The Azimuth Project
Land use



Land use is the human use of land. Land use involves the management and modification of natural environment or wilderness into built environment such as fields, pastures, and settlements.

From Wikipedia:

Land use and land management practices have a major impact on natural resources including water, soil, nutrients, plants and animals. Land use information can be used to develop solutions for natural resource management issues such as salinity and water quality. For instance, water bodies in a region that has been deforested or having erosion will have different water quality than those in areas that are forested.

Here is an estimate of land use carbon dioxide fluxes from Houghton:

LU changes

The major effect of land use on land cover since 1750 has been deforestation of temperate regions. More recent significant effects of land use include urban sprawl, soil erosion, soil degradation, salinization, and desertification. Land-use change, together with use of fossil fuels, are the major anthropogenic sources of carbon dioxide, a dominant greenhouse gas.

Land use management

Fire tracking and estimation


Also see pages on agriculture, deforestation, peatlands, ecosystem services and the section on the carbon footprint of livestock.


ABSTRACT Five new estimates of global net annual emissions of carbon from land use and land-use change collectively describe a gradually increasing trend in emissions, from ∼0.6 PgC yr−1 in 1850 to ∼1.3 PgC yr−1 in the period 1950–2005, with an annual range that varies between ±0.2 and ±0.4 PgC yr−1 of the mean. All estimates agree in the upward trend from 1850 to ∼1950 but not thereafter. In recent decades, when rates of land-use change and biomass density should be better known than in the past, the estimates are more variable. Most analyses have used three quasi-independent estimates of land-use change that are based on national and international agricultural and forestry data of limited accuracy in many countries. Further, the estimates of biomass used in the analyses have a common but limited literature base, which fails to address the spatial variability of biomass density within ecosystems. In contrast to the sources of information that have been used to date, a combination of existing ground and remote sensing data are available to determine with far higher accuracy rates of land-use change, aboveground biomass density, and, hence, the net flux of carbon from land use and land-use change.

Creative Commons

Abstract. The Fire INventory from NCAR version 1.0(FINNv1) provides daily, 1 km resolution, global estimates of the trace gas and particle emissions from open burning of biomass, which includes wildfire, agricultural fires, and prescribed burning and does not include biofuel use and trash burning. Emission factors used in the calculations have beenupdated with recent data, particularly for the non-methane organic compounds (NMOC). The resulting global annual NMOC emission estimates are as much as a factor of 5 greater than some prior estimates.

Chemical speciation profiles, necessary to allocate the total NMOC emission estimates to lumped species for use by chemical transport models, are provided for three widely used chemical mechanisms: SAPRC99, GEOS-CHEM, and MOZART-4. Using these profiles, FINNv1 also provides global estimates of key organic compounds, including formaldehyde and methanol. Uncertainties in the emissions estimates arise from several of the method steps. The use of fire hot spots, assumed area burned, land cover maps, biomass consumption estimates, and emission factors all introduce error into the model estimates. The uncertainty in the FINNv1 emission estimates are about a factor of two; but, the global estimates agree reasonably well with other global inventories of biomass burning emissions for CO, CO2, and other species with less variable emission factors.

FINNv1 emission estimates have been developed specifically for modeling atmospheric chemistry and air quality in a consistent framework at scales from local to global. The product is unique because of the high temporal and spatial resolution, global coverage, and the number of species estimated. FINNv1 can be used for both hindcast and forecast or near-real time model applications and the results are being critically evaluated with models and observations whenever possible.

category: ecology