## 2 Materials and Methods
### Extraction of Land Parcels
#### 2.4.3 Image Segmentation  
In the context of increasing demand for methods capable of extracting value-added information from geospatial data, object-based image analysis (OBIA) is a framework  
Figure 4: NDVI multitemporal raster visualized in RGB. The red channel is associated with the NDVI values from 16 March; the green channel is associated with the NDVI values from 16 June; and the blue channel is associated with the NDVI values from 7 October. \begin{table}
\begin{tabular}{c c c} \hline
**ID** & **Acquisition Date** & **File Name** \\ \hline  
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\end{table} 
Table 3: _Cont._dedicated to dividing remote sensing imagery into meaningful image objects (or segments), rather than individual pixels [16]. Traditionally, OBIA includes two principal steps, image segmentation and object classification. Image segmentation refers to the partitioning of an image into spatially adjoining and homogenous regions (the segments), comprising the foundation for further analysis [17]. Therefore, segmentation algorithms aim at clustering neighboring pixels into objects that should ideally correspond to real-world features. As LPIS is a geodatabase designed for identifying and monitoring land parcels to manage agricultural subsidies, the ability to accurately extract parcel boundaries is a primary requirement. In this study, image segmentation serves as a fundamental step in assessing the potential of LPIS parcel-boundary extraction using PlanetScope data. Since agricultural parcels typically exhibit well-defined radiometric borders and homogeneous content, the segmentation of the multitemporal NDVI dataset was performed using the Edge method, which is particularly well-suited for identifying features with distinct boundaries and uniform internal texture [18]. The algorithm was implemented through an established workflow available in ENVI 6.1 named Segment Only Feature Extraction. It comprises the following steps:  
1. Creation and application of an input mask to filter out from the scene all the coverage classes not relevant for extraction of agricultural parcels. Among the land-cover classes provided in Land Cover Annual (2023), only surfaces covered by the classes Croplands, Grasslands, Forage Crops, and Pastures were considered for further analysis. 2. Per-band gradient computation. Application of the Sobel filter with a kernel size of 3 \(\times\) 3 to generate twenty-five gradient images from the PlanetScope NDVI multitemporal image set. The Sobel filter is an edge-detection method that maps the magnitude of local changes (the contrast) in the pixel values of an image [19]. Globally, these changes are measured by a gradient image, in which higher pixel values represent areas with higher pixel contrast. 3. Fusion of per-band gradients. The resulting set of twenty-five gradient images is fused into a single gradient image. As outlined in U.S. Patent No. 8,260,048 B2--which underpins the Segment Only Feature Extraction workflow implemented in ENVI 6.1--the fusion method MAX is used to perform this operation. This method selects, for each pixel location, the maximum value of gradient values across all bands. The MAX fusion approach effectively emphasizes the most salient spatial transitions present in any gradient images, closely matching the way humans perceive object boundaries [18]. 4. Computation and normalization of the cumulative relative histogram (CRH) of the gradient values (Figure 5). 5. Setting of scale-level threshold to filter the gradient image. Pixels in the gradient image with values lower than a selected cumulative relative histogram threshold (scale level) are replaced with the threshold value. Therefore, the application of the scale level modifies the gradient image, discarding from it the values in percentiles that fall below the specified threshold and discarding the areas with pixel contrast lower than a defined threshold. This process allows for control of the degree of over or under-segmentation produced by the algorithm.