Context Recent conceptual developments in ecosystem services research have revealed the need to elucidate the complex and unintended relationships between humans and the environment if we are to better understand and manage ecosystem services in practice. Objectives This study aimed to develop a model that spatially represents a complex human–environment (H–E) system consisting of heterogeneous social–ecological components and feedback mechanisms at multiple scales, in order to assess multi-dimensional (spatial, temporal, and social) trade-offs in ecosystem services. Methods We constructed an agent-based model and empirically calibrated it for a semi-arid region in Northeast China, and examined ecosystem service trade-offs derived from the Sloping Land Conversion Program (SLCP), which is based on payment for ecosystem services. This paper describes our model, named Inner Mongolia Land Use Dynamic Simulator (IM-LUDAS), using the overview, design concepts, and details + decision (ODD + D) protocol and demonstrates the capabilities of IM-LUDAS through simulations. Results IM-LUDAS represented typical characteristics of complex H–E systems, such as secondary and cross-scale feedback loops, time lags, and threshold change, revealing the following results: tree plantations expanded by the SLCP facilitated vegetation and soil restoration and household change toward off-farm livelihoods, as expected by the government; conversely, the program caused further land degradation outside the implementation plots; moreover, the livelihood changes were not large enough to compensate for income deterioration by policy-induced reduction in cropland. Conclusions IM-LUDAS proved itself to be an advanced empirical model that can recreate essential features of complex H–E systems and assess multi-dimensional trade-offs in ecosystem services.

Previous field research on the Horqin Sandy Land (China), which has suffered from severe desertification during recent decades, revealed how land use on a sand-dune topography affects both land degradation and restoration. This study aimed to depict the spatial distribution of local land use in order to shed more light on previous field findings regarding policies on a broader scale. We performed the following analyses with Panchromatic Remote-sensing Instrument for Stereo Mapping (PRISM) and Advanced Visible and Near Infrared Radiometer type 2 (AVNIR-2) images of Advanced Land Observing Satellite (ALOS): (1) object-based classification to discriminate preliminary classification of land-use types that were approximately differentiated by ordinary pixel-based analysis with spectral information; (2) digital photogrammetry to generate a digital surface model (DSM) with adequately high accuracy to represent undulating sand-dune topography; (3) geographic information system (GIS) analysis to classify major topographic types with the digital surface model (DSM); and (4) overlay of the two classification results to depict the local land-use types. The overall accuracies of the object-based and GIS-based classifications were high, at 93% (kappa statistic: 0.84) and 89% (kappa statistic: 0.81), respectively. The resultant local land-use map represents areas covered in previous field studies, showing where and how land degradation and restoration are likely to occur. This research can contribute to future environmental surveys, models, and policies in the study area.