Tree Shadows Research Articles

Performance enhancement and techno-economic analysis of photovoltaic modules under dynamic weather conditions using dual exponential sawtooth method

The maximum output power extracted from the Photovoltaic (PV) modules is mainly dependent on ambient temperature and solar irradiance. The photovoltaic modules performance is hugely influenced by the Partial Shading (PS) effect, which results in the reduction of output power. It is caused by shadows of buildings, trees, moving clouds and towers. Under PS conditions, shaded modules receive less irradiance as compared to the unshaded modules, and this leads to overheating of PV array. The present work has been developed by a technique based on Dual Exponential Sawtooth (DES) pattern to reconfigure the PV modules so as to increase the PV output power under PS conditions along with the energy savings and economical aspects. In this technique, the PV modules of Total Cross Tied (TCT) technique and their physical locations are arranged as per DES pattern to distribute the shading effect over the entire array without altering the electrical connections of the PV modules. The DES arrangement gives the enhanced power of 41.48% by reducing the effect on any row of the shaded modules. Further, it provides the details about power sharing percentage as the number of shaded modules increase and also the affect of dynamic shade variation over the entire PV array. The proposed method validation with the existing methods for the analysis, simulation and hardware experimentation shows better performance in terms of mismatch losses and fill factor. Further, the analysis has been extended for units generated and energy savings with each module rating of 1.5KW on 4×4 PV modules.

Hierarchical Integration of UAS and Sentinel-2 Imagery for Spruce Bark Beetle Grey-Attack Detection by Vegetation Index Thresholding Approach

This study aimed to examine the efficiency of the vegetation index (VI) thresholding approach for mapping deadwood caused by spruce bark beetle outbreak. For this, the study used upscaling from individual dead spruce detection by unmanned aerial (UAS) imagery as reference data for continuous spruce deadwood mapping at a stand/landscape level by VI thresholding binary masks calculated from satellite Sentinel-2 imagery. The study found that the Normalized Difference Vegetation Index (NDVI) was most effective for distinguishing dead spruce from healthy trees, with an accuracy of 97% using UAS imagery. The study results showed that the NDVI minimises cloud and dominant tree shadows and illumination differences during UAS imagery acquisition, keeping the NDVI relatively stable over sunny and cloudy weather conditions. Like the UAS case, the NDVI calculated from Sentinel-2 (S2) imagery was the most reliable index for spruce deadwood cover mapping using a binary threshold mask at a landscape scale. Based on accuracy assessment, the summer leaf-on period (June–July) was found to be the most appropriate for spruce deadwood mapping by S2 imagery with an accuracy of 85% and a deadwood detection rate of 83% in dense, close-canopy mixed conifer forests. The study found that the spruce deadwood was successfully classified by S2 imagery when the spatial extent of the isolated dead tree cluster allocated at least 5–7 Sentinel-2 pixels.

AGF-Net: adaptive global feature fusion network for road extraction from remote-sensing images

Road extraction from remote-sensing images is of great significance for vehicle navigation and emergency insurance. However, the road information extracted in the remote-sensing image is discontinuous because the road in the image is often obscured by the shadows of trees or buildings. Moreover, due to the scale difference of roads in remote-sensing images, it remains a computational challenge to extract small-size roads from remote-sensing images. To address those problems, we propose a road extraction method based on adaptive global feature fusion (AGF-Net). First, a dilated convolution strip attention (DCSA) module is designed from the encoder–decoder structure. It consists of the dilated convolution and the strip attention module, which adaptively emphasizes relevant features in vertical and horizontal directions. Then, multiple global feature fusion modules (GFFM) in the skip connection are designed to supplement the decoder with road detail features, and we design a multi-scale strip convolution module (MSCM) to implement the GFFM module to obtain multi-scale road information. We compare AGF-Net to state-of-the-art methods and report their performance using standard evaluation metrics, including Intersection over Union (IoU), F1-score, precision, and recall. Our proposed AGF-Net achieves higher accuracy compared to other existing methods on the Massachusetts Road Dataset, DeepGlobe Road Dataset, CHN6-CUG Road Dataset, and BJRoad Dataset. The IoU obtained on these datasets are 0.679, 0.673, 0.567, and 0.637, respectively.

Maximum power enhancement in solar PV modules through modified TCT interconnection method

This article presents a narrativemove towards for maximal power augmentation in poly-crystalline solar photovoltaic (PV) modules through the modified Total Cross Tied (TCT) interlinkconnection method. Worldwide, the cost of coal has increased after a pandemic rebound in industrial activity and it leads to demand for electricity. The maximum power extraction from sustainable energy sources plays a vital role as they are ample, eco-friendly, harmless, and won’t run out. Due to shading effects like the transition of clouds and tree shadows, the output power generated from the solar photovoltaic array is reduced uptill 60%. Several methodologies of array formation and reconfiguration strategies were developed to overcome the partial shading effects. In this proposed method, PV cells in modules are tied via modified TCT fashion with a bypass diode through each row. Real-time experimental analysis is performed with 4 × 4 cell connections. The current (I)-(V)voltage and Power(P)-(V) voltage characteristics are obtained under normal and various partial shading conditions. According to our proposed method, the percentage power enhancement achieved by row-wise partial shading increases from 10% to 60%, approximately 83.64, 82%,80%, 74.29%, 70%, and 64%. According to the conventional topology, the maximum fill factor is 0.28 in columnwise 25% shading ratio, whereas the proposed modified TCT PV array has a fill factor of 0.53, 89% higher. Compared to the proposed pattern, which has a maximum performance ratio of 0.86 for 10% shading condition. The modified TCT interconnected PV module connection extracts a maximum of 50% increased output power when compared to conventional PV modules. The performance of the projected modules is also examined in a one kW PV array.

Advanced road extraction using CNN-based U-Net model and satellite imagery

Road extraction from high-resolution remote sensing imagery (HRRSI) is a challenging task due to low spectral variation and the presence of complex background elements, such as the shadows of buildings and trees. Many techniques have struggled to maintain proper edges and boundaries while retaining the geometric features necessary for accurate non-linear road extraction. In this paper, we address these issues by proposing a deep learning approach that utilizes a fine-tuned U-Net model with a modified feature space in the basic U-Net architecture for semantic segmentation. We also incorporate the BRISQUE preprocessing technique to improve the performance. We experimented with a subset of 200 high-quality images from the Massachusetts roads dataset, prioritized based on their rank. Due to computer memory constraints, the images were resized to 256 by 256 pixels. The proposed method produced the accuracy of 95.45%.

Application of Spectrometry for Determining the Solar Radiation of Deciduous Trees’ Shade: A Passive Energy Conservation Approach for Mediterranean Climates

Deciduous trees are well known for controlling solar gains in buildings, contributing to energy savings in a sector that consumes 35% of global energy. However, there is still a lack of information about the real thermal impact that deciduous trees have. This work proposes a new method that is cheap and easy to implement to quantify the shading efficiency of different types of deciduous trees in hot seasons. The results can be applied in energy evaluations of buildings. The trees selected belong to the central valley of Chile, which is characterized by hot summers and cold winters. The trees selected can also be found in other parts of the world. A spectrometer is used for measuring the amount of solar radiation (irradiance) that is present in the shadow of trees, measuring wavelengths between 339 nm and 750 nm (mostly within the visible light range). The full referential irradiance spectrum of the site is obtained by calibrating the standard ASTM G-173-03. At the site, the spectrometer is used to obtain the visible light range, while the infrared radiation (IR) and ultraviolet (UV) radiation ranges are obtained from the literature. Our results indicate that the analyzed deciduous trees reduce an average of 82% of the solar radiation. This information will help project designers during the building energy efficiency design phase by representative modeling of the solar radiation gains allowed by deciduous trees.

Almond (Prunus sp.) Growth and Morphological Properties Affected by Super Absorbent Polymers and Phosphorus and Iron Fertilization

Almond (Prunus sp.) is produced in large amounts in the province of ChaharMahal and Bakhtiari, Iran. The effects of super absorbent polymers (SAPs, potassium acrylamide, KC3H5NO) singly, or in combination with phosphorus (P) and iron (Fe), on the growth and morphological properties of 20-year old almond orchards were tested in a two-year field experiment. It was a three-way factorial on the basis of a complete randomized block design testing almond growth affected by SAPs (control and 100 g), P (control and 750 g) using triple super phosphate, and Fe (control and 250 g) using FeSO4, placed at 70 cm depth of each tree shadow zone. The plant samples were collected and measured in July, four months after flowering. The trees used for sampling were similar in terms of age and cultivar. The green and woody nuts were collected in August and September, respectively. The perimeter of trunk environment (PTT), branch length (BL), branch length after three months (BLT), difference of branch length (DBL), number of almond fruits (NAF), weight of green, woody, and fresh nuts (GWN), and weight of woody and fresh nuts (WN) were determined. The tested treatments singly or in combination significantly improved plant morphological properties including BL, BLT, and WN. The important conclusion about the present research is the gradual release of water by SAPs, which makes the plant roots absorb water along with the essential nutrients including the tested P and Fe at the right time for a higher tree branch and nut production.

Politics of Representation

This article takes a feminist viewpoint to highlight the lives and religious identities of Muslim women who are victims of pervasive negative perceptions of imperialist discourses and restrictive cultural practices of native males. We intend to investigate the practicability of empowerment conferred by feminism in redefining their subjectivity as represented in Tariq Ali’s Shadows of the Pomegranate Tree (1991) and The Book of Saladin (1998). Drawing on the concepts of postcolonial theorists such as; Edward Said, Chandra Mohanty, Fatima Mernissi and Riffat Hassan, we interpret the female characters as reflective of the women’s struggles to renegotiate their identity. The novels under scrutiny address the difficulties of depicting Muslim women in a cultural setting dominated by images of religious fanaticism, violence, and female subordination. Ali articulates a particular ideology regarding the construction of Muslim women’s socio-religious identity(ies) that serve the interests of Muslim clergy and patriarchy. We suggest that these representations are a powerful resource Muslim Women can draw upon in constructing their identities. Finally, it is argued that a disruption of the stereotypes of Muslim women signals the potential for the compatibility of Muslim women’s distinct identities.

Towards a bionic IoT: Environmental monitoring using smartphone interrogated plant sensors.

The utilisation of plants directly as quantifiable natural sensors is proposed. A case study measuring surface wettability of Aucuba japonica, or Japanese Laurel, plants using a novel smartphone field interrogator is demonstrated. This plant has been naturalised globally from Asia. Top-down contact angle measurements map wettability on-site and characterise a range of properties impacting plant health, such as aging, solar and UV exposure, and pollution. Leaves at an early age or in the shadow of trees are found to be hydrophobic with contact angle θ ~ 99°, while more mature leaves under sunlight are hydrophilic with θ ~ 79°. Direct UVA irradiation at λ = 365 nm is shown to accelerate aging, changing contact angle of one leaf from slightly hydrophobic at θ ~ 91° to be hydrophilic with θ ~ 87° after 30 min. Leaves growing beside a road with heavy traffic are observed to be substantially hydrophilic, as low as θ ~ 47°, arising from increased wettability with particulate accumulation on the leaf surface. Away from the road, the contact angle increases as high as θ ~ 96°. The results demonstrate that contact angle measurements using a portable diagnostic IoT edge device can be taken into the field for environmental detection, pollution assessment and more. Using an Internet connected smartphone combined with a plant sensor allows multiple measurements at multiple locations together in real-time, potentially enabling tracking of parameter change anywhere where plants are present or introduced. This hybrid integration of widely distributed living organic systems with the Internet marks the beginning of a new bionic Internet-of-things (b-IoT).

A zero switch and sensorless reconfiguration approach for sustainable operation of roof‐top photovoltaic system during partial shading

AbstractPhotovoltaic (PV) roof‐top systems in real‐time environments are highly susceptible to the commonly occurring scenario of partial shading that occurs due to unavoidable conditions such as the shadow of buildings and trees. The occurrence of partial shading creates a serious mismatch among the modules of PV arrays that results in severe unexpected power losses in PV arrays. The conventional and hybrid configurations used for loss mitigation in PV arrays are sometimes unreliable and incapable of generating higher power during partial shading. Hence, this paper focuses on the development of a simple and effective technique for the reliable operation of PV arrays and loss reduction during the occurrence of partial shading. The technique is applicable to arbitrary array sizes having no dynamic parts and requires no switches and sensors for implementation to enhance the performance and operation of the system. The performance and reliability of the technique have been investigated under MATLAB simulation and real‐time scenarios by comparing it with various configurations under shading. The investigation reveals a higher power output by the proposed technique with an average conversion efficiency of higher than 90% and power enhancement of 19.07% than other configurations.

Road Segmentation in High-Resolution Images Using Deep Residual Networks

Automatic road detection from remote sensing images is a vital application for traffic management, urban planning, and disaster management. The presence of occlusions like shadows of buildings, trees, and flyovers in high-resolution images and miss-classifications in databases create obstacles in the road detection task. Therefore, an automatic road detection system is required to detect roads in the presence of occlusions. This paper presents a deep convolutional neural network to address the problem of road detection, consisting of an encoder-decoder architecture. The architecture contains a U-Network with residual blocks. U-Network allows the transfer of low-level features to the high-level, helping the network to learn low-level details. Residual blocks help maintain the network's training performance, which may deteriorate due to a deep network. The encoder and decoder structures generate a feature map and classify pixels into road and non-road classes, respectively. Experimentation was performed on the Massachusetts road dataset. The results showed that the proposed model gave better accuracy than current state-of-the-art methods.

:World Medievalism: The Middle Ages in Modern Textual Culture

:<i>World Medievalism: The Middle Ages in Modern Textual Culture</i>

Virtually Fenced Goats for Grazing Fire Prone Juniper in Coastal Norway Wildland–Urban Interface

For millennia, the coastal heathlands of Western Europe were managed by regular burning cycles for improved grazing. In recent decades, this practice has generally been neglected. In Norway, the result is accumulation of degenerated heather and highly combustible Juniperus communis (juniper) encroachment, i.e., an increasing fire threat to a rising number of homes in the wildland–urban interface (WUI). In the present study, goats grazing a 1.2 ha site partly encroached by fire-prone juniper were studied in a living lab approach. Twelve wethers (castrated male goats) wearing solar-powered Nofence GPS collars were virtually fenced to protect villa gardens bordering the site during eight weeks from 18 April 2022. Besides some early operator errors, tree shadows and cloudy days limiting battery charging, the system worked well. Photographs taken prior to and after the grazing revealed that only 39% of the junipers had 90+% remaining foliage while for 41%, the foliage was reduced to below 10%, making them far less fire-prone. The goats were frequently visited by neighborhood residents of all ages, school classes, and kindergartens. Moreover, local newspapers and TV stations broadcasted the initiative. Although divided regarding debarked deciduous trees, the neighbors stated that the goats were well accepted. Despite some limited loss of garden plants, all interviewed residents bordering the site were positive about the goats “bringing life to the forest” and, if possible, welcomed them back for other grazing periods. Virtually fenced grazing goats may represent a sustainable solution for reducing fire hazards in the WUI also elsewhere.

ROAD EXTRACTION BASED ON IMPROVED DEEPLABV3 PLUS IN REMOTE SENSING IMAGE

Abstract. Urban roads in remote sensing images will be disturbed by surrounding ground features such as building shadows and tree shadows, and the extraction results are prone to problems such as incomplete road structure, poor topological connectivity, and poor accuracy. For mountain roads, there will also be problems such as hill shadow or vegetation occlusion. We propose an improved Deeplabv3+ semantic segmentation network method. This method uses ResNeSt, which introduces channel attention, as the backbone network, and combines the ASPP module to obtain multi-scale information, thereby improving the accuracy of road extraction. Analysis of the experimental results on the Deeplglobe dataset shows that the intersection ratio and accuracy of the method in this paper are 63.15% and 73.16%, respectively, which are better than other methods.

MECA-Net: A MultiScale Feature Encoding and Long-Range Context-Aware Network for Road Extraction from Remote Sensing Images

Road extraction from remote sensing images is significant for urban planning, intelligent transportation, and vehicle navigation. However, it is challenging to automatically extract roads from remote sensing images because the scale difference of roads in remote sensing images varies greatly, and slender roads are difficult to identify. Moreover, the road in the image is often blocked by the shadows of trees and buildings, which results in discontinuous and incomplete extraction results. To solve the above problems, this paper proposes a multiscale feature encoding and long-range context-aware network (MECA-Net) for road extraction. MECA-Net adopts an encoder–decoder structure and contains two core modules. One is the multiscale feature encoding module, which aggregates multiscale road features to improve the recognition ability of slender roads. The other is the long-range context-aware module, which consists of the channel attention module and the strip pooling module, and is used to obtain sufficient long-range context information from the channel dimension and spatial dimension to alleviate road occlusion. Experimental results on the open DeepGlobe road dataset and Massachusetts road dataset indicate that the proposed MECA-Net outperforms the other eight mainstream networks, which verifies the effectiveness of the proposed method.

Monitoring dryland trees with remote sensing. Part B: Combining tree cover and plant architecture data to assess degradation and recovery of Argania spinosa woodlands of South Morocco

The argan woodlands of South Morocco represent an open-canopy dryland forest with traditional silvopastoral usage that includes browsing by goats, sheep and camels, oil production as well as agricultural use. In the past, these forests have undergone extensive clearing, but are now protected by the state. However, the remaining argan woodlands are still under pressure from intensive grazing and illegal firewood collection. Although the argan-forest area seems to be overall decreasing due to large forest clearings for intensive agriculture, little quantitative data is available on the dynamics and overall state of the remaining argan forest. To determine how the argan woodlands in the High Atlas and the Anti-Atlas had changed in tree-crown cover from 1972 to 2018 we used historical black and white HEXAGON satellite images as well as recent WorldView satellite images (see Part A of our study). Because tree shadows can oftentimes not be separated from the tree crown on panchromatic satellite images, individual trees were mapped in three size categories to determine if trees were unchanged, had decreased/increased in crown size or had disappeared or newly grown. The current state of the argan trees was evaluated by mapping tree architectures in the field. Tree-cover changes varied highly between the test sites. Trees that remained unchanged between 1972 and 2018 were in the majority, while tree mortality and tree establishment were nearly even. Small unchanged trees made up 48.4% of all remaining trees, of these 51% showed degraded tree architectures. 40% of small (re-) grown trees were so overbrowsed that they only appeared as bushes, while medium (3–7 m crown diameter) and large trees (&amp;gt;7 m) showed less degraded trees regardless if they had changed or not. Approaches like grazing exclusion or cereal cultivation lead to a positive influence on tree architecture and less tree-cover decrease. Although the woodland was found to be mostly unchanged 1972–2018, the analysis of tree architecture reveals that a lot of (mostly small) trees remained stable but in a degraded state. This stability might be the result of the small trees’ high degradation status and shows the heavy pressure on the argan forest.

Extraction of Rural Residential Land from Very-High Resolution UAV Images Using a Novel Semantic Segmentation Framework

Accurate recognition and extraction of rural residential land (RRL) is significant for scientific planning, utilization, and management of rural land. Very-High Resolution (VHR) Unmanned Aerial Vehicle (UAV) images and deep learning techniques can provide data and methodological support for the target. However, RRL, as a complex land use assemblage, exhibits features of different scales under VHR images, as well as the presence of complex impervious layers and backgrounds such as natural surfaces and tree shadows in rural areas. It still needs further research to determine how to deal with multi-scale features and accurate edge features in such scenarios. In response to the above problems, a novel framework named cascaded dense dilated network (CDD-Net), which combines DenseNet, ASPP, and PointRend, is proposed for RRL extraction from VHR images. The advantages of the proposed framework are as follows: Firstly, DenseNet is used as a feature extraction network, allowing feature reuse and better network design with fewer parameters. Secondly, the ASPP module can better handle multi-scale features. Thirdly, PointRend is added to the model to improve the segmentation accuracy of the edges. The research takes a plain village in China as the research area. Experimental results show that the Precision, Recall, F1 score, and Dice coefficients of our approach are 91.41%, 93.86%, 92.62%, and 0.8359, respectively, higher than other advanced models used for comparison. It is feasible in the task of high-precision extraction of RRL using VHR UAV images. This research could provide technical support for rural land planning, analysis, and formulation of land management policies.

Fusion of Lidar and Aerial Imagery to Map Wetlands and Channels via Deep Convolutional Neural Network

Wetlands and channels provide significant ecological and societal services. Mapping their locations and types at high resolution remains a challenge for scientific communities and regulatory agencies. In the U.S.A., the National Wetland Inventory (NWI) provides the best nationwide wetland maps, but the NWI for some locations has not been updated for up to four decades and includes omission errors. To address these problems, we developed a deep learning framework for identifying wetlands and channels from lidar point clouds and 1 m aerial images. Both deep learning and terrain analysis were applied to classify wetlands and identify channels. The deep learning classifier was a convolutional neural network that utilized both image color information and lidar-derived canopy height. When tested on a 94 km2 Ohio watershed, the method achieved a classification accuracy of 85.6%. Compared with the NWI maps, the results included 76% more forested wetlands by area and 168% more channels by length. It was also found that only leaf-off images were useful for detecting forested wetlands and typical commission errors (i.e., false positives) were attributed to tree shadows. The study demonstrates the advantages of combining lidar structural and aerial spectral information over the use of each alone and exemplifies the utility of deep learning as an effective means to leverage open-source data for high resolution mapping of wetlands.

Modeling Shadow with Voxel-Based Trees for Sentinel-2 Reflectance Simulation in Tropical Rainforest

Satellite-based gross primary production (GPP) estimation has uncertainties due to shadow fraction caused by the geometric relationship between the complex forest structure and the Sun. The virtual forests allow shadow fraction estimation without 3D measurements, but require optimal structural parameters. In this study, we developed the reflectance simulator (Canopy-level Shadow and Reflectance Simulator, CSRS) that considers tree shadows and the method to determine the optimal canopy shape for shadow fraction estimation. The target forest is any tropical evergreen forest which accounts for 58% of tropical forests. Firstly, we analyzed the effects of canopy shape on the reflectance simulation based on virtual forests created with different canopy shapes. This result was checked by Tukey’s honestly significant difference (HSD) test. Secondly, the optimal canopy shape was determined by comparing the reflectance from Sentinel-2 Band 4 (red) bottom of atmosphere reflectance with those simulated from virtual forests. Finally, the shadow fraction estimated from the virtual forest was evaluated. Since the focus of this study was to derive the optimal canopy shape, unmanned aerial vehicle (UAV) structure from motion (SfM) was used to obtain the parameters other than canopy shape and to validate the estimated shadow fraction. The results showed that when the Sun zenith angle (SZA) was more than 20°, significant differences were observed among canopy shapes. The least root mean square error (RMSE) for reflectance simulation was 0.385 from the canopy shape of a half ellipsoid. Moreover, the half ellipsoid also showed the smallest RMSE in estimating shadow fraction (0.032), which indicated the reliability and applicability of CSRS. This study is the first attempt to determine the optimal canopy shape for estimating shadow fraction and is expected to improve the accuracy of GPP estimation in the future.

Ferenc Andai. In the Hour of Fate and Danger.

Ferenc Andai. <i>In the Hour of Fate and Danger</i>.