Map Of Area Research Articles

CLIMATIC RISKS AND FOREST ECOSYSTEMS: THE INTERACTION BETWEEN DEFORESTATION AND CLIMATE CHANGE

The article provides a comprehensive analysis of the interaction between deforestation and climate change, with particular emphasis on the impact of military actions on the reduction of forest ecosystems. The research utilized general scientific methods (data analysis and synthesis), retrospective and comparative analysis, analytical-synthetic methods (study of scientific and statistical data, archival materials, etc.), and remote sensing techniques. The aim of the study was to identify the main causes of the reduction in forested areas in Ukraine and their role in the context of global climate change, as well as their significance for the socio-economic development of the country during wartime and the post-war period. The role of forest ecosystems as a source of a wide range of ecosystem services and as a significant component of the socio-economic status of the Ukrainian Polissya region is analyzed. Special attention is given to the interaction between the reduction of forest-covered areas and the intensification of climate change. The main factors contributing to the reduction of Ukraine’s forest ecosystems are presented, with a particular focus on forest fires, including those caused by military actions. It was established that from April 2023 to April 2024, the highest number of forest fires occurred in the eastern and southern parts of the country, where active military actions are ongoing. An automated methodology for decoding satellite images to detect areas of Scots pine dieback due to infestation by the pine shoot beetle was developed, resulting in a map of the study area showing the boundaries of forestry districts, the grid of forest compartments, the contours of damaged forest areas, and new dieback hotspots. The study demonstrates that forest ecosystems are essential for mitigating climate change and ensuring sustainable regional development. This is particularly true for the Ukrainian Polissya region, especially Zhytomyr Oblast, which has the highest forest cover in the country but also leads in the loss of forest-covered areas.

Spatial Assessment of Greenhouse Gas Emissions and Eutrophication Potential from Livestock Manure in Bangladesh

Large amounts of livestock manure production in Bangladesh have created a challenge to the environment by increasing greenhouse gas (GHG) emissions and eutrophication potential (EP) risk. Though some studies have identified the impact of manure on emissions, the consequences of manure exploitation on water bodies are very rare. This study investigated the effects of livestock manure on both air and water environments in the regional context of Bangladesh. Mathematical equations were used to assess manure generation, GHG emissions, manure leach-out amount, EP, and geographic information system (GIS) spatial analysis tools were applied to visualize the outcomes of the research. Between 1990 and 2020, the GHG emissions increased from 7451.26 to 13,244.45 kilotons CO2eq, and the amount of manure leach-out to water also increased from 236.49 to 493.75 kilotons over these 30 years of time. In 2023, the study demonstrated that approximately 216.97 million tons of livestock manure were produced in Bangladesh, which accounted for a total GHG emission of 16.61 million tons CO2eq, and about 62.19 million tons of manure leached out to water, which has a total EP of 295.22 kg N-eq/ha/year. This study offers regional pattern emission intensity and eutrophication-susceptible area maps, which indicate the stimulus of livestock manure across Bangladesh. A long-term impact study, distinctive map formation, and eutrophication susceptibility analysis will be helpful for implementing specific policies and strategies to improve the environment of the livestock sector in Bangladesh.

A method for measuring banana pseudo-stem phenotypic parameters based on handheld mobile LiDAR and IMU fusion.

Diameter and height are crucial morphological parameters of banana pseudo-stems, serving as indicators of the plant's growth status. Currently, in densely cultivated banana plantations, there is a lack of applicable research methods for the scalable measurement of phenotypic parameters such as diameter and height of banana pseudo-stems. This paper introduces a handheld mobile LiDAR and Inertial Measurement Unit (IMU)-fused laser scanning system designed for measuring phenotypic parameters of banana pseudo-stems within banana orchards. To address the challenges posed by dense canopy cover in banana orchards, a distance-weighted feature extraction method is proposed. This method, coupled with Lidar-IMU integration, constructs a three-dimensional point cloud map of the banana plantation area. To overcome difficulties in segmenting individual banana plants in complex environments, a combined segmentation approach is proposed, involving Euclidean clustering, Kmeans clustering, and threshold segmentation. A sliding window recognition method is presented to determine the connection points between pseudo-stems and leaves, mitigating issues caused by crown closure and heavy leaf overlap. Experimental results in banana orchards demonstrate that, compared with manual measurements, the mean absolute errors and relative errors for banana pseudo-stem diameter and height are 0.2127 cm (4.06%) and 3.52 cm (1.91%), respectively. These findings indicate that the proposed method is suitable for scalable measurements of banana pseudo-stem diameter and height in complex, obscured environments, providing a rapid and accurate inter-orchard measurement approach for banana plantation managers.

Application of optical images and analytical hierarchy process model for solid waste dumping site analysis in Kombolcha town, Northeastern, Ethiopia

Improper open dumping of solid wastes caused different serious problems in fast-growing towns in Ethiopia like Kombolcha town. This unsuitable solid waste dumping management brings health challenges by spreading diseases, contaminating water, polluting air, and spreading of different disease-causing insects such as mosquitoes. Therefore, selecting suitable solid waste dumping site analysis (SWDSA) is very essential to minimize these negative effects related to improper solid waste dumping in such towns. The primary goal of this study is to use optical images and the AHP model to find appropriate solid waste dumping sites in Kombolcha Town. The nine governing factors such as soil texture, geology, faults, groundwater well points (GWWPs, rivers, roads, built-up, Land use land cover (LULC) and slopes, were considered to achieve the aim of this study. These factors were extracted and delineated from the various optical data such as Landsat 8 images, digital elevation model, Google Earth images, geological map, soil map, and also field survey. The factor classes and factors were ranked and weighted utilizing a comparison matrix according to importance and finally by applying the AHP spatial analysis extension tool in Arc GIS software the SWDSA map of the study area was generated. Therefore, as a result of this study about 19.4 km2 (22.8 %), 22.8 km2 (26.8 %), 20.5 km2 (24.1 %), 16.1 km2 (18.9 %), and 6.2 km2 (7.3 %) of the study area falls in not suitable, less suitable, moderately suitable, suitable and highly suitable ranges respectively. Based on the result of this further analysis, D is the 1st suitable site with a score is 83.6 %, site B is the 2nd suitable site is a score is 83.2 %, site C is the 3rd suitable site with a score of 65.1 % and site A is the last suitable site and scored 60.4 %. Therefore, this study strongly recommends that the waste management municipal office of Kombolcha town and other concerned government and non-government bodies apply and consider these identified the best suitable solid waste dumping sites.

Characterization and Mapping of the Potential Area of Oil Palm Using Multi-Criteria Decision Analysis in a Geographic Information Systems Environment

This study presents a GIS-based Multi-Criteria Decision Analysis (MCDA) spatial model to assess land suitability for oil palm (OP) cultivation in rainfed conditions. Initially, twelve parameters, viz., rainfall, number of rainy days, mean temperature, RH, ground water level, soil pH, salinity, soil depth, surface texture, stoniness, slope, and drainage, were selected for assessing OP suitability in one of the states (Kerala). However, subsequent ground verification revealed significant discrepancies, which prompted refining the model by focusing on key parameters with greater accuracy and relevance. Accordingly, only five the most critical parameters affecting OP cultivation under rainfed conditions were selected through the rank sum method, and weights were assigned ac-cording to their significance. This study was aimed at creating a comprehensive tool for informed decision making in agricultural planning. District-level spatial data from reliable sources were utilized for Multi-Criteria Decision Analysis. Thematic rasters, representing key factors influencing land suitability, were created in a GIS. Utilizing MCDA techniques, a digital suitability map was generated in ArcGIS 10.3, delineating three distinct classes over an extensive area of 10.5 million hectares. Further, with an aim to focus on actual locations that can be readily planted with oil palm, the suitable locations identified were restricted to eight selected land use/land cover (LULC) classes. This strategic limitation aimed to facilitate the expansion of OP cultivation exclusively to areas deemed most suitable based on the identified criteria. The validation of this developed model involved comparing the suitability map generated with the performance of existing oil palm plantations across diverse locations. The reasonable similarity between the model’s predictions and real-world plantation outcomes validated the effectiveness of this MCDA spatial model. This model not only helps identify suitable locations for rainfed oil palm cultivation but also serves as a valuable tool for strategic decision making in agricultural land use planning.

Identification of Technosols formed from industrial dust in the area of Yazd city (Central Iran) by ground-based radiometry

Intensive industrial development in the form of metallurgical plants and quarries for mining of mineral resources in the vicinity of the city of Yazd resulted in significant dustiness of the ground layer of atmosphere and accumulation of industrial dust on the soil surface. At the locations of alloy steel production facilities, dust composition was dominated by particulate matter less than 1 μm (PM1), while near sand quarries, dust particles less than 10 μm (PM10) were predominant. The dust from these sources also differs in chemical composition (iron/silicon content ratio, pH). Registration with a pyranometer with a dark green filter with transmittance at a wavelength of 550 nanometers with a time interval of 20 seconds at a speed of 30 km/h during the movement along the specified routes allowed authors to identify zones with a stable increased content of particles of different sizes in the near-surface atmosphere. Sampling soil pits in these zones confirmed the presence on their surface of layers of dust deposits with a thickness of more than 5 cm, which allows them to be attributed to Technosols in accordance with the WRB soil classification. The soil map of the research area was compiled at a scale of 1 : 25 000 with the reflection of Technosols participation in the soil patterns. According to the map, more than three quarters of the study area are soil complexes including Technosols. Arid climatic conditions of the region lead to the accumulation of dust on the soil surface, which are practically not transformed and not removed from the soil, which results in their degradation and deterioration of the environmental conditions. Dust accumulation on the soil surface affects the state of vegetation cover of the region, which serves as a basis for indirect satellite indication of dusting zones. The method of revealing the dust layer on the soil surface based on the assessment of dustiness of the near-surface atmosphere using pyranometer can serve as a good complement to remote sensing methods.

Integration of the AHP Method and GIS Techniques for Mapping Areas Susceptible to Forest Fires in the Southern Amazon Region (Peru)

Integration of the AHP Method and GIS Techniques for Mapping Areas Susceptible to Forest Fires in the Southern Amazon Region (Peru)

The location of pain in cluster headache: Data from the International Cluster Headache Questionnaire.

To identify the most common locations of cluster headache pain from an international, non-clinic-based survey of participants with cluster headache, and to compare these locations to other cluster headache features as well as to somatotopic maps of peripheral, brainstem, thalamic, and cortical areas. Official criteria for cluster headache state pain in the orbital, supraorbital, and/or temporal areas, yet studies have noted pain extending beyond these locations, and the occipital nerve appears relevant, given the effectiveness of suboccipital corticosteroid injections and occipital nerve stimulation. Furthermore, cranial autonomic features vary between patients, and it is not clear if the trigeminovascular reflex is dermatome specific (e.g., do patients with maxillary or V2 division pain have more rhinorrhea?). Finally, functional imaging studies show early activation of the posterior hypothalamus in a cluster headache attack. However, the first somatosensory area to be sensitized is unclear; the first area can be hypothesized based on the complete map of pain locations. The International Cluster Headache Questionnaire was an internet-based cross-sectional survey that included a clickable pain map of the face. These data were compared to several other datasets: (1) a meta-analysis of 22 previous publications of pain location in cluster headache (consisting of 6074 patients); (2) four cephalic dermatome maps; (3) participants' survey responses for demographics, autonomic features, and effective medications; and (4) previously published somatotopic maps of the brainstem, thalamus, primary somatosensory cortex, and higher order somatosensory cortex. One thousand five hundred eighty-nine participants completed the pain map portion of the survey, and the primary locations of pain across all respondents was the orbital, periorbital, and temporal areas with a secondary location in the lower occiput; these primary and secondary locations were consistent with our meta-analysis of 22 previous publications. Of the four cephalic dermatomes (V1, V2, V3, and a combination of C2-3), our study found that most respondents had pain in two or more dermatomes (range 85.7% to 88.7%, or 1361-1410 of 1589 respondents, across the four dermatome maps). Dermatomes did not correlate with their respective autonomic features or with medication effectiveness. The first area to be sensitized in the canonical somatosensory pathway is either a subcortical (brainstem or thalamus) or higher order somatosensory area (parietal ventral or secondary somatosensory cortices) because the primary somatosensory cortex (area 3b) and somatosensory area 1 have discontinuous face and occipital regions. The primary pain locations in cluster headache are the orbital, supraorbital, and temporal areas, consistent with the official International Classification of Headache Disorders criteria. However, activation of the occiput in many participants suggests a role for the occipital nerve, and the pain locations suggest that somatosensory sensitization does not start in the primary somatosensory cortex.

Revisiting the 2023 wildfire season in Canada

The area burned by wildfires in Canada in 2023 is unprecedented in historical records. To help ensure the safety of communities and support the mobilization of firefighting resources, rapid detection of areas affected by wildfires is required. Satellite data are ideally suited to provide near real-time wildfire information over large areas. At the same time, clouds, smoke, and haze can obscure the collection of observations from sensors typically used for mapping purposes. Established methods using coarse spatial resolution satellites (e.g., MODIS, VIIRS) rely upon the combination of daily revisit to enable the rapid and reliable detection of large active fires, in full or in part, and the application of modeling (including spatial buffering) to infer additional, yet still obscured, areas. While timely, these initial maps of wildfire-impacted areas do not capture small fires (those smaller than 200 ha) and, importantly, are not intended to differentiate unburned areas within fire perimeters. To address these limitations, we used data from Sentinel-2A and -2B, and Landsat-8 and -9, which form a virtual constellation of four satellites to revisit and map burned area in Canada's forested ecosystems for the 2023 fire season. Availing upon the high temporal data density and using the Tracking Intra- and Inter-year Change algorithm (TIIC), an aggregate seasonal mapping of wildfires resulted in a total area affected by wildfires in 2023 of 12.74 Mha. Within this total area, 9.51 Mha of treed land cover was impacted. Shrubs and wetlands comprised most of the remaining non-treed area that was burned. Using a 2022 map of aboveground treed biomass (AGB), approximately 0.649 Pg of AGB was impacted by 2023 wildfires, representing an 11-fold increase in AGB impacts relative to a long-term annual average of treed AGB loss. Differences between the estimate of total burned area reported herein and the total burned area indicated by the Natural Resources Canada (NRCan) Fire M3 hotspot fire perimeters (18.64 Mha) were analyzed. Overall, estimates of burned area differed by 5.9 Mha, including over 1.13 Mha of water identified as burned within the NRCan perimeters. Differences in land cover and AGB impacts between the two products were also investigated and quantified. TIIC enables the near-continuous capture of areas impacted by fire through the fire season, allowing for within-year refinement of total burned area, rapid interrogation of land cover types impacted, and estimation of associated biomass consequences.

Remote Sensing Scene Classification using ConvNeXt-Tiny Model with Attention Mechanism and Label Smoothing

Remote Sensing Scene Classification (RSSC) is the discrete categorization of remote sensing images into various classes of scene categories based on their image content. RSSC plays an important role in many fields, such as agriculture, land mapping, and identification of disaster-prone areas. Therefore, a reliable and accurate RSSC algorithm is required to ensure the accuracy of land identification. Many existing studies in recent years have used deep learning methods, especially CNN combined with attention modules to solve this problem. This study focuses on solving the RSSC problem by proposing a deep learning-based method (CNN) with the ConvNeXt-Tiny model integrated with Efficient Channel Attention Module (ECANet) and label smoothing regularization (LSR). The ConvNeXt-Tiny model shows that a persistent superior outperforms the ‘large’ model in convinced metrics. The ConvNeXt-Tiny model also has a huge advantage in high-precision positioning and higher classification accuracy and localization precision in a variety of complicated scenarios of remote sensing scene recognition. The experiments in this study also aim to prove that the integration of the attention module and LSR into the basic CNN network can improve precision, because the attention module can strengthen important features and weaken features that are less useful for classification. The experimental results proved that the integration of ECANet and LSR in the ConvNeXt-Tiny base network obtained a higher precision of 0.38% in the UC-Merced dataset, 0.7% in the AID, and 0.4% in the WHU-RS19 dataset than the ConvNeXt-Tiny model without ECANet and LSR. The ConvNeXt-Tiny model with ECANet integration and LSR obtained an accuracy of 99.00±0.41% in the UC-Merced dataset, 95.08±0.20% in AID, and 99.50±0.31% in the WHU-RS19 dataset.

Sentinel 2 based burn severity mapping and assessing post-fire impacts on forests and buildings in the Mizoram, a north-eastern Himalayan region

The Increasing frequency and severity of forest fires worldwide highlights the need for more effective Burnt area mapping. Finding the effects of fire on vegetation and putting mitigation methods in place, depends on post-fire evaluation. In this study, the location of the burned regions and the severity of the fire were determined using high-resolution multi-spectral images from Sentinel 2 on Google Earth Engine (GEE) platform. Three widely used fire severity indices—differenced Normalized Burn Ratio (dNBR), Relativized Burn Ratio (RBR), and Relativized dNBR (RdNBR)—based on pre-fire Normalized Burn Ratio (NBR) and post-fire NBR—were computed and compared based on their accuracy using very high-resolution planet imagery fire points and equal number of random non fire points. Maps also validated with active fires, ground based photos and crowdsourced images. The accuracy (AUC) of the RdNBR map was 85%, RBR - 84% and dNBR −82%. The RdNBR index demonstrated highest level of accuracy. Then the loss to vegetation using pre-fire and post-fire NDVI was analysed. The analysis of pre-fire and post-fire NDVI provided insights into the extent of vegetation loss. The analysis of vegetation loss offered valuable information regarding the impact of fire on the affected areas. Google building dataset was used to monitor the percent of buildings under threat due to these fires. Around 8.77% of buildings were found in high severity region. Accurate mapping aids post-fire evaluation, guided mitigation strategies, and enhanced forest management and ecological restoration.

A Technical-Thematic Civil Protection Exercise in Italy: UAS Fleets-Based Activities Supporting Emergency Response in Seismic Scenarios

In October 2023, during the Italian Civil Protection Week, in Eastern Lombardy (Italy) a large technical-thematic seismic exercise called “EXE.Lomb.Est 2023” was organized, with the goal of testing the response of the Regional Civil Protection system for post-earthquake damage assessment activities. Within this context, the use of an unmanned aerial system (UAS), in particular the deployment of multi-rotors UAS teams, has been tested as support for the rapid mapping of a large area involving the simultaneous participation of different Italian institutions with UAS units. Coordinated flight planning design, safety issues, coordination and communication procedures, data management and delivery of the results are some of the main aspects investigated and presented in this work.

A multi-method approach for assessing groundwater vulnerability of shallow aquifers in the Marchfeld region (Austria)

Study regionMarchfeld region (Austria) Study focusA multi-method and multi-scale assessment of the intrinsic groundwater vulnerability to generic pollutants was carried out. At the regional scale, a parametric method, to assess the intrinsic groundwater vulnerability, and a transfer function model, to assess the travel time of a generic and non-reactive pollutant through the unsaturated zone, were applied. At the site-specific scale, the travel time of the peak concentration was evaluated by using a physically-based hydrological model. The comparison of results of different approaches allowed mutual validation and advanced the knowledge about the assessment of groundwater vulnerability. New hydrogeological insights for the regionTo assess the groundwater vulnerability, a detailed hydrogeological map of the study area was reconstructed. A large variability of hydrogeological, morphological and anthropic conditions was recognized. Alluvial aquifers formed by high-permeability deposits hosting shallow groundwater circulation are characterized by the highest groundwater vulnerability. Contrarily, lower groundwater vulnerability was recognized for aquifers formed by low-permeability deposits, favoring a reduction of infiltration processes and a major attenuation of pollutants’ potential effects. The presented multi-method approach revealed how comparing the results of a DRASTIC-like method and two process-based models can deliver hints regarding their suitability, different spatial densities and quality of required inputs, and effectiveness. Finally, the potential strong impact of some agricultural practices was confirmed.

Deep learning for geological mapping in the overburden area

This paper aims to achieve bedrock geologic mapping in the overburden area using big data, distributed computing, and deep learning techniques. First, the satellite Bouguer gravity anomaly with a resolution of 2′×2′ in the range of E66°-E96°, N40°-N55° and 1:5000000 Asia-European geological map are used to design a dataset for bedrock prediction. Then, starting from the gravity anomaly formula in the spherical coordinate system, we deduce the non-linear functional between rock density ρ and rock mineral composition m, content p, buried depth h, diagenesis time t and other variables. We analyze the feasibility of using deep neural network to approximate the above nonlinear generalization. The problem of solving deep neural network parameters is transformed into a non-convex optimization problem. We give an iterative, gradient descent-based solution algorithm for the non-convex optimization problem. Utilizing neural architecture search (NAS) and human-designed approach, we propose a geological-geophysical mapping network (GGMNet). The dataset for the network consists of both gravity anomaly and a priori geological information. The network has fast convergence speed and stable iteration during the training process. It also has better performance than a single neural network search or human-designed architectures, with the mean pixel accuracy (MAP) = 63.1% and the frequency weighted intersection over union (FWIoU) = 42.88. Finally, the GGMNet is used to predict the rock distribution of the Junggar Basin.

Assessment and mapping of priority areas for transboundary ecological conservation: Suggestions for the protection of the Altai Mountains in Central Asia

Political boundaries often do not coincide with ecological boundaries, and it is common for the same physical geographical unit to span multiple countries. Exploring establishing transboundary protected areas and ecological networks can effectively protect the integrity of natural ecosystems separated by political boundaries. Based on the integrated evaluation of ecosystem services function, ecological vulnerability, and ecosystem integrity, this study explored a methodological system for evaluating ecological protection priority areas in transboundary areas and selected the Altai Mountains, which span four countries, contain China, Kazakhstan, Russia, and Mongolia, as a typical case to conduct empirical research. The study found that the extremely important areas of ecosystem services function, ecological vulnerability, and ecosystem integrity showed transboundary contiguous characteristics. Furthermore, weighted overlay analysis identified the spatial distribution pattern of ecological protection priority areas in the Altai Mountains. We suggested establishing transboundary protected areas in the four countries’ transboundary areas, building the identified ecological protection priority areas and existing protected areas into a transboundary protected area network, and deepening a multi-party transboundary protection cooperation mechanism. This study can provide a theoretical and practical reference to achieve the 2030 biodiversity conservation goals.

Geospatial assessment of forest fire impacts utilizing high-resolution KazEOSat-1 satellite data

Forest fires or wildfires frequently occur in Kazakhstan, especially in the months from June to September, damaging the forest resources. Burnt area mapping is important for fire managers to take appropriate mitigation steps and carry out restoration activities after the fire event. In this study, KazEOSat-1 high-resolution satellite datasets are used to map the burnt area in the regions of Kazakhstan. KazEOSat-1 satellite is in a Sun-synchronous orbit, consisting of four bands, namely blue, green, red, and NIR multispectral bands, in 4 m spatial resolution, while panchromatic data are in 1 m spatial resolution. This study examined three spectral indices, namely AVI, BAI, and GEMI, for mapping the burnt area based on the four spectral bands NIR, blue, red, and green of the KazEOSat-1 satellite datasets. The DN values for each band are used to determine TOA reflectance, which is then used as a basis for deriving the aforementioned spectral indices. The results of spectral indices, AVI, BAI, and GEMI are compared based on a discriminative index (M) for quantifying the effectiveness of each index based on burned area derived from KazEOSat-1 datasets. The spectral index BAI shows higher M values than other indices; therefore, the index BAI has the higher capability to extract the burned area as compared with AVI and GEMI. Accuracy was calculated based on the number of forest fire incidents that fell in burned and unburned areas, and the results indicate that BAI shows the highest accuracy, whereas AVI shows the lowest accuracy among them. Therefore, the BAI has the highest ability for extracting the burned area using the KazEOSat-1 satellite datasets. As the revisit time period of KazEOSat is 3 days, this study will be useful to map the burnt area and fire progression in Kazakhstan.

Geospatial Assessment of Oil Spill’s Impact in Obio/Akpor Local Government Area Rivers State, Nigeria

Environmental degradation resulting from oil spills has emerged as a critical issue requiring immediate attention. This study uses geospatial techniques to investigate the impact of oil spills in Obio/Akpor Local Government Area of Rivers State. Landsat images, administrative map of the study area and historical oil spill data were data used in carrying out this study. The images were pre-processed through atmospheric and geometric corrections. Spatial analysis of four remote sensing indices-Normalized Difference Vegetation Index (NDVI) for assessing vegetation health, Normalized Difference Water Index (NDWI) for detecting water bodies, Land Surface Temperature (LST) for measuring surface temperature, and Normalized Difference Built-Up Index (NDBI) for identifying built-up areas-was conducted using the raster calculator in ArcGIS. The study employed the Analytical Hierarchy Process (AHP) to assign weights to these indices based their relative significance regarding oil spill impact. Subsequently, reclassification and integration of this information were done to generate Oil Spill Index (OSI) maps for 2003, 2013, and 2023. Validation of the Oil Spill Index (OSI) of various points before and after an oil spill incident was performed, specifically contrasting 2003 with 2013 and 2023 OSI values. This was to affirm the accuracy and predictive capability of the OSI maps in identifying oil spill-prone areas. Findings revealed OSI values for 2003 and 2013, indicating a range of 1.04 to 3.48 for 2003, with a noticeable increase observed in 2013, expanding the range to 3.08 to 4.72. Similarly, a comparison of OSI values between 2003 and 2023 indicates OSI values, ranging from 1.64 to 3.92 in 2003, then in 2023 after the oil spill incidents, there were notable changes in the OSI values, with the range shifting to 3.2 to 4.84. This suggests a significant increase in the impact of oil spill on the affected areas over the two-decade. This comprehensive index map serves as a tool for monitoring and assessing the effects of oil spills on the study area, thereby achieving the research objectives effectively.

Vandalism on city trees: a case study from KNUST campus, Ghana

ABSTRACT Tree vandalism has harmful consequences that impact the performance of trees, affecting the flow of their benefits to society. However, questions about the common forms of tree vandalism and factors contributing to a tree’s vulnerability to vandalism have not been well researched. Using the campus of Kwame Nkrumah University of Science and Technology as a case study, the current research identified the common forms of tree vandalism and determined the relationship between these forms and tree species, size, and location. The study area was stratified into three based on land use: Faculty area, Students’ residence, and Lecturers’ residence. Using the Quantum Geographic Information System (QGIS), 100 × 100 m grids were laid on the study area map, and 10% of the grids were randomly selected from each stratum. All trees within the selected grids were identified and assessed. A total of 185 incidents of tree damage were recorded from the 131 trees assessed across the three strata. The highest incidence of damage recorded was broken branches (90), while burnt trees had the lowest frequency of occurrence (6). A total of 61 trees from 19 species were recorded as vandalised according to our classification. Tree size and proximity to the road network did not influence the type of tree vandalism recorded. However, our study suggests that tree vandalism may be location-specific, with trees in highly visible and crowded areas more likely to be vandalised with acts that seek people’s attention, while those in isolated areas are less likely to be vandalised through attention-seeking acts. This study is among the few which has attempted to empirically document a common phenomenon that has received less attention and has been overlooked in several societies.

Flood vulnerability mapping in an urban area with high levels of impermeable coverage in southern Brazil

Flood vulnerability mapping in an urban area with high levels of impermeable coverage in southern Brazil

Construction of Three-Dimensional Semantic Maps of Unstructured Lawn Scenes Based on Deep Learning

Traditional automatic gardening pruning robots generally employ electronic fences for the delineation of working boundaries. In order to quickly determine the working area of a robot, we combined an improved DeepLabv3+ semantic segmentation model with a simultaneous localization and mapping (SLAM) system to construct a three-dimensional (3D) semantic map. To reduce the computational cost of its future deployment in resource-constrained mobile robots, we replaced the backbone network of DeepLabv3+, ResNet50, with MobileNetV2 to decrease the number of network parameters and improve recognition speed. In addition, we introduced an efficient channel attention network attention mechanism to enhance the accuracy of the neural network, forming an improved Multiclass MobileNetV2 ECA DeepLabv3+ (MM-ED) network model. Through the integration of this model with the SLAM system, the entire framework was able to generate a 3D semantic point cloud map of a lawn working area and convert it into octree and occupancy grid maps, providing technical support for future autonomous robot operation and navigation. We created a lawn dataset containing 7500 images, using our own annotated images as ground truth. This dataset was employed for experimental purposes. Experimental results showed that the proposed MM-ED network model achieved 91.07% and 94.71% for MIoU and MPA metrics, respectively. Using a GTX 3060 Laptop GPU, the frames per second rate reached 27.69, demonstrating superior recognition performance compared to similar semantic segmentation architectures and better adaptation to SLAM systems.