The present study analyzes for the first time the usefulness of the synergy between UAV-multispectral data and biomass-chlorophyll indices to discriminate and map carob trees dieback and damages. To achieve so, the UAV flight was performed over a carob forest located in a valley of a watershed in the Moroccan Middle-Atlas Mountain. The UAV data were rigorously pre-processed and twelve biomass-chlorophyll indices were implemented, analysed (spectrally and radiometrically), and validated using the ground truth. Then, a histogram thresholding classification was applied to the index offering the best performance. The results obtained pointed out that the TDVI and CIG indices have similar and good dynamic range values, and better performance than the other indices tested. They are well correlated, completely independent of soil background artefacts, and relatively avoid linearity and saturation problems. They showed a curvilinear relationship between their computed values and the considered classes (i.e., bare soil, healthy, dieback, and dead trees). The validation shows that TDVI and CIG are sensitive to the carob spatial variations, thus allowing an excellent land-use separating power, predicting early warning signals of dieback, and providing useful bio-physiological information about carob tree conditions. Furthermore, the results highlighted the radiometric and spectral performance of the DJI Phantom-4 camera for powerful sensitivities in discriminating carob forest classes. This simple and quick method can be a useful tool for decision support for monitoring and protecting carob forests on a large scale to promote sustainable development.

来源平台：2024 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS 2024)