30-m Resolution Research Articles

Active faults along Indonesia’s first high-speed rail line

Abstract The cities of Jakarta and Bandung, two vital hubs in the western part of Java, are now connected by a high-speed rail line. However, potential earthquake hazards arise as the line intersects two active faults in West Java. To address this concern, we conducted a detailed geomorphological analysis using high-resolution DEM data (8-meter resolution) combine with 30-m resolution data to locate fault lines along the rail infrastructure. In the north, the line intersects the West Java back-arc thrust, while in the south, it crosses the junction of the Lembang Fault and Cimandiri Fault in Padalarang. However, young deposits and vegetation still could obscure fault traces in specific areas observed from high-resolution DEM data. To overcome this challenge, we employed UAV drone mapping to capture finer geomorphology details. This information then combined with paleoseismology trenching to uncover the active structural features. The aim is to enhance the railway’s resilience; and build monitoring and early warning systems for the high-speed train operation system to seismic events.

National-scale 10-m maps of cropland use intensity in China during 2018–2023

The amount of actively cultivated land in China is increasingly threatened by rapid urbanization and rural population aging. Quantifying the extent and changes of active cropland and cropping intensity is crucial to global food security. However, national-scale datasets for smallholder agriculture are limited in spatiotemporal continuity, resolution, and precision. In this paper, we present updated annual Cropland Use Intensity maps in China (China-CUI10m) with descriptions of the extent of fallow/abandoned, actively cropped fields and cropping intensity at a 10-m resolution in recent six years (2018–2023). The dataset is produced by robust algorithms with no requirements for regional adjustments or intensive training samples, which take full advantage of the Sentinel-1 (S1) SAR and Sentinel-2 (S2) MSI time series. The China-CUI10m maps have achieved high accuracy when compared to ground truth data (Overall accuracy = 90.88%) and statistical data (R2 > 0.94). This paper provides the recent trends in cropland abandonment and agricultural intensification in China, which contributes to facilitating geographic-targeted cropland use control policies towards sustainable intensification of smallholder agricultural systems in developing countries.

Change analyses and prediction of land use and land cover changes in Bernam River Basin, Malaysia

Land use and land cover (LULC) change is a dynamic process which is significantly influenced by anthropogenic activities. Analysing historical LULC trends and predicting future dynamics is critical to provide insights for decision-makers and planners aiming for sustainable land management and development. This study focuses on the Bernam River Basin (BRB). It employs an integrated approach that combines the Multi-Layer Perceptron (MLP), the Cellular Automata (CA)-Markov algorithm, remote sensing, and Geographical Information System (GIS) techniques. Using multi-temporal 10m resolution Sentinel-2 Landsat imagery from 2010, 2020, and 2022, the study classified LULC into seven categories: water, forest, wetlands, agriculture, urban, barren, and rangeland areas. Change analysis from 2010 to 2020 was conducted, with 2022 validating predicted LULC transitions. The MLP model, trained on land change driver variables, facilitated the generation of transition potentials for simulating future LULC changes. A spatially explicit CA-Markov model implemented LULC change projections for 2022, 2025, 2050, and 2075, based on the transition potentials. The analysis reveals an annual increase of 0.24% in water, 0.61% in forest, and 2.11% in urban areas, while wetlands (2.69%), agriculture (2.47%), barren (3.51%), and rangeland (4.58%) experienced declines. The CA-Markov approach accurately predicted LULC transitions for 2022, validated through an error matrix with an overall accuracy of 91.56% based on 450 sampling points. Predictions for 2025–2075 indicate rising trends in water (1.76%), wetlands (29.18%), agriculture (60.08%), urban (96.53%), barren (0.59%), and rangeland areas (3.57%). Forests are expected to decrease by 12% (261.52 km2). The study identified agriculture and urban expansion as the primary drivers of LULC changes in the river basin. These findings provide critical information for regional authorities to formulate evidence-based policies and management strategies, ensuring the environmental sustainability of BRB. Furthermore, these predicted LULC patterns can be integrated into complementary models, such as the Soil and Water Assessment Tool, to assess the impacts of LULC changes on water resources.

Understanding Japan's Land-use Dynamics between 1987 and 2050 using Land Accounting and Scenario Analysis

Amidst global concerns about land use change and its far-reaching impact on biodiversity and human well-being, there is a growing need to understand how land use stock and flow changes over time through land use accounting. While existing studies on land accounting have focused on historical land changes, little attention has been paid to future transitions. This study assessed historical patterns and projected future shifts in land use dynamics from 1987 to 2050 across Japan by combining high-resolution land use and land cover datasets, land change simulations, and land accounting. In the analyses, particular attention was paid to the historical and future trends of farmland abandonment by leveraging data at 100-m resolution built on national vegetation surveys. High-resolution analysis of farmland abandonment issue with national scale in Japan is a novelty. From 1987 to 1998, the land stock analysis results showed a pronounced marked increase in residential land (10.4%) and grassland (16.9%); the flow analysis results showed that urban residential sprawl expansion was mainly formed by secondary (32.6%) and plantation (21.1%) forest areas, coinciding with increasing population and economic growth. Projections from 2010 to 2050 indicate a marked increase in abandoned farmland (67.2% per decade), a trend influenced by rapid population decline and presumably agricultural policies, especially significant in regions such as Hokkaido and Kyushu. The findings of this study are crucial for shaping policy and decision-making, underlining the need for sustainable land management strategies that effectively balance urban growth, agricultural productivity, and environmental preservation in Japan.

The effectiveness of natural reserves from the perspective of habitat quality in the southern section of the Hengduan Mountains, Southwestern China

Natural reserves (NRs) play key roles in habitat integrity conservation and biodiversity loss mitigation, and the assessment of the conservation effectiveness of NRs is needed to better manage them. Habitat quality (HQ) comprehensively reflects habitat integrity and biodiversity, but the conservation effectiveness of NRs from the perspective of HQ has rarely been determined at high spatial resolution. Taking the southern section of the Hengduan Mountains (SSHM) in Southwest China as an example, combining an InVEST-HQ model and spatiotemporal change detection methods, the effectiveness of NRs from the perspective of HQ at 30-m spatial resolution was assessed in this study. The effectiveness disparities of NRs across different properties (i.e., management level, conservation target, size, and establishment age) was analyzed and the human pressures on NRs was investigated. The results showed that the HQ of the NRs is good in the SSHM, with the area ratio of the Higher and Highest HQ ≥ 93%. Most of the NR area (94.11%) was effective at improving or maintaining a good HQ. With regard to NR properties, county NRs, NRs designated to conserving wild animals, middle NRs, and younger NRs were more effective, corresponding to management level, conservation target, size, and establishment age, respectively. The human footprint for an effective area is significantly lower than that for an ineffective area, consistent with higher HQ in the effective area and lower HQ in the ineffective area. These findings support the management and zoning of NRs in the SSHM to ensure their effectiveness.

Added utility of temperature zone information in remote sensing-based large scale crop mapping

Accurate crop cover maps are beneficial for various aspects like water resources management, crop yield prediction, regulation insurance policies, and investigation of the effects of climate change. When making large-scale crop classification, regional harvest time and phenological growth differences occur due to varying temperatures along the study area, and using regional temperature differences while performing crop cover classification may increase the map accuracy. Therefore, in this study, we investigated for the first time the contribution of temperature information over large areas to the classification of agricultural products. Agricultural crop mapping is performed over Türkiye using Sentinel-2 Level-2A images with 10-m spatial resolution acquired between March 15, 2019, and October 15, 2019. In addition to spectral bands, Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) are used as classification features. Twenty years of ERA5-Land 2-m temperature data is averaged to divide the study area into three temperature zones Low (LTZ), Medium (MTZ), and High-Temperature Zone (HTZ). Before the classification, feature selection using random forest importance is performed to select the most successful features. After that, a random forest classifier is created for each temperature zone. LTZ reached 89% overall accuracy (OA) with a 0.88 Kappa. MTZ reached 91% OA with 0.92 Kappa, and HTZ reached 94% OA with 0.94 Kappa, giving the best accuracy among the classifiers. Finally, test sets of all temperature zones are combined, and an OA of 92% with a Kappa of 0.93 is achieved with this combined test set. To test the advantage of temperature zoning, classification is also performed without the temperature zones, and it is observed that temperature zoning increases the OA and Kappa by 1%. A land cover classification map is then created using temperature zone classifiers with 34 crop classes and six non-agricultural classes.

Spatiotemporal evolution and driving mechanisms of desertification on the Mongolian Plateau

Desertification poses a severe ecological and environmental challenge in the Mongolian Plateau (MP). It is difficult to quantify desertification distribution using unified indicators in the entire MP, because of its complex physical geographic conditions and various climatic zones covered. To accurately address this challenge, the spatial distribution of desertification at a 30-m resolution from 1990 to 2020 were mapped in this study. The desertification potential occurrence zone was identified by using a moisture index on the MP firstly. The feature space model and five machine learning models were constructed to make the map based on Google Earth Engine and Landsat data. The spatiotemporal distribution of desertification were further analyzed, and the dominant drivers of desertification distribution and evolution were identified using Geodetector model. The results indicate that the potential occurrence area of desertification accounted for 83.88 % of the total land area. The gradient boosted tree model for desertification assessment has the best performance with the highest overall accuracy of 88.18 %. The year 2010 marked a pivotal transition from land degradation to land restoration in the MP. Between 2010 and 2020, desertified land continued to deteriorate extensively in the southern Mongolia, while Inner Mongolia, China, essentially entered a full recovery phase. Precipitation and land use emerged as the primary drivers of the spatial distribution of desertification on the Mongolian Plateau and Mongolia, with potential evapotranspiration and precipitation influencing the distribution of desertification in Inner Mongolia, China. Land use change was the primary driver of desertification evolution on the MP and Mongolia. This study constructs an indicator system and methodology suitable for desertification monitoring on the MP, addresses the lack of refined desertification data over a long time series, and provides scientific reference for decision-making support in combating desertification in this region, and other large arid and semi-arid areas in the world.

Mapping the dynamics of intensive forage acreage during 2008–2022 in Google Earth Engine using time series Landsat images and a phenology-based algorithm

The increasing demand for livestock feed in China has led to a remarkable increase in forage acreage, particularly in forage fields under intensive agricultural management. However, there are no maps currently available to demonstrate and document the spatial and temporal patterns of the continuous expansion of intensive forage. Here we proposed a pixel- and phenology-based approach to map intensive forage by utilizing time series Landsat images and the Google Earth Engine platform. Given the challenge of accurately identifying intensive forage fields in diverse regions using fixed multi-temporal Landsat data, along with the distinct phenological characteristics of intensive forage fields that undergo multiple annual harvests, we proposed an innovative data mining approach based on dynamic monthly composite images (DMCI). This approach aims to enhance the algorithm’s efficiency and precision in identifying intensive forage. As a pilot study, we analyzed all available Landsat images (1732 scenes) from 2008 to 2022 in a county located in the farming-pastoral ecotone of northern China and tracked the historical dynamics of intensive forage expansion over five epochs at three-year intervals. The accuracy assessment of the intensive forage maps for the five epochs using validation sample points showed that the overall accuracy and Mathews correlation coefficient ranged from 97.1 % to 98.9 % and 0.93 to 0.98 respectively. The intensive forage acreage in the study area exhibited a dramatic expansion from 2008 to 2022, particularly after the 2012s. This study demonstrates the potential of our DMCI- and phenology-based algorithm and time series Landsat images for tracking the dynamics of intensive forage acreage at 30-m resolution in temperate single-cropping regions.

Assessing the accuracy of remote sensing data products: A multi-granular spatial sampling method

Over the past two decades, remote sensing data have demonstrated significant potential across various fields. Accuracy assessment, as a crucial component, plays a vital role in ensuring the effective application of remote sensing data products. To assess the accuracy of remote sensing data products, this paper proposed a multi-granular spatial sampling method (MG-SSM). In the proposed MG-SSM, the assessed remote sensing data products were stratified into different layers based on spatial heterogeneity, quantified by calculating the aggregation index. Subsequently, multi-granular sampling units were defined for each layer based on spatial correlation, quantified using Moran’s I and Z-score. Finally, the accuracy of remote sensing data products was assessed by comparing it with the reference data. Using the classification results of Landsat 9 OLI/TIRS with a 30-m spatial resolution as assessed data, and the classification results of Sentinel-2 MSI with a 10-m spatial resolution as reference data, and employing overall accuracy (OA) and the Kappa coefficient as assessment metrics, the performance of MG-SSM was compared against other sampling methods, including SRS, ST, SY, SSS, and GLCM. The performances of SRS, ST, SY, SSS, GLCM, and MG-SSM were 74.41%, 75.39%, 76.57%, 78.47%, 76.77%, and 67.36% in terms of OA, and 0.6, 0.61, 0.64, 0.66, 0.64, and 0.46 in terms of the Kappa coefficient, respectively. The results indicated that MG-SSM exhibited the lowest values for overall accuracy and the Kappa coefficient, aligning with the objective of assessing the accuracy of remote sensing data products. This outcome is attributed to MG-SSM’s strategy of distributing more sample points in areas with lower aggregation of land-cover classes, which are more prone to classification errors. Collectively, the proposed MG-SSM enhanced the representativeness and reduced the information redundancy of the sample points, making it an efficient sampling method for assessing the accuracy of remote sensing data products.

Spatial patterns of urban expansion and cropland loss during 2017–2022 in Guangdong, China

Urban expansion often occurs at the expense of cropland loss, posing challenges to sustainable urban growth and food security. However, detailed investigations into urban expansion and cropland loss remain limited, particularly in regions with varying levels of urbanization. Here, we take Guangdong Province, China, as a case study to exemplify how urban expansion affects cropland using remotely sensed land use products. We adopted geospatial analysis, correlation indicators, and landscape metrics to uncover their spatial relationships at 10-m spatial resolutions. Results showed that urban areas increased by 6335 km2 while cropland decreased by 3780 km2 from 2017 to 2022. Notably, 41 % of newly expanded urban areas were from croplands, and 45 % of lost croplands were converted to urban areas. Western Guangdong experienced the largest extent of urban expansion and cropland loss, emerging as a hotspot region in recent years. Additionally, our analysis observed the increasing compactness of urban areas and the growing fragmentation of cropland landscapes over time. These findings shed light on the intricate dynamics between urban expansion and cropland loss in rapidly urbanizing regions, which provide valuable insights for sustainable urban development, agricultural practice, and land management in the future.

Multi-factor weighted image fusion method for high spatiotemporal tracking of reservoir drawdown area and its vegetation dynamics

Reservoir drawdown areas (RDAs) with distinct dry-wet cycles and vegetation dynamics have emerged as significant hotspots for carbon-related activities. However, high-resolution spatiotemporal tracking of the variations and vegetation dynamics of RDAs remains challenging because they often change dramatically and are controlled by both human activities and natural factors. Herein, a modified image fusion method was proposed to capture rapid variations in RDAs by integrating impact factor information into the analysis. The capability of the proposed method was tested in the Danjiangkou (DJK) Reservoir as it is the largest artificial freshwater lake in Asia with a highly variable RDA, since it is surrounded by gently sloping plains or hills. The results showed that the modified workflow produced reliable predictions (r=0.83,RMSE=0.097) compared with the original Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) workflow (r=0.60,RMSE=0.195), demonstrating improved capability for mapping water surface changes and vegetation dynamics. Using the proposed method, the 15-d variations in the RDA were derived from 2013 to 2022 with a 30-m resolution. The interannual maximum RDA was estimated to be 278 km2 after the dam was elevated in 2013. The Normalized Difference Vegetation Index (NDVI) decreased as inundation frequency (IF) increased. Mean NDVI in the growing season (May–October) decreased by 0.109 (17.6 %) and 0.156 (26.0 %) under 30 %–40 % IF and 60 %–70 % IF, respectively, compared with the vegetation under 0 %–10 % IF, which was referred as “natural” vegetation considering its rare inundation. Moreover, the mean growing season length decreased to only 63 and 19 d for 30 %–40 % IF and 60 %–70 % IF, respectively. Furthermore, 77.3 % of the RDA exhibited a decrease in NDVI, whereas 22.7 % showed an unusual increase, possibly due to the selection of dominant species well-adapted to inundation during vegetation succession. Overall, this study not only proposed a new method for the high spatiotemporal monitoring of RDAs, but also highlighted the importance of variations and vegetation dynamics within RDAs for accurate estimation of their carbon budgets.

Retrieval of high-resolution aerosol optical depth (AOD) using Landsat 8 imageries over different LULC classes over a city along Indo-Gangetic Plain, India.

Aerosol optical depth (AOD) serves as a crucial indicator for assessing regional air quality. To address regional and urban pollution issues, there is a requirement for high-resolution AOD products, as the existing data is of very coarse resolution. To address this issue, we retrieved high-resolution AOD over Kanpur (26.4499°N, 80.3319°E), located in the Indo-Gangetic Plain (IGP) region using Landsat 8 imageries and implemented the algorithm SEMARA, which combines SARA (Simplified Aerosol Retrieval Algorithm) and SREM (Simplified and Robust Surface Reflectance Estimation). Our approach leveraged the green band of the Landsat 8, resulting in an impressive spatial resolution of 30m of AOD and rigorously validated with available AERONET observations. The retrieved AOD is in good agreement with high correlation coefficients (r) of 0.997, a low root mean squared error of 0.035, and root mean bias of - 4.91%. We evaluated the retrieved AOD with downscaled MODIS (MCD19A2) AOD products across various land classes for cropped and harvested period of agriculture cycle over the study region. It is noticed that over the built-up region of Kanpur, the SEMARA algorithm exhibits a stronger correlation with the MODIS AOD product compared to vegetation, barren areas and water bodies. The SEMARA approach proved to be more effective for AOD retrieval over the barren and built-up land categories for harvested period compared with the cropping period. This study offers a first comparative examination of SEMARA-retrieved high-resolution AOD and MODIS AOD product over a station of IGP.

Detection of karst depression in Brazil comparing different semantic and instance segmentations and global digital elevation models

This research assessed the efficacy of deep segmentation in identifying and measuring natural karst depressions in the Bambuí Group's carbonate rocks in Western Bahia, Brazil. The investigation used five Global Digital Elevation Models (DEMs) with 30-m resolution: Advanced Spaceborne Thermal Emission and Reflection Radiometer - Global Digital Elevation Model version 3 (ASTER-GDEM v3), Advanced Land Observing Satellite World 3D – 30 m version 3.2 (AW3D30 v3.2), Copernicus 30 m global DEM (GLO-30), NASA Digital Elevation Model version 1 (NASADEM v1), and Shuttle Radar Topography Mission version 3 (SRTM v3). The karst feature detection analysis compared five semantic segmentation architectures: Feature Pyramid Networks (FPN), LinkNet, UNet, UNet++, DVL3+, using an EfficientNet-B7 backbone, one instance segmentation model (Mask-RCNN), and a semantic-to-instance conversion method. This comparison considers different datasets, including one, two, or eleven variables (DEM, DEM-based sink depth, and nine terrain attributes). Besides, we performed a combinatorial analysis of the DEMs to evaluate the improvement in karst feature detection. The methodology involved the generation of geomorphometric attributes, sample labeling from Sentinel-2 and Operational Land Imager-Landsat 8 datasets, training-validation-testing steps with 128 × 128 samples, reconstruction of large images for semantic and instance segmentation, and accuracy analysis. The findings revealed that GLO-30 and AW3D30 data were the most accurate, while ASTER GDEM performed poorly in both segmentation forms. Among the semantic models, FPN showed the highest accuracy. The 11-variable models preferentially outperformed those with fewer in both types of segmentation. The approach of semantic-to-instance conversion with Geographic Information System tools favored individualizing karst depressions and obtaining efficient quantification, consisting of an easy alternative to achieve instance segmentation. Reconstruction of large remote sensing images through sliding windows considering the specifics of instance and semantic segmentation demonstrated that smaller stride enhances object coverage and reduces the risk of losing crucial information, effectively improving the predictions. The combinatorial analysis for semantic and instance segmentation indicates that models incorporating more DEMs (4- and 5-DEM models) and variables achieve generally higher accuracy. The best result in semantic segmentation was combining the five DEM datasets using 11 variables, reaching an F-score of 85.06 and IoU of 74.00. In instance segmentation, the best result for the bounding box was the model that integrates AW3D30, GLO-30, NASADEM, and SRTM with eleven variables reaching Average Precision (AP) of 45.64, AP50 of 88.40 and AP75 of 39.38, while the best result for the segmentation mask was the model that integrates the five models with eleven variables with AP of 42.61, AP50 of 86.79 and AP75 of 35.43. These results demonstrate that integrating a wide range of DEM data, even the lowest performing ones, improves model generalization and accuracy in segmentation, leveraging strengths and mitigating individual weaknesses. Future work could explore high-resolution DEMs and the integration of various deep-learning methods.

Investigating the influence of sub-mesoscale current structures on Baltic Sea connectivity through a Lagrangian analysis

This study explores the impact of sub-mesoscale structures and vertical advection on the connectivity properties of the Baltic Sea using a Lagrangian approach. High-resolution flow fields from the General Estuarine Transport Model (GETM) were employed to compute Lagrangian trajectories, focusing on the influence of fine-scale structures on connectivity estimates. Six river mouths in the Baltic Sea served as initial positions for numerical particles, and trajectories were generated using flow fields with varying horizontal resolutions: 3D trajectories with 250m resolution as well as 2D trajectories with 250m and 1km resolutions. Several Lagrangian indices, such as mean transit time, arrival depths, and probability density functions of transit times, were analyzed to unravel the complex circulation of the Baltic Sea and highlight the substantial impact of sub-mesoscale structures on numerical trajectories. Results indicate that in 2D simulations, particles exhibit faster movement on the eastern side of the Gotland Basin in high-resolution compared to coarse-resolution simulations. This difference is attributed to the stronger coastal current in high-resolution compared to coarse-resolution simulations. Additionally, the study investigates the influence of vertical advection on numerical particle motion within the Baltic Sea, considering the difference between 3D and 2D trajectories. Findings reveal that denser water in the eastern and south-eastern areas significantly affects particle dispersion in 3D simulations, resulting in increased transit times. Conversely, regions in the North-western part of the basin accelerate particle movement in 3D compared to the 2D simulations. Finally, we calculated the average residence time of numerical particles exiting the Baltic Sea through the Danish strait. Results show an average surface layer residence time of approximately 790 days over an eight-year integration period, highlighting the relatively slow water circulation in the semi-enclosed Baltic Sea basin. This prolonged residence time emphasizes the potential for the accumulation of pollutants. Overall, the study underscores the pivotal role of fine-scale structures in shaping the connectivity of the Baltic Sea, with implications for understanding and managing environmental challenges in this unique marine ecosystem.

Remotely-sensed phenoclusters of Wisconsin’s forests, shrublands, and grasslands for biodiversity applications

Heterogeneous vegetation supports higher species richness than homogenous vegetation, which is why efficiently identifying heterogenous vegetation can be useful for biodiversity conservation. Satellite remote-sensing data provide an opportunity to generate vegetation heterogeneity metrics and to explore the phenology of vegetation patterns. Phenoclusters are vegetation types with similar phenological characteristics, and valuable for capturing vegetation habitat heterogeneity patterns. Our goal was to map phenoclusters for Wisconsin, USA, at 10-m spatial resolution based on land surface phenology metrics from EVI (Enhanced Vegetation Index) Sentinel-2 data. We characterized each phenocluster based on landcover composition and structure, phenology timing, and environmental factors, and compared them to bird species richness. We also calculated the diversity of phenoclusters at multiple spatial extents. We identified 14 phenoclusters in Wisconsin, each with distinct landcover composition and structure, and unique phenological characteristics. Our remotely-sensed phenoclusters effectively captured environmental gradients, with elevation and temperature emerging as the most important driving variables. Furthermore, the phenoclusters successfully captured bird biodiversity patterns, especially richness of forest and grassland specialist. Our results identified phenological patterns among Wisconsin’s forests, shrublands, and grasslands, capturing phenological timing both among and within the same tree species. Phenoclusters are a valuable tool for capturing vegetation habitat heterogeneity, phenology diversity and biodiversity patterns, as well as climate change effects.

Evaluating future flood risk mitigation capacities in major cities across China

Rapid urbanization and climate change have significantly altered urban hydrological processes. Previous research has highlighted their adverse effects on escalating risk of urban flood. Nevertheless, few assessments have quantified how future urban flood risk mitigation capacities evolve under various urban expansion and climate change scenarios. This study simulates urban expansion of major cities across China using future urban-regional environment simulation model at 30-m resolution under shared socioeconomic pathways scenarios (SSP). Concurrently, future precipitation intensity changes are assessed based on climate projection data. Finally, the urban flood risk mitigation capacity impacted by urban expansion and climate change is evaluated using the soil conservation service model. The results reveal: (1) The sizes of major cities are projected to expand 30% by 2050, and the north and east China are primary urban growth poles. (2) Major cities' precipitation intensity increase 0.4% in SSP126, but decline 5.4% and 2.3% in SSP245 and SSP585. (3) The flood risk mitigation capacities of major cities decrease 2.7% in SSP126 and 2.1% in SSP585, but increase 0.2% in SSP245. Most cities experiencing capacity decrease are located in the north and east China. These results provide valuable insights for the formulation of future urban flood adaptation and mitigation strategies.

Assessment of water shortage in underground aquifer in Abuja during dry season

Water shortage is a complex issue with severe implications for human well-being, agriculture, and ecosystems. Climate change, population growth, and inadequate water management practices contribute to this global problem. This study is to identify the causes of ground water scarcity and determine groundwater potential zones in the Abuja community. The research area's borehole coordinates, Landsat 8 satellite imagery, AsterDEM 30m resolution DEM data, and vertical electrical sounding (VES) data were all used to do this. Unsupervised classification of Landsat imagery was used to create a layer of land use and cover, image filtering techniques were used to improve the visualization of lineament extraction, and density analysis was used to quantify lineament density. AsterDEM data was subjected to surface analysis to acquire elevation data, while VES data were interpolated to produce spatial distributions of the resistivity and thickness parameters. To identify groundwater potential zones, multiple criterion analysis using the analytical hierarchical process (AHP) was utilized to reclassify variables such as land use, elevation, lineament density, resistivity, and thickness. To create the zones, these characteristics were weighted and layered. According to the findings, resistivity levels varied from 6.25 m to 676 m, and the geologic ground structure's thickness ranged from 0.27 m to 92.37 m. Elevation varied from 41 meters to 720 meters, while lineament density ranged from 0 to 0.04. 74% of the land was categorized as vegetated, 21% as built up, 2.7% as water bodies, and 2.3% as rock formations. Within Abuja, zones of groundwater potential were identified as being very low (5%), low (26.7%), marginally high (26.3%), moderately high (28.5%), and very high (13.4%)

High spatio-temporal resolution predictions of PM2.5 using low-cost sensor data

We generated PM2.5 predictions at a high spatio-temporal resolution in the Columbus, OH, Denver, CO, and Pittsburgh, PA metropolitan areas using low-cost PurpleAir sensor data. We used multiple modeling approaches, namely random forest (RF), random forest spatial interpolation (RFSI), space-time regression kriging (STRK), and random forest kriging (RFK). We trained separate models for each combination of hour, month, and city to predict PM2.5 concentrations at 8 a.m. and 6 p.m. on any specific day at a spatial resolution of 100m. In most cases, models that account for the spatio-temporal relationships (e.g., STRK, RFK, RFSI) show better performance than non-spatio-temporal machine learning models (e.g., RF). On average, considering all models of all cities, RFSI (mean MAE = 1.75, R2 = 0.67) and STRK (mean MAE = 1.74, R2 = 0.63) models perform better than RFK models (mean MAE = 2.11, R2 = 0.59), and STRK has clearest spatial patterns. We found that kriging models, especially STRK, are superior in capturing the spatio-temporal relationships and resemble the generic land use pattern of the city, while RFSI models are effective when dealing with very large datasets with missing cases. Our study demonstrates a multi-model approach that could inform low-cost sensor deployment to facilitate air quality modeling. Our high-resolution predictions could also facilitate studies on short-term, traffic-based exposure assessment.

Crowded and warmer: Unequal dengue risk at high spatial resolution across a megacity of India

The role of climate factors on transmission of mosquito-borne infections within urban landscapes must be considered in the context of the pronounced spatial heterogeneity of such environments. Socio-demographic and environmental variation challenge control efforts for emergent arboviruses transmitted via the urban mosquito Aedes aegypti. We address at high resolution, the spatial heterogeneity of dengue transmission risk in the megacity of Delhi, India, as a function of both temperature and the carrying-capacity of the human environment for the mosquito. Based on previous results predicting maximum mosquitoes per human for different socio-economic typologies, and on remote sensing temperature data, we produce a map of the reproductive number of dengue at a resolution of 250m by 250m. We focus on dengue risk hotspots during inter-epidemic periods, places where chains of transmission can persist for longer. We assess the resulting high-resolution risk map of dengue with reported cases for three consecutive boreal winters. We find that both temperature and vector carrying-capacity per human co-vary in space because of their respective dependence on population density. The synergistic action of these two factors results in larger variation of dengue’s reproductive number than when considered separately, with poor and dense locations experiencing the warmest conditions and becoming the most likely reservoirs off-season. The location of observed winter cases is accurately predicted for different risk threshold criteria. Results underscore the inequity of risk across a complex urban landscape, whereby individuals in dense poor neighborhoods face the compounded effect of higher temperatures and mosquito carrying capacity. Targeting chains of transmission in inter-epidemic periods at these locations should be a priority of control efforts. A better mapping is needed of the interplay between climate factors that are dominant determinants of the seasonality of vector-borne infections and the socio-economic conditions behind unequal exposure.

Mapping of secondary forest age in China using stacked generalization and Landsat time series

A national distribution of secondary forest age (SFA) is essential for understanding the forest ecosystem and carbon stock in China. While past studies have mainly used various change detection algorithms to detect forest disturbance, which cannot adequately characterize the entire forest landscape. This study developed a data-driven approach for improving performances of the Vegetation Change Tracker (VCT) and Continuous Change Detection and Classification (CCDC) algorithms for detecting the establishment of forest stands. An ensemble method for mapping national-scale SFA by determining the establishment time of secondary forest stands using change detection algorithms and dense Landsat time series was proposed. A dataset of national secondary forest age for China (SFAC) for 1 to 34 and with a 30-m spatial resolution was produced from the optimal ensemble model. This dataset provides national, continuous spatial SFA information and can improve understanding of secondary forests and the estimation of forest carbon storage in China.