Comparison of Five Spectral Indices and Six Imagery Classification Techniques for Assessment of Crop Residue Cover Using Four Years of Landsat Imagery
<p>Study area of the South Fork Watershed in Central Iowa. Major crops (corn and soybean) from the Cropland Data Layer (CDL) are shown on the zoom-in map.</p> "> Figure 2
<p>Percentage of corn and soybean planting dates (dots) from the NASS crop progress reports and dates of field visit and Landsat image acquisition.</p> "> Figure 3
<p>Percent residue cover based on SVMC for 2011.</p> "> Figure 4
<p>NDI5 vs. percent residue in 2011. Each point is a field.</p> "> Figure 5
<p>Comparison of Training R<sup>2</sup> and Training Accuracy for five spectral indices and four years. Example of NDI5 and 2011 is shown in red triangle.</p> "> Figure A1
<p>Results for spectral indices/classification techniques for 2011. (<b>a</b>) NDI5, (<b>b</b>) NDI7, (<b>c</b>) NDSVI, (<b>d</b>) NDTI, (<b>e</b>) STI, (<b>f</b>) MAHL, (<b>g</b>) MINDIST, (<b>h</b>) SAM, (<b>i</b>) MAXLI, (<b>j</b>) RANDTR, (<b>k</b>) SVMC, (<b>l</b>) ALL, (<b>m</b>) FOUR.</p> "> Figure A1 Cont.
<p>Results for spectral indices/classification techniques for 2011. (<b>a</b>) NDI5, (<b>b</b>) NDI7, (<b>c</b>) NDSVI, (<b>d</b>) NDTI, (<b>e</b>) STI, (<b>f</b>) MAHL, (<b>g</b>) MINDIST, (<b>h</b>) SAM, (<b>i</b>) MAXLI, (<b>j</b>) RANDTR, (<b>k</b>) SVMC, (<b>l</b>) ALL, (<b>m</b>) FOUR.</p> "> Figure A1 Cont.
<p>Results for spectral indices/classification techniques for 2011. (<b>a</b>) NDI5, (<b>b</b>) NDI7, (<b>c</b>) NDSVI, (<b>d</b>) NDTI, (<b>e</b>) STI, (<b>f</b>) MAHL, (<b>g</b>) MINDIST, (<b>h</b>) SAM, (<b>i</b>) MAXLI, (<b>j</b>) RANDTR, (<b>k</b>) SVMC, (<b>l</b>) ALL, (<b>m</b>) FOUR.</p> ">
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area and Data Collection
2.2. Remote Sensing Data and Methods
- Minimum Distance (MINDIST) assigns the class based on the class with the smallest Euclidean distance in n-space from the unclassified pixel to the mean of the known class [31].
- Maximum Likelihood (MAXLI) assigns the unclassified pixel to the class which is most probably a part of assuming each class in each band is normally distributed [31].
- Mahalanobis distance (MAHLDIST) is similar to maximum likelihood but assumes all class covariances are equal [31].
- Random tree (RANDTR) classified unclassified pixels based on a series of decisions which lead to the known classes [32].
- Spectral Angle Mapper (SAM) creates a modified spectra from the training data, based on the angle between the various bands. It assigns each unclassified pixel to the spectra that it matches the best [33].
- Support Vector Machine (SVM) uses a decision surface or optimal hyperplane that maximizes the difference between the classes [34].
2.3. Data Preparation
2.4. Accuracy Assessment
3. Results and Discussion
3.1. Results from Discussion
3.2. Appropriateness of Using Training R2 As a Surrogate for Validation Accuracy
3.3. Limitations
4. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A
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Band | L5 TM | L7 ETM+ | L8 OLI |
---|---|---|---|
Green | 520–600 | 520–600 | 530–590 |
Red | 630–690 | 630–690 | 640–670 |
NIR | 760–900 | 770–900 | 850–880 |
SWIR1 | 1550–1750 | 1550–1750 | 1570–1650 |
SWIR2 | 2080–2350 | 2080–2350 | 2110–2290 |
Date | Image | Field Visit Dates | Days between Visit and Image |
---|---|---|---|
31 May 2011 | Landsat 5 TM | 20–23 May 2011 | 11 |
13 June 2013 | Landsat 7 ETM+ | 29 May–1 June 2013 | 15 |
9 May 2015 | Landsat 7 ETM+ | 27–29 May 2015 | −18 |
25 May 2018 | Landsat 8 OLI | 30 May–1 June 2018 | −5 |
2011 | 2013 | ||||||
---|---|---|---|---|---|---|---|
% Residue | All | Training | Validation | % Residue | All | Training | Validation |
<15 | 3 | 2 | 1 | <15 | 5 | 3 | 2 |
15–30 | 37 | 25 | 12 | 15–30 | 16 | 11 | 5 |
30–60 | 20 | 13 | 7 | 30–60 | 17 | 11 | 6 |
>60 | 4 | 3 | 1 | >60 | 9 | 6 | 3 |
2015 | 2018 | ||||||
% Residue | All | Training | Validation | % Residue | All | Training | Validation |
<15 | 5 | 3 | 2 | <15 | 6 | 4 | 2 |
15–30 | 14 | 9 | 5 | 15–30 | 22 | 15 | 7 |
30–60 | 17 | 11 | 6 | 30–60 | 20 | 13 | 7 |
>60 | 2 | 1 | 1 | >60 | 10 | 7 | 3 |
Accuracy | 2011 | 2013 | 2015 | 2018 | AVG | SD | Rank AVG |
---|---|---|---|---|---|---|---|
NDI7 | 52.75 | 46.83 | 56.48 | 66.32 | 55.60 | 7.08 | 1 |
STI | 54.57 | 39.59 | 60.87 | 63.51 | 54.64 | 9.27 | 2 |
NDTI | 53.41 | 39.25 | 61.36 | 63.4 | 54.36 | 9.49 | 3 |
NDI5 | 49.77 | 49.01 | 55.35 | 61.16 | 53.82 | 4.89 | 4 |
SVMC | 65.48 | 49.34 | 47.99 | 48.75 | 52.89 | 7.28 | 5 |
NDSVI | 50.45 | 49.39 | 55.57 | 54.27 | 52.42 | 2.57 | 6 |
RANDTR | 64.17 | 44.3 | 52.65 | 46.17 | 51.82 | 7.77 | 7 |
MAHL | 56.86 | 48.77 | 48.81 | 48.04 | 50.62 | 3.62 | 8 |
MAXLI | 60.47 | 50.07 | 50.33 | 39.34 | 50.05 | 7.47 | 9 |
SAM | 59.72 | 45.69 | 41.6 | 40.59 | 46.90 | 7.64 | 10 |
MINDIST | 53.96 | 41.42 | 44.31 | 44.02 | 45.93 | 4.77 | 11 |
Would Be Rank | |||||||
ALL | 63.28 | 52.46 | 59.20 | 52.32 | 59.42 | 4.31 | 1 |
FOUR | 67.08 | 52.90 | 62.63 | 64.81 | 61.86 | 5.40 | 1 |
AVG | 56.51 | 45.79 | 52.30 | 52.32 | 51.73 | ||
SD | 5.06 | 3.89 | 6.08 | 9.39 | 2.98 |
NDI5 | Reference | Kappa = 0 | ||||
---|---|---|---|---|---|---|
Classified | 0–15 | 15–30 | 30–60 | 60–100 | ||
0–15 | Z = 0 | |||||
15–30 | ||||||
30–60 | 476 | 1362 | 2180 | 430 | overall accuracy | |
60–100 | 49.01 | |||||
NDTI | Reference | Kappa = 0.1472 | ||||
Classified | 0–15 | 15–30 | 30–60 | 60–100 | ||
0–15 | 161 | 225 | 240 | 9 | Z = 13.32 | |
15–30 | 215 | 530 | 315 | 15 | ||
30–60 | 100 | 577 | 771 | 122 | overall accuracy | |
60–100 | 30 | 854 | 284 | 39.25 |
Kappa | 2011 | 2013 | 2015 | 2018 | AVG | SD | Rank AVG |
---|---|---|---|---|---|---|---|
STI | 0.1405 | 0.1487 | 0.3487 | 0.4426 | 0.27 | 0.13 | 1 |
NDTI | 0.1336 | 0.1472 | 0.3594 | 0.4402 | 0.27 | 0.13 | 2 |
SVMC | 0.2815 | 0.2453 | 0.1599 | 0.2431 | 0.23 | 0.04 | 3 |
NDI7 | 0.1317 | 0.044 | 0.2684 | 0.4543 | 0.22 | 0.15 | 4 |
MAHL | 0.1898 | 0.2347 | 0.1597 | 0.2472 | 0.21 | 0.04 | 5 |
RANDTR | 0.2272 | 0.1755 | 0.231 | 0.1957 | 0.21 | 0.02 | 6 |
MAXLI | 0.2353 | 0.2657 | 0.2329 | 0.0895 | 0.21 | 0.07 | 7 |
NDI5 | 0.1165 | 0.0000 | 0.2394 | 0.3421 | 0.17 | 0.13 | 8 |
NDSVI | 0.1684 | 0.0165 | 0.2253 | 0.2092 | 0.15 | 0.08 | 9 |
SAM | 0.2093 | 0.1979 | 0.0585 | 0.1491 | 0.15 | 0.06 | 10 |
MINDIST | 0.1057 | 0.0717 | 0.1109 | 0.1835 | 0.12 | 0.04 | 11 |
Would Be Rank | |||||||
ALL | 0.2522 | 0.2078 | 0.2932 | 0.4204 | 0.29 | 0.07 | 1 |
FOUR | 0.2926 | 0.2658 | 0.3835 | 0.4727 | 0.35 | 0.08 | 1 |
AVG | 0.1763 | 0.1407 | 0.2176 | 0.2724 | 0.2018 | ||
SD | 0.0540 | 0.0901 | 0.0875 | 0.1217 | 0.0459 |
Z | 2011 | 2013 | 2015 | 2018 | AVG | SD | Rank AVG |
---|---|---|---|---|---|---|---|
STI | 15.73 | 13.5 | 26.03 | 51.5 | 26.69 | 15.08 | 1 |
NDTI | 14.98 | 13.32 | 26.75 | 51.19 | 26.56 | 15.13 | 2 |
SVMC | 30.02 | 22.16 | 12.13 | 25.18 | 22.37 | 6.54 | 3 |
NDI7 | 14.56 | 3.08 | 19.89 | 50.11 | 21.91 | 17.38 | 4 |
MAHL | 21.06 | 20.7 | 12.48 | 27.66 | 20.48 | 5.38 | 5 |
MAXLI | 25.98 | 25.13 | 18.65 | 9.11 | 19.72 | 6.75 | 6 |
RANDTR | 22.76 | 15.96 | 17.54 | 19.79 | 19.01 | 2.56 | 7 |
NDI5 | 13.08 | 0.00 | 17.66 | 35.03 | 16.44 | 12.54 | 8 |
SAM | 22.4 | 17.81 | 5.00 | 15.98 | 15.30 | 6.39 | 9 |
NDSVI | 19.91 | 0.75 | 16.58 | 19.69 | 14.23 | 7.89 | 10 |
MINDIST | 11.96 | 6.32 | 9.45 | 19.44 | 11.79 | 4.85 | 11 |
Would Be Rank | |||||||
ALL | 26.56 | 15.81 | 21.12 | 44.56 | 27.01 | 10.82 | 1 |
FOUR | 26.14 | 19.37 | 23.88 | 45.91 | 28.83 | 10.16 | 1 |
AVG | 19.31 | 12.61 | 16.56 | 29.52 | 19.50 | ||
SD | 5.49 | 8.44 | 6.26 | 14.53 | 4.58 |
NDI5 | Reference | Kappa = 0.227 | |||||
---|---|---|---|---|---|---|---|
Classified | 0–15 | 15–30 | 30–60 | 60–100 | |||
0–15 | 1 | 2 | Z = 1.72 | ||||
15–30 | 16 | 7 | |||||
30–60 | 1 | 7 | 6 | 2 | overall accuracy | ||
60–100 | 1 | 55.81 |
Comparison | R2 |
---|---|
Training R2 vs. Training Accuracy | 0.7 |
Training R2 vs. Validate Accuracy | 0.528 |
Training Accuracy vs. Training RMSE | 0.758 |
Training Accuracy vs. Validate RMSE | 0.584 |
Training Accuracy vs. Validate R2 | 0.025 |
Validate Accuracy vs. Training RMSE | 0.33 |
Validate Accuracy vs. Validate RMSE | 0.072 |
Validate Accuracy vs. Validate R2 | 0.024 |
Training Accuracy vs. Validate Accuracy | 0.237 |
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Stern, A.J.; Daughtry, C.S.T.; Hunt, E.R., Jr.; Gao, F. Comparison of Five Spectral Indices and Six Imagery Classification Techniques for Assessment of Crop Residue Cover Using Four Years of Landsat Imagery. Remote Sens. 2023, 15, 4596. https://doi.org/10.3390/rs15184596
Stern AJ, Daughtry CST, Hunt ER Jr., Gao F. Comparison of Five Spectral Indices and Six Imagery Classification Techniques for Assessment of Crop Residue Cover Using Four Years of Landsat Imagery. Remote Sensing. 2023; 15(18):4596. https://doi.org/10.3390/rs15184596
Chicago/Turabian StyleStern, Alan J., Craig S. T. Daughtry, E. Raymond Hunt, Jr., and Feng Gao. 2023. "Comparison of Five Spectral Indices and Six Imagery Classification Techniques for Assessment of Crop Residue Cover Using Four Years of Landsat Imagery" Remote Sensing 15, no. 18: 4596. https://doi.org/10.3390/rs15184596