2026-07-29 ペンシルベニア州立大学（Penn State）
＜関連情報＞
- https://www.psu.edu/news/research/story/drone-cameras-can-measure-pigments-indicating-surface-health-drylands-soils
- https://zslpublications.onlinelibrary.wiley.com/doi/10.1002/rse2.70082
高解像度マルチスペクトル画像を用いたシトネミン色素含有量と生物地殻機能指標のマッピング Mapping Scytonemin Pigment Content and Biocrust Functional Indicators Using High-Resolution Multispectral Imagery
Raúl Román, Tong Qiu, Fernando T. Maestre, Elizabeth A. La Rue, Sergio Vargas Zesati, Anthony Schaefer, Ryan V. Trexler, Nicole Pietrasiak, Estelle Couradeau
Remote Sensing in Ecology and Conservation Published: 23 June 2026
DOI:https://doi.org/10.1002/rse2.70082
ABSTRACT
Biocrusts are soil-surface assemblages of cryptogams, microbial communities, and soil particles, constituting the “living skin” of drylands. They are critical for global carbon and nitrogen cycles but are endangered by climate change. Safeguarding these critical components and their functions requires accurately measuring biocrust distribution and traits. Biocrust key functional indicators, such as pigments, provide information about their developmental stage, biomass, and functional state, but their remote quantification across spatial scales remains underdeveloped. We evaluated the potential of local-scale (tripod-acquired) multispectral imagery with machine learning to estimate pigments (scytonemin, chlorophyll a, carotenoids), soil organic carbon (SOC), and nitrogen (N) in two contrasting deserts: the Chihuahuan Desert (NM) and the Colorado Plateau (UT). High-resolution imagery (< 1 mm) alongside pigments and nutrient concentrations was collected. At NM site, support vector regressions (SVR) using blue and red bands achieved high accuracy for scytonemin (R2 = 0.93); red-edge band enhanced predictions for SOC and N (R2 = 0.95 and 0.80, respectively). In UT, predictive accuracy was lower (R2 < 0.6), likely due to scytonemin saturation. Scytonemin-based predictions of biocrust cover were less sensitive to moisture variability than chlorophyll a or carotenoids, suggesting scytonemin’s superiority as a biocrust index compared to previously developed indices. The best-performing local-scale models at the NM site successfully scaled to landscape-scale UAS imagery, allowing the remote prediction of pigments and nutrients and capturing relative differences associated with biocrust successional stages but did not capture the exact values of the modeled parameters compared to local measurements. The approach shows promise for future integration with satellite imagery to expand biocrust trait mapping at broader scales, offering a valuable tool for monitoring these key soil ecosystems and their functional attributes across diverse landscapes.
