English Abstract
Perennial crops are a key component of sustainable agriculture due to their higher carbon storage compared to annual systems. Improving their mapping is essential to support effective public policies. This study investigates the use of field observations, waveform Light Detection and Ranging (LiDAR) data from the Global Ecosystem Dynamics Investigation (GEDI), and hyperspectral imagery from EnMAP to identify spectral and structural features and assess their separability. Field data were used to develop an interpretation key for the analyzed classes. GEDI Level 1B waveforms were normalized to extract metrics such as the number of peaks, while structural attributes, including canopy height (RH95), were derived from GEDI Level 2A. From EnMAP imagery, spectral reflectance was obtained and processed using mean values, first- and second-order derivatives, and continuum removal. Differences among classes were evaluated using Tukey’s test and principal component analysis (PCA). Results show that waveform structure and canopy height significantly distinguish secondary vegetation from perennial crops. Additionally, spectral regions in the visible and shortwave infrared present significant differences between these classes. These findings demonstrate that the structural and spectral variables could improve the discrimination of perennial crops and help overcome challenges in separating them from secondary vegetation.