Predictive Ecological Land Classification From Multi-Decadal Satellite Imagery

Ecological land classifications serve diverse purposes including sample stratification, inventory, impact assessment and environmental planning. While popular, data-driven classification approaches can require large training samples, frequently with limited robustness to rapid environmental change. We evaluate the potential to derive useful, durable ecological land classifications from a synthesis of multi-decadal satellite imagery and geospatial environmental data. Using random forests and multivariate regression trees, we analyze 1982-2000 Landsat Thematic Mapper (L45) and 2013-2020 Harmonized Landsat Sentinel (HLS) imagery to develop and then test the predictive skill of an ecological land classification for monitoring Mediterranean-climate oak woodlands at the recently established Jack and Laura Dangermond Preserve (JLDP) near Point Conception, California. Image pixels were processed using spectral and temporal mixture models. Temporal mixture model residual scores were highly correlated with oak canopy cover trends between 2012 and 2020 (r(2) = 0.74, p << 0.001). The resulting topoclimatic-edaphic land classification effectively distinguished areas of systematically higher or lower oak dieback during 2012-2020 severe drought, with a fivefold difference in dieback rates between land classes. Our results highlight the largely untapped potential for developing predictive ecological land classifications from multi-decadal satellite imagery to guide scalable, ground-supported monitoring of rapid environmental change.