Coverage Path Planning with Semantic Segmentation for UAV in PV Plants

Solar energy is one of the most strategic energy sources for the world’s economic development. This has caused the number of solar photovoltaic plants to increase around the world; consequently, they are installed in places where their access and manual inspection are arduous and risky tasks. Recently, the inspection of photovoltaic plants has been conducted with the use of unmanned aerial vehicles (UAV). Although the inspection with UAVs can be completed with a drone operator, where the UAV flight path is purely manual or utilizes a previously generated flight path through a ground control station (GCS). However, the path generated in the GCS has many restrictions that the operator must supply. Due to these restrictions, we present a novel way to develop a flight path automatically with coverage path planning (CPP) methods. Using a DL server to segment the region of interest (RoI) within each of the predefined PV plant images, three CPP methods were also considered and their performances were assessed with metrics. The UAV energy consumption performance in each of the CPP methods was assessed using two different UAVs and standard metrics. Six experiments were performed by varying the CPP width, and the consumption metrics were recorded in each experiment. According to the results, the most effective and efficient methods are the exact cellular decomposition boustrophedon and grid-based wavefront coverage, depending on the CPP width and the area of the PV plant. Finally, a relationship was established between the size of the photovoltaic plant area and the best UAV to perform the inspection with the appropriate CPP width. This could be an important result for low-cost inspection with UAVs, without high-resolution cameras on the UAV board, and in small plants.

Fusing Sentinel-1 and -2 to Model GEDI-Derived Vegetation Structure Characteristics in GEE for the Paraguayan Chaco

Vegetation structure is a key component in assessing habitat quality for wildlife and carbon storage capacity of forests. Studies conducted at global scale demonstrate the increasing pressure of the agricultural frontier on tropical forest, endangering their continuity and biodiversity within. The Paraguayan Chaco has been identified as one of the regions with the highest rate of deforestation in South America. Uninterrupted deforestation activities over the last 30 years have resulted in the loss of 27% of its original cover. The present study focuses on the assessment of vegetation structure characteristics for the complete Paraguayan Chaco by fusing Sentinel-1, -2 and novel spaceborne Light Detection and Ranging (LiDAR) samples from the Global Ecosystem Dynamics Investigation (GEDI). The large study area (240,000 km²) calls for a workflow in the cloud computing environment of Google Earth Engine (GEE) which efficiently processes the multi-temporal and multi-sensor data sets for extrapolation in a tile-based random forest (RF) regression model. GEDI-derived attributes of vegetation structure are available since December 2019, opening novel research perspectives to assess vegetation structure composition in remote areas and at large-scale. Therefore, the combination of global mapping missions, such as Landsat and Sentinel, are predestined to be combined with GEDI data, in order to identify priority areas for nature conservation. Nevertheless, a comprehensive assessment of the vegetation structure of the Paraguayan Chaco has not been conducted yet. For that reason, the present methodology was developed to generate the first high-resolution maps (10 m) of canopy height, total canopy cover, Plant-Area-Index and Foliage-Height-Diversity-Index. The complex ecosystems of the Paraguayan Chaco ranging from arid to humid climates can be described by canopy height values from 1.8 to 17.6 m and canopy covers from sparse to dense (total canopy cover: 0 to 78.1%). Model accuracy according to median R² amounts to 64.0% for canopy height, 61.4% for total canopy cover, 50.6% for Plant-Area-Index and 48.0% for Foliage-Height-Diversity-Index. The generated maps of vegetation structure should promote environmental-sound land use and conservation strategies in the Paraguayan Chaco, to meet the challenges of expanding agricultural fields and increasing demand of cattle ranching products, which are dominant drivers of tropical forest loss.

Phenology of fine roots and shoots using high frequency temporal resolution images in a temperate larch forest

Purpose Understanding tree phenology reveals the underlying mechanisms through plant functional and productive activities and carbon sinks in forest ecosystems. However, previous research on tree phenology has focused on shoot dynamics rather than tree root dynamics. We aimed to explore seasonal temperature patterns of daily-based root and shoot dynamics by capturing high frequency plant images in a larch forest. Methods We monitored continuous images using an automated digital camera for shoot dynamics and a flatbed scanner for the fine root dynamics in the larch. Using the images, we analyzed the relationship between temperature and plant area index as shoot growth status and total root-area proportion of white and brown roots. Results Larch shoot production had a single mountain-shaped peak with a positive correlation between plant area index and air temperature. Fine root production had two peaks in the bimodal root-growth pattern in early summer and late autumn. Soil temperature was positively correlated with white root proportion and negatively correlated with brown root proportion. Conclusion We found differences between shoots and roots regarding temperature relationships. In particular, the automated flatbed scanner method for the root dynamics allowed the collection of detailed bimodal patterns of root production with shift from whitening to browning color, which had been previously overlooked. Such high frequency temporal resolution analysis can provide an in-depth of mechanisms of fine-root and shoot phenology through different stages of plant development in terms of growth and senescence.

Electromagnetic Induction Is a Fast and Non-Destructive Approach to Estimate the Influence of Subsurface Heterogeneity on Forest Canopy Structure

The spatial forest structure that drives the functioning of these ecosystems and their response to global change is closely linked to edaphic conditions. However, the latter properties are particularly difficult to characterize in forest areas developed on karst, where soil is highly rocky and heterogeneous. In this work, we investigated whether geophysics, and more specifically electromagnetic induction (EMI), can provide a better understanding of forest structure. We use EMI (EM31, Geonics Limited, Ontario, Canada) to study the spatial variability of ground properties in two different Mediterranean forests. A naturally post-fire regenerated forest composed of Aleppo pines and Holm oaks and a monospecific plantation of Altlas cedar. To better interpret EMI results, we used electrical resistivity tomography (ERT), soil depth surveys, and field observations. Vegetation was also characterized using hemispherical photographs that allowed to calculate plant area index (PAI). Our results show that the variability of ground properties contribute to explaining the variability in the vegetation cover development (plant area index). Vegetation density is higher in areas where the soil is deeper. We showed a significant correlation between edaphic conditions and tree development in the naturally regenerated forest, but this relationship is clearly weaker in the cedar plantation. We hypothesized that regular planting after subsoiling, as well as sylvicultural practices (thinning and pruning) influenced the expected relationship between vegetation structure and soil conditions measured by EMI. This work opens up new research avenues to better understand the interplay between soil and subsoil variability and forest response to climate change.

Sizing Methodology of Floating Photovoltaic Plants in Dams of Semi-Arid Areas

Floating photovoltaic (FPV) plants in reservoirs can contribute to reduce water evaporation, increase power generation efficiency, due to the cooling process, and reduce competitiveness in land use. Based on this motivation we propose a new methodology for sizing FPV plants in dams of semi-arid regions by using the Flood Duration Curve (FDC). The methodology innovations are: no use of commercial software, the possibility of choosing the reliability level, the application in reservoirs of semi-arid areas of the world and the use of a graphic analysis of the reservoir hydrological behavior. The case studies in the Brazilian and Australian semi-arid consider two scenarios: high reliability level (90%, scenario 1) and low reliability level (70%, scenario 2). The reliability level is linked to the electricity production; the evaporation reduction is proportional to the FPV plant area.

Community cooling infrastructure from waste heat among diverse building types in Rourkela Steel Township, India

Urban building energy modeling is an important field in the current decade due to the rising rate of urbanization, specifically in developing countries. The UN environment is promoting urban level space cooling approaches in the upcoming smart cities of India. Rourkela is a tier-2 steel township included within the ‘smart city’ mission in India and houses one of the largest Steel Plants of India, classified under Koppen Aw tropical climate zone. However it experiences extreme heat stress in the dry summer season before the onset of monsoons. The given study proposes an alternative cooling scenario utilizing waste heat from the rolling mill with which cooling in the range of 700-900 tons of nearly zero energy cooling can be made available in the surrounding areas, otherwise catered by an energy intensive cooling system reporting a COP of 2.45. This study can be further expanded to provide cooling to the nearby residential communities keeping the steel plant area as center point for community cooling infrastructure provision.

Hazardous area classification for wheat flour processing plant

Dust explosion possibly occurs in common unit operations such as mills, grinders, dryers, and other modes of transport. The basic element for the setting of hazardous zone types consist of identifies release sources, determination of classification region of hazardous area, overviewing the basic operation in wheat flour processing plant with their specification requirement and use of a suitable code or calculations to determine area scope. Therefore, this analysis can be more elaborate by classifying the hazardous area into several areas using the International Electro Technical Commission System for Certification to Standards Relating to Equipment for Use in Explosive Atmospheres standard. Thus, wheat flour processing plant area classification can be categorized according to three zones based on the quantity of an explosion into atmosphere and its release frequencies which are zones 20, zones 21, and zones 22. From the results, it can be summarized that zone 20 is almost inside or closer one with the main equipment located near the ignition source which could lead to dust explosion, whereas zone 21 and zone 22 comes after zone 20 which is a less hazardous area as compared to zone 20 areas.