Employing Machine Learning for Detection of Invasive Species using Sentinel-2 and AVIRIS Data: The Case of Kudzu in the United States
Abstract
:1. Introduction
1.1. Invasive Species
1.2. Kudzu Vine and Its Phenology
1.3. Objectives and Research Questions
- How accurately can Kudzu be classified using different machine learning algorithms on Sentinel-2 multispectral images versus AVIRIS hyperspectral data?
- What differences will be seen using several seasonal multispectral images compared to a single hyperspectral image?
- How does combining multispectral and hyperspectral data affect overall accuracy and does resampling have any influence on the result?
2. Data and Method
2.1. Area of Interest
2.2. Data
2.2.1. Remote Sensing Data
Sentinel-2
AVIRIS
2.2.2. Ground Truth Data
2.3. Study Workflow
2.3.1. Implementation Medium with Narratives
- RF-mtry
- NN-size & decay
- SVM-gamma & cost
- NB-laplace, usekernel & adjust
- BLR–iterations
2.3.2. Big Data and Dimensionality Reduction
- Sentinel-2 stack (56 bands) = 10 components
- AVIRIS stack (224 bands) = 2 components
- Resampled stacks (280 bands) = 7 components
2.4. Internal and External Validation
3. Results
- Accuracy–Percentage of samples correctly classified
- Kappa–Accuracy accounted for being correct by chance
- Lower Classification Accuracy–Lower classification accuracy bounds of the 95% confidence interval
- Upper Classification Accuracy–Upper classification accuracy bounds of the 95% confidence interval
- Null Accuracy–Expected accuracy of predicting the most frequent class
- McNemarPValue–Describes which class the model is best as predicting, where 1 is equal performance on both classes, and 0 is superior performance on one class.
External Validation
Expanded Area Classification
4. Discussion
4.1. Are the Achieved Results Useful? What Would Their Purpose be and What Kind of Appropriate Action Should be Taken?
4.2. Could this Approach be Used with Different Plants or in Different Areas?
5. Conclusions
Guidelines and Recommendations
Supplementary Materials
Author Contributions
Funding
Conflicts of Interest
References
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Season | Start | End |
---|---|---|
Spring | 1 April 2017 | 29 May 2017 |
Summer | 1 June 2017 | 01 September 2017 |
Autumn | 1 October 2017 | 29 November 2017 |
Winter | 12 December 2017 | 07 February 2018 |
Stack | # of Bands | Bands | Resolution |
---|---|---|---|
Sentinel-2 | 56 * | 2, 3, 4, 5, 6, 7, 8, 8a, 11, 12, SAVI & 3x Tasseled cap | 10 & 20 m |
AVIRIS | 224 | All | 16.6 m |
Resample (down) | 280 | Sentinel-2 + AVIRIS | 10 m |
Resample (up) | 280 | Sentinel-2 + AVIRIS | 16.6 m |
Image Stack | Classifier | Acc. | Kappa | Acc. Lower | Acc. Upper | Acc. Null | McNemarPValue |
---|---|---|---|---|---|---|---|
Sentinel-2 Multispectral Resolution: 10 m | BLR | 0.96 | 0.83 | 0.94 | 0.97 | 0.85 | 0.17 |
NB | 0.95 | 0.80 | 0.93 | 0.96 | 0.83 | 0.00 | |
NN | 0.96 | 0.86 | 0.94 | 0.97 | 0.83 | 0.12 | |
RF | 0.98 | 0.91 | 0.96 | 0.99 | 0.83 | 0.01 | |
SVM | 0.98 | 0.95 | 0.97 | 0.99 | 0.83 | 0.75 | |
AVIRIS Hyperspectral Resolution: 16.6 m | BLR | 0.94 | 0.72 | 0.92 | 0.96 | 0.86 | 0.00 |
NB | 0.87 | 0.51 | 0.84 | 0.90 | 0.83 | 0.28 | |
NN | 0.97 | 0.90 | 0.96 | 0.98 | 0.83 | 0.36 | |
RF | 0.96 | 0.85 | 0.94 | 0.97 | 0.83 | 1.00 | |
SVM | 0.92 | 0.70 | 0.89 | 0.94 | 0.83 | 1.00 | |
Resample (down) Resolution: 10 m | BLR | 0.96 | 0.83 | 0.94 | 0.97 | 0.85 | 0.17 |
NB | 0.95 | 0.80 | 0.93 | 0.96 | 0.83 | 0.00 | |
NN | 0.96 | 0.86 | 0.94 | 0.97 | 0.83 | 0.12 | |
RF | 0.98 | 0.91 | 0.96 | 0.99 | 0.83 | 0.01 | |
SVM | 0.98 | 0.95 | 0.97 | 0.99 | 0.83 | 0.75 | |
Resample (up) Resolution: 16.6 m | BLR | 0.95 | 0.82 | 0.93 | 0.97 | 0.84 | 1.00 |
NB | 0.95 | 0.82 | 0.93 | 0.97 | 0.93 | 0.01 | |
NN | 0.98 | 0.95 | 0.97 | 0.99 | 0.83 | 0.75 | |
RF | 0.98 | 0.91 | 0.96 | 0.99 | 0.83 | 0.01 | |
SVM | 0.97 | 0.89 | 0.95 | 0.98 | 0.83 | 0.01 |
Image Stack | Classifier | Confusion Matrix | Error 1 | Error 2 | Acc. 1 | Acc. 2 | |||
---|---|---|---|---|---|---|---|---|---|
Sentinel-2 | Class | 0 | 1 | ||||||
Multispectral | SVM | 0 | 542 | 9 | 551 | 0.009 | 0.016 | 0.991 | 0.984 |
Resolution: 10 m | 1 | 5 | 100 | 105 | 0.083 | 0.048 | 0.917 | 0.952 | |
total | 547 | 109 | 656 | ||||||
AVIRIS | Class | 0 | 1 | ||||||
Hyperspectral | NN | 0 | 535 | 7 | 542 | 0.022 | 0.013 | 0.978 | 0.987 |
Resolution: 16.6 m | 1 | 12 | 102 | 114 | 0.064 | 0.105 | 0.936 | 0.895 | |
total | 547 | 109 | 656 | ||||||
Resampled (up) | Class | 0 | 1 | ||||||
Resolution: 10 m | SVM | 0 | 541 | 4 | 545 | 0.011 | 0.007 | 0.989 | 0.993 |
1 | 6 | 105 | 111 | 0.037 | 0.054 | 0.963 | 0.946 | ||
total | 547 | 109 | 656 | ||||||
Resampled (down) | Class | 0 | 1 | ||||||
Resolution: 16.6 m | NN | 0 | 541 | 4 | 545 | 0.011 | 0.007 | 0.989 | 0.993 |
1 | 6 | 105 | 111 | 0.037 | 0.054 | 0.963 | 0.946 | ||
total | 547 | 109 | 656 |
Grid | Registry Points | Detected Points | Accuracy |
---|---|---|---|
Sentinel | 55 | 35 | 64% |
AVIRIS | 55 | 10 | 18% |
Combined, 10 m | 55 | 33 | 60% |
Combined, 16 m | 55 | 36 | 66% |
Testing the BLR algorithm without PCA, using only hyper-spectral data | |||||||
Class | 0 | 1 | Omission e. | Commission e. | Producer acc. | User acc. | |
0 | 529 | 3 | 532 | 0.013 | 0.006 | 0.987 | 0.994 |
1 | 7 | 103 | 110 | 0.028 | 0.064 | 0.972 | 0.936 |
total | 536 | 106 | 642 | ||||
Testing the NB algorithm without PCA, using only hyper-spectral data | |||||||
Class | 0 | 1 | Omission e. | Commission e. | Producer acc. | User acc. | |
0 | 441 | 41 | 482 | 0.192 | 0.085 | 0.808 | 0.915 |
1 | 105 | 68 | 173 | 0.376 | 0.607 | 0.624 | 0.393 |
total | 546 | 109 | 655 | ||||
Testing the SVM algorithm without PCA, using only hyper-spectral data | |||||||
Class | 0 | 1 | Omission e. | Commission e. | Producer acc. | User acc. | |
0 | 543 | 0 | 543 | 0.005 | 0.000 | 0.995 | 1.000 |
1 | 3 | 109 | 112 | 0.000 | 0.027 | 1.000 | 0.973 |
total | 546 | 109 | 655 |
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Jensen, T.; Seerup Hass, F.; Seam Akbar, M.; Holm Petersen, P.; Jokar Arsanjani, J. Employing Machine Learning for Detection of Invasive Species using Sentinel-2 and AVIRIS Data: The Case of Kudzu in the United States. Sustainability 2020, 12, 3544. https://doi.org/10.3390/su12093544
Jensen T, Seerup Hass F, Seam Akbar M, Holm Petersen P, Jokar Arsanjani J. Employing Machine Learning for Detection of Invasive Species using Sentinel-2 and AVIRIS Data: The Case of Kudzu in the United States. Sustainability. 2020; 12(9):3544. https://doi.org/10.3390/su12093544
Chicago/Turabian StyleJensen, Tobias, Frederik Seerup Hass, Mohammad Seam Akbar, Philip Holm Petersen, and Jamal Jokar Arsanjani. 2020. "Employing Machine Learning for Detection of Invasive Species using Sentinel-2 and AVIRIS Data: The Case of Kudzu in the United States" Sustainability 12, no. 9: 3544. https://doi.org/10.3390/su12093544