Data Gathering
Different sources of Data Gathering for GIS and RS:[edit]
Geographic Information Systems (GIS) plays a crucial role in various fields, including land use mapping and modelling, climate change monitoring, urban planning, agriculture, forestry, and disaster management. These technologies enable the collection, storage, analysis, and visualization of spatial and temporal data, aiding stakeholders, and decision-makers in understanding complex spatial relationships and patterns. The effectiveness of the GIS studies depends heavily on the quality, quantity, and suitability of the data used. In recent years, significant advancements have been made in data gathering techniques, enhancing the accuracy, resolution, and coverage of spatial data. We discuss some of the advancements and their implications in the field of GIS:
- Satellite Imagery: Satellite imagery remains one of the primary sources of data for GIS and Remote Sensing. Advances in satellite technology and better-quality lenses have led to the development of high-resolution imaging satellites capable of capturing detailed spatial information over large areas. There are five types of data collected and generated by the satellites: Spatial, spectral temporal, radiometric, geometric, based on the type of resolutions satellites possess.
The launch of satellites such as Landsat, Sentinel, and WorldView has revolutionized the way researchers collect data for environmental monitoring, land cover mapping, and urban planning. Furthermore, the availability of open-access satellite data repositories, such as NASA's Earth Observing System Data and Information System (EOSDIS) and the European Space Agency's (ESA) Copernicus program, has made the access to satellite imagery easier and free, enabling researchers worldwide to conduct spatial analysis and monitoring.
- Aerial Photography: Ranging from utilizing pigeons to huge kites as a source of clicking aerial photography to advanced photography tools, it remains a valuable tool for collecting high-resolution spatial data, especially in areas where satellite imagery may be limited or obscured by cloud cover. Advances in unmanned aerial vehicle (UAV) technology have made it easier and more cost-effective to capture aerial imagery for GIS and Remote Sensing studies. Aerial photography has proved useful time and again in multiple fields where we need much higher resolution and a closer view on the current situation, such as disaster management, and species identification, etc.
UAVs equipped with cameras, LiDAR sensors, and other remote sensing instruments can collect data with precision and flexibility, making them ideal for such applications.
- LiDAR: Light Detection and Ranging (LiDAR) technology have revolutionized the way researchers collect elevation data for GIS. LiDAR sensors mounted on aircraft or UAVs emit laser pulses towards the Earth's surface and measure the time it takes for the pulses to return, allowing for the generation of highly accurate elevation models. LiDAR data are used in various applications, including flood mapping, terrain analysis, urban planning, and forest inventory. Recent advancements in LiDAR technology, such as multi-beam and full-waveform LiDAR, have further improved the accuracy and resolution of elevation data, enabling researchers to better understand the Earth's surface and its dynamics.
- Crowdsourced Data Collection: Crowdsourced data collection platforms, such as OpenStreetMap (OSM) and citizen science projects, have emerged as valuable sources of spatial data for GIS and Remote Sensing studies. These platforms allow volunteers and citizen scientists to contribute geospatial data, such as road networks, land use information, and environmental observations, through collaborative mapping efforts.
Crowdsourced data complement traditional sources of spatial data and can provide valuable insights into local conditions and phenomena. However, challenges related to data quality, consistency, and validation remain, highlighting the need for robust quality assurance mechanisms and integration with existing datasets.
- Challenges and Future Directions:
Advancements in data gathering techniques have significantly enhanced the capabilities of GIS and Remote Sensing studies, enabling researchers to address complex spatial problems and contribute to sustainable development and environmental conservation efforts. The integration of satellite imagery, aerial photography, LiDAR, and crowdsourced data collection platforms has expanded the scope and applicability of spatial analysis and monitoring across various disciplines. But, despite the advancements in data gathering techniques for GIS and Remote Sensing studies, several challenges persist. These include data interoperability issues, data quality assurance, and the need for improved spatial resolution and coverage in remote and inaccessible areas. Future directions in data gathering may include the integration of emerging technologies such as artificial intelligence (AI) and machine learning for automated feature extraction and classification from remote sensing data. Additionally, efforts to improve data sharing, collaboration, and capacity-building initiatives in developing countries will be crucial for advancing GIS and Remote Sensing research on a global scale.
Here is a table outlining some of the commonly used data types in GIS and Remote Sensing along with some sources:
| Data Type | Description | Online Sources |
|---|---|---|
| Satellite Imagery | High-resolution images captured by satellites | NASA Earth Observing System Data and Information System (EOSDIS), USGS Earth Explorer, ESA Copernicus Open Access Hub, Google Earth Engine, DigitalGlobe Open Data Program, Planet |
| Aerial Photography | High-resolution images captured from aircraft or UAVs | USGS Earth Explorer, DigitalGlobe Open Data Program, Commercial providers (e.g., Nearmap) |
| LiDAR Data | Elevation data collected using LiDAR sensors | USGS Earth Explorer, NOAA Digital Coast, Commercial providers (e.g., Harris Geospatial Solutions) |
| Land Cover Data | Information about land cover types and land use categories | USGS Earth Explorer, European Environment Agency (EEA) CORINE Land Cover, MODIS Land Cover Dynamics, GlobCover |
| Topographic Data | Information about elevation, terrain, and landforms | USGS Earth Explorer, National Elevation Dataset (NED), SRTM Digital Elevation Model (DEM), OpenTopography |
| Weather Data | Meteorological data such as temperature, precipitation, etc. | NOAA National Centers for Environmental Information (NCEI), NASA Earth Science Data, European Centre for Medium-Range Weather Forecasts (ECMWF) |
| Soil Data | Information about soil properties and characteristics | USDA Soil Data Mart, European Soil Data Centre (ESDAC), SoilGrids |
| Hydrological Data | Information about water bodies, rivers, and watersheds | USGS National Hydrography Dataset (NHD), HydroSHEDS, European Environment Agency (EEA) Water Information System (WISE) |
| Population | Demographic information about human populations | WorldPop, UN World Urbanization Prospects, US Census Bureau |
| Transportation Data | Information about roads, highways, and transportation networks | OpenStreetMap (OSM), HERE Maps, TomTom, National transportation agencies (e.g., US Department of Transportation) |