Land cover Sample Clauses
The Land Cover clause defines the types and extent of surface features present on a specific parcel of land, such as vegetation, water bodies, buildings, or bare soil. In practice, this clause may specify requirements for maintaining certain types of vegetation, restrictions on clearing land, or obligations to restore land cover after construction or other activities. Its core function is to ensure that the land is used and maintained in accordance with agreed environmental, aesthetic, or regulatory standards, thereby preventing disputes and promoting responsible land management.
Land cover. Climate change is expected to impact crop yield and affect cropland needs, as change in harvested area is part of the adaptation response that farmers adopt in response to yield changes. Two effects can be expected: as yields decrease, farmers can expand their harvested area to compensate for a part of their losses, in particular if prices on the markets increase. But in case some substitution across crops is possible, farmers may also adopt alternative crops providing them more profit opportunities or abandon the land to grow crops in other regions with more favorable conditions. Non-market impacts on the other hand receive no price or no complete price in the optimization algorithm. In the case of land expansion, the optimization only considers the land- expansion costs but not for the environmental damage of removing the natural vegetation.
Land cover. Land cover will change with changing climate and demography. This is mainly of importance for growing urban areas and the development of forested area in Europe. The applied land cover data were produced by The International Institute for Applied Systems Analysis (IIASA) based on the A2 scenario of the IPCC assessment report. The A2 describes a very heterogeneous world with high population growth, slow economic development and slow technological change. This scenario gives similar climate signals as the A1B used in the precipitation data until ca. 2060. ▇▇▇▇▇▇▇▇ and ▇▇▇▇▇▇▇ (2007) described the dataset as follows: “The simulation tool IISASA-FAO AWZ model uses detailed agronomic based knowledge to simulate availability and use of land resources, farm-level management options, and crop production potentials as a function of climate. At the same time, it employs detailed spatial biophysical and socio-economic data sets to distribute its computations at fine gridded intervals over the entire globe (▇▇▇▇▇▇▇ et al, 2002).
Land cover. 5.1.3.1 First approach
Land cover. Land cover is one of the factors that contributes to landslide susceptibility together with topography and geology. While topography and geology are assumed to remain constant during the next 100 years, land cover will most likely be altered by a changing climate and demography. Changes in land cover are mainly caused by two processes, urbanization and a change in agricultural practices in different parts of Europe. It can be easily seen from the map in Figure 3-1 that some major cities such as London or Malmö in Sweden are growing. The biggest changes are visible in Spain, France and Germany where much of the current farmland is turned into grassland (destabilization) or forested areas (stabilization) in the future scenarios. Land cover has a limited effect on the overall landslide hazard, but forested areas are less susceptible for landslides than croplands or pasture. Therefore a conversion to forest will lead to a stabilization of presently unstable slopes.
Figure 3-1: Change of land cover from 2010 to 2090. Red pixels show areas where the land cover is changing in a future climate scenario.
Land cover. Land cover data was obtained from the European Space Agency (ESA) GlobCover project. The land cover map counts 22 land cover classes defined with the United Nations (UN) Land Cover Classification System (LCCS) (Figure 12). This data is at a 300 meter resolution. Aboard the ESA Environmental Satellite (ENVISAT), launched into orbit in 2002, is a wide field-of-view imaging spectrometer measuring the solar radiation reflected by the Earth in 15 spectral bands. This device is called MERIS. MERIS is able to achieve global coverage in 3 days (Defourny, ▇▇▇▇▇▇▇, ▇▇▇▇▇▇▇▇▇, & ▇▇▇▇▇, 2011). The land cover map created by the GlobCover project uses data from MERIS to classify areas into 22 different classes. For trachoma, LF and STH cumulative prevalence data, only 10 classes were present in relevant sites. The 10 different classes were grouped into 6 classes for analysis. Artificial surfaces/urban areas were used as the referent group. Rain fed croplands maintained their own group while mosaic croplands/vegetation and mosaic vegetation/croplands were merged into one group. Closed to open broadleaved evergreen and semi-deciduous forest and open broadleaved deciduous forest were merged into one group. Mosaic forest-shrub land/grassland, mosaic grassland/forest-shrub land and closed to open shrub land were merged into one group. Closed broadleaved forest permanently flooded (saline-brackish water) maintained its’ own group for LF analysis. For STH infection and trachoma analysis, the sixth group was closed to open grassland. Land cover is being considered as a candidate confounder of the poverty- trachoma association as previous studies have found land cover, urban surfaces in particular, meaningfully associated with trachoma distribution (▇▇▇▇▇▇▇▇ et al., 2010; ▇▇▇▇▇ et al., 2015). Land cover acts as a potential confounder of the poverty-STH association because soil moisture is known to influence the development and survival of STH ova and larvae (▇▇▇▇▇▇▇ et al., 2006) and may also affect poverty by influencing the success of crops and desirability of an area to inhabit. Land covers also acts as a potential confounder of the poverty-LF association because land cover has been found to be an important predictor of LF distribution. Croplands and grasslands in particular have been associated with high probabilities of LF infection (▇▇▇▇▇ et al., 2014).
Land cover. Change in land cover (either due to human actions or as a result of natural processes) will also modify landslide risk patterns.