Multispectral Remote Sensing Data Application in Modelling Non-Extensive Tsallis Thermodynamics for Mountain Forests in Northern Mongolia

• Published in: Entropy
• Main Authors: Robert Sandlersky, Nataliya Petrzhik, Tushigma Jargalsaikhan, Ivan Shironiya
• Format: Article in Journal/Newspaper
• Language: English
• Published: MDPI AG 2023
• Subjects: ecosystem; exergy; q-index; Tsallis non-extensive thermodynamics; order and control parameters; Landsat 8; Science; Q; Astrophysics; QB460-466; Physics; QC1-999; taiga
• Online Access: https://doi.org/10.3390/e25121653; https://doaj.org/article/8a95b86040d0454f811a90a26bf7245e

The imminent threat of Mongolian montane forests facing extinction due to climate change emphasizes the pressing need to study these ecosystems for sustainable development. Leveraging multispectral remote sensing data from Landsat 8 OLI TIRS (2013–2021), we apply Tsallis non-extensive thermodynamics to assess spatiotemporal fluctuations in the absorbed solar energy budget (exergy, bound energy, internal energy increment) and organizational parameters (entropy, information increment, q-index) within the mountain taiga–meadow landscape. Using the principal component method, we discern three functional subsystems: evapotranspiration, heat dissipation, and a structural-informational component linked to bioproductivity. The interplay among these subsystems delineates distinct landscape cover states. By categorizing ecosystems (pixels) based on these processes, discrete states and transitional areas (boundaries and potential disturbances) emerge. Examining the temporal dynamics of ecosystems (pixels) within this three-dimensional coordinate space facilitates predictions of future landscape states. Our findings indicate that northern Mongolian montane forests utilize a smaller proportion of received energy for productivity compared to alpine meadows, which results in their heightened vulnerability to climate change. This approach deepens our understanding of ecosystem functioning and landscape dynamics, serving as a basis for evaluating their resilience amid ongoing climate challenges.