From sky to road: Incorporating the satellite imagery into analysis of freight truck-related crash factors

Freight truck-related crashes in urban contexts have caused significant economic losses and casualties, making it increasingly essential to understand the spatial patterns of such crashes. Limitations regarding data availability have greatly undermined the generalizability and applicability of certain prior research findings. This study explores the potential of emerging geospatial data to delve deeply into the determinants of these incidents with a more generalizable research design. By synergizing high-resolution satellite imagery with refined GIS map data and geospatial tabular data, a rich tapestry of the road environment and freight truck operations emerges. To navigate the challenges of zero-inflated issues of the crash datasets, the Tweedie Gradient Boosting model is adopted. Results reveal a pronounced spatial heterogeneity between highway and urban non-highway road networks in crash determinants. Factors such as freight truck activity, intricate road network patterns, and vehicular densities rise to prominence, albeit with varying degrees of influence across highways and urban non-highway terrains. Results emphasize the need for context-specific interventions for policymakers, encompassing optimized urban planning, infrastructural overhauls, and refined traffic management protocols. This endeavor may not only elevate the academic discourse around freight truck-related crashes but also champion a data-driven approach towards safer road ecosystems for all.

Landsat observations of total suspended solids concentrations in the Pearl River Estuary, China, over the past 36 years

Information on long-term trends in total suspended solids (TSS) is critical for assessing aquatic ecosystems. However, the long-term patterns of TSS concentration (CTSS) and its latent drivers have not been well investigated. In this study, we developed and validated three semi-analysis algorithms for deriving CTSS using Landsat images. Subsequently, the long-term trends in CTSS in the Pearl River Estuary (PRE) from 1987 to 2022 and the driving factors were clarified. The developed algorithms yielded excellent performance in estimating CTSS, with mean absolute percentage errors <25% and root mean square errors of <13 mg/L. Long-term Landsat observations showed an overall decreasing trend and significant spatiotemporal dynamics of the CTSS in the PRE from 1987 to 2022. The analysis of driving factors suggested that industrial sewage, cropland, forests and grasslands, and built-up land were the four potential driving forces that explained 87.81% of the long-term variation in CTSS. This study not only provides 36-year recorded datasets of CTSS in estuary water, but also offers new insights into the complex mechanisms that regulate CTSS spatiotemporal dynamics for water resource management.

Exploring the spatial association between the distribution of temperature and urban morphology with green view index

Urban heat islands will occur if city neighborhoods contain insufficient green spaces to create a comfortable environment, and residents' health will be adversely affected. Current satellite imagery can only effectively identify large-scale green spaces and cannot capture street trees or potted plants within three-dimensional building spaces. In this study, we used a deep convolutional neural network semantic segmentation model on Google Street View to extract environmental features at the neighborhood level in Taipei City, Taiwan, including the green vegetation index (GVI), building view factor, and sky view factor. Monthly temperature data from 2018 to 2021 with a 0.01° spatial resolution were used. We applied a linear mixed-effects model and geographically weighted regression to explore the association between pedestrian-level green spaces and ambient temperature, controlling for seasons, land use information, and traffic volume. Their results indicated that a higher GVI was significantly associated with lower ambient temperatures and temperature differences. Locations with higher traffic flows or specific land uses, such as religious or governmental, are associated with higher ambient temperatures. In conclusion, the GVI from street-view imagery at the community level can improve the understanding of urban green spaces and evaluate their effects in association with other social and environmental indicators.

Assessing Tunisia's urban air quality using combined lichens and Sentinel-5 satellite integration

In Tunisia, urban air pollution is becoming a bigger problem. This study used a combined strategy of biomonitoring with lichens and satellite mapping with Sentinel-5 satellite data processed in Google Earth Engine (GEE) to assess the air quality over metropolitan Tunis. Lichen diversity was surveyed across the green spaces of the Faculty of Science of Tunisia sites, revealing 15 species with a predominance of pollution-tolerant genera. The Index of Atmospheric Purity (IAP) calculated from the lichen data indicated poor air quality. Spatial patterns of pollutants sulfur dioxide (SO2), ozone (O3), nitrogen dioxide (NO2), carbon monoxide (CO), and aerosol index across Greater Tunis were analyzed from Sentinel-5 datasets on the GEE platform. The higher values of these indices in the research area indicate that it may be impacted by industrial activity and highlight the considerable role that vehicle traffic plays in air pollution. The results of the IAP, IBL, and the combined ground-based biomonitoring and satellite mapping techniques confirm poor air quality and an environment affected by atmospheric pollutants which will enable proactive air quality management strategies to be put in place in Tunisia's rapidly expanding cities.

How accurate are existing land cover maps for agriculture in Sub-Saharan Africa?

Satellite Earth observations (EO) can provide affordable and timely information for assessing crop conditions and food production. Such monitoring systems are essential in Africa, where food insecurity is high and agricultural statistics are sparse. EO-based monitoring systems require accurate cropland maps to provide information about croplands, but there is a lack of data to determine which of the many available land cover maps most accurately identify cropland in African countries. This study provides a quantitative evaluation and intercomparison of 11 publicly available land cover maps to assess their suitability for cropland classification and EO-based agriculture monitoring in Africa using statistically rigorous reference datasets from 8 countries. We hope the results of this study will help users determine the most suitable map for their needs and encourage future work to focus on resolving inconsistencies between maps and improving accuracy in low-accuracy regions.

Global WaterPack - The development of global surface water over the past 20 years at daily temporal resolution

Open surface water across the globe is essential for many life forms and is an important source for human settlements, agriculture, and industry. The presence and variation in time and space is influenced by different natural conditions (e.g. climate, topography, geology) and human use (e.g. irrigation, flood protection). The information on the spatial and temporal distribution of open surface water is fundamental for many disciplines and is also required as an essential parameter for hydrological and climatological modelling. Here, we present a dataset derived from satellite earth observation, which is based on more than 6.3 million single MODIS products with a volume of approx. 300 TB. The resulting dataset reflects the situation of open surface water on a global scale for each day over the time period from 2003 to 2022 at a spatial resolution of 250 m. The dataset enables the analysis of the development of lake and reservoir surface areas, freezing cycles, and inundation areas.

Multispectral analysis-ready satellite data for three East African mountain ecosystems

The East African mountain ecosystems are facing increasing threats due to global change, putting their unique socio-ecological systems at risk. To monitor and understand these changes, researchers and stakeholders require accessible analysis-ready remote sensing data. Although satellite data is available for many applications, it often lacks accurate geometric orientation and has extensive cloud cover. This can generate misleading results and make it unreliable for time-series analysis. Therefore, it needs comprehensive processing before usage, which encompasses multi-step operations, requiring large computational and storage capacities, as well as expert knowledge. Here, we provide high-quality, atmospherically corrected, and cloud-free analysis-ready Sentinel-2 imagery for the Bale Mountains (Ethiopia), Mounts Kilimanjaro and Meru (Tanzania) ecosystems in East Africa. Our dataset ranges from 2017 to 2021 and is provided as monthly and annual aggregated products together with 24 spectral indices. Our dataset enables researchers and stakeholders to conduct immediate and impactful analyses. These applications can include vegetation mapping, wildlife habitat assessment, land cover change detection, ecosystem monitoring, and climate change research.

An evaluative technique for drought impact on variation in agricultural LULC using remote sensing and machine learning

Drought events threaten freshwater reservoirs and agricultural productivity, particularly in semi-arid regions characterized by erratic rainfall. This study evaluates a novel technique for assessing the impact of drought on LULC variations in the context of climate change from 2018 to 2022. Various data sources were harnessed, encompassing Sentinel-2 satellite imagery for LULC classification, climate data from the CHIRPS and AgERA5 databases, geomorphological data from JAXA's ALOS satellite, and a drought indicator (Vegetation Health Index (VHI)) derived from MODIS data. Two classifier models, namely gradient tree boost (GTB) and random forest (RF), were trained and assessed for LULC classification, with performance evaluated by overall accuracy (OA) and kappa coefficient (K). Notably, the GTB model exhibited superior performance, with OA > 90% and a K > 0.9. Over the period from 2018 to 2022, Fez experienced LULC changes of 19.92% expansion in built-up areas, a 34.86% increase in bare land, a 17.86% reduction in water bodies, and a 37.30% decrease in agricultural land. Positive correlations of 0.81 and 0.89 were observed between changes in agricultural LULC, rainfall, and VHI. Furthermore, mild drought conditions were identified in the years 2020 and 2022. This study emphasizes the importance of AI and remote sensing techniques in assessing drought and environmental changes, with potential applications for improving existing drought monitoring systems.

A straightforward approach for the rapid detection of red Noctiluca scintillans blooms from satellite imagery

Red Noctiluca scintillans (RNS), a prominent species of dinoflagellate known for its conspicuous size and ability to form blooms, exhibits heterotrophic behavior and functions as a microzooplankton grazer within the marine food web. In this study, a straightforward technique referred to as the blue-green index (BGI) has been introduced for the purpose of distinguishing and discerning RNS from neighboring waters, owing to its pronounced absorption in the blue-green spectral range. This method has been applied across a range of satellite imagery, encompassing both multi-spectral and hyperspectral sensors. The study delved into three instances of bloom occurrences caused by RNS: firstly, in November 2014 and April 2022 off the western coast of Guangdong, and secondly, in February 2021 within the Beibu Gulf. The notable bloom event in the Beibu Gulf during February 2021 extended across an expansive area totaling 6933.5 km2. The motion speed and direction of the RNS bloom patches were also derived from successive satellite images. The recently introduced BGI method demonstrates insensitivity to suspended sediment, though its successful application necessitates accurate atmospheric correction. Subsequent efforts will involve the quantification of RNS blooms in a more precise manner, utilizing hyperspectral satellite data grounded in optimized band configurations.

Climate velocities and species tracking in global mountain regions

Mountain ranges contain high concentrations of endemic species and are indispensable refugia for lowland species that are facing anthropogenic climate change1,2. Forecasting biodiversity redistribution hinges on assessing whether species can track shifting isotherms as the climate warms3,4. However, a global analysis of the velocities of isotherm shifts along elevation gradients is hindered by the scarcity of weather stations in mountainous regions5. Here we address this issue by mapping the lapse rate of temperature (LRT) across mountain regions globally, both by using satellite data (SLRT) and by using the laws of thermodynamics to account for water vapour6 (that is, the moist adiabatic lapse rate (MALRT)). By dividing the rate of surface warming from 1971 to 2020 by either the SLRT or the MALRT, we provide maps of vertical isotherm shift velocities. We identify 17 mountain regions with exceptionally high vertical isotherm shift velocities (greater than 11.67 m per year for the SLRT; greater than 8.25 m per year for the MALRT), predominantly in dry areas but also in wet regions with shallow lapse rates; for example, northern Sumatra, the Brazilian highlands and southern Africa. By linking these velocities to the velocities of species range shifts, we report instances of close tracking in mountains with lower climate velocities. However, many species lag behind, suggesting that range shift dynamics would persist even if we managed to curb climate-change trajectories. Our findings are key for devising global conservation strategies, particularly in the 17 high-velocity mountain regions that we have identified.

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 30 m 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.

High resolution optical and acoustic remote sensing datasets of the Puck Lagoon

The very shallow marine basin of Puck Lagoon in the southern Baltic Sea, on the Northern coast of Poland, hosts valuable benthic habitats and cultural heritage sites. These include, among others, protected Zostera marina meadows, one of the Baltic's major medieval harbours, a ship graveyard, and likely other submerged features that are yet to be discovered. Prior to this project, no comprehensive high-resolution remote sensing data were available for this area. This article describes the first Digital Elevation Models (DEMs) derived from a combination of airborne bathymetric LiDAR, multibeam echosounder, airborne photogrammetry and satellite imagery. These datasets also include multibeam echosounder backscatter and LiDAR intensity, allowing determination of the character and properties of the seafloor. Combined, these datasets are a vital resource for assessing and understanding seafloor morphology, benthic habitats, cultural heritage, and submerged landscapes. Given the significance of Puck Lagoon's hydrographical, ecological, geological, and archaeological environs, the high-resolution bathymetry, acquired by our project, can provide the foundation for sustainable management and informed decision-making for this area of interest.

Mapping Road Surface Type of Kenya Using OpenStreetMap and High-resolution Google Satellite Imagery

Identifying road surface types (paved or unpaved) can ensure road vehicle safety, reduce energy consumption, and promote economic development. Existing studies identified road surface types by using sensors mounted on mobile devices and high-resolution satellite images that are not openly accessible, which makes it difficult to apply them to large-scale (e.g., national or regional) study areas. Addressing this issue, this study developed a dataset of road surface types (paved and unpaved) for the national road network of Kenya, containing 1,267,818 road segments classified as paved or unpaved. To accomplish this, this study proposes a method that integrates crowdsourced geographic data (OpenStreetMap) and Google satellite imagery to identify road surface types. The accuracy, recall, and F1 score of the method were all above 0.94, validating the effectiveness of the method. The data sources of the method are freely available, and the method may be applied to other countries and regions. The dataset developed based on the method can provide data support and decision support for local governments to improve road infrastructure.

Urban heat load assessment in Zagreb, Croatia: a multi-scale analysis using mobile measurement and satellite imagery

A limited number of meteorological stations and sparse data challenge microclimate assessment in urban areas. Therefore, it is necessary to complement these data with additional measurements to achieve a denser spatial coverage, enabling a detailed representation of the city's microclimatic features. In this study, conducted in Zagreb, Croatia, mobile air temperature measurements were utilized and compared with satellite-derived land surface temperature (LST). Here, air temperature measurements were carried out using bicycles and an instrument with a GPS receiver and temperature probe during a heat wave in June 2021, capturing the spatial pattern of air temperature to highlight the city's microclimate characteristics (i.e. urban heat load; UHL) in extremely hot weather conditions. Simultaneously, remotely sensed LST was retrieved from the Landsat-8 satellite. Air temperature measurements were compared to city-specific street type classification, while neighbourhood heat load characteristics were analysed based on local climate zones (LCZ) and LST. Results indicated significant thermal differences between surface types and urban forms and between street types and LCZs. Air temperatures reached up to 35 °C, while LST exceeded 40 °C. City parks, tree-lined streets and areas near blue infrastructure were 1.5-3 °C cooler than densely built areas. Temperature contrasts between LCZs in terms of median LST were more emphasised and reached 9 °C between some classes. These findings highlight the importance of preserving green areas to reduce UHL and enhance urban resilience. Here, exemplified by the city of Zagreb, it has been demonstrated that the use of multiple datasets allows a comprehensive understanding of temperature patterns and their implications for urban climate research.

Nanoparticles in terrestrial sediments and the behavior of the spectral optics of Sentinel-3B OLCI Satellite images in a river basin of UNESCO World Cultural and Natural Heritage

The dangerous chemical elements associated with nanoparticles (NPs) and ultra-fine sediment particles in hydrological bays have the capacity to move contaminants to large oceanic regions. The general objective of this study is to quantify the major chemical elements present in NPs and ultra-fine particles in aquatic sediments sampled from Guanabara Bay and compare these data to values determined through spectral optics using the Sentinel-3B Satellite OLCI (Ocean Land Color Instrument) during the winter and summer seasons of 2018, 2019, 2020, 2021, and 2022. This is done to highlight the impacts anthropogenic environmental hazards have on the marine ecosystem and human beings. Ten aquatic sediment field collection points were selected by triangulated irregular network (TIN). Samples were subjected to analysis by X-ray diffraction (XRD), scanning electron microscopy (SEM), electron dispersion spectroscopy (EDS), and transmission electron microscopy (TEM), which enabled a detailed analysis using scanning transmission electron microscopy (STEM). Geospatial analyses using Sentinel-3B OLCI Satellite images considered Water Full Resolution (WFR) at 300 m resolution, in neural network (NN), normalized at 0.83 µg/mg. A maximum average spectral error of 6.62% was utilized for the identification of the levels of Absorption Coefficient of Detritus and Gelbstoff (ADG443_NN) at 443 m-1, Chlorophyll-a (CHL_NN) (m-3), and Total Suspended Matter (TSM_NN) (g m-3) at 581 sample points. The results showed high levels of ADG443_NN, with average values as high as of 4444 m-1 (summer 2021). When related to the analyses of nanoparticulate sediments and ultrafine particles collected in the field, they showed the presence of major chemical elements such as Ge, As, Cr, and others, highly toxic to human health and the aquatic environment. The application of satellite and terrestrial surveys proved to be efficient, in addition to the possibility of this study being applied to other hydrological systems on a global scale.

A comparative study of satellite altimetry-based and DEM-based methods for estimating lake water volume changes

This study compared two different methods, the satellite altimetry-based and DEM (digital elevation model)-based, for estimating lake water volume changes. We focused on 34 lakes in China as the testing sites to compare the two methods for lake water volume changes from 2005 to 2020. The satellite altimetry-based method used water levels provided by the DAHITI (Database for Hydrological Time Series of Inland Waters) data and surface areas derived from Landsat imagery. The DEM-based method used the SRTM DEM data in combination with Landsat-derived lake extents. Our results showed a high degree of consistency in lake water volume changes estimated between the two methods (R2 > 0.90), but each method has its limitations. In terms of temporal coverage, the satellite altimetry-based method with the DAHITI data is limited by missing water level data in certain periods. The performance of the DEM-based method in extracting lake shore boundaries in regions with flat terrains (slope <1.5°) is not satisfactory. The DEM-based method has complete regional applicability (100%) in the Tibetan Plateau (TP) Lake Region, yet its effectiveness drops significantly in the Xinjiang and Eastern China Plain Lake Regions, with applicability rates of 50 and 40%, respectively.

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.

Satellite imagery as a management tool for monitoring water clarity across freshwater ponds on Cape Cod, Massachusetts

Water clarity serves as both an indicator and a regulator of biological function in aquatic systems. Large-scale, consistent water clarity monitoring is needed for informed decision-making. Inland freshwater ponds and lakes across Cape Cod, a 100-km peninsula in Massachusetts, are of particular interest for water clarity monitoring. Secchi disk depth (SDD), a common measure of water clarity, has been measured intermittently for over 200 Cape Cod ponds since 2001. Field-measured SDD data were used to estimate SDD from satellite data, leveraging the NASA/USGS Landsat Program and Copernicus Sentinel-2 mission, spanning 1984 to 2022. Random forest machine learning models were generated to estimate SDD from satellite reflectance data and maximum pond depth. Spearman rank correlations (rs) were "strong" for Landsat 5 and 7 (rs = 0.78 and 0.79), and "very strong" for Landsat 8, 9, and Sentinel-2 (rs = 0.83, 0.86, and 0.80). Mean absolute error also indicated strong predictive capacity, ranging from 0.65 to 1.05 m, while average bias ranged from -0.20 to 0.06 m. Long- and recent short-term changes in satellite-estimated SDD were assessed for 193 ponds, selected based on surface area and the availability of maximum pond depth data. Long-term changes between 1984 and 2022 established a retrospective baseline using the Mann-Kendall test for trend and Theil-Sen slope. Generally, long-term water clarity improved across the Cape; 149 ponds indicated increasing water clarity, and 8 indicated deteriorating water clarity. Recent short-term changes between 2021 and 2022 identified ponds that may benefit from targeted management efforts using the Mann-Whitney U test. Between 2021 and 2022, 96 ponds indicated deteriorations in water clarity, and no ponds improved in water clarity. While the 193 ponds analyzed here constitute only one quarter of Cape Cod ponds, they represent 85% of its freshwater surface area, providing the most spatially and temporally comprehensive assessment of Cape Cod ponds to date. Efforts are focused on Cape Cod, but can be applied to other areas given the availability of local field data. This study defines a framework for monitoring and assessing change in satellite-estimated SDD, which is important for both local and regional management and resource prioritization.

One to twelve-month-ahead forecasting of MODIS-derived Qinghai Lake area, using neuro-fuzzy system hybridized by firefly optimization

Lakes, as the main sources of surface water, are of great environmental and ecological importance and largely affect the climatic conditions of the surrounding areas. Lake area fluctuations are very effective on plant and animal biodiversity in the areas covered. Hence, accurate and reliable forecasts of ​lake area might provide the awareness of water and climate resources and the survival of various species dependent on area fluctuations. Using machine learning methods, the current study numerically predicted area fluctuations of ​China's largest lake, Qinghai, over 1 to 12 months ahead of lead time. To this end, Moderate Resolution Imaging Spectroradiometer (MODIS) sensor images were used to monitor the monthly changes in the area of ​the lake from 2000 to 2021. Predictive inputs included the MODIS-derived lake area time latency specified by the autocorrelation function. The data was divided into two periods of the train (initial 75%) and test (final 25%), and the input combinations were arranged so that the model in the test period could be used to predict 12 scenarios, including forecast horizons for the next 1 to 12 months. The adaptive neuro-fuzzy inference system (ANFIS) was utilized as a predictive model. The firefly algorithm (FA) was also used to optimize ANFIS and improve its accuracy, as a hybrid model ANFIS-FA. Based on evaluation criteria such as root mean square error (RMSE) (477-594 km2) and R2 (88-92%), the results confirmed the acceptable accuracy of the models in all forecast horizons, even long-term horizons (10 months, 11 months, and 12 months). Based on the normalized RMSE criterion (0.095-0.125), the models' performance was reported to be appropriate. Furthermore, the firefly algorithm improved the prediction accuracy of the ANFIS model by an average of 16.9%. In the inter-month survey, the models had fewer forecast errors in the dry months (February-March) than in the wet months (October-November). Using the current method can provide remarkable information about the future state of lakes, which is very important for managers and planners of water resources, environment, and natural ecosystems. According to the results, the current approach is satisfactory in predicting MODIS-derived fluctuations of Qinghai Lake area and has research value for other lakes.

Next-level vegetation health index forecasting: A ConvLSTM study using MODIS Time Series

The Vegetation Health Index (VHI) is a metric used to assess the health and condition of vegetation, based on satellite-derived data. It offers a comprehensive indicator of stress or vigor, commonly used in agriculture, ecology, and environmental monitoring for forecasting changes in vegetation health. Despite its advantages, there are few studies on forecasting VHI as a future projection, particularly using up-to-date and effective machine learning methods. Hence, the primary objective of this study is to forecast VHI values by utilizing remotely sensed images. To achieve this objective, the study proposes employing a combined Convolutional Neural Network (CNN) and a specific type of Recurrent Neural Network (RNN) called Long Short-Term Memory (LSTM), known as ConvLSTM. The VHI time series images are calculated based on the Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST) data obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard the Terra and Aqua satellites. In addition to the traditional image-based calculation, the study suggests using global minimum and global maximum values (global scale) of NDVI and LST time series for calculating the VHI. The results of the study showed that the ConvLSTM with a 1-layer structure generally provided better forecasts than 2-layer and 3-layer structures. The average Root Mean Square Error (RMSE) values for the 1-step, 2-step, and 3-step ahead VHI forecasts were 0.025, 0.026, and 0.026, respectively, with each step representing an 8-day forecast horizon. Moreover, the proposed global scale model using the applied ConvLSTM structures outperformed the traditional VHI calculation method.

Advancing crop classification in smallholder agriculture: A multifaceted approach combining frequency-domain image co-registration, transformer-based parcel segmentation, and Bi-LSTM for crop classification

Agricultural Remote Sensing has the potential to enhance agricultural monitoring in smallholder economies to mitigate losses. However, its widespread adoption faces challenges, such as diminishing farm sizes, lack of reliable data-sets and high cost related to commercial satellite imagery. This research focuses on opportunities, practices and novel approaches for effective utilization of remote sensing in agriculture applications for smallholder economies. The work entails insights from experiments using datasets representative of major crops during different growing seasons. We propose an optimized solution for addressing challenges associated with remote sensing-based crop mapping in smallholder agriculture farms. Open source tools and data are used for inter and intra-sensor image registration, with a root mean square error of 0.3 or less. We also propose and emphasize on the use of delineated vegetation parcels through Segment Anything Model for Geospatial (SAM-GEOs). Furthermore a Bidirectional-Long Short-Term Memory-based (Bi-LSTM) deep learning model is developed and trained for crop classification, achieving results with accuracy of more than 94% and 96% for validation sets of two data sets collected in the field, during 2 growing seasons.

Spatio-temporal correspondence of aerosol optical depth between CMIP6 simulations and MODIS retrievals over India

In the present work, the aerosol optical depth (AOD) at 550 nm of the moderate resolution imaging spectroradiometer (MODIS) onboard the Terra satellite was utilised to evaluate the AOD simulations of newly emerged general circulation models (GCMs) of coupled model intercomparison project-phase 6 (CMIP6) over the Indian landmass. Further, the AOD from the CMIP6 models has been compared with its previous generation models from CMIP5 to examine the extent of uncertainties in AOD with reference to the MODIS AOD datasets. The evolution of aerosols over India using the different shared socioeconomic pathways (SSPs) has also been studied till the year 2050. The results show that the CMIP5 and CMIP6 models underestimated the mean annual AOD of the Indian region as a whole. A multi-model mean (MMM) of thirteen GCMs from CMIP6 showed an underestimation of AOD by 40 to 60% over the Indo-Gangetic plains, while an overestimation of 60 to 80% in AOD was observed over the Peninsular and Central Indian regions in comparison with MODIS for the study period of 2001 to 2014. In future simulations, the pathway SSP370 has shown a significant increasing trend of AOD whereas SSP126 and SSP585 have shown significant decreasing trends of AOD by the year 2050. In the future, the changes in the AOD will mainly be contributed by the anthropogenic aerosols (AOA, BC, and Sulphates) emissions in all SSPs. The large bias of MMM with the MODIS requires further research in terms of analysing the accuracy of emission datasets that have been used to simulate the AODs by the CMIP6 models over the Indian region.

Improving on mapping long-term surface water with a novel framework based on the Landsat imagery series

Surface water plays a crucial role in the ecological environment and societal development. Remote sensing detection serves as a significant approach to understand the temporal and spatial change in surface water series (SWS) and to directly construct long-term SWS. Limited by various factors such as cloud, cloud shadow, and problematic satellite sensor monitoring, the existent surface water mapping datasets might be short and incomplete due to losing raw information on certain dates. Improved algorithms are desired to increase the completeness and quality of SWS datasets. The present study proposes an automated framework to detect SWS, based on the Google Earth Engine and Landsat satellite imagery. This framework incorporates implementing a raw image filtering algorithm to increase available images, thereby expanding the completeness. It improves OTSU thresholding by replacing anomaly thresholds with the median value, thus enhancing the accuracy of SWS datasets. Gaps caused by Landsat7 ETM + SLC-off are respired with the random forest algorithm and morphological operations. The results show that this novel framework effectively expands the long-term series of SWS for three surface water bodies with distinct geomorphological patterns. The evaluation of confusion matrices suggests the good performance of extracting surface water, with the overall accuracy ranging from 0.96 to 0.97, and user's accuracy between 0.96 and 0.98, producer's accuracy ranging from 0.83 to 0.89, and Matthews correlation coefficient ranging from 0.87 to 0.9 for several spectral water indices (NDWI, MNDWI, ANNDWI, and AWEI). Compared with the Global Reservoirs Surface Area Dynamics (GRSAD) dataset, our constructed datasets promote greater completeness of SWS datasets by 27.01%-91.89% for the selected water bodies. The proposed framework for detecting SWS shows good potential in enlarging and completing long-term global-scale SWS datasets, capable of supporting assessments of surface-water-related environmental management and disaster prevention.

Remote Sensing-Based Extension of GRDC Discharge Time Series - A Monthly Product with Uncertainty Estimates

The Global Runoff Data Center (GRDC) data set has faced a decline in the number of active gauges since the 1980s, leaving only 14% of gauges active as of 2020. We develop the Remote Sensing-based Extension for the GRDC (RSEG) data set that can ingest legacy gauge discharge and remote sensing observations. We employ a stochastic nonparametric mapping algorithm to extend the monthly discharge time series for inactive GRDC stations, benefiting from satellite imagery- and altimetry-derived river width and water height observations. After a rigorous quality assessment of our estimated discharge, involving statistical validation, tests and visual inspection, results in the extension of discharge records for 3377 out of 6015 GRDC stations. The quality of discharge estimates for the rivers with a large or medium mean discharge is quite satisfactory (average KGE value > 0.5) however for river reaches with a low mean discharge the average KGE value drops to 0.33.The RSEG data set regains monitoring capability for 83% of total river discharge measured by GRDC stations, equivalent to 7895 km3/month.

Ground far-red sun-induced chlorophyll fluorescence and vegetation indices in the US Midwestern agroecosystems

Sun-induced chlorophyll fluorescence (SIF) provides an opportunity to study terrestrial ecosystem photosynthesis dynamics. However, the current coarse spatiotemporal satellite SIF products are challenging for mechanistic interpretations of SIF signals. Long-term ground SIF and vegetation indices (VIs) are important for satellite SIF validation and mechanistic understanding of the relationship between SIF and photosynthesis when combined with leaf- and canopy-level auxiliary measurements. In this study, we present and analyze a total of 15 site-years of ground far-red SIF (SIF at 760 nm, SIF760) and VIs datasets from soybean, corn, and miscanthus grown in the U.S. Corn Belt from 2016 to 2021. We introduce a comprehensive data processing protocol, including different retrieval methods, calibration coefficient adjustment, and nadir SIF footprint upscaling to match the eddy covariance footprint. This long-term ground far-red SIF and VIs dataset provides important and first-hand data for far-red SIF interpretation and understanding the mechanistic relationship between far-red SIF and canopy photosynthesis across various crop species and environmental conditions.

Transitioning from MODIS to VIIRS Global Water Reservoir Product

Reservoirs play a crucial role in regulating water availability and enhancing water security. Here, we develop NASA's Visible Infrared Imaging Radiometer Suite (VIIRS) based Global Water Reservoir (GWR) product, consisting of measurements of reservoir area, elevation, storage, evaporation rate, and evaporation loss for 164 large global reservoirs. The dataset is available at 8-day and monthly temporal resolutions. Since the Moderate Resolution Imaging Spectroradiometer (MODIS) is close to the end of its life, we further evaluated the consistency between MODIS and VIIRS-based GWR to ensure continuity to the 20+ year MODIS GWR product. Independent assessment of VIIRS reservoir storage (8-day) retrievals against in-situ measurements shows an average of R2 = 0.84, RMSE = 0.47 km3, and NRMSE = 16.45%. The evaporation rate has an average of R2 = 0.56, RMSE = 1.32 mm/day, and NRMSE = 28.14%. Furthermore, results show good consistency (R2 ≥ 0.90) between the VIIRS and MODIS-based product components, confirming that long-term data continuity can be achieved. This dataset can provide valuable insights for long-term trend analysis, hydrological modeling, and understanding hydroclimatic extremes in the context of reservoirs.

ENVINet5 deep learning change detection framework for the estimation of agriculture variations during 2012-2023 with Landsat series data

Remote sensing is one of the most important methods for analysing the multitemporal changes over a certain period. As a cost-effective way, remote sensing allows the long-term analysis of agricultural land by collecting satellite imagery from different satellite missions. Landsat is one of the longest-running world missions which offers a moderate-resolution earth observation dataset. Land surface mapping and monitoring are generally performed by incorporating classification and change detection models. In this work, a deep learning-based change detection (DCD) algorithm has been proposed to detect long-term agricultural changes using the Landsat series datasets (i.e., Landsat-7, Landsat-8, and Landsat-9) during the period 2012 to 2023. The proposed algorithm extracts the features from satellite data according to their spectral and geographic characteristics and identifies seasonal variability. The DCD integrates the deep learning-based (Environment for visualizing images) ENVI Net-5 classification model and posterior probability-based post-classification comparison-based change detection model (PCD). The DCD is capable of providing seasonal variations accurately with distinct Landsat series dataset and promises to use higher resolution dataset with accurate results. The experimental result concludes that vegetation has decreased from 2012 to 2023, while build-up land has increased up to 88.22% (2012-2023) for Landsat-7 and Landsat-8 datasets. On the other side, degraded area includes water (3.20-0.05%) and fallow land (1-0.59%). This study allows the identification of crop growth, crop yield prediction, precision farming, and crop mapping.

Using deep transfer learning and satellite imagery to estimate urban air quality in data-poor regions

Urban air pollution is a critical public health challenge in low-and-middle-income countries (LMICs). At the same time, LMICs tend to be data-poor, lacking adequate infrastructure to monitor air quality (AQ). As LMICs undergo rapid urbanization, the socio-economic burden of poor AQ will be immense. Here we present a globally scalable two-step deep learning (DL) based approach for AQ estimation in LMIC cities that mitigates the need for extensive AQ infrastructure on the ground. We train a DL model that can map satellite imagery to AQ in high-income countries (HICs) with sufficient ground data, and then adapt the model to learn meaningful AQ estimates in LMIC cities using transfer learning. The trained model can explain up to 54% of the variation in the AQ distribution of the target LMIC city without the need for target labels. The approach is demonstrated for Accra in Ghana, Africa, with AQ patterns learned and adapted from two HIC cities, specifically Los Angeles and New York.

Modeling NO2 air pollution variation during and after COVID-19-regulation using principal component analysis of satellite imagery

By implementing Principal Component Analysis (PCA) of multitemporal satellite data, this paper presents modeling solutions for air pollutant variation in three scenarios related to COVID-19 lockdown: pre, during, and after lockdown. Tropospheric NO2 satellite data from Sentinel-5P was used. Two novel PCA-models were developed: Weighted Principal Component Analysis (WPCA) and Rescaled Principal Component Analysis (RPCA). Model results were tested for goodness-of-fit to empirical NO2 data. The models were used to predict actual near-surface NO2 concentrations. Model-predicted NO2 concentrations were validated with NO2 data acquired at ground monitoring stations. Besides, meteorological bias affecting NO2 was assessed. It was found that the weather component had substantial impact on NO2 built-ups, propitiating air pollutant decrease during lockdown and increase after. WPCA and RPCA models well fitted to observed NO2. Both models accurately estimated near-surface NO2 concentrations. Modeled NO2 variation results evidenced the prolongated effect of the total lockdown (up to half a year). Model-predicted NO2 concentrations were found to highly correlate with monitoring station NO2 data collected on the ground. It is concluded that PCA is reliable in identifying and predicting air pollution variation patterns. The implementation of PCA is recommended when analyzing other pollutant gases.

Exploring machine learning and statistical approach techniques for landslide susceptibility mapping in Siwalik Himalayan Region using geospatial technology

Landslides are a natural threat that poses a severe risk to human life and the environment. In the Kumaon mountains region in Uttarakhand (India), Nainital is among the most vulnerable areas prone to landslides inflicting harm to livelihood and civilization due to frequent landslides. Developing a landslide susceptibility map (LSM) in this Nainital area will help alleviate the probability of landslide occurrence. GIS and statistical-based approaches like the certainty factor (CF), information value (IV), frequency ratio (FR) and logistic regression (LR) are used for the assessment of LSM. The landslide inventories were prepared using topography, satellite imagery, lithology, slope, aspect, curvature, soil, land use and land cover, geomorphology, drainage density and lineament density to construct the geodatabase of the elements affecting landslides. Furthermore, the receiver operating characteristic (ROC) curve was used to check the accuracy of the predicting model. The results for the area under the curves (AUCs) were 87.8% for logistic regression, 87.6% for certainty factor, 87.4% for information value and 84.8% for frequency ratio, which indicates satisfactory accuracy in landslide susceptibility mapping. The present study perfectly combines GIS and statistical approaches for mapping landslide susceptibility zonation. Regional land use planners and natural disaster management will benefit from the proposed framework for landslide susceptibility maps.

A Bayesian model for predicting monthly fire frequency in Kenya

This study presents a comprehensive analysis of historical fire and climatic data to estimate the monthly frequency of vegetation fires in Kenya. This work introduces a statistical model that captures the behavior of fire count data, incorporating temporal explanatory factors and emphasizing the predictive significance of maximum temperature and rainfall. By employing Bayesian approaches, the paper integrates literature information, simulation studies, and real-world data to enhance model performance and generate more precise prediction intervals that encompass actual fire counts. To forecast monthly fire occurrences aggregated from the Moderate Resolution Imaging Spectroradiometer (MODIS) data in Kenya (2000-2018), the study utilizes maximum temperature and rainfall values derived from global GeoTiff (.tif) files sourced from the WorldClim database. The evaluation of the widely used Negative Binomial (NB) model and the proposed Bayesian Negative Binomial (BNB) model reveals the superiority of the latter in accounting for seasonal patterns and long-term trends. The simulation results demonstrate that the BNB model outperforms the NB model in terms of Root Mean Square Error (RMSE), and Mean Absolute Scaled Error (MASE) on both training and testing datasets. Furthermore, when applied to real data, the Bayesian Negative Binomial model exhibits better performance on the test dataset, showcasing lower RMSE (163.22 vs. 166.67), lower MASE (1.12 vs. 1.15), and reduced bias (-2.52% vs. -2.62%) compared to the NB model. The Bayesian model also offers prediction intervals that closely align with actual predictions, indicating its flexibility in forecasting the frequency of monthly fires. These findings underscore the importance of leveraging past data to forecast the future behavior of the fire regime, thus providing valuable insights for fire control strategies in Kenya. By integrating climatic factors and employing Bayesian modeling techniques, the study contributes to the understanding and prediction of vegetation fires, ultimately supporting proactive measures in mitigating their impact.

Monitoring of wetland turbidity using multi-temporal Landsat-8 and Landsat-9 satellite imagery in the Bisalpur wetland, Rajasthan, India

Satellite imagery has emerged as the predominant method for performing spatial and temporal water quality analyses on a global scale. This study employs remote sensing techniques to monitor the water quality of the Bisalpur wetland during both the pre and post-monsoon seasons in 2013 and 2022. The study aims to investigate the prospective use of Landsat-8 (L8) and Landsat-9 (L9) data acquired from the Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) for the temporal monitoring of turbidity. Concurrently, the study examines the relationship of turbidity with water surface temperature (WST) and chlorophyll-a (Chl-a) concentrations. We utilized visible and near-infrared (NIR) bands to conduct a single-band spectral response analysis of wetland turbidity. The results reveal a notable increase in turbidity concentration in May 2022, as this timeframe recorded the highest reflectance (0.28) in the NIR band. Additionally, the normalized difference turbidity index (NDTI) formula was used to assess the overall turbidity levels in the wetland. The results indicated that the highest concentration was observed in May 2013, with a value of 0.37, while the second-highest concentration was recorded in May 2022, with a value of 0.25. The WST was calculated using thermal band-10 in conjunction with Chlorophyll-a, utilizing the normalized difference chlorophyll index (NDCI). The regression analysis shows a positive correlation between turbidity and WST, as indicated by R2 values of 0.41 in May 2013 and 0.40 in May 2022. Furthermore, a robust positive relationship exists between turbidity and Chl-a, with a high R2 value of 0.71 in May 2022. These findings emphasize the efficacy of the L8 and L9 datasets for conducting temporal analyses of wetland turbidity, WST, and Chl-a. Additionally, this research underscores the critical role of satellite imagery in assessing and managing water quality, particularly in situations where in-situ data is lacking.

Assessing long-term trends in vegetation cover change in the Xilin River Basin: Potential for monitoring grassland degradation and restoration

Under the influence of climate change and human activities, the problem of grassland degradation is becoming increasingly severe. Detection of changes in vegetation cover is crucial for a better understanding of the interaction between humans and ecosystems. This study maps changes in vegetation cover using the Google Earth Engine (GEE). We used 36 years of Landsat satellite imagery (1985-2020) in the Xilin River Basin, China, to classify grassland conditions and validated the results with field observation data. The overall classification of the model accuracy assessment was 83.3%. The Dynamic Reference Vegetation Cover Method (DRCM) was adopted to remove the effect of interannual variation of rainfall, allowing to focus on the impact of human activities on vegetation cover changes. The results identify five categories of vegetation cover changes: significantly increased, potentially increased, stable, potentially decreased, and significantly decreased. The reference level is derived from the most persistent land surface coverage across different grassland types and all years. Overall, 9.3% of the study area had a significant increase in vegetation cover, 14.2% a potential increase, 48.6% of the area showed a stable vegetation condition, 9.8% showed a potential decrease, and 18.1% a significant decrease in vegetation cover. The largest proportion of combined potential and significant reduction was 35.2% for desert grassland, where the vegetation faced the most severe reduction. This study will provide a basis for identifying grassland degradation and developing scientific management policies.

Relationship between floating marine debris accumulation and coastal fronts in the Northeast coast of the USA

Floating marine debris (FMD) is one of the world's most concerning issues due to its potential impact on biodiversity, communities, and ecosystem services. FMD transport and concentrations are driven by fronts, generated by oceanographic processes, and the accumulation of FMD has been reported in gyres, eddies, tidal fronts, salinity fronts, and coastal fronts. This study explores the relationship between fronts and FMD accumulation in the Gulf of Maine (GoM) and the surrounding coastal areas (USA). Frontal edge detection algorithms were applied to sea surface temperature (SST) imagery from the Moderate-resolution Imaging Spectroradiometer (MODIS) between 2002 and 2012. Frontal location is spatially correlated with FMD concentrations collected by the Sea Education Association. Higher concentrations of FMD are associated with frontal frequencies (FF) of 5-10 %. FMD is trapped between fronts and the coastline in accumulation zones. These results highlight the need to consider coastal FMD hotspots, given these are areas of high biodiversity value.

Satellite mapping reveals extensive industrial activity at sea

The world's population increasingly relies on the ocean for food, energy production and global trade1-3, yet human activities at sea are not well quantified4,5. We combine satellite imagery, vessel GPS data and deep-learning models to map industrial vessel activities and offshore energy infrastructure across the world's coastal waters from 2017 to 2021. We find that 72-76% of the world's industrial fishing vessels are not publicly tracked, with much of that fishing taking place around South Asia, Southeast Asia and Africa. We also find that 21-30% of transport and energy vessel activity is missing from public tracking systems. Globally, fishing decreased by 12 ± 1% at the onset of the COVID-19 pandemic in 2020 and had not recovered to pre-pandemic levels by 2021. By contrast, transport and energy vessel activities were relatively unaffected during the same period. Offshore wind is growing rapidly, with most wind turbines confined to small areas of the ocean but surpassing the number of oil structures in 2021. Our map of ocean industrialization reveals changes in some of the most extensive and economically important human activities at sea.

Assessing coastal bathymetry and climate change impacts on coastal ecosystems using Landsat 8 and Sentinel-2 satellite imagery

Coastal ecosystems are facing heightened risks due to human-induced climate change, including rising water levels and intensified storm events. Accurate bathymetry data is crucial for assessing the impacts of these threats. Traditional data collection methods can be cost-prohibitive. This study investigates the feasibility of using freely accessible Landsat and Sentinel satellite imagery to estimate bathymetry and its correlation with hydrographic chart soundings in Port Klang, Malaysia. Through analysis of the blue and green spectral bands from the Landsat 8 and Sentinel 2 datasets, a bathymetry map of Port Klang's seabed is generated. The precision of this derived bathymetry is evaluated using statistical metrics like Root Mean Square Error (RMSE) and the coefficient of determination. The results reveal a strong statistical connection (R2 = 0.9411) and correlation (R2 = 0.7958) between bathymetry data derived from hydrographic chart soundings and satellite imagery. This research not only advances our understanding of employing Landsat imagery for bathymetry assessment but also underscores the significance of such assessments in the context of climate change's impact on coastal ecosystems. The primary goal of this research is to contribute to the comprehension of Landsat imagery's utility in bathymetry evaluation, with the potential to enhance safety protocols in seaport terminals and provide valuable insights for decision-making concerning the management of coastal ecosystems amidst climate-related challenges. The findings of this research have practical implications for a wide range of stakeholders involved in coastal management, environmental protection, climate adaptation and disaster preparedness.

Assessment of seasonal warming trends at the Nile Delta: a paradigm for human-induced climate change

Given its modern geographical and geomorphological characteristics, along with rapid socio-economic changes, the Nile Delta stands out as one of the world's most dynamic landscapes. The key drivers of the land use change in this region have been the reclamation of delta margins, changes in agricultural practices, and urban expansion. The present study aims to explore the variations in the seasonal daytime and nighttime trends of the land surface temperatures (LST) at this active agronomic system in response to the seasonal variations of vegetation cover as revealed by the normalized difference vegetation index (NDVI) during the past two decades. The data were exclusively acquired from the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument for the period from January 2001 to December 2021, where geospatial and statistical analyses were accomplished to construct a LST/NDVI spatio-temporal pattern throughout the Nile Delta. Results revealed a robust negative and a significant relationship between the NDVI and the diurnal LST with high regression coefficients (R2) ranging from 0.78 to 0.97 (p value < 0.05). Maximal seasonal warming trends occurred during harvesting seasons (springs and falls), while the least warming was recorded during winters (the growing seasons). It was also observed that the nocturnal warming (0.72°C/decade) was almost as double as the corresponding value of the daytime trend (0.33°C/decade). The study recognized a seasonal climatic warming throughout the Nile Delta influenced by the human-induced land use change and agricultural practices.

A Satellite Imagery Dataset for Long-Term Sustainable Development in United States Cities

Cities play an important role in achieving sustainable development goals (SDGs) to promote economic growth and meet social needs. Especially satellite imagery is a potential data source for studying sustainable urban development. However, a comprehensive dataset in the United States (U.S.) covering multiple cities, multiple years, multiple scales, and multiple indicators for SDG monitoring is lacking. To support the research on SDGs in U.S. cities, we develop a satellite imagery dataset using deep learning models for five SDGs containing 25 sustainable development indicators. The proposed dataset covers the 100 most populated U.S. cities and corresponding Census Block Groups from 2014 to 2023. Specifically, we collect satellite imagery and identify objects with state-of-the-art object detection and semantic segmentation models to observe cities' bird's-eye view. We further gather population, nighttime light, survey, and built environment data to depict SDGs regarding poverty, health, education, inequality, and living environment. We anticipate the dataset to help urban policymakers and researchers to advance SDGs-related studies, especially applying satellite imagery to monitor long-term and multi-scale SDGs in cities.

Spatiotemporal variation in the vegetation cover of Peshawar Basin in response to climate change

Climate factors like temperature, precipitation, humidity, and sunshine time exert a profound influence on vegetation. The intricate interplay between the two is crucial to understand in the face of changing climate to develop mitigation strategies. In the current exploration, we delve how climate variability (CV) has impacted the vegetation in the Peshawar Basin (PB) using remote sensing data tools. The trend of climatic variability was investigated using the modified Mann-Kendall test and Sen's slope statistics. The changing climatic parameters were regressed on the Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI). The NDVI was further analyzed for spatiotemporal variability under land surface temperature (LST) influence. Results revealed that among the climate factors, average annual temperature and solar radiation have a significant (p < 0.05) negative impact on vegetation while precipitation and relative humidity significantly (p < 0.05) influence NDVI positively. The overall positive trend shows that vegetation improved between 2001 and 2020 with time, however some years (2010, 2012, 2014, 2016, and 2017) with low NDVI. NDVI varied in space considerably due to climatic extremes brought on by CV and the urbanization of agricultural land. NDVI regressed on LST showed that there was no or very little vegetation in the grids with high LST. The study concluded that the region is significantly impacted by both CV-related extreme weather events and anthropogenic activities. The vegetation is improving, but it is in danger of being destroyed by deforestation due to CV and human activities that exacerbate the risk of future calamities. To protect vegetation and avoid disasters, there is an immense need for adaptation and mitigation measures to deal with the region's fast-changing environment. The study urges local authorities to create climate-resilient governmental policies and supports regional sustainable development and vegetation restoration.

Machine learning models for predicting vegetation conditions in Mahanadi River basin

The vegetation of a river basin is affected by various climate factors, such as precipitation and land surface temperature (LST). This study explores the best machine learning model for the prediction of normalized difference vegetation index (NDVI) with LST and precipitation as input parameters. The study also determines the correlation between NDVI, LST, and precipitation of the Mahanadi basin from 2003 to 2021. Monthly precipitation data was extracted from the Center for Hydrometeorology and Remote Sensing (CHRS) portal. The Moderate Resolution Imaging Spectroradiometer (MODIS) products were used to derive the LST and NDVI using Google Earth Engine (GEE). Four different machine learning models were used to predict the NDVI of the Mahanadi basin: linear regression (LR), random forest (RF), support vector regression (SVR), and k-nearest neighbors (KNN). The coefficient of determination (R2), root mean square error (RMSE), mean square error (MSE), mean absolute error (MAE), and explained variance score (EVS) were calculated to evaluate the performance of the models. The results show that the RF model has the highest R2 value in both the training and testing sets among these models, indicating that it is the most optimal among these models for predicting NDVI. The SVR model has the lowest RMSE value in the training set, but the KNN model has the lowest RMSE value in the testing set. The results also show that there is a positive correlation between precipitation and NDVI, a negative correlation between precipitation and LST, and between NDVI and LST. This study provides insights into the relationship between NDVI, LST, and precipitation, and the best machine-learning model for predicting NDVI. The findings of this study can be used to improve the management of river basins and to predict the effects of climate change on vegetation.

Satellite hyperspectral imaging technology as a potential rapid pollution assessment tool for urban landfill sites: case study of Ghazipur and Okhla landfill sites in Delhi, India

Hyperspectral imaging technology has been used for biochemical analysis of Earth's surface exploiting the spectral reflectance signatures of various materials. The new-generation Italian PRISMA (PRecursore IperSpettrale dellaMissione Applicativa) hyperspectral satellite launched by the Italian space agency (ASI) provides a unique opportunity to map various materials through spectral signature analysis for recourse management and sustainable development. In this study PRISMA hyperspectral satellite imagery-based multiple spectral indices were generated for rapid pollution assessment at Ghazipur and Okhla landfill sites in Delhi, India. It was found that the combined risk score for Okhla landfill site was higher than the Ghazipur landfill site. Various manmade materials identified, exploiting the hyperspectral imagery and spectral signature libraries, indicated presence of highly saline water, plastic (black, ABS, pipe, netting, etc.), asphalt tar, black tar paper, kerogen BK-Cornell, black paint and graphite, chalcocite minerals, etc. in large quantities in both the landfill sites. The methodology provides a rapid pollution assessment tool for municipal landfill sites.

Determination of long-term volume change in lakes by integration of UAV and satellite data: the case of Lake Burdur in Türkiye

Monitoring the water levels and volume changes of lakes and reservoirs enables us to understand the importance of better protection and managing water resources in an ecologically optimum manner. Although some lakes, such as Burdur Lake, are not a source of drinking water, they are home to many endangered animals, endemic plants, and some species. Therefore, monitoring the changes in these lakes over time is important for various reasons. While water level measurement stations in lakes and wetlands provide important information, it may not always be possible to obtain this data. In this study, we investigated the long-term changes in Burdur Lake, a Ramsar site, by integrating the Digital Elevation Model (DEM) obtained by the unmanned aerial vehicle (UAV) with shoreline information obtained from the Landsat mission. This study aimed to investigate the usability, advantages, and disadvantages of the UAV-Landsat integration for volume calculation. As a result, we successfully determined the water level as r= 0.999 and the cumulative volume loss at a rate of 97.5%. Burdur Lake experienced a significant reduction in its area decreasing from 206 to 120 km2 (42%) between 1984 and 2022. Furthermore, the water volume of the lake decreased by 2.70 km3 over a span of 38 years. This study demonstrates the potential and limitations of the presented UAV-remote sensing integration. Our proposed method is beneficial for determining short and long-term water levels and volumetric changes with high accuracy.

High-resolution maps show that rubber causes substantial deforestation

Understanding the effects of cash crop expansion on natural forest is of fundamental importance. However, for most crops there are no remotely sensed global maps1, and global deforestation impacts are estimated using models and extrapolations. Natural rubber is an example of a principal commodity for which deforestation impacts have been highly uncertain, with estimates differing more than fivefold1-4. Here we harnessed Earth observation satellite data and cloud computing5 to produce high-resolution maps of rubber (10 m pixel size) and associated deforestation (30 m pixel size) for Southeast Asia. Our maps indicate that rubber-related forest loss has been substantially underestimated in policy, by the public and in recent reports6-8. Our direct remotely sensed observations show that deforestation for rubber is at least twofold to threefold higher than suggested by figures now widely used for setting policy4. With more than 4 million hectares of forest loss for rubber since 1993 (at least 2 million hectares since 2000) and more than 1 million hectares of rubber plantations established in Key Biodiversity Areas, the effects of rubber on biodiversity and ecosystem services in Southeast Asia could be extensive. Thus, rubber deserves more attention in domestic policy, within trade agreements and in incoming due-diligence legislation.

Assessing potential of the Geostationary Littoral Imaging and Monitoring Radiometer (GLIMR) for water quality monitoring across the coastal United States

The Geostationary Littoral Imaging and Monitoring Radiometer (GLIMR) will provide unique high temporal frequency observations of the United States coastal waters to quantify processes that vary on short temporal and spatial scales. The frequency and coverage of observations from geostationary orbit will improve quantification and reduce uncertainty in tracking water quality events such as harmful algal blooms and oil spills. This study looks at the potential for GLIMR to complement existing satellite platforms from its unique geostationary viewpoint for water quality and oil spill monitoring with a focus on temporal and spatial resolution aspects. Water quality measures derived from satellite imagery, such as harmful algal blooms, thick oil, and oil emulsions are observable with glint <0.005 sr-1, while oil films require glint >10-5 sr-1. Daily imaging hours range from 6 to 12 h for water quality measures, and 0 to 6 h for oil film applications throughout the year as defined by sun glint strength. Spatial pixel resolution is 300 m at nadir and median pixel resolution was 391 m across the entire field of regard, with higher spatial resolution across all spectral bands in the Gulf of Mexico than existing satellites, such as MODIS and VIIRS, used for oil spill surveillance reports. The potential for beneficial glint use in oil film detection and quality flagging for other water quality parameters was greatest at lower latitudes and changed location throughout the day from the West and East Coasts of the United States. GLIMR scan times can change from the planned ocean color default of 0.763 s depending on the signal-to-noise ratio application requirement and can match existing and future satellite mission regions of interest to leverage multi-mission observations.

Spatio-Temporal Change of Land Use/Land Cover and Vegetation Using Multi-MODIS Satellite Data, Western Ethiopia

Land use and land cover (LULC) change and variability are some of the challenges to present-day water resource management. The purpose of this study was to determine LULC and Normalized Difference Vegetation Index (NDVI) fluctuations in western Ethiopia during the last 20 years. The first part of the study used MODIS LULC data for the change analysis, change detection, and spatial and temporal coverage in the study region. In the second part, the study analyzes the NDVI change and its spatial and temporal coverage. In this study, The Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data were applied to determine LULC and NDVI changes over four different periods. Evergreen broadleaf forests, deciduous broadleaf forests, mixed forests, woody savannas, savannas, grasslands, permanent wetlands, croplands, urban and built-up lands, and water bodies are the LULC in the period of analysis. The overall classification accuracy for the classified image from 2001 to 2020 was 85.4% and the overall kappa statistic was 81.2%. The results indicate a substantial increase in woody savannas, deciduous broadleaf, grasslands, permanent wetlands, and mixed forest areas by 119.6%, 57.7% 45.2%, 37%, and 21.3%, respectively, followed by reductions in croplands, water bodies, savannas, and evergreen broadleaf forest by 90.1%, 19.8%, 13.2%, and 4.8%, respectively, for the catchment between 2001 and 2020. The result also showed that the area's vegetation cover increased by 64% from 2001 to 2022. This study could provide valuable information for water resource and environmental management as well as policy and decision-making.

Land cover and forest health indicator datasets for central India using very-high resolution satellite data

Satellite imagery has been used to provide global and regional estimates of forest cover. Despite increased availability and accessibility of satellite data, approaches for detecting forest degradation have been limited. We produce a very-high resolution 3-meter (m) land cover dataset and develop a normalized index, the Bare Ground Index (BGI), to detect and map exposed bare ground within forests at 90 m resolution in central India. Tree cover and bare ground was identified from Planet Labs Very High-Resolution satellite data using a Random Forest classifier, resulting in a thematic land cover map with 83.00% overall accuracy (95% confidence interval: 61.25%-90.29%). The BGI is a ratio of bare ground to tree cover and was derived by aggregating the land cover. Results from field data indicate that the BGI serves as a proxy for intensity of forest use although open areas occur naturally. The BGI is an indicator of forest health and a baseline to monitor future changes to a tropical dry forest landscape at an unprecedented spatial scale.

Detection of goat herding impact on vegetation cover change using multi-season, multi-herd tracking and satellite imagery

The frequency and severity of Mediterranean forest fires are expected to worsen as climate change progresses, heightening the need to evaluate understory fuel management strategies as rigorously as possible. Prescribed small-ruminant foraging is considered a sustainable, cost-effective strategy, but demonstrating a link between animal presence and vegetation change is challenging. This study tested whether the effect of small-ruminant herd presence in Mediterranean woodlands can be detected by integrating remote sensing and herd tracking at the landscape scale. The daily foraging routes of seven shepherded goat herds that exploited a 100-km2 forested area of the Judean Hills, Israel, were tracked over six years using GPS (Global Positioning System) collars. Herd locations were converted to stocking rates, with units of animal-presence-days per unit area per defined time period, and mapped at a spatial resolution of 10 m. We estimated pixel-level vegetation cover change based on a time series of 63 monthly Landsat-8 images expressed as the normalized soil-adjusted vegetation index (SAVI). Spatiotemporal trend analysis assessed the magnitude and direction of change, and a random forest machine-learning algorithm estimated the relative impact on vegetation cover change of environmental factors as well as the herd-related factors of stocking rate that accrued over six years and distance to the closest corral. The last two factors were among the most influential factors determining vegetation cover change in the regional and individual-herd analyses. In some respects, the permanent herds differed in their spatial pattern of stocking rate from the mobile herds that periodically relocated their night corral throughout the year, but stocking rate scaled logarithmically for all herds individually and combined. The combination of multi-season GPS tracking, remote sensing, and machine-learning techniques, applied at a regional scale, detected herd impacts on vegetation cover trends, consistent with livestock foraging being an effective tool for fuel reduction in Mediterranean woodlands.

Dust detection and susceptibility mapping by aiding satellite imagery time series and integration of ensemble machine learning with evolutionary algorithms

To mitigate the impact of dust on human health and the environment, it is crucial to create a model and map that identifies the areas susceptible to dust. The present study focused on identifying dust occurrences in the Bushehr province of Iran between 2002 and 2022 using moderate-resolution imaging spectroradiometer (MODIS) imagery. Subsequently, an ensemble machine learning model was improved to prepare a dust susceptibility map (DSM). The study employed differential evolution (DE), genetic algorithm (GA), and flower pollination algorithm (FPA) - three evolutionary algorithms - to enhance the random forest (RF) ensemble model. A spatial database was created for modeling, including 519 dust occurrence points (extracted from MODIS imagery) and 15 factors affecting dust (Slope, bulk density, aspect, clay, altitude, sand, rainfall, lithology, soil order, distance to river, soil texture, normalized difference vegetation index (NDVI), soil water content, land cover, and wind speed). By utilizing the differential evolution (DE) algorithm, we determined the significance of these factors in impacting dust occurrences. The results indicated that altitude, wind speed, and land cover were the most influential factors, while the distance to the river, bulk density, and soil texture had less impact on dust occurrence. Data were preprocessed using multicollinearity analysis and the frequency ratio (FR) approach. For this research, three RF-based meta-heuristic optimization algorithms, namely RF-FPA, RF-GA, and RF-DE, were created for DSM. The effectiveness prediction of the constructed models by indexes of root-mean-square-error (RMSE), the area under the receiver operating characteristic (AUC-ROC), and coefficient of determination (R2) from best to worst were RF-DE (RMSE = 0.131, AUC-ROC = 0.988, and R2 = 0.93), RF-GA (RMSE = 0.141, AUC-ROC = 0.986, and R2 = 0.919), RF-FPA (RMSE = 0.157, AUC-ROC = 0.981, and R2 = 0.9), and RF (RMSE = 0.173, AUC-ROC = 0.964, and R2 = 0.878). The results showed that combining evolutionary algorithms with an RF model improves the accuracy of dust susceptibility modeling.

Assessing spatiotemporal changes in landcover using geospatial and remote sensing techniques in the Southern fringes of Chennai

Peri-urban areas are transitional zones on the outer boundaries of cities. These regions have immense growth potential, and it is necessary to observe the land use landcover changes to understand the dynamics of these transformations. The area selected for this study is towards the Southern fringe of Chennai, Tamil Nādu, India, and is analyzed using multi-spectral satellite imagery from Landsat 5 and 8. The primary objective of the study is to assess the change in landcover classes, namely water, land, and vegetation, over a 30-year study period between 1991 and 2021. The peri-urban regions majorly are arable land. Hence, NDVI is considered a suitable index to monitor the landcover changes. The spatiotemporal analysis indicates an increase of 19.43% in land /barren areas towards the Northern parts near the study area and along the transit and industrial corridors. No significant changes are observed in the areas of vegetation that could be attributed to efforts taken to conserve reserve forests and increase green zones in newer developments. A steep depletion of 46.86% of water bodies observed in the region also corresponds to water scarcity problems. Accuracy was assessed using ground-truthing methods, computing the confusion matrix and Kappa coefficient. NDVI is used efficiently in the landcover classification but does not indicate the difference between built-up areas and barren land. Change detection map prepared using ARCGIS indicates the areas that have been converted to other landcover over a period of 30 years. The study reveals an urgent need to bring in policy decisions to conserve waterbodies and green spaces in the initial stages of urban planning for sustainable developments in the fringe areas.

Detection and mapping of water and chlorophyll-a spread using Sentinel-2 satellite imagery for water quality assessment of inland water bodies

Water quality monitoring of reservoirs is currently a significant challenge in the tropical regions of the world due to limited monitoring stations and hydrological data. Remote sensing techniques have proven to be a powerful tool for continuous real-time monitoring and assessment of tropical reservoirs water quality. Although many studies have detected chlorophyll-a (Chl-a) concentrations as a proxy to represent nutrient contamination, using Sentinel 2 for eutrophic or hypereutrophic inland water bodies, mainly reservoirs, minimal efforts have been made for oligotrophic and mesotrophic reservoirs. The present study aimed to develop a modeling framework to map and estimate spatio-temporal variability of Chl-a levels and associated water spread using the Modified Normalized Difference Water Index (MNDWI) and Maximum Chlorophyll Index (MCI). Moreover, the impact of land use/land cover type of the contributing watershed in the oligo-mesotrophic reservoir, Bhadra (tropical reservoir), for 2018 and 2019 using Sentinel 2 satellite data was analyzed. The results show that the water spread area was higher in the post-monsoon months and lower in the summer months. This was further validated by the correlation with reservoir storage, which showed a strong relationship (R2 = 0.97, 2018; R2 = 0.93, 2019). The estimated Chl-a spread was higher in the winter season, because the reservoir catchment was dominated by deciduous forest, producing a large amount of leaf litter in tropical regions, which leads to an increase in the level of Chl-a. It was found that Chl-a spread in the reservoir, specifically at the inlet sources and near agricultural land practices (western parts of the Bhadra reservoir). Based on the findings of this study, the MCI spectral index derived from Sentinel 2 data can be used to accurately map the spread of Chl-a in diverse water bodies, thereby offering a robust scientific basis for effective reservoir management.