Land Use Versus Land Cover

Before classifying an image, you must decide what you are mapping: the physical surface (land cover) or how humans use it (land use). This distinction shapes your training data, class labels, and how you interpret results.

Learning objectives

  • Distinguish between land cover and land use.
  • Recognize why the same spectral signature can represent different classes.
  • Choose appropriate class labels for your classification project.
  • Understand limitations of spectral-only classification for land use mapping.

Why it matters

Confusing land use with land cover leads to misclassification. A grassy area looks the same to a satellite whether it is a park, a golf course, or a cattle pasture. Understanding this distinction helps you set realistic expectations for your classifier.

Key definitions

Term Definition Examples
Land Cover Physical material on the Earth's surface Forest, water, bare soil, impervious surface, grass
Land Use Human activity or function of the land Residential, commercial, agriculture, recreation, conservation

The same cover, different uses

Consider these examples where identical spectral signatures represent different land uses:

Land Cover Possible Land Uses
Grass/Herbaceous Park, golf course, pasture, lawn, sports field, airport
Forest Conservation area, timber harvest, recreation, watershed protection
Water Reservoir, aquaculture, natural lake, wastewater treatment
Impervious Residential, commercial, industrial, transportation

Key insight

Satellites see land cover, not land use. A classifier trained on spectral bands can reliably distinguish forest from water, but it cannot tell a city park from a cattle ranch based on reflectance alone. Land use mapping often requires ancillary data: parcel boundaries, building footprints, road networks, or temporal patterns.

LULC: The hybrid approach

Many classification schemes combine both concepts into "Land Use/Land Cover" (LULC) classes. Common examples include:

  • NLCD (National Land Cover Database): Primarily land cover with some use-based classes like "Developed" at different intensities.
  • CORINE (Europe): Hierarchical system mixing cover and use.
  • FAO LCCS: Classification system designed to separate cover from use when needed.

Try it: Examine a LULC dataset

Load NLCD in Earth Engine and explore the class legend:

// Load NLCD 2021
var nlcd = ee.Image('USGS/NLCD_RELEASES/2021_REL/NLCD/2021');
var landcover = nlcd.select('landcover');

// Add to map with built-in palette
Map.setCenter(-98.5, 39.5, 5);
Map.addLayer(landcover, {}, 'NLCD 2021');

// Print the class names
print('NLCD classes:', landcover.get('landcover_class_names'));

Notice how "Developed" classes mix land cover (impervious percentage) with land use (urban).

Common mistakes

  • Training "agriculture" samples on bare fields some months and crops other months.
  • Expecting a classifier to distinguish "residential" from "commercial" using only spectral data.
  • Mixing classification schemes (for example, calling grass "pasture" in one sample and "lawn" in another).
  • Ignoring that the same pixel may be classified differently at different times of year.

Quick self-check

  1. Can a satellite distinguish a forest used for conservation from one used for logging?
  2. Why might "Developed, High Intensity" be considered a hybrid LULC class?
  3. What additional data would help classify grass as either "park" or "pasture"?

Next steps