Improved Cropland Abandonment Detection with Deep Learning Vision Transformer (DL-ViT) and Multiple Vegetation Indices
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Datasets
2.3. Software
2.4. Image Preprocessing
- (1)
- The first step involves the creation of subsets of the original images to focus specifically on the study area of interest, allowing for efficient computational processing and analysis of the relevant image data.
- (2)
- To generate multispectral imagery for our study, a layer-stacked image was assembled by combining georeferenced images. The resulting imagery consists of four bands: B, G, R, and NIR.
- (3)
- A color-normalized (Brovey) sharpening technique [29] was applied to convert the 8 m resolution GF-6 satellite images to 2 m resolution for sample labeling and results verification of the classifier. The Brovey Transform sharpens the multispectral image by combining it with the panchromatic image using a weighted ratio. The basic idea is to emphasize the spatial details from the panchromatic image while preserving the spectral information from the multispectral image. The technique is based on the following mathematical expression (Equation (1)):
- (4)
- Since the GF-6 images sometimes did not cover the entire study area, mosaicking was conducted by merging two or more images of the same season to obtain a comprehensive view of the entire study area. This ensured that all relevant features and land cover patterns were captured in the analysis.
- (5)
- To ensure consistency and compatibility with other datasets, the images were projected to a standard coordinate system. This step facilitated seamless integration and comparison with other geospatial data.
- (6)
- Additionally, in order to derive terrain-related information, slope calculation was performed in degrees. This allowed for assessing topographic characteristics and their potential influence on land cover dynamics.
- (7)
- To achieve accurate reflectance values and compensate for atmospheric effects, Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes (FLAASH) atmospheric correction [30] was employed. The primary objective of FLAASH is to eliminate the atmospheric interference in remote sensing data, thereby enhancing data accuracy and reliability for a wide spectrum of applications. This correction accounted for atmospheric conditions and improved the reliability of subsequent analyses. FLAASH works according to the following mathematical formulas as in Equations (2) and (3).
- (8)
- Lastly, radiometric calibration was carried out to normalize the pixel values across the image, ensuring consistent and accurate measurements.
2.5. Vegetation Indices (VIs)
2.6. Optimizing Image Selection for Classifier
2.7. Classes and Image Labeling
2.8. Cropland Abandonment Rate
2.9. Vision Transformer Model (ViT)
2.10. Performance Metrics
2.11. Comparison with Other Methods
- (1)
- Deep convolutional neural network (DCNN) [41] can automatically construct the training dataset and execute the classification of multispectral satellite images through deep neural networks.
- (2)
- (3)
- Vegetation–soil–pigment indices and synthetic-aperture radar (SAR) time-series images (VSPS) [44] comprise a multi-temporal indicator-based, large-area mapping framework, which facilitates the automatic identification of active croplands.
- (4)
- Redundancy analysis (RDA) [17] entails a direct gradient analysis methodology that succinctly captures the linear relationship between the response variable components of a cluster of redundant explanatory variables. This technique can quantify the contribution rate of determinants to the phenomenon of cropland abandonment.
3. Results
3.1. Choosing the Optimal Multiband Composite Image for ViT
3.2. Inter-Annual Land Use Dynamics and Assessing Classification Accuracy
Classifier Performance Evaluation
3.3. Spatiotemporal Analysis of Cropland Abandonment: Distribution, Magnitude, Patterns, and Trends
3.4. Explanatory Variables
3.5. Comparative Analysis of Contemporary Approaches for Cropland Abandonment Detection
3.6. Comparison of Employed VIs
4. Discussion
4.1. Understanding Spatiotemporal Dynamics and Factors of Cropland Abandonment: Methodological Advancements and Insights
4.2. Contrasting Trends in Cropland Abandonment
4.3. Shortcomings and Limitations
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A
Appendix B
Appendix C
VIs | Spectral Bands | Sensitivity to Vegetation | Robustness to Noise | Ease of Calculation | Studies |
---|---|---|---|---|---|
Normalized Difference Vegetation Index (NDVI) | R and NIR | High | Moderate | Easy | [61] |
Dead Fuel Index (DFI) | R, NIR, and SWIR | Low | Moderate | Moderate | [49] |
Normalized Difference Vegetation Index (NDVI) | R and NIR | High | Moderate | Easy | |
Normalized Difference Water Index (NDWI) | NIR and SWIR | High | Low | High | [17] |
Normalized Difference Soil Index (NDSI) | R and SWIR | High | High | Moderate | |
Enhanced Vegetation Index (EVI2) | R, NIR, and SWIR | Very High | Low | High | |
Enhanced Vegetation Index (EVI) | R, NIR, and SWIR | Very High | High | Moderate | [13] |
Normalized Difference Vegetation Index (NDVI) | R and NIR | High | Moderate | Easy | [21] |
Normalized Difference Snow Index (NDSI) | NIR and SWIR | High | High | Easy | |
Dry Bare-Soil Index (DBSI) | R and NIR | High | High | Moderate | [62] |
Enhanced Vegetation Index (EVI2) | R, NIR, and SWIR | Very High | Low | High | [63] |
Normalized Difference Vegetation Index (NDVI) | R and NIR | High | Moderate | Easy | [23] |
Normalized Difference Vegetation Index (NDVI) | R and NIR | High | Moderate | Easy | [24] |
Normalized Difference Vegetation Index (NDVI) | R and NIR | High | Moderate | Easy | [25] |
Green Normalized Difference Vegetation Index (GNDVI) | G and NIR | High | Moderate | Easy | |
Enhanced Vegetation Index (EVI) | R, NIR, and SWIR | Very High | High | Moderate | |
Normalized Difference Infrared Index (NDII) | R and NIR | High | High | Easy | |
Normalized Burn Ratio (NBR) | SWIR and NIR | High | Low | Easy | |
Normalized Difference Building Index (NDBI) | NIR and SWIR | High | Moderate | Easy | [22] |
Normalized Difference Vegetation Index (NDVI) | R and NIR | High | Moderate | Easy | |
Normalized Difference Vegetation Index (NDVI) | R and NIR | High | Moderate | Easy | [18] |
Normalized Difference Vegetation Index (NDVI) | R and NIR | High | Moderate | Easy | Proposed VIs |
Soil-adjusted Vegetation Index (SAVI) | R and NIR | High | Low | Easy | |
Modified Soil-adjusted Vegetation Index (MSAVI) | R and NIR | High | High | Easy | |
Perpendicular Vegetation Index (PVI) | G and NIR | High | High | Easy | |
Red-Edge Triangulated Vegetation Index (RTVICore) | R, NIR, and SWIR | Very High | High | Moderate | |
Simple Ratio (SR) | R and NIR | High | Low | Easy |
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Features | GF-2 | GF-6 | ||
---|---|---|---|---|
Frequency (GHz) | 8–12.5 | 4–8 | ||
Temporal Resolution (d) | 5 | 1 (PMC)–4 (WFV) | ||
Altitude (km) | 631 | 634 km × 647 km | ||
Swath Width (km) | One camera 23 km (45 km with two combined cameras) | 95 (PMC), 860 (WFV) | ||
Spatial Resolution (m) | PAN: 0.8 | MS: 3.2 | PAN: 2 | MS: 8 |
Bands/Wavelength (μm) | Pan: 0.45–90 | B1/blue: 0.45–0.52 | Pan: 0.45–90 | B1/blue: 0.45–0.52 |
B2/green: 0.52–0.59 | B2/green: 0.52–0.6 | |||
B3/red: 0.63–0.69 | B3/red: 0.63–0.69 | |||
B4/NIR: 0.77–0.89 | B4/NIR: 0.76–0.90 | |||
Cloud Coverage (%) | 5–10 | 5–10 | ||
Date of Image Accusation (year/month/day) | 2020/09/07 and 2021/10/18 | 2019/04/09, 2019/08/10, 2020/06/02, 2021/04/11, 2022/05/03, 2022/10/10, and 2023/04/19 |
VIs | Formula | Reference |
---|---|---|
Normalized Difference Vegetation Index (NDVI) | [33] | |
Soil-Adjusted Vegetation Index (SAVI) | [34] | |
Transformed Soil-Adjusted Vegetation Index (TSAVI) | [35] | |
Perpendicular Vegetation Index (PVI) | [36] | |
Red-Edge Triangulated Vegetation Index (RTVICore) | [37] | |
Simple Ratio (SR) | [38] |
VIs | F1 Score | OA |
---|---|---|
NDVI | 0.82 | 0.82 |
SAVI | 0.83 | 0.83 |
TSAVI | 0.82 | 0.83 |
PVI | 0.84 | 0.84 |
RTVICore | 0.84 | 0.83 |
SR | 0.84 | 0.82 |
NDVI, SAVI, PVI | 0.86 | 0.87 |
All VIs | 0.88 | 0.91 |
Precision | Recall | F1 Score | OA | |
---|---|---|---|---|
Spring 2019 | 0.9032 | 0.8858 | 0.8885 | 0.9114 |
Fall 2019 | 0.8696 | 0.8945 | 0.8767 | 0.9328 |
Spring 2020 | 0.8014 | 0.8790 | 0.8047 | 0.9042 |
Fall 2020 | 0.8756 | 0.8574 | 0.8497 | 0.9078 |
Spring 2021 | 0.8995 | 0.8998 | 0.8986 | 0.9386 |
Fall 2021 | 0.8409 | 0.8877 | 0.8580 | 0.9139 |
Spring 2022 | 0.8914 | 0.8888 | 0.8839 | 0.9454 |
Fall 2022 | 0.8319 | 0.8982 | 0.8579 | 0.9332 |
Spring 2023 | 0.8611 | 0.8784 | 0.8683 | 0.9416 |
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Share and Cite
Karim, M.; Deng, J.; Ayoub, M.; Dong, W.; Zhang, B.; Yousaf, M.S.; Bhutto, Y.A.; Ishfaque, M. Improved Cropland Abandonment Detection with Deep Learning Vision Transformer (DL-ViT) and Multiple Vegetation Indices. Land 2023, 12, 1926. https://doi.org/10.3390/land12101926
Karim M, Deng J, Ayoub M, Dong W, Zhang B, Yousaf MS, Bhutto YA, Ishfaque M. Improved Cropland Abandonment Detection with Deep Learning Vision Transformer (DL-ViT) and Multiple Vegetation Indices. Land. 2023; 12(10):1926. https://doi.org/10.3390/land12101926
Chicago/Turabian StyleKarim, Mannan, Jiqiu Deng, Muhammad Ayoub, Wuzhou Dong, Baoyi Zhang, Muhammad Shahzad Yousaf, Yasir Ali Bhutto, and Muhammad Ishfaque. 2023. "Improved Cropland Abandonment Detection with Deep Learning Vision Transformer (DL-ViT) and Multiple Vegetation Indices" Land 12, no. 10: 1926. https://doi.org/10.3390/land12101926
APA StyleKarim, M., Deng, J., Ayoub, M., Dong, W., Zhang, B., Yousaf, M. S., Bhutto, Y. A., & Ishfaque, M. (2023). Improved Cropland Abandonment Detection with Deep Learning Vision Transformer (DL-ViT) and Multiple Vegetation Indices. Land, 12(10), 1926. https://doi.org/10.3390/land12101926