  Natural protected areas are vital for biodiversity, climate change
mitigation, and supporting ecological processes. Despite their significance,
comprehensive mapping is hindered by a lack of understanding of their
characteristics and a missing land cover class definition. This paper aims to
advance the explanation of the designating patterns forming protected and wild
areas. To this end, we propose a novel framework that uses activation
maximization and a generative adversarial model. With this, we aim to generate
satellite images that, in combination with domain knowledge, are capable of
offering complete and valid explanations for the spatial and spectral patterns
that define the natural authenticity of these regions. Our proposed framework
produces more precise attribution maps pinpointing the designating patterns
forming the natural authenticity of protected areas. Our approach fosters our
understanding of the ecological integrity of the protected natural areas and
may contribute to future monitoring and preservation efforts.

Computer Vision and Pattern Recognition;Artificial Intelligence