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.

通过使用最大激活和生成对抗模型的新框架，本研究旨在生成能够完整有效解释定义自然真实性的保护区域的空间和光谱模式的卫星图像，进而推动对保护和野生地区形成模式的解释。这个研究的方法能够生成更精确的归因地图，指明保护区域的自然真实性的形成模式，并促进对保护自然地区生态完整性的理解，为未来的监测和保护工作做出贡献。