Nagabhatla, N., Padmanabhan, M., Kühle, P., Vishnudas, S., Betz, L., & Niemeyer, B. (2015). LCLUC as an entry point for transdisciplinary research–reflections from an agriculture land use change study in South Asia. Journal of environmental management, 148, 42-52.
This article highlights applied understanding of classifying earth imaging data for land cover land use change (LCLUC) information. Compared to the many previous studies of LCLUC, the present study is innovative in that it applied geospatial data, tools and techniques for transdisciplinary research. It contributes to a wider discourse on practical decision making for multi-level governance. Undertaken as part of the BioDIVA project, the research adopted a multi-tiered methodical approach across three key dimensions: socioecology as the sphere of interest, a transdisciplinary approach as the disciplinary framework, and geospatial analysis as the applied methodology. The area of interest was the agroecosystem of Wayanad district in Kerala, India (South Asia). The methodology was structured to enable analysis of multi-scalar and multi-temporal data, using Wayanad as a case study. Three levels of analysis included: District (Landsat TM-30m), Taluk or sub-district (ASTER-15m) and Village or Gram Panchayat (GeoEye-0.5m). Our hypothesis, that analyzing patterns of land use change is pertinent for up-to-date assessment of agroecosystem resources and their wise management is supported by the outcome of the multi-tiered geospatial analysis. In addition, two examples from the project that highlight the adoption of LCLUC by different disciplinary experts are presented. A sociologist assessed the land ownership boundary for a selected tribal community. A faunal ecologist used it to assess the effect of landscape structure on arthropods and plant groups in rice fields. Furthermore, the Google Earth interface was used to support the overall validation process. Our key conclusion was that a multi-level understanding of the causes, effects, processes and mechanisms that govern agroecosystem transformation requires close attention to spatial, temporal and seasonal dynamics, for which the incorporation of local knowledge and participation of local communities is crucial.