imageryintro(1grass)

🛰️ imageryintro(1grass) — Image processing in GRASS 💖

okay so bestie, welcome to the GRASS User's Manual page for imageryintro(1grass) — it's giving "everything you ever wanted to know about doing image processing in GRASS" and honestly? iconic. let's go 🦋

---

✨ Image processing in general ✨

GRASS ships a powerful suite of tools for processing and analysing geospatial raster data — think satellite imagery, aerial photography, the whole vibe. Its image processing capabilities cover a whole range of preprocessing operations: data import, georeferencing, radiometric calibration, atmospheric correction, you name it. It's especially suited for Earth observation data, so you can analyse multispectral and temporal datasets like a boss. GRASS supports advanced stuff too — image classification, sensor fusion, point cloud statistics. And calculating vegetation indices lets you dig into environmental, agricultural, and land cover dynamics. There's also an extensive collection of addons that levels up the image processing even more, especially for Earth observation and remote sensing. 💅

Digital numbers and physical values (reflection/radiance-at-sensor)

Satellite imagery is usually stored in Digital Numbers (DN) to keep the storage volume small — like, the originally sampled analog physical value (color, temperature, etc) gets stored as a discrete representation in 8-16 bits. For example, Landsat data are stored in 8bit values (i.e., ranging from 0 to 255); other satellite data may be stored in 10 or 16 bits. Having data stored in DN means these data are not yet the observed ground reality, bestie. Such data are called "at-satellite" — for example, the amount of energy sensed by the sensor of the satellite platform is encoded in 8 or more bits. That energy is called radiance-at-sensor. To get physical values from DNs, satellite image providers use a linear transform equation (y = a * x + b) to encode the radiance-at-sensor in 8 to 16 bits. And you can flip DNs back into physical values by applying the reverse formula: (x = (y - b) / a). 🔁

The GRASS module i.landsat.toar easily transforms Landsat DN to radiance-at-sensor (top of atmosphere, TOA). The equivalent module for ASTER data is i.aster.toar. For other satellites, r.mapcalc can be employed.

Reflection/radiance-at-sensor and surface reflectance

So once you've got radiance-at-sensor, the atmosphere is still influencing the signal recorded at the sensor. 😭 This atmospheric interaction with the sun energy reflected back into space by ground/vegetation/soil needs to be corrected. Why? Because atmospheric conditions change over time. So to gain comparability between Earth surface images taken at different times, the atmosphere needs to be removed — converting at-sensor values (which are top of atmosphere) to surface reflectance values. 🌈

In GRASS, there are two ways to apply atmospheric correction for satellite imagery:

• A simple, less accurate way for Landsat is with i.landsat.toar, using the DOS correction method.
• The more accurate way is using i.atcorr (which supports many satellite sensors).

The atmospherically corrected sensor data represent surface reflectance, which ranges theoretically from 0% to 100%. Note that this level of data correction is the proper level of correction to calculate vegetation indices. 👑

In GRASS, image data are identical to raster data. However, a couple of commands are explicitly dedicated to image processing. The geographic boundaries of the raster/imagery file are described by the north, south, east, and west fields. These values describe the lines which bound the map at its edges. These lines do NOT pass through the center of the grid cells at the edge of the map, but along the edge of the map itself. mind the gap ✨

As a general rule in GRASS:

1. Raster/imagery output maps have their bounds and resolution equal to those of the current region.
2. Raster/imagery input maps are automatically cropped/padded and rescaled (using nearest-neighbor resampling) to match the current region.

---

📥 Imagery import 📥

The module r.in.gdal offers a common interface for many different raster and satellite image formats. On top of that, it also offers options such as on-the-fly project creation or extension of the default region to match the extent of the imported raster map. For special cases, other import modules are available. Just know: always the full map is imported. Imagery data can be grouped (e.g. channel-wise) with i.group. 🎀

For importing scanned maps, you'll need to create a x,y-project, scan the map in the desired resolution, save it into an appropriate raster format (e.g. tiff, jpeg, png, pbm), and then use r.in.gdal to import it. Based on reference points the scanned map can be rectified to obtain geocoded data. 🔥

---

🏷️ Semantic label information 🏷️

Semantic labels are a description which can be stored as metadata. To print available semantic labels relevant for multispectral satellite data, use i.band.library. r.semantic.label allows assigning of these satellite imagery band references as defined in i.band.library. Semantic labels are also used in signature files of imagery classification tools. Therefore, signature files of one imagery or raster group can be used to classify a different group with identical semantic labels. ✨

New enhanced classification workflow involving semantic labels. With r.support any sort of semantic label the user wishes may be added (i.e., not only those registered in i.band.library). Semantic labels are supported also by the temporal GRASS modules. 💜

---

⚙️ Image processing operations ⚙️

GRASS raster/imagery map processing is always performed in the current region settings (see g.region) — i.e. the current region extent and current raster resolution is used. If the resolution differs from that of the input raster map(s), on-the-fly resampling is performed (nearest neighbor resampling). If that's not the vibe you're going for, the input map(s) has/have to be resampled beforehand with one of the dedicated modules. no shortcuts bestie 🫶

---

🌍 Geocoding of imagery data 🌍

GRASS is able to geocode raster and image data of various types:

• unreferenced scanned maps by defining four corner points (i.group, i.target, g.gui.gcp, i.rectify)
• unreferenced satellite data from optical and Radar sensors by defining a certain number of ground control points (i.group, i.target, g.gui.gcp, i.rectify)
• interactive graphical Ground Control Point (GCP) manager
• orthophoto generation based on DEM: i.ortho.photo
• digital handheld camera geocoding: modified procedure for i.ortho.photo

---

🌈 Visualizing (true) color composites 🌈

To quickly combine the first three channels to a near natural color image, the GRASS command d.rgb can be used or the graphical GIS manager (wxGUI). It assigns each channel to a color which is then mixed while displayed. With a bit more work of tuning the grey scales of the channels, nearly perfect colors can be achieved. Channel histograms can be shown with d.histogram. 💅

---

🌱 Calculation of vegetation indices 🌱

An example for indices derived from multispectral data is the NDVI (normalized difference vegetation index). To study the vegetation status with NDVI, the Red and the Near Infrared channels (NIR) are taken as used as input for simple map algebra in the GRASS command r.mapcalc:

ndvi = 1.0 * (nir - red)/(nir + red)

With r.colors an optimized "ndvi" color table can be assigned afterward. Also other vegetation indices can be generated likewise. obsessed 💚

---

🌡️ Calibration of thermal channel 🌡️

The encoded digital numbers of a thermal infrared channel can be transformed to degree Celsius (or other temperature units) which represent the temperature of the observed land surface. This requires a few algebraic steps with r.mapcalc which are outlined in the literature to apply gain and bias values from the image metadata. 🥵

---

🤖 Image classification 🤖

Single and multispectral data can be classified to user defined land use/land cover classes. In case of a single channel, segmentation will be used. GRASS supports the following methods:

• Radiometric classification:
  • Unsupervised classification (i.cluster, i.maxlik) using the Maximum Likelihood classification method
  • Supervised classification (i.gensig or g.gui.iclass, i.maxlik) using the Maximum Likelihood classification method
• Combined radiometric/geometric (segmentation based) classification:
  • Supervised classification (i.gensigset, i.smap)
• Object-oriented classification:
  • Unsupervised classification (segmentation based: i.segment)

Kappa statistic can be calculated to validate the results (r.kappa). Covariance/correlation matrices can be calculated with r.covar.

Note — signatures generated for one scene are suitable for classification of other scenes as long as they consist of same raster bands (semantic labels match). This comes handy when classifying multiple scenes from a single sensor taken in different areas or different times. 🎯

---

🧬 Image fusion 🧬

In case of using multispectral data, improvements of the resolution can be gained by merging the panchromatic channel with color channels. GRASS provides the HIS (i.rgb.his, i.his.rgb) and the Brovey and PCA transform (i.pansharpen) methods. glow up ✨

---

🛠️ Radiometric corrections 🛠️

Atmospheric effects can be removed with i.atcorr. Correction for topographic/terrain effects is offered in i.topo.corr. Clouds in LANDSAT data can be identified and removed with i.landsat.acca. Calibrated digital numbers of LANDSAT and ASTER imagery may be converted to top-of-atmosphere radiance or reflectance and temperature (i.aster.toar, i.landsat.toar). bye bye clouds 👋

---

⏳ Time series processing ⏳

GRASS also offers support for time series processing (r.series). Statistics can be derived from a set of coregistered input maps such as multitemporal satellite data. The common univariate statistics and also linear regression can be calculated. 📈

---

💧 Evapotranspiration modeling 💧

In GRASS, several types of evapotranspiration (ET) modeling methods are available:

• Reference ET: Hargreaves (i.evapo.mh), Penman-Monteith (i.evapo.pm);
• Potential ET: Priestley-Taylor (i.evapo.pt);
• Actual ET: i.evapo.time.

Evaporative fraction: i.eb.evapfr, i.eb.hsebal01. 🌊

---

🔥 Energy balance 🔥

Emissivity can be calculated with i.emissivity. Several modules support the calculation of the energy balance:

• Actual evapotranspiration for diurnal period (i.eb.eta);
• Evaporative fraction and root zone soil moisture (i.eb.evapfr);
• Sensible heat flux iteration (i.eb.hsebal01);
• Net radiation approximation (i.eb.netrad);
• Soil heat flux approximation (i.eb.soilheatflux).

---

👀 See also 👀

• GRASS Wiki page: Image processing
• The GRASS 4 Image Processing manual 📖 (the vintage one, she's got lore)
• Introduction into raster data processing
• Introduction into 3D raster data (voxel) processing
• Introduction into vector data processing
• Introduction into temporal data processing
• Database management
• Projections and spatial transformations

---

✨ SOURCE CODE ✨

Available at: Image processing in GRASS source code (history)

Accessed: Friday May 08 10:38:23 2026

Okay so you want to go poke at the actual code? Respect. That link up there is where it lives, and yeah we did clock the exact timestamp because precision is the whole personality here. 🫶

---

Main index | Imagery index | Topics index | Keywords index | Graphical index | Full index

---

© 2003-2026 GRASS Development Team, GRASS 8.5.0 Reference Manual

---

And that's a wrap bestie!! 💖 This was imageryintro(1grass) straight out of the GRASS 8.5.0 Reference Manual — go forth and process some imagery, you absolute icon. 🎀✨🦋 slay, periodt, no cap 💅👑