## 3 Results
### Classification Accuracy Assessment
Figure 5 shows the total forest cover change in 1980s, 2000s, and 2020s based on our study as according to the statistical value of the country's total forest estimated by the FAO. According to past studies in the region of interest, the current temporal forest cover analysis in the Swat district area for the years 1968, 1990, and 2007 reveals a yearly rate of deforestation of 1.86%, 1.28%, and 0.80% including the scrub forest, agro-forest, and alpine forest regions, consecutively [76]. The Swat and Shangla regions experienced an average yearly gross deforestation rate of 0.81% between 2001 and 2009. Moreover, between 1996 and 2008, the Hazara as well as the Malakand regions had an annual forest cover rate of change of 1.32%. [PERSON] reports that long-term analyses show a 50 percent decline in forested land in the Kaghan and Naran Valley of Khyber Pakhtunkhwa province between 1847 and 1990 [77]. This research's findings are consistent with those of others that have showed increased yearly rate of deforestation in a number of developing countries. FAO analyses (2005) indicate a 23.3% loss in the forest land of Pakistan from 1990 to 2005, further supporting the findings for a faster rate of deforestation. The Environment Assessment Programme for the Asia-Pacific region found that between 1981 and 1990 there was an annual drop of 0.6%. (UNEP and ICHMOD, 1998, pp. 29-31). In a similar vein, [PERSON] and [PERSON] (2003) estimated that the annual rate of deforestation in the United States during the 1990s was 1.5%. In Africa, [PERSON] and [PERSON] found in 2009 that agricultural land had grown from around 200 million hectares to 340 million hectares over a 25-year period. This expansion was due to a loss of forest cover, which decreased by 16% [12; 78]. According to [PERSON] and [PERSON], from 1989 to 2004 Belize lost 30% of its forest cover, with deforestation being more prevalent in regions close to roads in 2010 [79]. From 1963 until 1993, [PERSON] found a 30% increase in crop land in the central Himalaya region in India at the cost of a 5% decrease in forest. [PERSON], Sivrikaya, aakir, and Kose in 2008 observed an annual decrease of 0.42 percent of forest cover in Trabzon, Turkey, between 1975 and 2000 [56; 80]. Around the Changbai Biosphere Reserve in China, [PERSON], Wallin, and Hoa (1997) found an average rate of forest loss of 1.12%. Integrating time-varying remote sensing data into auxiliary datasets increases the variety of spectral signatures used to train the model; therefore, this finding makes sense. A model's capacity to accurately categorize picture pixels that differ from the most prevalent spectral signatures of the classifications improves in proportion to the amount of variation included in the training data. This confirms the importance of integrating spatiotemporal datasets of satellite remote sensing image classification, as demonstrated in prior studies. [81; 82; 83]. Even though it is hard to ascertain the classification performance with reality or with the actual features on the ground, constructing matrix errors may reduce the classification errors that could result from the clustering of spectral pixel values during the classification process. This is despite the fact that it is impossible to compare the accuracy of classification with reality [84; 85]. One of the most popular techniques in assessing classification accuracy is the confusion matrix; however, it may not always reflect reality because accuracy can be influenced by a variety of factors, such as availability of very high-resolution datasets. Additionally, the accuracy may be impacted by the shadows of mountain slopes, where the topography is quite rocky and mountainous. According to this study's findings, forest loss was mostly a result of human activity and was particularly severe along roads, rivers, and at the top and bottom of several ridges. Climate change-related factors such as drought, insufficient precipitation, or flood, on the other hand, can exacerbate forest loss.