Mapping impervious surface area increase and urban pluvial flooding using Sentinel Application Platform (SNAP) and remote sensing data.

Expansion of urban impervious surface (UIA) and increased urban pluvial flooding (UPF) have an impact on urban dynamics, socioeconomic activities, and our environment. Therefore, monitoring the increase in UIS and its effect on UPF is essential. The notion of this research is based on the mapping of impervious surface area increase in three major cities of Pakistan. There were two key objectives: (i) Mapping impervious surface area growth using the global impervious surface area index (GISAI) on Google Earth Engine from 1992 to 2022 and (ii) mapping the pluvial flood extent in selected urban areas using Sentinel-1 Ground Range Detected (GRD) data. Thus, we have utilized the GISAI for mapping urban impervious surface area (UISA) using Landsat time-series data on GEE. Our research findings revealed that about 16.8%, 23.5%, and 16.4% of the impervious surface have been increased in Islamabad, Lahore, and Karachi, respectively. Also, Lahore city has the highest overall accuracy, aiming at the GISAI of 93%, followed by Karachi and Islamabad with an overall accuracy of 86% and 85%, respectively. The results indicated that urban flooding has occurred in those areas where the ISA has grown during the last three decades. It shows significant changes in the impervious surface area that cause enhanced urban pluvial flooding in major cities of Pakistan. Also, Sentinel-1 data and the SNAP tool significantly mapped flooded areas in the selected zones. So, providing cities and local governments with increased quick flood detection capabilities is essential. It can also provide feasible policy recommendations for Pakistan decision-makers in city management. Therefore, we suggest a modeling-based solution to identify high-risk locations in major cities for upcoming UPF events.

Assessing urban wetlands dynamics in Wuhan and Nanchang, China.

Urban wetlands play a crucial role in sustainable social development. However, current research mainly focuses on specific wetland types, and fine extraction of urban wetlands remains a challenge. This study proposes a fine extraction framework based on hierarchical decision trees and shape features for urban wetlands, using Sentinel-2 remote sensing data to obtain detailed wetland data of Wuhan and Nanchang from 2016 to 2022. Our framework applies random forests to classify land cover, extracts urban fine wetlands by hierarchical decision trees and shape features, and assesses the dynamics of wetlands in the two cities. We also analyzed and discussed the characteristics of urban wetlands in the two cities. The results show that wetland accuracies of Wuhan and Nanchang are greater than 84.5 % and 82.9 %, respectively. The wetland areas of Wuhan in 2016, 2019, and 2022 are 1969.4 km2, 1713.8 km2, and 1681.1 km2, while those in Nanchang are 1405.9 km2, 1361.6 km2, and 766.9 km2. Inland wetlands are the main wetland types in both regions, with lake wetlands accounting for the highest proportion (over 40 %). The urban wetlands in the two cities exhibit different spatial and temporal evolution patterns, with varying change trends of wetland area and the structural proportions of fine wetlands. Besides, Wuhan's urban wetlands are primarily located in the south, while Nanchang's urban wetlands are concentrated in the east, exhibiting higher spatial and temporal dynamics. Analysis suggests that the reduced urban wetlands from 2016 to 2022 are related to fluctuating decreasing precipitation, growing population, and gross domestic product (GDP). Our study provides support for the conservation of urban wetland resources in Wuhan and Nanchang and highlights the need for targeted management strategies.

Vegetation as the catalyst for water circulation on global terrestrial ecosystem.

Global climate change is expected to further intensify the global water cycle, leading to more rapid evaporation and more intense precipitation. At the same time, the growth and expansion of natural vegetation caused by climate change and human activities create potential conflicts between ecosystems and humans over available water resources. Clarifying how terrestrial ecosystem evapotranspiration responds to global precipitation and vegetation facilitates a better understanding of and prediction for the responses of global ecosystem energy, water, and carbon budgets under climate change. Relying on the spatial and temporal distribution of evapotranspiration, precipitation, and solar-induced chlorophyll fluorescence (SIF) from remote sensing platforms, we decouple the interaction mechanism of evapotranspiration, precipitation, and vegetation in linear and nonlinear scenarios using correlation and partial correlation analysis, multiple linear regression analysis, and binning. Major conclusions are as follows: (1) As a natural catalyst of the global water cycle, vegetation plays a crucial role in regulating the relationship between climate change and the water­carbon-energy cycle. (2) Vegetation, a key parameter affecting the water cycle, participates in the entire water cycle process. (3) The increase in vegetation productivity and photosynthesis plays a dominant role in promoting evapotranspiration in vegetated areas, while the increase in precipitation dominates the promotion of evapotranspiration in non-vegetated areas.

Impact of global urban expansion on the terrestrial vegetation carbon sequestration capacity.

Continuous urban expansion has a negative impact on the potential of terrestrial vegetation. Till now, the mechanism of such impact remains unclear, and there have been no systematic investigations. In this study, we design a theoretical framework by laterally bridging urban boundaries to explain the distress of regional disparities and longitudinally quantify the impacts of urban expansion on net ecosystem productivity (NEP). The findings demonstrate that global urban expanded by 37.60 × 104 km2 during 1990-2017, which is one of the causes of vegetation carbon loss. Meanwhile, certain climatic changes (e.g., rising temperature, rising CO2, and nitrogen deposition) caused by urban expansion indirectly boosted vegetation carbon sequestration potential through photosynthetic enhancement. The direct decrease in NEP due to the urban expansion (occupying 0.25 % of the Earth's land area) offsets the 1.79 % increase due to the indirect impact. Our findings contribute to a better understanding of the uncertainty associated with urban expansion towards carbon neutrality and provide a scientific reference for sustainable urban development worldwide.

Modelling land use/land cover (LULC) change dynamics, future prospects, and its environmental impacts based on geospatial data models and remote sensing data.

The dynamic change in land use/land cover (LULC) caused by rapid urbanization has become a major concern in Lahore, causing a variety of socioeconomic and environmental issues relating to air quality. As a result, it is important to monitor existing LULC change detection, future projection, and the increase in atmospheric pollutants in Lahore. This research work makes use of Landsat, GIOVANNI, SRTM DEM, and vector data. The four key steps of the research approach are as follows: (i) LULC classification from 2000 to 2020 using Lansat data and semi-automatic classification plugin (SCP); (ii) simulation of future prediction using Modules of Land Use Change Evaluation (MOLUSCE) prediction model; (iii) assessment of effects of land use change on air quality using GIOVANNI-NASA product; and (iv) monitoring, change detection, and result interpretation. According to the research findings, there was an increase in metropolitan areas and a decrease in vegetation, barren land, and water bodies for both historical and future projections. The findings also indicated that from 2000 to 2020, about 27.41% of the urban area expanded, with a decline of 42.13% in vegetation, 2.3% in the barren land, and 6.51% in water bodies, respectively. Between 2020 and 2040, the urban area is predicted to increase by 23.15%, while vegetation, barren land, and water bodies are expected to decrease by 28.05%, 1.8%, and 12.31%, respectively. Also, the atmospheric pollutants have been increased including NO2 (1.60%), SO2 (1.02%), CO2 (0.71%), CO (1.56%), O3 (0.15%), and CH4 (0.20%), respectively. And it is projected that by 2040, the average annual atmospheric concentration of NO2, SO2, CO2, CO, O3, and CH4 will be increased by 28.80%, 18.36%, 12.78%, 28.08%, 2.70%, and 3.60%, respectively. In addition, it was also observed that the majority of the city's urban area expansion was found in the city's eastern and southern regions. Therefore, government should focus on natural resource conservation especially vegetation cover to reduce air pollutants concentration and the LULC effect.

Assessing the impact of drought-land cover change on global vegetation greenness and productivity.

Drought-land cover change (D-LCC) is considered to be an important stress factor that affects vegetation greenness and productivity (VG&P) in global terrestrial ecosystems. Understanding the effects of D-LCC on VG&P benefits the development of terrestrial ecosystem models and the prediction of ecosystem evolution. However, till today, the mechanism remains underexploited. In this study, based on the Theil-Sen median estimator and Mann-Kendall test, Hurst exponent evaluation and rescaled range analysis (R/S), Pearson and Partial correlation coefficient analyses, we explore the spatiotemporal distribution characteristics and future trends of Leaf area index (LAI), Net primary productivity (NPP), Solar-induced chlorophyll fluorescence (SIF), Standardized precipitation evapotranspiration index (SPEI), Soil moisture (SM), Land cover type (LC), and the impact mechanism of D-LCC on global VG&P. Our results provide four major insights. First, three independent satellite observations consistently indicate that the world is experiencing an increasing trend of VG&P: LAI (17.69 %), NPP (20.32 %) and SIF (16.46 %). Nonetheless, productivity-reducing trends are unfolding in some tropical regions, notably the Amazon rainforest and the Congo basin. Second, from 2001 to 2020, the frequency, severity, duration, and scope of global droughts have been increasing. Third, the impact of land cover change on global VG&P is region-dependent. Finally, our results indicate that the continuous growth of VG&P in the global vegetation area is likely to become more difficult to maintain.

Real-Time and Accurate UAV Pedestrian Detection for Social Distancing Monitoring in COVID-19 Pandemic.

Coronavirus Disease 2019 (COVID-19) is a highly infectious virus that has created a health crisis for people all over the world. Social distancing has proved to be an effective non-pharmaceutical measure to slow down the spread of COVID-19. As unmanned aerial vehicle (UAV) is a flexible mobile platform, it is a promising option to use UAV for social distance monitoring. Therefore, we propose a lightweight pedestrian detection network to accurately detect pedestrians by human head detection in real-time and then calculate the social distancing between pedestrians on UAV images. In particular, our network follows the PeleeNet as backbone and further incorporates the multi-scale features and spatial attention to enhance the features of small objects, like human heads. The experimental results on Merge-Head dataset show that our method achieves 92.22% AP (average precision) and 76 FPS (frames per second), outperforming YOLOv3 models and SSD models and enabling real-time detection in actual applications. The ablation experiments also indicate that multi-scale feature and spatial attention significantly contribute the performance of pedestrian detection. The test results on UAV-Head dataset show that our method can also achieve high precision pedestrian detection on UAV images with 88.5% AP and 75 FPS. In addition, we have conducted a precision calibration test to obtain the transformation matrix from images (vertical images and tilted images) to real-world coordinate. Based on the accurate pedestrian detection and the transformation matrix, the social distancing monitoring between individuals is reliably achieved.

Assessment of the importance of increasing temperature and decreasing soil moisture on global ecosystem productivity using solar-induced chlorophyll fluorescence.

The accurate assessment of the global gross primary productivity (GPP) of vegetation is the key to estimating the global carbon cycle. Temperature (Ts) and soil moisture (SM) are essential for vegetation growth. It is acknowledged that the global Ts has shown an increasing trend, yet SM has shown a decreasing trend. However, the importance of SM and Ts changes on the productivity of global ecosystems remains unclear, as SM and Ts are strongly coupled through soil-atmosphere interactions. Using solar-induced chlorophyll fluorescence (SIF) as a proxy for GPP and by decoupling SM and Ts changes, our investigation shows Ts plays a more important role in SIF in 60% of the vegetation areas. Overall, increased Ts promotes SIF by mitigating the resistance from SM's reduction. However, the importance of SM and Ts varies, given different vegetation types. The results show that in the humid zone, the variation of Ts plays a more important role in SIF, but in the arid and semi-arid zones, the variation of SM plays a more important role; in the semi-humid zone, the disparity in the importance of SM and Ts is difficult to unravel. In addition, our results suggest that SIF is very sensitive to aridity gradients in arid and semi-arid ecosystems. By decoupling the intertwined SM-Ts impact on SIF, our study provides essential evidence that benefits future investigation on the factors the influence ecosystem productivity at regional or global scales.

Enhanced image prior for unsupervised remoting sensing super-resolution.

Numerous approaches based on training low-high resolution image pairs have been proposed to address the super-resolution (SR) task. Despite their success, low-high resolution image pairs are usually difficult to obtain in certain scenarios, and these methods are limited in the actual scene (unknown or non-ideal image acquisition process). In this paper, we proposed a novel unsupervised learning framework, termed Enhanced Image Prior (EIP), which achieves SR tasks without low/high resolution image pairs. We first feed random noise maps into a designed generative adversarial network (GAN) for satellite image SR reconstruction. Then, we convert the reference image to latent space as the enhanced image prior. Finally, we update the input noise in the latent space with a recurrent updating strategy, and further transfer the texture and structured information from the reference image. Results on extensive experiments on the Draper dataset show that EIP achieves significant improvements over state-of-the-art unsupervised SR methods both quantitatively and qualitatively. Our experiments on satellite (SuperView-1) images reveal the potential of the proposed approach in improving the resolution of remote sensing imagery compared with the supervised algorithms. Source code is available at https://github.com/jiaming-wang/EIP.

Arsenic in a groundwater environment in Bangladesh: Occurrence and mobilization.

Groundwater with an excessive level of Arsenic (As) is a threat to human health. In Bangladesh, out of 64 districts, the groundwater of 50 and 59 districts contains As exceeding the Bangladesh (50 µg/L) and WHO (10 µg/L) standards for potable water. This review focuses on the occurrence, origin, plausible sources, and mobilization mechanisms of As in the groundwater of Bangladesh to better understand its environmental as well as public health consequences. High As concentrations mainly was mainly occur from the natural origin of the Himalayan orogenic tract. Consequently, sedimentary processes transport the As-loaded sediments from the orogenic tract to the marginal foreland of Bangladesh, and under the favorable biogeochemical circumstances, As is discharged from the sediment to the groundwater. Rock weathering, regular floods, volcanic movement, deposition of hydrochemical ore, and leaching of geological formations in the Himalayan range cause As occurrence in the groundwater of Bangladesh. Redox and desorption processes along with microbe-related reduction are the key geochemical processes for As enrichment. Under reducing conditions, both reductive dissolution of Fe-oxides and desorption of As are the root causes of As mobilization. A medium alkaline and reductive environment, resulting from biochemical reactions, is the major factor mobilizing As in groundwater. An elevated pH value along with decoupling of As and HCO3- plays a vital role in mobilizing As. The As mobilization process is related to the reductive solution of metal oxides as well as hydroxides that exists in sporadic sediments in Bangladesh. Other mechanisms, such as pyrite oxidation, redox cycling, and competitive ion exchange processes, are also postulated as probable mechanisms of As mobilization. The reductive dissolution of MnOOH adds dissolved As and redox-sensitive components such as SO42- and oxidized pyrite, which act as the major mechanisms to mobilize As. The reductive suspension of Mn(IV)-oxyhydroxides has also accelerated the As mobilization process in the groundwater of Bangladesh. Infiltration from the irrigation return flow and surface-wash water are also potential factors to remobilize As. Over-exploitation of groundwater and the competitive ion exchange process are also responsible for releasing As into the aquifers of Bangladesh.

User Identification across Asynchronous Mobility Trajectories.

With the popularity of location-based services and applications, a large amount of mobility data has been generated. Identification through mobile trajectory information, especially asynchronous trajectory data has raised great concerns in social security prevention and control. This paper advocates an identification resolution method based on the most frequently distributed TOP-N (the most frequently distributed N regions regarding user trajectories) regions regarding user trajectories. This method first finds TOP-N regions whose trajectory points are most frequently distributed to reduce the computational complexity. Based on this, we discuss three methods of trajectory similarity metrics for matching tracks belonging to the same user in two datasets. We conducted extensive experiments on two real GPS trajectory datasets GeoLife and Cabspotting and comprehensively discussed the experimental results. Experimentally, our method is substantially effective and efficiency for user identification.

High arsenic contamination and presence of other trace metals in drinking water of Kushtia district, Bangladesh.

Drinking water with excessive concentration levels of arsenic (As) is a great threat to human health. A hydrochemical approach was employed in 50 drinking water samples (collected from Kushtia district, Bangladesh) to examine the occurrence of geogenic As and the presence of trace metals (TMs), as well as the factors controlling As release in aquifers. The results reveal that the drinking water of shallow aquifers is highly contaminated by As (6.05-590.7 µg/L); 82% of samples were found to exceed the WHO recommended limit (10 µg/L) for potable water, but the concentrations of Si, B, Mn, Sr, Se, Ba, Fe, Cd, Pb, F, U, Ni, Li, and Cr were within safe limits. The Ca-HCO3-type drinking water was identified as having high contents of As, pH and HCO3-, a medium-high content EC, and low concentrations of NO3-, SO42-, K+, and Cl-. The significant correlation between As and NO3- indicates that NO3- might be attributed to the use of phosphate fertilizers and a factor responsible for enhancing As in aquifers. The study also reports that the occurrence of high As and the presence of TMs in drinking water may be a result of local anthropogenic activities, such as irrigation, intensive land use and the application of agrochemicals. The insignificant correlation between As and SO42- demonstrated that As is released from SO42- minerals under reducing conditions. An elevated pH value along with decoupling of As and HCO3- plays a vital role in mobilizing As to aquifer systems. Moreover, the positive relationship between As and Si indicated that As is transported in the biogeochemical environment. The reductive suspension of Mn(IV)-oxyhydroxides also accelerated the As mobilization process. Over exploitation of tube-well water and the competitive ion exchange process are also responsible for the release of As in aquifers.

Estimating Forest Aboveground Biomass by Combining Optical and SAR Data: A Case Study in Genhe, Inner Mongolia, China.

Estimation of forest aboveground biomass is critical for regional carbon policies and sustainable forest management. Passive optical remote sensing and active microwave remote sensing both play an important role in the monitoring of forest biomass. However, optical spectral reflectance is saturated in relatively dense vegetation areas, and microwave backscattering is significantly influenced by the underlying soil when the vegetation coverage is low. Both of these conditions decrease the estimation accuracy of forest biomass. A new optical and microwave integrated vegetation index (VI) was proposed based on observations from both field experiments and satellite (Landsat 8 Operational Land Imager (OLI) and RADARSAT-2) data. According to the difference in interaction between the multispectral reflectance and microwave backscattering signatures with biomass, the combined VI (COVI) was designed using the weighted optical optimized soil-adjusted vegetation index (OSAVI) and microwave horizontally transmitted and vertically received signal (HV) to overcome the disadvantages of both data types. The performance of the COVI was evaluated by comparison with those of the sole optical data, Synthetic Aperture Radar (SAR) data, and the simple combination of independent optical and SAR variables. The most accurate performance was obtained by the models based on the COVI and optical and microwave optimal variables excluding OSAVI and HV, in combination with a random forest algorithm and the largest number of reference samples. The results also revealed that the predictive accuracy depended highly on the statistical method and the number of sample units. The validation indicated that this integrated method of determining the new VI is a good synergistic way to combine both optical and microwave information for the accurate estimation of forest biomass.

Thin cloud removal from single satellite images.

A novel method for removing thin clouds from single satellite image is presented based on a cloud physical model. Given the unevenness of clouds, the cloud background is first estimated in the frequency domain and an adjustment function is used to suppress the areas with greater gray values and enhance the dark objects. An image, mainly influenced by transmission, is obtained by subtracting the cloud background from the original cloudy image. The final image with proper color and contrast is obtained by decreasing the effect of transmission using the proposed max-min radiation correction approach and an adaptive brightness factor. The results indicate that the proposed method can more effectively remove thin clouds, improve contrast, restore color information, and retain detailed information compared with the commonly used image enhancement and haze removal methods.

Invariant matching method for different viewpoint angle images.

In recent years, many methods have been put forward to improve the image matching for different viewpoint images. However, these methods are still not able to achieve stable results, especially when large variation in view occurs. In this paper, an image matching method based on affine transformation of local image areas is proposed. First, local stable regions are extracted from the reference image and the test image, and transformed to circular areas according to the second-order moment. Then, scale invariant features are detected and matched in the transformed regions. Finally, we use epipolar constraint based on the fundamental matrix to eliminate wrong corresponding pairs. The goal of our method is not to increase the invariance of the detector but to improve the final performance of the matching results. The experimental results demonstrate that compared with the traditional detectors the proposed method provides significant improvement in robustness for different viewpoint images matching in the 2D scene and 3D scene. Moreover, the efficiency is greatly improved compared with affine scale invariant feature transform (Affine-SIFT).

Color constancy enhancement under poor illumination.

In this Letter, the color constancy and its realization were studied and a novel color constancy image enhancement algorithm under poor illumination was presented. The purpose of this algorithm is to maintain the hue of an image during the processing so that the change of saturation can be minimized. The original image was first multiplied by a scale parameter obtained by the adaptive quadratic function to enhance the luminance, and then the edge details were restored by a shifting parameter. Numerical results of the Simon Fraser University (SFU) image database indicated that the proposed algorithm performed much better in preserving the hue and saturation and avoiding color distortion compared with the existing image enhancement algorithms.