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The integration of remote sensing technologies, such as Mobile Mapping Systems (MMS), Ground Penetrating Radar (GPR), airborne LiDAR, high-speed terrestrial laser scanner and airborne multispectral cameras, is transforming the execution and management of large-scale infrastructure projects. These tools enable efficient and accurate data collection without requiring operators to be physically present in the observed area, enhancing the ability to map, analyze, and model both surface and subsurface features. This paper explores the synergy of these technologies, focusing on their application in critical sectors such as climate change mitigation, smart cities, and digital twins. It examines the benefits of technology integration, the challenges of data interpretation, and the opportunities for improving project efficiency and sustainability. Through a detailed analysis of each remote sensing technology, this paper highlights their potential to redefine large-scale project execution, driving innovation in infrastructure development and to bring business benefits to organisation which implements it. [ABSTRACT FROM AUTHOR]

This study provides a comprehensive bibliometric and in-depth analysis of artificial intelligence (AI) and machine learning (ML) applications in environmental monitoring, based on 4762 publications from 1991 to 2024. The research highlights a notable increase in publications and citations since 2010, with China, the United States, and India emerging as leading contributors. Key areas of research include air and water quality monitoring, climate change modeling, biodiversity assessment, and disaster management. The integration of AI with emerging technologies, such as the Internet of Things (IoT) and remote sensing, has significantly expanded real-time environmental monitoring capabilities and data-driven decision-making. In-depth analysis reveals advancements in AI/ML methodologies, including novel algorithms for soil mapping, land-cover classification, flood susceptibility modeling, and remote sensing image analysis. Notable applications include enhanced air quality predictions, water quality assessments, climate impact forecasting, and automated wildlife monitoring using AI-driven image recognition. Challenges such as the "black-box" nature of AI models, the need for high-quality data in resource-constrained regions, and the complexity of real-time disaster management are also addressed. The study highlights ongoing efforts to develop explainable AI (XAI) models, which aim to improve model transparency and trust in critical environmental applications. Future research directions emphasize improving data quality and availability, fostering interdisciplinary collaborations across environmental and computer sciences, and addressing ethical considerations in AI-driven environmental management. These findings underscore the transformative potential of AI and ML technologies for sustainable environmental management, offering valuable insights for researchers and policymakers in addressing global environmental challenges. [ABSTRACT FROM AUTHOR]

Abstract This study provides a comprehensive bibliometric and in-depth analysis of artificial intelligence (AI) and machine learning (ML) applications in environmental monitoring, based on 4762 publications from 1991 to 2024. The research highlights a notable increase in publications and citations since 2010, with China, the United States, and India emerging as leading contributors. Key areas of research include air and water quality monitoring, climate change modeling, biodiversity assessment, and disaster management. The integration of AI with emerging technologies, such as the Internet of Things (IoT) and remote sensing, has significantly expanded real-time environmental monitoring capabilities and data-driven decision-making. In-depth analysis reveals advancements in AI/ML methodologies, including novel algorithms for soil mapping, land-cover classification, flood susceptibility modeling, and remote sensing image analysis. Notable applications include enhanced air quality predictions, water quality assessments, climate impact forecasting, and automated wildlife monitoring using AI-driven image recognition. Challenges such as the “black-box” nature of AI models, the need for high-quality data in resource-constrained regions, and the complexity of real-time disaster management are also addressed. The study highlights ongoing efforts to develop explainable AI (XAI) models, which aim to improve model transparency and trust in critical environmental applications. Future research directions emphasize improving data quality and availability, fostering interdisciplinary collaborations across environmental and computer sciences, and addressing ethical considerations in AI-driven environmental management. These findings underscore the transformative potential of AI and ML technologies for sustainable environmental management, offering valuable insights for researchers and policymakers in addressing global environmental challenges.

Soil moisture (SM) plays a crucial role in land–atmosphere interaction systems, directly influencing evapotranspiration, photosynthesis, and the water dynamics of the soil surface. Invariably, SM is negatively impacted by disturbances such as fires, which are becoming more frequent across semi-arid ecosystems. Different ecological restoration activities have been implemented to mitigate the impacts of disturbance that, when left untreated, can worsen the effects of recurrent droughts and accelerate desertification and land degradation processes. To measure and monitor the dynamics of SM, advanced techniques and tools have been developed that integrate remote sensing and in situ measurement. This review encompasses various themes on the application of remote sensing for measuring and monitoring SM dynamics in semi-arid ecosystems at different scales. We focused our analysis on the western United States region and thus have developed a review on the following topics: (a) the different data sources (e.g., satellite, unmanned aerial vehicles), (b) approaches to measure field-based SM, and (c) algorithms and techniques to model SM at different scales. We summarize these topics by emphasizing repeatable approaches for the transparent estimation of this variable, identifying current data gaps, and highlighting future trends to fulfill the expanding demand for SM monitoring strategies. [ABSTRACT FROM AUTHOR]

High-resolution remote sensing platforms are crucial to map land use/cover (LULC) types. Unmanned aerial vehicle (UAV) technology has been widely used in the northern hemisphere, addressing the challenges facing low- to medium-resolution satellite platforms. This study establishes the scalability of Sentinel-2 LULC classification with ground-linked UAV orthoimages to large African ecosystems, particularly the Burunge Wildlife Management Area in Tanzania. It involved UAV flights in 19 ground-surveyed plots followed by upscaling orthoimages to a 10 m × 10 m resolution to guide Sentinel-2 LULC classification. The results were compared with unguided Sentinel-2 using the best classifier (random forest, RFC) compared to support vector machines (SVMs) and maximum likelihood classification (MLC). The guided classification approach, with an overall accuracy (OA) of 94% and a kappa coefficient (k) of 0.92, outperformed the unguided classification approach (OA = 90%; k = 0.87). It registered grasslands (55.2%) as a major vegetated class, followed by woodlands (7.6%) and shrublands (4.7%). The unguided approach registered grasslands (43.3%), followed by shrublands (27.4%) and woodlands (1.7%). Powerful ground-linked UAV-based training samples and RFC improved the performance. The area size, heterogeneity, pre-UAV flight ground data, and UAV-based woody plant encroachment detection contribute to the study's novelty. The findings are useful in conservation planning and rangelands management. Thus, they are recommended for similar conservation areas. [ABSTRACT FROM AUTHOR]

Forests play a vital role in maintaining ecological balance and provide numerous benefits. The monitoring and managing of large-scale forest plantations can be challenging and expensive. In recent years, advancements in remote sensing technologies, such as lightweight drones and object-oriented image analysis, have opened up new possibilities for efficient and accurate forest plantation monitoring. This study aimed to explore the utility of lightweight drones as a cost-effective and accurate method for mapping plantation characteristics in two 50 ha forest plots in the Nayla Range, Jaipur. By combining aerial photographs collected by the drone with photogrammetry and limited ground survey data, as well as topography and edaphic variables, this study examined the relative contribution of drone-derived plantation canopy information. The results demonstrate the immense potential of lightweight drones and object-oriented image analysis in providing valuable insights for optimizing silvicultural operations and planting trees in complex forest environments. [ABSTRACT FROM AUTHOR]

Structural health assessment is a critical aspect of maintaining the longevity and safety of built infrastructure. It involves inspection, monitoring and maintenance of structures. The aim of the study is to adopt the most appropriate nondestructive testing method for structural health monitoring (SHM) that increases the longevity of structures under observation. The study examines diverse methods for assessing the health of structures, ranging from traditional visual inspections to advanced technologies such as nondestructive testing (NDT) and SHM systems. It discusses the strengths and limitations of visual inspections and various NDT techniques, emphasizing the importance of each in different contexts. Then it explores the emerging field of SHM, highlighting the integration of sensors, data analytics and wireless communication for real-time monitoring. Additionally, it addresses the use of remote sensing technologies, such as satellites and drones, for large-scale assessments. It concludes by identifying the challenges and future directions, emphasizing the role of interdisciplinary collaboration and technological advancements in ensuring the resilience and sustainability of civil infrastructure. [ABSTRACT FROM AUTHOR]

The integration of remote sensing technologies, such as Mobile Mapping Systems (MMS), Ground Penetrating Radar (GPR), airborne LiDAR, high-speed terrestrial laser scanner and airborne multispectral cameras, is transforming the execution and management of large-scale infrastructure projects. These tools enable efficient and accurate data collection without requiring operators to be physically present in the observed area, enhancing the ability to map, analyze, and model both surface and subsurface features. This paper explores the synergy of these technologies, focusing on their application in critical sectors such as climate change mitigation, smart cities, and digital twins. It examines the benefits of technology integration, the challenges of data interpretation, and the opportunities for improving project efficiency and sustainability. Through a detailed analysis of each remote sensing technology, this paper highlights their potential to redefine large-scale project execution, driving innovation in infrastructure development and to bring business benefits to organisation which implements it.

Soil moisture (SM) plays a crucial role in land–atmosphere interaction systems, directly influencing evapotranspiration, photosynthesis, and the water dynamics of the soil surface. Invariably, SM is negatively impacted by disturbances such as fires, which are becoming more frequent across semi-arid ecosystems. Different ecological restoration activities have been implemented to mitigate the impacts of disturbance that, when left untreated, can worsen the effects of recurrent droughts and accelerate desertification and land degradation processes. To measure and monitor the dynamics of SM, advanced techniques and tools have been developed that integrate remote sensing and in situ measurement. This review encompasses various themes on the application of remote sensing for measuring and monitoring SM dynamics in semi-arid ecosystems at different scales. We focused our analysis on the western United States region and thus have developed a review on the following topics: (a) the different data sources (e.g., satellite, unmanned aerial vehicles), (b) approaches to measure field-based SM, and (c) algorithms and techniques to model SM at different scales. We summarize these topics by emphasizing repeatable approaches for the transparent estimation of this variable, identifying current data gaps, and highlighting future trends to fulfill the expanding demand for SM monitoring strategies.

High-resolution remote sensing platforms are crucial to map land use/cover (LULC) types. Unmanned aerial vehicle (UAV) technology has been widely used in the northern hemisphere, addressing the challenges facing low- to medium-resolution satellite platforms. This study establishes the scalability of Sentinel-2 LULC classification with ground-linked UAV orthoimages to large African ecosystems, particularly the Burunge Wildlife Management Area in Tanzania. It involved UAV flights in 19 ground-surveyed plots followed by upscaling orthoimages to a 10 m × 10 m resolution to guide Sentinel-2 LULC classification. The results were compared with unguided Sentinel-2 using the best classifier (random forest, RFC) compared to support vector machines (SVMs) and maximum likelihood classification (MLC). The guided classification approach, with an overall accuracy (OA) of 94% and a kappa coefficient (k) of 0.92, outperformed the unguided classification approach (OA = 90%; k = 0.87). It registered grasslands (55.2%) as a major vegetated class, followed by woodlands (7.6%) and shrublands (4.7%). The unguided approach registered grasslands (43.3%), followed by shrublands (27.4%) and woodlands (1.7%). Powerful ground-linked UAV-based training samples and RFC improved the performance. The area size, heterogeneity, pre-UAV flight ground data, and UAV-based woody plant encroachment detection contribute to the study’s novelty. The findings are useful in conservation planning and rangelands management. Thus, they are recommended for similar conservation areas.

Forests play a vital role in maintaining ecological balance and provide numerous benefits. The monitoring and managing of large-scale forest plantations can be challenging and expensive. In recent years, advancements in remote sensing technologies, such as lightweight drones and object-oriented image analysis, have opened up new possibilities for efficient and accurate forest plantation monitoring. This study aimed to explore the utility of lightweight drones as a cost-effective and accurate method for mapping plantation characteristics in two 50 ha forest plots in the Nayla Range, Jaipur. By combining aerial photographs collected by the drone with photogrammetry and limited ground survey data, as well as topography and edaphic variables, this study examined the relative contribution of drone-derived plantation canopy information. The results demonstrate the immense potential of lightweight drones and object-oriented image analysis in providing valuable insights for optimizing silvicultural operations and planting trees in complex forest environments.

Background: Technology has been identified as a potential solution to alleviate resource gaps and augment care delivery in dementia care settings such as hospitals, long-term care, and retirement homes. There has been an increasing interest in using real-time location systems (RTLS) across health care settings for older adults with dementia, specifically related to the ability to track a person's movement and location., Objective: In this study, we aimed to explore the factors that influence the adoption or nonadoption of an RTLS during its implementation in a specialized inpatient dementia unit in a tertiary care rehabilitation hospital., Methods: The study included data from a brief quantitative survey and interviews from a convenience sample of frontline participants. Our deductive analysis of the interview used the 3 categories of the Fit Between Individuals, Task, and Technology framework as follows: individual and task, individual and technology, and task and technology. The purpose of using this framework was to assess the quality of the fit between technology attributes and an individual's self-reported intentions to adopt RTLS technology., Results: A total of 20 health care providers (HCPs) completed the survey, of which 16 (80%) participated in interviews. Coding and subsequent analysis identified 2 conceptual subthemes in the individual-task fit category, including the identification of the task and the perception that participants were missing at-risk patient events. The task-technology fit category consisted of 3 subthemes, including reorganization of the task, personal control in relation to the task, and efficiency or resource allocation. A total of 4 subthemes were identified in the individual-technology fit category, including privacy and personal agency, trust in the technology, user interfaces, and perceptions of increased safety., Conclusions: By the end of the study, most of the unit's HCPs were using the tablet app based on their perception of its usefulness, its alignment with their comfort level with technology, and its ability to help them perform job responsibilities. HCPs perceived that they were able to reduce patient search time dramatically, yet any improvements in care were noted to be implied, as this was not measured. There was limited anecdotal evidence of reduced patient risk or adverse events, but greater reported peace of mind for HCPs overseeing patients' activity levels., (©Lynn Haslam-Larmer, Alisa Grigorovich, Leia Shum, Andria Bianchi, Kristine Newman, Andrea Iaboni, Josephine McMurray. Originally published in JMIR Aging (https://aging.jmir.org), 08.04.2024.)

We examine the methodological and practical issues related to the application of information technologies and space-based monitoring in ethnological examination of projects of Arctic industrial development. The necessity and prospects of using these technologies to assess the resource productivity of the territory are illustrated by the example of the project of the extraction of alluvial gold in the basin of the Mokrundya stream of the Srednekolymskii ulus (district) of the Sakha (Yakutia) Republic. In particular, this is necessary for analyzing the distribution of dominant plant communities, determining the phytomass of pasture plants, stocks of wild plants, and reindeer grazing capacity, and for calculating the losses of the indigenous peoples of the North in the influence zone of the project. A geobotanical map of the study area is provided. It is also shown that the use of space-based monitoring in carrying out an ethnological examination of projects and determining the damage caused to the local population as a result of the impact on traditional land use practices (hunting, gathering wild plants, and reindeer husbandry) makes it possible to take an inventory of pasture lands of reindeer husbandry and reduce the cost of these projects in comparison with traditional methods (field surveys of the territory, and aerial imaging surveys). The possibilities of comparing interannual images can also provide insight into the dynamics of changes in the territory under the influence of anthropogenic activity and taking into account climatic transformations. [ABSTRACT FROM AUTHOR]

Featured Application: This review article aims to highlight the capabilities and limitations of a wide range of remote sensing (RS) technologies for their possible use for automated data acquisition on construction job sites. More attention is given to integrated RS technologies for the purpose of tracking, localization, and 3D modeling in construction projects. This article is expected to facilitate the use of RS technologies for automating progress reporting, improving safety and productivity in construction. Near real-time tracking of construction operations and timely progress reporting are essential for effective management of construction projects. This does not only mitigate potential negative impact of schedule delays and cost overruns but also helps to improve safety on site. Such timely tracking circumvents the drawbacks of conventional methods for data acquisition, which are manual, labor-intensive, and not reliable enough for various construction purposes. To address these issues, a wide range of automated site data acquisition, including remote sensing (RS) technologies, has been introduced. This review article describes the capabilities and limitations of various scenarios employing RS enabling technologies for localization, with a focus on multi-sensor data fusion models. In particular, we have considered integration of real-time location systems (RTLSs) including GPS and UWB with other sensing technologies such as RFID, WSN, and digital imaging for their use in construction. This integrated use of technologies, along with information models (e.g., BIM models) is expected to enhance the efficiency of automated site data acquisition. It is also hoped that this review will prompt researchers to investigate fusion-based data capturing and processing. [ABSTRACT FROM AUTHOR]

Alterations to Louisiana's river systems and local hydrology have resulted in reduced freshwater, sediment, and nutrient inputs to wetland landscapes, causing significant negative impacts on marsh productivity and stability. This study set out to assess regional- and basin-scale impacts of river connectivity and sediment availability on wetland productivity. Satellite data were used in conjunction with river discharge, river sediment concentration, and wetland accretion data to evaluate correlations between river connectivity and wetland productivity and stability. Significant correlations were observed between river connectivity and Normalized Difference Vegetation Index (NDVI) and Aggregation Index (AI) values across a 10 year period of analysis. Moderate correlations (r2 = 0.51) between mean NDVI and AI values were observed for all wetland vegetation in coastal Louisiana. Middle Coast wetlands had the highest river connectivity and significantly higher aboveground productivity, spatial integrity, and wetland area. The Chenier Plain, with moderate sediment and nutrient inputs, consisted primarily of moderate productivity and integrity. The majority of the inactive Deltaic Plain, which is largely sediment deprived, consists of landscapes with the lowest wetland productivity and spatial integrity. This study linked wetland area, configuration, and productivity with river connectivity to provide an enhanced understanding of river and sediment importance for wetland stability and restoration. [ABSTRACT FROM AUTHOR]

Near real-time tracking of construction operations and timely progress reporting are essential for effective management of construction projects. This does not only mitigate potential negative impact of schedule delays and cost overruns but also helps to improve safety on site. Such timely tracking circumvents the drawbacks of conventional methods for data acquisition, which are manual, labor-intensive, and not reliable enough for various construction purposes. To address these issues, a wide range of automated site data acquisition, including remote sensing (RS) technologies, has been introduced. This review article describes the capabilities and limitations of various scenarios employing RS enabling technologies for localization, with a focus on multi-sensor data fusion models. In particular, we have considered integration of real-time location systems (RTLSs) including GPS and UWB with other sensing technologies such as RFID, WSN, and digital imaging for their use in construction. This integrated use of technologies, along with information models (e.g., BIM models) is expected to enhance the efficiency of automated site data acquisition. It is also hoped that this review will prompt researchers to investigate fusion-based data capturing and processing.

The Malaria Frontier Hypothesis (MFH) is the current model for predicting malaria emergence in the Brazilian Amazon. It has two important dimensions, ‘settlement time’ and ‘malaria incidence’, and its prediction are: malaria incidence peaks five years after the initiation of human settlement and declines towards zero after an estimated 10 years. Although MFH is currently accepted, it has been challenged recently. Herein, we described a novel method for estimating settlement timeline by using remote sensing technology integrated in an open-software geographic information system. Surprisingly, we found that of the majority of the rural settlements with high malaria incidence are more than 10 years old.

China has a significant portion of land in landslide-prone areas, and remote sensing technologies are becoming a tool of choice to investigate and monitor landslides. Although much progress has been made with remote sensing technologies and their applications in China, there is no systematical summary report. Thus, we summarize Synthetic Aperture Radar (SAR), optical remote sensing, and laser technologies currently being used in China, and their associated platforms (space-borne, air-borne, and ground-based). Multi-temporal optical images and time series SAR are often used to detect active landslides at time scales of years and months. The latest optical images and SAR intensity images are usual adopted to map fresh landslides, especial for coseismic landslides. LiDAR technology has been widely applied to identify ancient landslides. Combining advantages and limitations of every technology, an integrated space-air-ground collaborative investigation strategy has been proposed for early identification and early warning of landslides. Additionally, a comprehensive landslide investigation integrating multidisciplinary approaches, including remote sensing, geology, and geophysical exploration, could be a further developing trend, because remote sensing technologies just provide surface information, while a complete understanding of landslides requires more than surface information – knowledge of geotechnical parameters, geological features, and field conditions is also needed. • SAR, optical remote sensing and laser technologies have been applied for landslide investigations in China. • China has proposed remote sensing strategies for different landslides. • China has carried out successful remote-sensing applications on different scales. • Multidisciplinary integration for landslide investigation should be the further trend. [ABSTRACT FROM AUTHOR]

Urban forests and green infrastructures at large are of critical importance for contemporary cities as they provide a wide range of ecosystem services (ESS) that enhance the quality of life of urban dwellers. Remote sensing technologies have greatly contributed to assessing and mapping the spatial distribution of ESS in urban areas, although more research is needed given the availability of new sensors from multiple satellites and platforms and the particular characteristics of urban environments (e.g., high heterogeneity). This Special Issue hosts papers focusing on the temporal and spatial dynamics of urban forests with special attention given to the most recent remote sensing technologies as well as advanced methods for processing geospatial data and extracting meaningful information.

IREA (an Institute of the Italian National Research Council) develops methodologies and technologies for acquisition, processing, fusion and interpretation of images and data obtained by using electromagnetic sensors (operating on satellite, aircraft and in situ), aimed at monitoring environment and territory, at non-invasive diagnostic and at electromagnetic risk assessment. These scientific activities have a significant follow-up in the fields of Cultural Heritage (CH) monitoring and protection. IREA is an excellence level Institute with a very solid international visibility, gained thanks to a very good scientific production and through the involvement in national and international projects. IREA is also involved in the organization of scientific events, conferences, summer schools and contributes to the editorial board of several international journals. Awards have been assigned to IREA laboratories and researchers who collaborate with many CH institutions in Italy and abroad.

Reliable and accurate mapping and extraction of key forest indicators of ecosystem development and health, such as aboveground biomass (AGB) and aboveground carbon stocks (AGCS) is critical in understanding forests contribution to the local, regional and global carbon cycle. This information is critical in assessing forest contribution towards ecosystem functioning and services, as well as their conservation status. This work aimed at assessing the applicability of the high resolution 8-band WorldView-2 multispectral dataset together with environmental variables in quantifying AGB and aboveground carbon stocks for three forest plantation species i.e. Eucalyptus dunii (ED), Eucalyptus grandis (EG) and Pinus taeda (PT) in uMgeni Catchment, South Africa. Specifically, the strength of the Worldview-2 sensor in terms of its improved imaging agilities is examined as an independent dataset and in conjunction with selected environmental variables. The results have demonstrated that the integration of high resolution 8-band Worldview-2 multispectral data with environmental variables provide improved AGB and AGCS estimates, when compared to the use of spectral data as an independent dataset. The use of integrated datasets yielded a high R 2 value of 0.88 and RMSEs of 10.05 t ha −1 and 5.03 t C ha −1 for E. dunii AGB and carbon stocks; whereas the use of spectral data as an independent dataset yielded slightly weaker results, producing an R 2 value of 0.73 and an RMSE of 18.57 t ha −1 and 09.29 t C ha −1 . Similarly, high accurate results ( R 2 value of 0.73 and RMSE values of 27.30 t ha −1 and 13.65 t C ha −1 ) were observed from the estimation of inter-species AGB and carbon stocks. Overall, the findings of this work have shown that the integration of new generation multispectral datasets with environmental variables provide a robust toolset required for the accurate and reliable retrieval of forest aboveground biomass and carbon stocks in densely forested terrestrial ecosystems. [ABSTRACT FROM AUTHOR]

IREA (an Institute of the Italian National Research Council) develops methodologies and technologies for acquisition, processing, fusion and interpretation of images and data obtained by using electromagnetic sensors (operating on satellite, aircraft and in situ), aimed at monitoring environment and territory, at non-invasive diagnostic and at electromagnetic risk assessment. These scientific activities have a significant follow-up in the fields of Cultural Heritage (CH) monitoring and protection. IREA is an excellence level Institute with a very solid international visibility, gained thanks to a very good scientific production and through the involvement in national and international projects. IREA is also involved in the organization of scientific events, conferences, summer schools and contributes to the editorial board of several international journals. Awards have been assigned to IREA laboratories and researchers who collaborate with many CH institutions in Italy and abroad. [ABSTRACT FROM AUTHOR]

It is difficult in a dental setting to accurately diagnose sleep bruxism and to objectively assess the severity, frequency or natural history of the condition in an individual patient. Yet this information is essential for the management of sleep bruxism and to plan appropriate dental treatment. The objective of this study was to clinically test a device that could be used to record bruxism events in a home environment. Pressure sensors were developed for use under the surface of an occlusal splint, and circuitry was designed to facilitate the recording and wireless transmission of the pressure sensor signal to a computer. Controlled mandibular movements were carried out in vivo to simulate bruxism and non-bruxism patterns. These patterns of force application were graphically presented to two examiners who were asked to identify the type of activity represented by the force curves. Examiners were largely able to distinguish bruxism from non-bruxism activity; the sensitivity ranged from 80% to 100% and the specificity from 75% to 100%. Using sensors in an occlusal splint, it is possible to recognise the typical tooth contact patterns seen in sleep bruxism. Such a device may be useful for monitoring sleep bruxism over an extended period at home. [ABSTRACT FROM AUTHOR]

Urban forests and green infrastructures at large are of critical importance for contemporary cities as they provide a wide range of ecosystem services (ESS) that enhance the quality of life of urban dwellers. Remote sensing technologies have greatly contributed to assessing and mapping the spatial distribution of ESS in urban areas, although more research is needed given the availability of new sensors from multiple satellites and platforms and the particular characteristics of urban environments (e.g., high heterogeneity). This Special Issue hosts papers focusing on the temporal and spatial dynamics of urban forests with special attention given to the most recent remote sensing technologies as well as advanced methods for processing geospatial data and extracting meaningful information. [ABSTRACT FROM AUTHOR]

The United Nations Sustainable Development Goals (SDGs), announced in September 2015, present a vision of achieving a higher level of human health and well-being worldwide by the year 2030. The SDG targets specific to water and sanitation call for more detailed monitoring and response to understand the coverage and quality of safely managed sources. It is hoped that improved monitoring of water and sanitation interventions will reveal more cost-effective and efficient ways of meeting the SDGs. In this paper, we review the landscape of approaches that can be used to support and improve on the water and sanitation targets SDG 6.1, “By 2030, achieve universal and equitable access to safe and affordable drinking water for all”, and SDG 6.2, “By 2030, achieve access to adequate and equitable sanitation and hygiene for all and end open defecation, paying special attention to the needs of women and girls and those in vulnerable situations”. [ABSTRACT FROM AUTHOR]