Above Ground Level Research Articles

Wind Energy Analysis in the Coastal Region of Bangladesh

Diversifying the energy mix of Bangladesh is becoming indispensable not only to improve its energy security, but also for a more sustainable economic development. This study focused on mapping the wind potential of southern coastal areas of Bangladesh to estimate the wind energy potential, along with the reduction in carbon emissions due to wind energy. Analysis of the carbon footprint was based on the annual energy production (AEP) from the selected low-wind turbine generators (WTGs). The time series-measured and -predicted wind data were incorporated with the high-resolution mesoscale and microscale wind re-source mapping technique at 60, 80, and 100 m above ground level (AGL). Coupling mesoscale and microscale modeling provided reliable mapping results for the commercially exploitable wind resource and was verified by ground-based wind measurement. The results revealed that, among the selected areas, two sites named Charfashion and Monpura have a promising annual mean wind speed of 7.3 m/s at 100 m AGL for energy generation. Different WTGs with ranges of 1–3.3 MW were used to estimate the wind energy generation capacity at different sites in the study area. A WTG with a 1 MW wind energy generation capacity installed at 60 m AGL in the selected site has the potential to produce 2.79 GWh/year of clean energy, reducing 1781.689 tons of CO2 per year, whereas a 3.3 MW WTG at 80 m AGL can produce 18.99 GWh/year of energy, reducing 12,098.54 tons of CO2 per year, and a 1.6 MW WTG at 100 m AGL produces 11.04 GWh/year of energy, cutting 7035.028 tons of CO2 per year. With its reliable scientific and time-tested wind energy estimation method, this research is very important for the development of wind energy in the southern coastal areas of Bangladesh to meet the increasing energy demands through initiating the development of renewable energy to improve the energy security and reduce the carbon emissions of the country.

Steps to build a DIY low-cost fixed-wing drone for biodiversity conservation.

Despite the proved usefulness of drones in biodiversity studies, acquisition costs and difficulties in operating, maintaining and repairing these systems constrain their integration in conservation projects, particularly for low-income countries. Here we present the steps necessary to build a low-cost fixed-wing drone for environmental applications in large areas, along with instructions to increase the reliability of the system and testing its performance. Inspired by DIY (Do It Yourself) and open source models, this work prioritizes simplicity and accounts for cost-benefit for the researcher. The DIY fixed-wing drone developed has electric propulsion, can perform pre-programmed flight, can carry up to 500 g payload capacity with 65 minutes flight duration and flies at a maximum distance of 20 km. It is equipped with a RGB (Red, Green and Blue) sensor capable of obtaining 2.8 cm per pixel Ground Sample Distance (GSD) resolution at a constant altitude of 100 m above ground level (AGL). The total cost was $995 which is substantially less than the average value of similar commercial drones used in biodiversity studies. We performed 12 flight tests in auto mode using the developed model in protected areas in Brazil, obtaining RGB images that allowed us to identify deforestation spots smaller than 5 m2 and medium-sized animals. Building DIY drones requires some technical knowledge and demands more time than buying a commercial ready-to-fly system, but as proved here, it can be less expensive, which is often crucial in conservation projects.

Evaluating surface renewal models for estimating sensible heat flux above and within a coffee agroforestry system

The surface renewal method offers an inexpensive way to estimate the sensible heat flux (H). When combined with measurements of net radiation and soil heat flux, latent heat flux (evapotranspiration) can be estimated indirectly. This study evaluated the performance of two surface renewal models to estimate H in a traditional coffee agroforestry system. The first model (Snyder96) is the commonly used approach in which H is calculated from the average temperature ramp amplitude and duration, which in turn are calculated from the 2nd, 3rd, and 5th order structure functions. The second model (Chen97) is a reformulation of Snyder96 in which H is calculated from the 3rd order structure function and friction velocity (u*). The models were calibrated and evaluated with eddy covariance data. Tests were conducted at three levels (above-canopy, above-coffee, above-ground), with each level having two replicate plots. Both models showed generally good agreement with the observations when using humidity-corrected sonic temperature data. Estimates from Chen97 agreed slightly better with measurements than those from Snyder96. In addition, the results suggest that for Chen97 the same calibration can be used for the above-canopy and above-coffee levels and for positive and negative H. Also for the above-ground level the combined empirical coefficient of Chen97 was similar to those for the other levels, but the variability between plots was too high to draw conclusions. Conversely, the results showed that Snyder96 requires separate calibrations for each level and for positive and negative H. For Chen97, similarly good results were obtained when using frequency response-corrected thermocouple data. For Snyder96, results were also good above the canopy, but not within the canopy, particularly close to the ground. Chen97 needs wind speed to calculate u*. Wind speed is needed for both models if the signals of thicker, stronger thermocouples are corrected for the frequency response error.

Characteristics of Energy Dissipation Rate Observed from the High-Frequency Sonic Anemometer at Boseong, South Korea

The characteristics of low-level turbulence at Boseong, located on the southern coast of South Korea, were investigated in terms of eddy dissipation rate (EDR) using 1-year (2018) of wind data obtained from the Boseong Meteorological Observatory (BMO), a World Meteorological Organization testbed. At BMO, a 307 m tall tower is installed on which four high-frequency (20 Hz) sonic anemometers are mounted at 60, 140, and 300 m above ground level (AGL). In addition, a sonic anemometer at 2.5 m AGL is located to the south of the tower. EDRs are estimated from the wind measurements based on three different EDR estimation methods. The first two methods use the inertial dissipation method derived from Kolmogorov turbulence theory, and the third uses a maximum likelihood estimation assuming a von Kármán spectral model. Reasonable agreement was obtained between the three methods with various fluctuations, including diurnal variations for all seasons, while the EDR calculated from the third method displayed slightly higher EDR values than the other two methods. The result of the analysis showed that the mean (standard deviations) of logarithms of EDR had larger values as height decreased (increased), and the means were higher in the unstable planetary boundary layer (PBL) than in the stable PBL for this heterogeneous location adjacent to the coastlines. The probability density functions (PDFs) of the EDRs showed that the distribution was well-represented by a lognormal distribution in both the stable and unstable PBL, although the PDFs at the lowest level (2.5 m) deviated from those at other levels due to surface effects. Seasonal variations in the PDFs showed that there was less difference in the shape of the PDFs depending on atmospheric stability in the wintertime. Finally, we calculate the 1-yr statistics of the observed EDR, which will be used for future LLT forecast systems in Korea.

High-latitude precipitation: Snowfall regimes at two distinct sites in Scandinavia

AbstractThe prevailing snowfall regimes at two Scandinavian sites, Haukeliseter, Norway and Kiruna, Sweden, are documented using ground-based in-situ and remote sensing methods. Micro Rain Radar (MRR) profiles indicate three distinct snowfall regimes occur at both sites: shallow, deep, and intermittent snowfall. The shallow snowfall regime produces the lowest mean snowfall rates and radar echo tops are confined below 1.5 km above ground level (AGL). Shallow snowfall occurs under areas of large scale subsidence with a moist boundary layer and dry air aloft. The atmospheric ridge coinciding with shallow snowfall is highly anomalous over Haukeliseter, but is more common in Kiruna where shallow snowfall was frequently observed. The shallow snowfall particle size distributions (PSDs) are broad with lower particle concentrations than other regimes, especially small particles. Deep snowfall events exhibit MRR profiles that extend above 2 km AGL, and tend to be associated with weak low pressure and high relative humidity throughout the troposphere. The PSDs in deep events are narrower with high concentrations of small particles. Increasing MRR reflectivity towards the surface suggests aggregation as a possible growth process during deep snowfall events. The heaviest mean snowfall rates are associated with intermittent events that are characterized by deep MRR profiles, but have variations in intensity and height. The intermittent regime is associated with anomalous, deep low pressure along the coast of Norway, and enhanced relative humidity at lower levels. The PSDs reveal high concentrations of small and large particles. The analysis reveals that there are unique characteristics of shallow, deep, and intermittent snowfall regimes that are common between the sites.

Evaluation of Wind Energy Potential in Rayong Province, Thailand by WAsP Program

Over the past decades, Wind energy is one of the alternative energy or renewable sources, which has been harvested to produce electricity. Our research aims to study the wind potential of the areas in the Rayong provinces of Thailand. Data from meteorological stations were collected every 10 minutes for of 3 years (2017-2019), with a measuring tower at 10-meter height above ground level (AGL). The annual average wind speeds were investigated in Rayong (2.02 m/s) with Weibull Probability Distribution Function (PDF). The annual average power density in Rayong regions was 13 W/m2. In all locations, wind direction was detected mainly from Southwest (SSW) and the yearly maximum wind power capacity is 94.376 MWh. The capacity factor of 21.5 % was noticed. With relatively low wind speed was noticed in Rayong provinces of Thailand, a small wind turbine installed at 30 meters would be recommended as a cost-effective way to convert wind power to electricity.

Linking behavioral states to landscape features for improved conservation management.

A central theme for conservation is understanding how animals differentially use, and are affected by change in, the landscapes they inhabit. However, it has been challenging to develop conservation schemes for habitat‐specific behaviors.Here we use behavioral change point analysis to identify behavioral states of golden eagles (Aquila chrysaetos) in the Sonoran and Mojave Deserts of the southwestern United States, and we identify, for each behavioral state, conservation‐relevant habitat associations.We modeled behavior using 186,859 GPS points from 48 eagles and identified 2,851 distinct segments comprising four behavioral states. Altitude above ground level (AGL) best differentiated behavioral states, with two clusters of short‐distance movement behaviors characterized by low AGL (state 1 AGL = 14 m (median); state 2 AGL = 11 m) and two associated with longer‐distance movement behaviors and characterized by higher AGL (state 3 AGL = 108 m; state 4 AGL = 450 m).Behaviors such as perching and low‐altitude hunting were associated with short‐distance movements in updraft‐poor environments, at higher elevations, and over steeper and more north‐facing terrain. In contrast, medium‐distance movements such as hunting and transiting were over gentle and south‐facing slopes. Long‐distance transiting occurred over the desert habitats that generate the best updraft.This information can guide management of this species, and our approach provides a template for behavior‐specific habitat associations for other species of management concern.

Impacts of Varying Concentrations of Cloud Condensation Nuclei on Deep Convective Cloud Updrafts—A Multimodel Assessment

AbstractThis study presents results from a model intercomparison project, focusing on the range of responses in deep convective cloud updrafts to varying cloud condensation nuclei (CCN) concentrations among seven state-of-the-art cloud-resolving models. Simulations of scattered convective clouds near Houston, Texas, are conducted, after being initialized with both relatively low and high CCN concentrations. Deep convective updrafts are identified, and trends in the updraft intensity and frequency are assessed. The factors contributing to the vertical velocity tendencies are examined to identify the physical processes associated with the CCN-induced updraft changes. The models show several consistent trends. In general, the changes between the High-CCN and Low-CCN simulations in updraft magnitudes throughout the depth of the troposphere are within 15% for all of the models. All models produce stronger (~+5%–15%) mean updrafts from ~4–7 km above ground level (AGL) in the High-CCN simulations, followed by a waning response up to ~8 km AGL in most of the models. Thermal buoyancy was more sensitive than condensate loading to varying CCN concentrations in most of the models and more impactful in the mean updraft responses. However, there are also differences between the models. The change in the amount of deep convective updrafts varies significantly. Furthermore, approximately half the models demonstrate neutral-to-weaker (~−5% to 0%) updrafts above ~8 km AGL, while the other models show stronger (~+10%) updrafts in the High-CCN simulations. The combination of the CCN-induced impacts on the buoyancy and vertical perturbation pressure gradient terms better explains these middle- and upper-tropospheric updraft trends than the buoyancy terms alone.

NILAI EKONOMI POTENSI JASA LINGKUNGAN MENYERAP KARBON DI TAMAN WISATA ALAM BULUH CINA KABUPATEN KAMPAR PROVINSI RIAU

Forest is the most dominant area in composing the role of environmental services. One of the environmental services of forests is carbon absorption services. Forests play an important role in climate change, namely as carbon sinks and carbon storage (stock). Nature Tourism Park is one of the ecosystems that becomes CO2 storage and absorbent. The study was conducted for 1 (one) month in Buluh Cina Nature Tourism Park. This study aims to determine the potential of environmental services to absorb carbon, the economic value of environmental services to absorb carbon, and how the role of the government and society in the management of Buluh Cina Nature Tourism Park. The research method used in this study is a field survey method that measures tree diameters and determines tree species to determine carbon potential, the benefit transfer method to calculate the economic value of carbon absorption and indepht interviews to determine the role of government and society in environmental management in Buluh Cina Nature Tourism Park. The results showed that the potential of environmental services to absorb carbon aboveground level in Buluh Cina Nature Tourism Park was 358,919 tons / ha or 345,757,150 tons when extrapolated to the total breadth of Buluh Cina Nature Tourism Park. Furthermore, the results of the calculation using the benefit transfer method of the economic value of carbon absorbing services in the Buluh Cina Nature Tourism Park are Rp. 69,696,398,680, -. For the role of the government and the Society, the respondents consisted of the Head of the Buluh Cina Nature Tourism Park, the Head of Buluh Cina Village, Ninik Mamak and the Buluh Cina Village Society.The role of government and society cannot be separated from one another. The government and the society together maintain, protect and manage the Buluh Cina Nature Tourism Park in order to achieve environmental sustainability.

Antarctic atmospheric boundary layer observations with the Small Unmanned Meteorological Observer (SUMO)

Abstract. Between January 2012 and June 2017 a small unmanned aerial system (sUAS), known as the Small Unmanned Meteorological Observer (SUMO), was used to observe the state of the atmospheric boundary layer in the Antarctic. During six Antarctic field campaigns, 116 SUMO flights were completed. These flights took place during all seasons over both permanent ice and ice-free locations on the Antarctic continent and over sea ice in the western Ross Sea. Sampling was completed during spiral ascent and descent flight paths that observed the temperature, humidity, pressure and wind up to 1000 m above ground level and sampled the entire depth of the atmospheric boundary layer, as well as portions of the free atmosphere above the boundary layer. A wide variety of boundary layer states were observed, including very shallow, strongly stable conditions during the Antarctic winter and deep, convective conditions over ice-free locations in the summer. The Antarctic atmospheric boundary layer data collected by the SUMO sUAS, described in this paper, can be retrieved from the United States Antarctic Program Data Center (https://www.usap-dc.org, last access: 8 March 2021). The data for all flights conducted on the continent are available at https://doi.org/10.15784/601054 (Cassano, 2017), and data from the Ross Sea flights are available at https://doi.org/10.15784/601191 (Cassano, 2019).

Evaluation of Arctic Water Vapor Profile Observations from a Differential Absorption Lidar

The continuous measuring of the vertical profile of water vapor in the boundary layer using a commercially available differential absorption lidar (DIAL) has only recently been made possible. Since September 2018, a new pre-production version of the Vaisala DIAL system has operated at the Iqaluit supersite (63.74°N, 68.51°W), commissioned by Environment and Climate Change Canada (ECCC) as part of the Canadian Arctic Weather Science project. This study presents its evaluation during the extremely dry conditions experienced in the Arctic by comparing it with coincident radiosonde and Raman lidar observations. Comparisons over a one year period were strongly correlated (r > 0.8 at almost all heights) and exhibited an average bias of +0.13 ± 0.01 g/kg (DIAL-sonde) and +0.18 ± 0.02 g/kg (DIAL-Raman). Larger differences exhibiting distinct artifacts were found between 250 and 400 m above ground level (AGL). The DIAL’s observations were also used to conduct a verification case study of operational numerical weather prediction (NWP) models during the World Meteorological Organization’s Year of Polar Prediction. Comparisons to ECCC’s global environmental multiscale model (GEM-2.5 km and GEM-10 km) indicate good agreement with an average bias < 0.16 g/kg for the higher-resolution (GEM-2.5 km) models. All models performed significantly better during the winter than the summer, likely due to the winter’s lower water vapor concentrations and decreased variability. This study provides evidence in favor of using high temporal resolution lidar water vapor profile measurements to complement radiosonde observations and for NWP model verification and process studies.

Determination of Surface Precipitation Type Based on the Data Fusion Approach

Hazardous events related to atmospheric precipitation depend not only on the intensity of surface precipitation, but also on its type. Uncertainty related to determination of the precipitation type (PT) leads to financial losses in many areas of human activity, such as the power industry, agriculture, transportation, and many more. In this study, we use machine learning (ML) algorithms with the data fusion approach to more accurately determine surface PT. Based on surface synoptic observations, ERA5 reanalysis, and radar data, we distinguish between liquid, mixed, and solid precipitation types. The study domain considers the entire area of Poland and a period from 2015 to 2017. The purpose of this work is to address the question: “How can ML techniques applied in observational and NWP data help to improve the recognition of the surface PT?” Despite testing 33 parameters, it was found that a combination of the near-surface air temperature and the depth of the warm layer in the 0–1000 m above ground level (AGL) layer contains most of the signal needed to determine surface PT. The accrued probability of detection for liquid, solid, and mixed PTs according to the developed Random Forest model is 98.0%, 98.8%, and 67.3%, respectively. The application of the ML technique and data fusion approach allows to significantly improve the robustness of PT prediction compared to commonly used baseline models and provides promising results for operational forecasters.

Field Performance of a Solid Set Canopy Delivery System Configured for High-Density Tall Spindle Architecture Trained Apple Canopies

HighlightsVariants of a solid set canopy delivery system were evaluated in a high-density apple orchard.A pair of hollow-cone nozzles installed in three tiers had optimum spray performance for studied crop growth stages.A shower-down arrangement of emitters was the simplest design but had lesser deposition on abaxial leaf surfaces.Abstract. Optimally configured solid set canopy delivery system (SSCDS) based spraying has potential to improve tree-fruit crop pest and disease management by reducing application time and eliminating dependence on ground conditions. In such an effort, this study attempted to optimize SSCDS variants. Four different emitter types (E1 to E4) installed in different mounting configurations (C1 to C4) were evaluated for spray deposition and coverage in a high-density apple orchard trained in tall spindle architecture. Emitters E1, E2, and E4 had full circle spray patterns, and E3 had a hollow-cone pattern. Configuration C1 had a pair of E1 emitters spraying in a vertical plane and installed between two trees at 1.5 m above ground level (AGL). Another E1 emitter spraying in a horizontal plane was mounted atop each tree at 3.3 m AGL. Configurations C2 and C4 had emitters (E2 and E4, respectively) mounted atop each tree at 3.3 m AGL, and C3 had a pair of E3 emitters installed in a three-tier arrangement between two trees in the crop row. During field trials, a tree canopy about 3.0 m tall was divided into three zones (0 to 1.4 m, >1.4 to 2.2 m, and >2.2 to 3.0 m AGL) as bottom, middle, and top canopy zones, respectively. Mylar cards were used to quantify spray deposition using fluorometry, and water-sensitive papers (WSPs) were used to quantify coverage using image processing. Configuration C3 with 80° hollow-cone nozzles in a twin-emitter, three-tier arrangement had the highest overall spray deposition (581.1 ±77.8 ng cm-2, mean ± standard error) and coverage (18.4% ±4.1%). Moreover, C3 also had a significantly higher coverage on the abaxial surfaces of leaves compared to the other configurations. Configurations C1 was non-optimal because it lacked abaxial surface coverage as the canopy grew in the middle and late growth stages. Moreover, significant spray runoff from leaf surfaces was observed visually in the middle zone for C1 during the middle and late stages. This may be attributed to canopy growth around the emitters. Configurations C2 and C4, with emitters in a shower-down arrangement, had the highest deposition and coverage in the top canopy zone compared to the middle and bottom zones. Configurations C2 and C4 also had significantly higher spray coverage on the adaxial surfaces of leaves compared to the abaxial surfaces. Overall, despite the complex design of configuration C3 with six emitters per tree, it may be the most ideal arrangement for agrochemical application in an apple orchard trained in tall spindle architecture. For commercial feasibility, we recommend exploring this three-tier SSCDS configuration with low-cost emitter alternatives. Pertinent continuing efforts have been published by our group in which we successfully modified low-cost irrigation emitters, and the resulting three-tier SSCDS configurations had improved spray performance over expensive hollow-cone nozzles. Keywords: Fixed spray system, High-density apple orchard, Solid set canopy delivery system, Spray coverage, Spray deposition, SSCDS.

Wind Speed Prediction Using Hybrid 1D CNN and BLSTM Network

As the world witnesses population increase, the global power demand is increasing and the need for exploring other alternative clean and self-renewable sources of energy such as wind has become necessary. For optimal operation of the wind farms and stability of the grid, wind prediction ahead of time is of key importance. An accurate forecast of wind speed is often difficult due to the unpredictable nature of the wind. In this work, we utilized different machine learning models and proposed a hybrid machine learning approach. This approach combines 1D convolutional neural network (CNN) and bidirectional long short term memory (BLSTM) network for accurate prediction of short term wind prediction at different heights above ground level (AGL). The 1D CNN model extracts high-level features of the input wind speed data. The extracted features are then fed as input to the BLSTM network for wind speed prediction. The wind speed time series data used in this study are measured at 18, and 98 meters AGL. The study further presents a relationship between the utilized models and prediction accuracy at different heights. The forecasting performance of the models tends to increase as the height AGL increases. A real-world case study is implemented to demonstrate the effectiveness of the proposed CNN-BLSTM method in Saudi Arabia. The mean absolute error (MAE), mean squared error (MSE), root mean squared error (RMSE), and mean absolute percentage error (MAPE) are used as performance indices to evaluate the performance of the proposed CNN-BLTSM model. The corresponding results show that the proposed method outperforms other benchmark models.

Design and analysis of 3D printed UAV wheel

Unmanned Aerial Vehicles of group 1 category are small in size with Maximum Gross Take-off Weight between 0 and 20 lbs and normal operating altitude as 1200 AGL (Above Ground Level). Light weighting in aerial vehicles is one of the major concerns and is mostly favoured by the change in materials. Tyres used for UAVs are extensively made up of rubber and are quite heavy in weight thus rising a concern. These rubber tyres add to the dead weight during the flight and also adds to the drag. For this study, lightweight 3D – printed wheels for better performance and lesser drag during the flight are designed and analysed for the group I UAV vehicles. The target is to reduce the weight by 50%, to achieve this, a rigorous analysis was performed for choosing the best possible configuration using various 3D – printing materials like ABS, PLA, Nylon and Carbon fibre mix. Additive manufacturing commonly known as 3D – printing favours the production of high strength light weighing parts. It also offers a broader range of design selection and favours iterative design approach and thus was opted as the method of production. The iterative design approach was followed to obtain the best results. Wheels with 4, 5, and 6 spokes were considered for the study. The wheel designs were modelled in Solidworks and analysis was conducted in Ansys using Finite Element Analysis to study the static and dynamic strength of the wheel in the radial direction, ignoring the bending and torsion of wheels during landing. Furthermore, the Charpy impact test was conducted on the specimen to study the crack propagation on impact.

Construction of the shopping center Ada Mall in Belgrade

Ada Shopping Mall is located at the corner of Radnička and Paštrovićeva Street in Belgrade, closed to Lake Ada Ciganlija. The building has a gross area of about 100.000m². It consisted of reinforced concrete structure with three underground and four above-ground levels and a steel roof structure. The foundation pit protection was made by bored and bored secant piles in combination with two different Top-Down methods during construction. The excavation depth was up to 25 m. As the building was built in rock mass, during excavation blasting was applied. During construction a monitoring system was implemented. This paper provides an overview of the construction process of shopping mall.

Changes in Root Architecture and Aboveground Traits of Red Clover Cultivars Driven by Breeding to Improve Persistence

Red clover persistence has been one of the major targets for the most breeding programs worldwide. A phenotypic characterization at above- and belowground level was performed to a set of highly persistent red clover populations. The objective was to identify phenotypic changes occurring after empirical selection for plant survival, which is the consensus criteria of persistence. Eleven red clover populations were established on mesocosms of polyvinyl chloride (PVC) tubes of 11 cm in diameter and 100 cm depth, containing as substrate a mixture (v/v) of sand, vermiculite, soil and perlite. The trial was organized in a randomized complete block design with four replicates, each replicate consisting of five mesocosms per population and one plant per mesocosm. In total, 220 mesocosms were handled. At aboveground level, growth parameters, specific leaf area (SLA) and shoot dry matter (ShootDM) were measured. At belowground level, root morphology (volume, diameter and length) and topology (altitude, external path length [EPL] and dichotomous branching index [DBI]) were measured through image analyses. Analyses of variance were performed implementing a phenotypic linear mixed model using the Restricted Maximum Likelihood method. Additionally, variance components were estimated and broad-sense heritability was calculated for each phenotypic trait. Highly persistent cultivars exhibited 30% higher ShootDM and 10% lower leaf size and SLA than the oldest low-persistent cultivar Quiñequeli. At root level, they showed 20, 50 and 50% higher crown diameter, root length density and root volume than Quiñequeli, respectively, but 20% lower DBI. Root traits exhibited medium-low values of genetic control; broad sense heritability ranged between 0.20 and 0.48. In conclusion, highly persistent red clover cultivars and experimental lines bred in Chile modified their phenotypic expression of individual plants at shoot and root levels relative to the oldest low persistent cultivar Quiñequeli. Associations among above- and belowground traits offer opportunities for designing more efficient selection strategies. For instance, the strong relationship between SLA and root traits offers tremendous potential for indirect phenotypic selection.

Evaluation of Wind Energy Potential in a Sahelian Zone: A Case Study of Ouahigouya, Burkina Faso

This study investigates the characteristics of the wind energy potential in Ouahigouya, Burkina Faso. For this purpose, eleven-years data (2006-2016) of wind speed (at 10 m above ground level (AGL)) and surface temperature data from Burkina Faso meteorological agency and wind speed (50 m AGL) from the NASA were used. Based on the mean vertical profile of wind speed at monthly and annual scales, the Weibull function was used to characterize the wind speed frequency distribution and to calculate the wind power density. Major results indicate that the peaks of mean wind speeds are estimated at 3.51 m.s-1 (May) and 5.18 m.s-1 (March) for the 20 m and 50 m altitudes respectively. The mean annual wind speed at the 20 m AGL at the site is estimated at 2.87 m.s-1 while at 50 m AGL, a value of 4.33 m.s-1 is recorded. The estimated wind power density reveals that the largest amounts of energy are generally collected during the months corresponding to the dry season with peaks in March or May depending on the height. The amount of energy available at 20 m AGL varies from 32.90 W/m2 (September) to 84.97 W/m2 (May). At 50 m AGL, this value varies respectively from 68.68 W/m2 (August) to 346.47 W/m2 (March). Average annual power density amounts are estimated at 65.92 W/m2 at 20 m and 223.17 W/m2 at 50 m, respectively, for a growth rate of 70.46 %. In view of these results, the Ouahigouya site could be conducive to the installation of small and medium-sized turbines to supply rural communities with electrical energy.

An assessment of the aboveground phytomass and carbon levels of the forests of northern Telangana, India, using a geospatial technique

ABSTRACT An assessment of aboveground phytomass and carbon levels was conducted in the South-Central region of India, in the Telangana State, situated on the high Deccan Plateau. The study is based on field observations at 30 sites using cluster sampling. The sites were selected on homogeneity maps prepared using satellite data of forest cover type maps, forest cover density maps, normalized difference vegetation indices and maps of administrative boundaries. Field observations such as forest types, canopy cover, species, girth and plant height were made for assessing basal area, bole volume, biomass and carbon, first for each tree, then for each plot and then for the entire region. The basal area varied from 0.09 to 40.94 m2/ha, and the phytomass ranged from 0.07 to 174.69 t/ha. The total phytomass in the study area was about 6709.623 t, with a mean value of 859.10 t/ha per site (i.e. 250 × 250 m). The predicted carbon was 5.02 million tons (Mt) (standard deviation [SD] 9.542) against the total phytomass of 10.46 Mt (SD 19.878) for the study area. These data can provide essential information for helping to establish forest policies that support a more sustainable development of local forest resources, as well as for structuring forest management working plans. Key policy insights The study reveals the observed and predicted biomass and carbon of northern Telangana, India. The key purpose of the study is to determine the biomass and carbon content of northern Telangana, in light of the Kyoto Protocol. For the study area, the predicted carbon is 5.02 Mt against the total biomass of 10.46 Mt. The forest density, phytomass and carbon maps are the first of their kind at a landscape scale for the Telangana State of India. These data help to assess and inform forest policies, which contribute towards the sustainable development of forest resources through preserving the ecological integrity of this landscape.

Assessing the Influence of UAV Altitude on Extracted Biophysical Parameters of Young Oil Palm

The information on biophysical parameters—such as height, crown area, and vegetation indices such as the normalized difference vegetation index (NDVI) and normalized difference red edge index (NDRE)—are useful to monitor health conditions and the growth of oil palm trees in precision agriculture practices. The use of multispectral sensors mounted on unmanned aerial vehicles (UAV) provides high spatio-temporal resolution data to study plant health. However, the influence of UAV altitude when extracting biophysical parameters of oil palm from a multispectral sensor has not yet been well explored. Therefore, this study utilized the MicaSense RedEdge sensor mounted on a DJI Phantom–4 UAV platform for aerial photogrammetry. Three different close-range multispectral aerial images were acquired at a flight altitude of 20 m, 60 m, and 80 m above ground level (AGL) over the young oil palm plantation area in Malaysia. The images were processed using the structure from motion (SfM) technique in Pix4DMapper software and produced multispectral orthomosaic aerial images, digital surface model (DSM), and point clouds. Meanwhile, canopy height models (CHM) were generated by subtracting DSM and digital elevation models (DEM). Oil palm tree heights and crown projected area (CPA) were extracted from CHM and the orthomosaic. NDVI and NDRE were calculated using the red, red-edge, and near-infrared spectral bands of orthomosaic data. The accuracy of the extracted height and CPA were evaluated by assessing accuracy from a different altitude of UAV data with ground measured CPA and height. Correlations, root mean square deviation (RMSD), and central tendency were used to compare UAV extracted biophysical parameters with ground data. Based on our results, flying at an altitude of 60 m is the best and optimal flight altitude for estimating biophysical parameters followed by 80 m altitude. The 20 m UAV altitude showed a tendency of overestimation in biophysical parameters of young oil palm and is less consistent when extracting parameters among the others. The methodology and results are a step toward precision agriculture in the oil palm plantation area.