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Multispectral data specifications

The Globhe standards for multispectral data

The Globhe standards will be applied if others are not specified in your order.

Multispectral drone data captures light across specific wavelengths beyond the visible spectrum, including ultraviolet (UV) and infrared (IR) regions. By simultaneously gathering data from multiple spectral bands, these data products offer unparalleled insights into the composition and characteristics of various surfaces, objects, and environments.

Multispectral drone data has a wide range of applications across various industries, especially agriculture (crop health monitoring, precision farming, and crop classification).

The electromagnetic spectrum is made of different wavelengths, each with different energies and bearing valuable information. However, only a small portion of these wavelengths fall within the range visible to the human eye (visible light). When we observe objects, what is visible is a reflected color spectrum comprised of red, green, and blue (RGB) hues. Standard RGB cameras capture wavelengths corresponding to visible light. Multispectral cameras have specialized optics and filters capable of detecting wavelengths beyond the visible spectrum, specifically delving into the realm of infrared wavelengths. Typically, multispectral imaging measures light in 3 to 15 spectral bands.

Multispectral data are especially useful for monitoring vegetation and crops' health. Plants and soil absorb and reflect wavelengths from sunlight depending on their contents. For example, when a plant is healthy and engaged in photosynthesis, it will absorb much red and blue light and reflect green and more infrared light. Looking at a chlorophyll-rich (green) plant you can see the wavelength of light that the plant reflects, not the ones it absorbs (blue and red). Chlorophyll production in leaves results in a lot of infrared light reflectance, visible using multispectral sensors.

The multispectral sensors can capture red, green, blue, plus red edge (RE) and near-infrared (NIR) bands (the latter two highlighting changes in chlorophyll content in plants, oftentimes an indicator of plant diseases or stress.

Plant health / NDVI maps

Vegetation indices, created using multispectral data, are used for analyzing plants/crops and detecting anything from irrigation stress to pest infestations to weeds. The most common index in remote sensing is the Normalized Difference Vegetation Index (NDVI). NDVI compares the reflectance of the Red band with that of the near-infrared band with values between 0 (no biomass = sick plant) and 1 (healthy plant). Simplifying, when looking at a NDVI map, the greener the map produced the more healthy and productive the vegetation is.

Deliverable specifications

Flight height

Definition: the altitude or elevation at which a drone operates during an aerial mission or flight.

Globhe standard: 110 meters AGL*, in line with national and local regulations in place.

*Above Ground Level = the altitude or vertical distance between the drone's current position and the Earth's surface.

Preferably using the terrain follow mode*, where possible.

*Terrain follow mode = flight mode available on certain drones that allows them to automatically adjust their altitude and maintain a consistent distance above the ground or terrain below. When the terrain follow mode is engaged, the drone utilizes various sensors and algorithms to detect and track the ground's elevation. It uses available terrain models as a reference to continuously adjust its flight altitude and compensate for changes in the terrain, such as hills, valleys, or uneven surfaces. By following the terrain contours, the drone can capture consistent and precise data, while enhancing the safety and efficiency of drone operations.


Definition: the rate at which the drone can travel through the air or move from one location to another. It is a measure of how quickly the drone can cover a certain distance within a given period of time. The speed of a drone is typically expressed in terms of a linear velocity, often measured in meters per second (m/s). The speed of a drone can vary depending on various factors, including its design, size, weight, propulsion system, flight mode, and external factors (such as wind speed and direction).

Globhe standard: 3 - 5 m/s and adjusted to meet the technical requirements.

Image overlap

Definition: the degree of redundancy or overlap between consecutive images captured during a drone flight mission. It is typically expressed as the percentage (%) of overlap between adjacent images along (front overlap) and perpendicular (side overlap) to the flight direction. Image overlap is needed to ensure the accuracy and quality of the resulting drone data. The overlap allows for better stitching and alignment of the images during post-processing (photogrammetry), enabling the creation of seamless data.

Globhe standard: 75% front overlap, 70% side overlap.

Spatial resolution / Ground Sampling Distance (GSD)

Definition: the level of detail and clarity with which the physical features on the Earth's surface are captured and represented in thermal maps. The spatial resolution defines the size of the smallest object that can be distinguished in drone images and it is typically measured in terms of the ground sampling distance (GSD), which represents the physical distance on the ground covered by each pixel. The GSD achieved is a function of the drone sensor/camera specifications and the flight height.

Globhe standard: multispectral data's GSD depends on flight altitude and sensor used. It is generally up to 1/5 of the resolution of an equivalent RGB orthomosaic map.

Ground Control Points (GCPs)

Definition: precisely located reference points on the Earth's surface that are used in conjunction with drone imagery or data to enhance the accuracy and georeferencing of the captured information. GCPs serve as known geographic control markers that provide a reliable connection between the drone's coordinate system and real-world geographical coordinates. GCPs are measured using markers placed on the ground, such as aerial targets with known coordinates. These markers are visible and easily identifiable in the drone imagery. The GCPs are surveyed or measured using high-precision surveying equipment, such as GNSS stations/receivers to determine their precise three-dimensional coordinates (latitude, longitude, altitude).

Globhe standard: not needed for simple mapping purposes. Around 10 GCPs every 100 hectares are required for more accurate work such as flood mapping/modelling or infrastructure surveys.

Absolute accuracy

Definition: the degree of conformity between the recorded or measured geographical positioning and the true or actual values of the features or properties being captured by drones. It represents the level of correctness, precision, and reliability of the data or measurements obtained from drone operations. I.e. if the position of a road in the reconstructed model is close to its actual position on the Earth, then the absolute accuracy is high.

Adding precisely measured GCPs or using RTK/PPK drones can greatly improve absolute accuracy. So the accuracy of the deliverables highly depends on the accuracy of the GNSS receiver of the drone or the GNSS station, and if GCPs are used or not.

Globhe standard: highly dependant on the deliverables GSD and application. Few meters if no GCPs are used and up to 10 cm of horizontal and vertical accuracy if GCPs are used.

Coordinate Reference System (CRS)

Definition: standardized system for defining and interpreting geographic coordinates, allowing for accurate and consistent positioning of features on the Earth's surface. In the context of drone data and mapping, the CRS plays a vital role in establishing a common reference point for geospatial data captured by drones. It consists of three key elements: coordinate system, map projection, and datum. The coordinate system specifies how locations are defined on a two- or three-dimensional plane using coordinate values. The map projection converts the curved surface of the Earth into a flat surface, facilitating the representation of spatial data on maps or digital screens. The datum provides a reference frame for measuring and aligning coordinates to the Earth's surface. There are various types of CRSs used in geospatial applications, including geographic coordinate systems (e.g., latitude and longitude), projected coordinate systems (e.g., Universal Transverse Mercator), and local coordinate systems (e.g., State Plane Coordinate System). Each CRS has its own set of properties, units of measurement, and accuracy characteristics.

Globhe standard: WGS 84 / (EPSG: 4326) / Meters.

Data format

Globhe standard: multispectral and NDVI drone data are primarily represented as images captured by the multispectral sensors mounted on drones. The images are saved as geotagged JPG. If photogrammetry is used to create 2D orthomosaic maps: geotagged 2D orthomosaics are saved as GeoTIFF (.tiff).

Definition: geotagging consists in adding geographic information to the image file, such as the latitude and longitude coordinates of where the image was taken. Overall a geotagged 2D thermal orthomosaic map combines the visual information captured by a drone's camera with embedded geographical metadata, providing valuable location-based context to the processed orthomosaic map.

Drone models and sensors

GLOBHE relies on a wide range of commercial drone models available through our Crowddroners, including but not limited to multirotors (i.e. quadcopters, hexacopter, octocopters) and fixed-wing, mounted with every sort of sensor as payload*.

*Payload = the additional equipment or devices that are carried or attached to a drone in order to perform specific functions or tasks. These payloads can vary depending on the purpose and capabilities of the drone. The most common drone payloads are RGB cameras for aerial photography or videography, sensors for data collection (such as thermal, multispectral imaging, or LiDAR sensors), and specialized equipment for tasks like seedlings, crop spraying, or search and rescue operations.

If the drone model and/or sensor are not selected, the GLOBHE team will choose the suitable drone needed to meet technical requirements, also based on availability.


Margherita Bruscolini
Head of Drones

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