfort models

Collecting intensive longitudinal thermal preference data from building occupants is emerging as an innovative means of characterizing the performance of buildings and the people who use them. These techniques have occupants giving subjective feedback using smartphones or smartwatches frequently over the course of days or weeks. The intention is that the data will be collected with high spatial and temporal diversity to best characterize a building and the occupant’s preferences. But in reality, leaving the occupant to respond in an ad-hoc or fixed interval way creates unneeded survey fatigue and redundant data. This paper outlines a scenario-based (virtual experiment) method for optimizing data sampling using a smartwatch to achieve comparable accuracy in a personal thermal preference model with fewer data. This method uses BIM-extracted spatial data and Graph Neural Network-based (GNN) modeling to find regions of similar comfort preference to identify the best scenarios for triggering the occupant to give feedback. This method is compared to two baseline scenarios that use conventional zoning and a generic 4x4 square meter grid method from two field-based datasets. The results show that the proposed Build2Vec method has an 18%–23% higher overall sampling quality than the spaces-based and square-grid-based sampling methods. The Build2Vec method also performs similar to the baselines when removing redundant occupant feedback points but with better scalability potential.

Assessing the structure of coastal forested wetland using field and remote sensing data

Coastal Swamp Oak Forest (CSOF), a supratidal wetland community dominated by Casuarina glauca, is a widely distributed coastal ecosystem along Australia's east coast. These wetland communities are highly valuable for providing ecosystem services, including carbon sequestration. Positioned within the supratidal zone of estuaries – and often abutting upper intertidal saltmarsh and/or mangrove – CSOF may be vulnerable to salinity intrusion and increased tidal inundation due to sea-level rise. To understand spatial patterns of vegetation composition and structure in CSOF, field-based (in-situ) and remote-sensing approaches were employed on the Minnamurra floodplain, New South Wales, Australia. In-situ vegetation surveys within 23 field plots located along seaward to landward transects revealed large variations in mean tree height (2.5–13.1 m) and tree densities (100–8700 trees/ha). Unmanned Aerial Vehicles with Structure from Motion (UAV-SfM), and airborne Light Detection and Ranging (LiDAR) approaches returned mean plot canopy height estimates ranging between 0.1 and 12.8 m. Comparison of vegetation metrics between remote sensors (UAV-SfM and LiDAR) demonstrated similar capacities (R2 values > 0.85) to capture CSOF vegetation height. Comparison of field and spatial metrics elucidated a moderate correlation between the datasets for maximum canopy height (R2 > 0.6) which can be partially explained by the different spatial scales of measurement among these approaches. Canopy height, Normalised Difference Vegetation Index (NDVI), and point density (i.e., vegetation density) estimates were each positively correlated with elevation above mean sea-level. This coincides with indications of plant stress and/or mortality at the seaward edge of CSOF, and in topographic depressions. These findings suggest physico-chemical gradients exert a strong control on CSOF vegetation structure and health, with implications for the current acceleration of sea-level rise. When combined, remote sensing and field-based datasets are useful to characterise and quantify CSOF structure and distribution and can therefore be employed in future assessments of this understudied ecosystem.

Wheat-Net: An Automatic Dense Wheat Spike Segmentation Method Based on an Optimized Hybrid Task Cascade Model.

Precise segmentation of wheat spikes from a complex background is necessary for obtaining image-based phenotypic information of wheat traits such as yield estimation and spike morphology. A new instance segmentation method based on a Hybrid Task Cascade model was proposed to solve the wheat spike detection problem with improved detection results. In this study, wheat images were collected from fields where the environment varied both spatially and temporally. Res2Net50 was adopted as a backbone network, combined with multi-scale training, deformable convolutional networks, and Generic ROI Extractor for rich feature learning. The proposed methods were trained and validated, and the average precision (AP) obtained for the bounding box and mask was 0.904 and 0.907, respectively, and the accuracy for wheat spike counting was 99.29%. Comprehensive empirical analyses revealed that our method (Wheat-Net) performed well on challenging field-based datasets with mixed qualities, particularly those with various backgrounds and wheat spike adjacence/occlusion. These results provide evidence for dense wheat spike detection capabilities with masking, which is useful for not only wheat yield estimation but also spike morphology assessments.

The building blocks of igneous sheet intrusions: Insights from 3-D seismic reflection data

Abstract The propagating margins of igneous sills (and other sheet intrusions) may divide into laterally and/or vertically separated sections, which later inflate and coalesce. These components elongate parallel to and thus record the magma flow direction, and they can form either due to fracture segmentation (i.e., “segments”) or brittle and/or non-brittle deformation of the host rock (i.e., “magma fingers”). Seismic reflection data can image entire sills or sill-complexes in 3-D, and their resolution is often sufficient to allow us to identify these distinct elongate components and thereby map magma flow patterns over entire intrusion networks. However, seismic resolution is limited, so we typically cannot discern the centimeter- to meter-scale host rock deformation structures that would allow the origin of these components to be interpreted. Here, we introduce a new term that defines the components (i.e., “elements”) of sheet-like igneous intrusions without linking their description to emplacement mechanisms. Using 3-D seismic reflection data from offshore NW Australia, we quantify the 3-D geometry of these elements and their connectors within two sills and discuss how their shape may relate to emplacement processes. Based on seismic attribute analyses and our measurements of their 3-D geometry, we conclude that the mapped elements likely formed through non-elastic-brittle and/or non-brittle deformation ahead of the advancing sill tip, which implies they are magma fingers. We show that thickness varies across sills, and across distinct elements, which we infer to represent flow localization and subsequent thickening of restricted areas. The quantification of element geometries is useful for comparisons between different subsurface and field-based data sets that span a range of host rock types and tectonic settings. This, in turn, facilitates the testing of magma emplacement mechanisms and predictions from numerical and physical analogue experiments.

Detection of Outliers in LiDAR Data Acquired by Multiple Platforms over Sorghum and Maize

High-resolution point cloud data acquired with a laser scanner from any platform contain random noise and outliers. Therefore, outlier detection in LiDAR data is often necessary prior to analysis. Applications in agriculture are particularly challenging, as there is typically no prior knowledge of the statistical distribution of points, plant complexity, and local point densities, which are crop-dependent. The goals of this study were first to investigate approaches to minimize the impact of outliers on LiDAR acquired over agricultural row crops, and specifically for sorghum and maize breeding experiments, by an unmanned aerial vehicle (UAV) and a wheel-based ground platform; second, to evaluate the impact of existing outliers in the datasets on leaf area index (LAI) prediction using LiDAR data. Two methods were investigated to detect and remove the outliers from the plant datasets. The first was based on surface fitting to noisy point cloud data via normal and curvature estimation in a local neighborhood. The second utilized the PointCleanNet deep learning framework. Both methods were applied to individual plants and field-based datasets. To evaluate the method, an F-score was calculated for synthetic data in the controlled conditions, and LAI, the variable being predicted, was computed both before and after outlier removal for both scenarios. Results indicate that the deep learning method for outlier detection is more robust than the geometric approach to changes in point densities, level of noise, and shapes. The prediction of LAI was also improved for the wheel-based vehicle data based on the coefficient of determination (R2) and the root mean squared error (RMSE) of the residuals before and after the removal of outliers.

Using Convolutional Neural Networks for Detection and Morphometric Analysis of Carolina Bays from Publicly Available Digital Elevation Models

Carolina Bays are oriented and sandy-rimmed depressions that are ubiquitous throughout the Atlantic Coastal Plain (ACP). Their origin has been a highly debated topic since the 1800s and remains unsolved. Past population estimates of Carolina Bays have varied vastly, ranging between as few as 10,000 to as many as 500,000. With such a large uncertainty around the actual population size, mapping these enigmatic features is a problem that requires an automated detection scheme. Using publicly available LiDAR-derived digital elevation models (DEMs) of the ACP as training images, various types of convolutional neural networks (CNNs) were trained to detect Carolina bays. The detection results were assessed for accuracy and scalability, as well as analyzed for various morphologic, land-use and land cover, and hydrologic characteristics. Overall, the detector found over 23,000 Carolina Bays from southern New Jersey to northern Florida, with highest densities along interfluves. Carolina Bays in Delmarva were found to be smaller and shallower than Bays in the southeastern ACP. At least a third of Carolina Bays have been converted to agricultural lands and almost half of all Carolina Bays are forested. Few Carolina Bays are classified as open water basins, yet almost all of the detected Bays were within 2 km of a water body. In addition, field investigations based upon detection results were performed to describe the sedimentology of Carolina Bays. Sedimentological investigations showed that Bays typically have 1.5 m to 2.5 m thick sand rims that show a gradient in texture, with coarser sand at the bottom and finer sand and silt towards the top. Their basins were found to be 0.5 m to 2 m thick and showed a mix of clayey, silty, and sandy deposits. Last, the results compiled during this study were compared to similar depressional features (i.e., playa-lunette systems) to pinpoint any similarities in origin processes. Altogether, this study shows that CNNs are valuable tools for automated geomorphic feature detection and can lead to new insights when coupled with various forms of remotely sensed and field-based datasets.

European primary forest database v2.0

Primary forests, defined here as forests where the signs of human impacts, if any, are strongly blurred due to decades without forest management, are scarce in Europe and continue to disappear. Despite these losses, we know little about where these forests occur. Here, we present a comprehensive geodatabase and map of Europe’s known primary forests. Our geodatabase harmonizes 48 different, mostly field-based datasets of primary forests, and contains 18,411 individual patches (41.1 Mha) spread across 33 countries. When available, we provide information on each patch (name, location, naturalness, extent and dominant tree species) and the surrounding landscape (biogeographical regions, protection status, potential natural vegetation, current forest extent). Using Landsat satellite-image time series (1985–2018) we checked each patch for possible disturbance events since primary forests were identified, resulting in 94% of patches free of significant disturbances in the last 30 years. Although knowledge gaps remain, ours is the most comprehensive dataset on primary forests in Europe, and will be useful for ecological studies, and conservation planning to safeguard these unique forests.

Global patterns and climatic drivers of above- and belowground net primary productivity in grasslands.

Understanding patterns and determinants of net primary productivity (NPP) in global grasslands is ongoing challenges, especially for belowground NPP (BNPP) and its fraction (fBNPP). By developing a comprehensive field-based dataset, we revealed that, along with gradients of mean annual precipitation, actual evapotranspiration, and aridity, aboveground NPP (ANPP), BNPP, and total NPP (TNPP) exhibited hump-shaped patterns, whereas fBNPP showed an opposite trend. ANPP and TNPP showed positive correlations with mean annual temperature, but fBNPP was negatively correlated with it. The relationship between BNPP and climatic factors was considerably weak, indicating that BNPP was relatively stable regardless of the climate conditions. We also observed that the sensitivities of ANPP and BNPP to interannual temperature variability and those of BNPP to interannual precipitation fluctuations exhibited large variations among different study sites, and differed from those at the spatial scale. In contrast, the temporal sensitivities of ANPP to interannual precipitation variability were highly similar across all the individual sites and much smaller than those at the spatial scale. Overall, these results highlight that precipitation, temperature and evapotranspiration all play vital roles in shaping ANPP pattern and its partitioning to belowground and that the patterns of BNPP along climatic gradients do not mirror those of the ANPP.

River Water Quality of the Selenga-Baikal Basin: Part II—Metal Partitioning under Different Hydroclimatic Conditions

The partitioning of metals and metalloids between their dissolved and suspended forms in river systems largely governs their mobility and bioavailability. However, most of the existing knowledge about catchment-scale metal partitioning in river systems is based on a limited number of observation points, which is not sufficient to characterize the complexity of large river systems. Here we present an extensive field-based dataset, composed of multi-year data from over 100 monitoring locations distributed over the large, transboundary Selenga River basin (of Russia and Mongolia), sampled during different hydrological seasons. The aim is to investigate on the basin scale, the influence of different hydroclimatic conditions on metal partitioning and transport. Our results showed that the investigated metals exhibited a wide range of different behaviors. Some metals were mostly found in the dissolved form (84–96% of Mo, U, B, and Sb on an average), whereas many others predominantly existed in suspension (66–87% of Al, Fe, Mn, Pb, Co, and Bi). Nevertheless, our results also showed a consistently increasing share of metals in dissolved form as the metals were transported to the downstream parts of the basin, closer to the Lake Baikal. Under high discharge conditions (including floods), metal transport by suspended particulate matter was significantly greater (about 2–6 times). However, since high and low water conditions could prevail simultaneously at a given point of time within the large river basin, e.g., as a result of on-going flood propagation, snap-shot observations of metal partitioning demonstrated contrasting patterns with domination of both particulate and dissolved phases in different parts of the basin. Such heterogeneity of metal partitioning is likely to be found in many large river systems. These results point out the importance of looking into different hydroclimatic conditions across space and time, both for management purposes and contaminant modeling efforts at the basin scale.

Parameterising resuspension in aquaculture waste deposition modelling

Sustainable expansion of global aquaculture depends on a thorough understanding of environmental impacts. Open-water culture operations produce waste food and faeces, the benthic impacts of which are a focus of regulation. Seabed interactions of wastes are complex, depending on current velocity, seabed substrate and waste material characteristics. The accuracy achieved in modelling intensity and spatial extent of impacts is contingent upon the representation of this interaction and its implications for resuspension. We used benthic flumes to study resuspension processes at 11 salmon aquaculture sites, covering a range of sediment types. Erosion rates and critical entrainment stress were computed at the cage edge and between 100 and 500 m away, characterising seabed erodibility in highly impacted and less impacted sediments. Heavily impacted cage-edge sediments had an erosion threshold (mean 0.02 N m-2) that was an order of magnitude lower, and markedly less heterogeneous, than that of nearby less impacted sediments (mean across sites 0.19 N m-2). This likely reflects a seabed which was smothered by waste material close to the depositional centre. Further out, the covering of waste material is less continuous, thinner, and more admixed with underlying sediments. Bed erosion rates were found to be a linear function of excess stress. The results provide important information on how benthic flumes can be deployed to collect spatial and temporal data for parameterisation of erosion and entrainment processes in numerical waste transport simulation models such as DEPOMOD, and the comparatively large field-based dataset should contribute to the goal of allowing a more realistic representation of particulate waste in these models.

Field-based investigation of aged biochar coupled with summer legumes effect on wheat yield in Pakistan

There is a debate about whether the aged biochar effect can increase the crop yield or not. Herein, a field-based experimental data set and analysis provide the information on the aged biochar effect coupled with summer legumes on the yield of subsequent wheat. Briefly, in summer 2016, three different types of legumes i.e. mungbean, sesbania, and cowpea were grown with the intention of grain for human consumption, green manuring for soil fertility improvement, and fodder for livestock consumption. A fallow was also adjusted in the experiment with the purpose of comparison. Biochar was added to each experimental plot in triplicates at the rate of 0, 5, and 10 tons ha-1. After the harvesting of legumes, the biomass of each sesbania treatment plot was mixed in the field while the biomass of mungbean and cowpea were removed from each respective plot. To investigate the aged biochar effect, the wheat crop was grown on the same field layout and design (randomized complete block) of legumes. The data analysis highlighted that significantly maximum grain yield (kg ha-1), biological yield (kg ha-1); thousand-grain weight (g), and straw yield (kg ha-1) were obtained in the plots mixed with sesbania. Regarding the aged biochar effect, maximum yield was obtained in the plots with 10 tons ha-1treatment dose. Additionally, the interaction of aged biochar coupled with legumes was non-significant. In conclusion, this work could prove that aged biochar coupled with summer legumes enhanced the yield of subsequent wheat on a sustainable basis due to its long-term numerous benefits to the soil-plant system.

Hydro-geomorphic assessment of erosion intensity and sediment yield initiated debris-flow hazards at Wadi Dahab Watershed, Egypt

ABSTRACT This study attempts to assess slope and channel erosion for modelling their implications on debris-flow occurrences in Wadi Dahab Watershed (WDW). Remote sensing and Geographic Information System (GIS) were integrated to appraise erosion rates from a hillslope and channel storage throughout WDW. A mass-wasting database was built initially for modelling hazard zones and validating the final map using a bivariate statistical analysis. An Erosion Hazard Model (EHM) was developed to evaluate the erosion intensity and sediment yield throughout WDW and to prognosticate the hazard zones due to debris-flows. The EHM was developed based on hydrological and geomorphic controls which are responsible for disintegrating bedrocks, delivering detritus downslopes, and accelerating debris through channels. Multi-source datasets, including topographic and geologic maps, climatic, satellite images, aerial photographs, and field-based datasets, were used to derive factors associated with the hydro-geomorphic processes. A spatial prediction of erosion intensity was obtained by the integration of both static and dynamic factors generated hazards in GIS platform. The erosion intensity map classifies WDW relatively to five intensity zones in which the most hazardous zones are distributed in steep sloping terrains and structurally controlled channels covered by metamorphic and clastic rocks. The erosion intensity map was correlated and tested against the debris-flows dataset which was not used during the spatial modelling process. The statistical correlation analysis has confirmed that the debris-flow locations increase exponentially in the high erosion intensity zones. The holistic integration approach provides the promising model for forecasting critical zones prone to erosion intensity and their associated hazards in WDW.

Automatic identification for field butterflies by convolutional neural networks

In today’s competitive conditions, producing fast, inexpensive and reliable solutions are an objective for engineers. Development of artificial intelligence and the introduction of this technology to almost all areas have created a need to minimize the human factor by using artificial intelligence in the field of image processing, as well as to make a profit in terms of time and labor. In this paper, we propose an automated butterfly species identification model using deep neural networks. We collected 44,659 images of 104 different butterfly species taken with different positions of butterflies, the shooting angle, butterfly distance, occlusion and background complexity in the field in Turkey. Since many species have a few image samples we constructed a field-based dataset of 17,769 butterflies with 10 species. Convolutional Neural Networks (CNNs) were used for the identification of butterfly species. Comparison and evaluation of the experimental results obtained using three different network structures are conducted. Experimental results on 10 common butterfly species showed that our method successfully identified various butterfly species.

Comparison of terrestrial LiDAR and digital hemispherical photography for estimating leaf angle distribution in European broadleaf beech forests

Leaf inclination plays a crucial role in regulating the radiation, carbon and water fluxes in plant canopies. Accurate measurement of the probability density function of leaf inclination (i.e., the leaf angle distribution or leaf inclination distribution function (LIDF)), is very important for modelling photosynthesis as well as measuring leaf area index. In spite of its importance, in situ measurement of LIDF is very challenging. Both digital hemispherical photography (DHP) and terrestrial LiDAR (TLS) have been used to measure LIDF. However, the consistency and relative accuracy of these two techniques has never been evaluated.In this research, we aimed to evaluate which in situ technique, either DHP or TLS, could measure LIDF more accurately, with respect to both field-based and synthetic datasets. The field-based datasets were collected from 36 natural European beech stands covering a range of forest structures. The synthetic datasets were generated from 44 virtual forest scenes using TLS and DHP simulators. Due to the inability of differing leaf and woody materials in DHP, the average plant inclination angle (θ¯) from DHP and TLS was selected for LIDF comparison. The average inclination angle (θ¯) was retrieved from TLS point clouds using a geometric method, and from DHP using three gap fraction inversion methods including the NC method (Norman and Campbell, 1989), as well as the CAN-EYE and Hemisfer software.Results from the field-based datasets showed a significant difference and inconsistency between the average inclination angle (θ¯) retrieved from TLS and DHP (respectively θ¯∈(40°, 58°) from TLS, (15°, 86°) from DHP NC, (36°, 78°) from DHP CAN-EYE, (0°, 67°) from DHP Hemisfer). Results from the synthetic datasets demonstrated that the accuracy of θ¯ from TLS was considerably higher than that obtained from DHP (R2: 0.90 > 0.74; RMSE: 5.38° < 13.30°).This study demonstrated that the LIDF estimated from TLS and DHP were not coherent. Based on the experimental results as well as deduction from theoretical arguments, we recommended using TLS when measuring leaf inclination in broadleaf forests, especially for stands with a heterogeneous structure.

Optimizing biomass estimates of savanna woodland at different spatial scales in the Brazilian Cerrado: Re-evaluating allometric equations and environmental influences

Cerrado is the second largest biome in South America and accounted for the second largest contribution to carbon emissions in Brazil for the last 10 years, mainly due to land-use changes. It comprises approximately 2 million km2 and is divided into 22 ecoregions, based on environmental conditions and vegetation. The most dominant vegetation type is cerrado sensu stricto (cerrado ss), a savanna woodland. Quantifying variation of biomass density of this vegetation is crucial for climate change mitigation policies. Integrating remote sensing data with adequate allometric equations and field-based data sets can provide large-scale estimates of biomass. We developed individual-tree aboveground biomass (AGB) allometric models to compare different regression techniques and explanatory variables. We applied the model with the strongest fit to a comprehensive ground-based data set (77 sites, 893 plots, and 95,484 trees) to describe AGB density variation of cerrado ss. We also investigated the influence of physiographic and climatological variables on AGB density; this analysis was restricted to 68 sites because eight sites could not be classified into a specific ecoregion, and one site had no soil texture data. In addition, we developed two models to estimate plot AGB density based on plot basal area. Our data show that for individual-tree AGB models a) log-log linear models provided better estimates than nonlinear power models; b) including species as a random effect improved model fit; c) diameter at 30 cm above ground was a reliable predictor for individual-tree AGB, and although height significantly improved model fit, species wood density did not. Mean tree AGB density in cerrado ss was 22.9 tons ha-1 (95% confidence interval = ± 2.2) and varied widely between ecoregions (8.8 to 42.2 tons ha-1), within ecoregions (e.g. 4.8 to 39.5 tons ha-1), and even within sites (24.3 to 69.9 tons ha-1). Biomass density tended to be higher in sites close to the Amazon. Ecoregion explained 42% of biomass variation between the 68 sites (P < 0.01) and shows strong potential as a parameter for classifying regional biomass variation in the Cerrado.