You have a limited budget for a GIS project. How do you choose which remote sensing data sets to prioritize?

That’s true. Based on the objective which should be met, the relevant dataset will be selected. On the other hand, when coming to budget management, it could be a trade-off in some cases and, in some cases no; other techniques and complementary datasets can be acquired to compensate, e.g., low spatial resolution satellite imagery.

Considerar temas esenciales como el tipo de fenómeno a monitorear o plasmar, la temporalidad, el nivel de detalle aceptable para capturar datos remotos y complementar con información secundaria. Ver disponibilidad de datos abiertos en geoportales o sitios de descarga gratuita de imágenes satelitales para completar ciertos sectores en los cuales el fenómeno no tenga incidencia por ejemplo, la deforestación no se presentará en zonas urbanas constituidas. Por tanto, es esencial conocer cuáles son las prioridades del proyecto para poder elegir los insumos principales y seleccionar alternativas apropiadas y complementarias para los productos derivados.

Selecting the right remote sensing image for a GIS project is essential and depends on specific needs: feature requirements (e.g., vegetation health), location (e.g., coastal erosion), type of POI (e.g., urban planning), resolution (e.g., deforestation monitoring), data availability (e.g., land use mapping), and processing capabilities (e.g., disaster response). Tailoring imagery to these factors ensures accurate, detailed analysis and successful project outcomes.

To maximize the value of your remote sensing data on a limited budget: Align data with project goals: Ensure data directly supports project objectives. Consider data availability: Prioritize free or low-cost data sources. Evaluate data quality: Choose data with known accuracy and reliability. Balance data needs: Weigh the trade-offs between spatial and temporal resolution.

Here are four free data sources that can be used: Free and open data: USGS EarthExplorer: Offers a wide range of Landsat, Sentinel, and other satellite imagery. Google Earth Engine: Cloud-based platform with access to huge datasets. NASA EarthData Search: Explore NASA's Earth science data archives. OpenStreetMap: Free and open source map data. However a concrete answer will depend on the type of project, the temporality, and the extent of study area

The quality of remote sensing data is paramount, as it directly impacts the accuracy of your GIS analyses. When you have a limited budget, it's vital to understand the trade-offs between different data types. Optical imagery is useful for detailed visual analysis but can be obstructed by clouds or lack nighttime capturing capabilities. In contrast, Synthetic Aperture Radar (SAR) can penetrate cloud cover and collect data regardless of lighting conditions, but it may not be necessary for all projects. Assessing the spectral resolution—how many and what range of wavelengths can be detected—is also important, especially for environmental studies

It's worth mentioning that some countries are providing remote sensed data at no cost at all. For instance, many EU countries provide satellite and aerial images as open data following the EU Directive of high value data. Also, some global portals are providing such valuable data for free.

Most common datasets are free or just need a login to be created in order to access. Often times paid datasets specialize in certain aspects (ie. higher spatial, temporal, spectral resolution). Many analyses can be done with the free datasets. However, if paid data is needed and the study area is large I suggest this: Run preliminary analyses with the free data to identify potential areas of interest and purchase the images in said areas. Though everyone's situation is unique, working with a small budget can be difficult so we need to sometimes tap into our creative sides to stay within the requirements placed upon us.