30-m Resolution Research Articles

Identify and map coastal aquaculture ponds and their drainage and impoundment dynamics

Sustainable management of coastal aquaculture ponds could achieve win-win between food and economic benefits and ecological conservation including waterbird. In this study, 5,790 Harmonized Landsat and Sentinel-2 images from July 2021 to June 2022 and 498 Sentinel-1 images from July 2021, August 2021, and June 2022 as supplementary data were collected to calculate multiple water indices. Based on Otsu algorithm to distinguish between water and non-water region and Savitzky-Golay filtering to optimize time series, coastal aquaculture ponds were identified using the SNIC. Furthermore, their drainage and impoundment phases were determined using the Dynamic Time Warping-Kmeans++ method. Finally, a new 30-m resolution dataset at the national scale of China was generated with an overall accuracy greater than 90 % for both the pond map and the drainage and impoundment phases. Our observations revealed that the total area was 7,919.53 km2, with the largest pond area in Shandong Province. Among the coastal aquaculture ponds, 27.95 % were seasonal aquaculture ponds, 70.32 % were yearlong aquaculture ponds, and 1.49 % were abandoned aquaculture ponds. Drainage start dates, end dates, and durations were calculated based on abrupt changes in the water proportion time series. Drainage start dates were concentrated from September to December, while drainage end dates were from January to April. Drainage durations of coastal aquaculture ponds ranged from two weeks to six months, with Shanghai Municipality having the longest drainage durations and Taiwan Province having the shortest drainage durations. The findings could provide scientific support for modifying the drainage and impoundment phases of coastal aquaculture ponds to achieve the win–win goal of improving economic development and protecting waterbirds or improving offshore water quality.

Characterizing long-term tree species dynamics in Canada’s forested ecosystems using annual time series remote sensing data

Mapping tree species and changes in species distribution over time enables monitoring of successional dynamics and linking of realized species distributions to regional, climatic, and disturbance processes. Such knowledge is valuable for informing forest management activities regarding the role of climate change and various types of forest disturbances on tree species distributions and successional processes. In this study, we used time-series Landsat imagery to produce annual maps of dominant tree species (n = 37 tree species classes) from 1984 to 2022 at a 30-m spatial resolution for the 650 Mha of Canada’s forested ecosystems. The classification approach was based on spectral, geographic, climatic, and topographic descriptive metrics and was independently calibrated and validated with data from Canada’s National Forest Inventory. Preliminary results were informed by disturbance events and post-processed to ensure consistency of the annual species maps. Assessment of the resulting annual species maps using independent validation data resulted in an overall accuracy of 86.1 % ± 0.14 % (95 %-confidence interval). Over the study period, Canada’s treed area increased from 335 to 359 Mha. The area dominated by conifers increased in absolute terms but decreased in relative terms (from 86 % to 84.9 %), especially in western ecozones where wildfire is the main agent of disturbance. The area dominated by black spruce (Picea mariana), the most common tree species in Canada, remained stable, but its relative area decreased by 4.6 %. The area dominated by balsam fir (Abies balsamea, 1.5 %), trembling aspen (Populus tremuloides, 0.9 %), jack pine (0.5 %), and sugar maple (Acer saccharum, 0.4 %) increased. In burned areas, the prevalence of black spruce (74.7 %) and jack pine (Pinus banksiana, 14.6 %) was greater than their prevalence in Canada’s forested ecosystems overall (58.7 % and 3.8 %, respectively), while trembling aspen (2.4 % vs. 10 %) and subalpine fir (1.1 % vs. 4.6 %; Abies lasiocarpa) were underrepresented in burned areas. The area dominated by black spruce was underrepresented in harvested areas (46.8 % vs. 58.7 %) whereas balsam fir (6.9 % vs. 3 %), Engelmann spruce (5.5 % vs. 2 %; Picea engelmannii), red spruce (1.8 % vs. 0.4 %; Picea rubens), lodgepole pine (11.2 % vs. 5.6 %; Pinus contorta), Douglas-fir (2.7 % vs. 1.3 %; Pseudotsuga menziesii), and western hemlock (4.8 % vs. 2 %; Tsuga heterophylla) were found to be overrepresented. Both post-fire and post-harvest dynamics indicated a general trend of regeneration to the same pre-disturbance tree species, with post-harvest landscapes exhibiting a greater variety of different dominant tree species. These results provide valuable information for sustainable forest management and can inform conservation strategies by helping to better understand the short- and long-term effects of forest disturbances on species presence and distribution.

Health effects of greenspace morphology: Large, irregular-shaped, well-connected, and close-clustered greenspaces may reduce mortality risks, especially for neighborhoods with higher aging levels

The healthcare burden has intensified with urbanization and aging populations in many global cities. While the health effects of urban greenspaces have been well documented, little is known about the associations between greenspace morphological features and health, especially in a high-density city with significant aging populations. Drawing on land use data with 10-m resolution, we assessed seven greenspace morphological metrics in terms of size (the percentage of greenspace, the largest pixel index, the average greenspace area), fragmentation (the patch density), shape (the average weighted shape index), connectedness (the cohesion index), and proximity (the aggregation index). We further conducted an ecological study to examine their associations with all-cause and three cause-specific (cardiovascular disease, respiratory disease, and cancer) mortality. Results from the negative binomial regression models revealed protective effects of five greenspace morphology metrics, including the percentage of greenspace, the largest pixel index, the average weighted shape index, the cohesion index, and the aggregation index, on mortality. The shape index showed the greatest effects, with every 1 Standard Deviation (SD) increase in the shape index linked to a reduction of 22.1% (95% CI: 22.0%–31.0%) in all-cause mortality, 22.1% (12.2%–30.8%) in mortality from cardiovascular diseases, 25.0% (14.0%–34.6%) in mortality from respiratory diseases, and 22.0% (12.3%–30.6%) in mortality from cancers. Moreover, stratified analyses revealed that the health effects of the cohesion index and the aggregation index were significantly more pronounced in neighborhoods with higher aging levels. Our findings highlight the significance of greenspace morphology features, beyond greenspace quantity, in improving residents’ health, particularly for societies with high aging populations.

Leveraging Open-Source Geographic Databases to Enhance the Representation of Landscape Heterogeneity in Ecological Models.

Wildlife abundance and movement are strongly impacted by landscape heterogeneity, especially in cities which are among the world's most heterogeneous landscapes. Nonetheless, current global land cover maps, which are used as a basis for large-scale spatial ecological modeling, represent urban areas as a single, homogeneous, class. This often requires urban ecologists to rely on geographic resources from local governments, which are not comparable between cities and are not available in underserved countries, limiting the spatial scale at which urban conservation issues can be tackled. The recent expansion of community-based geographic databases, for example, OpenStreetMap (OSM), represents an opportunity for ecologists to generate large-scale maps geared toward their specific research needs. However, computational differences in language and format, and the high diversity of information within, limit the access to these data. We provide a framework, using R, to extract geographic features from the OSM database, classify, and integrate them into global land cover maps. The framework includes an exhaustive list of OSM features describing urban and peri-urban landscapes and is validated by quantifying the completeness of the OSM features characterized, and the accuracy of its final output in 34 cities in North America. We portray its application as the basis for generating landscape variables for ecological analysis by using the OSM-enhanced map to generate an urbanization index, and subsequently analyze the spatial occupancy of six mammals throughout Chicago, Illinois, USA. The OSM features characterized had high completeness values for impervious land cover classes (50%-100%). The final output, the OSM-enhance map, provided an 89% accurate representation of the landscape at 30m resolution. The OSM-derived urbanization index outperformed other global spatial data layers in the spatial occupancy analysis and concurred with previously seen local response trends, whereby lagomorphs and squirrels responded positively to urbanization, while skunks, raccoons, opossums, and deer responded negatively. This study provides a roadmap for ecologists to leverage the fine resolution of open-source geographic databases and apply it to spatial modeling by generating research-specific landscape variables. As our occupancy results show, using context-specific maps can improve modeling outputs and reduce uncertainty, especially when trying to understand anthropogenic impacts on wildlife populations.

Urban forest cover and ecosystem service response to fire varies across California communities

Urban fires that result from wildfires are an emerging, extreme event affecting communities and urban forests globally. However, much of the fire effects on urban ecosystems literature is primarily focused on Wildland-Urban Interface (WUI) areas, correlates of building loss, risk mitigation, and wildland vegetation and fuels. Three recent urban fires in California USA provided an opportunity to explore these effects on urban forests: the 2017 Tubbs (Santa Rosa) and Thomas (Ventura) and the 2018 Camp fire that burned Paradise. Accordingly, we analyzed pre- and post-fire neighborhood level urban tree cover (nUTC) change over 5 years using Sentinel and LiDAR data (10m resolution). Then, we explored the effects of fire severity on changes in several regulating ecosystem services (e.g., carbon, air pollution, stormwater). Findings from the Rapid Assessment of Vegetation Condition after Wildfire severity processes and other geospatial datasets show that fire effects were patchy with fire severity within neighborhoods ranging from unburned to extreme. We found that ~5 years after the fires, and relative to adjacent non-fire affected neighborhoods, Ventura and Santa Rosa’s nUTC is recovering to pre-fire levels while Paradise’s nUTC is being consistently lost over time. Ventura and Santa Rosa had 20-25% of their fire affected area outside established WUI boundaries. However, local-scale urban tree cover and ecosystem service supply are lagged, and recovery time depends on the surrounding biome and socio-ecological context. In inland, higher elevation communities – like Paradise – the recovery of nUTC and the associated ecosystem services might materialize over a much longer timeframe. Our study provides a roadmap to assess the response of urban wildfire-affected tree cover and ecosystem services across space and time. It is also one of the first assessments of fire effects on urban ecosystems and communities outside of WUI boundaries that are now experiencing increased threat from wildfires globally.

Mapping South Africa's canopy-forming kelp forests using low-cost, high-resolution Sentinel-2 imagery

Globally, kelp forests play a vital role in providing ecological, social and economic services. They contribute to the livelihoods and resilience of many coastal communities. To date, a gap exists in contemporary, comprehensive and consistent maps of South African kelp forests, since they were last mapped partially in 2006 by covering 900 km of South Africa's coastline. This study addresses this gap by producing a comprehensive remotely sensed high-resolution 2016 map of canopy forming coastal kelp forests. Using the 10m resolution Sentinel-2 A and B spectral bands 4 (RED) and 8 (NIR), an expert derived Normalized Difference Vegetation Index (NDVI) threshold was used to detect surface protruding kelp forests along 1300 km coastline. The results were validated with expert verification and using Google Earth, data from historic maps, remote sensing and biomass estimation of South African kelp resources. Our study demonstrates the aptitude of Sentinel-2 for detecting canopy-forming kelps at low cost along South Africa's west and south coast. We report the encountered drawbacks in mapping an expansive coastline with varied sea conditions including wave activity, tides and turbidity. Comprehensive ground validation, long-term monitoring sites and the use of time series datasets remain a gap towards improving the calibration of remote sensing methods and long-term monitoring of South African kelp forests. Our results provide a foundation for continuous monitoring of South African kelp forests as well as exploring other unmapped kelp/similar habitats to inform research and environmental management.

Settlement Suitability Analysis: The Case of Bursa City

Abstract: Nowadays, the existing built-up area texture in cities has started to shift towards areas that are not suitable for settlement. In this sense, settlement suitability analyses play a critical role in the realisation of sustainable development goals. Detailed analyses are required to formulate an effective settlement strategy in regions with large and diverse natural features such as Bursa. In this study, in order to determine the areas suitable for settlement in the study area, which covers the borders of Osmangazi, Nilüfer and Yıldırım districts, which are the central districts of Bursa city, a settlement suitability analysis was carried out with the Analytical Hierarchy Process (AHP) method using 9 parameters consisting of slope, elevation, lithology, land use, precipitation, temperature, distance to stream, distance to fault and erosion. The main underlying data used in the study are 10m resolution Digital Elevation Model (DEM), lithology, land use, erosion, precipitation, and temperature (WorldClim) data of Bursa city. As a result of the analysis, it was determined that 9% of the city is very suitable, 44% is less suitable and 47% is not suitable. The area where the city was established is in very suitable and moderate suitable class. It is observed that the settlements established in the study area, especially in the areas close to the fault line and the main river branch, are in the moderate suitability class. In addition, it has been determined that the erosion risk is in the low class, and the settlements far from the main river branch and fault line are in the very favourable class. While it is observed that Bursa city is located at appropriate values in terms of topography, it is seen that especially the southern part of the city is not sufficiently suitable in terms of distance to fault lines. The results obtained aim to contribute to sustainable settlement planning and to provide a guide for the methods and processes to be used in settlement suitability analysis.

Mapping 10-m harvested area in the major winter wheat-producing regions of China from 2018 to 2022.

Winter wheat constitutes approximately 20% of China's total cereal production. However, calculations of total production based on multiplying the planted area by the yield have tended to produce overestimates. In this study, we generated sample points from existing winter wheat maps and obtained samples for different years using a temporal migration method. Random forest classifiers were then constructed using optimized features extracted from spectral and phenological characteristics and elevation information. Maps of the harvested and planted areas of winter wheat in Chinese eight provinces from 2018 to 2022 were then produced. The resulting maps of the harvested areas achieved an overall accuracy of 95.06% verified by the sample points, and the correlation coefficient between the CROPGRIDS dataset is about 0.77. The harvested area was found to be about 13% smaller than the planted area, which can primarily be attributed to meteorological hazards. This study represents the first attempt to map the winter wheat harvested area at 10-m resolution in China, and it should improve the accuracy of yield estimation.

Outdoor light at night, air pollution and risk of incident type 2 diabetes

BackgroundAir pollution and outdoor light at night (LAN) have been reported to be related to type 2 diabetes (T2D). However, their interaction with risk of T2D remains uncertain. Therefore, our study aimed to explore the relationship between outdoor LAN, air pollution and incident T2D. MethodsOur study included a cohort of 24,147 subjects recruited from 2015 to 2018 in Ningbo, China. Land use regression models were used to evaluate particulate matter with a diameter ≤2.5 μm (PM2.5), ≤10 μm (PM10) and nitrogen dioxide (NO2). Satellite images data with a spatial resolution of 500m was used to estimate outdoor LAN levels. T2D new cases were identified by medical records based on health information system. Cox proportional hazards models were used to estimate Hazard ratios (HRs) and 95% confidence intervals (CIs). Moreover, we investigated the multiplicative and additive interactions between air pollution and outdoor LAN. ResultsDuring 108,908 person-years of follow-up period, 1016 T2D incident cases were identified. The HRs (95% CIs) were 1.22 (1.15, 1.30) for outdoor LAN, 1.20 (1.00, 1.45) for PM2.5, 1.23 (1.11, 1.35) for PM10 and 1.19 (1.04, 1.37) for NO2 in every interquartile range increase, respectively. Furthermore, significant interactions were observed between outdoor LAN and NO2. ConclusionsOur findings indicated that air pollution and outdoor LAN were positively associated with T2D. Moreover, we observed an interaction between outdoor LAN and NO2 suggesting that stronger associations for outdoor LAN and T2D in areas with higher levels of NO2, and for NO2 and T2D in areas with higher levels of outdoor LAN.

Using of high spatial resolution images to evaluate the thematic accuracy of land use and occupation maps with the Kappa index

The objective from this article evaluates the thematic quality of automatic mappings with supervised classification for land use and land cover, using high spatial resolution satellite images as "ground truth". This consider the advancement of remote sensing technologies has enabled the acquisition of satellite images with various spatial resolutions, which are essential for thematic mapping and automatic classifiers in the context of land use and land cover mapping. In fact, the wide availability of high, medium, and low spatial resolution satellite images has significantly optimized the time and resources required by using more accurate classifiers during data processing. The image used for verification of this paper was GeoEye, with a spatial resolution of 0.5m, dated October 2023. The images submitted to the automatic classifier were Sentinel-2A with a spatial resolution of 10m and Planet with a spatial resolution of 5m, both from the same satellite pass period (October 2023) over the study area, aiming to avoid seasonal and phenological variations in vegetation, as well as changes in the environment due to anthropogenic intervention. The classification method adopted was Maximum Likelihood (MAXVER). The classification accuracy was rigorously evaluated to ensure the reliability of the results using the Kappa index, assessing the agreement between the observed and expected classifications. Based on the methods presented, the set of mapped classes in this study showed good accuracy for the Planet image and very good accuracy for the Sentinel image.

The effect of urban form parameters on annual and diurnal cycles of land surface temperature with 30-meter hourly resolution

Understanding dynamic changes in urban land surface temperature (U-LST) is vital for improving urban thermal environment. Previous studies noted a close association between two/three-dimensional (2D/3D) urban form parameters (UFPs) and U-LST, but divergent conclusions emerged due to study scale and data spatiotemporal resolution. This study investigated the effect of 2D/3D UFPs on multi-temporal (hourly, diurnal, and annual) U-LSTs with 30-m spatial resolution, which is the first exploration of such dynamics at this fine scale. In addition, the influence of local climate zones (LCZs) was considered. Results showed that 2D/3D vegetation UFPs have the greatest summer cooling effect at 21:00, while 3D building UFP sky view factor (SVF) impacts minimum nighttime temperature more (5:00). The independently total explained variances (R2) of 2D UFPs (0.2–0.7) on seasonal average U-LSTs were higher than that of 3D UFPs (0.1–0.4) in most LCZs. LCZ 3 and LCZ 2 had the highest seasonal and daytime U-LST, respectively. Interestingly, SVF replaced normalized difference vegetation index as the dominant factor in LCZs 4–6 and LCZs A-C after summer. Findings of this study are useful for guiding urban planning, formulating urban heat island mitigation policies, and promoting sustainable development goals (SDGs 11, 13, 15) of cities and communities.

A dataset of NDVI for the vegetation growing season in Central Asia with a resolution of 250 m (2001–2020) and 30 m (2020)

Central Asia is the largest arid and semi-arid region in the Northern Hemisphere, and its ecological environment is extremely fragile and susceptible to the effects of global climate change. Maintaining the stability of the region's ecosystem is crucial to global economic and social development due to its unique geographic location. Vegetation serves as a significant indicator of the ecological environment, and the spatial and temporal distribution pattern, as well as the change trend of vegetation, are important indicators in evaluating the ecological status of the region. The Normalized Difference Vegetation Index (NDVI) is a commonly used remote sensing index to study vegetation, which characterizes the spatio-temporal changes of vegetation. This dataset utilized MODIS13Q1 to generate long-term time series growing season mean NDVI data with a spatial resolution of 250m in Central Asia from 2001 to 2020. To obtain the 2020 growing season mean NDVI data with a higher spatial resolution of 30m, the Cubist algorithm based on rule segmentation regression was utilized to fuse the Landsat data and MODIS data. Meanwhile, this dataset carried out quality control of data products from three aspects: data source quality control, model training optimization, and model independent verification to ensure the accuracy and reliability of data. The generation of this dataset provides powerful data support for the analysis of vegetation dynamic change and spatial pattern in Central Asia.

An automatic method for extracting sand dunes based on slope cost distance from digital elevation models

Sand dunes significantly impact climate, water resources, and human infrastructure, reflecting sand sources and wind conditions. However, their complex and heterogeneous terrain leads to fragmented and poorly continuous dune extraction results. Focusing on the Badain Jaran Desert, this study developed a sand dune extraction method using slope cost distance analysis with FABDEM data. First, positive and negative terrains were segmented using 30 m resolution digital elevation data. Next, slope patches were classified based on specific criteria: slope less than 1.5°, patch area greater than 0.01 km2, and patches entirely within negative terrains. The filtered patches served as source data, and slope raster data were used to calculate slope cost distance. Sand dunes and interdune areas were delineated based on a predefined slope cost threshold. Post-processing, such as smoothing and vector conversion, yielded the final extraction results. The method effectively extracted compound transverse megadunes and pyramid megadunes, addressing misclassification issues and accurately defining interdune areas. It demonstrated faster processing speeds compared to traditional methods, with high precision (89.38 % & 90.37 %), accuracy (96.5 % & 92.89 %), and recall (94.77 % & 98.63 %). The results are more complete and less fragmented, aiding in the extraction of micro-scale dune features and revealing macro-scale spatial patterns, which are beneficial for studying dune formation, development, and wind dynamics.

Assessment of Landscape Change and its Relationships with Surface Temperature and Geo-spatial Indices during 1992 - 2022 at Haldia Industrial Region, West Bengal, India

Unplanned local development and alterations in Land Use and Land Cover (LULC) are an early alert for towns and cities around the world. The population pressure has resulted in the conversion of natural land into impermeable areas, which has altered the surface energy budget and created microclimatic differences locally. The main objective of the present study is to estimate LULC changes and its relation to increases Land Surface Temperature (LST) in the Haldia Industrial Region (HIR) of West Bengal using multi-temporal Landsat 5 Thematic Mapper (TM) of 1992 and Landsat 8 Operational Land Imager (OLI) of 2022 with 30-m spatial resolution images were obtained from the USGS website. The supervised classification method was applied by using Maximum Likelihood classification (MLC) to classify the satellite images into various LULC classes such as settlement, waterbody, vegetation, agriculture land, and fallow land, respectively. The result suggested an overall increase of settlement area from 13.49 km2 in 1992 to 44.54 km2 in 2022. On the other hand, the agriculture land decreased from 214.26 km2 to 152.34 km2 due to fast urbanization, decline green lands, and expansion of barren lands. The outcome result from LST was exhibited that the LST value has increased 3.77°C during 1992 to 2022 in this study area. The Normalized Difference Vegetation Index (NDVI) was showed a strong negative relationship with LST with a determination coefficient (R2) values found as 0.491 (1992) and 0.356 (2022). The positive determination coefficient between LST with Normalized Difference Water Index (NDWI) was found as 0.0002 (1992), and 0.0417 (2022). R2 values were estimated as 0.174 (1992), and 0.0347 (2022) between LST with Normalized Difference Built-up Index (NDBI) that indicated a positive determination coefficient. The findings enhanced understanding of the relationship between urban LST and LULC in developing an inclusive climate resilience policy and making the HIR more sustainable to the effects of climate change.

Mapping of temperate upland habitats using high-resolution satellite imagery and machine learning

Upland habitats provide vital ecological services, yet they are highly threatened by natural and anthropogenic stressors. Monitoring these vulnerable habitats is fundamental for conservation and involves determining information about their spatial locations and conditions. Remote sensing has evolved as a promising tool to map the distribution of upland habitats in space and time. However, the resolutions of most freely available satellite images (e.g., 10-m resolution for Sentinel-2) may not be sufficient for mapping relatively small features, especially in the heterogeneous landscape—in terms of habitat composition—of uplands. Moreover, the use of traditional remote sensing methods, imposing discrete boundaries between habitats, may not accurately represent upland habitats as they often occur in mosaics and merge with each other. In this context, we used high-resolution (2 m) Pleiades satellite imagery and Random Forest (RF) machine learning to map habitats at two Irish upland sites. Specifically, we investigated the impact of varying spatial resolutions on classification accuracy and proposed a complementary approach to traditional methods for mapping complex upland habitats. Results showed that the accuracy generally improved with finer spatial resolution data, with the highest accuracy values (80.34% and 79.64%) achieved for both sites using the 2-m resolution datasets. The probability maps derived from the RF-based fuzzy classification technique can represent complex mosaics and gradual transitions occurring in upland habitats. The presented approach can potentially enhance our understanding of the spatiotemporal dynamics of habitats over large areas.

High-resolution greenspace dynamic data cube from Sentinel-2 satellites over 1028 global major cities

Greenspace, offering multifaceted ecological and socioeconomic benefits to the nature system and human society, is integral to the 11th Sustainable Development Goal pertaining to cities and communities. Spatially and temporally explicit information on greenspace is a premise to gauge the balance between its supply and demand. However, existing efforts on urban greenspace mapping primarily focus on specific time points or baseline years without well considering seasonal fluctuations, which obscures our knowledge of greenspace’s spatiotemporal dynamics in urban settings. Here, we combined spectral unmixing approach, time-series phenology modeling, and Sentinel-2 satellite images with a 10-m resolution and nearly 5-day revisit cycle to generate a four-year (2019–2022) 10-m and 10-day resolution greenspace dynamic data cube over 1028 global major cities (with an urbanized area >100 km2). This data cube can effectively capture greenspace seasonal dynamics across greenspace types, cities, and climate zones. It also can reflect the spatiotemporal dynamics of the cooling effect of greenspace with Landsat land surface temperature data. The developed data cube provides informative data support to investigate the spatiotemporal interactions between greenspace and human society.

Long-term annual mapping and spatial-temporal dynamic analysis of winter wheat in Shandong Province based on spatial-temporal data fusion (2000-2022).

Winter wheat, as one of the world's key staple crops, plays a crucial role in ensuring food security and shaping international food trade policies. However, there has been a relative scarcity of high-resolution, long time-series winter wheat maps over the past few decades. This study utilized Landsat and Sentinel-2 data to produce maps depicting winter wheat distribution in Google Earth Engine (GEE). We further analyzed the comprehensive spatial-temporal dynamics of winter wheat cultivation in Shandong Province, China. The gap filling and Savitzky-Golay filter method (GF-SG) was applied to address temporal discontinuities in the Landsat NDVI (Normalized Difference Vegetation Index) time series. Six features based on phenological characteristics were used to distinguish winter wheat from other land cover types. The resulting maps spanned from 2000 to 2022, featuring a 30-m resolution from 2000 to 2017 and an improved 10-m resolution from 2018 to 2022. The overall accuracy of these maps ranged from 80.5 to 93.3%, with Kappa coefficients ranging from 71.3 to 909% and F1 scores from 84.2 to 96.9%. Over the analyzed period, the area dedicated to winter wheat cultivation experienced a decline from 2000 to 2011. However, a notable shift occurred with an increase in winter wheat acreage observed from 2014 to 2017 and a subsequent rise from 2018 to 2022. This research highlights the viability of using satellite observation data for the long-term mapping and monitoring of winter wheat. The proposed methodology has long-term implications for extending this mapping and monitoring approach to other similar areas.

Outdoor artificial light-at-night and cardiometabolic disease risk: an urban perspective from the Catalan GCAT cohort study.

We investigated the association between outdoor artificial light-at-night (ALAN) exposure and cardiometabolic risk in the GCAT study. We included 9,752 participants from Barcelona (59% women). We used satellite images (30m resolution) and estimated photopic illuminance and the circadian-regulation relevant melanopic illuminance (melanopic EDI). We explored the association between ALAN exposure and prevalent obesity, hypertension, and diabetes with logistic regressions. We assessed the relationship with incident cardiometabolic diseases ascertained through electronic health records (mean follow-up 6.5 years) with Cox proportional hazards regressions. We observed an association between photopic illuminance and melanopic EDI and prevalent hypertension, Odds ratio (OR) = 1.09 (95% CI, 1.01-1.16) and 1.08 (1.01-1.14) per interquartile range increase (0.59 and 0.16 lux, respectively). Both ALAN indicators were linked to incident obesity (hazard ratio [HR] = 1.29, 1.11-1.48 and 1.19, 1.05-1.34) and haemorrhagic stroke (HR = 1.73, 1.00-3.02 and 1.51, 0.99-2.29). Photopic illuminance was associated with incident hypercholesterolemia in all participants (HR = 1.17, 1.05-1.31) and with angina pectoris only in women (HR = 1.55, 1.03-2.33). Further research in this area and increased awareness on the health impacts of light pollution are needed. Results should be interpreted carefully since satellite-based ALAN data do not estimate total individual exposure.

Estimation of 100 m root zone soil moisture by downscaling 1 km soil water index with machine learning and multiple geodata

Root zone soil moisture (RZSM) is crucial for agricultural water management and land surface processes. The 1 km soil water index (SWI) dataset from Copernicus Global Land services, with eight fixed characteristic time lengths (T), requires root zone depth optimization (Topt) and is limited in use due to its low spatial resolution. To estimate RZSM at 100-m resolution, we integrate the depth specificity of SWI and employed random forest (RF) downscaling. Topographic synthetic aperture radar (SAR) and optical datasets were utilized to develop three RF models (RF1: SAR, RF2: optical, RF3: SAR + optical). At the DEMMIN experimental site in northeastern Germany, Topt (in days) varies from 20 to 60 for depths of 10 to 30 cm, increasing to 100 for 40–60 cm. RF3 outperformed other models with 1 km test data. Following residual correction, all high-resolution predictions exhibited strong spatial accuracy (R ≥ 0.94). Both products (1 km and 100 m) agreed well with observed RZSM during summer but overestimated in winter. Mean R between observed RZSM and 1 km (100 m; RF1, RF2, and RF3) SWI ranges from 0.74 (0.67, 0.76, and 0.68) to 0.90 (0.88, 0.81, and 0.82), with the lowest and highest R achieved at 10 cm and 30 cm depths, respectively. The average RMSE using 1 km (100 m; RF1, RF2, and RF3) SWI increased from 2.20 Vol.% (2.28, 2.28, and 2.35) at 30 cm to 3.40 Vol.% (3.50, 3.70, and 3.60) at 60 cm. These negligible accuracy differences underpin the potential of the proposed method to estimate RZSM for precise local applications, e.g., irrigation management.

Mapping pedestrian network level outdoor heat hazard distributions in Philadelphia

With the rise of global temperature, many cities are suffering from more and more frequent extreme heat in hot summers. Quantitative information on the spatial distributions of urban heat has become more and more important for extreme heat mitigation and adaptation in cities. This study first investigated the fine-level heat hazard distributions at the sidewalk and building block level from the pedestrian perspective in Philadelphia, Pennsylvania. The urban microclimate modeling based on a high-resolution urban geometrical model was used to generate the 1m resolution outdoor heat hazard map in the study area. The sidewalk map was overlaid on the generated high-resolution heat hazard map to estimate the sidewalk level heat hazard. Based on the sidewalk level heat hazard map, this study further calculated the heat hazard level in the 400m walkshed along sidewalks for each building block. The building level hazard data were then aggregated at the census tract level to compare with the socioeconomic and racial/ethnic variables. The result shows that neighborhoods with higher proportion of African Americans have a higher heat hazard level in Philadelphia. This study would provide new insights for developing more thermally comfortable and pedestrian-friendly neighborhoods in the context of climate change.