Tracking savanna vegetation structure in South Africa by extension of GEDI canopy metrics with Landsat, Sentinel-2, and PALSAR

Abstract

1Adaptive management of savanna ecosystems requires frequent monitoring of woody vegetation structure, and although vegetation structure and changes may be captured with repeat airborne lidar, it is spatially and temporally limited across African savannas. As an alternative, this study evaluates the extension of spaceborne waveform lidar canopy metrics (RH98, Cover, Foliage Height Diversity) from the Global Ecosystem Dynamics Investigation (GEDI) across the Greater Kruger region in South Africa using moderate resolution optical sensors (Landsat and Harmonized Landsat Sentinel-2 [HLS]), L-band Synthetic Aperture Radar (PALSAR-1 and -2), and topographic and soil covariates. We compared the performance of 14 predictor sets incorporating different sensor combinations and temporal processing methods (LandTrendr and CCDC) in random forest models using temporal cross-validation to assess extrapolation accuracy. The most parsimonious fusion model (LandTrendr + SAR + topography/soils) achieved RMSEs of 3.04 m for RH98, 13.38% for Cover and 0.34 for FHD, which was comparable to more complex models using HLS and CCDC. All models demonstrated good temporal transferability with minimal bias but tended to overestimate low values and underestimate high values, which muted the estimated magnitude of change. Annual canopy structure maps derived from the best model captured expected spatial patterns and were used in model-based estimators to quantify changes in areas impacted by elephants, timber harvesting, fuelwood extraction, and woody encroachment. Extending GEDI metrics with moderate-resolution sensors thus offers a viable approach for large-scale savanna monitoring and detecting change in high impact areas.

Plant phenotyping relevance

GEDIと光学・SARセンサーを融合して樹冠構造形質を推定し、複数モデルの性能検証と年次マッピングを行うことが中心であり、植物形質の計測手法として適格です。

Code and data availability

The supplied blocks describe GEDI/Landsat/HLS/PALSAR data processing and random forest modeling, but contain no data or code availability statement, no public repository, and no author-provided URL for the paper's datasets, maps, models, or analysis code. All data sources mentioned (GEDI, Landsat, PALSAR, iSDA soils)都是

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