Impact of Satellite-Derived Land Cover Resolution Using Machine Learning and Hydrological Simulations
"> Figure 1
<p>Jhelum River Basin (Study Area).</p> "> Figure 2
<p>(<b>a</b>) SVM Classified Landsat 8 Image; (<b>b</b>) Random Forest Classified Landsat 8 Image; (<b>c</b>) Combine Classified Landsat 8 Image; (<b>d</b>) SVM Classified Sentinel 2 Image; (<b>e</b>) Random Forest Classified Sentinel 2 Image; (<b>f</b>) Combine Classified Sentinel 2 Image. Details of legend is explained in <a href="#remotesensing-15-05338-t001" class="html-table">Table 1</a>.</p> "> Figure 3
<p>Areal Coverage Comparison between Global Land cover 2009 and SVM classified Land Cover 2019.</p> "> Figure 3 Cont.
<p>Areal Coverage Comparison between Global Land cover 2009 and SVM classified Land Cover 2019.</p> "> Figure 4
<p>Rainfall Comparison for Simulations of Land Cover 2009 and Land Cover 2019.</p> "> Figure 5
<p>Snowfall Comparison for Simulations of Land Cover 2009 and Land Cover 2019.</p> "> Figure 6
<p>Surface Runoff Comparison for Simulations of Land Cover 2009 and Land Cover 2019.</p> "> Figure 7
<p>Lateral Runoff Comparison for Simulations of Land Cover 2009 and Land Cover 2019.</p> "> Figure 8
<p>Water Yield Comparison for Simulations of Land Cover 2009 and Land Cover 2019.</p> "> Figure 9
<p>Evapotranspiration Comparison for Simulations of Land Cover 2009 and Land Cover 2019.</p> "> Figure 10
<p>Sediment Yield Comparison for Simulations of Land Cover 2009 and Land Cover 2019.</p> "> Figure 11
<p>Potential Evapotranspiration Comparison for Simulations of Land Cover 2009 and Land Cover 2019.</p> "> Figure 12
<p>Impact of Land-cover Classes on Surface Runoff.</p> "> Figure 13
<p>Impact of Land-cover Classes on Curve Number.</p> ">
Abstract
:1. Introduction
2. Materials and Methods
2.1. Classification Algorithms
2.2. Classification Assessment
- P_Accuracy = the producer’s accuracy column shows false negatives, or errors of omission. The producer’s accuracy indicates how accurately the classification results meet the expectation of the creator.
- U_Accuracy = the user’s accuracy column shows false positives, or errors of commission, in which pixels are incorrectly classified as a known class when they should have been classified as something else.
- κ = Kappa Coefficient.
- C = Column Total.
- D = Row Total.
2.3. Hydrologic Model
2.4. Temporal Data
2.5. Application Framework
3. Results
3.1. Land Cover Classification
3.2. Accuracy Assessment
3.3. Hydrologic Simulations
3.4. Sensitivity Analysis
4. Discussion and Conclusions
Research Innovation and Future Recommendations
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Class Names | Landsat 8 SVM Classified Areas in m2 | Landsat 8 Random Forest Classified Areas in m2 | Landsat 8 Combine Classified Areas in m2 |
---|---|---|---|
Rain-Fed Crops (CRIR) | 4,792,441,500 | 3,822,347,700 | 29,006,659,800 |
Dry Crops (CRDY) | 5,282,307,000 | 5,707,529,100 | 6,445,646,100 |
Row Agriculture (AGRR) | 1,920,349,800 | 2,327,472,000 | 3,679,992,900 |
Crops And Grasses (CRGR) | 1,338,019,200 | 1,197,567,000 | 3,066,940,800 |
Broad Leaf Forest (FRSE) | 1,176,307,200 | 1,105,245,900 | 2,306,052,000 |
Forest Deciduous (FRSD) | 1,440,661,500 | 1,093,562,100 | 334,854,000 |
Needle Leaf Forest (FRSE) | 2,512,664,100 | 2,599,309,800 | 2,286,873,000 |
Mixed Forest (FRST) | 1,313,891,100 | 1,049,804,100 | 4,826,664,900 |
Mosaic Shrubs and Grasses (MISG) | 411,715,800 | 662,919,300 | 867,705,300 |
Mosaic Grasses and Shrubs (MIGS) | 874,218,600 | 1,264,524,300 | 1,557,319,500 |
Shrubs Land (SHRB) | 1,513,336,500 | 1,726,434,900 | 1,525,024,800 |
Herbaceous Land (RNGE) | 4,416,347,700 | 4,186,488,600 | 1,673,035,200 |
Urban Settlements (URBN) | 4,232,900,700 | 6,272,032,500 | 1,901,830,500 |
Barren Land (BARE) | 973,674,900 | 847,521,900 | 3,589,747,200 |
Water Bodies (WATB) | 1,234,278,900 | 647,824,500 | 1,968,434,100 |
Perennial Ice Cover (WATR) | 1,762,086,600 | 684,617,400 | 708,812,100 |
Class Names | Landsat 8 SVM Classified Areas in m2 | Landsat 8 Random Forest Classified Areas in m2 | Landsat 8 Combine Classified Areas in m2 |
---|---|---|---|
Rain-Fed Crops (CRIR) | 5,982,550,146 | 4,822,768,963 | 24,582,564.49 |
Dry Crops (CRDY) | 3,680,562,256 | 4,568,699,832 | 6,985,752,514 |
Row Agriculture (AGRR) | 1,744,995,559 | 2,045,955,144 | 7,293,704,112 |
Crops And Grasses (CRGR) | 603,689,880.1 | 1,388,018,688 | 4,223,103,570 |
Broad Leaf Forest (FRSE) | 612,557,897.2 | 848,808,205.6 | 226,860,643.3 |
Forest Deciduous (FRSD) | 1,354,356,714 | 661,014,099.2 | 78,211,039.68 |
Needle Leaf Forest (FRSE) | 4,461,742,689 | 3,674,827,342 | 2,000,841,830 |
Mixed Forest (FRST) | 918,870,604.2 | 1435,203,322 | 5,744,628,795 |
Mosaic Shrubs and Grasses (MISG) | 128,296,487.7 | 825,203,831.7 | 334,754,181.1 |
Mosaic Grasses and Shrubs (MIGS) | 664,754,050.5 | 1,175,044,814 | 1,020,541,339 |
Shrubs Land (SHRB) | 872,673,001.6 | 940,767,766.2 | 198,469,806.8 |
Herbaceous Land (RNGE) | 9,454,880,784 | 7,538,788,679 | 1,023,609,337 |
Urban Settlements (URBN) | 1848,227,219 | 1,972,648,721 | 1,560,824,053 |
Barren Land (BARE) | 133,631,926.5 | 105,183,218.5 | 2,181,308,750 |
Water Bodies (WATB) | 860,517,556.8 | 394,829,712 | 891,638,807 |
Perennial Ice Cover (WATR) | 202,416,3276 | 2,850,595,271 | 649,561,151.2 |
Class Names | Landsat 8 Classified Image Accuracy Values | |||||
---|---|---|---|---|---|---|
Support Vector Machines | Random Forest | Combine Classification | ||||
P_Accuracy | U_Accuracy | P_Accuracy | U_Accuracy | P_Accuracy | U_Accuracy | |
Rain-Fed Crops (CRIR) | 0.67 | 0.40 | 0.71 | 0.50 | 0.80 | 0.80 |
Dry Crops (CRDY) | 1.00 | 0.70 | 0.88 | 0.70 | 0.88 | 0.70 |
Row Agriculture (AGRR) | 0.89 | 0.80 | 1.00 | 0.50 | 0.82 | 0.90 |
Crops and Grasses (CRGR) | 1.00 | 0.60 | 0.83 | 0.50 | 1.00 | 0.60 |
Broad Leaf Forest (FRSE) | 0.91 | 1.00 | 0.88 | 0.70 | 1.00 | 1.00 |
Forest Deciduous (FRSD) | 1.00 | 0.90 | 0.75 | 0.60 | 1.00 | 0.80 |
Needle Leaf Forest (FRSE) | 1.00 | 0.90 | 0.60 | 0.90 | 0.53 | 0.90 |
Mixed Forest (FRST) | 0.83 | 1.00 | 0.57 | 0.80 | 0.67 | 0.40 |
Mosaic Shrubs and Grasses (MISG) | 0.83 | 1.00 | 0.70 | 0.70 | 0.83 | 1.00 |
Mosaic Grasses and Shrubs (MIGS) | 0.64 | 0.90 | 0.58 | 0.70 | 0.59 | 1.00 |
Shrubs Land (SHRB) | 0.69 | 0.90 | 0.75 | 0.60 | 0.82 | 0.90 |
Herbaceous Land (RNGE) | 0.83 | 1.00 | 0.45 | 0.90 | 0.59 | 1.00 |
Urban Settlements (URBN) | 0.75 | 0.30 | 0.67 | 0.20 | 1.00 | 0.30 |
Barren Land (BARE) | 0.56 | 0.90 | 0.38 | 0.50 | 0.43 | 0.30 |
Water Bodies (WATB) | 1.00 | 0.90 | 0.89 | 0.80 | 1.00 | 0.80 |
Perennial Ice Cover (WATR) | 0.91 | 1.00 | 0.71 | 1.00 | 1.00 | 0.90 |
Kappa | 0.81 | 0.64 | 0.75 | |||
Overall Accuracy (%) | 82.50 | 66.25 | 76.87 |
Class Names | Sentinel 2 Classified Image Accuracy Values | |||||
---|---|---|---|---|---|---|
Support Vector Machines | Random Forest | Combine Classification | ||||
P_Accuracy | U_Accuracy | P_Accuracy | U_Accuracy | P_Accuracy | U_Accuracy | |
Rain-Fed Crops (CRIR) | 0.83 | 0.50 | 1.00 | 0.60 | 0.75 | 0.60 |
Dry Crops (CRDY) | 0.71 | 0.50 | 0.86 | 0.60 | 0.75 | 0.60 |
Row Agriculture (AGRR) | 0.67 | 0.60 | 1.00 | 0.70 | 0.91 | 1.00 |
Crops and Grasses (CRGR) | 1.00 | 0.40 | 1.00 | 0.70 | 1.00 | 0.50 |
Broad Leaf Forest (FRSE) | 0.75 | 0.90 | 1.00 | 0.90 | 0.88 | 0.70 |
Forest Deciduous (FRSD) | 0.73 | 0.80 | 1.00 | 0.80 | 0.67 | 0.40 |
Needle Leaf Forest (FRSE) | 0.44 | 0.40 | 0.83 | 1.00 | 0.73 | 0.80 |
Mixed Forest (FRST) | 0.54 | 0.70 | 0.90 | 0.90 | 0.45 | 0.50 |
Mosaic Shrubs and Grasses (MISG) | 0.67 | 0.40 | 0.91 | 1.00 | 0.71 | 0.50 |
Mosaic Grasses and Shrubs (MIGS) | 0.59 | 1.00 | 0.71 | 1.00 | 0.89 | 0.80 |
Shrubs Land (SHRB) | 0.78 | 0.70 | 0.56 | 0.90 | 1.00 | 0.90 |
Herbaceous Land (RNGE) | 0.43 | 0.90 | 0.45 | 1.00 | 0.32 | 1.00 |
Urban Settlements (URBN) | 0.75 | 0.30 | 1.00 | 0.10 | 1.00 | 0.10 |
Barren Land (BARE) | 0.14 | 0.10 | 0.67 | 0.60 | 0.40 | 0.60 |
Water Bodies (WATB) | 0.91 | 1.00 | 0.88 | 0.70 | 1.00 | 0.70 |
Perennial Ice Cover (WATR) | 0.64 | 0.90 | 0.77 | 1.00 | 0.69 | 0.90 |
Kappa | 0.61 | 0.78 | 0.66 | |||
Overall Accuracy (%) | 63.12 | 78.12 | 66.25 |
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Hanif, F.; Kanae, S.; Farooq, R.; Iqbal, M.R.; Petroselli, A. Impact of Satellite-Derived Land Cover Resolution Using Machine Learning and Hydrological Simulations. Remote Sens. 2023, 15, 5338. https://doi.org/10.3390/rs15225338
Hanif F, Kanae S, Farooq R, Iqbal MR, Petroselli A. Impact of Satellite-Derived Land Cover Resolution Using Machine Learning and Hydrological Simulations. Remote Sensing. 2023; 15(22):5338. https://doi.org/10.3390/rs15225338
Chicago/Turabian StyleHanif, Fatima, Shinjiro Kanae, Rashid Farooq, M. Rashid Iqbal, and Andrea Petroselli. 2023. "Impact of Satellite-Derived Land Cover Resolution Using Machine Learning and Hydrological Simulations" Remote Sensing 15, no. 22: 5338. https://doi.org/10.3390/rs15225338