Satellite imagery is a valuable source of geospatial data for GIS analysis, but it also poses some challenges and limitations. How can you acquire and use satellite imagery effectively for your GIS projects? In this article, you will learn some best practices for selecting, accessing, processing, and analyzing satellite imagery for GIS.
The first step is to determine what kind of satellite imagery you need for your GIS analysis. Satellite imagery can vary in spatial, temporal, spectral, and radiometric resolution, as well as in coverage, cost, and availability. Depending on your analysis objectives, you may need high-resolution imagery for detailed mapping, or low-resolution imagery for large-scale monitoring. You may also need imagery from different spectral bands, such as visible, infrared, or microwave, to capture different features and phenomena. Additionally, you may need to consider the frequency and timeliness of the imagery, as well as the licensing and copyright issues.
When acquiring satellite imagery for GIS, pick the right imagery like choosing the right lens for a camera. Consider resolution - think of it like getting a clearer picture. Match the image date with your project timeline, much like selecting photos from the right time. Choosing suitable imagery enhances the quality of GIS analysis, ensuring accurate and valuable insights for your project.
This might help you to understand the imagery characteristics... Think of radiometric resolution as a time interval between two acquisitions in the same position... Think of spatial resolution as that smallest area satellite is able to capture, remember most of the image pixels are square once you see spatial resolution is 30m means one side of square is 30m and hence the total smallest area covered by the sensor is 900sqm..
Las mejores prácticas para adquirir imágenes satelitales para el análisis SIG incluyen los siguientes pasos: Identificar las necesidades de datos: Determina qué tipo de información necesitas y para qué propósito. Esto te ayudará a seleccionar el tipo de imagen satelital adecuada. Conocer las fuentes de datos: Investiga y compara las diferentes fuentes de imágenes satelitales disponibles, como proveedores comerciales, agencias espaciales y plataformas de datos abiertos. Evaluar la resolución espacial y temporal: Considera la resolución espacial (nivel de detalle) y la frecuencia con la que se capturan las imágenes (resolución temporal) para asegurarte de que cumplan con tus requisitos.
When acquiring satellite imagery for GIS analysis, adhere to best practices for optimal results. Define project needs and choose a satellite accordingly; NASA EOSDIS, ESA, Airbus, and Maxar offer diverse options. Commercial providers like Maxar and Airbus offer high-resolution options. Understand licensing terms to ensure legal compliance. Review metadata, check for atmospheric conditions, and align temporal aspects with analysis requirements. Preprocess and calibrate for accuracy, ensuring compatibility with GIS data in terms of coordinate systems. Utilize image fusion for multispectral analysis, and be cautious of compression methods. Conduct a quality assessment, keeping detailed metadata records for reproducibility
For acquiring satellite imagery for GIS analysis, consider factors like spatial resolution, spectral bands, and temporal frequency. Choose imagery from reputable providers like Landsat or Sentinel for global coverage. Assess the cloud cover percentage to ensure clear visibility of the area of interest. Match the spectral bands to your analysis needs, considering infrared for vegetation or thermal bands for temperature analysis. Opt for high spatial resolution for detailed analysis but be mindful of file sizes. Regularly update imagery for temporal analysis. Utilize open-access platforms like NASA Earthdata or commercial providers such as DigitalGlobe. Carefully review licensing agreements and metadata to comply with usage restrictions.
When looking for the best sources of satellite imagery for GIS analysis, there are many platforms and providers to choose from. Google Earth and Google Maps are two of the most widely used options, as they offer access to high-resolution satellite imagery from various sensors and dates, which can be exported as georeferenced images or KML files. USGS Earth Explorer is another popular choice, allowing users to search and download satellite imagery from various missions with the ability to filter by date, location, cloud cover, and more. ESA Copernicus Open Access Hub provides free access to imagery from the Copernicus program, such as Sentinel-1, Sentinel-2, and Sentinel-3, as well as other products and services like land monitoring, marine environment, and emergency management. Finally, NASA Earthdata is a great resource for accessing satellite imagery and data from various NASA missions like Aqua, Terra, and Suomi NPP while also providing access to tools and resources like data analysis, visualization, and education.
I am always use Google Earth Engine to acquire the newest multispectral data due to its ability to easily query and process data. You dont have export all of the data, you can clip it directly. In GEE you could also create image composite that have been cloud masked. Then you can download it after the process have been done.
Existen diversas plataformas en las cuales se puede obtener imágenes satelitales: ??Sentinel Playground?puede ser una referencia para empezar a manejar? búsqueda de imágenes y filtrado de bandas para componer?imágenes a falso color?utilizando la misión Sentinel 2. ??Google Earth Engine?permite acceder a infinidad de colecciones de imágenes satélite y procesar los datos en la nube en tan solo unos segundos. ??El visor del?Servicio Geológico de los Estados Unidos?(USGS)?también es una de las plataformas de referencia donde descargar datos satelitales. ??La NASA dispone de su propio visor?World View?desde donde se puede acceder a imágenes satélite y datos espaciales. ??La herramienta?Landsat Viewer?es muy útil.
For optimal satellite imagery in GIS, consider resolution and recency, akin to choosing clear and recent photos. Verify data sources, much like checking the credibility of information. Utilize reputable providers like you'd rely on trusted photographers for quality pictures. Cross-reference multiple sources for accuracy, similar to fact-checking. Employing these practices ensures reliable and valuable satellite imagery for effective GIS analysis.
Some great resources for accessing satellite imagery and data are: USGS Earth Explorer, NASA Earth Observing System Data and Information Center, Copernicus Open Access Hub, Planet Explorer, OpenStreetMap, Google Earth Engine, European Space Agency, NOAA's National Centers for Environmental Information (NCEI) and many more. With these websites are GIS Servers, you can find live data and GIS Imagery for further analysis. Remember, some datasets might be encrypted and might require registration for usage. Some of these websites might require account creation for access and downloading of resources from GIS Servers.
Beaucoup trop compliqué... Pour pas grand chose. L'IGN est une excellente source de donnée WMS en France. Je ne pense pas que tout ce blabla ci dessus soit nécessaire
The third step in GIS analysis is to process the satellite imagery. Preprocessing steps, such as georeferencing, projection, resampling, mosaicking, clipping, enhancement, and classification are often required to improve the quality and usability of the imagery. Georeferencing assigns spatial coordinates to the imagery, while projection transforms the imagery from its original coordinate system to a standard or desired coordinate system. Resampling changes the pixel size or resolution of the imagery, while mosaicking combines multiple images of the same area into one. Clipping extracts a subset or area of interest from the imagery, and enhancement applies various techniques to improve its appearance and clarity. Finally, classification assigns labels or categories to the pixels or segments of the imagery.
Estoy de acuerdo en que el proceso de imágenes satelitales permite el saneamiento y la mejora de las mismas, un tip de este proceso es el “Use Layer Georeferencing” y la corrección de imágenes satelitales con Atmosphere Correction / ATCOR Ground Reflectance. -Atmosphere Reflectance (TOA) y ATCOR Ground Reflectance.
Processing the imagery can include several steps. This include: Image selection, Pre-processing (Radiometric Correction, Geometric Correction), Enhancement Techniques (Pan-Sharpening, Contrast Adjustment, Color Balancing), Mosaicking, Feature Extraction, Cloud and Noise Removal, Data Integration, Metadata Documentation, Quality Assessment, Analysis and Interpretation. Different software and tools like ENVI, QGIS, ArcGIS, or specialized remote sensing software packages offer functionalities to perform these processing steps. The specific workflow may vary based on the software used and the nature of the GIS analysis being conducted.
To process satellite imagery, use dedicated software, comparable to fine-tuning a photo on your smartphone. Improve clarity, like adjusting image settings. Correct distortions, similar to straightening a skewed photo. Assemble images seamlessly for a complete view, resembling the creation of a panorama. These actions guarantee precise and practical satellite data for your GIS analysis.
A lot of the time you may need to work on multiple images which require cross sensor calibration before imge calibration. Do it for homogeneity for pixel, resolution, tone, unwanted objects from the tile as per your AOI.
Los equipos empleados para tomar las imágenes satelitales pueden generar errores, ruido, o distorsiones. Por eso es necesario realizar: Correcciones radiométricas.? Correcciones geométricas.? Estas correcciones son necesarias e importantes para la corrección de los errores inherentes en las imágenes satelitales. Otros efectos que son más complicados deben de ser corregidos de forma empírica, utilizando puntos de control, información de alturas, relieve y conocimiento técnico del operador. Todo esto sirve para lograr un adecuado procesamiento de la imagen. Además se deben de realizar ciertas mejoras para obtener la imagen corregida.
The final step is to analyze the satellite imagery for your GIS analysis. This can provide a wealth of information and insights for various GIS applications, such as land use, land cover, vegetation, water, urban, and environmental analysis. Common methods include change detection which involves comparing two or more images of the same area or scene from different dates or times, to identify the changes that have occurred. Another method is NDVI which involves calculating the normalized difference vegetation index (NDVI) to measure the greenness or health of vegetation. PCA is also used which is a statistical technique that reduces the dimensionality and redundancy of the imagery. Lastly, supervised and unsupervised learning are methods that involve applying machine learning algorithms to discover patterns, trends, or anomalies.
Analyze processed imagery using GIS tools, much like examining details on a map. Identify patterns and trends, like spotting landmarks in a city. Measure distances, akin to using a scale on a map. This approach allows for comprehensive insights, making the best use of satellite data in your analysis.
In GIS analysis, key software like ArcGIS and QGIS are essential for processing satellite imagery. ArcGIS offers sophisticated georeferencing, projection, and raster analysis, ideal for complex geographic studies. QGIS excels in data visualization and manipulation, including resampling and mosaicking. For advanced image processing, ERDAS IMAGINE and ENVI are crucial, with ERDAS specializing in land cover classification and ENVI in spectral analysis. These tools enable detailed analysis and meaningful insights from satellite imagery, essential in fields like environmental management and urban planning.
Analyzing satellite imagery in GIS involves several techniques and approaches to extract valuable information and insights. Here are common methods for imagery analysis: Image Classification, Change Detection, Vegetation Indices, Object-Based Image Analysis, Land Use/Land Cover Mapping, Feature Extraction, Terrain Analysis, Supervised/Unsupervised Segmentation, Time-Series Analysis and Accuracy Assessment. These analyses are often performed using remote sensing software packages like ENVI, ERDAS IMAGINE, QGIS with plugins, or ArcGIS with specialized extensions.
Las imágenes por satélite tienen muchas aplicaciones prácticas en diversos sectores. Mediante el análisis y utilizando el software adecuado se puede realizar y clasificar el tipo de cobertura del suelo, detectar cambios o evaluar la salud de los cultivos, estimar áreas quemadas, seguir el progreso de los huracanes o temas referentes a clima, temas militares, entre otros usos.
Using satellite images is a cost effective way in addressing many challenges and resolving issues compared with other methods and approaches. However, the accuracy and quality of the outcomes are very dependent on the type and source of space data. Then, the way images are processed and analyzed will also contribute significantly to the overall performance of the GIS analysis!
Every step depends on the application workflow from choosing the right satellite images to their processing, analysis, and information retrieval. Specifications of satellite images vary and so is their utilisation and analysis. The need for supplementary data along with satellite images is another parameter for GIS analysis.
In my experience, legal issues are also to be considered while purchasing and ingesting satellite imagery. For sensitive areas, there are country level restrictions where you are allowed only a certain level of resolutions, eg: border areas, army bases etc.
In GIS analysis, beyond software proficiency, programming skills in languages like Python and R are increasingly valuable. Python, with its extensive libraries like GDAL and PyQGIS, allows for automation, customization, and complex spatial analysis, integrating seamlessly with tools like ArcGIS and QGIS. R, known for its statistical prowess, excels in data manipulation, visualization, and spatial statistics, making it indispensable for in-depth geographic data analysis and modeling. Mastery in these programming languages enhances a GIS professional's ability to handle large datasets, perform sophisticated analyses, and develop innovative solutions in areas like environmental monitoring, urban planning, and data-driven decision-making.
I live in Colombia. Many areas here are cloudy and it is not easy to get images without clouds to check certain features. It is good then to understand the weather of the regions, for example, when is the dry season, years of "El ni?o" phenomeno, wind cycles etc. Then you can search your area of interest limited by dates that will give you more probability of finding what you need.