## 4 Results and Discussion
### Sample Code for Accuracy Assessment
#### 4.3.3 Landscape Level  
* **AI:** The landscape's configuration is measured by AI. The values lie between 0 and 100; 0 indicates maximally disaggregated classes, and 100 indicates maximally aggregated classes. When the value increases from 0 to 100, maximally aggregated classes begin to develop. Figure 21 and Figure 26 depict a fragmented landscape with many edges. AI values for both years are nearly identical. * **CONTAG:** CONTAG was used to measure the landscape structure at the landscape level. Here, values lie between 0 and 100; 0 indicates that all cells are unevenly distributed, and 100 indicates that all cells are equally adjacent to all other classes. CONTAG values in 2011 were 69.94 and 65.19 in 2021, as shown in Figure 22 and Figure 26, which describe the cells of the classes as being equally adjacent to all other classes in the landscape, but the values are reduced in 2021 because the study area is degrading, on average. * **DCAD:** DCAD is a core area metric. DCAD values lie below 0, and 0 indicates that no patch contains a disjunct core area. It increases without limit as disjunct core areas become more present, i.e., patches become larger and less complex. Figure 23 and Figure 26 show that patches of the study area become larger because the DCAD values increased in 2021 (44.86), as there are more disjoint core areas in 2021 than in 2011. * **ED:** ED is a metric that measures both area and edge. Here, values lie above 0 and increase without limits; 0 indicates only one patch, and when they increase above 0 without limit, the landscape becomes patchier. The edge density corresponds to the density of edges in the landscape in relation to the area of the landscape. Metrics describe the configuration of the landscape. The edge density of the landscape is increased so that the landscape of the study area was patchier in 2021 than in 2011, as shown in Figure 24 and Figure 26.
* **ENT:** The ENT assesses landscape class diversity (thematic complexity). The calculated marginal entropy of the measured landscape ranges from 0.642376459 (2011) to 0.97  
Figure 20: Graphical and pictorial representation of the highest and lowest values of a PAFRACE, class-level landscape metric. All metrics indicate that landscape degradation and fragmentation are based on observable shifts toward reduced spatial heterogeneity, which are influenced by specific developments in the study area. Figure 27 illustrates that large agricultural patches have been fragmented due to infrastructure expansion, including the construction of canals, new road networks, and industrial zones. This leads to increased barren and fallow land patches and a notable rise in urban and built-up areas. While fragmentation can sometimes contribute to biodiversity by creating ecotones, in this context, it indicates a decline in agricultural continuity and habitat connectivity. The transformation from contiguous cropland to smaller, disconnected patches disrupts the traditional agricultural landscape  
Figure 26: Graphical representation of the results of a landscape metric at landscape level. Figure 24: Pictorial representation of an ED, landscape-level landscape metric. Figure 25: Pictorial representation of an ENT, landscape-level landscape metric. and increases the likelihood of habitat fragmentation, especially in the eastern part of the study area. Therefore, the observed fragmentation is less about ecological enrichment and more about land use changes that compromise landscape integrity and spatial continuity. The observed changes in the landscape structure across patch, class, and landscape levels highlight the critical importance of taking proactive measures to prevent further degradation. Metrics such as increased fragmentation, reduced core areas, and irregular patch shapes signal ecological stress, which, if left unchecked, could lead to the erosion of biodiversity, loss of agricultural productivity, and disruption of ecosystem services.