Establishment and verification of anthropogenic speciated VOCs emission inventory of Central China.

Improving the accuracy of anthropogenic volatile organic compounds (VOCs) emission inventory is crucial for reducing atmospheric pollution and formulating control policy of air pollution. In this study, an anthropogenic speciated VOCs emission inventory was established for Central China represented by Henan Province at a 3 km × 3 km spatial resolution based on the emission factor method. The 2019 VOCs emission in Henan Province was 1003.5 Gg, while industrial process source (33.7%) was the highest emission source, Zhengzhou (17.9%) was the city with highest emission and April and August were the months with the more emissions. High VOCs emission regions were concentrated in downtown areas and industrial parks. Alkanes and aromatic hydrocarbons were the main VOCs contribution groups. The species composition, source contribution and spatial distribution were verified and evaluated through tracer ratio method (TR), Positive Matrix Factorization Model (PMF) and remote sensing inversion (RSI). Results show that both the emission results by emission inventory (EI) (15.7 Gg) and by TR method (13.6 Gg) and source contribution by EI and PMF are familiar. The spatial distribution of HCHO primary emission based on RSI is basically consistent with that of HCHO emission based on EI with a R-value of 0.73. The verification results show that the VOCs emission inventory and speciated emission inventory established in this study are relatively reliable.

Multi-source machine learning and spaceborne remote sensing data accurately predict three-dimensional soil moisture in an in-service uranium disposal cell.

One reason arid and semi-arid environments have been used to store waste is due to low groundwater recharge, presumably limiting the potential for meteoric water to mobilize and transport contaminants into groundwater. The U.S. Department of Energy Office of Legacy Management (LM) is evaluating selected uranium mill tailings disposal cell covers to be managed as evapotranspiration (ET) covers, where vegetation is used to naturally remove water from the cover profile via transpiration, further reducing deep percolation. An important parameter in monitoring the performance of ET covers is soil moisture (SM). If SM is too high, water may drain into tailings material, potentially transporting contaminants into groundwater; if SM is too low, radon flux may increase through the cover. However, monitoring SM via traditional instrumentation is invasive, expensive, and may fail to account for spatial heterogeneity, especially over vegetated disposal cells. Here we investigated the potential for non-invasive SM monitoring using radar remote sensing and other geospatial data to see if this approach could provide a practical, accurate, and spatially comprehensive tool to monitor SM. We used theoretical simulations to analyze the sensitivity of multi-frequency radar backscatter to SM at different depths of a field-scale (3 ha) drainage lysimeter embedded within an in-service LM disposal cell. We then evaluated a shallow and deep form of machine learning (ML) using Google Earth Engine to integrate multi-source observations and estimate the SM profile across six soil layers from depths of 0-2 m. The ML models were trained using in situ SM measurements from 2019 and validated using data from 2014 to 2018 and 2020-2021. Model predictors included backscatter observations from satellite synthetic aperture radar, vegetation, temperature products from optical infrared sensors, and accumulated, gridded rainfall data. The radar simulations confirmed that the lower frequencies (L- and P-band) and smaller incidence angles show better sensitivity to deeper soil layers and an overall larger SM dynamic range relative to the higher frequencies (C- and X-band). The ML models produced accurate SM estimates throughout the soil profile (r values from 0.75 to 0.94; RMSE = 0.003-0.017 cm3/cm3; bias = 0.00 cm3/cm3), with the simpler shallow-learning approach outperforming a selected deep-learning model. The ML models we developed provide an accurate, cost-effective tool for monitoring SM within ET covers that could be applied to other vegetated disposal cell covers, potentially including those with rock-armored covers.

Assessment of meteorological drought impacts on rainfed agriculture using remote sensing-derived biomass productivity.

Assessing drought impacts is necessary for pursuing sustainable development goals relevant to food security and land degradation. Data availability is a major restriction and remote sensing has been promoted for this purpose. Version 3 of WaPOR has been released in 2023, which provides global coverage of remote sensing-derived water productivity indicators and could allow improved analysis of drought impacts, but validation is still needed. This study explores the utility of remote sensing-derived productivity data from WaPOR as a proxy indicator for agricultural drought impacts. The analysis utilized (1) production surveys, (2) meteorological measurements for drought analysis, and (3) remote sensing-derived gross and net biomass water productivities (GBWP & NBWP) and total biomass production (TBP). All layers were analyzed against the Standardized Precipitation and Standardized Precipitation Evapotranspiration Indices (SPI and SPEI) over drought-vulnerable locations in Irbid and Madaba governorates in Jordan. Strong and significant correlations (R2 0.5-0.8, P < 0.05) were obtained between drought intensities and GBWP and NBWP layers, particularly in the May-Sep periods. These correlations were higher than previously tested remotely sensed indicators for agricultural drought impacts. Water productivity and biomass production averages were lower during drier periods and higher during wet periods, but pairwise testing did not reveal significant differences. There is sufficient evidence that WaPOR data demonstrates behavior that reflects agricultural response to drought, and further assessment in other agroclimatic zones is recommended. This could potentially allow for enhanced evaluation of management strategies, decision support, and policy recommendations for drought mitigation.

Temporal assessment of forest cover dynamics in response to forest fires and other environmental impacts using AI.

The rapid reduction of forests due to environmental impacts such as deforestation, global warming, natural disasters such as forest fires as well as various human activities is an escalating concern. The increasing frequency and severity of forest fires are causing significant harm to the ecosystem, economy, wildlife, and human safety. During dry and hot seasons, the likelihood of forest fires also increases. It is crucial to accurately monitor and analyze the large-scale changes in the forest cover to ensure sustainable forest management. Remote sensing technology helps to precisely study such changes in forest cover over a wide area over time. This research analyzes the impact of forest fires over time, identifies hotspots, and explores the environmental factors that affect forest cover change. Sentinel-2 imagery was utilized to study changes in Brunei Darussalam's forest cover area over five years from 2017 to 2022. An object-based approach, Simple Non-Iterative Clustering (SNIC), is employed to cluster the region using NDVI values and analyze the changes per cluster. The results indicate that the area of the clusters reduced where fire incidence occurred as well as the precipitation dropped. Between 2017 and 2022, the increased forest fires and decreased precipitation levels resulted in the change in cluster areas as follows: 66.11%, 69.46%, 68.32%, 73.88%, 77.27%, and 78.70%, respectively. Additionally, hotspots in response to forest fires each year were identified in the Belait district. This study will help forest managers assess the causes of forest cover loss and develop conservation and afforestation strategies.

Delineating groundwater potential zones using integrated remote sensing and GIS in Lahore, Pakistan.

Groundwater depletion and water scarcity are pressing issues in water-limited regions worldwide, including Pakistan, where it ranks as the third-largest user of groundwater. Lahore, Pakistan, grapples with severe groundwater depletion due to factors like population growth and increased agricultural land use. This study aims to address the lack of comprehensive groundwater availability data in Lahore's semi-arid region by employing GIS techniques and remote sensing data. Various parameters, including Land Use and Land Cover (LULC), Rainfall, Drainage Density (DD), Water Depth, Soil Type, Slope, Population Density, Road Density, Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-Up Index (NDBI), Moisture Stress Index (MSI), Water Vegetation Water Index (WVWI), and Land Surface Temperature (LST), are considered. Thematic layers of these parameters are assigned different weights based on previous literature, reclassified, and superimposed in weighted overlay tool to develop a groundwater potential zones index map for Lahore. The groundwater recharge potential zones are categorized into five classes: Extremely Bad, Bad, Mediocre, Good, and Extremely Good. The groundwater potential zone index (GWPZI) map of Lahore reveals that the majority falls within the Bad to Mediocre recharge potential zones, covering 33% and 28% of the total land area in Lahore, respectively. Additionally, 14% of the total area falls under the category of Extremely Bad recharge potential zones, while Good to Extremely Good areas cover 19% and 6%, respectively. By providing policymakers and water supply authorities with valuable insights, this study underscores the significance of GIS techniques in groundwater management. Implementing the findings can aid in addressing Lahore's groundwater challenges and formulating sustainable water management strategies for the city's future.

Unraveling impacts on carbon, water, and energy exchange of Pinus plantations in South American temperate ecosystems.

Tree plantations are expanding in southern South America and their effects on ecosystem services, particularly climate regulation, are still not well understood. Here, we used remote sensing techniques and a paired design to analyze ≈33,000 ha of Pinus plantations along a broad geographical and environmental gradient (26-43° South latitude, 54-72° West longitude). Radiation interception, surface temperature, evapotranspiration, and albedo were assessed both in tree plantations stands and in adjacent uncultivated areas. Additionally, the climatic impact of tree plantations was quantified by analyzing changes in atmospheric radiative forcing and its carbon (C) equivalent. Tree plantations intercepted more radiation when replacing steppes, grasslands, and shrublands but not when replacing forests. The control exerted on radiation interception by precipitation decreased in both space and time after tree plantation. Furthermore, evapotranspiration notably increased in tree plantations. The lower albedo of tree plantations compared to uncultivated adjacent areas induces global warming through the biophysical pathway. Thus, the climate benefits of afforestation through C sequestration can be counteracted by 18 to 83 % due to albedo changes. It is necessary to fully consider the biophysical effects and water footprint of tree plantations in public policies that promote them, as well as in international carbon accounting mechanisms.

Surface water monitoring from 1984 to 2021 based on Landsat time-series images and Google Earth Engine.

Dynamic monitoring of surface water bodies is essential for understanding global climate change and the impact of human activities on water resources. Satellite remote sensing is characterized by large-scale monitoring, timely updates, and simplicity, and it has become an important means of obtaining the distribution of surface water bodies. This study is based on a long time-series Landsat satellite images and the Google Earth Engine (GEE) platform, focusing on Anhui Province in China, and proposes a method for extracting surface water that combines water indices, Bias-Corrected Fuzzy Clustering Method (BCFCM), and OTSU threshold segmentation. The spatial distribution of surface water in Anhui Province was obtained from 1984 to 2021, and further analysis was conducted on the spatiotemporal characteristics of surface water in each city and three major river basins within the province. The results indicated that the overall accuracy of water extraction in this study was 94.06 %. Surface water in Anhui was most abundant in 1998 and least in 2001, with more distribution in the south than in the north. Northern Anhui is dominated by rivers, while southern Anhui has more lakes. Permanent surface water with an inundation frequency of above 75 % covered approximately 4341 km2, accounting for 32.03 % of the total water, while seasonal water with an inundation frequency between 5 % and 75 % covered about 6661 km2, accounting for 49.15 % of the total water, others were considered temporary surface water. By comparing our results with the global annual surface water released by the Joint Research Centre (JRC), we found that our study performed better in extracting lakes and rivers in terms of completeness, but the extraction results for aquaculture areas were slightly less than the JRC dataset. Overall, the long-term surface water dataset established in this study can effectively supplement the existing datasets and provide important references for regional water resource investigation, management, as well as flood monitoring.

Advancing forest carbon stocks' mapping using a hierarchical approach with machine learning and satellite imagery.

Remote sensing of forests is a powerful tool for monitoring the biodiversity of ecosystems, maintaining general planning, and accounting for resources. Various sensors bring together heterogeneous data, and advanced machine learning methods enable their automatic handling in wide territories. Key forest properties usually under consideration in environmental studies include dominant species, tree age, height, basal area and timber stock. Being proxies of stand productivity, they can be utilized for forest carbon stock estimation to analyze forests' status and proper climate change mitigation measures on a global scale. In this study, we aim to develop an effective machine learning-based pipeline for automatic carbon stock estimation using solely freely available and regularly updated satellite observations. We employed multispectral Sentinel-2 remote sensing data to predict forest structure characteristics and produce their detailed spatial maps. Using the Extreme Gradient Boosting (XGBoost) algorithm in classification and regression settings and management-level inventory data as reference measurements, we achieved quality of predictions of species equal to 0.75 according to the F1-score, and for stand age, height, and basal area, we achieved an accuracy of 0.75, 0.58 and 0.56, respectively, according to the R2. We focused on the growing stock volume as the main proxy to estimate forest carbon stocks on the example of the stem pool. We explored two approaches: a direct approach and a hierarchical approach. The direct approach leverages the remote sensing data to create the target maps, and the hierarchical approach calculates the target forest properties using predicted inventory characteristics and conversion equations. We estimated stem carbon stock based on the same approach: from Earth observation imagery directly and using biomass and conversion factors developed for the northern regions. Thus, our study proposes an end-to-end solution for carbon stock estimations based on the complexation of inventory data at the forest stand level, Earth observation imagery, machine learning predictions and conversion equations for the region. The presented approach enables more robust and accurate large-scale assessments using limited annotated datasets.

Estimating the spatiotemporal distribution of PM2.5 concentrations in Tianjin during the Chinese Spring Festival: Impact of fireworks ban.

SETTING: off fireworks during the Spring Festival (SF) is a traditional practice in China. However, because of its environmental impact, the Chinese government has banned this practice completely. Existing evaluations of the effectiveness of firework prohibition policies (FPPs) lack spatiotemporal perspectives, making it difficult to comprehensively assess their effects on air quality. Consequently, this study used remote sensing technology based on aerosol optical depth and multiple variables, compared nine statistical learning methods, and selected the optimal model, transformer, to estimate daily spatiotemporal continuous PM2.5 concentration datasets for Tianjin from 2016 to 2020. The overall model accuracy reached a root mean square error of 15.30 µg/m³, a mean absolute error of 9.55 µg/m³, a mean absolute percentage error of 21.07%, and an R2 of 0.88. Subsequently, we analysed the variations in PM2.5 concentrations from three time dimensions-the entire year, winter, and SF periods-to exclude the impact of interannual variations on the experimental results. Additionally, we quantitatively estimated firework-specific PM2.5 concentrations based on time-series forecasting. The results showed that during the three years following the implementation of the FPPs, firework-specific PM2.5 concentrations decreased by 52.70%, 49.76%, and 86.90%, respectively, compared to the year before the implementation of the FPPs. Spatially, the central urban area and industrial zones are more affected by FPPs than the suburbs. However, daily variations of PM2.5 concentrations during the SF showed that high concentrations of PM2.5 produced in a short period will return to normal rapidly and will not cause lasting effects. Therefore, the management of fireworks needs to consider both environmental protection and the public's emotional attachment to traditional customs, rather than simply imposing a blanket ban on fireworks. We advocate improving firework policies in four aspects-production, sales, supervision, and control-to promote sustainable development of the ecological environment and human society.

TROPOMI unravels transboundary transport pathways of atmospheric carbon monoxide in Tibetan Plateau.

Numerous studies have reported in situ monitoring and source analysis in the Tibetan Plateau (TP), a region crucial for climate systems. However, a gap remains in understanding the comprehensive distribution of atmospheric pollutants in the TP and their transboundary pollution transport. Here, we analyzed the high-resolution satellite TROPOMI observations from 2018 to 2023 in Tibet and its surrounding areas. Our result reveals that, contrary to the results from in situ surface CO monitoring, Tibet exhibits a distinct seasonality in atmospheric carbon monoxide total column average mixing ratio (XCO), with higher levels in summer and lower levels in winter. This distinctive seasonal pattern may be related to the TP's 'air pump' effect and the Asia summer monsoon. Before 2022, the annual growth rate of XCO in Tibet was 1.63 %·year-1; however, it declined by 6.88 % in 2022. Source analysis and satellite observations suggest that CO from South Asia may enter Tibet either by crossing the Himalayas or through the Yarlung Zangbo Grand Canyon. We discovered that spring outbreaks of open biomass burning (OBB) in South and Southeast Asia led to an 11.57-27.98 % increase in XCO over Tibet. Favorable wind pattern and unique topography of the canyon promote the high concentrations CO transport to Tibet. Our greater concern is whether the TP will experience more severe transboundary pollution in the future.

Research on remote sensing ecological livability index based on Google Earth Engine: a case study from Urumqi-Changji-Shihezi urban cluster.

The U-Chang-Shi (Urumqi-Changji-Shihezi) urban cluster, located at the heart of Xinjiang, boasts abundant natural resources. Over the past two decades, rapid urbanization, industrialization, and climate change have significantly threatened the region's ecological livability. To comprehensively, scientifically, and objectively assess the ecological livability of this area, this study leverages the Google Earth Engine (GEE) platform and multi-source remote sensing data to develop a comprehensive evaluation metric: the Remote Sensing Ecological Livability Index (RSELI). This aims to examine the changes in the ecological livability of the U-Chang-Shi urban cluster from 2000 to 2020. The findings show that despite some annual improvements, the overall trend in ecological livability is declining, indicating that the swift pace of urbanization and industrialization has placed considerable pressure on the region's ecological environment. Land use changes, driven by urban expansion and the growth in agricultural and industrial lands, have progressively encroached upon existing green spaces and water bodies, further deteriorating the ecological environment. Additionally, the region's topographical features have influenced its ecological livability; large terrain fluctuations have made soil erosion and geological disasters common. Despite the central plains' vast rivers providing ample water resources, over exploitation and ill-conceived hydrological constructions have led to escalating water scarcity. The area near the Gurbantunggut Desert in the north, with its extremely fragile ecological environment, has long been unsuitable for habitation. This study provides a crucial scientific basis for the future development of the U-Chang-Shi urban cluster and hopes to offer theoretical support and practical guidance for the sustainable development and ecological improvement of the region.

Study on the applicability of FAI linear fitting model in the extraction of cyanobacterial blooms.

Currently, more and more lakes around the world are experiencing outbreaks of cyanobacterial blooms, and high-precision and rapid monitoring of the spatial distribution of algae in water bodies is an important task. Remote sensing technology is one of the effective means for monitoring algae in water bodies. Studies have shown that the Floating Algae Index (FAI) is superior to methods such as the Standardized Differential Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) in monitoring cyanobacterial blooms. However, compared to the NDVI method, the FAI method has difficulty in determining the threshold, and how to choose the threshold with the highest classification accuracy is challenging. In this study, FAI linear fitting model (FAI-L) is selected to solve the problem that FAI threshold is difficult to determine. Innovatively combine FAI index and NDVI index, and use NDVI index to find the threshold of FAI index. In order to analyze the applicability of FAI-L to extract cyanobacterial blooms, this paper selected multi-temporal Landsat8, HJ-1B, and Sentinel-2 remote sensing images as data sources, and took Chaohu Lake and Taihu Lake in China as research areas to extract cyanobacterial blooms. The results show that (1) the accuracy of extracting cyanobacterial bloom by FAI-L method is generally higher than that by NDVI and FAI. Under different data sources and different research areas, the average accuracy of extracting cyanobacterial blooms by FAI-L method is 95.13%, which is 6.98% and 18.43% higher than that by NDVI and FAI respectively. (2) The average accuracy of FAI-L method for extracting cyanobacterial blooms varies from 84.09 to 99.03%, with a standard deviation of 4.04, which is highly stable and applicable. (3) For simultaneous multi-source image data, the FAI-L method has the highest average accuracy in extracting cyanobacterial blooms, at 95.93%, which is 6.77% and 13.26% higher than NDVI and FAI methods, respectively. In this paper, it is found that FAI-L method shows high accuracy and stability in extracting cyanobacterial blooms, and it can extract the spatial distribution of cyanobacterial blooms well, which can provide a new method for monitoring cyanobacterial blooms.

The ant and the grasshopper: Contrasting responses and behaviors to water stress of riparian trees along a hydroclimatic gradient.

Riparian trees are particularly vulnerable to drought because they are highly dependent on water availability for their survival. However, the response of riparian tree species to water stress varies depending on regional hydroclimatic conditions, making them unevenly vulnerable to changing drought patterns. Understanding this spatial variability in stress responses requires a comprehensive assessment of water stress across broader spatial and temporal scales. Yet, the precise ecophysiological mechanisms underlying these responses remain poorly linked to remotely sensed indices. To address this gap, the implementation of remote sensing methods coupled with in situ validation is essential to obtain consistent results across diverse spatial and temporal contexts. We conducted a multi-tool analysis combining multispectral and thermal remote sensing indices with in situ ecophysiological measurements at different temporal scales to analyze the responses of white poplar (Populus alba) to seasonal changes in drought along a hydroclimatic gradient. Using this approach, we demonstrate that white poplars along the Rhône River (France) exhibit contrasting responses and behaviors during drought depending on the latitudinal context. White poplars in a Mediterranean climate show rapid stomatal closure to reduce water loss and maintain high minimum water potential levels, although this results in a decrease in remotely sensed greenness. Conversely, white poplars located upstream in a temperate climate show high transpiration and stable greenness but lower minimum water potential and water content. A site in the middle of the gradient has intermediate responses. These results demonstrate that white poplars along a climate gradient can have a range of responses to drought along the iso/anisohydricity continuum. These results are important for future climatic conditions because they show that the same species can have different mechanisms of drought resilience, even in the same river valley. This raises questions regarding how these riparian tree populations will respond to future climatic and hydrological conditions.

Assessing climate-driven impacts on Karakoram glacier surge: A geospatial analysis highlighting Shishper Glacial Lake outburst flood events (2019-2022) as prime example.

The Northern Areas of Pakistan encompass the Hindukush, Karakoram, and Himalayan mountain ranges witnessing glacier surging, exacerbated by climate warming. As glaciers rapidly melt, ravines experience heightened blockage and migration, obstructing stream discharges and forming expansive ice-dammed lakes. The rupture of these natural dams triggers Glacial Lake Outburst Floods downstream in the primary glacier's ravine. The catastrophic Glacial Lake Outburst Floods in 2022 across the Karakoram ranges in Northern Pakistan prompted this study. It focuses on Shishper Glacier Lake. The aim is to provide complete flood observations and their devastating effects on downstream communities. Analysis of Landsat 08 Imagery reveals the evolution of Shishper Glacier Lake from its initiation in November 2018 to the catastrophic GLOF in May 2022. The lake reached a maximum area of 0.32 km2 in 2019 and its successive breaches on June 22, 2019, and May 29, 2020, reduced it to 0.018 km2. Draining continued until July 2021, shrinking the lake area to 0.009 km2. A noteworthy 2.73 °C temperature increase in 2022 correlated with an expansion of the lake area to 0.33 km2, culminating in the GLOF on May 7th, 2022. The study emphasizes the critical need for mapping, assessing, and monitoring surging glaciers and glacier-formed lakes in the Karakoram ranges to safeguard downstream communities from potential hazards.

Analysis of environmental variables and deforestation in the amazon using logistical regression models.

In this study, we applied a multivariate logistic regression model to identify deforested areas and evaluate the current effects on environmental variables in the Brazilian state of Rondônia, located in the southwestern Amazon region using data from the MODIS/Terra sensor. The variables albedo, temperature, evapotranspiration, vegetation index, and gross primary productivity were analyzed from 2000 to 2022, with surface type data from the PRODES project as the dependent variable. The accuracy of the models was evaluated by the parameters area under the curve (AUC), pseudo R2, and Akaike information criterion, in addition to statistical tests. The results indicated that deforested areas had higher albedo (25%) and higher surface temperatures (3.2 °C) compared to forested areas. There was a significant reduction of the EVI (16%), indicating water stress, and a decrease in GPP (18%) and ETr (23%) due to the loss of plant biomass. The most precise model (91.6%) included only surface temperature and albedo, providing important information about the environmental impacts of deforestation in humid tropical regions.

Detecting rare earth elements using EnMAP hyperspectral satellite data: a case study from Mountain Pass, California.

Rare earth elements (REEs) exhibit diagnostic absorption features in the visible-near infrared region, enabling their detection and identification via spectroscopic methods. Satellite-based remote sensing mapping of REEs, however, has not been attainable so far due to the necessity for high-quality hyperspectral data to resolve their narrow absorption features. This research leverages EnMAP hyperspectral satellite data to map REEs in Mountain Pass, California-a mining area known to host bastnaesite-Ce ore in sövite and beforsite carbonatites. By employing a polynomial fitting technique to characterize the diagnostic absorption features of Neodymium (Nd) at ∼740 and ∼800 nm, the surface occurrence of Nd was successfully mapped at a 30m pixel resolution. The relative abundance of Nd was represented using the continuum-removed area of the 800 nm feature. The resulting map, highlighting hundreds of anomalous pixels, was validated through laboratory spectroscopy, surface geology, and high-resolution satellite imagery. This study marks a major advancement in REE exploration, demonstrating for the first time, the possibility of directly detecting Nd in geologic environments using the EnMAP hyperspectral satellite data. This capability can offer a fast and cost-effective method for screening Earth's surfaces for REE signature, complementing the existing exploration portfolio and facilitating the discovery of new resources.

Fine classification of rice fields in high-resolution remote sensing images.

Fine-grained management of rice fields can enhance the yield and quality of rice crops. Challenges in achieving fine classification include interference from similar vegetation, the irregularity of natural field shapes, and complex scale variations. This paper introduces Rice Attention Cascade Network (RACNet), for the fine classification of rice fields in high-resolution satellite remote sensing imagery. The network employs the Hybrid Task Cascade network as the base framework and uses spectral and indices mixed multimodal data as input to reinforce the feature differentiation of similar vegetation. Initially, a Channel Attention Deformable-ResNet (CAD-ResNet) was designed to enhance the feature representation of rice on different channels. Deformable convolution improves the ability of CAD-ResNet to capture irregular field shapes. Then, to address the issue of complex scale changes, the multi-scale features extracted by the CAD-ResNet are progressively fused using an Asymptotic Feature Pyramid, reducing the loss of scale information between non-adjacent layers. Experiments on the Meishan rice dataset show that the proposed method is capable of accurate instance segmentation for fragmented or irregularly shaped rice fields. The evaluation metric AP50 of RACNet reaches 50.8%.

The urban air quality nexus: Assessing the interplay of land cover change and air pollution in emerging South Asian cities.

Air quality degradation presents a significant public health challenge, particularly in rapidly urbanizing regions where changes in land use/land cover (LULC) can dramatically influence pollution levels. This study investigates the association between LULC changes and air pollution (AP) in the five fastest-growing cities of Bangladesh from 1998 to 2021. Leveraging satellite data from Landsat and Sentinel-5P, the analysis reveals a substantial increase in urban areas and sparse vegetation, with declines in dense vegetation and water bodies over this period. Urban expansion was most pronounced in Sylhet (22-254%), while Khulna experienced the largest increase in sparse vegetation (2-124%). Dense vegetation loss was highest in Dhaka (20-77%) and water bodies (9-59%) over this period. Concentrations of six major air pollutants (APTs) - aerosol index, CO, HCHO, NO2, O3, and SO2 - were quantified, showing alarmingly high levels in densely populated industrial and commercial zones. Pearson's correlation indicates strong positive associations between APTs and urban land indices (R > 0.8), while negative correlations exist with vegetation indices. Geographically weighted regression modeling identifies city centers with dense urban built-up as pollution hotspots, where APTs exhibited stronger impacts on land cover changes (R2 > 0.8) compared to other land classes. The highest daily emissions were observed for O3 (1031 tons) and CO (356 tons) at Chittagong in 2021. In contrast, areas with substantial green cover displayed weaker pollutant-land cover associations. These findings underscore how unplanned urbanization drives AP by replacing natural land cover with emission sources, providing crucial insights to guide sustainable urban planning strategies integrating pollution mitigation and environmental resilience.

Assessing deep pools and water spread dynamics in semi-arid Banas River, India: a geospatial approach for conservation and sustainable management.

The deep pools are considered vital habitats for both aquatic and terrestrial biodiversity in arid and semi-arid rivers. These 'refugia' habitats sustain the aquatic biodiversity of local and regional importance when water flow ceases. Banas is an ecologically unique and non-perennial river in the Ganga Basin originating from the Aravalli Range and flowing through the semi-arid region of Rajasthan, India. This study maps and characterises the deep pools in the water stressed river using Sentinel-2 satellite data (2019-2022). Mapping and analysis were done using geospatial tools and field data. The composite map reported 2.18 km2 (0.6% of the total area) and 72.42 km2 (19.0% of the total area) of permanent water spread in the floodplain and reservoirs of Banas River, respectively with seasonal variations. A total of 558 contiguous habitats with varying sizes (50 to 314,422 m2) were delineated and most of them were located in the downstream of Bisalpur Dam especially along the river meandering. The composition of the area under different land use land cover classes in the riparian zone varied across the deep pools with medium land use intensity. The high proportion of vegetation and cropland near and far from the riparian buffer indicated existence of the natural and agrarian landscapes, respectively. The indications of various ecosystem services by the deep pools necessitate spatial quantification. Additionally, impact of the various anthropogenic threats on aquatic habitats recommends measures for habitat restoration and conservation planning of Banas River.

Marine debris induced by the Great East Japan Earthquake and Tsunami: A multi-sensor remote sensing assessment.

Using satellite remote sensing, we show the distribution, dominant type, and amounts of marine debris off the northeast coast of Japan after the Great East Japan Earthquake on 11 March 2011 and subsequent tsunami. Extensive marine debris was found on March 12, with the maximal amount found on March 13. The debris was found to be mainly wood (possibly lumber wood), with an estimated 1.5 million metric tons in an elongated water area of 6800 km2 (18 km E-W and 380 km N-S) near parallel to the coast between 36.75°N and 40.25°N. The amount decreased rapidly with time, with scattered debris patches captured in high-resolution satellite images up to April 6. These results provide new insights on the initial distribution of the Japanese Tsunami Marine Debris, which may be used to help find bottom deposition of debris and help refine numerical models to predict the debris trajectory and fate. SYNOPSIS: Marine debris induced by the 2011 Great East Japan Earthquake and Tsunami is found to be mainly composed of wood and possibly lumber wood from constructions, with maximum amount on 13 March 2011 distributed within a narrow band of ∼18 km near parallel to the northeast coast of Japan between 36.75°N and 40.25°N.