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In recent decades, climatic pressures have altered the forested landscape of Bavaria. Widespread loss of trees has unevenly impacted the entire state, of which 37% is covered by forests (5% more than the national average). In 2018 and 2019—due in large part to drought and subsequent insect infestations—more tree-covered areas were lost in Bavaria than in any other German state. Moreover, the annual crown condition survey of Bavaria has revealed a decreasing trend in tree vitality since 1998. We conducted a systematic literature review regarding the remote sensing of forests in Bavaria. In total, 146 scientific articles were published between 2008 and 2023. While 88 studies took place in the Bavarian Forest National Park, only five publications covered the whole of Bavaria. Outside of the national park, the remaining 2.5 million hectares of forest in Bavaria are understudied. The most commonly studied topics were related to bark beetle infestations (24 papers); however, few papers focused on the drivers of infestations. The majority of studies utilized airborne data, while publications utilizing spaceborne data focused on multispectral; other data types were under-utilized- particularly thermal, lidar, and hyperspectral. We recommend future studies to both spatially broaden investigations to the state or national scale and to increase temporal data acquisitions together with contemporaneous in situ data. Especially in understudied topics regarding forest response to climate, catastrophic disturbances, regrowth and species composition, phenological timing, and in the sector of forest management. The utilization of remote sensing data in the forestry sector and the uptake of scientific results among stakeholders remains a challenge compared to other heavily forested European countries. An integral part of the Bavarian economy and the tourism sector, forests are also vital for climate regulation via atmospheric carbon reduction and land surface cooling. Therefore, forest monitoring remains centrally important to attaining more resilient and productive forests. [ABSTRACT FROM AUTHOR]

Rapid urbanization and climate change exacerbate the urban heat island effect, increasing the vulnerability of urban residents to extreme heat. Although many studies have assessed urban heat vulnerability, there is a significant lack of standardized criteria and references for selecting indicators, building models, and validating those models. Many existing approaches do not adequately meet urban planning needs due to insufficient spatial resolution, temporal coverage, and accuracy. To address this gap, this paper introduces the U-HEAT framework, a conceptual model for analyzing urban heat vulnerability. The primary objective is to outline the theoretical foundations and potential applications of U-HEAT, emphasizing its conceptual nature. This framework integrates machine learning (ML) with remote sensing (RS) to identify urban heat vulnerability at both long-term and detailed levels. It combines retrospective and forward-looking mapping for continuous monitoring and assessment, providing essential data for developing comprehensive strategies. With its active learning capacity, U-HEAT enables model refinement and the evaluation of policy impacts. The framework presented in this paper offers a standardized and sustainable approach, aiming to enhance practical analysis tools. It highlights the importance of interdisciplinary research in bolstering urban resilience and stresses the need for sustainable urban ecosystems capable of addressing the complex challenges posed by climate change and increased urban heat. This study provides valuable insights for researchers, urban administrators, and planners to effectively combat urban heat challenges. [ABSTRACT FROM AUTHOR]

Land degradation, desertification and tree mortality related to global climate change have been in the spotlight of remote sensing research in recent decades since extreme climatic events could affect the composition, structure, and biogeography of forests. However, the complexity of tree mortality processes requires a holistic approach. Herein, we present the first global assessment and a historical perspective of forest tree mortality by reviewing both remote sensing and meteorological ground-based studies. We compiled 254 papers on tree mortality that make use of remotely sensed products, meteorological ground-based monitoring, and climatic drivers, focusing on their spatial and temporal patterns and the methods applied while highlighting research gaps. Our core results indicate that international publications on tree mortality are on the increase, with the main hotspots being North America (39%) and Europe (26%). Wetness indicators appear as the barometer in explaining tree mortality at a local scale, while vegetation indicators derived from multispectral optical sensors are promising for large-scale assessments. We observed that almost all of the studies we reviewed were based on less than 25 years of data and were at the local scale. Longer timeframes and regional scale investigations that will include multiple tree species analysis could have a significant impact on future research. [ABSTRACT FROM AUTHOR]

As the second most predominant greenhouse gas, methane-targeted emission mitigation holds the potential to decelerate the pace of global warming. Satellite remote sensing is an important monitoring tool, and we review developments in the satellite detection of methane. This paper provides an overview of the various types of satellites, including the various instrument parameters, and describes the different types of satellite retrieval algorithms. In addition, the currently popular methane point source quantification method is presented. Based on existing research, we delineate the classification of methane remote sensing satellites into two overarching categories: area flux mappers and point source imagers. Area flux mappers primarily concentrate on the assessment of global or large-scale methane concentrations, with a further subclassification into active remote sensing satellites (e.g., MERLIN) and passive remote sensing satellites (e.g., TROPOMI, GOSAT), contingent upon the remote sensing methodology employed. Such satellites are mainly based on physical models and the carbon dioxide proxy method for the retrieval of methane. Point source imagers, in contrast, can detect methane point source plumes using their ultra-high spatial resolution. Subcategories within this classification include multispectral imagers (e.g., Sentinel-2, Landsat-8) and hyperspectral imagers (e.g., PRISMA, GF-5), contingent upon their spectral resolution disparities. Area flux mappers are mostly distinguished by their use of physical algorithms, while point source imagers are dominated by data-driven methods. Furthermore, methane plume emissions can be accurately quantified through the utilization of an integrated mass enhancement model. Finally, a prediction of the future trajectory of methane remote sensing satellites is presented, in consideration of the current landscape. This paper aims to provide basic theoretical support for subsequent scientific research. [ABSTRACT FROM AUTHOR]

Extreme forest fires have historically been a significant concern in Canada, the Russian Federation, the USA, and now pose an increasing threat in boreal Europe. This paper deals with application of the wildFire cLimate impacts and Adaptation Model (FLAM) in boreal forests. FLAM operates on a daily time step and utilizes mechanistic algorithms to quantify the impact of climate, human activities, and fuel availability on wildfire probabilities, frequencies, and burned areas. In our paper, we calibrate the model using historical remote sensing data and explore future projections of burned areas under different climate change scenarios. The study consists of the following steps: (i) analysis of the historical burned areas over 2001–2020; (ii) analysis of temperature and precipitation changes in the future projections as compared to the historical period; (iii) analysis of the future burned areas projected by FLAM and driven by climate change scenarios until the year 2100; (iv) simulation of adaptation options under the worst-case scenario. The modeling results show an increase in burned areas under all Representative Concentration Pathway (RCP) scenarios. Maintaining current temperatures (RCP 2.6) will still result in an increase in burned area (total and forest), but in the worst-case scenario (RCP 8.5), projected burned forest area will more than triple by 2100. Based on FLAM calibration, we identify hotspots for wildland fires in the boreal forest and suggest adaptation options such as increasing suppression efficiency at the hotspots. We model two scenarios of improved reaction times—stopping a fire within 4 days and within 24 h—which could reduce average burned forest areas by 48.6% and 79.2%, respectively, compared to projected burned areas without adaptation from 2021–2099. [ABSTRACT FROM AUTHOR]

The occurrence patterns of forest insect pests and diseases have been altered by global events such as climate change. Recent developments in improved monitoring methods and tools for data analyses provide new opportunities to understand the causes and consequences of such changes. Using a variety of management tools, forest pest management programs can mitigate the influence of global changes on forest health. The goal of this Special Issue is to improve our understanding of the root causes of changes that have induced global changes. Fifteen papers are included in this Special Issue, covering several issues in forest pest management. One paper reviews the causes of Korean oak wilt, and another paper discusses fourteen invasive tree pests in Russia. The remaining thirteen papers cover issues related to the monitoring and management of forest pests. These studies provide a better understanding of the causes of change in the patterns of forest pests under the influence of global changes. These reviews also contribute to the development of forest-pest-management strategies to mitigate such impacts on forests due to global changes. [ABSTRACT FROM AUTHOR]

Temperate forests are particularly exposed to climate change and the associated increase in weather extremes. Droughts, storms, late frosts, floods, heavy snowfalls, or changing climatic conditions such as rising temperatures or more erratic precipitation are having an increasing impact on forests. There is an urgent need to better assess the impacts of climate change and extreme weather events (EWEs) on temperate forests. Remote sensing can be used to map forests at multiple spatial, temporal, and spectral resolutions at low cost. Different approaches to forest change assessment offer promising methods for a broad analysis of the impacts of climate change and EWEs. In this review, we examine the potential of Earth observation for assessing the impacts of climate change and EWEs in temperate forests by reviewing 126 scientific papers published between 1 January 2014 and 31 January 2024. This study provides a comprehensive overview of the sensors utilized, the spatial and temporal resolution of the studies, their spatial distribution, and their thematic focus on the various abiotic drivers and the resulting forest responses. The analysis indicates that multispectral, non-high-resolution timeseries were employed most frequently. A predominant proportion of the studies examine the impact of droughts. In all instances of EWEs, dieback is the most prevailing response, whereas in studies on changing trends, phenology shifts account for the largest share of forest response categories. The detailed analysis of in-depth forest differentiation implies that area-wide studies have so far barely distinguished the effects of different abiotic drivers at the species level. [ABSTRACT FROM AUTHOR]

As a consequence of global climate change, sea level rise (SLR) presents notable risks to both urban and natural areas located near coastlines. For developing effective strategies to mitigate and adapt to these risks, it is essential to evaluate the potential impacts of SLR in coastal areas. While substantial research has been conducted on mapping the broad-scale impacts of SLR based on scenarios of Global Mean Sea Level (GMSL), consideration of regional scenarios, systematic classification, and distinct stages of SLR have been largely overlooked. This gap is significant because SLR impacts vary by region and by the level of SLR, so adaptations, planning, and decision-making must be adapted to local conditions. This paper aims to precisely identify the landscape and urban morphology changes caused by the impact of SLR for each foot of elevation increase based on remote sensing technologies, focusing on St. Johns County, Florida, and Chatham County, Georgia. These two counties are both situated along the southeastern coastline of the United States but with completely different urban forms due to distinct historical and cultural developments. Regional forecasting SLR scenarios covering the period from 2020 to 2100 were utilized to assess the landscape transformation and urban changes, incorporating selected landscape and urban metrics to calculate quantitative data for facilitating comparative analyses. This study investigated gradual alterations in urban morphology and green infrastructure both individually and in combination with the effect on wetlands due to SLR. The mapping outcomes of this research were generated by employing comprehensive remote sensing data. The findings of this research indicated that, when the sea level rose to 3 feet, the wetlands would experience notable alterations, and the level of fragmentation in urban built areas would progressively increase, causing most of the metric data to exhibit a pronounced decline or increase. [ABSTRACT FROM AUTHOR]

Water bodies can offer local microclimates that have the potential to attenuate the effects of urban heat islands by reducing local temperature. This capability is shaded when the river is channelized. In such cases, the river temperature rises during hot periods, leading to negative impacts on the water quality. The main aim of this paper is to develop a local simple model to predict the temperature of the Dâmbovița River at its exit from Bucharest City, the capital of Romania. The location is chosen based on the historical critical impacts, in terms of extreme heatwaves that took place during hot summers, as well as future possible risks due to climate change. The water temperature prediction model is based on an artificial neural network that uses the Levenberg–Marquardt algorithm, due to its stability and rapid convergence capabilities. The model forecasts, with an accuracy of ±1 °C, the water temperature in an ungauged, downstream location, as a function of measured air and upstream water temperatures. The proposed model represents a first attempt to provide water managers in Bucharest City with a useful tool that will allow them to take timely measures to counteract the unwanted effects that can be generated by high water temperatures. [ABSTRACT FROM AUTHOR]

Globally, forest ecosystems, especially subtropical forests, play a central role in biogeochemical cycles and climate regulation, demonstrating their irreplaceable function. The subtropical region of China, characterized by its unique forest ecosystem, complex terrain, climate heterogeneity, diverse vegetation types, and frequent human activities, underscores the importance of the in-depth study of its net primary productivity (NPP). This paper employs the eddy covariance–light use efficiency (EC-LUE) model to quantitatively estimate the gross primary productivity (GPP) of this region from 2001 to 2018, followed by an estimation of the actual net primary productivity (ANPP) using the carbon use efficiency (CUE). The results showed that over these 18 years, the annual average ANPP was 677.17 gC m−2 a−1, exhibiting an overall increasing trend, particularly in mountainous areas, reserves, and the cultivated lands of the northeastern plains, whereas a significant decrease was observed around the urban agglomerations on the southeast coast. Furthermore, the Thornthwaite memorial model was applied to calculate the potential net primary productivity (PNPP), and diverse scenarios were set to quantitatively evaluate the impact of climate change and human activities on the vegetation productivity in the study area. It was found that in areas where the ANPP increased, both human activities and climate change jointly influenced ANPP dynamics; in areas with a decreased ANPP, the impact of human activities was particularly significant. Additionally, the heterogeneous distribution of ANPP across different altitudinal gradients and the driving effects of various climatic factors were analyzed. Finally, a partial correlation analysis was used to examine the relationships between the temperature, precipitation, and ANPP. This study indicated that temperature and precipitation have a substantial impact on the growth and distribution of vegetation in the region, yet the extent of this influence shows considerable variation among different areas. This provides a robust scientific basis for further research and understanding of the carbon dynamics of subtropical forest ecosystems and their role in the global carbon cycle. [ABSTRACT FROM AUTHOR]

The net primary productivity (NPP) of vegetation is an important indicator reflecting the vegetation dynamics and carbon sequestration capacity in a region. In recent years, China has implemented policies to carry out ecological protection. To understand the changes in the distribution of vegetation NPP in China and the influence of climate factors, the Carnegie–Ames–Stanford approach (CASA) model was used to estimate the NPP from 2001 to 2020. In this paper, several sets of measurement datasets and products were collected to evaluate the effectiveness of the model and suggestions were provided for the modification of the CASA model based on the evaluation results. In addition to the correlation analysis, this paper presents a statistical method for analyzing the quantitative effects in individual climatic factors on NPP changes in large regions. The comparison found that the model has a better estimation effect on grassland and needleleaf forest. The estimation error for the evergreen needleleaf forest (ENF) and deciduous broadleaf forest (DBF) decreases with the warming of the climatic zone, while the evergreen broadleaf forest (EBF) and deciduous needleleaf forest (DNF) do the opposite. The changes in total CASA NPP were consistent with the trends of other products, showing a dynamic increasing trend. In terms of the degree of correlation between the NPP changes and climatic factors, the NPP changes were significantly correlated with temperature in about 10.39% of the vegetation cover area and with precipitation in about 26.92% of the vegetation cover area. It was found that the NPP variation had a negative response to the temperature variation in Inner Mongolia grasslands, while it had a positive but small effect (±10 g C) in the Qinghai–Tibet Plateau grasslands. Precipitation had a facilitative effect on the grassland NPP variation, while an increase in the annual precipitation of more than 200 mm had an inhibitory effect in arid and semi-arid regions. This study can provide data and methodological reference for the ecological assessment of large-scale regional and climate anomalous environments. [ABSTRACT FROM AUTHOR]

Orphaned oil and gas wells represent an important environmental and economic development concern in areas where historical energy exploration and production activity pre-dated regulations on well abandonment documentation practices. From an economic development perspective, these wells pose a subsurface risk to infrastructure development, while the environmental impact of orphaned wells is largely associated with uncontrolled emissions of both fluids and gasses, most notably atmospheric methane. Often neglected in regulatory oversight, methane emissions from orphaned wells contribute to global greenhouse gas concentrations and without proper mitigation, these emissions contribute to and further exacerbate global climate change processes. An inherent challenge of remediation efforts targeting orphaned wells is the consistent observation that only a fraction of located and identified wells produce the majority of methane emissions, yet no methodology exists to effectively separate out heavy emitters without visiting and assessing each and every well. We demonstrate that emitting wells have certain defined characteristics largely pertaining to the presence and integrity of metal casing to the surface, which can be distinguished as an organized high intensity magnetic anomaly in low-altitude UAV-based aeromagnetic surveys. In this paper, we present a database of identified and ground-truthed wellsites correlated to their magnetic signatures and provide a roadmap to initial prioritization of wellsites for subsequent remediation activities that can be implemented in complex environments where other survey options are not feasible. In a regulatory environment where resources dedicated to wellsite remediation are limited, we propose implementing this methodology as a key element of a targeted approach to wellsite prioritization for subsequent remediation activity. [ABSTRACT FROM AUTHOR]

The generation and propagation of internal waves in the ocean are a common phenomenon that plays a pivotal role in the transport of mass, momentum, and energy, as well as in global climate change. Internal waves serve as a critical component of oceanic processes, contributing to the redistribution of heat and nutrients in the ocean, which, in turn, has implications for global climate regulation. However, the automatic identification of internal waves in oceanic regions from remote sensing images has presented a significant challenge. In this research paper, we address this challenge by designing a data augmentation approach grounded in a modified deep convolutional generative adversarial network (DCGAN) to enrich MODIS remote sensing image data for the automated detection of internal waves in the ocean. Utilizing t-distributed stochastic neighbor embedding (t-SNE) technology, we demonstrate that the feature distribution of the images produced by the modified DCGAN closely resembles that of the original images. By using t-SNE dimensionality reduction technology to map high-dimensional remote sensing data into a two-dimensional space, we can better understand, visualize, and analyze the quality of data generated by the modified DCGAN. The images generated by the modified DCGAN not only expand the dataset's size but also exhibit diverse characteristics, enhancing the model's generalization performance. Furthermore, we have developed a deep neural network named "WaveNet," which incorporates a channel-wise attention mechanism to effectively handle complex remote sensing images, resulting in high classification accuracy and robustness. It is important to note that this study has limitations, such as the reliance on specific remote sensing data sources and the need for further validation across various oceanic regions. These limitations are essential to consider in the broader context of oceanic research and remote sensing applications. We initially pre-train WaveNet using the EuroSAT remote sensing dataset and subsequently employ it to identify internal waves in MODIS remote sensing images. Experiments show the highest average recognition accuracy achieved is an impressive 98.625%. When compared to traditional data augmentation training sets, utilizing the training set generated by the modified DCGAN leads to a 5.437% enhancement in WaveNet's recognition rate. [ABSTRACT FROM AUTHOR]

Climate dictates the critical aspects of human environmental conditions. The frequency and intensity of extreme weather conditions due to human-induced climate change have alarmingly increased. Consequently, climate change directly affects environmental sustainability and human mortality in the short term and creates prolonged and complicated long-term indirect grave risks. This paper examines three-level environmental impact risks associated with climate change on human mortality. It proposes a conceptual framework for developing an empirical event-based human mortality database related to climate change and communication strategies to enhance global environmental adaptation, resilience, and sustainability. [ABSTRACT FROM AUTHOR]

Climate change is now one of the most formidable threats to the livelihoods of resource-poor communities in low-income developing countries world-wide. Addressing this challenge continues to be undermined by the conspicuous absence of actionable adaptation strategies that are potentially capable of enhancing our capacities to realize the Millennial Sustainable Development Goals that seek to securitize access to adequate food supplies for everybody. This paper attempts to address this limitation by providing an improvised geostatistical methodology that integrates multi-source data to map the adaptive capacities of vulnerable communities in a selected South African local municipality, whose livelihoods are largely dependent on rain-fed agriculture. The development of this methodology was based on the use scripts that were compiled in Python and used to test-try its usefulness through a case-study-based assessment of the climate-change-adaptive capacities of local communities in Raymond Mhlaba Local Municipality (RMLM), Eastern Cape Province, South Africa. A Bayesian maximum entropy framework-based technique was used to overcome the lack of missing soil moisture data, which we included because of its reliable usefulness as a surrogate indicator of climate-change-driven variations in this variable on the sustainability of rain-fed agriculture. Analysis of the results from a sampling universe of 124 communities revealed that 65 and 56 of them had high and medium adaptive capacities, respectively, with the remaining 3 having low adaptive capacities. This finding indicates that more than half of the communities in the municipality's communities have limited capabilities to cope with climate change's impacts on their livelihoods. Although our proposed methodology is premised on findings from a case-study-based investigation, it is still extremely useful because it demonstratively shows that there is tremendous scope for the scientific community to provide objectively informed insights that can be used to enhance the adaptive capacities of those in need of the badly needed but difficult-to-access information. Added to this is the fact that our proposed methodology is not only applicable for use under different environmental settings but also capable of allowing us to cost-effectively tap into the rich, wide-ranging, freely accessible datasets at our disposal. The aim of this submission is to show that although we have the information, we need to address these persevering challenges by exploring innovative approaches to translate the knowledge we have into actionable climate-change-adaptation strategies. [ABSTRACT FROM AUTHOR]

Floods occur throughout the world and are becoming increasingly frequent and dangerous. This is due to different factors, among which climate change and land use stand out. In Mexico, they occur every year in different areas. Tabasco is a periodically flooded region, causing losses and negative consequences for the rural, urban, livestock, agricultural, and service industries. Consequently, it is necessary to create strategies to intervene effectively in the affected areas. Different strategies and techniques have been developed to mitigate the damage caused by this phenomenon. Satellite programs provide a large amount of data on the Earth's surface and geospatial information processing tools useful for environmental and forest monitoring, climate change impacts, risk analysis, and natural disasters. This paper presents a strategy for the classification of flooded areas using satellite images obtained from synthetic aperture radar, as well as the U-Net neural network and ArcGIS platform. The study area is located in Los Rios, a region of Tabasco, Mexico. The results show that U-Net performs well despite the limited number of training samples. As the training data and epochs increase, its precision increases. [ABSTRACT FROM AUTHOR]

In China, irrigation plays a fundamental role in food production, which is hampered by water security, rising population and climate change. To ensure food security and formulate agricultural and irrigation policies, decision-makers need detailed grid-level information on the location of irrigated areas. Unfortunately, this information is not easily available as national irrigation maps are frequently outdated and often have a coarse spatial resolution. In this paper, we present new irrigation maps for China at a spatial resolution of 30 arc seconds (~1 × 1 km) that cover the period between 2005 and 2015. The maps were created using a synergy approach, which combines and integrates regional and global irrigation maps, cropland maps and subnational statistics. The maps were calibrated to subnational irrigation statistics and validated using an external dataset with geocoded information on the location of irrigated areas. The maps show, at the detailed spatial level, how much irrigation expanded over the period of 2005–2015. The proposed synergy approach is flexible and can easily be applied to create irrigation maps for other regions. [ABSTRACT FROM AUTHOR]

The observation of the sea through remote sensing technologies plays a fundamentalan role in understanding the state of health of marine fauna species and their behaviour. Mesoscale phenomena, such as upwelling, countercurrents, and filaments, are essential processes to be analysed because their occurrence involves, among other things, variations in the density of nutrients, which, in turn, influence the biological parameters of the habitat. Indeed, there is a connection between the biogeochemical and physical processes that occur within a biological system and the variations observed in its faunal populations. This paper concerns the proposal of an automatic classification system, namely the Mesoscale Events Classifier, dedicated to the recognition of marine mesoscale events. The proposed system is devoted to the study of these phenomena through the analysis of sea surface temperature images captured by satellite missions, such as EUMETSAT's Metop and NASA's Earth Observing System programmes. The classification of these images is obtained through (i) a preprocessing stage with the goal to provide a simultaneous representation of the spatial and temporal properties of the data and enhance the salient features of the sought phenomena, (ii) the extraction of temporal and spatial characteristics from the data and, finally, (iii) the application of a set of rules to discriminate between different observed scenarios. The results presented in this work were obtained by applying the proposed approach to images acquired in the southwestern region of the Iberian peninsula. [ABSTRACT FROM AUTHOR]

The retreat rates of Arctic coasts have increased in recent decades at many sites, and an essential part of coasts considered accumulative before have turned erosional due to global climate changes and construction in the coastal zone. In this paper, we study a 7 km long coastal section of the western Gydan Peninsula in a new construction area. Based on the interpretation of multi-temporal satellite imagery, we assessed coastal dynamics in distinct periods from 1972 to 2020. We analyzed the geological structure of the coast as well as changes in hydrometeorological parameters with time, and considering the human impact, we proposed the main drivers of spatial and temporal variations of coastal dynamics. The studied low-lying sandy accumulative marine terrace was more or less stable in the period before construction (1972–2014). However, with the area's development, the coast dynamics changed drastically: in 2014–2017, three-quarters of the studied area experienced retreat, and the average retreat rate amounted to 5.8 m/yr, up to 28.5 m/yr near the construction sites. We relate this coastal erosion intensification to human impact combined with the growth of hydrometeorological forcing. Although coastal erosion slowed down after 2017, the retreat trend remained. In the coming years, with Arctic climate warming, erosion of the studied coast will continue. [ABSTRACT FROM AUTHOR]

In line with the United Nations Sustainable Development Goal (SDG) 6, the main goal of the Special Issue on "Remote sensing for water resources and environmental management" was to solicit papers from a diverse range of scientists around the world on the use of cutting-edge remote sensing technologies to assess and monitor freshwater quality, quantity, availability, and management to ensure water security. Special consideration was given to scientific manuscripts that covered, but were not limited to, the development of geospatial techniques and remote sensing applications for detecting, quantifying, and monitoring freshwater water resources, identifying potential threats to water resources and agriculture, as well as other themes related to water resources and environmental management at various spatial scales. The Special Issue attracted over thirteen peer-reviewed scientific articles, with the majority of manuscripts originating from China. Most of the studies made use of satellite datasets, ranging from coarse spatial resolution data, such as the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GRACE-FO), to medium spatial resolution data, such as the Landsat series, ERA5, Modern-Era Retrospective Analysis for Research and Application Land version 2 reanalysis product (MERRA2), CLSM and NOAH ET, and MODIS (Moderate Resolution Imaging Spectroradiometer). Google Earth Engine (GEE) data, together with big data processing techniques, such as the remote sensing-based energy balance model (ALEXI/DisALEXI approach) and the STARFM data fusion technique, were used for analyzing geospatial datasets. Overall, this Special Issue demonstrated significant knowledge gaps in various big data image processing techniques and improved computing processes in assessing and monitoring water resources and the environment at various spatial and temporal scales. [ABSTRACT FROM AUTHOR]

The SWAT model has been widely used to simulate snowmelt runoff in cold regions thanks to its ability of representing the effects of snowmelt and permafrost on runoff generation and confluence. However, a core method used in the SWAT model, the temperature index method, assumes both the dates for maximum and minimum snowmelt factors and the snowmelt temperature threshold, which leads to inaccuracies in simulating snowmelt runoff in seasonal snowmelt regions. In this paper, we present the development and application of an improved temperature index method for SWAT (SWAT+) in simulating the daily snowmelt runoff in a seasonal snowmelt area of Northeast China. The improvements include the introduction of total radiation to the temperature index method, modification of the snowmelt factor seasonal variation formula, and changing the snowmelt temperature threshold according to the snow depth derived from passive microwave remote sensing data and temperature in the seasonal snowmelt area. Further, the SWAT+ model is applied to study climate change impact on future snowmelt runoff (2025–2054) under the climate change scenarios including SSP2.6, SSP4.5, and SSP8.5. Much improved snowmelt runoff simulation is obtained as a result, supported by several metrics, such as MAE, RE, RMSE, R2, and NSE for both the calibration and validation. Compared with the baseline period (1980–2019), the March–April ensemble average snowmelt runoff is shown to decrease under the SSP2.6, SSP4.5, and SSP8.5 scenario during 2025–2054. This study provides a valuable insight into the efficient development and utilization of spring water resources in seasonal snowmelt areas. [ABSTRACT FROM AUTHOR]

Climate change-driven forces and anthropogenic interventions have led to considerable changes in coastal zones and shoreline positions, resulting in coastal erosion or sedimentation. Shoreline change detection through cost-effective methods and easy-access data plays a key role in coastal management, where other effective parameters such as land-use/land-cover (LULC) change should be considered. This paper presents a remotely sensed shoreline monitoring in Sandbanks Provincial Park, Ontario, Canada, from 1984 to 2021. The CoastSat toolkit for Python and a multilayer perceptron (MLP) neural network classifier were used for shoreline detection, and an unsupervised change detection framework followed by a postclassification change detection method was implemented for LULC classification and change detection. The study assessed the recent coastal erosion and accretion trends in the region in association with spatiotemporal changes in the total area of the West and East Lakes, the transition between LULC classes, extreme climate events, population growth, and future climate projection scenarios. The results of the study illustrate that the accretion trend apparently can be seen in most parts of the study area since 1984 and is affected by several factors, including lake water-level changes, total annual precipitations, sand movements, and other hydrologic/climatic parameters. Furthermore, the observed LULC changes could be in line with climate change-driven forces and population growth to accelerate the detected accretion trend in the East and West Lakes. In total, the synergistic interaction of the investigated parameters would result in a greater accretion trend along with a lower groundwater table amid even a low carbon scenario. The discussed findings could be beneficial to regional/provincial authorities, policymakers, and environmental advocates for the sustainable development of coastal communities. [ABSTRACT FROM AUTHOR]

Rapid urbanisation is affecting people in different ways, with some becoming more vulnerable to the impacts of climate change. Africa's cities are projected to be home to nearly 60% of the continent's population by 2050. In conjunction with climate change, these cities are experiencing critical environmental challenges, including changes in the urban thermal environment. Urban areas generally exhibit significantly higher air and surface temperatures than their surrounding rural areas, resulting in urban heat islands. However, little has been done to synthesise existing knowledge and identify the key research gaps in this area, particularly in Africa. This paper focuses on the combined effects of urbanisation and climate change on the urban thermal environment in Africa, and provides a comprehensive review of results, major advances and the dominant direction of research. Our review of 40 publications from peer-reviewed journals from 2000 to 2021 revealed that South Africa, Ethiopia and Nigeria were most frequently studied, and satellite imagery-based data and analysis were used predominantly. Results from a few studies have shown the practical implications for urban land-use planning, informal settlement management, human wellbeing and productivity, energy use, air pollution and disease spread. Integrated approaches, strengthening planning institutions, and early warning systems are proposed to address climate change. Low-income groups are emphasised in efforts to help people cope with heat stress. Solutions based on land use and land cover dynamics and blue–green infrastructure are mentioned but are in need of further research. Cities with similar patterns of urbanisation, geographies and climate conditions could benefit from multi-disciplinary research collaboration to address the combined impacts of rapid urbanisation and climate change. [ABSTRACT FROM AUTHOR]

A significant challenge posed by changing climates is how water cycling and surficial and subsurface water availability will be affected at global and regional scales. Such alterations are critical as they often lead to increased vulnerability in ecosystems and human society. Understanding specifically how climate change affects water resource variability in different locations is of critical importance to sustainable development in different parts of the world. The papers included in this special issue focus on three broad perspectives associated with water resource variability and climate change. Six papers employ remote sensing, meteorological station-based observational data, and tree-ring records to empirically determine how water resources have been changing over historical time periods. Eight of the contributions focus on modeling approaches to determine how known processes are likely to manifest themselves as climate shifts over time. Two others focus on human perceptions and adaptation strategies in the midst of unstable or unsettled water availability. The findings and methods presented in this collection of papers provide important contributions to the increased study and awareness of climate change on water resources. [ABSTRACT FROM AUTHOR]

The number and area of alpine lakes in Tien Shan (TS) are rapidly growing in response to a warming climate and retreating glaciers. This paper presents a comparative analysis of lake classification and changes by dividing alpine lakes (within a 10 km buffer of the glacier margins) into four types (supraglacial lakes, proglacial lakes, extraglacial lakes and non-glacial lakes), and subsequently determining the driving forces of change across the TS region from 1990 to 2015. The analysis utilized multiple satellite images and climatic data from gridded data sets and meteorological station observations. The results indicate that the total number and area of glacial lakes continuously increased during the study period, whereas non-glacial lakes intermittently expanded. Specifically, the total number and area of all glacial lakes (supraglacial lakes, proglacial lakes and extraglacial lakes) increased by 45.45% and 27.08%, respectively. Non-glacial lakes, in contrast, increased in quantity and area by 23.92% and 19.01%, respectively. Alpine lakes are closer to glaciers at high altitudes; in fact, some (e.g., proglacial lakes) are connected to glacier termini, and these show the highest expansion speed during the study period. The area of proglacial lakes expanded by 60.32%. Extraglacial lakes expanded by 21.06%. Supraglacial lakes, in marked contrast to the other types, decreased in area by 3.74%. Widespread rises in temperature and glacier wastage were the primary cause of the steady expansion of glacial lakes, particularly those linked to small- and medium-sized glaciers distributed in the Eastern TS where glacial lakes have rapidly increased. Both proglacial and extraglacial lakes expanded by 6.47%/a and 2%/a, respectively, from 1990 to 2015. While these proglacial and extraglacial lakes are located in largely glacierized areas, lakes in the Central TS exhibited the slowest expansion, increasing in area by 1.44%/a and 0.74%/a, respectively. Alterations in non-glacial lake areas were driven by changes in precipitation and varied spatially over the region. This study has substantial implications for the state of water resources under the complex regional changes in climate in the TS and can be used to develop useful water-resource management and planning strategies throughout Central Asia. [ABSTRACT FROM AUTHOR]

The Loess Plateau (LP) of China, which is the pilot region of the "Grain to Green Project" (GGP), has received worldwide attention due to its significant changes in the natural and social environment. Investigation of vegetation variations in response to climate change and human activities is vital for providing support for further ecological restoration planning. This paper aimed to monitor vegetation dynamics of the LP with trend comparisons of various vegetation types, disentangle the effects of climate variations and ecological programs on vegetation variations, and detect the consistency of vegetation variations. More specifically, vegetation dynamics during 1982–2015 were analyzed using the Global Inventory Modelling and Mapping System third-generation Normalized Difference Vegetation Index (GIMMS NDVI3g) data with the application of Breaks for Additive Season and Trend (BFAST) and Hurst Exponent. The results showed that: (1) Vegetation manifested a significant greening trend (0.013 decade−1p < 0.01) in the LP during 1982–2015, and a breakpoint (BP) was detected in 1999, which was the beginning of the GGP. Interannual NDVI after the BP (ABP) showed more than 3.5 times greening rates compared to the NDVI before the BP (BBP). (2) Human activities dominated the vegetation variation (accounted for 59.46% of vegetation variation), among which reforestation and land-use change with steep slopes (i.e., ≥15°) lead to the greening after the GGP implementation. (3) Future trends should be noticed in the Forest Zone and Forest-Grass Zone, where the greening trends tend to slow down or even reverse in the southern LP. The long-term GIMMS NDVI3g time series and multiple geospatial analyses of this study might facilitate a better understanding of the mechanisms of vegetation variations for the assessment of the large restoration programs in fragile ecosystems. [ABSTRACT FROM AUTHOR]

The Grain for Green Project (GGP) has considerably improved the vegetation cover of the Loess Plateau, as well as changed the carbon and water coupling process of local vegetation to a certain extent. Water use efficiency (WUE) is a crucial measure for evaluating ecosystem responses to global climate change and is a key indicator of the carbon–water coupling between terrestrial ecosystems and the environment. A comprehensive understanding of the impact of vegetation reconstruction on WUE on the Loess Plateau is of great significance to the vegetation growth and contribution to sustainable of the Loess Plateau. In recent years, scholars have gained a more comprehensive understanding of the distribution and drivers of WUE on the Loess Plateau. However, through the study of carbon and water coupling in the Loess Plateau, it is found that the effects of different vegetation restoration levels on WUE are still to be studied in depth in terms of spatial and temporal heterogeneity and long timeseries. In this paper, we analyzed the trends of Normalized Difference vegetation cover (NDVI) and WUE from 2001 to 2010 and 2011 to 2020, respectively, to research at the WUE of the vegetation in this area in relation to vegetation restoration. It was found that the Loess Plateau's vegetation WUE rose from 2001 to 2020 at a rate of 0.023 g C kg−1 H2O per year, and that the increase from 2011 to 2020 was more significant than the growth from 2000 to 2010. The Loess Plateau's area with a growing trend in vegetation water use rate increased from 77.12% in 2001–2010 to 88.63% in 2011–2020, with the majority of the increased area occurring in the northeastern Inner Mongolia region. After 20 years of the reforestation project, the area where NDVI and WUE increased simultaneously accounted for 71.54% of the Loess Plateau, the area where NDVI increased but WUE decreased accounted for 10.95% of the Loess Plateau, and the area where NDVI increased but WUE decreased accounted for 7.15% of the Loess Plateau. The correlation between temperature precipitation and WUE was not significant for the whole Loess Plateau, further indicating that the increase in vegetation cover was the main reason for the increase in vegetation water efficiency. Therefore, the effect of vegetation cover on WUE should be fully considered when vegetation restoration is carried out on the Loess Plateau. [ABSTRACT FROM AUTHOR]

As an important part of the Earth's environmental system, sandy soils are particularly sensitive to changes in the climatic environment. As one of the four major desert regions in China, the MuUs desert has transformed from a desert to an oasis after more than half a century of ecological management. In this paper, we analyzed the spatial and temporal patterns of surface albedo, evapotranspiration, and fraction vegetation cover in the MuUs desert based on the Global Land Surface Satellite (GLASS) product with high spatial and temporal resolution and assessed the relationships between their variability and snow cover, air temperature, and precipitation. It is of great significance to understand the effect of desertification control and climate change after the conversion of land surface types in the MuUs region. The results show that the desertification control in the MuUs area has achieved remarkable results since 1982. The fraction vegetation coverage of the MuUs desert showed a significant increasing trend, with an interannual change rate of 1.32% each decade−1. The surface albedo of MuUs desert decreased significantly. Affected by vegetation and snow cover, it was lower in summer and higher in winter. The evapotranspiration showed a significant upward trend, higher in summer and lower in winter, which is significantly correlated with the changes in surface albedo, air temperature, and vegetation. In addition, the local-scale biophysical effects caused by vegetation change have influenced the climate of the MuUs region, manifested as the increase in precipitation and air temperature. In general, with the support of relevant policies and human construction projects, the overall ecological environment in the MuUs desert is developing in a good way. [ABSTRACT FROM AUTHOR]

The boreal hemisphere has been experiencing increasing extreme hot and dry conditions over the past few decades, consistent with anthropogenic climate change. The continental extension of this phenomenon calls for tools and techniques capable of monitoring the global to regional scales. In this context, satellite data can satisfy the need for global coverage. The main objective we have addressed in the present paper is the capability of infrared satellite observations to monitor the vegetation stress due to increasing drought and heatwaves in summer. We have designed and implemented a new water deficit index (wdi) that exploits satellite observations in the infrared to retrieve humidity, air temperature, and surface temperature simultaneously. These three parameters are combined to provide the water deficit index. The index has been developed based on the Infrared Atmospheric Sounder Interferometer or IASI, which covers the infrared spectral range 645 to 2760 cm−1 with a sampling of 0.25 cm−1. The index has been used to study the 2017 heatwave, which hit continental Europe from May to October. In particular, we have examined southern Italy, where Mediterranean forests suffer from climate change. We have computed the index's time series and show that it can be used to indicate the atmospheric background conditions associated with meteorological drought. We have also found a good agreement with soil moisture, which suggests that the persistence of an anomalously high water deficit index was an essential driver of the rapid development and evolution of the exceptionally severe 2017 droughts. [ABSTRACT FROM AUTHOR]

Aircraft-Induced Clouds (AICs), colloquially called contrails, form from the emission of soot from jet engines during cruise flight in favorable atmospheric conditions. AICs absorb, scatter, and reflect shortwave and longwave radiation. This radiative transfer has a cooling effect during the day; however, the night experiences an overwhelming warming effect, which leads to an overall warming effect on Earth, contributing to anthropogenically propelled climate change. Reducing AICs significantly mitigates aviation's contribution to climate change by reducing the disruption in Earth's radiation budget. Researchers have proposed AIC Abatement Programs (AAPs) to increase cruise flight levels without additional fuel burn. In order to effectively implement AAPs, it is crucial to be able to accurately identify AICs from publicly available aerial and satellite imagery. This study aims at the identification of AICs from hyperspectral imagery to help the effective implementation of an AAP and to mitigate climate change. This paper describes a method for the hyperspectral analysis of aerial images in order to accurately identify AICs through a case study based in West Virginia. The results show that both the Adaptive Coherence Estimator and the Matched Filter algorithms based on unique in-scene spectra were successful in the isolation of the AICs from other cloud types and the background. It is found that AICs can be identified with 84% confidence in this case study. The method, a case study, and future works are provided. [ABSTRACT FROM AUTHOR]

Urban Heat Islands increase surface temperatures which impact the health and well-being of urban populations. Radiative forcing is impacted by changes to the land surface associated with urbanization that are particularly significant immediately after sunset. This paper aimed to analyze the behavior of UHI in different Local Climate Zones (LCZ) in Bragança city (Portugal), using Air Temperature (Ta), satellite images (Landsat 8), and on-site data. The methodology included a seasonal approach, integrating data with different scales (spatial, radiometric, and spectral) and qualitative and quantitative analyses. Google Earth Engine (GEE) optimized the processing time and computation requirement to generate the Land Surface Temperature (LST) maps. The integration of data with different scales corroborated the complementation of information/analysis and detected the correlation between the Ta and LST. However, the identification of the UHI was compromised due to the time of the passage of Landsat 8, and it was identified as the Urban Cool Island (UCI), a complementary effect of UHI, supporting the results of previous studies and for the use of Remote Sensing (RS) for thermal effects analysis. [ABSTRACT FROM AUTHOR]

Artificial and natural water bodies, such as reservoirs, ponds, rivers and lakes, are important components of water-related ecosystems; they are also important indicators of the impact of human activities and climate change on surface water resources. However, due to the global and regional lack of artificial and natural water bodies data sets, understanding of the changes in water-related ecosystems under the dual impact of human activities and climate change is limited and scientific and effective protection and restoration actions are restricted. In this paper, artificial and natural water bodies data sets for China are developed for the years 2000, 2005, 2010, 2015 and 2020 based on satellite remote sensing surface water and artificial water body location sample data sets. The characteristics and causes of the temporal and spatial distributions of the artificial and natural water bodies are also analyzed. The results revealed that the area of artificial and natural water bodies in China shows an overall increasing trend, with obvious differences in spatial distribution during the last 20 years, and that the fluctuation range of artificial water bodies is smaller than that of natural water bodies. This research is critical for understanding the composition and long-term changes in China's surface water system and for supporting and formulating scientific and rational strategies for water-related ecosystem protection and restoration. [ABSTRACT FROM AUTHOR]

Remote sensing technologies are extensively applied to prevent, monitor, and forecast hazardous risk conditions in the present-day global climate change era. This paper presents an overview of the current stage of remote sensing approaches employed to study coastal and delta river regions. The advantages and limitations of Earth Observation technology in characterizing the effects of climate variations on coastal environments are also presented. The role of the constellations of satellite sensors for Earth Observation, collecting helpful information on the Earth's system and its temporal changes, is emphasized. For some key technologies, the principal characteristics of the processing chains adopted to obtain from the collected raw data added-value products are summarized. Emphasis is put on studying various disaster risks that affect coastal and megacity areas, where heterogeneous and interlinked hazard conditions can severely affect the population. [ABSTRACT FROM AUTHOR]

The headwaters of the Yangtze River are a complicated system composed of different eco-environment elements. The abnormal moisture and energy exchanges between the atmosphere and earth systems caused by global climate change are predicted to produce drastic changes in these eco-environment elements. In order to study the dynamic effect and ecological stress in the eco-environment, we adapted the Double Attention Mechanism (DAM) to improve the performance of the DeepLab V3+ network in large-scale semantic segmentation. We proposed Elements Fragmentation (EF) and Elements Information Content (EIC) to quantitatively analyze the spatial distribution characteristics and spatial relationships of eco-environment elements. In this paper, the following conclusions were drawn: (1) we established sample sets based on "Sentinel-2" remote sensing images using the interpretation signs of eco-environment elements; (2) the mAP, mIoU, and Kappa of the improved DeepLab V3+ method were 0.639, 0.778, and 0.825, respectively, which demonstrates a good ability to distinguish the eco-environment elements; (3) between 2015 and 2021, EF gradually increased from 0.2234 to 0.2394, and EIC increased from 23.80 to 25.32, which shows that the eco-environment is oriented to complex, heterogeneous, and discontinuous processes; (4) the headwaters of the Yangtze River are a community of life, and thus we should build a multifunctional ecological management system with which to implement well-organized and efficient scientific ecological rehabilitation projects. [ABSTRACT FROM AUTHOR]

The world has a long record of shoreline and related erosion problems due to the impacts of climate change/variability in sea level rise. This has made coastal systems and large inland water environments vulnerable, thereby activating research concern globally. This study is a bibliometric analysis of the global scientific production of data sources and tools for shoreline change analysis and detection. The bibliometric mapping method (bibliometric R and VOSviewer package) was utilized to analyze 1578 scientific documents (1968–2022) retrieved from Scopus and Web of Science databases. There is a chance that in the selection process one or more important scientific papers might be omitted due to the selection criteria. Thus, there could be a bias in the present results due to the search criteria here employed. The results revealed that the U.S.A. is the country with the most scientific production (16.9%) on the subject. Again, more country collaborations exist among the developed countries compared with the developing countries. The results further revealed that tools for shoreline change analysis have changed from a simple beach transect (0.1%) to the utilization of geospatial tools such as DSAS (14.6%), ArcGIS/ArcMap (13.8%), and, currently, machine learning (5.1%). Considering the benefits of these geospatial tools, and machine learning in particular, more utilization is essential to the continuous growth of the field. Found research gaps were mostly addressed by the researchers themselves or addressed in other studies, while others have still not been addressed, especially the ones emerged from the recent work. For instance, the one on insights for reef restoration projects focused on erosion mitigation and designing artificial reefs in microtidal sandy beaches. [ABSTRACT FROM AUTHOR]

Wetlands are some of the most important ecosystems on Earth. They play a key role in alleviating floods and filtering polluted water and also provide habitats for many plants and animals. Wetlands also interact with climate change. Over the past 50 years, wetlands have been polluted and declined dramatically as land cover has changed in some regions. Remote sensing has been the most useful tool to acquire spatial and temporal information about wetlands. In this paper, seven types of sensors were reviewed: aerial photos coarse-resolution, medium-resolution, high-resolution, hyperspectral imagery, radar, and Light Detection and Ranging (LiDAR) data. This study also discusses the advantage of each sensor for wetland research. Wetland research themes reviewed in this paper include wetland classification, habitat or biodiversity, biomass estimation, plant leaf chemistry, water quality, mangrove forest, and sea level rise. This study also gives an overview of the methods used in wetland research such as supervised and unsupervised classification and decision tree and object-based classification. Finally, this paper provides some advice on future wetland remote sensing. To our knowledge, this paper is the most comprehensive and detailed review of wetland remote sensing and it will be a good reference for wetland researchers. [ABSTRACT FROM AUTHOR]

The increasing need for food in recent years means that environmental protection and sustainable agriculture are necessary. For this, smart agricultural systems and autonomous robots have become widespread. One of the most significant and persistent problems related to robots is 3D path planning, which is an NP-hard problem, for mobile robots. In this paper, efficient methods are proposed by two metaheuristic algorithms (Incremental Gray Wolf Optimization (I-GWO) and Expanded Gray Wolf Optimization (Ex-GWO)). The proposed methods try to find collision-free optimal paths between two points for robots without human intervention in an acceptable time with the lowest process costs and efficient use of resources in large-scale and crowded farmlands. Thanks to the methods proposed in this study, various tasks such as tracking crops can be performed efficiently by autonomous robots. The simulations are carried out using three methods, and the obtained results are compared with each other and analyzed. The relevant results show that in the proposed methods, the mobile robots avoid the obstacles successfully and obtain the optimal path cost from source to destination. According to the simulation results, the proposed method based on the Ex-GWO algorithm has a better success rate of 55.56% in optimal path cost. [ABSTRACT FROM AUTHOR]

Crop planting area and spatial distribution information have important practical significance for food security, global change, and sustainable agricultural development. How to efficiently and accurately identify crops in a timely manner by remote sensing in order to determine the crop planting area and its temporal–spatial dynamic change information is a core issue of monitoring crop growth and estimating regional crop yields. Based on hundreds of relevant documents from the past 25 years, in this paper, we summarize research progress in relation to farmland vegetation identification and classification by remote sensing. The classification and identification of farmland vegetation includes classification based on vegetation index, spectral bands, multi-source data fusion, artificial intelligence learning, and drone remote sensing. Representative studies of remote sensing methods are collated, the main content of each technology is summarized, and the advantages and disadvantages of each method are analyzed. Current problems related to crop remote sensing identification are then identified and future development directions are proposed. [ABSTRACT FROM AUTHOR]

With its high water potential, the Ziz basin is one of the most important basins in Morocco. This paper aims to develop a methodology for spatiotemporal monitoring of the water quality of the Hassan Addakhil dam using remote sensing techniques combined with a modeling approach. Firstly, several models were established for the different water quality parameters (nitrate, dissolved oxygen and chlorophyll a) by combining field and satellite data. In a second step, the calibration and validation of the selected models were performed based on the following statistical parameters: compliance index R2, the root mean square error and p-value. Finally, the satellite data were used to carry out spatiotemporal monitoring of the water quality. The field results show excellent quality for most of the samples. In terms of the modeling approach, the selected models for the three parameters (nitrate, dissolved oxygen and chlorophyll a) have shown a good correlation between the measured and estimated values with compliance index values of 0.62, 0.56 and 0.58 and root mean square error values of 0.16 mg/L, 0.65 mg/L and 0.07 µg/L for nitrate, dissolved oxygen and chlorophyll a, respectively. After the calibration, the validation and the selection of the models, the spatiotemporal variation of water quality was determined thanks to the multitemporal satellite data. The results show that this approach is an effective and valid methodology for the modeling and spatiotemporal mapping of water quality in the reservoir of the Hassan Addakhil dam. It can also provide valuable support for decision-makers in water quality monitoring as it can be applied to other regions with similar conditions. [ABSTRACT FROM AUTHOR]

Within an E.U.-funded project, BESS (Pocket Beach Management and Remote Surveillance System), the notion of a geographic information system is an indispensable tool for managing the dynamics of georeferenced data and information for any form of territorial planning. This notion was further explored with the creation of a WebGIS portal that will allow local and regional stakeholders/authorities obtain an easy remote access tool to monitor the status of pocket beaches (PB) in the Maltese Archipelago and Sicily. In this paper, we provide a methodological approach for the implementation of a WebGIS necessary for very detailed dynamic mapping and visualization of geospatial coastal data; the description of the dataset necessary for the monitoring of coastal areas, especially the PBs; and a demonstration of a case study for the PBs of Sicily and Malta by using the methodology and the dataset used during the BESS project. Detailed steps involved in the creation of the WebGIS are presented. These include data preparation, data storage, and data publication and transformation into geo-services. With the help of different Open Geospatial Consortium protocols, the WebGIS displays different layers of information for 134 PBs including orthophotos, sedimentological/geomorphological beach characteristics, shoreline evolution, geometric and morphological parameters, shallow water bathymetry, and photographs of pocket beaches. The WebGIS allows not only for identifying, evaluating, and directing potential solutions to present and arising issues, but also enables public access and involvement. It reflects a platform for future local and regional coastal zone monitoring and management, by promoting public/private involvement in addressing coastal issues and providing local public administrations with an improved technology to monitor coastal changes and help better plan suitable interventions. [ABSTRACT FROM AUTHOR]

Mangrove forests, distributed in the tropical and subtropical regions of the world, are in a constant flux. They provide important ecosystem goods and services to nature and society. In recent years, the carbon sequestration potential and protective role of mangrove forests from natural disasters is being highlighted as an effective option for climate change adaptation and mitigation. The forests are under threat from both natural and anthropogenic forces. However, accurate, reliable, and timely information of the distribution and dynamics of mangrove forests of the world is not readily available. Recent developments in the availability and accessibility of remotely sensed data, advancement in image pre-processing and classification algorithms, significant improvement in computing, availability of expertise in handling remotely sensed data, and an increasing awareness of the applicability of remote sensing products has greatly improved our scientific understanding of changing mangrove forest cover attributes. As reported in this special issue, the use of both optical and radar satellite data at various spatial resolutions (i.e., 1 m to 30 m) to derive meaningful forest cover attributes (e.g., species discrimination, above ground biomass) is on the rise. This multi-sensor trend is likely to continue into the future providing a more complete inventory of global mangrove forest distributions and attribute inventories at enhanced temporal frequency. The papers presented in this "Special Issue" provide important remote sensing monitoring advancements needed to meet future scientific objectives for global mangrove forest monitoring from local to global scales. [ABSTRACT FROM AUTHOR]

An accurate assessment of the spatial–temporal variations in regional net ecosystem productivity (NEP), water use efficiency (WUE), and carbon use efficiency (CUE) are vital for understanding the water–carbon cycle. We analyzed the spatial–temporal patterns of the NEP, WUE, and CUE in the middle reaches of the Yellow River (MRYR) from 2001 to 2022, and the factors that influenced them using remote sensing data, NEP estimation models, and various statistical methods. The results indicate that the recovery of the ecosystem in the MRYR is a result of the combined effects of climate change and human activities. Climate change in the MRYR led to warming and humidification from 2001 to 2022. The NEP, WUE, and CUE were characterized by increasing trends, with average growth rates of 7.75 gC m−2a−1, 0.012 gC m−2 mm−1a−1, and 0.009a−1, respectively. For four vegetation types, the interannual rates of change were, in descending order, grassland, cropland, shrubs, and forest. Spatially, the NEP, WUE, and CUE showed significant regional heterogeneity, increasing from the northwest to the southeast. Based on an analysis of the interannual anomalies, precipitation accumulation contributed to carbon sink accumulation. The correlation of the NEP, WUE, and CUE with the drought severity index (DSI) was high, and their correlation with precipitation showed latitudinal zonality, which suggests that precipitation (PRE) is the main climatic factor influencing the water–carbon cycle in the MRYR rather than temperature (TEM). There were 67,671.27 km2 of land that changed use during 2001–2022, and 15.07 Tg of NEP was added to these areas. [ABSTRACT FROM AUTHOR]

Extreme precipitation events induced by tropical cyclones have increased frequency and intensity, significantly impacting human socioeconomic activities and ecological environments. This study systematically examines the spatiotemporal characteristics of these events across Hainan Island and their influencing factors using GsMAP satellite precipitation data and tropical cyclone track data. The results indicate that while the frequency of typhoon events in Hainan decreased by 0.3 events decade−1 from 1949 to 2020, extreme precipitation events have increased significantly since 2000, especially in the eastern and central regions. Different typhoon tracks have distinct impacts on the island, with Track 1 (Northeastern track) and Track 2 (Central track) primarily affecting the western and central regions and Track 3 (Southern track) impacting the western region. The impact of typhoon precipitation on extreme events increased over time, being the greatest in the eastern region, followed by the central and western regions. Incorporating typhoon precipitation data shortened the recurrence interval of extreme precipitation in the central and eastern regions. Diurnal peaks occur in the early morning and late evening, primarily affecting coastal areas. Typhoon duration (CC_max = 0.850) and wind speed (CC_max = 0.369) positively correlated with extreme precipitation, while the pressure was negatively correlated. High sea surface temperature areas were closely associated with extreme precipitation events. The atmospheric circulation indices showed a significant negative correlation with extreme precipitation, particularly in the western and central regions. ENSO events, especially sea surface temperature changes in the Niño 1 + 2 region (−0.340 to −0.406), have significantly influenced typhoon precipitation characteristics. These findings can inform region-specific disaster prevention and mitigation strategies for Hainan Island. [ABSTRACT FROM AUTHOR]

Massive accumulations of invasive brown algae Rugulopteryx okamurae are exacerbating environmental and socio-economic issues on the Mediterranean and potentially Atlantic coasts. These golden tides, likely intensified by global change processes such as changes in wind direction and intensity and rising temperatures, pose increasing challenges to coastal management. This study employs the Normalized Difference Vegetation Index (NDVI), with values above 0.08 from Level-2 Sentinel-2 imagery, to effectively monitor these strandings along the coastline of Los Lances beach (Tarifa, Spain) in the Strait of Gibraltar Natural Park from 2018 to 2022. Los Lances beach is one of the most affected by the R. okamurae bioinvasion in Spain. The analysis reveals that wind direction determines the spatial distribution of biomass accumulated on the shore. The highest average NDVI values in the western patch were observed with south-easterly winds, while in the eastern patch, higher average NDVI values were recorded with south-westerly, westerly and north-westerly winds. The maximum coverage correlates with elevated temperatures and minimal rainfall, peaking between July and October. Leveraging these insights, we propose a replicable methodology for the early detection and strategic pre-shore collection of biomass, which could facilitate efficient coastal cleanup strategies and enhance biomass utility for biotechnological applications. This approach promises cost-effective adaptability across different geographic areas impacted by golden tides. [ABSTRACT FROM AUTHOR]

The Tibetan Plateau (TP), with its unique geographical and climatic conditions, holds a significant role in global climate change. Therefore, it is particularly urgent to fully understand its vegetation phenology. Herbaceous plants are widely distributed in the TP. However, previous studies have predominantly examined the impact of air temperature on the end date of the vegetation growing season (EOS), with less emphasis on the influence of land surface temperature (LST). In this study, the dynamic changes in the EOS from 2001 to 2020 were analyzed by utilizing the Normalized Difference Vegetation Index (NDVI) data published by NASA. Furthermore, the impact of climate change on the EOS was examined, and the dominant factor (air temperature, LST, or precipitation) influencing the EOS was identified. The main findings were as follows: the average annual EOS predominantly occurred between day of year (DOY) 240 and 280, with an advance from the edge of the plateau to the center. The EOS across the entire region displayed a marginal tendency towards delay, with an average rate of 0.017 days/year. Among all vegetation, shrubs showed the most pronounced delay at a rate of 0.04 days/year. In terms of precipitation, the impact of climate change increased precipitation in both summer and autumn, which could delay EOS. In terms of temperature, an increase in summer Tmin, autumn air temperatures and summer LST delayed the EOS, while an increase in autumn LST advanced the EOS. Compared to air temperature and precipitation, LST had a stronger controlling effect on the EOS (the largest pixel area dominated by LST). These results could offer new insights for enhancing the parameters of vegetation phenology models across the TP. [ABSTRACT FROM AUTHOR]

Currently, the global climate system is complex and ever-changing, with multiple factors influencing climate change. The Qinghai–Tibet Plateau, known as the "Third Pole" of the Earth, is particularly sensitive to global climate change. Without timely and scientific research on the ecological environment of the Qinghai–Tibet Plateau and without summarizing relevant adaptive strategies, global climate change will impact the sustainable development of the plateau. This study utilized Landsat remote sensing images from 2000 to 2020 to extract the snow cover area and snow temperature of the Qinghai–Tibet Plateau using the snow frequency threshold method. The study analyzed the spatiotemporal characteristics of snow cover and temperature over the 20-year period and investigated some of the climate and topographical driving factors influencing their changes. The results showed that from 2000 to 2020, the permanent snow cover area in the Qinghai–Tibet Plateau region showed a fluctuating decreasing trend, reducing from approximately 12.34 thousand km2 to around 9.01 thousand km2; the permanent snow temperature showed an initial increase followed by a decrease during the same period. The highest annual average snow temperature was approximately −3.478 °C, while the lowest annual average temperature was around −8.150 °C. Over the 20-year period, the snow cover area in the plateau was negatively correlated with temperature and precipitation, while snow temperature was positively correlated with temperature and precipitation. The snow cover in the weak wind areas of the plateau showed a significant reduction. Areas with higher average wind speeds, such as shaded slopes and semi-shaded slopes, had larger snow cover areas. These research findings provide important insights into the protection and management of the ecological environment of the Qinghai–Tibet Plateau. [ABSTRACT FROM AUTHOR]

Glacial lakes are important freshwater resources in southern Tibet. However, glacial lake outburst floods have significantly jeopardized the safety of local residents. To better understand the changes in glacial lakes in response to climate change, it is necessary to conduct a long-term evaluation on the areal dynamics of glacial lakes, assisted with local observations. Here, we propose an innovative method of classification and stacking extraction to accurately delineate glacial lakes in southwestern Tibet from 1990 to 2020. Based on Landsat images and meteorological data, we used geographic detectors to detect correlation factors. Multiple regression models were used to analyze the driving factors of the changes in glacier lake area. We combined bathymetric data of the glacial lakes with the changes in climatic variables and utilized HEC-RAS to determine critical circumstances for glacial lake outbursts. The results show that the area of glacial lakes in Nyalam County increased from 27.95 km2 in 1990 to 52.85 km2 in 2020, and eight more glacial lakes were observed in the study area. The glacial lake area expanded by 89.09%, where we found significant growth from 2015 to 2020. The correlation analysis between the glacial lake area and climate change throughout the period shows that temperature and precipitation dominate the expansion of these lakes from 1990 to 2020. We also discover that the progressive increase in water volume of glacial lakes can be attributed to the constant rise in temperature and freeze–thaw of surrounding glaciers. Finally, the critical conditions for the glacial lake's outburst were predicted by using HEC-RAS combined with the changes in the water volume and climatic factors. It is concluded that GangxiCo endures a maximum water flow of 4.3 × 108 m3, and the glacial lake is in a stable changing stage. This conclusion is consistent with the field investigation and can inform the prediction of glacial lake outbursts in southwestern Tibet in the future. [ABSTRACT FROM AUTHOR]

The trophic state of Lake Ladoga was studied during the period 1997–2019, using the Copernicus Marine Environmental Monitoring Service (CMEMS) GlobColour-merged chlorophyll-a OC5 algorithm (GlobColour CHL-OC5) satellite observations. Lake Ladoga, in general, is mesotrophic but certain parts of the lake have been eutrophic since the 1960s due to the discharge of wastewater from industrial, urban, and agricultural sources. Since then, many ecological assessments of the Lake's state have been made. These studies have indicated that various changes are taking place in the lake and continuous monitoring of the lake is essential to update the current knowledge of its state. The aim of this study was to assess the long-term trend in chl-a in Lake Ladoga. The results showed a gradual reduction in chl-a concentration, indicating a moderate improvement. Chl-a concentrations (minimum-maximum values) varied spatially. The shallow southern shores did not show any improvement while the situation in the north is much better. The shore areas around the functioning paper mill at Pitkäranta and city of Sortavala still show high chl-a values. These findings provide a general reference on the current trophic state of Lake Ladoga that could contribute to improve policy and management strategies. It is assumed that the present warming trend of surface water may result in phytoplankton growth increase, thus partly offsetting a decrease in nutrient load. Precipitation is thought to be increasing, but the influence on water quality is less clear. Future studies could assess the current chemical composition to determine the state of water quality of Lake Ladoga. [ABSTRACT FROM AUTHOR]

Remote sensing (RS) plays an important role gathering data in many critical domains (e.g., global climate change, risk assessment and vulnerability reduction of natural hazards, resilience of ecosystems, and urban planning). Retrieving, managing, and analyzing large amounts of RS imagery poses substantial challenges. Google Earth Engine (GEE) provides a scalable, cloud-based, geospatial retrieval and processing platform. GEE also provides access to the vast majority of freely available, public, multi-temporal RS data and offers free cloud-based computational power for geospatial data analysis. Artificial intelligence (AI) methods are a critical enabling technology to automating the interpretation of RS imagery, particularly on object-based domains, so the integration of AI methods into GEE represents a promising path towards operationalizing automated RS-based monitoring programs. In this article, we provide a systematic review of relevant literature to identify recent research that incorporates AI methods in GEE. We then discuss some of the major challenges of integrating GEE and AI and identify several priorities for future research. We developed an interactive web application designed to allow readers to intuitively and dynamically review the publications included in this literature review. [ABSTRACT FROM AUTHOR]

The impact of climate change over the coming decades will increase the likelihood of many species undergoing genetic alterations or even becoming extinct. Vegetation and belowground organisms are more vulnerable to the intensified impact of climate change due to a possible lack of genetic plasticity and limited mobility. Organisms are inter-dependable in ecosystems; hence, this study focused on the impact of climate change, examining the soil condition in Africa, vegetation responses and the overview of species' responses to climate change through a bibliometric study and an analysis of remote sensing information. The bibliometric study examines climate change-related literature published from 1999 to 2019, collected from the Web of Science and Scopus database platforms, and this reveals an overall rapid increase in the number of climate change publications in Africa, with South Africa occupying a leading position in all the studied parameters. The spatially based information on soil moisture, temperature and the photosynthetic activities of vegetation affirmed that there is increasing amount of drought in Africa with more impact in northern, southern and eastern Africa. African countries, especially in the above-mentioned regions, need to urgently invest in support programs that will ease the impact of climate change, particularly on food security. [ABSTRACT FROM AUTHOR]