Spatial Scales Research Articles

Spatial distribution and environmental correlations of Culex pipiens pallens (Diptera: Culicidae)in Haidian district, Beijing.

Culex pipiens pallens Coquillett, 1898(Diptera: Culicidae)was the dominant health threat to mosquito species in Beijing, and it is important to unravel the spatial distribution and environmental correlations of Cx. pipiens pallens in Beijing. 3S technology methods and spatial statistics were used to clarify the distribution pattern. Subsequently, linear and spatial regression were performed to detect the environmental factors linked with the density of Cx. pipiens pallens. The same "middle peak" spatial distribution pattern was observed for Cx. pipiens pallens density at the community, subdistrict, and loop area levels in our study area. In addition, there were various correlated environmental factors at the community and subdistrict scales. At the community scale, the summary values of the Modified Normalized Difference Water Index (MNDWI) in 2 km buffer zone (MNDWI_2K) were negatively correlated, and the summary values of Normalized Difference Built-up Index (NDBI) in 800 m buffer zone (NDBI_800) was positively correlated to the Cx. pipiens pallens density. However, the summary values of Normalized Difference Vegetation Index and Nighttime Light Index significantly affected Cx. pipiens pallens density at the subdistrict scale. Our findings provide insight into the spatial distribution pattern of Cx. pipiens pallens density and its associated environmental risk factors at different spatial scales in the Haidian district of Beijing for the first time. The results could be used to predict the Cx. pipiens pallens density as well as the risk of lymphatic filariasis (LF) infection, which would help implement prevention and control measures to prevent future risks of biting and LF transmission in Beijing.

The global pattern of epiphytic liverwort disparity: insights from Frullania

The geographical and ecological patterns of morphological disparity are crucial to understand how species are assembled within communities in the context of the evolutionary history, morphological evolution and ecological interactions. However, with limited exceptions, rather few studies have been conducted on the global pattern of disparity, particularly in early land plants. Here we explored the spatial accumulation of disparity in a morphologically variable and species rich liverwort genus Frullania in order to test the hypothesis of latitude disparity gradient. We compiled a morphological data set consisting of eight continuous traits for 244 currently accepted species, and scored the species distribution into 19 floristic regions worldwide. By reconstructing the morphospace of all defined regions and comparisons, we identified a general Gondwana-Laurasia pattern of disparity in Frullania. This likely results from an increase of ecological opportunities and / or relaxed constraints towards low latitudes. The lowest disparity occurred in arid tropical regions, largely due to a high extinction rate as a consequence of paleoaridification. There was weak correlation between species diversity and disparity at different spatial scales. Furthermore, long-distance dispersal may have partially shaped the present-day distribution of Frullania disparity, given its frequency and the great contribution of widely distributed species to local morphospace. This study not only highlighted the crucial roles of paleoenvironmental changes, ecological opportunities, and efficient dispersal on the global pattern of plant disparity, but also implied its dependence on the ecological and physiological function of traits.

Crowdsourcing RTK: a new GNSS positioning framework for building spatial high-resolution atmospheric maps based on massive vehicle GNSS data

High-quality spatial atmospheric delay correction information is essential for achieving fast integer ambiguity resolution (AR) in precise positioning. However, traditional real-time precise positioning frameworks (i.e., NRTK and PPP-RTK) depend on spatial low-resolution atmospheric delay correction through the expensive and sparsely distributed CORS network. This results in limited public appeal. With the mass production of autonomous driving vehicles, more cost-effective and widespread data sources can be explored to create spatial high-resolution atmospheric maps. In this study, we propose a new GNSS positioning framework that relies on dual base stations, massive vehicle GNSS data, and crowdsourced atmospheric delay correction maps (CAM). The map is easily produced and updated by vehicles equipped with GNSS receivers in a crowd-sourced way. Specifically, the map consists of between-station single-differenced ionospheric and tropospheric delays. We introduce the whole framework of CAM initialization for individual vehicles, on-cloud CAM maintenance, and CAM-augmented user-end positioning. The map data are collected and preprocessed in vehicles. Then, the crowdsourced data are uploaded to a cloud server. The massive data from multiple vehicles are merged in the cloud to update the CAM in time. Finally, the CAM will augment the user positioning performance. This framework forms a beneficial cycle where the CAM’s spatial resolution and the user positioning performance mutually improve each other. We validate the performance of the proposed framework in real-world experiments and the applied potency at different spatial scales. We highlight that this framework is a reliable and practical positioning solution that meets the requirements of ubiquitous high-precision positioning.

Evapotranspiration in the Amazon Basin: Couplings, hydrological memory and water feedback

The hydrological cycle of the Amazon Basin is characterized by a multitude of interactions between the atmosphere and surface processes, involving several temporal and spatial scales. Evapotranspiration is a key variable in the water and energy balance, particularly in this watershed that is home to the world largest rainforest. This study analyzed evapotranspiration in the Amazon Basin using the concepts of Coupling (one way relationship between two variables), Memory (temporal permanence of a state or anomaly in the behavior of one variable) and Feedback (two way relationships between two variables), during a 62-year period (12/1959–11/2021). This study found that the seasonality of precipitation, mainly due to the South American Monsoon, determines when evapotranspiration is mainly driven by (or coupled with) net radiation at the surface or soil moisture. Moreover, in the regions where seasonality of precipitation is strong, the previous hydroclimatic conditions due to hydrological memory (reaching just over 90 days in shallow soils) influences the evapotranspiration during the relatively dry season. Regarding water feedback, this study found that the dependence of seasonal precipitation of the Amazon Basin from their hydrobiological processes (fraction of precipitation recycled by Amazonian evapotranspiration) increases with the distance from the Atlantic Ocean, mainly during the non-monsoonal circulation. This process is particularly remarkable in the Amazon-Andes transition zone. However, the spatial pattern and height of water recycled in this watershed is mainly determined by the dominant circulation pattern of atmospheric fluxes in each season.

Analyzing the Energy Usage of a Community and the Benefits of Energy Storage

Understanding the energy usage of a community is crucial for policymaking, energy planning, and achieving sustainable development. The advent of the smart grid has made it feasible to gather fine-grain energy usage data at large-scales, providing us with new opportunities to understand demand patterns at different spatial and temporal scales. In this paper, we conduct a large-scale empirical study of energy usage of 14,849 residential and commercial energy consumers from a small city in the United States. We conduct a wide ranging analysis of energy usage at multiple granularities—citywide, transformer-level, and individual home levels. In doing so, we demonstrate how city-wide smart meter datasets can answer a variety of questions on energy consumption, such as the impact of weather on energy usage. For example, we show that extreme weather events significantly increase energy usage, e.g., by 36% and 11.5% on hot summer and cold winter days, respectively. As another example, we show 19.2% of transformers in the grid get overloaded during peak load periods. Finally, we evaluate the impact of incorporating varying amounts of energy storage within the distribution grid and the impact such deployments will have on the peak demand patterns seen by the grid as well as the ability to reduce overloads seen by distribution transformers during peak periods.

Bridging senses of place and mobilities scholarships to inform social-ecological systems governance: A research agenda

Uncertainty and change are increasingly commonplace as communities respond to impacts of social-ecological change including climate change, and dangerous levels of pollution. Given the extent of these crises, new approaches are needed to support responses. Here we identify challenges and discuss insights that the nexus of Senses of place (SoP) and mobilities research offers in navigating such uncertainty. We conducted a two-round Delphi, followed by a workshop, and collaborative writing process with a global network of researchers with expertise in either or both SoP and mobilities research. Participants identified five challenges at the place-mobility nexus that emerge when a social-ecological system is disrupted. We use the 2022 Odra River fish die-off to exemplify the identified challenges: 1) accounting for power dynamics, inequalities and motility; 2) doing justice to more-than human actors; 3) integrating multiple and sometimes nested spatial scales; 4) considering temporalities of place and mobilities, and 5) embracing multisensoriality. To address these challenges, we recommend drawing on diverse methods and knowledge co-creation processes that combine more-than-human perspectives, multisensoriality, and engage in the dynamic relations between places to understand people-place disruptions in the face of socio-spatial precarity. Addressing such knowledge gaps requires stronger collaboration of mobilities and place researchers.

Environmentally associated colour divergence does not coincide with population structure across Lesser Antillean anoles

Abstract Species distributed across heterogeneous environments may undergo local adaptation, which can be limited by the homogenizing effects of gene flow. Lesser Antillean anoles exhibit dorsal colour variation associated with dramatic shifts in environment across small spatial scales, providing an excellent system for studying the maintenance of local adaptation despite ongoing gene flow. The 1995 Soufriere Hills eruption on Montserrat provides an opportunity to understand how natural disasters may influence the evolution of insular species. Thus, our study had two main objectives. First, we collected genetic, phenotypic, and environmental data to investigate whether environmentally associated dorsal coloration reflects underlying population structure in three species of Lesser Antillean anoles. Second, we examined the short-term evolutionary consequences of volcanism by comparing pre- and posteruption samples of Anolis lividus using multilocus sequence data. We found all three species exhibited genetic isolation-by-distance but not isolation-by-environment. Furthermore, phenotypic differences within each species were not predicted by genetic distances, suggesting gene flow across colour morphs. We confirmed the repopulation of A. lividus in the impacted area and found genetic diversity equivalent to pre-eruption sampling. Our findings contribute to our understanding of local adaptation in spatially small-scale systems and the impact of catastrophic natural disasters on population structure.

Landscape influences on thermal sensitivity and predicted spatial variability among brook trout streams in the southeastern USA

AbstractWarming water temperatures as a result of climate change pose a major threat to coldwater organisms. However, the rate of warming is not spatially uniform due to surface‐ground‐water interactions and stream and watershed characteristics. Coldwater habitats that are most resistant to warming serve as thermal refugia and identifying their locations is critical to regional aquatic conservation planning. We quantified the thermal sensitivity of 203 streams providing current and potential habitat for brook trout (Salvelinus fontinalis) across nearly 1000 linear km of their native range in the southern and central Appalachian Mountains region, USA, and characterized their spatial variability with landscape variables available in the National Hydrography Dataset. Using the Bayesian framework, we calculated the maximum slope of the logistic function relating paired weekly mean air temperature and stream temperature as an index of stream thermal sensitivity. Streams differed greatly in thermal sensitivity and those with more resistant water temperature regimes (i.e., thermal refugia) were consistently characterized by southerly latitudes and groundwater input. Landscape variables derived from a principal component analysis explained 16% of the variation in thermal sensitivity, indicating that the existing landscape variables were modestly successful in explaining spatial thermal heterogeneity. Using our model and spatial interpolation, we predicted thermal sensitivity at 8695 stream segments potentially suitable for brook trout in the study region. Thermal refugia were more common southward presumably due to higher elevations, but elsewhere they were also clustered at finer spatial scales. Our analysis informs prioritizing habitat conservation and restoration of this native salmonid and other aquatic organisms that depend on coldwater habitats in a warming world.

Conceptual model of global plants entrapping plastics

Aquatic plants, seagrasses, macrophytes, mangroves, and riparian vegetation are responsible for some of the most important ecosystem services provided on the Earth. Given their role in trapping plastics along rivers, we propose a new ecosystem service of plastic entrapment by global plants. Although research started recently to study vegetation trapping plastics, little is known about the global patterns of plastic retention and remobilization by vegetation through different habitats. Given those gaps, we synthesize global data on plastic entrapment in plants providing a conceptual model to describe processes for plastic retention by vegetation. Our results demonstrate how vegetation has a pivotal role in entrapping plastics across spatial and temporal scales, finding the higher density of plastics on plants rather than in the adjacent water area. Furthermore, we proposed a conceptual model (i.e., Plant Plastic Pathway) of plants entrapping plastics, highlighting spatial and temporal scales of plastic retention and release processes in different habitats. Thus, we anticipate our conceptual model to be a starting point for more sophisticated future studies, putting effort into looking at plastic-vegetation dynamics. Our conceptual model may have a crucial effect if applied to plastic hotspot area detection with clean-up and mitigation actions in riverine ecosystems.

Addressing the spatial disparity of COVID-19 vaccination services: A spatial optimisation approach

The COVID-19 pandemic has posed global public health threats. The mass COVID-19 vaccination has continuously played a central role in containing this pandemic. To maximise the benefits of COVID-19 vaccination, equal accessibility to vaccination at different spatial scales is critical. Limited research has investigated the spatial disparity and location optimisation of COVID-19 vaccination services. In this study, we utilised the spatial big data of COVID-19 vaccination sites and used the Gini index to measure the spatial disparity of COVID-19 vaccination service levels. Then, we leveraged spatial optimisation models to enhance the spatial deployment of vaccination sites in the study area of England, UK. The results demonstrate the feasibility and efficiency of the proposed methods. Specifically, the spatial disparity of vaccination services is largely mitigated by the optimised configuration of vaccination sites, as the Gini index decreases from 0.84 (before optimisation) to 0.12 (after optimisation). The suggested vaccination sites would improve the equality of vaccination services and reduce the financial costs of maintaining the vaccination programme, which has implications for public health policymakers. This research illustrates that spatial optimisation could be instrumental in effectively addressing public health crises.

Expanding the spatial scale in DNA-based monitoring schemes: ascertainment bias in transnational assessments

Harmonising methodology between countries is crucial in transborder population monitoring. However, immediate application of alleged, established DNA-based methods across the extended area can entail drawbacks and may lead to biases. Therefore, genetic methods need to be tested across the whole area before being deployed. Around 4,500 brown bears (Ursus arctos) live in Norway, Sweden, and Finland and they are divided into the western (Scandinavian) and eastern (Karelian) population. Both populations have recovered and are connected via asymmetric migration. DNA-based population monitoring in Norway and Sweden uses the same set of genetic markers. With Finland aiming to implement monitoring, we tested the available SNP-panel developed to assess brown bears in Norway and Sweden, on tissue samples from a representative set of 93 legally harvested individuals from Finland. The aim was to test for ascertainment bias and evaluate its suitability for DNA-based transnational-monitoring covering all three countries. We compared results to the performance of microsatellite genotypes of the same individuals in Finland and against SNP-genotypes from individuals sampled in Sweden (N = 95) and Norway (N = 27). In Finland, a higher resolution for individual identification was obtained for SNPs (PI = 1.18E-27) compared to microsatellites (PI = 4.2E-11). Compared to Norway and Sweden, probability of identity of the SNP-panel was slightly higher and expected heterozygosity lower in Finland indicating ascertainment bias. Yet, our evaluation show that the available SNP-panel outperforms the microsatellite panel currently applied in Norway and Sweden. The SNP-panel represents a powerful tool that could aid improving transnational DNA-based monitoring of brown bears across these three countries.

Answering the key stakeholder questions about the impact of offshore wind farms on marine life using hypothesis testing to inform targeted monitoring

Abstract Stakeholders need scientific advice on the environmental impacts of offshore wind (OW) before the facilities are installed. The utility of conventional environmental monitoring methods as a basis for forecasting OW impacts is limited because they do not explain the causes of the observed effects. We propose a multistep approach, based on process-oriented hypothesis testing, targeted monitoring and numerical modeling, to answer key stakeholder questions about planning an OW facility: Q1—Where do we place future OW farms so that impacts on the ecosystem are minimized? Q2—Which species and ecosystem processes will be impacted and to what degree? Q3—Can we mitigate impacts and, if so, how? and Q4—What are the risks of placing an OW facility in one location vs. another? Hypothesis testing can be used to assess impacts of OW facilities on target species-ecological process. This knowledge is transferable and is broadly applicable, a priori, to assess suitable locations for OW (Q1). Hypothesis testing can be combined with monitoring methods to guide targeted monitoring. The knowledge generated can identify the species/habitats at risk (Q2), help selecting/developing mitigation measures (Q3), and be used as input parameters for models to forecast OW impacts at a large spatial scale (Q1; Q4).

Impacts of water regulation through a reservoir on drought dynamics and propagation in the Pilica River watershed

Study regionThe Pilica River watershed, located in central Poland, is the largest left tributary of the Vistula River basin. Study focusThe study aims to assess the impact of human activities on drought dynamics by considering the specific role of the Sulejów Reservoir. Identifying factors contributing to drought development is essential for decision-makers to implement appropriate management measures. The temporal overlap method is used to distinguish between climate and human-driven droughts. The changes in drought propagation are assessed using the time shift method at various accumulation time scales. To assess the spatial distribution of drought, we applied the Soil and Water Assessment Tool (SWAT) model to simulate soil moisture and streamflow response at a spatial scale. New hydrological insights for the regionThe findings highlight that about 75% of meteorological droughts lead to soil moisture droughts, and about 70% result in hydrological droughts. The results show the contribution of human activities to soil moisture drought (∼25%) and hydrological drought (∼10%), indicating that not all droughts are driven by meteorological factors alone. The key finding suggests a shorter and longer time scale of around 3 and 12 months for soil moisture drought and hydrological drought propagation analysis, respectively. Moreover, the decrease in the duration and severity of drought during 2005–2019 compared to 1997–2004 suggests that the magnitude of the reservoir impact depends on its purpose and operational strategies.

Experimental‐Assisted Approach to Develop a Numerical Model for Simulating the Reaction Propagation in Reactive Multilayers

One outstanding feature of self‐propagating reactions is their ability to release heat of reaction over both temporal and spatial scales, enabling the sustained progression of the reaction after a local ignition. They propagate in the form of a continuous reaction front through the mixture of the starting materials. Previous research on reactive materials has predominantly focused on unraveling the microstructure property relationships influencing released energy in reacting multilayers. This involved considering coupled differential equations, including the heat conduction equation and Fick's law. In this study, the introduction of a purely thermal numerical macroscale model is made, incorporating two states of material properties that differentiate between the thermal characteristics before and after phase formation. The homogenization of material properties before the phase formation is accomplished through the consideration of directional‐temperature‐dependent thermal conductivity and temperature‐dependent‐specific heat capacity. The energy‐release function is derived using experimental data for the reaction velocity depending on bilayer thickness. This model allows for the exploration of reaction motion and temperature profiles, achieving qualitative conformity with experimental measurements for freestanding foil, and necessitating reasonable computational effort.

The MOSAiC Distributed Network: Observing the coupled Arctic system with multidisciplinary, coordinated platforms

Central Arctic properties and processes are important to the regional and global coupled climate system. The Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) Distributed Network (DN) of autonomous ice-tethered systems aimed to bridge gaps in our understanding of temporal and spatial scales, in particular with respect to the resolution of Earth system models. By characterizing variability around local measurements made at a Central Observatory, the DN covers both the coupled system interactions involving the ocean-ice-atmosphere interfaces as well as three-dimensional processes in the ocean, sea ice, and atmosphere. The more than 200 autonomous instruments (“buoys”) were of varying complexity and set up at different sites mostly within 50 km of the Central Observatory. During an exemplary midwinter month, the DN observations captured the spatial variability of atmospheric processes on sub-monthly time scales, but less so for monthly means. They show significant variability in snow depth and ice thickness, and provide a temporally and spatially resolved characterization of ice motion and deformation, showing coherency at the DN scale but less at smaller spatial scales. Ocean data show the background gradient across the DN as well as spatially dependent time variability due to local mixed layer sub-mesoscale and mesoscale processes, influenced by a variable ice cover. The second case (May–June 2020) illustrates the utility of the DN during the absence of manually obtained data by providing continuity of physical and biological observations during this key transitional period. We show examples of synergies between the extensive MOSAiC remote sensing observations and numerical modeling, such as estimating the skill of ice drift forecasts and evaluating coupled system modeling. The MOSAiC DN has been proven to enable analysis of local to mesoscale processes in the coupled atmosphere-ice-ocean system and has the potential to improve model parameterizations of important, unresolved processes in the future.

Different roads, same destination: The shared future of plant ecophysiology and ecohydrology.

Terrestrial water fluxes are substantially mediated by vegetation, while the distribution, growth, health, and mortality of plants are strongly influenced by the availability of water. These interactions, playing out across multiple spatial and temporal scales, link the disciplines of plant ecophysiology and ecohydrology. Despite this connection, the disciplines have provided complementary, but largely independent, perspectives on the soil-plant-atmosphere continuum since their crystallization as modern scientific disciplines in the late 20th century. This review traces the development of the two disciplines, from their respective origins in engineering and ecology, their largely independent growth and maturation, and the eventual development of common conceptual and quantitative frameworks. This common ground has allowed explicit coupling of the disciplines to better understand plant function. Case studies both illuminate the limitations of the disciplines working in isolation, and reveal the exciting possibilities created by consilience between the disciplines. The histories of the two disciplines suggest opportunities for new advances will arise from sharing methodologies, working across multiple levels of complexity, and leveraging new observational technologies. Practically, these exchanges can be supported by creating shared scientific spaces. This review argues that consilience and collaboration are essential for robust and evidence-based predictions and policy responses under global change.

Enhancing flood verification using Signal Detection Theory (SDT) and IoT Sensors: A spatial scale evaluation

Inundation mapping by flood simulation is subject to uncertainties which spatial accuracy assessment could not be processed as normal mapping does. This paper presents a comprehensive study on the spatial accuracy of flood verification using Internet of Things (IoT) sensors in several flood-prone regions in Taiwan, including Changhua, Yunlin, Chiayi, Tainan, Kaohsiung, and Pingtung, during three storm events in 2021. The study adopts Signal Detection Theory (SDT) and proposes an ensemble-based approach that combines multiple evaluation metrics to enhance the accuracy and reliability of flood verification in these regions. The study examines the inundation availabilities between flood observation from IoT sensors and flood simulation. A range of spatial scales varies from 100 m to 1000 m and also administrative divisions are considered including neighborhood-level and village-level scales. The results show that increasing the spatial scale generally leads to higher True Positives, indicating improved flood detection, but it also results in increased False Positives, indicating more misclassification of non-flooded points. The findings reveal that the optimal spatial scale for all cities/counties is 450 m which indicates the averaged spatial accuracy of the flood simulation. However, there is notable variation in the optimal spatial scale across different regions. The ensemble approach demonstrates high consistency in determining the optimal spatial scale, as eight out of ten metrics consistently identify the same value. Additionally, the study compares flood verification across administrative divisions in Taiwan, finding that the village-level spatial scale performs best in Changhua and Yunlin counties, while the neighborhood-level scale is preferred in Tainan, Kaohsiung, and Pingtung. This research provides a valuable framework to estimate inundation mapping accuracy under flood verification using IoT sensors in the specified region of Taiwan and highlights the importance of considering local conditions and administrative divisions in determining the optimal spatial scale. These findings can contribute to the improvement of flood detection and monitoring systems in the area with IoT flood sensors.

Dynamics of dissolved inorganic carbon (DIC) and stable C isotope ratios (δ13CDIC) in a tropical watershed with diversified land use in São Paulo State, Brazil

The fluvial transport of dissolved inorganic carbon (DIC) is an important component of the global carbon cycle. Herein, we assessed the dynamics of DIC and the C stable isotopic composition (δ13CDIC) in a watershed with diversified land use in São Paulo State (Brazil), more specifically in the Sorocaba River basin (SRB) and considered the temporal and spatial scales. For this purpose, twelve fluvial samples at each sampling point (e.g., S1, S2, S3, S4 and S5) were collected in the SRB, from June 2009 to May 2010, which represented one complete hydrological cycle that included the extremes of the rainfall and discharge regimes. In addition, the δ13CDIC values were also characterized in the wet and dry season at all sampling points to verify their seasonal variability. The results reflected the seasonal variation in discharges, water temperatures, and electrical conductivities. Most of the DIC was transported in the wet season at all sampling points, where the less negative δ13CDIC values were characterized. The natural sources of DIC are associated with atmospheric/soil CO2 consumption. The anthropogenic impacts on both [DIC] and δ13CDIC are linked to untreated urban sewage that is discharged directly into the Sorocaba River, as well as to aquatic photosynthesis in the Itupararanga Reservoir. From 1970 to 2020, the modeling proposed in this study indicated that the annual flux of DIC (Friver) varied from 9.0 to 78.7 t km−2 a−1, confirming that the El Niño Southern Oscillation (ENSO) controlled Friver in the SRB, with higher and lower Friver values occurring during strong El Niño (EN) and La Niña (LN) years. The average Friver value was 20 t km−2 a−1, which is higher than those obtained in natural several temperate and tropical watersheds due to the influences of urban areas on [DIC] in the SRB.

Exploring the effects of crop growth differences on radar vegetation index response and crop height estimation using dynamic monitoring model

Abstract. Synthetic aperture radar (SAR) has emerged as a promising technology for monitoring crop plant height due to its ability to capture the geometric properties of crops. Radar vegetation index (RVI) has been extensively utilized for qualitative and quantitative remote sensing monitoring of vegetation growth dynamics. However, the combination of crop, growing environment, and temporal dynamics makes crop monitoring data a complex task. Despite the relatively simple underlying mechanisms of this phenomenon, there is still a need for more research to identify specific vegetation structures that correspond to changes in the response of vegetation indices. Building upon this premise, this study utilized a dynamic monitoring model to conduct dynamic monitoring of plant height for three common crops: rice, wheat, and maize. The findings revealed that (1) models developed for specific spatial and temporal scales of particular crop varieties may not accurately predict crop growth in different regions or with different varieties in a timely manner, due to growth variations; (2) these models maintain accuracy over a range of plant heights, such as rice at around 70cm, wheat at around 50cm, and maize at around 150cm; (3) among the three crops, planting density was identified as the main factor influencing the differences in RVI response. This research contributes to our comprehension of the dynamic response of RVI to different growth conditions in crops, and offers valuable insights and references for agricultural monitoring.

Population health implications of exposure to pervasive military aircraft noise pollution.

While the adverse health effects of civil aircraft noise are relatively well studied, impacts associated with more intense and intermittent noise from military aviation have been rarely assessed. In recent years, increased training at Naval Air Station Whidbey Island, USA has raised concerns regarding the public health and well-being implications of noise from military aviation. This study assessed the public health risks of military aircraft noise by developing a systematic workflow that uses acoustic and aircraft operations data to map noise exposure and predict health outcomes at the population scale. Acoustic data encompassing seven years of monitoring efforts were integrated with flight operations data for 2020-2021 and a Department of Defense noise simulation model to characterize the noise regime. The model produced contours for day-night, nighttime, and 24-h average levels, which were validated by field monitoring and mapped to yield the estimated noise burden. Established thresholds and exposure-response relationships were used to predict the population subject to potential noise-related health effects, including annoyance, sleep disturbance, hearing impairment, and delays in childhood learning. Over 74,000 people within the area of aircraft noise exposure were at risk of adverse health effects. Of those exposed, substantial numbers were estimated to be highly annoyed and highly sleep disturbed, and several schools were exposed to levels that place them at risk of delay in childhood learning. Noise in some areas exceeded thresholds established by federal regulations for public health, residential land use and noise mitigation action, as well as the ranges of established exposure-response relationships. This study quantified the extensive spatial scale and population health burden of noise from military aviation.We employed a novel GIS-based workflow for relating mapped distributions of aircraft noise exposure to a suite of public health outcomes by integrating acoustic monitoring and simulation data with a dasymetric population density map. This approach enables the evaluation of population health impacts due to past, current, and future proposed military operations. Moreover, itcanbemodified for application to other environmental noise sources and offers an improved open-sourcetool to assess the population health implications of environmental noise exposure, inform at-risk communities, and guide efforts in noise mitigation and policy governing noise legislation, urban planning, and land use.