Vegetation classification by satellite image processing in a dry area of north-eastern Syria

Derivation of biomass information for semi-arid areas using remote-sensing data

“…Possible applications Example studies Sustainable land-use management -Support drought detection -Give advice to farmers regarding good grazing places -Information on the amount of forage, its quality as well as distribution -Understanding of wildlife feeding patterns -Build up management guidelines on the suitability of specific habitats -Quantifying the effect of grazing intensities -Crop forecasting -Locust forecasting and hatching Rosema (1993), Wylie et al (1995), Du Plessis (1999), Qi and Wallace (2002), Dech et al (2003), Kogan et al (2004), Kawamura et al (2005), Mutanga and Rugege (2006) Fire risk assessment -Fire risk assessment for savannah ecosystems -Selection of sites for controlled burning -Development of early warning systems for fire management -Fuel modelling and fire behaviour simulation Pereira et al (1995), Kraus and Samimi (2002), Sannier et al (2002), Mutanga and Rugege (2006), Verbesselt et al (2006) Climate change and degradation -Understand the exchange of energy and CO 2 between vegetation and the atmosphere -Understand the role these regions play in the biochemical cycles -Examine yield and food production -Assess both degradation and ecosystem recovery -Plan protective measures for areas under high risk of desertification -Assist the decision-making process for the declaration of areas experiencing drought exceptional circumstances Pieper (1988), Kennedy (1989), Rosema (1993), Running et al (1995), Pickup (1996), Imeson and Lavee (1998), McVicar andJupp (1998), Shoshany (2000), Diouf and Lambin (2001), Hirata et al (2001), Moleele et al (2001), Collins et al (2009) …”

“…The vegetation cover in semi-arid and arid regions is of high importance for protection against wind and storms. As the level of soil protection provided by, for example, shrubs is directly proportional to their biomass/cover (Calvão and Palmeirim 2004), the quantification of biomass is essential to identify and monitor areas under high risk of degradation/desertification (Hirata et al 2001, Moleele et al 2001 and to assess the degradation status of semi-arid regions (e.g. Liu et al 2003, Baumann 2009).…”

Land cover classification using multi‐temporal MERIS vegetation indices

“…For example, using NDVI data, Running et al (1995) present a decision tree classification based on the permanence of above-ground biomass, longevity of leaves, and leaf type. Critically, the use of a vegetation index can yield a classification that is more accurate than one derived from the data used in its calculation (Anderson et al 1993, Nemani et al 1993, Hirata et al 2001. This feature, together with the ready availability of NDVI data in major archives (e.g.…”

Determination of green aboveground biomass in desert steppe using litter-soil-adjusted vegetation index

“…Green aboveground biomass is also a key ecological variable in arid and semiarid grassland [Eisfelder et al, 2012], and influences important environmental processes, such as soil erosion, environmental degradation, and desertification [Verstraete, 1986;Eswaran et al, 2001;Hirata et al, 2001;Moleele et al, 2001;Mulligan, 2009]. Therefore, a great need exists for the establishment of robust and transferable methods for green aboveground biomass estimation in arid and semiarid grassland.…”