Cover Data Research Articles

Reproducible WiSDM: a workflow for reproducible invasive alien species risk maps under climate change scenarios using standardized open data

IntroductionSpecies distribution models (SDMs) are often used to produce risk maps to guide conservation management and decision-making with regard to invasive alien species (IAS). However, gathering and harmonizing the required species occurrence and other spatial data, as well as identifying and coding a robust modeling framework for reproducible SDMs, requires expertise in both ecological data science and statistics.MethodsWe developed WiSDM, a semi-automated workflow to democratize the creation of open, reproducible, transparent, invasive alien species risk maps. To facilitate the production of IAS risk maps using WiSDM, we harmonized and openly published climate and land cover data to a 1 km2 resolution with coverage for Europe. Our workflow mitigates spatial sampling bias, identifies highly correlated predictors, creates ensemble models to predict risk, and quantifies spatial autocorrelation. In addition, we present a novel application for assessing the transferability of the model by quantifying and visualizing the confidence of its predictions. All modeling steps, parameters, evaluation statistics, and other outputs are also automatically generated and are saved in a R markdown notebook file.ResultsOur workflow requires minimal input from the user to generate reproducible maps at 1 km2 resolution for standard Intergovernmental Panel on Climate Change (IPCC) greenhouse gas emission representative concentration pathway (RCP) scenarios. The confidence associated with the predicted risk for each 1km2 pixel is also mapped, enabling the intuitive visualization and understanding of how the confidence of the model varies across space and RCP scenarios.DiscussionOur workflow can readily be applied by end users with a basic knowledge of R, does not require expertise in species distribution modeling, and only requires an understanding of the ecological theory underlying species distributions. The risk maps generated by our repeatable workflow can be used to support IAS risk assessment and surveillance.

Global poverty estimation using private and public sector big data sources

Household surveys give a precise estimate of poverty; however, surveys are costly and are fielded infrequently. We demonstrate the importance of jointly using multiple public and private sector data sources to estimate levels and changes in wealth for a large set of countries. We train models using 63,854 survey cluster locations across 59 countries, relying on data from satellites, Facebook Marketing information, and OpenStreetMaps. The model generalizes previous approaches to a wide set of countries. On average, across countries, the model explains 55% (min = 14%; max = 85%) of the variation in levels of wealth at the survey cluster level and 59% (min = 0%; max = 93%) of the variation at the district level, and the model explains 4% (min = 0%; max = 17%) and 6% (min = 0%; max = 26%) of the variation of changes in wealth at the cluster and district levels. Models perform best in lower-income countries and in countries with higher variance in wealth. Features from nighttime lights, OpenStreetMaps, and land cover data are most important in explaining levels of wealth, and features from nighttime lights are most important in explaining changes in wealth.

Glucocorticoids and land cover: a largescale comparative approach to assess a physiological biomarker for avian conservation.

As humans alter landscapes worldwide, land and wildlife managers need reliable tools to assess and monitor responses of wildlife populations. Glucocorticoid (GC) hormone levels are one common physiological metric used to quantify how populations are coping in the context of their environments. Understanding whether GC levels can reflect broad landscape characteristics, using data that are free and commonplace to diverse stakeholders, is an important step towards physiological biomarkers having practical application in management and conservation. We conducted a phylogenetic comparative analysis using publicly available datasets to test the efficacy of GCs as a biomarker for large spatial-scale avian population monitoring. We used hormone data from HormoneBase (51 species), natural history information and US national land cover data to determine if baseline or stress-induced corticosterone varies with the amount of usable land cover types within each species' home range. We found that stress-induced levels, but not baseline, positively correlated with per cent usable land cover both within and across species. Our results indicate that GC concentrations may be a useful biomarker for characterizing populations across a range of habitat availability, and we advocate for more physiological studies on non-traditional species in less studied populations to build on this framework. This article is part of the theme issue 'Endocrine responses to environmental variation: conceptual approaches and recent developments'.

Remote sensing in the analysis between forest cover and COVID-19 cases in Colombia

This article explores the relationship between forest cover and coronavirus disease 2019 (COVID-19) cases in Colombia using remote sensing techniques and data analysis. The study focuses on the CORINE land cover methodology's five main land cover categories: artificial territory, agricultural territories, forests and semi-natural areas, humid areas, and water surfaces. The research methodology involves several phases of the unified method of analytical solutions for data mining (ASUM-DM). Data on COVID-19 cases and forest cover are collected from the Colombian National Institute of Health and Advanced Land Observation Satellite (ALOS PALSAR), respectively. Land cover data is processed using QGIS software. The results indicate an inverse relationship between forest cover and COVID-19 cases, as evidenced by Pearson's index ρ of -0.439 (p-value <0.012). In addition, a negative correlation is observed between case density and forests and semi-natural areas, one of the land cover categories. The findings of this study suggest that higher forest cover is associated with lower numbers of COVID-19 cases in Colombia. The results could potentially inform government organizations and policymakers in implementing strategies and policies for forest conservation and the inclusion of green areas in densely populated urban areas.

Lossless data hiding technique reducing cover data size for compressed videos

Lossless data hiding technique reducing cover data size for compressed videos

Study on the Spatiotemporal Evolution and Influencing Factors of Forest Coverage Rate (FCR): A Case Study on Yunnan Province Based on Remote Sensing Image Interpretation

The study of the forest coverage rate (FCR) is related to the ecological environment and sustainable development goals (SDGs) of a region. In light of the lack of an organic integration method of “spatiotemporal evolution, correlation analysis, and change prediction” and the lack of a methodology that integrates methods of “remote sensing (RS) and GIS, multi-phase LUCC, and construction of econometric models” in the research methods at present, this study focus on Yunnan, a typical border province located in China with a relatively fragile “innate” ecological environment, as the research area. Based on the interpretation of land use/land cover (LULC) data retrieved from seven periods RS images (1990, 1995, 2000, 2005, 2010, 2015, and 2020), the spatiotemporal evolution of FCR in 129 counties was analyzed. Complementary research methods, such as the spatial econometric model, geographically weighted regression (GWR), and the geographic detector (GD), are used to reveal the influencing factors of FCR. Finally, this study predicts the FCRs of 129 counties in Yunnan from 2025 to 2050. The FCR in Yunnan presents an increasing trend year by year, increasing from 28.96% in 1990 to 49.05% in 2020. In addition, it exhibits spatial agglomeration characteristics with fewer values in the east and more in the west. The analysis of influencing factors show that the increases in the per capita GDP, land utilization rate, and annual average temperature, and the implementation of the Conversion of Cultivated Land into Forest Project (CCFP) will significantly improve the FCR, while the increases in the population density land reclamation rate, the proportion of construction land area, and the proportion of soil erosion land area will significantly reduce the FCR. Furthermore, the FCR is influenced by multiple factors, and the relative factors observed not only show significant spatial differences, but also present complex and diverse patterns, with the additional characteristics of being interwoven and overlapping. This study contributes to expanding and improving the methods and pathways of exploring the spatiotemporal evolution characteristics of FCR in ecologically fragile areas using RS methods, providing a reference for increasing FCR and improving the ecological environment’s quality in Yunnan Province and other ecologically fragile areas.

Not just crop or forest: an integrated land cover map for agricultural and natural areas

Due to the key role surrounding landscape plays in ecological processes, a detailed characterization of land cover is critical for researchers and conservation practitioners. Unfortunately, in the United States, land cover data are split across thematic datasets that emphasize agricultural or natural vegetation, but not both. To address this gap, we merged two datasets, the LANDFIRE National Vegetation Classification (NVC) and USDA-NASS Cropland Data Layer (CDL), to produce integrated ‘Spatial Products for Agriculture and Nature’ (SPAN). Our workflow leveraged strengths of the NVC and the CDL to create detailed rasters comprising both agricultural and natural land-cover classes. We generated SPAN annually from 2012–2021 for the conterminous United States, quantified agreement and accuracy of SPAN, and published the complete computational workflow. In our validation analyses, we found that approximately 5.5% of NVC agricultural pixels conflicted with the CDL, but we resolved most conflicts, leaving only 0.6% of agricultural pixels unresolved in SPAN. These ready-to-use rasters characterizing both agricultural and natural land cover will be widely useful in environmental research and management.

Assessment of the Impact of Population Reduction on Grasslands with a New “Tool”: A Case Study on the “Mountainous Banat” Area of Romania

The landscapes and, implicitly, the surfaces of secondary grasslands in the mountain areas have been intensively modified and transformed by humans. In this context, this paper analyses the spatial and temporal changes of grassland surfaces following the impact of human population reduction. Thus, the study proposes the implementation of the Grassland Anthropic Impact Index (GAII) as a “measurement tool” to functionally link the two components, grassland surface and human population. The spatiotemporal analyses are based on Corine Land Cover data and demographic data, processed via Geographic Information Systems (GIS) methods and the Land Change Modeler (LCM) tool. The research shows that over a period of 28 years, the population, which was continuously decreasing, caused a series of transformations to the grasslands over an area of 33343 ha. The influence of the reduction in the number of inhabitants was also demonstrated by the direction of the changes produced in the grassland surfaces: in the better populated areas, the grasslands expanded over lands with other uses, and in the sparsely populated areas, they were abandoned. GAII values generally increase with the decrease of the population in the target area, meaning that for an inhabitant (potential user) a greater grassland surface is reported, resulting in a greater responsibility for the management of this resource on a space and time scale. Following the evaluation of the trend of the last 28 years, it was observed that the depopulation of mountain areas can be seen as a threat to grassland ecosystems, either through the transition to other categories of use, or through abandonment. The implications of these phenomena are much broader: they produce chain reactions and affect other components of the regional geosystem.

Crop phenology reshapes the food‐safety landscape for roe deer in an agroecosystem

Abstract Understanding the behavioural adjustments of wildlife in anthropized landscapes is key for promoting sustainable human–wildlife coexistence. Little is known, however, about how synanthropic species navigate spatio‐temporal variation in the availability of food and cover that are shaped by human practices such as agriculture. Animal habitat use is predominantly driven by spatial and temporal variations in food and cover, as individuals respond to fluctuations in the trade‐off between food acquisition and risk avoidance. In agroecosystems, the availability of high‐quality forage and cover is dependent on agricultural practices (e.g., harvesting) and crop phenology, providing an ideal opportunity to evaluate how wildlife adjust their behaviour in a heterogeneous human‐dominated landscape. We investigated the influence of crop phenology on the behaviour of European roe deer (Capreolus capreolus) to infer the functional roles of crop types in the food‐cover landscape. We analysed the habitat selection and activity patterns of 105 GPS‐collared adult female roe deer using a unique data set combining field‐specific land cover data, region‐specific estimates of crop phenology and weekly harvesting data for three common crops in a French agroecosystem. We found very distinct habitat selection and activity patterns according to crop type, phenological stage and time of day. Wheat and artificial meadows were strongly selected at night‐time during the early and post‐harvest stages only, when roe deer were highly active, suggestive of feeding activity. On the contrary, roe deer strongly selected maize during the day when it was high enough to provide cover, when they were less active, indicating that it was primarily used for refuge. These patterns depended on the availability of more ‘natural’ cover, suggesting that mature maize may substitute for ‘natural’ cover when the latter is scarce. Synthesis and application: Our work highlights the importance of behavioural plasticity and habitat complementation in the persistence of this synanthropic species in agroecosystems. This behavioural adjustment may buffer the consequences of the reduction in natural habitats that accompanies intensification of agricultural production and has implications for understanding how agricultural practices shape the food‐safety trade‐off of wildlife living in these highly modified landscapes.

The influence of landscape composition and configuration on breeding waterfowl pair abundance in Wisconsin

AbstractHabitat loss and fragmentation have negatively impacted breeding birds across the world. Across guilds, grassland‐dependent birds have experienced the largest proportional loss of their breeding population while wetland‐dependent birds have realized overall net gains in part due to focused conservation efforts. However, some species within the wetland‐dependent guild have a strong dependence on non‐wetland land cover types during the annual cycle and therefore may be equally sensitive to reductions in the composition and altered configuration of upland landcover, such as grasslands. We explored the influence of landcover composition and configuration on the number of breeding pairs of a breeding habitat generalist, the mallard (Anas platyrhynchos), and a grassland‐dependent breeding species, the blue‐winged teal (Spatula discors), in agriculturally‐intensive landscapes of Wisconsin, USA. Because of the extensive landscape alteration and habitat fragmentation that has occurred, we expected mallards to have a more utilitarian response to varying landscape composition compared to a grassland nesting obligate like blue‐winged teal. We used helicopter surveys conducted across 8 years (2001–2003, 2006–2009, and 2012) and remotely‐sensed land cover data to investigate the influence of habitat associations on relative abundance of breeding pairs. Model selection indicated that landscape composition models outperformed landscape configuration and null models for both species. Consistent with our predictions, we found that mallard pair counts were positively influenced by a greater number of land cover covariates compared to blue‐winged teal. Both blue‐winged teal and mallard breeding pairs were positively related to increased composition of emergent and scrub‐shrub wetlands as well as upland grassland. Additionally, we found that mallard pairs were positively related to forested, cattail (Typha sp.), and open water wetland types, whereas predicted blue‐winged teal pair abundance was negatively related to forested wetlands and had a nonlinear relationship and declined when surveyed land sections were comprised of more than 30% cattail‐dominated wetlands and 20% open‐water wetlands. Increased quantities of cattail‐dominated and open‐water wetland cover types comparatively provide less habitat for blue‐winged teal and may reflect broader shifts in habitat composition that have likely resulted from agricultural intensification and stabilized hydrology. Conservation activities that preserve existing nesting land cover types or restore hydrologically‐dynamic emergent wetlands in proximity to upland grassland cover could mutually benefit both species.

Detection of Population Density, LULC Variation and Cross-Regional Similarities Using K-Means Clustering Algorithm in Istanbul Example

In this study, the effect of urban sprawl on land change in Istanbul was examined using Geographic Information System (GIS) technologies and the CORINE Land Cover (CLC) data set produced for the years 1990-2018 and population information. According to this; It has been determined that urban sprawl in the study area has increased due to population growth, especially industrial units, city structures, mines and construction sites have increased by approximately 9%, while maquis areas, arable, mixed agricultural areas and forest areas have decreased by 9%. According to the K-means application, similarities in the districts were revealed between 1990 and 2018. According to the results obtained, it was determined that the districts that were in clusters with similar characteristics in the 1990s changed over time and were located in different clusters. As a result, it is predicted in the study that urban sprawl will increase further due to population growth in Istanbul.

Socio-ecological regime shifts in New England (USA), 1620–2020

Relationships between nature and society are made manifest in the production of landscapes. Consequently, landscape changes indicate changes in the relationships between nature and society. Forged at regional scales over long periods of time, nature/society relationships, like natural and social systems, exhibit periods of equilibrium, stability, and incremental change that eventually give way to new periods of equilibrium, stability, and incremental change in which causal relationships have changed. The paper presents a landscape changebased, grounded theory periodization of the New England (USA) region’s Anthropocene history. Its intent is to provide temporal boundaries within which processes, events, records, and artifacts can be examined within shared socio-ecological frames of reference, a first step in the development of new socio-ecologically-based historical narratives. Locating the “inaugural moment” of New England’s Anthropocene epoch at the establishment of Plymouth Colony in 1620, the beginning of England’s colonialization of this forested North American region, the paper presents and interprets regularity analyses of population density, land-use/land cover, and other data related to landscape shaping processes, identifying socio-ecological regime shifts from the aboriginal Late Woodlands regime to the English Colonial regime and subsequent shifts to the American Industrial regime in 1830 and the American Post-Industrial regime in 1970 along with their nested, subsidiary regimes. Previous periodizations of the region’s history are discussed, and a narrative of the region’s Anthropocene history is outlined based on the paper’s periodization. It is observed that displacements of a socio-ecological regime serve as resources for the next regime, that ghosts of past regimes are present in today’s environmental challenges, but that socio-ecological regime shifts are difficult to forecast.

Study on surface deformation pattern in mine closure area of complex karst mountainous region based on SBAS-InSAR technology

Liupanshui City in Guizhou Province represents a karst mountainous region with a delicate geological environment. The area has a long history of coal mining, and several coal mines have been progressively closed in recent years. However, even after closure of coal mines, the fractured rock mass in the mined-out areas continues to undergo deformation over a specific period. In karst mountainous regions, this deformation is affected by various factors including elevation, slope, precipitation, and vegetation. In this study, we employed SBAS-InSAR technology to construct a time series of surface deformation data from January 2019 to May 2022 within Liupanshui City’s LuJiaZhai-DaPingDi Minefield. Subsequently, this data was comprehensively analyzed in conjunction with time series vegetation cover, monthly precipitation, elevation, and slope data from the identical period. The key findings of this research are as follows: 1) After the closure of the mine, the subsidence area gradually stabilized, yet the volume of subsidence continues to increase. The subsidence area primarily occurs near the MaiZiGou Coal Mine air-mining area, which had a relatively short closure time, as well as in higher elevation areas in the southern and eastern parts of the minefield. Specifically, the maximum cumulative subsidence and the subsidence rate during the study period reached −60.3 mm and −21.83 mm/a, respectively. 2) Surface deformation is closely linked to slope, vegetation type, and rainfall, with subsidence rates and amounts noticeably higher during the rainy season than in the dry season. 3) Surface vegetation cover types display varied effects on deformation, with grassland or shrub areas being more sensitive to precipitation than forests. Forests, especially pine and fir, show a delayed subsidence response to rain, typically 2–3 months post-rainy season onset. This inhibitory effect lessens with increasing slope, particularly beyond a 25° threshold, where responsiveness to precipitation and associated subsidence significantly increase. The findings of this study hold substantial scientific implications for the restoration and management of closed mining areas, as well as the prevention of geological hazards in karst complex mountainous regions.

High‐resolution dataset of nocturnal air temperatures in Bern, Switzerland (2007–2022)

AbstractTo prepare for a hotter future, information on intra‐urban temperature distributions is crucial for cities worldwide. In recent years, different methods to compute high‐resolution temperature datasets have been developed. Such datasets commonly originate from downscaling techniques, which are applied to enhance the spatial resolution of existing data. In this study, we present an approach based on a fine‐scaled low‐cost urban temperature measurement network and a formerly developed land use regression approach. The dataset covers mean nocturnal temperatures of 16 summers (2007–2022) of a medium‐sized urban area with adapted land cover data for each year. It has a high spatial (50 m) and temporal (daily) resolution and performs well in validation (RMSEs of 0.70 and 0.69 K and mean biases of +0.41 and −0.19 K for two validation years). The dataset can be used to examine very detailed statistics in space and time, such as first heatwave per year, cumulative heat risks or inter‐annual variability. Here, we evaluate the dataset with two application cases regarding urban planning and heat risk assessment, which are of high interest for both researchers and practitioners. Due to potential biases of the low‐cost measurement devices during daytime, the dataset is currently limited to night‐time temperatures. With minor adaptions, the presented approach is transferable to cities worldwide in order to set a basis for researchers, city administrations and private stakeholders to address their heat mitigation and adaptation strategies.

An observational analysis of the eastern tropical Pacific Shallow Meridional Circulation using YOTC data and pilot balloons from Isla del Coco, Costa Rica

The low-level circulation of the atmosphere over Isla del Coco has been studied and the presence of a northerly wind at low levels of the atmosphere in the eastern tropical Pacific, in addition to the deep Hadley circulation cell, has been confirmed. Using data from pilot balloons (May 1997 through January 1999, October 2007 through April 2008), the northern flow is between 1 and 5 km high, depending on the time of year, with a maximum speed located between 2 and 3 km above the surface. The generating mechanism of the surface return flow in the Hadley circulation cell has been formulated as a sea breeze, Ekman pumping of the boundary layer, and it could even be a response to the Rossby wave generated by warming in the Chocó region. These results agree with those obtained from the ERA5 reanalysis (January 1979 through December 2020), which show that the southern return cell varies in position and height during the course of the year, with a poorly organized circulation in March and strengthening from July to February. The incorporation of data of low, medium and high cloud cover from Year of the Tropical Convection (boreal summer 2009) evidenced the presence of high-level clouds in the ITCZ region and low-level clouds to the south of the ITCZ, latitudes where the south surface circulation cell is located.

Mapping Soil Organic Carbon Stock and Uncertainties in an Alpine Valley (Northern Italy) Using Machine Learning Models

In this study, we conducted a comprehensive analysis of the spatial distribution of soil organic carbon stock (SOC stock) and the associated uncertainties in two soil layers (0–10 cm and 0–30 cm; SOC stock 10 and SOC stock 30, respectively), in Valchiavenna, an alpine valley located in northern Italy (450 km2). We employed the digital soil mapping (DSM) approach within different machine learning models, including multivariate adaptive regression splines (MARS), random forest (RF), support vector regression (SVR), and elastic net (ENET). Our dataset comprised soil data from 110 profiles, with SOC stock calculations for all sampling points based on bulk density (BD), whether measured or estimated, considering the presence of rock fragments. As environmental covariates for our research, we utilized environmental variables, in particular, geomorphometric parameters derived from a digital elevation model (with a 20 m pixel resolution), land cover data, and climatic maps. To evaluate the effectiveness of our models, we evaluated their capacity to predict SOC stock 10 and SOC stock 30 using the coefficient of determination (R2). The results for the SOC stock 10 were as follows: MARS 0.39, ENET 0.41, RF 0.69, and SVR 0.50. For the SOC stock 30, the corresponding R2 values were: MARS 0.45, ENET 0.48, RF 0.65, and SVR 0.62. Additionally, we calculated the root-mean-squared error (RMSE), mean absolute error (MAE), the bias, and Lin’s concordance correlation coefficient (LCCC) for further assessment. To map the spatial distribution of SOC stock and address uncertainties in both soil layers, we chose the RF model, due to its better performance, as indicated by the highest R2 and the lowest RMSE and MAE. The resulting SOC stock maps using the RF model demonstrated an accuracy of RMSE = 1.35 kg m−2 for the SOC stock 10 and RMSE = 3.36 kg m−2 for the SOC stock 30. To further evaluate and illustrate the precision of our soil maps, we conducted an uncertainty assessment and mapping by analyzing the standard deviation (SD) from 50 iterations of the best-performing RF model. This analysis effectively highlighted the high accuracy achieved in our soil maps. The maps of uncertainty demonstrated that the RF model better predicts the SOC stock 10 compared to the SOC stock 30. Predicting the correct ranges of SOC stocks was identified as the main limitation of the methodology.

Employing Copernicus Land Service and Sentinel-2 Satellite Mission Data to Assess the Spatial Dynamics and Distribution of the Extreme Forest Fires of 2023 in Greece

In 2023, Greece faced its worst wildfire season, with nine major fires causing unprecedented environmental damage of 1470.31 km2. This article uses Copernicus Land Monitoring Service and Sentinel-2 data, employing advanced remote sensing and GIS techniques to analyze spatial dynamics, map burn severity, assess fire extent, and highlight pre-fire tree density and land cover. The study focuses on the catastrophic fire in the Evros region and the damage to the National Forest Park of Dadia–Lefkimmi–Soufli. It also analyzes significant fires in Rhodes, Attica, Thessaly, Evia, Corfu, and Magnesia, emphasizing the compounded challenges posed by terrain, climate, and human factors in those areas. Additionally, the climate data for each affected area were compared with the weather conditions prevailing at the time of the fires. Copernicus Land Cover and Tree Density data are integrated to aid future management, assessment, and restoration. The analysis of maps and fire statistics underscores a notable pattern: areas with higher pre-fire tree density experienced correspondingly higher burn severity. This research underscores the crucial role of such data in assessing wildfire impact. In addition, compared with Copernicus Emergency Management Service, the burned area maps validate the accuracy and reliability of the utilized satellite data. The total burned area was assessed with a high accuracy rate of 96.28%.

Estimating the effects of vegetation and increased albedo on the urban heat island effect with spatial causal inference

The urban heat island effect causes increased heat stress in urban areas. Cool roofs and urban greening have been promoted as mitigation strategies to reduce this effect. However, evaluating their efficacy remains a challenge, as potential temperature reductions depend on local characteristics. Existing methods to characterize their efficacy, such as computational fluid dynamics and urban canopy models, are computationally burdensome and require a high degree of expertise to employ. We propose a data-driven approach to overcome these hurdles, inspired by recent innovations in spatial causal inference. This approach allows for estimates of hypothetical interventions to reduce the urban heat island effect. We demonstrate this approach by modeling evening temperature in Durham, North Carolina, using readily retrieved air temperature, land cover, and satellite data. Hypothetical interventions such as lining streets with trees, cool roofs, and changing parking lots to green space are estimated to decrease evening temperatures by a maximum of 0.7–0.9 ^{circ } hbox {C}, with reduced effects on temperature as a function of distance from the intervention. Because of the ease of data access, this approach may be applied to other cities in the U.S. to help them come up with city-specific solutions for reducing urban heat stress.

Green Space Morphology and School Myopia in China

China has experienced both rapid urbanization and major increases in myopia prevalence. Previous studies suggest that green space exposure reduces the risk of myopia, but the association between myopia risk and specific geometry and distribution characteristics of green space has yet to be explored. These must be understood to craft effective interventions to reduce myopia. To evaluate the associations between myopia and specific green space morphology using novel quantitative data from high-resolution satellite imaging. This prospective cohort study included students grades 1 to 4 (aged 6 to 9 years) in Shenzhen, China. Baseline data were collected in 2016-2017, and students were followed up in 2018-2019. Data were analyzed from September 2020 to January 2022. Eight landscape metrics were calculated using land cover data from high-resolution Gaofen-2 satellite images to measure area, aggregation, and shape of green space. The 2-year cumulative change in myopia prevalence at each school and incidence of myopia at the student level after 2 years were calculated as main outcomes. The associations between landscape metrics and school myopia were assessed, controlling for geographical, demographic, and socioeconomic factors. Principal component analyses were performed to further assess the joint effect of landscape metrics at the school and individual level. A total of 138 735 students were assessed at baseline. Higher proportion, aggregation, and better connectivity of green space were correlated with slower increases in myopia prevalence. In the principal component regression, a 1-unit increase in the myopia-related green space morphology index (the first principal component) was negatively associated with a 1.7% (95% CI, -2.7 to -0.6) decrease in myopia prevalence change at the school level (P = .002). At the individual level, a 1-unit increase in myopia-related green space morphology index was associated with a 9.8% (95% CI, 4.1 to 15.1) reduction in the risk of incident myopia (P < .001), and the association remained after further adjustment for outdoor time, screen time, reading time, and parental myopia (adjusted odds ratio, 0.88; 95% CI, 0.80 to 0.97; P = .009). Structure of green space was associated with a decreased relative risk of myopia, which may provide guidance for construction and renovation of schools. Since risk estimates only indicate correlations rather than causation, further interventional studies are needed to assess the effect on school myopia of urban planning and environmental designs, especially size and aggregation metrics of green space, on school myopia.

Exploring the relation between urban landscape service values and different infrastructures through crowdsourced data

ABSTRACT This research focuses on exploring and mapping Ankara’s urban points of interest and their values on different urban infrastructure types using geolocated data from Flickr. All publicly available data dated between 2005 and 2021 from Flickr were obtained through application programming interface and the infrastructure types were reclassified as green, blue, grey, and yellow, using the land use and land cover data based on Corine 2018. Spatial patterns for cultural service flow of landscape features and/or landscapes on different urban infrastructure types were mapped using Kernel density estimation in QGIS 3.21. The hotspots were assessed in terms of the cultural services they offered based on the Millennium Ecosystem Assessment (MA) framework, site visits, and observations. The findings showed that the city core mostly provided cultural heritage, identity, and recreational values. Results for the rural and peri-urban areas indicated that natural landscapes and protected areas stood out for their provision of cultural services, particularly for their recreational values. Our research identified user-intensive geographical clusters and hotspots that could benefit from interventions and enhancement for a more sustainable urban environment.