Google Earth Pro facilitated the acquisition of satellite imagery to monitor deforestation in Dhaka, Bangladesh. Multiple years of images were systematically captured from specific locations, allowing comprehensive analysis of tree cover reduction. The imagery displays diverse aspect ratios based on satellite perspectives and possesses high resolution, suitable for remote sensing. Each site provided 5 to 35 images annually, accumulating data over a ten-year period. The dataset classifies images into three primary categories: tree cover, deforested regions, and masked images. Organized by year, it comprises both raw and annotated images, each paired with a JSON file containing annotations and segmentation masks. This organization enhances accessibility and temporal analysis. Furthermore, the dataset is conducive to machine learning initiatives, particularly in training models for object detection and segmentation to evaluate environmental alterations.

This study assessed the spatial and temporal variations of land cover in the agricultural areas of the Al-Hassa oasis, Kingdom of Saudi Arabia (KSA). Change detection technique was applied in order to classify variations among different surface cover aspects, during three successive stages between 1985 and 2017 (i.e., 1985 to 1999 (14 years), 1999 to 2013 (14 years), and 2013 to 2017 (4 years)), using two scenarios. During the first stage, significant urban sprawl (i.e., 3,200 ha) occurred on bare lands within the old oasis, while only 590 ha of the oasis’s vegetation area was occupied by urban cover. However, the final stage revealed rapid urban development (1,270 ha by 2017) within the oasis’s vegetation region, while no urban sprawl occurred on bare lands (area of 1,900 ha, same as that in 1999–2013). Vegetation cover of around 1,000 ha changed to the bare soil class, in addition to the areas that were occupied by the urban class (1,700 ha in total). The study provides quantitative information on the influence of urban development on the spatial changes in vegetation cover of the oasis, especially during recent decades.

IntroductionUrbanization converts vegetated lands into impervious surfaces and often degrades vegetation carbon sequestration in urban ecosystems. At the same time, the impact on urban vegetation growth from urban expansion could be spatially diverse given different natural environments and urban management practices.MethodsHere we applied time-series remotely sensed images and analyzed the urban growth for all the prefecture-level cities across China during 2001–2019, and compared the impact of urbanization on vegetation carbon uptake proxied by MODIS (MOD17A2H) net primary productivity (NPP) on Google Earth Engine platform.ResultsThe result indicated that at the national scale, the carbon uptake flux in urban areas was only 19% compared to that in the nonurban vegetated counterparts. The total urban area expanded by 22% and the vegetation carbon uptake in the newly urbanized zones was averagely reduced by 16% during the period, but with high spatio-temporal heterogeneity among cities and with exceptions demonstrating even improved NPP, highlighting diversified responses of vegetation carbon sequestration to urban sprawl. The changes of vegetation carbon sequestration in response to urbanization were found to be spatially clustered.DiscussionWe conclude that urban land management strategies unique to cities may attribute to the diversified responses of vegetation carbon capture to urbanization.

Accurately and timely quantifying the dynamics of urban sprawl is essential for improving land use efficiency and land use planning. However, existing research mainly focused on the sprawl of a single city or urban agglomerations in China, while national scale studies mainly used short-term remote sensing data to quantify urban sprawl. Therefore, we still lack a long term and most up-to-date understanding of urban sprawl in China, especially in different regions, cities of different sizes. Here, we quantified the spatiotemporal dynamics of urban sprawl using urban sprawl index (USI) based on the latest population census, and analyzed its driving force in China during 2000 ~ 2020 using the optimal parameters-based geographical detector. The results showed that in the past two decades, Chinese cities were still experiencing urban sprawl, with an average USI of 3.04, indicating that the average annual growth rate of urban lands was 3.04% higher than that of the urban population. Overall, the sprawl showed a slowdown, with the USI dropping from 3.55 in 2000 ~ 2010 to 2.53 in 2010 ~ 2020. Among regions, urban sprawl was more severe in the western region, where the USI was 5.33 during 2000 ~ 2020 and 89.3% of the cities exceeded the national average sprawl speed. Recently, the driving force of transportation on urban sprawl substantially increased. In the future, the Territorial Spatial Planning should pay attention to confining excessive sprawl of small- and medium-sized cities in the western region of China.

The Global Monthly and Seasonal Urban and Land Backscatter Time Series, 1993-2020, is a multi-sensor, multi-decadal, data set of global microwave backscatter, for 1993 to 2020. It assembles data from C-band sensors onboard the European Remote Sensing Satellites (ERS-1 and ERS-2) covering 1993-2000, Advanced Scatterometer (ASCAT) onboard EUMETSAT satellites for 2007-2020, and the Ku-band sensor onboard the QuikSCAT satellite for 1999-2009, onto a common spatial grid (0.05 degree latitude /longitude resolution) and time step (both monthly and seasonal). Data are provided for all land (except high latitudes and islands), and for urban grid cells, based on a specific masking that removes grid cells with > 50% open water or < 20% built land. The all-land data allows users to choose and evaluate other urban masks. There is an offset between C-band and Ku-band backscatter from both vegetated and urban surfaces that is not spatially constant. There is a strong linear correlation (overall R-squared value = 0.69) between 2015 ASCAT urban backscatter and a continental-scale gridded product of building volume, across 8,450 urban grid cells (0.05 degree resolution) from large cities in Europe, China, and the United States.

This dataset comprises approximately 7

According to many previous studies, application of remote sensing for the complex and heterogeneous urban environments in Sub-Saharan African countries is challenging due to the spectral confusion among features caused by diversity of construction materials. Resorting to classification based on spectral indices that are expected to better highlight features of interest and to be prone to unsupervised classification, this study aims (1) to evaluate the effectiveness of index-based classification for Land Use Land Cover (LULC) using an unsupervised machine learning algorithm Product Quantized K-means (PQk-means); and (2) to monitor the urban expansion of Luanda, the capital city of Angola in a Logistic Regression Model (LRM). Comparison with state-of-the-art algorithms shows that unsupervised classification by means of spectral indices is effective for the study area and can be used for further studies. The built-up area of Luanda has increased from 94.5 km2 in 2000 to 198.3 km2 in 2008 and to 468.4 km2 in 2018, mainly driven by the proximity to the already established residential areas and to the main roads as confirmed by the logistic regression analysis. The generated probability maps show high probability of urban growth in the areas where government had defined housing programs.

This dataset contains 20 years of urban expansion data for 46 case studies located in Sub-Saharan Africa. For more information, see the MAUPP website. The dataset can also be explored from your browser in an interactive web map. For each case study, the following files are provided:

Filename: builtup_

overview

The project aims to extend the CAP LTER long-term, land-use/land-cover (LULC) datasets to facilitate environmental change monitoring and social-ecological studies regarding urban sprawl and dynamics, urban heat islands, and outdoor water consumption, among others. Six LULC maps at 30 m resolution were previously created from 1985 to 2010 at five year intervals (Zhang and Li, 2017). This project updates that suite with a seventh map for 2015. As with the prior set, systematic object-based classification was utilized to ensure map consistency and direct comparison capability over time. The map comprises 11 land-use/land-cover classes with an overall accuracy of 89.1%.

literature cited

• Zhang, Y. and X. Li. 2017. Land cover classification of the CAP LTER study area at five-year intervals from 1985 to 2010 using Landsat imagery ver 1. Environmental Data Initiative. https://doi.org/10.6073/pasta/dab4db27974f6c8d5b91a91d30c7781d (Accessed 2022-07-13).

The urban growth of cities is insatiable. It grows over the farm that once nourished it, over the streams that quench its thirst, and even over the forest that treated it as its own. Now to sustain the ever-growing appetite, it has to set up elaborate systems, funded extensively, to source resources from outlying lands. Cities barely incorporate ecosystem services in their intransigent land-use plans. This negligence increases socio-economic exclusion, food insecurity and burdens existing ecosystems. The study will be based on Noida, in India, which was an expanse of agrarian land that has transmuted into a landscape of fragmented green voids with issues like water crisis, pollution, shrinking productive and regulatory landscapes. These Urban voids have been termed as recreation spaces in the land use plans of Noida but have been rendered barren as no specific purpose has been assigned to them. The study explores the dynamic relationship between society and the agrarian landscape, i.e How the society influences the agrarian regime, concurrently, the agrarian landscape shapes the society. Secondly, analyse the production of ecosystem services, patterns in land-use change, and variation in climate and environment. Ultimately, explore the possibilities of a novel model for the fragmented voids.The data in the following data sets were retrieved through Landsat images, Gis and primary survey. Land Use land coverIn statistical terms, the percentage of Built-Up in Noida area was 29.52% during 2010 which was increased to 55.03% in 2016. It is also showing some positive land use analysis in which the wastelands are getting reduced and are getting replaced by vegetative areas which is showing an increasing trend over the years. With the increase in urbanization, the urban vegetation is also increasing with a decrease in the Open Land. It can also be seen through the graph that the built up has increased rapidly during 2016. The built-up area was found to be 63.17 sqkm during the year 2010 which further expanded to 90 sqkm in 2016. Simultaneously, the cultivated lands were found to be 94.76 sqkm in 2010 which had shrunk to 52.29 sqkm in 2016. There is a minor change in the area of the water body also with the area declining from 3.64 sqkm in 2010 to 2.06 sqkm in 2016.Impervious surfacesThe fast growth in population and expansion of urban built areas has led to the transformation of the natural landscape into impervious surfaces. Remote sensing-based estimate of impervious surface area (ISA) has emerged as an important indicator for the assessment of water resources depletion in urban areas and developed a correlation between land-use change and their potential impact on urban hydrology. The results observed by analysis of multi-temporal satellite images show an extreme temporal change in the growth of ISA in the city. The ISA observed for the year 2001 is 28 sq. km; in 2007, its increase was 48 sq. km and was 132 in 2014Agriculture coverStatistics show that the percentage of agricultural land has been drastically reduced, displacing farmers to the banks of Yamuna and Hindon. Such change in the land-use use pattern has also forced the population to change their occupation to adapt to the developing cityscape. The dense weave in the urban fabric has also forced green spaces inter-woven to shrink considerably. As we look into the master planning of Noida we understand how green areas and agricultural areas are being drastically reduced in order to bring in new development. The new master plan of 2031 showcases that only a fragment of floodplains is left as green areas. In reality, even these flood plains are encroached by sand mafias and temporary furniture markets. The green spaces have also fragmented and hence the biodiversity is reducing in Noida. All the canals are disconnected and have been converted into dumping areas. For example, the Shadra canal is disconnected from its green areas and has become a nuisance to the neighbourhood.Green cover The dense weave in the urban fabric has also forced green spaces inter-woven to shrink considerably. As we look into the master planning of Noida we understand how green areas and agricultural areas are being drastically reduced in order to bring in new development. The green spaces have also fragmented throughout the decade and hence the biodiversity is reducing in Noida. The method of setting up manicured green spaces doesn’t promote a sustainable ecosystem. All the interwoven green areas (recreation land use) when seen in the primary survey it was observed that all them have been encroached by various buildings, some are also left barren and abandoned. Some green areas have been converted into parks but these parks soon got barren as one single activity in such large green areas was not able to cater to it. Hydrological coverThere has been a stark reduction in the waterbodies of Noida. The maps show the disconnection and loss of water bodies in Noida in the past 2 decades.NORMALIZED DIFFERENCE VEGETATION INDEX According to the results achieved through this exercise, it can be seen that the overall vegetation of Noida has reduced distinctively. In terms of area under vegetation. From 430.53 Ha in 2000, forest reduced to 337.41 Ha. In 2016. While grassland reduced from 10211.8 Ha. to 8377.56 Ha. From the above images, it can be seen that the city doesn't have a definite pattern of growth overall but have spread across in every direction. It can be seen that though the change in the forest remains negligible in terms of percentage (though, a reduction of 430.53 Ha. to 337.4 Ha), the grassland has reduced by dramatically by 10%. In 2000, the area which came under grassland was 10211.8 Ha and in 2016, it reduced to 8377.56 Ha. BUILT DENSITY According to the map above it can be seen that density is increasing in the new coming development areas along the expressway, putting more pressure on resources and creating a higher urban heat island effect. Along the expressway, agricultural lands are being sold to developers and more high rise high-density housing are coming.

There are so many problems confronting most contemporary cities in the recent time particularly among the less developed countries around the world. These problems have been recognized to be the product of lack of urban planning by the authority in-charge as well as individual members of the society. However, the negative relationship between urban population and urban development has been identified using different methodologies. The prime objective is to apply the technique of Remote Sensing and GIS technology to examine the trend, pattern, the relationship between sprawl and population as well as the socio-economic implications of urban sprawl in Ibadan. However, the population is estimated to increase by 68.5% between year 2000 and 2020 (2,207,829 – 3,223,429) while the corresponding projected land consumption is also expected to rise by 58.5% (52,220.3 – 89, 192.3 ha) which implies that both would have doubled but the population is likely to double itself much faster than the land mass. Similarly, there was a significant change in the land use of land cover between 1986 and 2000 and a good example was the farmland which had decreased by 67.9% between this periods. The implication of this growth on the socioeconomic well being of the population is that urban development would have encroached on the urban fringe where urban and periurban agriculture is being practiced leading to acute shortage of fresh food supply to the urban populace, while similarly the sprawl is likely to result in slums development.

The Cellular Automata (CA) based SLEUTH model has emerged as a widely applied model to many cities for land use land cover (LULC) change and urban growth modelling due to its simplicity, robustness, and ease of implementation. The present study employed a rigorous sensitivity testing of self-modifying constants, Monte Carlo runs and critical slope to determine their influence on model calibration performance. Calibration performance has been examined in terms of statistical measures i.e., urban area, clusters, edges, mean cluster size, and cluster radius, best model fitness measure (i.e., Optimal SLEUTH Metrics (OSM)), overall accuracy percentage and hit-miss-false alarm method have been used. The sensitivity analysis reveals the optimum values for self-modifying parameters as {1.3, 0.10, 0.90, and 1.25} for boom, bust, critical low and critical high respectively; Monte Carlo runs as sixty (60) and critical slope as 15 to simulate the urban growth of the study area.

This dataset represents land cover for 7 sequential snapshots (1990, 2000, 2003, 2005, 2007, 2013 and 2015) over the Perth Metropolitan Region, Western Australia (WA) derived from medium resolution Landsat data. Cloud free imagery was acquired in or close to the month of July coinciding with WA's winter months coinciding with peak green-up facilitating the greatest contrast between spectrally similar surfaces (e.g. bare earth and urban). Imagery was first standardised and normalised to remove inherent residual noise (e.g. differences in modelled atmospheric correction parameters) whilst permitting classification of all imagery based upon a single classification model. The model was computed from the 2005 image representing the month post maximum rainfall of all considered imagery associated with peak greenness and maximum spectral separability. Classification of the normalised data was achieved with the Import Vector Machine (IVM) algorithm following a hybrid forward/backward strategy that adds import vectors whilst continuously testing validity in each step, producing a sparse and more accurate classification solution. Classified land cover data is provided in raster format (.tif) and divided into the classes: bare earth (1), grassland (2), low urban albedo (e.g. asphalt (3)), water (4), forest (5) and high urban albedo (e.g. concrete (6)). Please see MacLachlan et al. (2017) for further details. Supplement to: MacLachlan, A.; Biggs, E.; Roberts, G.; Boruff, B. Urban Growth Dynamics in Perth, Western Australia: Using Applied Remote Sensing for Sustainable Future Planning. Land 2017, 6, 9. doi:10.3390/land6010009 Also available at the pangea data publisher for earth and environmental science. doi: doi.pangaea.de/10.1594/PANGAEA.871017

The Earth Observation market is experiencing robust growth, driven by increasing demand for high-resolution imagery across various sectors. From precision agriculture and urban planning to environmental monitoring and disaster response, the applications are diverse and expanding rapidly. While precise market size figures for 2025 are unavailable, considering a conservative estimate based on typical industry growth rates and reported CAGR figures, the market is likely valued at approximately $15 billion USD in 2025. This reflects significant expansion from the historical period (2019-2024). Assuming a CAGR of 7% (a reasonable figure given the sector's trajectory), the market is projected to reach approximately $25 billion by 2033. Key drivers include advancements in satellite technology, leading to improved image resolution and data accessibility. The rise of big data analytics and cloud computing facilitates efficient processing and interpretation of the vast datasets generated, further fueling market expansion. However, the market is not without its challenges. High initial investment costs for satellite development and launch, along with data security concerns and regulatory complexities, pose significant restraints on growth. The market is segmented by various factors, including spatial resolution, sensor type, application, and geographical location. Leading players such as DigitalGlobe, Planet Labs, and Maxar Technologies are continuously innovating, pushing the boundaries of image acquisition and analysis. Future growth will be influenced by ongoing technological advancements, government initiatives promoting space exploration, and increasing private sector investment in the sector. Successful navigation of these restraints and effective exploitation of emerging opportunities will be crucial for sustained growth in the coming decade.

Click to add a brief description of the dataset (Markdown and LaTeX enabled). Abstract This dataset comprises approximately 7,100 satellite images paired with corresponding demographic and travel behavior data spanning 2012-2023 (excluding 2020) across United States counties. The satellite imagery consists of 256×256 pixel Landsat 8 Collection 2 Level 2 surface reflectance composites covering 10 km² areas around county centroids, processed to create cloud-free annual median representations. Demographic data includes 25 key variables from the U.S. Census Bureau's American Community Survey (ACS) 1-year estimates, encompassing population statistics, age distributions, racial composition, and educational attainment levels. Travel behavior metrics capture transportation modes, commute patterns, vehicle availability, and temporal travel characteristics for counties with populations exceeding 65,000. This multimodal spatiotemporal dataset enables research at the intersection of remote sensing, urban planning, and transportation analysis, providing a unique resource for studying the co-evolution of built environments, demographic patterns, and mobility behaviors over an 11-year period. The dataset supports applications in predictive modeling, urban development forecasting, transportation planning, and socioeconomic analysis using machine learning and computer vision techniques. Provide: Satellite Imagery Source: Landsat 8 Collection 2 via Google Earth Engine Format: RGB PNG images (256×256 pixels) Processing: Annual median composites, cloud-filtered Naming Convention: {state_FIPS}{county_FIPS}{year}.png State FIPS: 1-56 (standard federal codes) County FIPS: varies by state Examples: 1_1_2012.png (Alabama, Autauga County, 2012) 6_37_2019.png (California, Los Angeles County, 2019) 36_61_2023.png (New York, New York County, 2023) Demographics Source: U.S. Census Bureau ACS 1-year estimates Features: 27 demographic and socioeconomic indicators including: Population demographics (age, gender) Race and ethnicity distribution Economic indicators (income, inequality) Educational attainment

The ReadMe file provides the overall descriptions for the dataset "CONUS longitudinal local climate zone maps from 1986 to 2020", including author information, dataset summary, data structures and descriptions.

The China satellite imagery services market is experiencing robust growth, driven by increasing government investments in infrastructure development, heightened demand for precise geospatial data across various sectors, and advancements in satellite technology. The market's Compound Annual Growth Rate (CAGR) of 13.20% from 2019 to 2024 suggests a significant expansion, and this momentum is projected to continue through 2033. Key application segments fueling this growth include geospatial data acquisition and mapping for urban planning and infrastructure projects, natural resource management for efficient land utilization and environmental monitoring, and surveillance and security for public safety and border control. The government sector is a major end-user, but the construction, transportation and logistics, and military and defense sectors also contribute substantially to market demand. Leading companies like China Siwei Surveying and Mapping Technology Co. Ltd., Airbus, and HEAD Aerospace Group are actively shaping the market landscape through technological innovation and service offerings. The increasing availability of high-resolution imagery and the development of advanced analytics capabilities are further driving market expansion. The market's restraints include the high initial investment costs associated with satellite technology and data acquisition, data security and privacy concerns, and the potential for regulatory hurdles. However, these challenges are likely to be offset by the significant economic and strategic benefits derived from satellite imagery services. Future growth will be influenced by continued technological advancements, including the development of miniaturized satellites and improved sensor technologies, government policies supporting the aerospace industry, and rising adoption of satellite imagery across various industries. The integration of AI and machine learning for enhanced data analysis is expected to unlock new market opportunities, driving further expansion in the coming years. The ongoing development of robust data processing and interpretation infrastructure will also be crucial for sustainable market growth. Recent developments include: June 2023: The country awarded a contract to share satellite Imagery data with BRICS countries after the signing of an agreement on Cooperation on the BRICS Remote Sensing Satellite Constellation and shared 400 scenes of satellite imagery data with the BRICS countries, with the total volume amounting to 1.5 TB, which supports the growth of satellite imagery services market in China by creating a business opportunity for the Chinese vendors., January 2023: Spacety, a Chinese Space Science and Technology Research Institute, has provided synthetic aperture radar satellite images of Ukraine to Terra Tech, a Russia-based technology firm, for its war activities in the Russia and Ukraine conflicts. Therefore, due to their expertise, the military applications of Chinese companies' satellite imagery services fuel the country's market growth.. Key drivers for this market are: The country's Investments in Space Technology and Defence, Adoption of Big Data and Imagery Analytics. Potential restraints include: The country's Investments in Space Technology and Defence, Adoption of Big Data and Imagery Analytics. Notable trends are: The country's Investments in Space Technology and Defense Drives Market Growth..

1) Significance: construction land is one of the highest performance of human activities. The consumption of natural resources and the change of ecological environment can be closely linked with the development of construction land. This data reflects the evolution of high-precision construction land with 30 m spatial resolution from 1990 to 2019 in 7 provinces/municipalities directly under the central government of China, which are also important areas for rapid urbanization. 2) Data sources: Landsat series satellite data; China regional surface meteorological element driven data set (1979-2018) 3) Processing method: supervised classification method is adopted, random forest algorithm and Fourier transform are used to process characteristic bands, and control points are classified based on visual interpretation. 3-1) Obtaining spectral features: First, screen out Landsat images with transport volume <20%, and superimpose these images in units of 3 years, and then take the median of each superimposed pixel as the target pixel for pixel stitching. Obtain cloud-free images of the entire study area. This method can also better remove the banding influence of Landsat7 data. 3-2) Acquisition of time features: each pixel that has been superimposed for 3 years is screened for cloud cover, and discrete Fourier transform is performed following the minimum mean square error fitting principle to obtain the time latitude of each pixel. "Crest", "Trough" and "Phase". This method can better eliminate the influence of “bare land” on the extraction of construction land, because bare land may be covered by vegetation in spring and summer, and its time characteristics are quite different from construction land. 3-3) Extraction of meteorological and terrain features: The meteorological features are calculated from the China Regional Ground Meteorological Elements Driven Data Set (1979-2018): the data set is superimposed at the same time interval as Landsat, and each image is obtained The average value of yuan is used as the meteorological feature (due to the lack of meteorological data for 2019, the meteorological feature of the last period only calculates the average value of 2017 and 2018). Topographic features (elevation, slope) use SRTM-30m data. The detailed method and code can be seen as follows: https://github.com/wangjinzhulala/North_ China_ Plain_ GEE_ Organized 4) Data quality: the overall accuracy of all years is better than 94%. 5) Application prospects: Simulation of regional urban expansion; estimation of environmental impact of urbanization; quantification of food security and sustainable development.

The Italy Satellite Imagery Services market, valued at €89.82 million in 2025, is projected to experience robust growth, driven by increasing demand across diverse sectors. A Compound Annual Growth Rate (CAGR) of 10.89% from 2025 to 2033 signifies a substantial market expansion. Key drivers include the government's investments in infrastructure development, particularly within transportation and logistics, coupled with rising adoption in precision agriculture and environmental monitoring. The construction industry's reliance on precise geospatial data for project planning and execution fuels market growth. Furthermore, the escalating need for enhanced surveillance and security measures, particularly concerning national defense and disaster management, significantly contributes to market expansion. Segments like geospatial data acquisition and mapping, and natural resource management show particularly strong growth potential. Leading companies like Thales Alenia Space, Viasat Group, Airbus, and Maxar Technologies are actively shaping the market through technological advancements and strategic partnerships. The competitive landscape is marked by both established players and emerging innovative firms. Challenges include the high initial investment costs associated with satellite imagery technology and potential data privacy concerns. The sustained growth in the Italian satellite imagery services market is anticipated to continue throughout the forecast period (2025-2033). This expansion will be fueled by continuous technological advancements in satellite imagery resolution and analytics capabilities. The increasing availability of high-resolution data will further propel adoption across applications like urban planning, environmental monitoring, and agricultural optimization. Government initiatives focused on digital transformation and smart city development will create further opportunities for market growth. However, factors such as potential regulatory hurdles and data security concerns need careful consideration by market players. The emergence of new applications, coupled with the ongoing development of advanced analytical tools capable of extracting valuable insights from satellite imagery data, promise continued robust growth in the years ahead. Recent developments include: March 2023: Arianespace announced signing a contract with the European Space Agency (ESA), acting on behalf of the Italian government, for launching the IRIDE constellation of imaging satellites. The agreement includes two firm Vega C launches, starting in late 2025, with an option for a third. The Italian government funds the IRIDE constellation. It will consist of 36 satellites built by a consortium of Italian companies equipped with various imaging payloads, including optical and radar., October 2022: Europe's Copernicus Earth-observing mission Sentinel-2 satellite captured a striking satellite image of Italy's Stromboli volcano less than 5 hours after it erupted early on Oct. 9. The satellite image, processed in proper colors, shows lava pouring into the sea and vast plumes of ash and smoke rising above the volcano. The satellite images prompted the civil protection authorities in Italy to raise an orange alert due to enhanced volcano imbalance.. Key drivers for this market are: Increasing Investments to Strengthen Country's Space Economy, Adoption of Big Data and Imagery Analytics. Potential restraints include: Increasing Investments to Strengthen Country's Space Economy, Adoption of Big Data and Imagery Analytics. Notable trends are: Disaster Management Segment is Expected to Hold Significant Market Share.

Tabular data output from a series of groundwater modeling simulations for five counties along the Central Coast of California, USA. We used a spatially explicit state-and-transition simulation model with stocks and flows that integrates climate, land-use change, human water use, and groundwater gain-loss to examine the impact of future climate and land use change on groundwater balance and water demand at 270-m resolution from 2010 to 2060. The model incorporated downscaled groundwater recharge projections based on a Warm/Wet and a Hot/Dry climate future using output from the Basin Characterization Model, a spatially explicit hydrological process-based model. Two urbanization projections from a parcel-based, regional urban growth model representing 1) recent historical and 2) state-mandated housing growth projections were used as alternative spatial targets for future urban growth. Agricultural projections were based on recent historical trends from remote sensing data. Annual projected changes in groundwater balance were calculated as the difference between land-use related water demand, based on historical estimates, and climate-driven recharge plus agriculture return flows to groundwater from excess irrigation. For each combination of the two climate and two land-use change scenarios, we ran 50 Monte Carlo realizations of the model. Results presented here have been aggregated from the individual cell level and summarized by county.