The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Publications Copernicus
Articles | Volume XLVIII-M-1-2023
21 Apr 2023
 | 21 Apr 2023


I. T. Bueno, J. F. G. Antunes, A. P. S. G. D. D. Toro, J. P. S. Werner, A. C. Coutinho, G. K. D. A. Figueiredo, R. A. C. Lamparelli, J. C. D. M. Esquerdo, and P. S. G. Magalhães

Keywords: Clustering, Agricultural crops, OBIA, Random Forest, Spectro-temporal signature, Intra-class variability

Abstract. Land use and land cover (LULC) classification has long been an essential topic in Earth Observation research and plays a key role in the sustainable development of agriculture. This study evaluated the accuracy of LULC classification based on an initial clustering step in a heterogeneous agricultural landscape using PlanetScope imagery while checking for variability among their Normalized Difference Vegetation Index (NDVI) temporal signatures. We adopt an object-based image analysis to generate image-objects and then extract statistical information of PlanetScope spectral bands and vegetation indices as input information for classification. The exploratory analysis focused on the double crop class and calculated the distance between NDVI temporal signatures of paired land parcels. We applied an unsupervised clustering technique along with Random Forest algorithm based on multiple tests to classify and analyse gains and losses in accuracies produced by these approaches. Our results showed that the initial clustering method outperformed the non-clustered classification of LULC in overall accuracy measures. The exploratory analysis demonstrated that double crops might present high intra-class variability and diverse crop calendars for neighbour land parcels. The accuracies achieved represent promising opportunities for the sufficiently accurate classification of such areas, and the knowledge of the intra-class variability allows the analyst to infer the temporal dynamics of crop fields. We reinforce that further work could assess other types of classifiers, especially in areas with a large number of crop types and distinct management practices.