## 5 Results and Discussion
### Consolidated Trends
#### 5.1.2 SITS and Geographical Object-Based Approaches  
The geographic object-based image analysis (GEOBIA) expands the analysis over the pixel reflectance information, segmenting features; dividing images in geo-objects selected by shape, compactness, and texture; collecting samples; and performing classifications using geo-objects as a basic unit of analysis [73]. GEOBIA minimizes the within-class spectral variability by assigning all pixels in the object to an identical LULC class, exploring the spatial information implicit within orbital images, and integrating contextual and semantic relationships among geo-objects [60]. With the rise of Landsat-like application, the object-based approach performed better than the pixel-based one in urban [74], agricultural [26; 65], and natural areas [75], mitigating granular effects and improving the change detection [61; 76]. However, this approach remains rarely employed to analyze SITS data [75]. In the agricultural context, for example, despite the advantages to delineate crop fields, the implementation of object-based frameworks for the spatio-temporal analysis of medium-resolution imagery is limited [77]. The major difficulty is the alignment of objects along the time because of the agricultural landscape dynamics, which do not necessarily improve crop classification by using object-based methods [76].  
Nevertheless, some alternatives were developed using the object-based approach and SITS. [PERSON] et al. [10] proposed the enrichment of pixel SITS with contextual features, strengthening the connection among pixels from the same segment. [PERSON] et al. [74] showed the suitability of a geographic object-based image change analysis (GEOBICA) with L8/OLI and S2/MSI images for detecting urban changes. [PERSON] et al. [78] developed an object-oriented method to analyze L8/OLI SITS, identifying and grouping spatio-temporal entities that evolve similarly. They highlighted that combining GEOBIA, VIs, and bands allows for discriminating similar spatio-temporal phenomena better than using only pixel's reflectance and bands. [PERSON] and [PERSON] [79] attested the potential of object-based approaches to delineate crop field boundaries in multi-temporal S2/MSI data to improve crop mapping in a heterogeneous landscape. [PERSON] and [PERSON] [80] illustrated how Landsat object edgesand VIs SITS can improve cropland mapping in South America, presenting low geometric errors of crop field estimates. [PERSON] et al. [81] integrated temporal and spatial segmentation of Landsat SITS to generate agricultural objects, aiming to improve the land abandonment detection in the Caucasus region. The change detection using geo-objects achieved an OA equal to 97% for mapping six classes, performing better than pixel-level change detection (82%). [PERSON] et al. [82] compared object- and pixel-based approaches to map 15 LULC classes in Beijing, China, using L8/OLI data to explore the influence of repeated sampling on classifications considering different training sample sizes. The object-based approach performed better, and repeated sampling led to increasing accuracies. [PERSON] and [PERSON] [36] integrated L8/OLI and S2/MSI data and applied an object-based classification method to improve the separability of 12 LULC classes in Andalucia, Spain, achieving an OA equal to 88%.