Evaluation of Land Surface Phenology for Autumn Leaf Color Change Based on Citizen Reports across Japan

Autumn foliage color is an important phenological characteristic associated with climate and appeals to populations as a cultural ecosystem service (CES). Land surface phenology (LSP) analyzed using time-series remotely sensed imagery can facilitate the monitoring of autumn leaf color change (ALCC); however, the monitoring of autumn foliage by LSP approaches is still challenging because of complex spatio-temporal ALCC patterns and observational uncertainty associated with remote sensing sensors. Here, we evaluated the performance of several LSP analysis approaches in estimation of LSP-based ALCCs against the ground-level autumn foliage information obtained from 758 sightseeing (high CES) sites across Japan. The ground information uniquely collected by citizens represented ALCC stages of greening, early, peak, late, and defoliation collected on a daily basis. The ALCC was estimated using a second derivative approach, in which normalized difference vegetation index (NDVI), kernel normalized difference vegetation index (kNDVI), enhanced vegetation index (EVI), two-band enhanced vegetation index (EVI2), and green red vegetation index (GRVI) were applied based on MODerate resolution Imaging Spectroradiometer (MODIS) MOD09A1 with four (Beck, Elmore, Gu, and Zhang) double logistic smoothing methods in 2020. The results revealed inconsistency in the estimates obtained using different analytical methods; those obtained using EVI with the Beck model estimated the peak stage of the ALCC relatively well, while the estimates obtained using other indices and models had high discrepancies along with uncertainty. Our study provided insights on how the LSP approach can be improved toward mapping the CESs offered by autumn foliage.

Transformation of matter in living organisms during growth and evolution

Growth of an organism involves transformations of the state of matter from unstructured food or photosynthate into the highly organized matter in the living organism. Biological evolution involves random changes in the structure of DNA that lead to changes in the organization of the matter in an organism. Thermodynamic data show the organized biomass in living organisms has the same thermodynamic properties as a random mixture of the same elemental composition and is not in an energetically metastable, low entropy state. Therefore, the central thesis of this work is that building biological structures and organization from foodstuffs incurs no direct thermodynamic cost. The implication is that growth and evolution occur with little or no thermodynamic cost. In consequence, the fundamental difference between living biomass and lifeless organic sludge is in the information constraints that direct and govern the organization of the system. These constraints within a living organism override random processes to produce an organized distribution of biomass within the organism. Similarly, the information in DNA constrains the outcome of biological evolution across organisms within a population of a species in a predictable way that leads to convergent evolution. Although individuals and molecules act or are acted upon in a random manner, the outcome in a constrained system is predictable within an organism and across organisms. As a consequence evolution will produce similar outcomes at the macro level in similar environments. Stochastic determinism is proposed as a method that could be used to model convergent evolution.