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With the continued advancement of spaceborne hyperspectral sensors, hyperspectral remote sensing is evolving as an increasingly pivotal tool for high-precision global monitoring applications. Novel image spectroscopy data, e.g., the PRecursore IperSpettrale della Missione Applicativa (PRISMA) and Environmental Mapping and Analysis Program (EnMAP), can rapidly and non-invasively capture subtle spectral information of terrestrial vegetation, facilitating the precise retrieval of the required vegetation parameters. As critical vegetation traits, Leaf Mass per Area (LMA) and Equivalent Water Thickness (EWT) hold significant importance for comprehending ecosystem functionality and the physiological status of plants. To address the demand for high-precision vegetation parameter datasets, a hybrid modeling approach was proposed in this study, integrating the radiative transfer model PROSAIL and neural network models to retrieve LMA and EWT from PRISMA and EnMAP images. To achieve this objective, canopy reflectance was simulated via PROSAIL, and the optimal band combinations for LMA and EWT were selected as inputs to train neural networks. The evaluation of the hybrid inversion models over field measurements showed that the RMSE values for the LMA and EWT were 4.11 mg·cm−2 and 9.08 mg·cm−2, respectively. The hybrid models were applied to PRISMA and EnMAP images, resulting in LMA and EWT maps displaying adequate spatial consistency, along with cross-validation results showing high accuracy (RMSELMA = 5.78 mg·cm−2, RMSEEWT = 6.84 mg·cm−2). The results demonstrated the hybrid inversion model's universality and applicability, enabling the retrieval of vegetation parameters from image spectroscopy data and offering a valuable contribution to hyperspectral remote sensing for vegetation monitoring, though the availability of field measurement data remained a significant challenge. [ABSTRACT FROM AUTHOR]

During the beam current calibration process, accurate guidance of the beam current to the metal target is a challenging issue for proton accelerators. To address this challenge, we propose the use of beam orbital parameters combined with reinforcement learning algorithms to achieve automatic beam calibration. This study introduces a system architecture that employs edge intelligent acceleration nodes based on deep learning acceleration techniques. We designed a system to predict BPM parameters using a cascaded backpropagation neural network (CBPNN) that is informed by the physical structure. This system serves as an environmental map for reinforcement learning, aiding beam current correction. The CBPNN was implemented on the acceleration node to hasten the forward inference process, leveraging sparsification, quantization algorithms, and pipelining techniques. Our experimental results demonstrated that the simulated inference speed reached 28 μs with FPGA hardware as the edge acceleration node, achieving forward inference speeds 35.66 and 12.66 times faster than those of the CPU and GPU. The energy efficiency ratio was 10.582 MOPS/W, which was 989 and 410 times that of the CPU and GPU, respectively. This confirms the designed architecture's energy efficiency and low latency attributes. [ABSTRACT FROM AUTHOR]

The mining activity is fundamental for human development, but it requires changes in the environment, negatively affecting soil organic carbon (SOC) stock. The only way to restore the original SOC stock is via rehabilitation practices. Considering adopting more efficient techniques and technologies for the rehabilitation process, this study aimed to assess SOC stock in rehabilitating iron minelands and to use remote sensing tools to predict it. The study was conducted in two waste piles (WP-W and WP-S4) each presenting a chronosequence (initial, intermediate, and advanced stages) in addition to the forest (reference), all located in the Carajás Mineral Province, southeastern Amazon, Brazil. Results showed a gradual increase in the SOC stock for the WP-S4 from the initial to the advanced stage (average of 26.5 Mg ha−1). There were no differences among the rehabilitation stages for the WP-W. The difference in SOC stocks between the two waste piles is mainly related to the soil fertility status, indicating a higher SOC accumulation potential for the WP-S4. Principal component analysis revealed that the attributes presenting major influence on the SOC stock are K, SOC, cation exchange capacity, B, and total N (positive); and soil bulk density, pH, and sand (negative). The regression model results showed that the remote sensing indices Normalized Difference Vegetation Index (NDVI) and Bare Soil Index (BSI) are useful to derive digital maps of SOC stock and assist in the monitoring of the rehabilitation in the waste piles of the iron mineland. [ABSTRACT FROM AUTHOR]

Environmental mapping serves as a crucial element in Simultaneous Localization and Mapping (SLAM) algorithms, playing a pivotal role in ensuring the accurate representation necessary for autonomous robot navigation guided by SLAM. Current SLAM systems predominantly rely on grid-based map representations, encountering challenges such as measurement discretization for cell fitting and grid map interpolation for online posture prediction. Splines present a promising alternative, capable of mitigating these issues while maintaining computational efficiency. This paper delves into the efficiency disparities between B-Spline surface mapping and discretized cell-based approaches, such as grid mapping, within indoor environments. B-Spline Online SLAM and FastSLAM, utilizing Rao-Blackwellized Particle Filter (RBPF), are employed to achieve range-based mapping of the unknown 2D environment. The system incorporates deep learning networks in the B-Spline curve estimation process to compute parameterizations and knot vectors. The research implementation utilizes the Intel Research Lab benchmark dataset to conduct a comprehensive qualitative and quantitative analysis of both approaches. The B-Spline surface approach demonstrates significantly superior performance, evidenced by low error metrics, including an average squared translational error of 0.0016 and an average squared rotational error of 1.137. Additionally, comparative analysis with Vision Benchmark Suite demonstrates robustness across different environments, highlighting the effectiveness of B-Spline SLAM for real-world applications. [ABSTRACT FROM AUTHOR]

Nitrogen oxides (NOx = NO + NO2) are of great concern due to their impact on human health and the environment. In recent years, machine learning (ML) techniques have been widely used for surface NO2 estimation with rapid developments in computational power and big data. However, the uncertainties inherent to such retrievals are rarely studied. In this study, a novel ML framework has been developed, enhanced with uncertainty quantification techniques, to estimate surface NO2 and provide corresponding data‐induced uncertainty. We apply the Boosting Ensemble Conformal Quantile Estimator (BEnCQE) model to infer surface NO2 concentrations over Western Europe at the daily scale and 1 km spatial resolution from May 2018 to December 2021. High NO2 mainly appears in urban areas, industrial areas, and roads. The space‐based cross‐validation shows that our model achieves accurate point estimates (r = 0.8, R2 = 0.64, root mean square error = 8.08 μg/m3) and reliable prediction intervals (coverage probability, PI‐50%: 51.0%, PI‐90%: 90.5%). Also, the model result agrees with the Copernicus Atmosphere Monitoring Service (CAMS) model. The quantile regression in our model enables us to understand the importance of predictors for different NO2 level estimations. Additionally, the uncertainty information reveals the extra potential exceedance of the World Health Organization (WHO) 2021 limit in some locations, which is undetectable by only point estimates. Meanwhile, the uncertainty quantification allows assessment of the model's robustness outside existing in‐situ station measurements. It reveals challenges of NO2 estimation over urban and mountainous areas where NO2 is highly variable and heterogeneously distributed. Plain Language Summary: Inferring surface NO2 concentrations is an effective way to monitor and mitigate NOx pollution which is of great concern due to its impact on human health and the environment. Machine learning (ML) techniques have been widely used for surface NO2 estimation with rapid developments in computational power and big data. However, such estimations can be uncertain due to inherent errors in the data, and this uncertainty is rarely studied. We develop a novel ML framework to estimate surface NO2 concentrations and provide corresponding uncertainty information. We infer surface NO2 levels over Western Europe at the daily scale and 1 km spatial resolution from May 2018 to December 2021. Our model's performance is reliable as verified by in‐situ station measurements and an independent physics‐based model. We observe NO2 hotspots over urban areas, industrial areas, and major roads. The uncertainty quantification (UQ) techniques allow us to analyze the influence of different input data on estimating different NO2 levels. The UQ also helps to identify potential NO2 exceedances of the WHO 2021 limit, which have not been observed in previous research. Additionally, we assess the model's robustness outside of in‐situ stations and witness the challenge of NO2 estimation over urban and mountainous areas. Key Points: A novel and reliable machine learning model with uncertainty quantification is applied to infer surface NO2 levels over Western EuropeOur work uncovers how predictors impact model inference of various surface NO2 levels differentlyOur approach identifies areas of high uncertainty in surface NO2 mapping and potential environmental risks from overlooking uncertainty [ABSTRACT FROM AUTHOR]

To address the challenges of sparse point clouds in current MIMO millimeter-wave radar environmental mapping, this paper proposes a dense 3D millimeter-wave radar point cloud environmental mapping algorithm. In the preprocessing phase, a radar SLAM-based approach is introduced to construct local submaps, which replaces the direct use of radar point cloud frames. This not only reduces data dimensionality but also enables the proposed method to handle scenarios involving vehicle motion with varying speeds. Building on this, a 3D-RadarHR cross-modal learning network is proposed, which uses LiDAR as the target output to train the radar submaps, thereby generating a dense millimeter-wave radar point cloud map. Experimental results across multiple scenarios, including outdoor environments and underground tunnels, demonstrate that the proposed method can increase the point cloud density of millimeter-wave radar environmental maps by over 50 times, with a point cloud accuracy better than 0.1 m. Compared to existing algorithms, the proposed method achieves superior environmental map reconstruction performance while maintaining a real-time processing rate of 15 Hz. [ABSTRACT FROM AUTHOR]

In agricultural field inspection robots, constructing accurate environmental maps and achieving precise localization are essential for effective Light Detection And Ranging (LiDAR) Simultaneous Localization And Mapping (SLAM) navigation. However, navigating in occluded environments, such as mapping distortion and substantial cumulative errors, presents challenges. Although current filter-based algorithms and graph optimization-based algorithms are exceptionally outstanding, they exhibit a high degree of complexity. This paper aims to investigate precise mapping and localization methods for robots, facilitating accurate LiDAR SLAM navigation in agricultural environments characterized by occlusions. Initially, a LiDAR SLAM point cloud mapping scheme is proposed based on the LiDAR Odometry And Mapping (LOAM) framework, tailored to the operational requirements of the robot. Then, the GNU Image Manipulation Program (GIMP) is employed for map optimization. This approach simplifies the map optimization process for autonomous navigation systems and aids in converting the Costmap. Finally, the Adaptive Monte Carlo Localization (AMCL) method is implemented for the robot's positioning, using sensor data from the robot. Experimental results highlight that during outdoor navigation tests, when the robot operates at a speed of 1.6 m/s, the average error between the mapped values and actual measurements is 0.205 m. The results demonstrate that our method effectively prevents navigation mapping distortion and facilitates reliable robot positioning in experimental settings. [ABSTRACT FROM AUTHOR]

In robotics, reinforcement learning can train controllers or agents to find optimal solutions for complex tasks by enabling the robot to interact repeatedly with the environment. The reward function is an important aspect that guides any reinforcement algorithm to find the desired solution successfully. This work examines two deep reinforcement learning approaches, one uses a Deep Q‐Network(DQN) and the other a deep deterministic policy gradient (DDPG) algorithm, applicable to navigation tasks for mobile robots. Comparison between different reward schemes for both algorithms is one of the main focuses in this work. The methodology is implemented in the simulation for a mobile robot called TurtleBot3. The task for the robot is to navigate through obstacles from an initial location to a goal position. Finally, the trained end‐to‐end navigation stack is also implemented on the actual TurtleBot3 in a real environment. The robot uses a Lidar sensor to detect obstacles. The Lidar measurements and the relative position and angle of the robot to the target location are the inputs to the controller. The TurtleBot3 also utilizes distance information from its Lidar sensor to create an environmental map using the simultaneous localization and mapping (SLAM) technique. Additionally, given an initial position, the robot employs its inertial measurement unit (IMU) sensor and encoders for precise localization. [ABSTRACT FROM AUTHOR]

Mapping landslide cracks is essential in understanding landslide dynamics and evolution across space and time. Each landslide crack's depth, direction, and width are related to the stress and strain imposed on the landslide body. Moreover, their spatial distribution can indicate areas where the landslide can extend, mainly if located in the upper part of the main landslide scarp. Even though the cracks leave a distinct pattern on the landslide body when fresh or when there is a high contrast between the bare soil and surrounding vegetation, these patterns gradually diminish over time, making their detection difficult. The problem of landslide cracks mapping in various environmental conditions and having different ages was tackled in the current study using very high spatial resolution unmanned aerial vehicle (UAV) aerial imagery and derived products in conjunction with deep learning models. U-Net and DeepLab CNN models were applied using masked and non-masked training samples with different tile sizes. As the tile size decreases, the performance metrics, such as precision, recall, and F1-score, generally decrease. Overall, the lowest accuracy was approximately 0.79 for non-mask samples and tile size of 64 pixels, and reached over 0.93 for masked samples and tile size of 512 pixels. [ABSTRACT FROM AUTHOR]

Gold mining, even under strict environmental regulations, inevitably causes environmental impacts and liabilities. To address this, the study proposes a framework for environmental zoning around gold mines based on the distribution of potentially toxic elements (PTEs), pollution indices, and bioindicators. Soil samples were collected from municipalities affected by gold mining projects, and concentrations of As, Cd, Pb, and Cr were measured. Then, the Pollution Load Index (PLI) was calculated. A plant model was used for the biomonitoring of PTEs, and the Plant Vigor Index (PVI) was determined. Finally, environmental zoning was proposed through geospatial analysis combining PTEs, PLI, and PVI values. The concentrations of PTEs were as follows: As ranged from 1.7 to 892, Cd from 0.1 to 65.2, Pb from 18.5 to 2345, and Cr from 5.4 to 118.4. Spearman's rank correlation showed significant relations (ρ > 0.76) between bioindicators and PTE concentrations and PLI. The PVI correlated significantly with PTE concentrations (ρ − 0.41 to − 0.67) and PLI (ρ − 0.65). The municipalities were categorized into three zones: highly contaminated, moderately contaminated, and minimally contaminated. Overall, the environmental zoning maps serve as a management tool for environmental monitoring. [ABSTRACT FROM AUTHOR]

Appropriate environment management requires an understanding of how mining activity alters environmental characteristics and how those changes affect an area. Therefore, to reduce the adverse effects of mining activity on the land, it becomes crucial to have relevant information about responses to environmental degradation. This study aims to assess the impact of semi-mechanised and artisanal mining activities on the land cover using remote sensing data and photogrammetric analysis, in the Mbale locality, Northern Cameroon. For this purpose, the maximum likelihood classification algorithm of the supervised classification method combined with field surveys was used to map environmental changes, based on Sentinel-2 images of 2019, 2021, and 2023. Normalized Difference Vegetation Index (NDVI), Normalised Difference Water Index (NDWI), Brithness index (BI), and Soil crust index (SCI), were calculated to assess changes in vegetation, bare soil, water body, and exploited area. The orthophoto obtained from photogrammetric processing was performed to outline river network change through visual interpretation techniques and to calculate the volume of pits created by mining. The result of classified images indicated that vegetation cover decreased by 11.74% over the studied years. However, bare soil and exploited areas increased by 9.2% and 5.4% respectively. The calculated spectral indices show that between 2019 and 2023 the locality of Mbale considerably lost its vegetation cover, in favor of bare soil. The color of the soil and the granulometric size of the topsoil have also changed. The photogrammetry analysis highlighted the deviation of the main river and estimated the volume of pits created by mining activity to 22188.7 m3. The mining activities caused a loss of the vegetation cover, generated big pits, and multiple deviations of the Lom River from its natural course, which have a substantial negative influence on the ecosystem. Such data can be used for long-term environmental management, reclamation and rehabilitation monitoring, and mining area restoration. [ABSTRACT FROM AUTHOR]

Biologically inspired intelligent methods have been applied to various sensing systems in order to extract features from a huge size of raw sensing data. For example, point cloud data can be applied to human activity recognition, multi-person tracking, and suspicious person detection, but a single RGB-D camera is not enough to perform the above tasks. Therefore, this study propose a 3D environmental map-building method integrating point cloud data measured via multiple RGB-D cameras. First, a fast multi-scale of distributed batch-learning growing neural gas (Fast MS-DBL-GNG) is proposed as a topological feature extraction method in order to reduce computational costs because a single RGB-D camera may output 1 million data. Next, random sample consensus (RANSAC) is applied to integrate two sets of point cloud data using topological features. In order to show the effectiveness of the proposed method, Fast MS-DBL-GNG is applied to perform topological mapping from several point cloud data sets measured in different directions with some overlapping areas included in two images. The experimental results show that the proposed method can extract topological features enough to integrate point cloud data sets, and it runs 14 times faster than the previous GNG method with a 23% reduction in the quantization error. Finally, this paper discuss the advantage and disadvantage of the proposed method through numerical comparison with other methods, and explain future works to improve the proposed method. [ABSTRACT FROM AUTHOR]

Fluoride (iF) is an inorganic element commonly present in groundwater in central Mexico and is considered a health risk when it exceeds the Mexican drinking water standard of 1.5 mg/L. Prolonged exposure to iF can cause various adverse health effects, such as dental fluorosis and neurological effects, particularly in children. A rapid and cost-effective strategy to identify possible areas where the resident population may be at risk of exposure to this contaminant is the health risk estimation methodology of the Pan American Health Organization (PAHO). The aim of this study was to estimate the environmental risk of iF exposure in residents of Jerez, Zacatecas, Mexico, and subsequently construct risk maps to identify areas of the city where there is a higher risk of exposure. Fifty-five tap water samples were collected from households to determine iF concentrations using the ion-selective electrode method. Based on these environmental values, the hazard quotient (HQ) was estimated following the health risk estimation methodology. Subsequently, risk maps were generated from these values to visualize the spatial distribution of high-risk areas within the city. The iF concentrations in tap water ranged from 1.3 to 7.3 mg/L (ppm), with only one sample below the standard of 1.5 mg/L. We estimated HQ values ranging from 0.63 to 3.73. Exposure to iF in tap water is a health problem that must be addressed, necessitating risk communication actions that enable the population to safeguard its health through simple measures, thereby avoiding future health costs. [ABSTRACT FROM AUTHOR]

Unalaska Island, Alaska, served as a US military base during World War II. The military installed bases on Unalaska and nearby islands, many of which were built adjacent to Unangan communities. The military used toxic compounds in its operations and left a legacy of pollution that may pose health risks to residents and local wildlife. The goals of this study were to identify hotspots of contamination remaining at Unalaska formerly used defense (FUD) sites, evaluate the risk posed by arsenic, and examine "no US Department of Defense action indicated" (NDAI) status determinations for FUD sites near communities. We compiled soil chemistry data from remediation reports prepared by the US Army Corps of Engineers at 18 FUD sites on and near Unalaska. Nine had past and/or active remediation projects and on‐site sampling data. Eight sites did not have sampling data and were characterized as NDAI. One site was listed as closed. For the nine sites with sampling data, we compiled data for 22 contaminants of concern (COC) and compared concentrations to soil cleanup levels for human health (18 AAC 75.341). We mapped contaminant concentrations exceeding these levels to identify hotspots of contamination. We found that concentrations of some of the 22 COC exceeded Alaska cleanup levels despite remediation efforts, including diesel range organics, arsenic, and lead. The highest COC concentrations were at the FUD site adjacent to the City of Unalaska. A quantitative risk assessment for arsenic found that the risk of exposure through drinking water is low. We highlight concerns with NDAI designations and current remedial practices at remote FUD sites located adjacent to communities. Our data suggest the need for further remediation and monitoring efforts on Unalaska for certain contaminants and research to examine potential threats to human and animal health associated with these sites. Integr Environ Assess Manag 2024;20:1420–1431. © 2024 SETAC Key Points: We found that concentrations of some of the 22 contaminants of concern (COC) exceeded Alaska cleanup levels despite remediation efforts, including diesel range organics, arsenic, and lead, and that the highest COC concentrations were present at the formerly used defense (FUD) site adjacent to the City of Unalaska.We conducted a quantitative risk assessment for arsenic and found that the risk of exposure through drinking water is low.We highlight concerns with NDAI designations and current remedial practices at remote FUD sites located adjacent to communities.Our data suggest the need for further remediation and monitoring efforts on Unalaska for certain contaminants and research to examine potential threats to human and animal health associated with these sites. [ABSTRACT FROM AUTHOR]

Research background: Corporate social responsibility (CSR) demonstrates that a business should be interested in broader social issues rather than on merely those impacting profit margins. Thus, enterprises across all sectors need to focus on the pillars of CSR, which can ultimately give them a competitive advantage. Previous research papers have focused mainly on the level of CSR in particular industries or how CSR activities are communicated in these industries. Purpose of the article: The paper focuses on demonstrating the level of CSR performance in the chosen central European countries in the context of corporate earnings and specifying the impact of the economic sectors on the level of CSR performance, which is mapped by the environmental, social, and governance (ESG) score. Methods: This study used the ESG score, an indicator of the level of CSR performance, and financial and accounting data of 490 publicly traded enterprises from Central Europe. It also applied correlation analysis, the Kruskal–Wallis test and cluster agglomerative hierarchical clustering. Findings & value added: The results have proved that the CSR performance of central European enterprises is positively associated with the level of corporate earnings in all NACE sectors. This knowledge broadens the existing literature on this topic. The study also revealed statistically significant differences in the development of the CSR concept across the sectors. Then, cluster agglomerative hierarchical clustering identified the groups of sectors with homogenous approaches to CSR. This provides information on the homogeneity or heterogeneity of CSR performance across different industries, which is useful information not only for investors and other stakeholders, but also for researchers. [ABSTRACT FROM AUTHOR]

A substantial surge in ESG research has triggered a wide dissemination of research across various domains, underscoring the need for an extensive appraisal of the complex ESG landscape. This paper is grounded on a sample of 441 documents retrieved from the Web of Science database, spanning from 2007 to 2023. A strategic mapping was performed to decipher the cumulative scientific knowledge by delving into the interrelations among various facets and identifying the field's basic, motor, niche, and emerging themes. We also explore the thematic evolution of ESG across distinct time frames, indicating ESG's inception, evolution, and diversification over the years. Our findings contribute to the ESG literature by categorizing it into seven clusters across the four quadrants. Results of the strategic mapping accentuate the obsolete, over‐researched and promising areas of ESG research. We find research on CSR, socially responsible investments and ESG‐firm performance themes as over‐researched areas calling for diversification. Promising research areas include the emerging association of ESG with portfolio construction, green innovations and investments, ESG controversies, information asymmetry, divergence, greenwashing, AI‐enabled universal rating mechanism, up‐grading and down‐grading of ESG scores and ESG‐linked compensations. Thematic evolution of ESG research underpins the culmination of its interplay with sustainability, emerging markets, board diversity, and green financing. Finally, the factorial analysis strengthens the reliability of the main findings, making it robust. This paper stands as the initial venture providing a comprehensive exploration of conceptual linkages, shifts, and advancements within the ESG literature through thematic mapping, offering an original contribution to the field. [ABSTRACT FROM AUTHOR]

Environmental, social, and governance (ESG) policies have increased to the forefront of global discourse, appealing to the consideration of international and national institutes alike. To stay ahead of the curve and build long‐term competitive advantage, companies must prioritize proactive ESG research and integrate its findings into their strategies. Additionally, comprehensive ESG research is crucial to inform effective policy interventions that address pressing environmental, social, and governance issues. This article leverages Scopus data to conduct a bibliometric analysis of ESG research from 2007 to 2022, aiming to map the field's intellectual landscape and identify key themes driving its current state. The study highlights the top contributors, institutions, and journals that have focused on ESG issues. Moreover, it examines co‐citation networks, collaboration networks, and keyword co‐occurrences, which provides a comprehensive overview of intellectual structure. This study contributes to the understanding of the ESG research landscape and provides insights for future research directions. [ABSTRACT FROM AUTHOR]

The article discusses the lack of attention and protection given to wetlands in Latin America, particularly the wetlands of the Paraná Delta River. Wetlands are important ecosystems that serve as fresh water reservoirs and support a significant amount of biodiversity. However, 59% of Latin American wetlands are deteriorating, making it the region with the highest rate of wetland deterioration. The article highlights the recent events in Argentina where an influx of capybaras in a residential area brought attention to the environmental consequences of receding wetlands. The capybaras became a symbol of resistance and sparked a movement for the protection of wetlands. The article also introduces the concept of "wetland poetics," which refers to poetic expressions that engage with the specificities of wetland environments and contribute to environmental preservation. The article analyzes the works of three poets who explore wetlands within Argentina and emphasizes the importance of regional and ecological perspectives in understanding and addressing environmental challenges. The text discusses the work of Natalia Garay, a poet and visual artist from Paraná, Argentina, who explores wetland poetics and regional identity. Garay's poem "Smoke Altar" examines the impact of industrial ranching and intentional fires on the wetlands, portraying the pollution as disrupting social relations and likening people to livestock. The poem also highlights the erasure of Indigenous conceptions of the landscape and calls for the restoration of the wetlands. The text also mentions Mario Castells, another poet whose work addresses [Extracted from the article]

Digital maps structure how we understand and interact with space while simultaneously affording agency and the potential for an alternative use of space. This article investigates how digital maps exercise data and spatial agency. Our empirical object is the environmental mobile mapping application water refill map (WRM, Hong-Te/ Fengcha Action 奉茶行動) in Taiwan. Drawing on the walk-through method and semi-structured interviews with WRM's founders, we examine how the app leverages the power of digital mapping to encourage participation in environmental activities. We argue that by facilitating data and spatial agency, digital maps enable placemaking at the cognitive and hermeneutic levels. They also turn closed spaces into public ones, thus aiding place-shaping. Meanwhile, this digital environmental activism takes place in a field of contestation and negotiation between agency and structure, apps and infrastructural platforms, collective action and individual power, and environmentalism and (adjusted) commercialism. [ABSTRACT FROM AUTHOR]

AbstractEnvironmental governance outcomes hinge on the design and implementation of management decisions. Yet, available research methodologies can be limited in their ability to capture the complexity of decision-making and implementation processes and in turn predict and explain environmental governance outcomes. We present participatory process mapping as a method for examining the pathways that emerge and evolve over time that result in natural resource management decisions and on-the-ground outcomes, as perceived by participants in collaborative processes. The approach leverages a large-N comparative design, participatory diagraming within semi-structured interviews, and mental modeling toward a method that can fit into both quantitative and qualitative research designs and analysis. The method is highly flexible, offering a pathway toward capturing causal relationships while also enabling incorporation of diverse elements and relationships. Natural resource management is nonlinear and is shaped by diverse social-ecological elements, and process mapping diagrams elicited through this method reflect that reality. [ABSTRACT FROM AUTHOR]

This paper primarily studies the path planning problem for UAV formations guided by semantic map information. Our aim is to integrate prior information from semantic maps to provide initial information on task points for UAV formations, thereby planning formation paths that meet practical requirements. Firstly, a semantic segmentation network model based on multi-scale feature extraction and fusion is employed to obtain UAV aerial semantic maps containing environmental information. Secondly, based on the semantic maps, a three-point optimization model for the optimal UAV trajectory is established, and a general formula for calculating the heading angle is proposed to approximately decouple the triangular equation of the optimal trajectory. For large-scale formations and task points, an improved fuzzy clustering algorithm is proposed to classify task points that meet distance constraints by clusters, thereby reducing the computational scale of single samples without changing the sample size and improving the allocation efficiency of the UAV formation path planning model. Experimental data show that the UAV cluster path planning method using angle-optimized fuzzy clustering achieves an 8.6% improvement in total flight range compared to other algorithms and a 17.4% reduction in the number of large-angle turns. [ABSTRACT FROM AUTHOR]

Inadequate management and treatment of wastewater pose significant threats, including environmental pollution, degradation of water quality, depletion of global water resources, and detrimental effects on human well-being. Biogranulation technology has gained increasing traction for treating both domestic and industrial wastewater, garnering interest from researchers and industrial stakeholders alike. However, the literature lacks comprehensive bibliometric analyses that examine and illuminate research hotspots and trends in this field. This study aims to elucidate the global research trajectory of scientific output in biogranulation technology from 1992 to 2022. Utilizing data from the Scopus database, we conducted an extensive analysis, employing VOSviewer and the R-studio package to visualize and map connections and collaborations among authors, countries, and keywords. Our analysis revealed a total of 1703 journal articles published in English. Notably, China emerged as the leading country, Jin Rencun as the foremost author, Bioresource Technology as the dominant journal, and Environmental Science as the prominent subject area, with the Harbin Institute of Technology leading in institutional contributions. The most prominent author keyword identified through VOSviewer analysis was "aerobic granular sludge," with "sequencing batch reactor" emerging as the dominant research term. Furthermore, our examination using R Studio highlighted "wastewater treatment" and "sewage" as notable research terms within the field. These findings underscore a diverse research landscape encompassing fundamental aspects of granule formation, reactor design, and practical applications. This study offers valuable insights into biogranulation potential for efficient wastewater treatment and environmental remediation, contributing to a sustainable and cleaner future. [ABSTRACT FROM AUTHOR]

This article is a comprehensive empirical overview of environmental protection and conservation nonprofits' discourse on social media. To what extent have these nonprofits framed climate change in their public discourse and how has it evolved over time? How do organizational characteristics and resources affect their social media behavior? To address our research questions, we use machine learning with texts—specifically topic modeling—to track the activity of 120 environmental nonprofits during a 14-year time span on X, formerly known as Twitter. Our analysis of more than 1.3 million tweets shows that climate change, although not closely aligned with the missions for more than half of the top tweeting organizations included in our sample, has consistently been a prevalent priority issue on their social media agendas for more than a decade. This heightened attention to climate change discourse by the environmental nonprofit sector denotes their uniform efforts to inspire government for climate action. [ABSTRACT FROM AUTHOR]

With the global population growth and increasing food demand, the development of precision agriculture has become particularly critical. In precision agriculture, accurately identifying areas of nitrogen stress in crops and planning precise fertilization paths are crucial. However, traditional coverage path-planning (CPP) typically considers only single-area tasks and overlooks the multi-area tasks CPP. To address this problem, this study proposed a Regional Framework for Coverage Path-Planning for Precision Fertilization (RFCPPF) for crop protection UAVs in multi-area tasks. This framework includes three modules: nitrogen stress spatial distribution extraction, multi-area tasks environmental map construction, and coverage path-planning. Firstly, Sentinel-2 remote-sensing images are processed using the Google Earth Engine (GEE) platform, and the Green Normalized Difference Vegetation Index (GNDVI) is calculated to extract the spatial distribution of nitrogen stress. A multi-area tasks environmental map is constructed to guide multiple UAV agents. Subsequently, improvements based on the Double Deep Q Network (DDQN) are introduced, incorporating Long Short-Term Memory (LSTM) and dueling network structures. Additionally, a multi-objective reward function and a state and action selection strategy suitable for stress area plant protection operations are designed. Simulation experiments verify the superiority of the proposed method in reducing redundant paths and improving coverage efficiency. The proposed improved DDQN achieved an overall step count that is 60.71% of MLP-DDQN and 90.55% of Breadth-First Search–Boustrophedon Algorithm (BFS-BA). Additionally, the total repeated coverage rate was reduced by 7.06% compared to MLP-DDQN and by 8.82% compared to BFS-BA. [ABSTRACT FROM AUTHOR]

Nanospray desorption electrospray ionization (nano‐DESI) is an ambient ionization mass spectrometry imaging (MSI) approach that enables spatial mapping of biological and environmental samples with high spatial resolution and throughput. Because nano‐DESI has not yet been commercialized, researchers develop their own sources and interface them with different commercial mass spectrometers. Previously, several protocols focusing on the fabrication of nano‐DESI probes have been reported. In this tutorial, we discuss different hardware requirements for coupling the nano‐DESI source to commercial mass spectrometers, such as the safety interlock, inlet extension, and contact closure. In addition, we describe the structure of our custom software for controlling the nano‐DESI MSI platform and provide detailed instructions for its usage. With this tutorial, interested researchers should be able to implement nano‐DESI experiments in their labs. [ABSTRACT FROM AUTHOR]

Involving local people in natural area management is very important to address the complexity of environmental management issues for the sustainable use of local resources. Participatory methods require the contribution of local stakeholders in decision-making. Mapping the environmental views of local communities allows for the detection of aspects that could facilitate management efforts. This research—in the frame of the Skyros LIFE project and the PAMNATURA project—measures the local acceptability of the conservation actions proposed by the LIFE project in Skyros island (Greece), in relation to recording local people's environmental orientation using the 15-item revised New Ecological Paradigm (NEP) scale. The research was carried out by means of questionnaires. The LIFE project proposed actions were grouped into three categories according to their theme, i.e., promoting (a) nature protection, (b) agro-pastoralism and (c) ecotourism. The sample seems to largely agree with the LIFE project proposals, while the results showed high scores for most of the NEP scale items, implying pro-environmental beliefs, though there appears to be disagreement or neutrality regarding a few items. The local acceptability of the proposed actions was found to correlate to local people's environmental concern measured through the NEP scale. Drawing on the results, the implementation of actions related directly or indirectly to economic activities and the preservation of traditional activities, as well as environmental education and interactive informing and consultation, could enhance local participation. Skyros island seems to offer an appropriate social background for applying participatory and adaptive management and implementing conservation programs. [ABSTRACT FROM AUTHOR]

Regions of provenance for forest reproductive materials are the basis for wise use of forest resources in a changing climate. In this work a modelling framework is proposed for silver fir (Abies alba Mill.) in Italy where genetic clusters described by nuclear microsatellites were combined with high-resolution climatic data. When the genetic clusters were too large or had an uncertain ecological niche expression, an additional subregion division-was evaluated according to a climatic assessment. Subsequently each genecological group (Region of Provenance, RoP) was projected in geographic space separately using species distribution modelling (SDM) procedure under current (1991–2020) and a future climate scenario derived from the 6th assessment report for the period 2041–2070. The final division into nine RoPs was able to explain 77.41% of the total climatic variance, a good trade-off between statistical significance and practical usability. The modelling steps then showed a large degree of ecological overlap between RoPs with some of them occurring in similar ecological environments but characterized by a different genetic structure. When projected at the continental scale, the Italian RoPs were found to be suitable for almost all the current European range of silver fir, with potential expansion in Nordic countries in the future, beyond the current distribution range. The study showed that the combination of genetic and ecological data can be a robust way to proceed in areas where a strong genetic differentiation between populations occurs, such as in Italy. New markers such as SNPs can then be used to detect adaptive traits and drive the selection of provenances for common garden experiments in areas where the SDM modelscurrently extrapolate potential sites outside the current natural range. [ABSTRACT FROM AUTHOR]

This review focuses on digital soil organic carbon (SOC) mapping at regional or national scales in spatial resolutions up to 1 km using open data remote sensing sources, emphasizing its importance in achieving United Nations' Sustainable Development Goals (SDGs) related to hunger, climate action, and land conservation. The literature review was performed according to scientific studies indexed in the Web of Science Core Collection database since 2000. The analysis reveals a steady rise in total digital soil mapping studies since 2000, with digital SOC mapping studies accounting for over 20% of these studies in 2023, among which SDGs 2 (Zero Hunger) and 13 (Climate Action) were the most represented. Notably, countries like the United States, China, Germany, and Iran lead in digital SOC mapping research. The shift towards machine and deep learning methods in digital SOC mapping has surged post-2010, necessitating environmental covariates like topography, climate, and spectral data, which are cornerstones of machine and deep learning prediction methods. It was noted that the available climate data primarily restrict the spatial resolution of digital SOC mapping to 1 km, which typically requires downscaling to harmonize with topography (up to 30 m) and multispectral data (up to 10–30 m). Future directions include the integration of diverse remote sensing data sources, the development of advanced algorithms leveraging machine learning, and the utilization of high-resolution remote sensing for more precise SOC mapping. [ABSTRACT FROM AUTHOR]

As an unprecedented stream of decametric hyperspectral observations becomes available from recent and upcoming spaceborne missions, effective algorithms are required to retrieve vegetation biophysical and biochemical variables such as leaf area index (LAI) and canopy chlorophyll content (CCC). In the context of missions such as the Environmental Mapping and Analysis Program (EnMAP), Precursore Iperspettrale della Missione Applicativa (PRISMA), Copernicus Hyperspectral Imaging Mission for the Environment (CHIME), and Surface Biology Geology (SBG), several retrieval algorithms have been developed based upon the turbid medium Scattering by Arbitrarily Inclined Leaves (SAIL) radiative transfer model. Whilst well suited to cereal crops, SAIL is known to perform comparatively poorly over more heterogeneous canopies (including forests and row-structured crops). In this paper, we investigate the application of hybrid radiative transfer models, including a modified version of SAIL (rowSAIL) and the Invertible Forest Reflectance Model (INFORM), to such canopies. Unlike SAIL, which assumes a horizontally homogeneous canopy, such models partition the canopy into geometric objects, which are themselves treated as turbid media. By enabling crown transmittance, foliage clumping, and shadowing to be represented, they provide a more realistic representation of heterogeneous vegetation. Using airborne hyperspectral data to simulate EnMAP observations over vineyard and deciduous broadleaf forest sites, we demonstrate that SAIL-based algorithms provide moderate retrieval accuracy for LAI (RMSD = 0.92–2.15, NRMSD = 40–67%, bias = −0.64–0.96) and CCC (RMSD = 0.27–1.27 g m−2, NRMSD = 64–84%, bias = −0.17–0.89 g m−2). The use of hybrid radiative transfer models (rowSAIL and INFORM) reduces bias in LAI (RMSD = 0.88–1.64, NRMSD = 27–64%, bias = −0.78–−0.13) and CCC (RMSD = 0.30–0.87 g m−2, NRMSD = 52–73%, bias = 0.03–0.42 g m−2) retrievals. Based on our results, at the canopy level, we recommend that hybrid radiative transfer models such as rowSAIL and INFORM are further adopted for hyperspectral biophysical and biochemical variable retrieval over heterogeneous vegetation. [ABSTRACT FROM AUTHOR]

Environmental mapping and robot navigation are the basis for realizing robot automation in modern agricultural production. This study proposes a new autonomous mapping and navigation method for gardening scene robots. First, a new LiDAR slam-based semantic mapping algorithm is proposed to enable the robots to analyze structural information from point cloud images and generate roadmaps from them. Secondly, a general robot navigation framework is proposed to enable the robot to generate the shortest global path according to the road map, and consider the local terrain information to find the optimal local path to achieve safe and efficient trajectory tracking; this method is equipped in apple orchards. The LiDAR was evaluated on a differential drive robotic platform. Experimental results show that this method can effectively process orchard environmental information. Compared with vnf and pointnet++, the semantic information extraction efficiency and time are greatly improved. The map feature extraction time can be reduced to 0.1681 s, and its MIoU is 0.812. The resulting global path planning achieved a 100% success rate, with an average run time of 4ms. At the same time, the local path planning algorithm can effectively generate safe and smooth trajectories to execute the global path, with an average running time of 36 ms. [ABSTRACT FROM AUTHOR]

Visual simultaneous localization and mapping (VSLAM) enhances the navigation of autonomous agents in unfamiliar environments by progressively constructing maps and estimating poses. However, conventional VSLAM pipelines often exhibited degraded performance in dynamic environments featuring mobile objects. Recent research in deep learning led to notable progress in semantic segmentation, which involves assigning semantic labels to image pixels. The integration of semantic segmentation into VSLAM can effectively differentiate between static and dynamic elements in intricate scenes. This paper provided a comprehensive comparative review on leveraging semantic segmentation to improve major components of VSLAM, including visual odometry, loop closure detection, and environmental mapping. Key principles and methods for both traditional VSLAM and deep semantic segmentation were introduced. This paper presented an overview and comparative analysis of the technical implementations of semantic integration across various modules of the VSLAM pipeline. Furthermore, it examined the features and potential use cases associated with the fusion of VSLAM and semantics. It was found that the existing VSLAM model continued to face challenges related to computational complexity. Promising future research directions were identified, including efficient model design, multimodal fusion, online adaptation, dynamic scene reconstruction, and end-to-end joint optimization. This review shed light on the emerging paradigm of semantic VSLAM and how deep learning-enabled semantic reasoning could unlock new capabilities for autonomous intelligent systems to operate reliably in the real world. [ABSTRACT FROM AUTHOR]

Despite rising levels of interest in global environmental challenges, progress towards the widespread adoption of pro-environmental behaviours remains slow and inconsistent. Previous literature identifies the importance of education for working to address this inconsistency between the environmental values people hold and their behaviours, commonly described as the value-action gap. To examine current knowledge in this area we conducted a semi-systematic review of published literature which explicitly brings together value-action gap thinking and environmental education research. Our findings reveal that major areas of focus across this literature are either on the role of specific pedagogical approaches or broader policy and institutional structure. Based on our analysis of the literature, we suggest five priorities for future research. This includes research which contextualises educational visions within existing curricula, research which examines intergenerational learning, research which considers possibilities for collective action, research which studies a greater diversity of global locations, and research which examines the whole school approach. [ABSTRACT FROM AUTHOR]

Unmanned surface vehicles (USVs) often operate in real-world environments with long voyage distances and complex routes. The use of a single-grid map model presents challenges, such as the high computational costs for high-resolution maps and loss of environmental information for low-resolution maps. This article proposes an environmental modeling method using a hybrid map that combines topology units and grids. The approach involves calibrating key nodes based on the watershed skeleton line, constructing a topology map using these nodes, decomposing the original map into unit maps, converting each unit map into a grid map, and creating a hybrid map environment model that comprises topology maps, unit map sets, and grid map sets. Then, the article introduces an improved genetic algorithm, called Genetic Algorithm with Feedback (FGA), to address path planning in hybrid maps. Experimental results demonstrate that FGA has better computational efficiency than other algorithms in similar experimental environments. In hybrid maps, path planning with FGA reduces the path lengths and time consumption, and the paths are more logical, smooth, and continuous. These findings contribute to enhancing the quality of path planning and the practical value of USVs. [ABSTRACT FROM AUTHOR]

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The Australasian Journal of Environmental Management's second issue of 2024 focuses on the digital divide in environmental management in Australasia. The articles discuss the role of digital transformation in addressing environmental challenges and the opportunities it presents. They highlight the importance of digital inclusion and call for comprehensive policies to ensure equitable access and enhance digital literacy. The issue also includes studies on various environmental management strategies in Australia and New Zealand, emphasizing the need for tailored and inclusive approaches to sustainability. The findings offer insights for policymakers and managers to achieve positive change. [Extracted from the article]

Artisanal small-scale mines (ASMs) in the Amazon Rainforest are an important cause of deforestation, forest degradation, biodiversity loss, sedimentation in rivers, and mercury emissions. Satellite image data are widely used in environmental decision-making to monitor changes in the land surface, but ASMs are difficult to map from space. ASMs are small, irregularly shaped, unevenly distributed, and confused (spectrally) with other land clearance types. To address this issue, we developed a reliable and efficient ASM detection method for the Tapajós River Basin of Brazil—an important gold mining region of the Amazon Rainforest. We enhanced detection in three key ways. First, we used the time-series segmentation (LandTrendr) Google Earth Engine (GEE) Application Programming Interface to map the pixel-wise trajectory of natural vegetation disturbance and recovery on an annual basis with a 2000 to 2019 Landsat image time series. Second, we segmented 26 textural features in addition to 5 spectral features to account for the high spatial heterogeneity in ASM pixels. Third, we trained and tested a Random Forest model to detect ASMs after eliminating irrelevant and redundant features with the Variable Selection Using Random Forests "ensemble of ensembles" technique. The out-of-bag error and overall accuracy of the final Random Forest was 3.73 and 92.6%, which are comparable to studies mapping large industrial mines with the normalized difference vegetation index (NDVI) and LandTrendr. The most important feature in our study was NDVI, followed by textural features in the near and shortwave infrared. Our work paves the way for future ASM regulation through large area monitoring from space with free and open-source GEE and operational satellites. Studies with sufficient computational resources can improve ASM monitoring with advanced sensors consisting of spectral narrow bands (Sentinel-2, Environmental Mapping and Analysis Program, PRecursore IperSpettrale della Missione Applicativa) and deep learning. [ABSTRACT FROM AUTHOR]

In some large buildings, the efficiency of path planning algorithms for mobile robots still faces great challenges. To deal with this problem, a path planning algorithm combining Voronoi region segmentation and path optimization (VSO) is proposed to achieve fast path planning in large-scale indoor scenario. The algorithm uses the generalized Voronoi graph (GVG) to construct the topological map from the environmental map, and then the initial heuristic path can be obtained quickly on the topological map. By sampling in the area around the initial path, the over-exploration of the workspace can be reduced. What's more, the algorithm selects waypoints to divide the sampling area into multiple sub-regions, and search paths in the sub-regions in parallel to further reduce the search space and improve the search efficiency. Finally, the path obtained from each sub-region is connected as the solution of the overall problem, and the optimization algorithm is used to smooth the final path. Simulation and robot experiments are conducted to verify the practicability and effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Simultaneous localization and mapping (SLAM) is one of the key technologies for agricultural robots to build maps and localize in complex orchard environments and realize unmanned autonomous operations. Due to the complexity of the orchard environment, the single canopy feature and the diffuse reflection of light caused by the leaves, etc., the map construction process of the orchard environment leads to mismatch and increases the cumulative error of the map construction. Aiming at the above problems, this paper proposes a navigation map construction method for orchard environment based on the fusion of Scan Context and NDT-ICP. The method firstly searches the Ring key quickly to get the candidate frames, and scores the similarity between the candidate frames and the current frame, and effectively detects the loopbacks by two-stage searching algorithm to reduce the false matches in the map of orchard environment. Meanwhile, a point cloud alignment method based on the fusion of normal distribution transform coarse alignment and iterative nearest point exact alignment is used to reduce the cumulative error of the orchard environment map. The results show that the improved algorithm compensates the drift of the point cloud map with higher mapping accuracy, better real-time performance, lower resource utilization, higher overlap between the trajectory estimation and the real trajectory, smoother loops, and a 4% reduction in CPU occupancy. In the complex orchard environment, the root mean square error and standard deviation of the trajectories of this paper's algorithm are 0.57 m and 0.19 m, which are 68% and 83% higher than those of the loop detection algorithms in the Lightweight Ground Optimized Lidar Trajectory Measurement and Multivariate Terrain Mapping (LeGO-LOAM), respectively. Accurate map construction and low drift pose estimation can be performed. The research algorithm effectively reduces the influence of mis-matching and large cumulative error in the process of map construction in the orchard environment, meets the demand for high-precision environmental mapping in the orchard environment, and provides technical support for promoting unmanned operation in the orchard environment. [ABSTRACT FROM AUTHOR]

Systematic reviews and maps are considered a reliable form of research evidence, but often neglect non-English-language literature, which can be a source of important evidence. To understand the barriers that might limit authors' ability or intent to find and include non-English-language literature, we assessed factors that may predict the inclusion of non-English-language literature in ecological systematic reviews and maps, as well as the review authors' perspectives. We assessed systematic reviews and maps published in Environmental Evidence (n = 72). We also surveyed authors from each paper (n = 32 responses), gathering information on the barriers to the inclusion of non-English language literature. 44% of the reviewed papers (32/72) excluded non-English literature from their searches and inclusions. Commonly cited reasons included constraints related to resources and time. Regression analysis revealed that reviews with larger author teams, authors from diverse countries, especially those with non-English primary languages, and teams with multilingual capabilities searched in a significantly greater number of non-English languages. Our survey exposed limited language diversity within the review teams and inadequate funding as the principal barriers to incorporating non-English language literature. To improve language inclusion and reduce bias in systematic reviews and maps, our study suggests increasing language diversity within review teams. Combining machine translation with language skills can alleviate the financial and resource burdens of translation. Funding applications could also include translation costs. Additionally, establishing language exchange systems would enable access to information in more languages. Further studies investigating language inclusion in other journals would strengthen these conclusions. [ABSTRACT FROM AUTHOR]

Volcano arcs, particularly the Sunda Arc, are predominant in Indonesia and constitute a key tectonic system in current geodynamics, making it essential for several stakeholders to consider geothermal exploration while taking into account the environmental impact. In previous reports, conventional methods were used in mapping environmental impact zones, leading to inefficiency in the monitoring process. Therefore, this study aimed to monitor flood in geothermal surrounding areas using machine learning and multisensor data. In the process, sentinel 1 GRD was adopted to map flood area distribution and wa s compared to Sentinel 2 optic data. Furthermore, Google Earth Engine was used to process several steps, including acquisition, polarization, measuring indices, and post-processing. Mapping was conducted at flooded areas in Ciwidey and Pasirjambu as geothermal surroundings. The results showed that flooded areas were located in the built-up areas as a part of watershed downstream. Ciwidey District had a wider flooded area than Pasirjambu. In June 2022, VH polarization of flooded areas in Ciwidey was recorded at -25, correlating with field data. Meanwhile, the comparison between VH polarization in Pasirjambu and Sentinel 2 indicated that flooded areas were associated with low NDVI and SAVI values, representing low infiltration. Google Earth Engine was capable of detecting several areas with different topography through optical and SAR data fusion. Differences in flood detection were discovered between VH and VV polarizations in the subset area around geothermal area. VH polarization was more accurate for detecting flood distribution due to the capability to acquire inundation data in densely vegetated areas. Additionally, the method can be used to distinguish permanent water bodies in the form of lakes/craters from flood inundations. This study concluded that machine learning and multi-sensor data facilitated the mapping and monitoring of flood distribution using both temporal analysis and a faster process. [ABSTRACT FROM AUTHOR]

This study presents the environmental mapping of the Chilika Lake coastal lagoon, India, using satellite images Landsat 8-9 OLI/TIRS processed using machine learning (ML) methods. The largest brackish water coastal lagoon in Asia, Chilika Lake, is a wetland of international importance included in the Ramsar site due to its rich biodiversity, productivity, and precious habitat for migrating birds and rare species. The vulnerable ecosystems of the Chilika Lagoon are subject to climate effects (monsoon effects) and anthropogenic activities (overexploitation through fishing and pollution by microplastics). Such environmental pressure results in the eutrophication of the lake, coastal erosion, fluctuations in size, and changes in land cover types in the surrounding landscapes. The habitat monitoring of the coastal lagoons is complex and difficult to implement with conventional Geographic Information System (GIS) methods. In particular, landscape variability, patch fragmentation, and landscape dynamics play a crucial role in environmental dynamics along the eastern coasts of the Bay of Bengal, which is strongly affected by the Indian monsoon system, which controls the precipitation pattern and ecosystem structure. To improve methods of environmental monitoring of coastal areas, this study employs the methods of ML and Artificial Neural Networks (ANNs), which present a powerful tool for computer vision, image classification, and analysis of Earth Observation (EO) data. Multispectral satellite data were processed by several ML image classification methods, including Random Forest (RF), Support Vector Machine (SVM), and the ANN-based MultiLayer Perceptron (MLP) Classifier. The results are compared and discussed. The ANN-based approach outperformed the other methods in terms of accuracy and precision of mapping. Ten land cover classes around the Chilika coastal lagoon were identified via spatio-temporal variations in land cover types from 2019 until 2024. This study provides ML-based maps implemented using Geographic Resources Analysis Support System (GRASS) GIS image analysis software and aims to support ML-based mapping approach of environmental processes over the Chilika Lake coastal lagoon, India. [ABSTRACT FROM AUTHOR]

The swift advancements in robotics have rendered navigation an essential task for mobile robots. While map-based navigation methods depend on global environmental maps for decision-making, their efficacy in unfamiliar or dynamic settings falls short. Current deep reinforcement learning navigation strategies can navigate successfully without pre-existing map data, yet they grapple with issues like inefficient training, slow convergence, and infrequent rewards. To tackle these challenges, this study introduces an improved two-delay depth deterministic policy gradient algorithm (LP-TD3) for local planning navigation. Initially, the integration of the long–short-term memory (LSTM) module with the Prioritized Experience Re-play (PER) mechanism into the existing TD3 framework was performed to optimize training and improve the efficiency of experience data utilization. Furthermore, the incorporation of an Intrinsic Curiosity Module (ICM) merges intrinsic with extrinsic rewards to tackle sparse reward problems and enhance exploratory behavior. Experimental evaluations using ROS and Gazebo simulators demonstrate that the proposed method outperforms the original on various performance metrics. [ABSTRACT FROM AUTHOR]

This paper introduces an innovative approach to 3D environmental mapping through the integration of a compact, handheld sensor package with a two-stage sensor fusion pipeline. The sensor package, incorporating LiDAR, IMU, RGB, and thermal cameras, enables comprehensive and robust 3D mapping of various environments. By leveraging Simultaneous Localization and Mapping (SLAM) and thermal imaging, our solution offers good performance in conditions where global positioning is unavailable and in visually degraded environments. The sensor package runs a real-time LiDAR-Inertial SLAM algorithm, generating a dense point cloud map that accurately reconstructs the geometric features of the environment. Following the acquisition of that point cloud, we post-process these data by fusing them with images from the RGB and thermal cameras and produce a detailed, color-enriched 3D map that is useful and adaptable to different mission requirements. We demonstrated our system in a variety of scenarios, from indoor to outdoor conditions, and the results showcased the effectiveness and applicability of our sensor package and fusion pipeline. This system can be applied in a wide range of applications, ranging from autonomous navigation to smart agriculture, and has the potential to make a substantial benefit across diverse fields. [ABSTRACT FROM AUTHOR]

Simultaneous Localization and Mapping (SLAM) poses distinct challenges, especially in settings with variable elements, which demand the integration of multiple sensors to ensure robustness. This study addresses these issues by integrating advanced technologies like LiDAR-inertial odometry (LIO), visual-inertial odometry (VIO), and sophisticated Inertial Measurement Unit (IMU) preintegration methods. These integrations enhance the robustness and reliability of the SLAM process for precise mapping of complex environments. Additionally, incorporating an object-detection network aids in identifying and excluding transient objects such as pedestrians and vehicles, essential for maintaining the integrity and accuracy of environmental mapping. The object-detection network features a lightweight design and swift performance, enabling real-time analysis without significant resource utilization. Our approach focuses on harmoniously blending these techniques to yield superior mapping outcomes in complex scenarios. The effectiveness of our proposed methods is substantiated through experimental evaluation, demonstrating their capability to produce more reliable and precise maps in environments with variable elements. The results indicate improvements in autonomous navigation and mapping, providing a practical solution for SLAM in challenging and dynamic settings. [ABSTRACT FROM AUTHOR]

Grasslands across the African continent are under pressure from climate change and human activities, particularly in arid ecosystems. From a remote sensing perspective, these ecosystems have not received much scientific attention, especially in Namibia. To address this knowledge gap, various remote sensing methods were implemented using new generation spaceborne imaging spectrometers amongst others. Therefore, this research provides a first methodological approach aimed at mapping and evaluating the distribution of grasslands within two private nature reserves, namely, the NamibRand Nature Reserve (NRNR) and ProNamib Nature Reserve (PNNR) with surrounding farmlands on the edge of Namib Sand Sea. The multi-sensor approach utilizes Mixture Tuned Matched Filtering (MTMF) and incorporated spectral information collected in the field to analyze grasslands. The research involves a sensor comparison of multispectral Sentinel-2 and PlanetScope data, hyperspectral data from Environmental Mapping and Analysis Programme (EnMAP) and PRecursore IperSpettrale della Missione Applicativa (PRISMA) and an additional data fusion product derived from Sentinel-2 and EnMAP imagery based on a Smoothing Filter-based Intensity Modulation Hypersharpening method (SFIMHS). Additionally, a unique spectral library of collected field spectra was established and inter-species spectral separability and intra-species spectral homogeneity was analyzed. This library presents newly published spectra of individual species. Due to dry initial conditions, the calculated spectral separability of individual grasses is limited, making only a mean endmember feasible for partial unmixing. The validation results of satellite comparison show that data fusion products (R² = 0.51 with Normalized Difference Vegetation Index (NDVI); R² = 0.66 with Soil Adjusted Vegetation Index (SAVI)) are more suitable for mapping arid grasslands than multispectral or hyperspectral data (all R² < 0.35). More research is required and potential methodological adjustments are discussed to further investigate the spatio-temporal dynamics of arid grasslands and to aid conservation efforts in the Greater Sossusvlei-Namib Landscape in line with the United Nations Decade of Restoration. [ABSTRACT FROM AUTHOR]

The 2015–17 Zika virus (ZIKV) epidemic in the Americas subsided faster than expected and evolving population immunity was postulated to be the main reason. Herd immunization is suggested to occur around 60–70% seroprevalence, depending on demographic density and climate suitability. However, herd immunity was only documented for a few cities in South America, meaning a substantial portion of the population might still be vulnerable to a future Zika virus outbreak. The aim of our study was to determine the vulnerability of populations to ZIKV by comparing the environmental suitability of ZIKV transmission to the observed seroprevalence, based on published studies. Using a systematic search, we collected seroprevalence and geospatial data for 119 unique locations from 37 studies. Extracting the environmental suitability at each location and converting to a hypothetical expected seroprevalence, we were able to determine the discrepancy between observed and expected. This discrepancy is an indicator of vulnerability and divided into three categories: high risk, low risk, and very low risk. The vulnerability was used to evaluate the level of risk that each location still has for a ZIKV outbreak to occur. Of the 119 unique locations, 69 locations (58%) fell within the high risk category, 47 locations (39%) fell within the low risk category, and 3 locations (3%) fell within the very low risk category. The considerable heterogeneity between environmental suitability and seroprevalence potentially leaves a large population vulnerable to future infection. Vulnerability seems to be especially pronounced at the fringes of the environmental suitability for ZIKV (e.g. Sao Paulo, Brazil). The discrepancies between observed and expected seroprevalence raise the question: "why did the ZIKV epidemic stop with large populations unaffected?". This lack of understanding also highlights that future ZIKV outbreaks currently cannot be predicted with confidence. Author summary: After the ZIKV epidemic in the Americas, it remains unclear if and when a resurgence of the ZIKV could occur. We used publicly available data on environmental suitability of transmission as well as seroprevalence data to estimate future vulnerability to ZIKV outbreaks. Our results show a considerable discrepancy between the observed seroprevalence, from past exposure to the virus on one hand, and the environmental suitability, which raised the question why the epidemic subsided before reaching the expected herd immunization threshold in many locations. This lack of understanding also highlights that future ZIKV outbreaks currently cannot be predicted with confidence. Although we cannot provide an answer to the question why the epidemic subsided when it did, we present a better quantification and geospatial mapping of the potential vulnerability to future outbreaks, which will be crucial for decision makers to prepare for future outbreaks. [ABSTRACT FROM AUTHOR]