As the global space sector enters the NewSpace era, developing countries such as Mongolia face significant challenges in developing functional space programs without the institutional legacy of established space powers. This study examines the gap between Mongolia’s long-term development strategy, “Vision 2050”, its sectoral policy guidance for space science and technology, and the absence of a comprehensive national space governance system. Using Qualitative Systems Analysis (QSA), the study compares governance arrangements across advanced, developing, and emerging spacefaring nations. The method enables comparative evaluation of institutional interactions, regulatory adaptability, and governance structures in national space systems where quantitative indicators remain limited or difficult to standardize. The findings indicate that direct replication of traditional “Old Space” institutional models often produces structural misalignment in latecomer states with limited financial and technological capacity. In response, the study proposes an Agile Governance Model (AGM) based on the Governance–Resilience–Agility (GRA) Triad. The model combines strategic coordination, adaptive regulation, and operational implementation through a flexible governance structure emphasizing phased legal development, public–private partnerships, and national capacity-building. The study further argues that latecomer nations can avoid rigid legacy institutions by adopting modular governance structures and targeted specialization in areas such as Earth observation, geospatial services, and ground infrastructure. By leveraging geographic advantages and human capital, Mongolia and other emerging space nations can strengthen digital sovereignty and improve institutional resilience. Adaptive governance approaches can also support their long-term participation in the global space economy.

Hyperspectral imaging, a rapidly evolving technique, furnishes exhaustive information on Earth's surface reflectance over an extensive spectrum of wavelengths. We have developed a more precise method for analyzing spectral data by meticulously selecting plant leaf pixels and employing a relative reference approach. This enhanced technique correlates camera pixel values with light intensity, bypassing the need for complex equations and thereby providing clearer insights into vegetation vitality. The findings of this study not only enhance the precision of hyperspectral imaging but also bolster our capability for ecological monitoring and understanding environmental health. A novel algorithm based on the red-edge slope was established to classify images, exploiting the spectral reflectance data gleaned from the hyperspectral Liquid Crystal Tunable Filter (LCTF) camera. Compared with conventional SVM and K-means algorithms, our novel algorithm demonstrated commendable performance, achieving an overall accuracy of 90% with a Kappa Coefficient of 0.86. This comparison highlights the novel algorithm's relative strength and potential for large-scale satellite and drone imagery analysis applications. The advancements facilitated by this research are instrumental in ecological assessments, conservation efforts, and resource management strategies, signaling a leap forward in the era of environmental monitoring and analysis.