Abstract
Land use and land cover (LULC) and their changes in share and number of classes can be documented by remote sensing techniques. Information on LULC is needed for the assessment of ecosystem services and is used as input data for mapping and modelling. This information is important for decision-making and management of ecosystems and landscapes. In this study, LULC were analysed in two agricultural areas in Northern Germany by means of a pixel-based maximum likelihood classification approach of 11 Landsat TM 5 scenes between 1987 and 2011 followed by a post-classification refinement using the tool IRSeL. In this time period, grassland declined by about 50 % in both case study areas. This loss in grassland area can be associated with changes in provisioning ecosystem services as the supply of fodder and crops and the number of livestock declined from 1987 to 2007. Furthermore, an on-going increase in maize cultivation area, which is nowadays more and more used as biomass for biogas production, documents the addition of another provisioning service, i.e., biomass for energy. Combining remote sensing and research on ecosystem services supports the assessment and monitoring of ecosystem services on different temporal, spatial, and semantic scales.
Highlights
The detection and monitoring of changes in land use / land cover (LULC) and in the supply of ecosystem services at various spatial scales are important research tasks
As detailed LULC maps are lacking for several countries or regions, remote sensing data is considered a valuable source for LULC data (Kennedy et al 2009)
The focus was on provisioning services (Kandziora et al 2013a) because they are the group of ecosystem services that can be directly deduced from LULC data, whereas regulating and cultural services strongly depend on the interaction of several ecosystem services and other factors like geological material, climate, topography, management measures and human perceptions
Summary
The detection and monitoring of changes in land use / land cover (LULC) and in the supply of ecosystem services at various spatial scales are important research tasks. The use of models such as InVEST (Polasky et al 2011) requires LULC maps as input data to model and map multiple ecosystem services (Leh et al 2013). Many of these studies emphasize the need to include the gained knowledge of LULC change and ecosystem service change in decision-making and management (Estoque and Murayama 2012) as well as to understand past changes (Lautenbach et al 2011) and predict future impacts on ecosystem services and human wellbeing (Lorencová et al 2013; Sexton et al 2013)
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