Lesson 1. An Introduction to working with multispectral satellite data in ArcGIS Pro In which we learn: • How to unpack tar and gz files from USGS EROS • The basic map interface in ArcGIS • How to add image files • What each individual band of Landsat spectral data looks like • The difference between: o Analysis-ready data: surface reflectance and surface temperature o Landsat Collection 1 Level 3 data: burned area and dynamic surface water o Sentinel2data o ISRO AWiFS and LISS-3 data Lesson 2. Basic image preprocessing In which we learn: • How to composite using the composite band tool • How to represent composite images • All about band combinations • How to composite using raster functions • How to subset data into a rectangle • How to clip to a polygon Lesson 3. Working with mosaic datasets In which we learn: o How to prepare an empty mosaic dataset o How to add images to a mosaic dataset o How to change symbology in a mosaic dataset o How to add a time attribute o How to add a time dimension to the mosaic dataset o How to view time series data in a mosaic dataset Lesson 4. Working with and creating derived datasets In which we learn: • How to visualize Landsat ARD surface temperature • How to calculate F° from K° using ARD surface temperature • How to generate and apply .lyrx files • How to calculate an NDVI raster using ISRO LISS-3 data • How to visualize burned areas using Landsat Level 3 data • How to visualize dynamic surface water extent using Landsat Level 3 data

Remote sensing (RS) data and geographic information system (GIS) techniques were used to monitor the changes in the Oasis agroecosystem of the pre-Saharan province of Errachidia, southeastern Morocco. The land use and land cover (LULC) change of the agroecosystem of this province was processed using Landsat time series with 5-year intervals of the last thirty years. The normalized difference vegetation index (NDVI) and the maximum likelihood classification (MLC) were categorized into five classes, including water bodies, cultivated land, bare land, built-up, and desertified land. The overall accuracy of the MLC maps was estimated to be higher than 90%. The finding showed a degradation trend represented by an increase in desertified lands, which tripled in the ten last years, passing from 20.62% in 2011 to 58.49% in 2022. The findings also depicted a decreasing trend in the cultivated area in this period passing from 174.2 km2 in 1991 to 82.2 km2 in 2022. Using NDWI, Landsat images from 1991 to 2021 depicted a strong association between the water reserve in Hassan Eddakhil dam in the upstream area and the LULC changes. The oases from the dam (upstream) to Er-Rissani (downstream) recorded high rates of decline with an increasing trend of desertification due to drought and overuse mainly of groundwater. The outputs of this research effort constitute a significant source of information that may be used to support further research and decision-makers to manage arid ecosystems and achieve the sustainable development goals (SDGs), precisely the SDGs 15 (Life on land).

Geographic Information System (GIS) Tools Market Outlook

The global Geographic Information System (GIS) tools market size was valued at approximately USD 10.8 billion in 2023, and it is projected to reach USD 21.5 billion by 2032, growing at a compound annual growth rate (CAGR) of 7.9% from 2024 to 2032. The increasing demand for spatial data analytics and the rising adoption of GIS tools across various industries are significant growth factors propelling the market forward.

One of the primary growth factors for the GIS tools market is the surging demand for spatial data analytics. Spatial data plays a critical role in numerous sectors, including urban planning, environmental monitoring, disaster management, and natural resource exploration. The ability to visualize and analyze spatial data provides organizations with valuable insights, enabling them to make informed decisions. Advances in technology, such as the integration of artificial intelligence (AI) and machine learning (ML) with GIS, are enhancing the capabilities of these tools, further driving market growth.

Moreover, the increasing adoption of GIS tools in the construction and agriculture sectors is fueling market expansion. In construction, GIS tools are used for site selection, route planning, and resource management, enhancing operational efficiency and reducing costs. Similarly, in agriculture, GIS tools aid in precision farming, crop monitoring, and soil analysis, leading to improved crop yields and sustainable farming practices. The ability of GIS tools to provide real-time data and analytics is particularly beneficial in these industries, contributing to their widespread adoption.

The growing importance of location-based services (LBS) in various applications is another key driver for the GIS tools market. LBS are extensively used in navigation, logistics, and transportation, providing real-time location information and route optimization. The proliferation of smartphones and the development of advanced GPS technologies have significantly increased the demand for LBS, thereby boosting the GIS tools market. Additionally, the integration of GIS with other technologies, such as the Internet of Things (IoT) and Big Data, is creating new opportunities for market growth.

Regionally, North America holds a significant share of the GIS tools market, driven by the high adoption of advanced technologies and the presence of major market players. The Asia Pacific region is expected to witness the highest growth rate during the forecast period, owing to increasing investments in infrastructure development, smart city projects, and the growing use of GIS tools in emerging economies such as China and India. Europe, Latin America, and the Middle East & Africa are also expected to contribute to market growth, driven by various government initiatives and increasing awareness of the benefits of GIS tools.

Component Analysis

The GIS tools market can be segmented by component into software, hardware, and services. The software segment is anticipated to dominate the market due to the increasing demand for advanced GIS software solutions that offer enhanced data visualization, spatial analysis, and decision-making capabilities. GIS software encompasses a wide range of applications, including mapping, spatial data analysis, and geospatial data management, making it indispensable for various industries. The continuous development of user-friendly and feature-rich software solutions is expected to drive the growth of this segment.

Hardware components in the GIS tools market include devices such as GPS units, remote sensing devices, and plotting and digitizing tools. The hardware segment is also expected to witness substantial growth, driven by the increasing use of advanced hardware devices that provide accurate and real-time spatial data. The advancements in GPS technology and the development of sophisticated remote sensing devices are key factors contributing to the growth of the hardware segment. Additionally, the integration of hardware with IoT and AI technologies is enhancing the capabilities of GIS tools, further propelling market expansion.

The services segment includes consulting, integration, maintenance, and support services related to GIS tools. This segment is expected to grow significantly, driven by the increasing demand for specialized services that help organizations effectively implement and manage GIS solutions. Consulting services assist organizations in selecting the right GIS tools and optimizing their use, while integration services ensure seamless integr

This data support the paper "A systematic review on the integration of remote sensing and GIS to forest and grassland ecosystem health attributes, indicators, and measures " by Irini Soubry, Thuy Doan, Thuan Chu and Xulin Guo 2021 in the journal of "Remote Sensing" by MDPI. It includes the "Search_Effort.csv" list with the keywords and number of studies selected for further examination, the "Potential_Studies.csv" with the post-filtering of suitability and notes related to each study, the "Metadata.csv" with the information collected for each metadata variable per study, and the "ExtractedData.csv" with the information collected for each extracted dta variable per study. More information about the data collection and procedures can be found in the respective manuscript.

As GIS and computing technologies advanced rapidly, many indoor space studies began to adopt GIS technology, data models, and analysis methods. However, even with a considerable amount of research on indoor GIS and various indoor systems developed for different applications, there has not been much attention devoted to adopting indoor GIS for the evaluation space usage. Applying indoor GIS for space usage assessment can not only provide a map-based interface for data collection, but also brings spatial analysis and reporting capabilities for this purpose. This study aims to explore best practice of using an indoor GIS platform to assess space usage and design a complete indoor GIS solution to facilitate and streamline the data collection, a management and reporting workflow. The design has a user-friendly interface for data collectors and an automated mechanism to aggregate and visualize the space usage statistics. A case study was carried out at the Purdue University Libraries to assess study space usage. The system is efficient and effective in collecting student counts and activities and generating reports to interested parties in a timely manner. The analysis results of the collected data provide insights into the user preferences in terms of space usage. This study demonstrates the advantages of applying an indoor GIS solution to evaluate space usage as well as providing a framework to design and implement such a system. The system can be easily extended and applied to other buildings for space usage assessment purposes with minimal development efforts.

The dataset used in this study is designed to facilitate the monitoring and detection of Harmful Algal Blooms (HABs) using Synthetic Aperture Radar (SAR) remote sensing and deep learning models. The dataset includes Sentinel-1 SAR C-band (TIF), Sentinel-2 MSI (TIF), and Water indices (TIF) that were utilized as input dataset in the deep learning model. The dataset used in this study originates from external sources and is not the property of the authors. If reused, proper attribution to the original sources is required in accordance with their respective citation guidelines. The authors have modified the dataset for research purposes.

The Remote Sensing Interpretation Software market is experiencing robust growth, driven by increasing demand across diverse sectors. The market size, while not explicitly stated, can be reasonably estimated based on the provided information and typical market growth rates for technology sectors. Considering a CAGR (Compound Annual Growth Rate) and the given study period (2019-2033), a conservative estimate for the 2025 market size might fall within the range of $5-7 billion USD. This growth is fueled by several key factors. Firstly, the expanding application of remote sensing in precision agriculture, facilitating optimized resource allocation and improved crop yields, is a significant driver. Secondly, the petroleum and mineral exploration sector leverages remote sensing for efficient resource identification and extraction, contributing substantially to market growth. Furthermore, advancements in cloud-based solutions are enhancing accessibility and scalability, lowering barriers to entry for various users. Government initiatives promoting the use of geospatial technologies in various sectors, particularly in developing economies, are creating significant opportunities. The integration of AI and machine learning in remote sensing interpretation tools is further accelerating market expansion, enabling faster and more accurate analysis. However, certain restraints are present. High initial investment costs associated with acquiring sophisticated software and hardware can limit adoption, especially among smaller companies and organizations. The need for skilled professionals to operate and interpret data effectively poses a challenge. Data security concerns and the potential for inaccuracies resulting from environmental factors or data processing errors also present hurdles. Despite these challenges, the market's future trajectory remains optimistic, particularly with ongoing technological advancements and increasing government and private sector investment driving innovation and accessibility. Segmentation by application (Petroleum and Mineral Exploration, Agriculture and Forestry, Medicine, Military, Meteorological, Research, etc.) and type (Cloud-based, On-Premise) reveals the market's diverse and expanding potential, with significant growth anticipated in cloud-based solutions driven by their scalability and accessibility. The competitive landscape comprises both established players like Hexagon, Microsoft, and IBM, and emerging companies like Sense Time and Geovis Technology, reflecting a dynamic and innovative market.

The remote sensing software market is experiencing robust growth, driven by increasing demand for geospatial data across various sectors. The market's expansion is fueled by advancements in sensor technology, satellite imagery availability, and the rising adoption of cloud-based solutions for data processing and analysis. Factors like the need for precise land management, environmental monitoring, urban planning, and defense applications are significant contributors to this growth. While precise figures for market size and CAGR are unavailable in the provided information, based on industry reports and trends, a reasonable estimation would place the 2025 market size at approximately $5 billion, experiencing a compound annual growth rate (CAGR) of around 8% during the forecast period (2025-2033). This growth trajectory is expected to continue, driven by the increasing integration of AI and machine learning algorithms within remote sensing software for improved data analysis and automation. The competitive landscape is marked by a mix of established players like PCI Geomatics, Hexagon, and Esri, and emerging technology providers. These companies are constantly innovating to offer advanced functionalities such as 3D modeling, image processing, and data visualization capabilities. However, high initial investment costs for software licenses and specialized hardware can present a barrier to entry for some organizations. Further, data security concerns and the need for specialized expertise in data interpretation can pose some challenges to market growth. Despite these constraints, the long-term prospects of the remote sensing software market remain highly positive, fueled by government initiatives promoting geospatial data accessibility and the ongoing development of more sophisticated and user-friendly software solutions. The increasing availability of affordable high-resolution imagery and the integration of remote sensing data with other data sources promise to further boost market expansion in the coming years.

Abstract Among the various characteristics of the Brazilian territory, one is foremost: the country has the second largest forest reserve on the planet, accounting for approximately 10% of the total recorded global forest formations. In this scenario, seasonally dry tropical forests (SDTF) are the second smallest forest type in Brazil, located predominantly in non-forested biomes, such as the Cerrado and Caatinga. Consequently, correct identification is fundamental to their conservation, which is hampered as SDTF areas are generally classified as other types of vegetation. Therefore, this research aimed to monitor the Land Use and Coverage in 2007 and 2016 in the continuous strip from the North of Minas Gerais to the South of Piauí, to diagnose the current situation of Brazilian deciduous forests and verify the chief agents that affect its deforestation and regeneration. Our findings were that the significant increase in cultivated areas and the spatial mobility of pastures contributed decisively to the changes presented by plant formations. However, these drivers played different roles in the losses/gains. In particular, it was concluded that the changes occurring to deciduous forests are particularly explained by pastured areas. The other vegetation types were equally impacted by this class, but with a more incisive participation of cultivation.

Having updated knowledge of cropland extent is essential for crop monitoring and food security early warning. Previous research has proposed different methods and adopted various datasets for mapping cropland areas at regional to global scales. However, most approaches did not consider the characteristics of farming systems and applied the same classification method in different agroecological zones (AEZs). Furthermore, the acquisition of in situ samples for classification training remains challenging. To address these knowledge gaps and challenges, this study applied a zone-specific classification by comparing four classifiers (random forest, the support vector machine (SVM), the classification and regression tree (CART) and minimum distance) for cropland mapping over four different AEZs in the Zambezi River basin (ZRB). Landsat-8 and Sentinel-2 data and derived indices were used and synthesized to generate thirty-five layers for classification on the Google Earth Engine platform. Training samples were derived from three existing landcover datasets to minimize the cost of sample acquisitions over the large area. The final cropland map was generated at a 10 m resolution.

The information here presented was imported from a published paper with the title ''Comparison of Different Cropland Classification Methods under Diversified Agroecological Conditions in the Zambezi River Basin'' which its reference is shown below. The dataset here presented was created based on the results of this study.

Bofana, J.; Zhang, M.; Nabil, M.; Wu, B.; Tian, F.; Liu, W.; Zeng, H.; Zhang, N.; Nangombe, S.S.; Cipriano, S.A.; Phiri, E.; Mushore, T.D.; Kaluba, P.; Mashonjowa, E.; Moyo, C. Comparison of Different Cropland Classification Methods under Diversified Agroecological Conditions in the Zambezi River Basin. Remote Sens. 2020, 12, 2096. https://doi.org/10.3390/rs12132096

The global satellite remote sensing market is experiencing robust growth, projected to reach $4911.2 million in 2025 and maintain a Compound Annual Growth Rate (CAGR) of 17.9% from 2025 to 2033. This expansion is fueled by several key drivers. Increased demand for precise and timely geospatial intelligence across various sectors, including agriculture, defense, and urban planning, is a major catalyst. Advances in sensor technology, offering higher resolution and improved spectral capabilities, are further enhancing the market's appeal. The decreasing cost of satellite launches and data processing also contributes to market accessibility and affordability. Furthermore, the rising adoption of cloud-based platforms for data storage and analysis is streamlining workflows and improving data accessibility for a wider range of users. While data security and privacy concerns represent potential restraints, the overall market trajectory suggests a positive outlook for continued expansion. The competitive landscape is characterized by a mix of established aerospace giants and innovative startups. Companies like Airbus, Boeing, and Lockheed Martin dominate the market with their extensive experience in satellite design, manufacturing, and launch services. However, new entrants such as Planet Labs are disrupting the industry by offering high-frequency imagery and data analytics services at more competitive price points. This competitive dynamic fosters innovation and helps drive down the overall cost of satellite remote sensing data, making it more accessible to a wider range of users and applications. Geographical growth is expected across all regions, with North America and Europe currently leading due to established infrastructure and robust technological capabilities. However, Asia-Pacific is poised for significant expansion driven by rapid economic growth and increasing investment in satellite technology.

Geospatial Solutions Market size was valued at USD 282.75 Billion in 2024 and is projected to reach USD 650.14 Billion by 2032, growing at a CAGR of 12.10% during the forecast period 2026-2032.

Geospatial Solutions Market: Definition/ Overview

Geospatial solutions are applications and technologies that use spatial data to address geography, location, and Earth's surface problems. They use tools like GIS, remote sensing, GPS, satellite imagery analysis, and spatial modelling. These solutions enable informed decision-making, resource allocation optimization, asset management, environmental monitoring, infrastructure planning, and addressing challenges in sectors like urban planning, agriculture, transportation, disaster management, and natural resource management. They empower users to harness spatial information for better understanding and decision-making in various contexts.

Geospatial solutions are technologies and methodologies used to analyze and visualize spatial data, ranging from urban planning to agriculture. They use GIS, remote sensing, and GNSS to gather, process, and interpret data. These solutions help users make informed decisions, solve complex problems, optimize resource allocation, and enhance situational awareness. They are crucial in addressing challenges and unlocking opportunities in today's interconnected world, such as mapping land use patterns, monitoring ecosystem changes, and real-time asset tracking.

This project will adapt an existing computer model for assessing the suitability of a site for construction of a living shoreline, apply the model to Perdido Bay/Wolf Bay/Ono Island complex in coastal Alabama; Lake Pontchartrain, Louisiana; and Galveston Bay, Texas, and develop an interactive decision support tool that allows for a rapid assessment of a site.

Supporting information for: REMAP: An online remote sensing application for land cover classification and monitoringcsv and json files for implementing land cover classifications using the remap, the remote ecosystem assessment and monitoring pipeline (https://remap-app.org/)Nearmap aerial photograph courtesy of Nearmap Pty Ltd.For further information see:Murray, N.J., Keith, D.A., Simpson, D., Wilshire, J.H., Lucas, R.M. (accepted) REMAP: A cloud-based remote sensing application for generalized ecosystem classifications. Methods in Ecology and Evolution.

The global satellite remote sensing software market is experiencing robust growth, driven by increasing demand across diverse sectors. While precise market size figures for 2025 aren't provided, considering a plausible CAGR of 10% (a conservative estimate given the technological advancements and expanding applications) and an assumed 2024 market size of $2 billion, we can project a 2025 market valuation of approximately $2.2 billion. This expansion is fueled by several key factors. Firstly, the agricultural sector is leveraging satellite imagery for precision farming, crop monitoring, and yield prediction, significantly enhancing efficiency and productivity. Secondly, advancements in water resource management are heavily reliant on remote sensing data for efficient irrigation and flood control. Furthermore, forest management and conservation efforts utilize this technology for deforestation monitoring and biodiversity assessment. The public sector, including government agencies and research institutions, is also a major consumer, relying on these tools for environmental monitoring, disaster response, and urban planning. The market is segmented by software type (open-source and non-open-source) and application, with non-open-source solutions currently commanding a larger share due to their advanced features and robust support. Growth is further propelled by continuous technological innovation leading to more sophisticated analytics capabilities and easier data accessibility. However, certain restraints hinder market expansion. High initial investment costs for software licenses and hardware can pose a significant barrier, particularly for smaller organizations. Furthermore, the need for specialized expertise to interpret and analyze the complex satellite data can limit widespread adoption. Data security and privacy concerns related to sensitive geographic information are also emerging challenges. Despite these limitations, the long-term outlook for the satellite remote sensing software market remains positive, fueled by ongoing technological advancements, increased government investments in space-based technologies, and the growing recognition of its importance in various sectors. The market is expected to continue its growth trajectory, creating opportunities for established players and new entrants alike. The diverse range of applications and continued integration with other technologies like AI and machine learning will significantly shape the future landscape of this market.

The global satellite remote sensing software market is experiencing robust growth, driven by increasing demand for precise geospatial data across diverse sectors. The market, currently estimated at $2.5 billion in 2025, is projected to achieve a Compound Annual Growth Rate (CAGR) of 12% from 2025 to 2033, reaching an estimated value of $7.2 billion by 2033. This expansion is fueled by several key factors. Firstly, the rising adoption of cloud-based solutions and the advancements in artificial intelligence (AI) and machine learning (ML) technologies are enhancing the analytical capabilities and accessibility of remote sensing data. Secondly, the growing need for efficient resource management in agriculture, water conservancy, and forestry is driving the demand for sophisticated software capable of processing and interpreting satellite imagery. Furthermore, governmental initiatives promoting the use of geospatial technologies for infrastructure development and environmental monitoring are contributing to market growth. The open-source software segment is expected to witness significant growth due to its cost-effectiveness and flexibility, while the non-open source segment will maintain its market share driven by its advanced features and dedicated support. Geographic regions such as North America and Europe are currently leading the market, driven by robust technological infrastructure and high adoption rates. However, emerging economies in Asia-Pacific are poised for significant growth owing to increasing investments in infrastructure and technological advancements. Despite the positive outlook, the market faces certain challenges. High initial investment costs for both software and hardware can be a barrier to entry for small and medium-sized enterprises (SMEs). Furthermore, the complexity of remote sensing data analysis and the need for skilled professionals to interpret the results can hinder wider adoption. Data security and privacy concerns, especially concerning sensitive geospatial information, also present hurdles for market expansion. Overcoming these challenges through collaborative partnerships, the development of user-friendly interfaces, and robust data security measures will be crucial for driving continued growth in the satellite remote sensing software market.

Discover the Remote Sensing Object Segmentation Dataset Perfect for GIS, AI driven environmental studies, and satellite image analysis.

Multi-temporal landslide inventories are important information for the understanding of landslide dynamics and related predisposing and triggering factors, and thus a crucial prerequisite for probabilistic hazard and risk assessment. Despite the great importance of these inventories, they do not exist for many landslide prone regions in the world. In this context, the recently evolving global-scale availability of high temporal and spatial resolution optical satellite imagery (RapidEye, Sentinel-2A/B, planet) has opened up new opportunities for the creation of these multi-temporal inventories. Taking up on these at the time still to be evolving opportunities, a semi-automated spatiotemporal landslide mapper was developed at the Remote Sensing Section of the GFZ Potsdam being capable of deriving post-failure landslide objects (polygons) from optical satellite time series data (Behling et al., 2014). The developed algorithm was applied to a 7500 km² study area using RapidEye time series data which were acquired in the frame of the RESA project (Project ID 424) for the time period between 2009 and 2013. A multi-temporal landslide inventory from 1986 to 2013 derived from multi-sensor optical satellite time series data is available as separate publications (Behling et al., 2016; Behling and Roessner, 2020). The resulting multi-temporal landslide inventory being subject of this data publication is supplementary to the article of Behling et al. (2014), which describes the developed spatiotemporal landslide mapper in detail. This landslide mapper detects landslide objects by analyzing temporal NDVI-based vegetation cover changes and relief-oriented parameters in a rule-based approach combining pixel- and object-based analysis. Typical landslide-related vegetation changes comprise abrupt disturbances of the vegetation cover in the result of the actual failure as well as post-failure revegetation which usually happens at a slower pace compared to vegetation growth in the surrounding undisturbed areas, since the displaced landslide masses are susceptible to subsequent erosion and reactivation processes. The resulting landslide-specific temporal surface cover dynamics in form of temporal trajectories is used as input information to detect freshly occurred landslides and to separate them from other temporal variations in the surrounding vegetation cover (e.g., seasonal vegetation changes or changes due to agricultural activities) and from permanently non-vegetated areas (e.g., urban non-vegetated areas, water bodies, rock outcrops). For a detailed description of the methodology of the spatiotemporal landslide mapper, please see Behling et al. (2014). The data are provided in vector format (polygons) in form of a standard shapefile contained in the zip-file Behling_et-al_2014_landslide_inventory_SouthernKyrgyzstan_2009_2013.zip and are described in more detail in the data description file.

LIDAR_PROJECT_POLY: Data consists of polygons representing the spatial footprints of LIDAR projects over BLM lands in Oregon and Washington.

Additional file 1. Web_of_Science_Full_Record_and_Cited_References_1026. (This data file contains the full record and cited references of 1026 articles exported from Web of Science).