Disentangling Soil, Shade, and Tree Canopy Contributions to Mixed Satellite Vegetation Indices in a Sparse Dry Forest

Abstract

Remote sensing (RS) for vegetation monitoring can involve mixed pixels with contributions from vegetation and background surfaces, causing biases in signals and their interpretations, especially in low-density forests. In a case study in the semi-arid Yatir forest in Israel, we observed a mismatch between satellite (Landsat 8 surface product) and tower-based (Skye sensor) multispectral data and contrasting seasonal cycles in near-infrared (NIR) reflectance. We tested the hypothesis that this mismatch was due to the different fractional contributions of the various surface components and their unique reflectance. Employing an unmanned aerial vehicle (UAV), we obtained high-resolution multispectral images over selected forest plots and estimated the fraction, reflectance, and seasonal cycle of the three main surface components (canopy, shade, and sunlit soil). We determined that the Landsat 8 data were dominated by soil signals (70%), while the tower-based data were dominated by canopy signals (95%). We then developed a procedure to resolve the canopy (i.e., tree foliage) normalized difference vegetation index (NDVI) from the mixed satellite data. The retrieved and corrected canopy-only data resolved the original mismatch and indicated that the spatial variations in Landsat 8 NDVI were due to differences in stand density, while the canopy-only NDVI was spatially uniform, providing confidence in the local flux tower measurements.

Keywords

• semi-arid pine forest
• mixed-pixel problem
• soil disturbances
• UAV multispectral data
• image classification
• canopy fraction