## 4 Results and Discussion
### Sample Code for Accuracy Assessment
#### 4.3.2 Class Level  
* The total area of all patches in the corresponding land class is the class area. This metric was used to calculate all class patches. Here, the range starts at 0 and increases without limit; 0 indicates the patch area of the class, and when it increases from 0, it indicates the decreasing size of the patch area. However, when it increases without limits, the patch area becomes larger. Figure 12 shows the largest cropland area (135.40) in 2011 and the lesser waterbodies area (2.52) in 2021. * **Aggregation index (A):*
* This index measures the district's aggregation. The higher the value of the AI, the more aggregated it is, and the lower the value, the more disaggregated it is. Here, the range lies between 0 and 100; 0 indicates the maximum  
Figure 11: Graphical and pictorial representation of the highest and lowest values of a FRAC, patch-level landscape metric. Figure 12: Graphical and pictorial representation of the highest and lowest values of a CA, class-level landscape metric. disaggregated class, and 100 indicates maximally aggregated classes. Figure 13 depicts the fact that cropland patches were compact or aggregated in nature in both years, but fragmentation occurred in the landscape with lower values, implying urban patch desegregation in 2011. Finally, in 2021, small fragmented areas in waterbodies were observed, and in 2011, small fragmented patches were observed in other classes. * **CLUMPY:** The aggregation of class patches is estimated by CLUPMY. CLUMPY has a range of \(-1\) to \(1\), where \(-1\) indicates maximally disaggregated, 0 indicates randomly distributed, and 1 indicates maximally aggregated classes. In this study, cropland was more aggregated in 2021 with a value of 0.87, whereas the value increased from 2011 to 2021 (0.66-0.87), indicating disaggregation or fragmentation, as shown in Figure 14.  
Figure 14: Graphical and pictorial representation of the highest and lowest values of a CLUMPY, class-level landscape metric. Figure 13: Graphical and pictorial representation of the highest and lowest values of an AI, class-level landscape metric. * ED is also used to assess the fragmentation and spatial heterogeneity of the landscape. Here, the range starts at 0 and increases without limits; 0 denotes that only one patch is present (and the landscape boundary is not included), and it increases without limits, indicating that the landscape has become patchier. During the study period of 2011-2021, the value of edge density was higher in barren and fallow land (124.87) than in the other classes that compute the level of fragmentation. In 2021, compact or clumped urban growth experienced relatively higher values (74.78) in urban and built-up class areas, as shown in Figure 15.
* Division is a metric for aggregation. It can be defined as the probability that two randomly selected cells are not in the same class patch. The landscape division index has a negative relationship with effective patch size. Here, the range starts at 0 and goes to 1. Here, 0 indicates only one present patch, which means equally divided cells, and 1 indicates that all patches of the class are single cells, which means approaching division. Cropland is close to zero in both years in Figure 16 because it is the dominant class in the study area. Figure 15: Graphical and pictorial representation of the highest and lowest values of an ED, class-level landscape metric. * **CPLAND:**: CPLAND is the percentage of the class core area in relation to the total landscape area. The value of CPLAND lies between 0 and 100, as 0 indicates all patches, and it increases toward 100, indicating patches that are larger while being rather simple in shape. The PLAND values show a growth trend in the urban and built-up classes over the last decade. The higher percentage growth of built-up area (8.90%) from 2011 to 2021 is shown in Figure 17.  
Figure 16: Graphical and pictorial representation of the highest and lowest values of a DIVISION, class-level landscape metric.