Although the terms land cover and land use are often used interchangeably, their actual meanings are quite distinct. Land cover refers to the surface cover on the ground, whether vegetation, urban infrastructure, water, bare soil or other. Identifying, delineating and mapping land cover is important for global monitoring studies, resource management, and planning activities. Identification of land cover establishes the baseline from which monitoring activities (change detection) can be performed, and provides the ground cover information for baseline thematic maps.
Land use refers to the purpose the land serves, for example, recreation, wildlife habitat, or agriculture. Land use applications involve both baseline mapping and subsequent monitoring, since timely information is required to know what current quantity of land is in what type of use and to identify the land use changes from year to year. This knowledge will help develop strategies to balance conservation, conflicting uses, and developmental pressures. Issues driving land use studies include the removal or disturbance of productive land, urban encroachment, and depletion of forests.
It is important to distinguish this difference between land cover and land use, and the information that can be ascertained from each. The properties measured with remote sensing techniques relate to land cover, from which land use can be inferred, particularly with ancillary data or a priori knowledge.
Land cover / use studies are multidisciplinary in nature, and thus the participants involved in such work are numerous and varied, ranging from international wildlife and conservation foundations, to government researchers, and forestry companies. Regional (in Canada, provincial) government agencies have an operational need for land cover inventory and land use monitoring, as it is within their mandate to manage the natural resources of their respective regions. In addition to facilitating sustainable management of the land, land cover and use information may be used for planning, monitoring, and evaluation of development, industrial activity, or reclamation. Detection of long term changes in land cover may reveal a response to a shift in local or regional climatic conditions, the basis of terrestrial global monitoring.
Ongoing negotiations of aboriginal land claims have generated a need for more stringent knowledge of land information in those areas, ranging from cartographic to thematic information.
Resource managers involved in parks, oil, timber, and mining companies, are concerned with both land use and land cover, as are local resource inventory or natural resource agencies. Changes in land cover will be examined by environmental monitoring researchers, conservation authorities, and departments of municipal affairs, with interests varying from tax assessment to reconnaissance vegetation mapping. Governments are also concerned with the general protection of national resources, and become involved in publicly sensitive activities involving land use conflicts.
Land use applications of remote sensing include the following:
"...let me make this perfectly clear..."
This is a TM scene of Calgary, Canada, where the 1988 Winter Olympics were held. Calgary appears quite blue; the agricultural fields to the east are red, while grazing land to the west is green. Abutting the southwest corner of the city, is a long rectangular section of land stretching towards the west that is darker and more monotone than the other areas around it. This is the area of the Sarcee Reserve (T'suu T'ina) which has been held by native people, and protected from urbanization and residential construction. Of all the land on the image, this land is the closest to the original state of the Calgary region before agriculture and settlements reworked the landscape. It looks like an oasis amidst suburbia and farmland.
As the Earth's population increases and national economies continue to move away from agriculture based systems, cities will grow and spread. The urban sprawl often infringes upon viable agricultural or productive forest land, neither of which can resist or deflect the overwhelming momentum of urbanization. City growth is an indicator of industrialization (development) and generally has a negative impact on the environmental health of a region.
The change in land use from rural to urban is monitored to estimate populations, predict and plan direction of urban sprawl for developers, and monitor adjacent environmentally sensitive areas or hazards. Temporary refugee settlements and tent cities can be monitored and population amounts and densities estimated.
Analyzing agricultural vs. urban land use is important for ensuring that development does not encroach on valuable agricultural land, and to likewise ensure that agriculture is occurring on the most appropriate land and will not degrade due to improper adjacent development or infrastructure.
Why remote sensing?
With multi-temporal analyses, remote sensing gives a unique perspective of how cities evolve. The key element for mapping rural to urban landuse change is the ability to discriminate between rural uses (farming, pasture forests) and urban use (residential, commercial, recreational). Remote sensing methods can be employed to classify types of land use in a practical, economical and repetitive fashion, over large areas.
Requirements for rural / urban change detection and mapping applications are 1) high resolution to obtain detailed information, and 2) multispectral optical data to make fine distinction among various land use classes.
Sensors operating in the visible and infrared portion of the spectrum are the most useful data sources for land use analysis. While many urban features can be detected on radar and other imagery (usually because of high reflectivity), VIR data at high resolution permits fine distinction among more subtle land cover/use classes. This would permit a confident identification of the urban fringe and the transition to rural land usage. Optical imagery acquired during winter months is also useful for roughly delineating urban areas vs. non-urban. Cities appear in dramatic contrast to smooth textured snow covered fields.
Radar sensors also have some use for all urban/rural delineation applications, due to the ability of the imaging geometry to enhance anthropogenic features, such as buildings, in the manner of corner reflectors. The optimum geometric arrangement between the sensor and urban area is an orientation of linear features parallel to the sensor movement, perpendicular to the incoming incident EM energy.
Generally, this type of application does not require a high turnaround rate, or a frequent acquisition schedule.
Canada vs. International
Throughout the world, requirements for rural/urban delineation will differ according to the prevalent atmospheric conditions. Areas with frequently cloudy skies may require the penetrating ability of radar, while areas with clear conditions can use airphoto, optical satellite or radar data. While the land use practices for both rural and urban areas will be significantly different in various parts of the world, the requirement for remote sensing techniques to be applied (other than the cloud-cover issue) will be primarily the need for fine spatial detail.
Case study (example)
This image of land cover change provides multitemporal information in the form of urban growth mapping. The colours represent urban land cover for two different years. The green delineates those areas of urban cover in 1973, and the pink, urban areas for 1985. This image dramatically shows the change in expansion of existing urban areas, and the clearing of new land for settlements over a 12 year period. This type of information would be used for upgrading government services, planning for increased transportation routes, etc.
Land cover mapping serves as a basic inventory of land resources for all levels of government, environmental agencies, and private industry throughout the world. Whether regional or local in scope, remote sensing offers a means of acquiring and presenting land cover data in a timely manner. Land cover includes everything from crop type, ice and snow, to major biomes including tundra, boreal or rainforest, and barren land.
Regional land cover mapping is performed by almost anyone who is interested in obtaining an inventory of land resources, to be used as a baseline map for future monitoring and land management. Programs are conducted around the world to observe regional crop conditions as well as investigating climatic change on a regional level through biome monitoring. Biomass mapping provides quantifiable estimates of vegetation cover, and biophysical information such as leaf area index (LAI), net primary productivity (NPP) and total biomass accumulations (TBA) measurements - important parameters for measuring the health of our forests, for example.
Why remote sensing?
There is nothing as practical and cost efficient for obtaining a timely regional overview of land cover than remote sensing techniques. Remote sensing data are capable of capturing changes in plant phenology (growth) throughout the growing season, whether relating to changes in chlorophyll content (detectable with VIR) or structural changes (via radar). For regional mapping, continuous spatial coverage over large areas is required. It would be difficult to detect regional trends with point source data. Remote sensing fulfills this requirement, as well as providing multispectral, multisource, and multitemporal information for an accurate classification of land cover. The multisource example image shows the benefit of increased information content when two data sources are integrated. On the left is TM data, and on the right it has been merged with airborne SAR.
For continental and global scale vegetation studies, moderate resolution data (1km) is appropriate, since it requires less storage space and processing effort, a significant consideration when dealing with very large area projects. Of course the requirements depend entirely on the scope of the application. Wetland mapping for instance, demands a critical acquisition period and a high resolution requirement.
Coverage demand will be very large for regional types of surveying. One way to adequately cover a large area and retain high resolution, is to create mosaics of the area from a number of scenes.
Land cover information may be time sensitive. The identification of crops, for instance canola, may require imaging on specific days of flowering, and therefore, reliable imaging is appropriate. Multi-temporal data are preferred for capturing changes in phenology throughout the growing season. This information may be used in the classification process to more accurately discriminate vegetation types based on their growing characteristics.
While optical data are best for land cover mapping, radar imagery is a good replacement in very cloudy areas.
Case study (example)
NBIOME: Classification of Canada's Land Cover
A major initiative of the Canada Centre for Remote Sensing is the development of an objective, reproducible classification of Canada's landcover. This classification methodology is used to produce a baseline map of the major biomes and land cover in Canada, which can then be compared against subsequent classifications to observe changes in cover. These changes may relate to regional climatic or anthropogenic changes affecting the landscape.
The classification is based on NOAA-AVHRR LAC (Local Area Coverage) (1km) data. The coarse resolution is required to ensure efficient processing and storage of the data, when dealing with such a large coverage area. Before the classification procedure, cloud -cover reduced composites of the Canadian landmass, each spanning 10 day periods are created. In the composite, the value for each pixel used is the one most cloud free of the ten days. This is determined by the highest normalized difference vegetation index (NDVI) value, since low NDVI is indicative of cloud cover (low infrared reflectance, high visible reflectance). The data also underwent a procedure to minimize atmospheric, bidirectional, and contamination effects.
The composites consist of four channels, mean reflectance of AVHRR channels 1 and 2, NDVI and area under the (temporal NDVI) curve. 16 composites (in 1993) were included in a customized land cover classification procedure (named: classification by progressive generalization), which is neither a supervised nor unsupervised methodology, but incorporates aspects of both. The classification approach is based on finding dominant spectral clusters and conducting progressive merging methodology. Eventually the clusters are labelled with the appropriate land cover classes. The benefit is that the classification is more objective than a supervised approach, while not controlling the parameters of clustering, which could alter the results.