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In the United States, hundreds of thousands of undocumented orphan wells have been abandoned, leaving the burden of managing environmental hazards to governmental agencies or the public. These wells, a result of over a century of fossil fuel extraction without adequate regulation, lack basic information like location and depth, emit greenhouse gases, and leak toxic substances into groundwater. For most of these wells, basic information such as well location and depth is unknown or unverified. Addressing this issue necessitates innovative and interdisciplinary approaches for locating, characterizing, and mitigating their environmental impacts. Our survey of the United States revealed the need for tools to identify well locations and assess conditions, prompting the development of technologies including machine learning to automatically extract information from old records (95%+ accuracy), remote sensing technologies like aero-magnetometers to find buried wells, and cost-effective methods for estimating methane emissions. Notably, fixed-wing drones equipped with magnetometers have emerged as cost-effective and efficient for discovering unknown wells, offering advantages over helicopters and quadcopters. Efforts also involved leveraging local knowledge through outreach to state and tribal governments as well as citizen science initiatives. These initiatives aim to significantly contribute to environmental sustainability by reducing greenhouse gases and improving air and water quality.

California's diverse geography and meteorological conditions necessitate models capturing fine-grained patterns of air pollution distribution. This study presents the development of high-resolution (100 m) daily land use regression (LUR) models spanning 1989-2021 for nitrogen dioxide (NO2), fine particulate matter (PM2.5), and ozone (O3) across California. These machine learning LUR algorithms integrated comprehensive data sources, including traffic, land use, land cover, meteorological conditions, vegetation dynamics, and satellite data. The modeling process incorporated historical air quality observations utilizing continuous regulatory, fixed site saturation, and Google Streetcar mobile monitoring data. The model performance (adjusted R2) for NO2, PM2.5, and O3 was 84 %, 65 %, and 92 %, respectively. Over the years, NO2 concentrations showed a consistent decline, attributed to regulatory efforts and reduced human activities on weekends. Traffic density and weather conditions significantly influenced NO2 levels. PM2.5 concentrations also decreased over time, influenced by aerosol optical depth (AOD), traffic density, weather, and land use patterns, such as developed open spaces and vegetation. Industrial activities and residential areas contributed to higher PM2.5 concentrations. O3 concentrations exhibited no significant annual trend, with higher levels observed on weekends and lower levels associated with traffic density due to the scavenger effect. Weather conditions and land use, such as commercial areas and water bodies, influenced O3 concentrations. To extend the prediction of daily NO2, PM2.5, and O3 to 1989, models were developed for predictors such as daily road traffic, normalized difference vegetation index (NDVI), Ozone Monitoring Instrument (OMI)-NO2, monthly AOD, and OMI-O3. These models enabled effective estimation for any period with known daily weather conditions. Longitudinal analysis revealed a consistent NO2 decline, regulatory-driven PM2.5 decreases countered by wildfire impacts, and spatially variable O3 concentrations with no long-term trend. This study enhances understanding of air pollution trends, aiding in identifying lifetime exposure for statewide populations and supporting informed policy decisions and environmental justice advocacy.

Terrestrial evapotranspiration is the second-largest component of the land water cycle, linking the water, energy, and carbon cycles and influencing the productivity and health of ecosystems. The dynamics of ET across a spectrum of spatiotemporal scales and their controls remain an active focus of research across different science disciplines. Here, we provide an overview of the current state of ET science across in situ measurements, partitioning of ET, and remote sensing, and discuss how different approaches complement one another based on their advantages and shortcomings. We aim to facilitate collaboration among a cross-disciplinary group of ET scientists to overcome the challenges identified in this paper and ultimately advance our integrated understanding of ET.

Animals disperse seeds in various ways that affect seed deposition sites and seed survival, ultimately shaping plant species distribution, community composition, and ecosystem structure. Some animal species can disperse seeds through multiple pathways (e.g., defecation, regurgitation, epizoochory), each likely producing distinct seed dispersal outcomes. We studied how seed traits (size and toughness) interact with disperser species to influence seed dispersal pathway and how this ultimately shapes the proportion of seeds deposited in various habitat types. We focused on three frugivorous species of duikers (African forest antelopes) in the Dja Faunal Reserve, a tropical rainforest in southern Cameroon. Duikers can both defecate and regurgitate seeds, the latter predominantly occurring during rumination at their bedding sites (or "nests"). We located duiker nests and dungs along 18 linear 1-km-transects to assess: (1) how seed traits affect the likelihood of dispersal via defecation versus regurgitation, (2) if defecated versus regurgitated seeds are deposited at different rates in different forest types (assessed by indigenous Baka), microhabitats, and forest structural attributes (measured by drone lidar), and (3) if these differ between three duiker species that vary in size and diel activity patterns. We found that duikers predominantly defecated small seeds (10 mm length), the latter including 25 different plant species. The three duiker species varied in their nesting habits, with nocturnal bay duikers (Cephalophus dorsalis) nesting in dense understory vegetation at proportions 3-4 times higher than Peter's and yellow-backed duikers (Cephalophus callipygus and Cephalophus silvicultor). As a result, bay duikers deposited larger regurgitated seeds at a higher rate in habitats with denser understory where lianas and palms predominate and near fallen trees. This directed regurgitation seed deposition likely plays an important and unique role in forest succession and structure. This study highlights the importance of ungulate seed dispersal by regurgitation, a vastly understudied process that could impact many ecosystems given the prevalence of ruminating ungulates worldwide.

Solar-induced fluorescence (SIF) is a proxy of ecosystem photosynthesis that often scales linearly with gross primary productivity (GPP) at the canopy scale. However, the mechanistic relationship between GPP and SIF is still uncertain, especially at smaller temporal and spatial scales. We deployed a ultra-hyperspectral imager over two grassland sites in California throughout a soil moisture dry down. The imager has high spatial resolution that limits mixed pixels, enabling differentiation between plants and leaves within one scene. We find that imager SIF correlates well with diurnal changes in leaf-level physiology and gross primary productivity under well-watered conditions. These relationships deteriorate throughout the dry down event. Our results demonstrate an advancement in SIF imaging with new possibilities in remotely sensing plant canopies from the leaf to the ecosystem. These data can be used to resolve outstanding questions regarding SIF's meaning and usefulness in terrestrial ecosystem monitoring.

The intensifying global opioid crisis, majorly attributed to fentanyl (FT) and its analogs, has necessitated the development of rapid and ultrasensitive remote/on-site FT sensing modalities. However, current approaches for tracking FT exposure through wastewater-based epidemiology (WBE) are unadaptable, time-consuming, and require trained professionals. Toward developing an extended in situ wastewater opioid monitoring system, we have developed a screen-printed electrochemical FT sensor and integrated it with a customized submersible remote sensing probe. The sensor composition and design have been optimized to address the challenges for extended in situ FT monitoring. Specifically, ZIF-8 metal-organic framework (MOF)-derived mesoporous carbon (MPC) nanoparticles (NPs) are incorporated in the screen-printed carbon electrode (SPCE) transducer to improve FT accumulation and its electrocatalytic oxidation. A rapid (10 s) and sensitive square wave voltammetric (SWV) FT detection down to 9.9 µgL-1 is thus achieved in aqueous buffer solution. A protective mixed-matrix membrane (MMM) has been optimized as the anti-fouling sensor coating to mitigate electrode passivation by FT oxidation products and enable long-term, intermittent FT monitoring. The unique MMM, comprising an insulating polyvinyl chloride (PVC) matrix and carboxyl-functionalized multi-walled carbon nanotubes (CNT-COOH) as semiconductive fillers, yielded highly stable FT sensor operation (> 95% normalized response) up to 10 h in domestic wastewater, and up to 4 h in untreated river water. This sensing platform enables wireless data acquisition on a smartphone via Bluetooth. Such effective remote operation of submersible opioid sensing probes could enable stricter surveillance of community water systems toward timely alerts, countermeasures, and legal enforcement.

Abstract: Gabon is one of 11 high-forest, low-deforestation (HFLD) countries in the world. It has the highest proportion of preserved forests in the Congo Basin and is the first country to create large forest carbon offset credits in the market. However, about 60% of forests in Gabon is allocated to logging concessions, causing concerns for forest degradation and the sustainability of carbon credits. Here, we use a combination of air- and space-borne remote sensing data and the-state-of-the-art gradient boosted regression trees to estimate forest structure and aboveground biomass carbon density (ACD) of trees at 100 m resolution for the year circa 2020. Mapping spatial variations of ACD across floristically diverse landscapes, we estimate average density and total living carbon storage of trees at the national and sub-national levels. The estimated ACD of trees in forestlands within the country was 142.12 ± 7.3 Mg C ha−1 with the highest values found in central Gabon (150.08 ± 5.8 Mg C ha−1) and on highlands (161.18 ± 6.7 Mg C ha−1). On average, in every region, ACD of forests found within logging concessions (149.89 ± 6.1 Mg C ha−1) was higher than unmanaged forests of unprotected areas (122.81 ± 4.4 Mg C ha−1), indicating the combined effects of logging in carbon-rich forests and increased productivity due to management. The country’s total estimated biomass carbon for trees (above and belowground) stored within the forests was 4.14 ± 0.3 Pg C with 68% found within logging concessions and 14% within protected areas. The map provides high precision and comprehensive assessment of carbon stocks of trees in Gabon’s forests, significantly improving the country’s prospects to implement climate mitigation policies and to participate in carbon markets.

Abstract: Quantifying evapotranspiration (ET) is critical to accurately predict vegetation health, groundwater recharge, and streamflow generation. Hillslope aspect, the direction a hillslope faces, results in variable incoming solar radiation and subsequent vegetation water use that drive ET. Previous work in watersheds with a single dominant vegetation type (e.g., trees) have shown that equator‐facing slopes (EFS) have higher ET compared to pole‐facing slopes (PFS) due to higher evaporative demand. However, it remains unclear how differences in vegetation type (i.e., grasses and trees) influence ET and water partitioning between hillslopes with opposing aspects. Here, we quantified ET and root‐zone water storage deficits between a PFS and EFS with contrasting vegetation types within central coastal California. Our results suggest that the cooler PFS with oak trees has higher ET than the warmer EFS with grasses, which is counter to previous work in landscapes with a singule dominant vegetation type. Our root‐zone water storage deficit calculations indicate that the PFS has a higher subsurface storage deficit and a larger seasonal dry down than the EFS. This aspect difference in subsurface water storage deficits may influence the subsequent replenishment of dynamic water storage, groundwater recharge and streamflow generation. In addition, larger subsurface water deficits on PFS may reduce their ability to serve as hydrologic refugia for oaks during multi‐year droughts. This research provides a novel integration of field‐based and remotely‐sensed estimates of ET required to properly quantify hillslope‐scale water balances. These findings emphasize the importance of resolving hillslope‐scale vegetation structure within Earth system models, especially in landscapes with diverse vegetation types.

Methane is a major contributor to anthropogenic greenhouse gas emissions. Identifying large sources of methane, particularly from the oil and gas sectors, will be essential for mitigating climate change. Aircraft-based methane sensing platforms can rapidly detect and quantify methane point-source emissions across large geographic regions, and play an increasingly important role in industrial methane management and greenhouse gas inventory. We independently evaluate the performance of five major methane-sensing aircraft platforms: Carbon Mapper, GHGSat-AV, Insight M, MethaneAIR, and Scientific Aviation. Over a 6 week period, we released metered gas for over 700 single-blind measurements across all five platforms to evaluate their ability to detect and quantify emissions that range from 1 to over 1,500 kg(CH4)/h. Aircraft consistently quantified releases above 10 kg(CH4)/h, and GHGSat-AV and Insight M detected emissions below 5 kg(CH4)/h. Fully blinded quantification estimates for platforms using downward-facing imaging spectrometers have parity slopes ranging from 0.76 to 1.13, with R2 values of 0.61 to 0.93; the platform using continuous air sampling has a parity slope of 0.5 (R2 = 0.93). Results demonstrate that aircraft-based methane sensing has matured since previous studies and is ready for an increasingly important role in environmental policy and regulation.

Coastal terrestrial-aquatic interfaces (TAIs) are crucial contributors to global biogeochemical cycles and carbon exchange. The soil carbon dioxide (CO2) efflux in these transition zones is however poorly understood due to the high spatiotemporal dynamics of TAIs, as various sub-ecosystems in this region are compressed and expanded by complex influences of tides, changes in river levels, climate, and land use. We focus on the Chesapeake Bay region to (i) investigate the spatial heterogeneity of the coastal ecosystem and identify spatial zones with similar environmental characteristics based on the spatial data layers, including vegetation phenology, climate, landcover, diversity, topography, soil property, and relative tidal elevation; (ii) understand the primary driving factors affecting soil respiration within sub-ecosystems of the coastal ecosystem. Specifically, we employed hierarchical clustering analysis to identify spatial regions with distinct environmental characteristics, followed by the determination of main driving factors using Random Forest regression and SHapley Additive exPlanations. Maximum and minimum temperature are the main drivers common to all sub-ecosystems, while each region also has additional unique major drivers that differentiate them from one another. Precipitation exerts an influence on vegetated lands, while soil pH value holds importance specifically in forested lands. In croplands characterized by high clay content and low sand content, the significant role is attributed to bulk density. Wetlands demonstrate the importance of both elevation and sand content, with clay content being more relevant in non-inundated wetlands than in inundated wetlands. The topographic wetness index significantly contributes to the mixed vegetation areas, including shrub, grass, pasture, and forest. Additionally, our research reveals that dense vegetation land covers and urban/developed areas exhibit distinct soil property drivers. Overall, our research demonstrates an efficient method of employing various open-source remote sensing and GIS datasets to comprehend the spatial variability and soil respiration mechanisms in coastal TAI. There is no one-size-fits-all approach to modeling carbon fluxes released by soil respiration in coastal TAIs, and our study highlights the importance of further research and monitoring practices to improve our understanding of carbon dynamics and promote the sustainable management of coastal TAIs.

With increased interest in forest restoration comes an urgent need to provide accurate, scalable, and cost‐effective monitoring tools. The ubiquity of smartphones has led to a surge in monitoring apps. We reviewed and assessed monitoring apps found through web searches and conversations with practitioners. We identified 42 apps that (1) automatically monitor indicators or (2) facilitate data entry. We selected the five most promising from the first category, based on their relevance, availability, stability, and user support. We compared them to traditional field techniques in a well‐studied restoration project in Costa Rica. We received further feedback from 15 collaborator organizations that evaluated these in their corresponding field restoration sites. Diameter measurements correlated well with traditional tape‐based measurements (R2 = 0.86–0.89). Canopy openness and ground cover showed weaker correlations to densiometer and quadrat cover measurements (R2 = 0.42–0.51). Apps did not improve labor efficiency but do preclude the purchase of specialized field equipment. The apps reviewed here need further development and validation to support monitoring adequately, especially in the tropics. Estimates of development and maintenance costs, as well as statistics on user uptake, are required for cost‐effective development. We recommend a coordinated effort to develop dedicated restoration monitoring apps that can speed up and standardize the collection of indicators and provide evidence on restoration outcomes alongside a centralized repository of this information.

Estimates of the land area occupied by wind energy differ by orders of magnitude due to data scarcity and inconsistent methodology. We developed a method that combines machine learning-based imagery analysis and geographic information systems and examined the land area of 318 wind farms (15,871 turbines) in the U.S. portion of the Western Interconnection. We found that prior land use and human modification in the project area are critical for land-use efficiency and land transformation of wind projects. Projects developed in areas with little human modification have a land-use efficiency of 63.8 ± 8.9 W/m2 (mean ±95% confidence interval) and a land transformation of 0.24 ± 0.07 m2/MWh, while values for projects in areas with high human modification are 447 ± 49.4 W/m2 and 0.05 ± 0.01 m2/MWh, respectively. We show that land resources for wind can be quantified consistently with our replicable method, a method that obviates >99% of the workload using machine learning. To quantify the peripheral impact of a turbine, buffered geometry can be used as a proxy for measuring land resources and metrics when a large enough impact radius is assumed (e.g., >4 times the rotor diameter). Our analysis provides a necessary first step toward regionalized impact assessment and improved comparisons of energy alternatives.

Abstract: Hyperspectral remote sensing has the potential to map numerous attributes of the Earth’s surface, including spatial patterns of biological diversity. Grasslands are one of the largest biomes on Earth. Accurate mapping of grassland biodiversity relies on spectral discrimination of endmembers of species or plant functional types. We focused on spectral separation of grass lineages that dominate global grassy biomes: Andropogoneae (C4), Chloridoideae (C4), and Pooideae (C3). We examined leaf reflectance spectra (350–2,500 nm) from 43 grass species representing these grass lineages from four representative grassland sites in the Great Plains region of North America. We assessed the utility of leaf reflectance data for classification of grass species into three major lineages and by collection site. Classifications had very high accuracy (94%) that were robust to site differences in species and environment. We also show an information loss using multispectral sensors, that is, classification accuracy of grass lineages using spectral bands provided by current multispectral satellites is much lower (accuracy of 85.2% and 61.3% using Sentinel 2 and Landsat 8 bands, respectively). Our results suggest that hyperspectral data have an exciting potential for mapping grass functional types as informed by phylogeny. Leaf‐level hyperspectral separability of grass lineages is consistent with the potential increase in biodiversity and functional information content from the next generation of satellite‐based spectrometers.

Cyanobacterial harmful algal blooms (cyanoHABs) can harm people, animals, and affect consumptive and recreational use of inland waters. Monitoring cyanoHABs is often limited. However, chlorophyll-a (chl-a) is a common water quality metric and has been shown to have a relationship with cyanobacteria. The World Health Organization (WHO) recently updated their previous 1999 cyanoHAB guidance values (GVs) to be more practical by basing the GVs on chl-a concentration rather than cyanobacterial counts. This creates an opportunity for widespread cyanoHAB monitoring based on chl-a proxies, with satellite remote sensing (SRS) being a potentially powerful tool. We used Sentinel-2 (S2) and Sentinel-3 (S3) to map chl-a and cyanobacteria, respectively, classified chl-a values according to WHO GVs, and then compared them to cyanotoxin advisories issued by the California Department of Water Resources (DWR) at San Luis Reservoir, key infrastructure in Californias water system. We found reasonably high rates of total agreement between advisories by DWR and SRS, however rates of agreement varied for S2 based on algorithm. Total agreement was 83% for S3, and 52%-79% for S2. False positive and false negative rates for S3 were 12% and 23%, respectively. S2 had 12%-80% false positive rate and 0%-38% false negative rate, depending on algorithm. Using SRS-based chl-a GVs as an early indicator for possible exposure advisories and as a trigger for in situ sampling may be effective to improve public health warnings. Implementing SRS for cyanoHAB monitoring could fill temporal data gaps and provide greater spatial information not available from in situ measurements alone.

Recent studies indicate that the Asian Water Tower (AWT) is at risk due to climate change, which can negatively impact water and food security in Asia. However, there is a lack of comprehensive information on lakes' spatial and temporal changes in this region. This information is crucial for understanding the risk magnitude and designing strategies. To fill this research gap, we analyzed 89,480 Landsat images from 1977 ± 2 to 2020 ± 2 to investigate the changes in the size of lakes recharged by the AWT. Our findings showed that out of the 209 lakes larger than 50 km2, 176 (84 %) grew during the wet season and 167 (81 %) during the dry season. 74 % of expanded lakes are located in the Inner Tibetan Plateau (TP) and Tarim basins. The lakes that shrank are found mainly in the Helmand, Indus, and Yangtze basins. Over the entire period, the area of shrinkage (55,077.028 km2 in wet season, 53,986.796 km2 in dry) markedly exceeded expansion (13,000.267 km2 in wet, 11,038.805 km2 in dry), with the drastic decline of the Aral Sea being a major contributor to shrinkage, accounting for 90 % of the total loss. From 1990 ± 2 to 2020 ± 2, alpine lakes mostly expanded, plain lakes mostly shrank, with the opposite trend from 1977 ± 2 to 1990 ± 2. Glacial loss and permafrost thawing under global warming in the Inner TP, Tarim Interior, Syr Darya, and Mekong basins were strongly correlated with lake expansion. However, permafrost discontinuities may prevent significant growth of lakes in the Indus and Ganges basins despite increased recharge. Our findings point to the prominence of the risk the lakes recharged by AWT face. Taking immediate action to manage these risks and adaptation is crucial as the AWT retreats and lake recharges are slowed.

Background NOAA’s Hazard Mapping System (HMS) smoke product comprises smoke plumes digitised from satellite imagery. Recent studies have used HMS as a proxy for surface smoke presence. Aims We compare HMS with airport observations, air quality station measurements and model estimates of near-surface smoke. Methods We quantify the agreement in numbers of smoke days and trends, regional discrepancies in levels of near-surface smoke fine particulate matter (PM2.5) within HMS polygons, and separation of total PM2.5 on smoke and non-smoke days across the contiguous US and Alaska from 2010 to 2021. Key results We find large overestimates in HMS-derived smoke days and trends if we include light smoke plumes in the HMS smoke day definition. Outside the western US and Alaska, near-surface smoke PM2.5 within areas of HMS smoke plumes is low and almost indistinguishable across density categories, likely indicating frequent smoke aloft. Conclusions Compared with airport, Environmental Protection Agency (EPA) and model-derived estimates, HMS most closely reflects surface smoke in the Pacific and Mountain regions and Alaska when smoke days are defined using only heavy plumes or both medium and heavy plumes. Implications We recommend careful consideration of biases in the HMS smoke product for air quality and public health assessments of fires.

Strawberries in California are grown in specific coastal areas where land is scarce and climate change threatens future production. Strawberry growers are under pressure to adopt sustainable production strategies such as crop rotation, but this practice requires more land than back-to-back planting. The objectives of this research were to quantify the rate of crop rotation across the three main strawberry producing regions in California (Ventura, Santa Maria, and Monterey Bay), and evaluate geographic and edaphic influences on crop rotation. All strawberry fields in the main strawberry producing regions of California were identified by satellite imagery and manual inspection for the years 2017 through 2022. ArcGIS Pro was used to outline each strawberry field and compare among years to determine the period between successive strawberry plantings. Edaphic characteristics and shapefiles for surrounding fields were retrieved from public datasets. The three regions significantly differed in their rates of crop rotation. On average, 95, 52, and 25% of strawberry hectares were rotated each year in the Monterey Bay, Santa Maria, and Ventura regions, respectively. Shorter rotation lengths were associated with reduced distance from the ocean and soil with a higher percentage of sand. Based on 2 years of disease surveys, fields infested with Macrophomina phaseolina tended to be further inland than fields infested with Fusarium oxysporum f. sp. fragariae in the Monterey Bay region. This study determined that distance from the ocean and soil texture are associated with crop rotation lengths in California strawberry production. Enough land may be available in the Santa Maria and Monterey Bay regions for growers to lengthen crop rotations, but water quality, social networks, and financial considerations that were outside the scope of this study are likely to limit the ability for strawberry growers to maximize the duration of crop rotations.

Sun-induced chlorophyll fluorescence (SIF) has proven to be advantageous in estimating gross primary production, despite the lack of a stable relationship. Satellite-based SIF measurements at Level 2 offer comprehensive global coverage and are available in near real time. However, these measurements are often limited by spatial and temporal sparsity, as well as discontinuities. These limitations primarily arise from incomplete satellite trajectories. Additionally, variability in cloud cover and periodic issues specific to the instruments can compromise data quality. Two families of methods have been developed to address data discontinuity: (1) machine learning-based gap-filling techniques and (2) geostatistical techniques (various forms of kriging). The former techniques utilize the relationships between ancillary data and SIF, while the latter usually rely on the available SIF data recordings and their covariance structure to provide estimates at unsampled locations. In this study, we create a synthetic approach for SIF gap filling by hybridizing the two approaches under the umbrella of kriging with external drift. We performed leave-one-out cross-validation of the OCO-2 SIF retrieval aggregates for the entire year of 2019, comparing three methods: ordinary kriging, ML-based estimation using ancillary data, and kriging with external drift. The Mean Absolute Error (MAE) for ML, ordinary kriging, and the hybrid approach was found to be 0.1399, 0.1318, and 0.1183 mW m2 sr−1 nm−1, respectively. We demonstrate that the performance of the hybrid approach exceeds both parent techniques due to the incorporation of information from multiple resources. This use of multiple datasets enriches the hybrid model, making it more robust and accurate in handling the spatio-temporal variability and discontinuity of SIF data. The developed framework is portable and can be applied to SIF retrievals at various resolutions and from various sources (satellites), as well as extended to other satellite-measured variables.

Mountain shrublands are widespread habitats of the European Alps. Shrub encroachment into above treeline grazed lands profoundly modifies biodiversity and ecosystem functioning. Yet, mountain shrublands remain overlooked in vegetation distribution modeling because it is difficult to distinguish them from productive grasslands. Here, we used the pigment-sensitive spectral indices based on Sentinel-2 bands within a specific phenological window, to produce a high-resolution distribution map of mountain shrublands in the French Alps. We evaluated the performance of our classification using a large dataset of vegetation plots and found that our model is highly sensitive to Ericaceous species which constitute most of the dense alpine shrublands in the French Alps. Our analysis of topoclimatic and land use factors limiting the shrubland distribution at regional scale found that, consistent with the ecophysiology of shrubs, expansion is limited by a combination of water deficit and temperature. We discussed the past and current land-use implications in the observed distribution and put forward hypotheses combining climate and land-use trajectories. Our work provides a baseline for monitoring mountain shrub dynamics and exploring the response of shrublands to past and ongoing climate and land use changes.

• The objective of the work is to develop CNN-based DL model for LULC classification. • An assessment of LULC is carried out for the classified maps of 2011 and 2021. • The change detection analysis of Chamarajanagar district is performed for one decade. • The proposed method is outperformed, with an accuracy of 95.27 % and 94.57 %. The change detection analysis of land use land cover (LULC) is an important task in several fields and applications such as environmental monitoring, urban planning, disaster management, and climate change studies. This study focuses on the use of remote sensing (RS) and geographic information systems (GIS) to identify the changes in Chamarajanagar district, which is located in Karnataka state, South India. This paper mainly focuses on the classification and change detection analysis of LULC in 2011 and 2021 using linear imaging self-scanning sensor-III (LISS-III) satellite images. Traditional methods for LULC classification involve manual interpretation of satellite images, which provides lower accuracy. Therefore, this paper proposed the Convolutional Neural Network (CNN)-based deep learning classification method for LULC classification. The main objective of the research work is to perform an accurate change detection of the Chamarajanagar district using the classified maps of the years 2011 and 2021. The proposed classification method is outperformed, with a classification accuracy of 95.27 % and 94.57 % for LISS-III satellite imagery of the years 2011 and 2021 respectively. Further, change detection analysis has been carried out using classified maps and results show a decline of 3.23 sq. km, 22.7 sq. km, and 3.83 sq. km in the areas covered by vegetation, agricultural land, and forest area, respectively. In other classes, such as built-up, water bodies, and barren land, an increase in land cover was observed by 5.59 sq. km, 1.99 sq. km, and 20.92 sq. km, respectively. [ABSTRACT FROM AUTHOR]

Automatic classification of remote sensing images using machine learning techniques is challenging due to the complex features of the images. The images are characterized by features such as multi-resolution, heterogeneous appearance and multi-spectral channels. Deep learning methods have achieved promising results in the analysis of remote sensing satellite images in the recent past. However, deep learning methods based on convolutional neural networks (CNN) experience difficulties in the analysis of intrinsic objects from satellite images. These techniques have not achieved optimum performance in the analysis of remote sensing satellite images due to their complex features, such as coarse resolution, cloud masking, varied sizes of embedded objects and appearance. The receptive fields in convolutional operations are not able to establish long-range dependencies and lack global contextual connectivity for effective feature extraction. To address this problem, we propose an improved deep learning-based vision transformer model for the efficient analysis of remote sensing images. The proposed model incorporates a multi-head local self-attention mechanism with patch shifting procedure to provide both local and global context for effective extraction of multi-scale and multi-resolution spatial features of remote sensing images. The proposed model is also enhanced by fine-tuning the hyper-parameters by introducing dropout modules and a decay linear learning rate scheduler. This approach leverages local self-attention for learning and extraction of the complex features in satellite images. Four distinct remote sensing image datasets, namely RSSCN, EuroSat, UC Merced (UCM) and SIRI-WHU, were subjected to experiments and analysis. The results show some improvement in the proposed vision transformer on the CNN-based methods. [ABSTRACT FROM AUTHOR]

Marine spatial planning (MSP) is one of the most important tools for ensuring sustainable use of marine areas. Although MSP is a well‐established method, its adoption in rapidly developing countries is a challenge. One of the main concerns is data adequacy, as the MSP process typically requires a large amount of spatial data on human activities, biodiversity, and socio‐ecological interactions within the planning area. Drawing from an institutional cooperation project in Zanzibar, Tanzania, we share our experience and demonstrate how to fill in socio‐ecological data gaps to support the development of MSP in areas with limited data availability. We developed a rapid and cost‐effective system for collecting biological data, which, together with remote sensing and place‐based participatory mapping, helped formulate the first pilot ecologically informed MSP for Zanzibar. By sharing our results and experiences, we aim to provide best practices, lessons learned, and recommendations for future projects with a similar ecological setting and socio‐economic context. [ABSTRACT FROM AUTHOR]

Remote sensing observations of the nascent solar wind in the solar corona imply that heavy ions are often faster and hotter than the plasma protons. In situ observations of the near-Earth solar wind with low collisional age show that hotter and faster states of heavy ions usually do not occur at the same time: heavy ions are either faster or hotter than the protons. This discrepancy highlights the need to understand the competing effects of heating, acceleration, Coulomb collisions, and instabilities on heavy ions in the inner heliosphere, from the solar corona to Earth. The Parker Solar Probe (PSP) is currently the closest spacecraft to the Sun and is anticipated to cross the outer boundary of the preferential heating and acceleration zone. Using in situ measurements from PSP, we find that larger drift speeds between α -particles and protons (V α p ) correspond to larger α -to-proton temperature ratios ( T α T p ) in the young solar wind close to the corona. This observation differs from in situ observations farther from the Sun (e.g., with Helios and Wind at 0.3–1 au). We believe that this difference is related to the speed of the solar wind, as well as the distance from the Sun. The anti-correlation between V α p and T α T p in fast wind away from the Sun is potentially related to the energy transfer from the drift kinetic energy of α-particles to proton thermal energy due to drift instabilities. In the young slow wind, heavy ions exhibit signs of both preferential heating and preferential acceleration, but are not affected much by drift instabilities. We identify the outer boundary of the preferential heating and acceleration zone for heavy ions in the young slow wind to be at a distance of around 0.16 au, beyond which the effects of Coulomb collisions weaken the effects of preferential heating and acceleration. [ABSTRACT FROM AUTHOR]

This study investigates land use and land cover changes in the Teiči Strict Nature Reserve, Latvia, from 1982 to 2023 and models potential future changes based on four legislative scenarios extending to 2064. The research aims to assess the effectiveness of existing conservation zoning in relation to historical and projected changes in land use and land cover, addressing the ongoing debate between fixed and adaptable conservation strategies. The study employs remote sensing imagery, geographical information system data, and land use and land cover modeling methods to analyze historical changes and predict future trends. The results suggest substantial land use and land cover changes over the past four decades, including intensified urbanization, agricultural expansion, and a decline in peat bogs and forested areas. Scenario projections indicate that continued land use and land cover changes could further challenge the efficiency of current conservation strategies, with varying outcomes depending on legislative measures and climate change impacts. The study concludes that adaptive management and variable conservation zoning are necessary to address these dynamic changes and preserve the reserve's ecological integrity. The results emphasize the importance of integrating predictive modeling into conservation planning to improve flexibility and sustainability in protected areas. [ABSTRACT FROM AUTHOR]

Polarization photodetectors (pol-PDs) have widespread applications in geological remote sensing, machine vision, biological medicine, and so on. However, commercial pol-PDs use bulky and complicated optical systems with lenses, polarizers, and mechanical spools, which are complex and cumbersome, and respond slowly. Inspired by the desert ants' compound eyes, we developed a single-shot pol-PD based on four-directional grating arrays capped perovskite single-crystal thin film without other standard polarization optics. Our pol-PD has a high detectivity, two orders of magnitude greater than that of commercial photodetectors, and exhibits high polarization sensitivity. The high performance of our pol-PD is due to the highly crystalline perovskite single-crystal thin film and regular nanograting structure, made by a nanoimprinting crystallization method. Our single-shot pol-PD is a compact on-chip optoelectronic device that demonstrates excellent performance in a wide range of applications including accurate bionic navigation, sharp image restoration in hazy scenes, stress visualization of polymers, and detection of cancerous areas in tissues without histological staining. [ABSTRACT FROM AUTHOR]

ABSTRACT Aim Location Time Period Major Taxa Studied Methods Results Main Conclusions To characterise the size and geometry of coral reefs on a global scale.Global.Present.Coral reefs.We process the Allen Coral Atlas database of shallow‐water tropical reefs to obtain a comprehensive and unprecedented inventory of coral reefs worldwide. We analyse different macroecological and morphological patterns, including size distribution, the area‐perimeter relationship, inter‐reef distance distribution, and the fractal dimension of individual reefs and coral provinces.We identified a total of 1,579,772 individual reefs worldwide (> 1000 m2), extending over a total of 52,423 km2 of ocean area with mean and median sizes of 3.32 and 0.3 ha, respectively. We unravelled three universal laws that are common to all coral reef provinces: the size‐frequency distribution and the inter‐reef distance distribution follow power laws with an exponent of 1.8 and 2.33, respectively. At the same time, the area‐perimeter relationship conforms to a power‐law with an exponent of 1.26. Furthermore, we demonstrate that coral reefs develop fractal patterns characterised by a perimeter fractal dimension of DP=1.3$$ {D}_P=1.3 $$ and a surface fractal dimension of DA=1.6$$ {D}_A=1.6 $$. Our analysis suggests that coral reefs tend to evolve from simple rounded filled shapes to more complex, elongated and less compact forms, developing into fractal structures with a consistent surface fractal dimension and an increasing perimeter fractal dimension as they grow.Coral reefs display intricate fractal‐like geometries and exhibit universal macroecological patterns, largely independent of their geographical location. The universality of the observed patterns suggests that these features possibly stem from the highly conserved interactions of biological, physical and chemical processes. Over geological scales, these processes lead to reef landscape patterns common among all provinces, providing new information relevant to reef growth modelling. [ABSTRACT FROM AUTHOR]

Hyperspectral remote sensing images obtained from cameras are characterized by high-dimensions and low quality, which makes them unfavorable for various analytics purposes. This is due to the presence of visible and invisible frequencies of the reflected light making it poorly reveal the spectral signatures of the image. Visual communication advancement has paved the need for Image Super-Resolution (SR) which recovers high-resolution images from low-resolution images. Several works were carried out earlier on image SR using variants of supervised and unsupervised models that still lack accuracy. In this paper, we propose an unsupervised learning model titled Gompertz Function–based Convergence War Accelerometric Optimization–GAN framework for generating of High-Resolution (HR) images. The framework comprises a pre-processing stage, where the incoming Low-Resolution (LR) image is preprocessed for noise removal by applying Shannon-Gaussian Filter (S-GF). Following is the Gradient Domain Approach based Tone-Mapping (TM). Skew correction is done to remove distortion and maintain original resolution that may change during TM stage. The next stage comprises the boundary and edge enhancement of the resulting preprocessed image generated by the method of Inverse Gradient Mapping (IGM) followed by patch extraction to extract minute low-frequency information from the resulting boundary and edge-enhanced image. The contrast of the enhanced patches is improved by removing blurriness effect. The preprocessed image patches are then fed into the Gompertz Function-based Convergence War Accelerometric Optimization – GAN for feature mapping on the trained SR Image features that are clustered using Krzanowski and Li- Kantorovich Metric-K-Means clustering Algorithm (KL-KM-KMA) for effective generation of SR image. The developed model is validated for both qualitative and quantitative measurements. Comparisons are made with several other state-of -the-art methods for accuracy of 98.05%, precision of 97.98%, inception score of 8.71, Fréchet Inception Distance of 36.4 with reduced clustering and training time proving the efficiency of the proposed model. [ABSTRACT FROM AUTHOR]

Soil moisture maps provide quantitative information that, along with climate and energy balance, is critical to integrate with hydrologic processes for characterizing landscape conditions. However, soil moisture maps are difficult to produce for natural landscapes because of vegetation cover and complex topography. Satellite-based L-band microwave sensors are commonly used to develop spatial soil moisture data products, but most existing L-band satellites provide only coarse scale (one to tens of kilometers grid size), information that is unsuitable for measuring soil moisture variation at hillslope or watershed-scales. L-band sensors are typically deployed on satellite platforms and aircraft but have been too large to deploy on small uncrewed aircraft systems (UAS). There is a need for greater spatial resolution and development of effective measures of soil moisture across a variety of natural vegetation types. To address these challenges, a novel UAS-based L-band radiometer system was evaluated that has recently been tested in agricultural settings. In this study, L-band UAS was used to map soil moisture at 3–50-m (m) resolution in a 13 square kilometer (km2) mixed grassland-forested landscape in Sonoma County, California. The results represent the first application of this technology in a natural landscape with complex topography and vegetation. The L-band inversion of the radiative transfer model produced soil moisture maps with an average unbiased root mean squared error (ubRMSE) of 0.07 m3/m3 and a bias of 0.02 m3/m3. Improved fine-scale soil moisture maps developed using UAS-based systems may be used to help inform wildfire risk, improve hydrologic models, streamflow forecasting, and early detection of landslides. [ABSTRACT FROM AUTHOR]

Nitrogen (N) fertilization is a major management requirement for wild blueberry fields. Its presence and estimation can be difficult given the perennial and heterogeneous nature of the plant, low N requirement, and residual N effects, resulting in the frequent over-application of N, excessive canopy growth, and resulting reduction in berry yields. Therefore, this study aimed to estimate nitrogen content and growth parameters using remote sensing approaches. Three trials were established in three commercial fields in Nova Scotia, Canada. An RCBD with 5 replicates and a plot size of 6 × 8 m with a 2 m buffer was used. Treatments consisted of 0, 20, 40, 60, and 100 kg N ha-1 of fertilizer. Using a DJI Matrice 300 UAV mounted with an RGB and a multispectral camera, aerial measurements were collected at 30 m altitude. Several field measurements including leaf nitrogen content (LNC), leaf area, floral bud numbers, stem height, and yield were conducted. Several vegetation indices (VIs) were computed for each plot, and correlation and regression analyses were conducted. Results indicated that treatments with high nitrogen rates had correspondingly high LAI measurements with the 60 kg ha-1 rate achieving the best growth parameters compared to the other treatments. LNC, LAI, and berry yield estimations using VIs [green leaf index (GLI), green red vegetation index (GRVI), and visible atmospheric red index (VARI)] produced significantly positive R2 values of 0.43, 0.48, and 0.30 respectively. Results from this study illustrated the potential of using VIs to estimate LNC, LAI, and berry yield parameters. It was established that the near-infrared VIs are the most effective in estimating differences in nitrogen rates, making them suitable for use in prescription maps for N fertilization applications. [ABSTRACT FROM AUTHOR]

The Awash River Basin is essential in combating climate change by absorbing atmospheric carbon. The goal of this study was to determine carbon stocks using the linear correlation between field inventory and remote sensing data in the basin. The regression model was created using each plot studied from field inventory and the corresponding vegetation indices (VI) values obtained from ArcGIS software in the basin's study area. The linear correlation values for estimated carbon stock with Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) were R2 = 0.36, R = 0.60, and R2 = 0.52, R = 0.72, respectively. In contrast, the values for the Soil-Adjusted Vegetation Index (SAVI) were R2 = 0.36 and R = 0.60. The results show that the relationship between carbon stock and EVI is stronger for estimating carbon stock than other indices (NDVI and SAVI). The linear regression model or equation developed for NDVI, EVI, SAVI, and field-level carbon stock was Y = 16.25x–1.093, Y = 8.935x–1.1254, and Y = 12.988x–1.0895, respectively. The estimated carbon stock from the EVI linear regression model was 15,904,158.24 tons, with an average carbon stock value of 111.96 tons per hectare. The findings concluded that EVI has a stronger correlation with carbon stock stand estimation than NDVI and SAVI. This study could serve as a baseline for the estimation of carbon stock using vegetation indices in different and large-scale land use land cover types. High-resolution multispectral imagery and cloud-based geospatial analysis platforms could provide more accurate results. [ABSTRACT FROM AUTHOR]

As global climate change intensifies, understanding the response mechanisms and adaptive capacities of ecosystems to climate change has become a core focus in environmental science. The Qinghai-Tibet Plateau (QTP), a region highly sensitive to global climate change, shows vegetation phenological shifts that reflect the ecosystem's response to climate fluctuations. However, how phenological metrics extracted from different vegetation indices affect our understanding of these shifts in the region remains unclear. This study analyzes the start (SOS) and end (EOS) of the growing season on the QTP from 1982 to 2015 using GIMMS NDVI3g data. These metrics were compared with phenological data derived from GIMMS LAI3g and MODIS EVI2 data. The results indicate that phenological metrics derived from different vegetation indices (NDVI, LAI, and EVI2) are generally consistent in their spatiotemporal distribution and show significant correlations. However, regional differences and temporal inconsistencies were observed. This comparative analysis reveals the strengths and limitations of various vegetation indices in phenological metric extraction. The results offer crucial insights for enhancing the precision of phenological modeling and highlight the significance of choosing suitable vegetation indices in future studies on phenology. [ABSTRACT FROM AUTHOR]

Introduction: Forest fires, grassland fires, heap coking, straw burning, and volcanic eruptions are thermal anomalies. They attract attention and are designated as high-temperature targets. They can be retrieved macroscopically and quickly by remote sensing technology. Methods: In temperature inversion, the mid-infrared (MIR, 3∼5 μm) and thermal infrared (TIR, 8∼14 μm) band data are most commonly used for temperature inversion. However, it is difficult to effectively retrieve the temperature of small-area high-temperature targets with them; the SWIR band data can perform this task more effectively. Additionally, inversion methods for short-wave infrared (SWIR, 1.3∼2.5 μm) and TIR band data are different. These differences lie in the mechanisms and models. Therefore, we use SWIR and TIR band data to retrieve heap coking temperature with Landsat 7 and Landsat 8 data. Results: SWIR data obtained the results 496∼651 K and 912 K, and TIR data obtained the results 313∼334 K and 320 K. Conclusion: The SWIR inversion results have higher accuracy than the TIR inversion results. The inversion results are closer to the actual temperature of local coking. For this reason, SWIR is more suitable for temperature inversion of small-area high-temperature targets. [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. [ABSTRACT FROM AUTHOR]

Reintroductions are increasingly being used as a conservation tool to restore species to areas where they once existed. Unfortunately, many reintroduction projects fail to establish viable populations. Climate suitability at release sites is thought to be important in determining reintroduction outcomes, and future climate change is an essential consideration for effective reintroduction planning. Climate change threatens species in a variety of ways, such as by impacting life history traits or causing spatial and temporal distribution mismatches of interdependent species. Hibernating species, such as the hazel dormouse (Muscardinus avellanarius), may be particularly susceptible to changes in climate. For example, milder winters may increase the number of interbout arousals during hibernation, which are energetically costly. Timing of food availability may also be impacted by changing climates, potentially causing mismatches between activity and feeding opportunities. Here, we use species distribution models (SDMs) to map climate suitability for dormice in the UK. We also investigate the impact of climate suitability on a long‐running dormouse reintroduction programme, providing the first such investigation for a reintroduced mammal. We find that higher levels of current climate suitability increase the probability of reintroduction success. We find no effect of climate suitability on adult dormouse counts at reintroduction sites, but dormouse counts decline with time since reintroduction. Future projections predict that climate change may lead to more widespread climate suitability for dormice in the UK, reflecting predicted changes in seasonality, winter temperature and precipitation. Our work demonstrates the importance of understanding changing climate suitability for reintroduction planning, with potential widespread applications of SDMs for conservation projects of low‐dispersing mammals. [ABSTRACT FROM AUTHOR]

Many freshwater forested wetlands along the southeastern United States coastline are rapidly transitioning from forest to marsh or open water, due to climate change-related disturbances. Recent studies have found early warning signals (EWS) of regime shifts in other ecosystems, but it is unclear if these can be detected for coastal wetlands. In this study, we examined the ability to detect EWS of regime shifts in coastal wetlands within the Albemarle Pamlico peninsula (APP), North Carolina, U.S.A. We used the Landsat record (1985–2021) to examine trends of normalized difference vegetation index (NDVI) time series for selected areas known to have undergone regime shifts. We found that while 77% of the APP was either stable or revegetating, 22% of the landscape underwent a decrease in NDVI that would indicate a transition from forest to marsh or open water. Of the areas that transitioned, about half (11%) experienced an abrupt decrease in NDVI and 10% experienced a gradual decline. Increasing standard deviation and skewness of time series could serve as EWS of abrupt transitions, but can also provide false negative and positives. Our results suggest that ecosystem transitions from a forest to a marsh or open water can occur both rapidly and slowly, and remote sensing of NDVI time series can help identify EWS for some areas, but not all. Our results allow for prioritization of conservation/restoration of coastlines which will become important in the face of climate change and sea level rise. [ABSTRACT FROM AUTHOR]

Within precision agriculture, yield mapping is important in the evaluation of crop management and delineation of management zones. It can also be used to assess within-field yield potential, in order to guide different precision agriculture practices. However, some farmers do not have a yield monitoring system, and some who do may obtain incomplete or erroneous yield data. This study examined the accuracy with which winter wheat (Triticum aestivum L.) yield could be mapped in 18 fields in southern Sweden using a simple empirical relationship between Sentinel-2 (ESA, Paris, France) data, vegetation index (VI) maps and combined harvester data collected in nearby fields. The results showed that a decrease in map resolution to 40 m reduced the error in the yield maps obtained. Normalised difference water index (NDWI) was the most efficient VI, while a combination of satellite data from earlier and later plant development (booting and milk development stages) performed slightly better than data for other development stages and combinations. The best-performing model at a within-field scale (40-m resolution) had an average mean absolute error (MAE) of 0.40 tonnes ha−1 in a leave-one-field-out cross validation. When the prediction model at field-means scale was applied on 69 farms in a 1055 km2 area, MAE was 0.75 tonnes ha−1 when comparing predictions with mean yields reported by farmers in a phone survey. Therefore, if adequate combined harvester and/or mean yield data are available, a modelling framework that translates satellite imagery into yield maps on-the-fly could be made available for different stakeholders via decision support systems for precision agriculture. [ABSTRACT FROM AUTHOR]

Expanding on our previous efforts demonstrating the feasibility of extracting chemical information in steady-state systems of nanoparticles under irradiation, we demonstrate that the Cherenkov spectra can be used to observe chemical changes in real-time, in situ, and for non-equilibrium systems under irradiation. This is done by way of a dose-resolved measurement of the localized plasmon resonance absorption band associated with the formation and growth of silver nanoclusters and nanoparticles produced via radiolytic reduction of a solution of silver ion under exposure to an electron beam. Additionally, the method utilized in this study provided a significant resolution with a simplified optical system. This paves the way for a technique to determine similar changes in the material in medicinal, industrial (nuclear industry), and research applications—and indeed in any application involving ionizing radiation. [ABSTRACT FROM AUTHOR]

Despite increasing efforts and investment in mangrove conservation, mangrove cover continues to decline globally. The extent to which protected area (PA) management effectively prevents mangrove loss globally across differing management objectives and governance types is not well understood. We combined remote sensing data with PA information to identify the extent and the drivers of mangrove loss across PAs with distinct governance types and protection levels based on categories developed by the International Union for Conservation of Nature (IUCN). Mangrove loss due to storms and erosion was prevalent across all governance types and most IUCN categories. However, the extent of human‐driven loss differed across governance types and IUCN categories. Loss was highest in national government PAs. Private, local, shared arrangement, and subnational government agencies had low human‐driven mangrove loss. Human‐driven loss was highest in PAs with the highest level of restrictions on human activities (IUCN category I) due to mangrove conversion to areas for commodity production (e.g., aquaculture), whereas PAs that allowed sustainable resource use (e.g., category VI) experienced low levels of human‐driven mangrove loss. Because category I PAs with high human‐driven loss were primarily governed by national government agencies, conservation outcomes in highly PAs might depend not only on the level of restrictions, but also on the governance type. Mangrove loss across different governance types and IUCN categories varied regionally. Specific governance types and IUCN categories thus seemed more effective in preventing mangrove loss in certain regions. Overall, we found that natural drivers contributed to global mangrove loss across all PAs, whereas human‐driven mangrove loss was lowest in PAs with subnational‐ to local‐level governance and PAs with few restrictions on human activities. [ABSTRACT FROM AUTHOR]

Low-level moisture is an important ingredient for forecasting severe storms, especially over the Great Plains where severe storms often develop along the dryline. Although ground-based observation systems such as lidar or radiosondes on weather balloons provide accurate information on low-level moisture, data are provided at limited locations, and the low temporal resolution of radiosondes makes it difficult to track a rapidly developing dryline. Geostationary satellites provide high spatial and temporal observation, but the channels of current geostationary satellites are mostly sensitive to water vapor at mid- to upper levels. However, the split window difference (SWD) between the "clean" window channel (10.3 μm) and the "dirty" window channel (12.3 μm) is commonly used for estimating low-level water vapor. However, this estimation is complicated by surface temperature contributions, dependence on the lapse rate, and nonlinear relationships between SWD and moisture. This study applies machine learning techniques to infer boundary layer precipitable water (BLPW) from Geostationary Operational Environmental Satellite (GOES) Advanced Baseline Imager (ABI) data. Since there are few observations that cover wide regions for training convolutional neural networks, especially for the atmosphere above the surface, High-Resolution Rapid Refresh (HRRR) model outputs are used as the truth for training. Statistical results show that using ABI channel 13, 14, and 15 brightness temperatures, skin temperature, solar zenith angle, and GOES total precipitable water product as additional inputs shows the lowest mean-square error (MSE). Case study results show that the model developed in this study is good at capturing strong moisture gradients and depicting a dryline. Significance Statement: Knowledge of low-level moisture is important for monitoring drylines and moisture convergence preceding convective initiation. Traditional observations lack the spatial resolution, spatial coverage, or temporal update frequency needed by forecasters. The latest generation of Geostationary Operational Environmental Satellite (GOES), the GOES-R series, provides high spatial resolution and rapid temporal updates. However, the estimation of low-level moisture using the Advanced Baseline Imager (ABI) is complicated by dependencies on other variables and nonlinear relationships. Machine learning is shown to provide good estimates of low-level moisture from ABI observations. This application of machine learning to GOES-R can improve the ability to monitor low-level moisture and forecast convective initiation. [ABSTRACT FROM AUTHOR]

Bitemporal supervised learning paradigm always dominates remote sensing change detection using numerous labeled bitemporal image pairs, especially for high spatial resolution (HSR) remote sensing imagery. However, it is very expensive and labor-intensive to label change regions in large-scale bitemporal HSR remote sensing image pairs. In this paper, we propose single-temporal supervised learning (STAR) for universal remote sensing change detection from a new perspective of exploiting changes between unpaired images as supervisory signals. STAR enables us to train a high-accuracy change detector only using unpaired labeled images and can generalize to real-world bitemporal image pairs. To demonstrate the flexibility and scalability of STAR, we design a simple yet unified change detector, termed ChangeStar2, capable of addressing binary change detection, object change detection, and semantic change detection in one architecture. ChangeStar2 achieves state-of-the-art performances on eight public remote sensing change detection datasets, covering above two supervised settings, multiple change types, multiple scenarios. [ABSTRACT FROM AUTHOR]

Turbidity (Turb) and Chlorophyll-a (Chl-a) are crucial indicators of water quality because they can reveal the presence of suspended particles and algae, respectively. Understanding the health of rivers and spotting long-term water quality changes can both benefit from monitoring these measures. Traditional methods of monitoring these parameters, like in-situ measurements, is time-consuming, expensive, and inconvenient in some places. Sentinel-2, a multispectral satellite, might offer a more workable and economical option for monitoring water quality, though. This study used 100 in-situ data collected from the Ganga River near Varanasi in the pre-monsoon season (pre-MS) and post-monsoon season (post-MS) in order to create a model for the prediction of optically active water quality parameters by combining Multispectral Instrument (MSI) data and machine learning method (Random Forest). To create spatial distribution maps for Chl-a and Turb, 14 spectral indices and band ratios were employed as independent variables. The results showed that the prediction accuracy for Turb (R2 = 0.91, MAE = 1.13 and MAPE=7.76 % during pre-MS and R2 = 0.93, MAE = 0.88 and MAPE=2.29 % during post-MS) and for Chl-a (R2 = 0.97, MAE = 0.59, and MAPE=2.07 % during pre-MS and R2 = 0.95, MAE = 0.61, and MAPE = 2.71 % during post-MS). The Ganga near Varanasi abruptly turned green due to an increase in algal bloom in May and June 2021. This study not only revealed the reasons behind the green appearance but also identified potential areas of concern or hotspots. In order to identify hotspot locations, drainage networks, point source discharge locations and LU-LC were used. [ABSTRACT FROM AUTHOR]

• A multiscale dilated attention module is proposed. • A multiscale spatial-spectral attention module is constructed. • Building a new type of residual network can better address the problems caused by increased network depth. • Using two regularization methods to improve model generalization ability. Hyperspectral imaging is an image obtained by combining spectral detection technology and imaging technology, which can collect electromagnetic spectra in the wavelength range of visible light to near-infrared. It is an important research content in the field of ground observation in hyperspectral remote sensing. However, hyperspectral image face significant challenges in classification task due to their high spectral dimensions, lack of labeled samples, and strong correlation between bands. In order to fully extract features from both spectral and spatial dimensions and improve classification accuracy in the case of limited training samples, a multiscale dilated attention network is proposed for hyperspectral image classification. First, a three-dimensional convolutional layer is used to extract the shallow features of the image. Then, a multiscale dilated attention module is proposed by combining dilated convolution and channel attention. Using ordinary convolution and dilated convolution to form different receptive fields. Channel attention is used to remodel the obtained multiscale features, enhancing the inter-channel correlation. After that, a multiscale spatial-spectral attention module is constructed using multiple asymmetric convolutions to obtain spatial and spectral attention features at different positions, further enhancing important feature suppression over non-important features. Finally, using softmax to classify the obtained features. Using Indian Pines, Pavia University, KSC and University of Houston as experimental datasets, the overall classification accuracy of this paper's method achieved 98.97%, 99.14%, 99.45%, and 98.56% respectively, using only 5%, 1%, 10%, and 10% of training samples per class. Compared with seven advanced classification methods, the experimental results show that the proposed method can achieve the highest classification accuracy with limited training samples. [ABSTRACT FROM AUTHOR]

Expansive soils cause frequent surface deformation due to their expansion and contraction, which is a serious engineering hazard, and long-term subsidence monitoring is a prerequisite for preventing and controlling expansive soil disasters. Currently, the conventional monitoring methods for the above issue include Interferometric Synthetic Aperture Radar (InSAR) technology, but InSAR is not suitable for uninterrupted monitoring of surface deformation and has low sensitivity. Meanwhile, it can't obtain multiple surface environmental parameters around the station. The Global Navigation Satellite System (GNSS), a system that can directly acquire surface deformation, has been widely used in landslide disaster monitoring, and in recent years, this technology has also been applied to the field of expansive soil disaster monitoring. At the same time, GNSS can also provide a constant stream of L-band microwave signals to obtain ground environmental information such as precipitable rainfall and soil moisture around the station. In previous studies of expansive soil hazards, GNSS technology has been mainly used to provide surface deformation information without exploring its potential to invert ground environmental information around stations. This paper proposes a ground-based GNSS remote sensing integrated monitoring system that integrates expanding land surface parameters such as "precipitable rainfall, soil moisture, and three-dimensional deformation" and analyses the ability of ground-based GNSS to be used for integrated monitoring of expanding soil hazards by combining ten years of consecutive observational data from GNSS stations along the coastal area of Houston. The experimental results show that the GNSS is capable of providing highly accurate time-series characterization of deformation, and inelastic subsidence in recent years has resulted in a cumulative permanent elevation loss of 2 cm along the Houston coast. The correlation coefficient between soil moisture extracted by the fifth-generation European reanalysis data (ERA5) and soil moisture inverted by ground-based GNSS is 0.514. At the same time, the GNSS was also able to monitor the zenithal precipitable water vapor (PWV) and soil moisture changes around the GNSS station and further analyze the response relationship among the three parameters, which could comprehensively evaluate the stability of expansive soils, avoiding the unreliability of relying on a single piece of monitoring information to assess the stability of expansive soils. We hope to construct a more comprehensive ground-based GNSS remote sensing monitoring system to better monitor expansive soil hazards. [ABSTRACT FROM AUTHOR]

• Analysis of data from HF Doppler sounding of daytime equatorial ionosphere was undertaken. • Daytime equatorial F-layer spread was observed to occur more frequently than previously thought. • Morning occurrences of spread F were mostly fossils from post-midnight spreading of the F-layer. • Afternoon occurrences of spread F were triggered by periodic structures in the F-layer plasma or by E-layer irregularities. A receiver that measures and logs Doppler shifts of high frequency (HF) radio signals reflected from the ionosphere was installed in Lagos (geographic: 6.48°N, 3.27°E; dip latitude −4.66°), Nigeria in March 2011. However, continuous monitoring of the ionosphere was facilitated by the installation of a transmitter in Abuja (geographic: 8.99°N, 7.39°E; dip latitude 1.01°), Nigeria in July 2019, dedicated to transmitting HF signals for this remote sensing system. This provided the opportunity to study ionospheric signatures in HF Doppler data obtained from an equatorial location. This paper presents a summary of key findings from analysis of data from the instrument during the first 28 months of operation from July 2019 to November 2021. In this work, a statistical analysis of two main daytime features in the Doppler data is presented. The first feature is irregularities that appear as spreading of the Doppler trace, while the second feature is wave structures consistent with travelling ionospheric disturbances (TIDs). In order to highlight trends in the data, the events were separated into morning (occurring from sunrise to 1159 LT) and afternoon (occurring from 1200 LT to sunset) events. The results showed that occurrences of either type of features were more frequent during the afternoon compared with morning hours. The occurrences of irregularities that appeared as a spread in the Doppler trace peaked in the month of July in the morning, while, in the afternoon these occurrences peaked in the month of March. The duration of most of these irregularities was ≥60 min. A peak in occurrences of TIDs was observed in the morning and afternoon epochs during the March equinox. It was also observed that over 80 % of occurrences of spreading in the Doppler trace during the equinoxes were associated with the occurrences of TIDs. In the month of July, despite a paucity of TIDs, there was a peak in occurrences of spreading in the Doppler trace. Overall, the lowest number of occurrences of both types of features was recorded in the month of August. Detailed analysis of selected events showed that the morning occurrences of spreading in the Doppler trace were mostly remnants of post-midnight spread F, while the afternoon occurrences were due to either TIDs or severe E-layer irregularities. The results from this study show the potential of the low-cost HF Doppler instrument as a real-time monitor of space weather activity at equatorial latitudes. However, further work will be required to establish what percentage of daytime spreading in the F-layer is caused by the remnants of nocturnal plasma bubbles, TIDs, or irregularities in the E-layer. [ABSTRACT FROM AUTHOR]

Traditional agroecosystems (TA) are considered more resilient to climatic extremes due to higher crop diversity and agroecological approach. The TA of Tének were studied to evaluate their drought tolerance. Open-ended questionnaire surveys, field visits, and botanical collections, with the participation of local farmers, were considered. Cultivated plant diversity was analyzed using the Shannon-Wiener diversity index (H'), and the Morisita-Horn index (CMH) in ten different agroecosystems in April and August. Remote sensing methods included the triangular greenness index (TGI). Additionally, normalized difference vegetation index (NDVI) and standardized precipitation index (SPI) were determined, covering the period from 2013 to 2022. The cultivated plant richness (P > 0.5382) and their abundance (P > 0.3622) did not change from April to August considering the total analyzed parcels. However, differences between types of agroecosystems were observed because higher diversity corresponded to managed jungle (te'lom) (H' = 1.5-1.7), and some parcels cultivated with maize (milpa) (H' = 1.02-1.54). Nevertheless, in August, lower similarity was observed in milpa (CMH = 0.23-0.87) due to the absence of maize (Zea mays L.) and other herbaceous annual crops. Landscape interconnection can be affirmed with TGI correlations obtained by comparison of different TA. The negative SPI values were associated with a decrease of NDVI, but Tének polyculture agroecosystems maintained higher NDVI values compared to rainfed monocropping during the analyzed period. Tének polyculture agroecosystems exhibited higher drought tolerance, although maize and other herbaceous plants are susceptible to disappearing during prolonged drought events, thereby increasing food insecurity. [ABSTRACT FROM AUTHOR]

The diverse range of uses of unmanned aerial vehicles has garnered significant attention in research. The scientific literature that supports the data obtained from UAVs recording information from various sensors is presented in this manuscript. It summarizes current developments in remote sensing, including radar, photogrammetry, thermal imaging, light detection and ranging sensors (LiDAR), data gathering, and analysis. It is predicated on the instruments' ability to gather and analyze accurate data. To identify some of the most urgent research problems, it also shows surveys based on research methodologies. The present research focuses on the proliferation and social effects of unmanned aerial vehicles (UAVs). It also encourages novice researchers to pursue this area of study and suggest novel approaches to the design or setup of these flying machines. UAVs have entirely transformed due to advancements in internet technology and current technologies which include camera defects, environmental monitoring, charging, impediments, crop monitoring, energy consumption, military applications, and technology gaps. [ABSTRACT FROM AUTHOR]