This master thesis in geography investigates how remote sens- ing can be used in Transnational Corporations (TNC) global Corporate Social Responsibility (CSR) initiatives. The study aims to delineate an accurate method in remote sensing to be used to monitor deforestation in global value chains. Research questions asked are 1) What are the current monitoring practises used by TNCs to monitor global value chains? 2) Which is the most user-friendly and accurate remote sensing technique to map deforestation? 3) How can remote sensing successfully be implemented in TNCs CSR-initiatives? The study is approached from two perspectives, building on theories of value chains, and qualitative methods to answer the first research question. While the second question is a method study, investigating how well a spectral approach versus a contextual approach can map deforest- ation in Landsat scenes. The results are compared with Global Forest Watch (GFW), and the highest accuracy were acquired from the WICS (Window Indipendent Context Segmentation) technique. Conclusions includes that remote sensing can be used in CSR initiatives, to establish a baseline level or as a fifth dimen- sion in a score sheet approach. However, inconclusive mapping of value chains are a big hinder today.