Ecological Informatics (Dec 2024)
A multi-source approach to mapping habitat diversity: Combination of multi-date multispectral satellite imagery and comparison with single-date hyperspectral results in a Mediterranean Natural Reserve
Abstract
The increasing availability of spaceborne hyperspectral satellite imagery opens new opportunities for forest habitat mapping and monitoring, but the limitation of its generally low temporal resolution must be considered. In this study, we compare the ability of single-date PRISMA (PRecursore IperSpettrale della Missione Applicativa), the hyperspectral satellite from the Italian Space Agency, with that of both single-date and multi-date Sentinel-2 (S2) and PlanetScope (PS) to detect and correctly classify various EUNIS habitat types distributed over a relatively small spatial extent (6000 ha) in a natural reserve in Central Italy. The case study deals with multiple levels of spectral similarity, as the dominant canopy species of the target forest habitat classes belong to the same genus (Quercus spp., both deciduous and evergreen species) as well as of different taxa (Pinus and Fraxinus spp.). We performed a pixel-based classification with the Random Forest algorithm using a set of 22 spectral indices computed on S2, and 12 on PS, and compared the results with those obtained by PRISMA (28 indices). A Canopy Height Model (CHM) was also used as an input variable for the classification. The single date classification of PlanetScope obtained lower overall accuracy (69 %) than what obtained with other sensors (PRISMA and Sentinel-2) in a previous study over the same area. Regarding the comparison between multi-date multispectral and single-date hyperspectral, 10-fold cross-validation results revealed that S2 achieves an out-of-bag error rate of approximately 16 %, and PS 19 %, while PRISMA result from previous study achieves 17 %. This demonstrates that a combination of spectral indices calculated during the growing season can capture phenological or physiological differences among the target species, which consequently results in a significant improvement in the classification accuracy of the multispectral sensors. Ultimately, classification results from all three sensors were combined to create probability maps for each forest class, identifying areas classified with a higher degree of certainty by each satellite tested and potentially contributing to forest management by defining areas with varying conservation levels.
