For a more detailed analysis, we selected the changes in land cover in 2001–2005, 2005–2010 and 2010–2015 with a study interval of five years. Table 2 shows that the category of evergreen coniferous forest decreased by 6,249 km2 during 2001–2005, then increased by 5,522 km2 from 2005–2010 and decreased by 18,932 km2 in the last five years of 2010–2015. The evergreen broadleaf forest category increased by 3,633 km2 during 2001–2005, then increased again by 3,060 km2 during 2005–2010, and decreased by 4,031 km2 in the last five years of 2010–2015. The broadleaf deciduous forest category decreased by 1,716 km2 during 2001–2005, then increased by 7084 km2 during 2005–2010, and decreased by 26,821 km2 in the last five years of 2010–2015. Between 2001 and 2005, the mixed forest category increased by 13,507 km2, between 2005 and 2010 it increased by 8,644 km2 and during the last five years of 2010–2015 it decreased by 37,976 km2. During the period 2001–2005, the category of closed shrub land increased by 1,518 km2, then decreased by 3,426 km2 in 2005–2010, and increased by 5,543 km2 in the last five years of 2010–2015. The open shrub-lands increase with 24642 Km2 during the 2001–2005, and then decreased with 9455, and 2020 Km2 during the 2005–2010, and 2010–2015. The grasslands increase with 45495 Km2 during the 2001–2005, and then decreased with 14804, and 12794 Km2 during the 2005–2010, and 2010–2015. During the 2005–2010, and 2010–2015 the croplands decreased with 16699 Km2 during the 2001–2005, and then increased with 2862, and 119800 Km2. During the 2010–2015 the permanent snow improved with 1646, and 14213 Km2 during the 2001–2005 and 2005–2010, and then decreased with 81254 Km2, respectively. From 2001 to 2015, the water bodies have decline by 9747, 4007 and 20868 km2, respectively.
Table 2 Land cover/land use classes and change detection areas for each class in Km2.
3.5. Annual Trends of LCLU over South Asia
During the 2001, 2005, 2010 and 2015, the annual LULC map of South Asia was generated by random forest classification method are showed in Fig. 3. It's worth noting that from 2001 to 2015, the pixels on the LULC map of South Asia did not change in South Asia derived from MCD12Q1 (Fig. 1). During the period of land use changing detection, and land change detection during 2001 to 2015, the map of South Asia was more or less similar. The results obtained indicate that the total area of the survey is 5.07 × 106 km2 are presented in Table 2. The area is divided into 16 land cover categories. The cultivated land in South Asia decreased considerably between 2001 and 2005, and then increased between 2005 and 2015. The significantly reduction of grasslands and the popularization of current agronomic techniques have considerably increased cultivated land (Congalton & Green, 2009; Yuke, 2019; Na et al., 2020). In the past 15 years, cultivated land in South Asia has increased significantly, while grasslands, forest savannas, broadleaf deciduous forests, bodies of water, and permanent ice and snow have decreased. In contrast, closed bush land, savanna, natural vegetation per capita, barren land, urban land and urbanized in South Asia decreased from 2001 to 2005, but increased from 2005 to 2015 Trend (Table 2). Reddy et al. (2017) confirmed the recent decline of other species and the increase in cultivated land. In this study, the exchange between wetlands, barren areas, and woody shrub-land was significant exchange to crop-lands. In South Asia, compared to other categories, the overall increase and change pattern of cultivated land, our results are similar to those of Yin (2008). However, the basic reason for the basic analysis of various LULC courses in South Asia is beyond the capacity of this article.
Based on above mentioned analysis its confirm the clear spatial change patterns in water bodies, grass-lands, mixed forests, closed shrub-lands, evergreen needle-leaf forests, crop-lands, woody savannas, savannas, open shrub-lands, permanent snow and ice. In Fig. 4, 5, 6, and 7, are indicated the trends of overall 16 categories during the 15 years study period. The trend of the significance was use statistically package by using t-test. The generally trends for water bodies, mixed forests, deciduous broad-leaf forests, croplands, savannas, permanent snow and ice are a significant increase during 2001 to 2015 study duration. While, grass-lands, woody savannas, evergreen broad-leaf forests, closed shrub-lands, evergreen needle-leaf forests, urban lands, permanent wet-lands, open shrub-lands, barren and natural vegetation mosaics are overall showed decrease trend. This is consistent with He et al. (2017). The general crop-lands trend is an improving order from 2001 to 2015 durations. This may be due to change of Green to Grain (Jung et al., 2006; Yao et al., 2017). But, the uncertainty of these trends may be bigger due to larger standard errors of forest and crop-lands compared with grass-lands (Congalton & Green, 2009). Which may cause due to unusual climate conditions or other pressures and general policy changes (Pinheiro et al., 2014; Zhang et al., 2016; Yuke et al., 2019), our supposition is that these differences might be work of art such as sensor drift as the effect of chronological incompatible differences of GIMMS NDVI data. Earlier research work has indicated also these temporal inconsistencies of GIMMS NDVI value (Klein et al., 2012; Tian et al., 2015).
The confusion or error matrix has become a reliable technique to express the precision of the categorization results derived from remote sensing data (Ibrakhimov et al., 2007; Zhang et al., 2016). The error matrix provides three commonly used precision measures (Foody, 2002; Denisko and Hoffman, 2018), such as producer precision, user precision, and overall (Kotsiantis, 2007; Henchiri et al., 2019). To assess the correctness of the present categorization, we generated a confusion matrix based on the verification data set. Although the selected samples may be spatially related and the training process are not included. The results of the classification in 2001, 2005, 2010 and 2015 showed that the general precision was 87.65%, 86.95%, 86.67% and 85.55%. Tables 3, 4, 5 and 6show the confusion matrix for the classification of land cover, respectively.
Table 3 Confusion matrix of land covers classification 2001, expressed in percentages.
|
Reference class
|
ENF
|
EBF
|
DBF
|
MF
|
CS
|
OS
|
WS
|
S
|
GL
|
PW
|
CL
|
UBL
|
NVM
|
PSI
|
B
|
WB
|
UA
|
Mapped class
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
ENF
|
85.11
|
1.03
|
0.81
|
|
|
|
0.85
|
0.68
|
0.42
|
|
0.53
|
|
|
|
|
1.72
|
81.63
|
EBF
|
|
93.81
|
2.44
|
0.61
|
|
|
2.54
|
1.37
|
|
1.49
|
|
|
1.75
|
|
|
|
84.26
|
DBF
|
|
2.06
|
88.62
|
1.82
|
|
|
3.39
|
4.11
|
0.84
|
|
|
6.67
|
4.39
|
|
|
1.72
|
74.66
|
MF
|
6.38
|
1.03
|
2.44
|
95.76
|
|
|
1.27
|
2.40
|
0.84
|
1.49
|
1.60
|
|
1.75
|
|
|
3.45
|
85.41
|
CS
|
|
|
|
|
85.71
|
|
|
|
|
|
|
|
|
|
|
|
100.00
|
OS
|
|
|
|
|
|
95.00
|
|
|
3.78
|
1.49
|
|
1.67
|
|
1.15
|
4.28
|
1.72
|
89.06
|
WS
|
|
|
1.63
|
|
|
|
89.41
|
3.08
|
|
4.48
|
|
|
3.51
|
|
|
1.72
|
91.74
|
S
|
2.13
|
1.03
|
0.81
|
|
14.29
|
|
1.69
|
86.64
|
0.84
|
4.48
|
2.66
|
3.33
|
5.26
|
|
|
1.72
|
90.36
|
GL
|
4.26
|
|
0.81
|
0.61
|
|
1.11
|
|
|
89.50
|
|
3.19
|
6.67
|
0.88
|
|
2.67
|
1.72
|
90.25
|
PW
|
|
|
|
|
|
|
|
0.34
|
|
85.07
|
1.60
|
8.33
|
2.63
|
|
|
|
82.61
|
CL
|
2.13
|
1.03
|
0.81
|
1.21
|
|
0.56
|
|
1.03
|
0.84
|
1.49
|
89.89
|
10.00
|
4.39
|
1.15
|
1.60
|
8.62
|
84.08
|
UBL
|
|
|
|
|
|
|
|
|
|
|
|
63.33
|
0.88
|
|
|
|
97.44
|
NVM
|
|
|
1.63
|
|
|
|
0.85
|
|
|
|
0.53
|
|
74.56
|
|
|
1.72
|
93.41
|
PSI
|
|
|
|
|
|
|
|
|
|
|
|
|
|
81.61
|
2.14
|
|
94.67
|
B
|
|
|
|
|
|
3.33
|
|
|
2.94
|
|
|
|
|
16.09
|
89.30
|
3.45
|
85.20
|
WB
|
|
|
|
|
|
|
|
0.34
|
|
|
|
|
|
|
|
72.41
|
97.67
|
PA
|
85.11
|
93.81
|
88.62
|
95.76
|
85.71
|
95.00
|
89.41
|
86.64
|
89.50
|
85.07
|
89.89
|
63.33
|
74.56
|
81.61
|
89.30
|
72.41
|
|
OA
|
87.65%
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
KC
|
86.51%
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|