Production of a Dynamic Cropland Mask by Processing Remote Sensing Image Series at High Temporal and Spatial Resolutions
"> Figure 1
<p>Number of images with less than 90% cloud cover for the different Test Sites. Mo: Morocco, Mad: Madagascar, Ar: Argentina, Be: Belgium, Ch: China, So: South Africa, Uk: Ukraine, US: United States (Maricopa), Fr: France, Ru: Russia, Pa: Pakistan, Bu: Burkina Faso.</p> "> Figure 2
<p>Percentage of images covered by clouds. Mo: Morocco, Mad: Madagascar, Ar: Argentina, Be: Belgium, Ch: China, So: South Africa, Uk: Ukraine, US: United States (Maricopa), Fr: France, Ru: Russia, Pa: Pakistan, Bu: Burkina Faso.</p> "> Figure 3
<p>Number of pixels from Sentinel proxy data, having a spatial resolution of 20 m, used in the validation of cropland masks. Mo: Morocco, Mad: Madagascar, Ar: Argentina, Be: Belgium, Ch: China, So: South Africa, Uk: Ukraine, US: United States (Maricopa), Fr: France, Ru: Russia, Pa: Pakistan, Bu: Burkina Faso.</p> "> Figure 4
<p>The proposed dynamic classification system.</p> "> Figure 5
<p>Supervised classification strategy for pixel-based approach.</p> "> Figure 6
<p>The proposed object-based classification system with a pre-filtering task.</p> "> Figure 7
<p>MS segmentation results obtained for different agricultural landscapes. The MS segmentation algorithm was applied to the six first principal components for all the sites. The same set of MS parameters was used for the four examples : <math display="inline"> <mrow> <msub> <mi>h</mi> <mi>s</mi> </msub> <mo>=</mo> <mn>10</mn> </mrow> </math>, <math display="inline"> <mrow> <msub> <mi>h</mi> <mi>r</mi> </msub> <mo>=</mo> <mn>0</mn> <mo>.</mo> <mn>65</mn> </mrow> </math> and <math display="inline"> <mrow> <msub> <mi>N</mi> <mrow> <mi>m</mi> <mi>i</mi> <mi>n</mi> </mrow> </msub> <mo>=</mo> <mn>10</mn> </mrow> </math>. From left to right, the first row contains the Argentina, France, Pakistan, Maricopa (US) true color RGB compositions. The second row contains the MS segmentation results obtained.</p> "> Figure 8
<p>The proposed object-based classification system with a post-processing task.</p> "> Figure 9
<p>On the left, the 2x2 confusion matrix displaying the number of correct and incorrect predictions made by the classifier. On the right, equations of the classical quality measures used in the following.</p> "> Figure 10
<p>Precision and Recall evaluation for the cropland areas for the different data sets in 12 sites (<b>a–l</b>). Three methodologies are compared: Pixel-Based (PB), Object-Based with a Pre-filtering task (P-OB) and Object-Based with a Post-filtering task (OB-P)</p> "> Figure 11
<p>Precision and Recall evaluation for the no-crop areas for the different data sets in 12 sites (<b>a–l</b>). Three methodologies are compared: Pixel-Based (PB), Object-Based with a Pre-filtering task (P-OB) and Object-Based with a Post-filtering task (OB-P)</p> "> Figure 12
<p>Kappa and Overall Accuracy (OA) evaluation for the cropland masks for the different data sets in 12 sites (<b>a–l</b>). Three methodologies are compared: Pixel-Based (PB), Object-Based with a Pre-filtering task (P-OB) and Object-Based with a Post-filtering task (OB-P)</p> "> Figure 13
<p>From top to bottom: Cropland mask results obtained after 12 months for test sites in Argentina, Belgium, BurkinaFaso, Maricopa, Pakistan, and France. From left to right: true color RGB composition, PB cropland results, P-OB cropland results and OB-P cropland results. Crops appear in white whereas no-crop areas appear in black.</p> ">
Abstract
:1. Introduction
2. Sentinel-2 Proxy Data
3. Dynamic Global Cropland Mask Processing Chain
3.1. Pixel-Based Supervised Classification (PB)
3.1.1. Computation of the Spectral Indices
3.1.2. Feature Extraction
Notation | Description |
---|---|
Maximum value | |
Mean value | |
Standard deviation value | |
Maximum difference in a sliding temporal neighborhood having a size w (default value w=2) | |
Minimum difference in a sliding temporal neighborhood having a size w (default value w=2). | |
Difference between and value estimating the transition jump | |
Maximum mean value in a sliding temporal neighborhood having a size w (default value w=2). | |
Length of the flat interval containing the peak area associated to value. | |
Surface of the flat interval area containing the peak value associated to | |
Maximum surface of the first positive derivative interval | |
Length of the first positive derivative interval associated to value | |
Rate of the first positive derivative interval associated to | |
Maximum surface of the first negative derivative interval | |
Length of the first negative derivative interval associated to value. | |
Rate of senescence of the first negative derivative interval associated to | |
Flag detecting if there is a bare soil transition before the positive derivative | |
Flag detecting if there is a bare soil transition after the negative derivative |
Notation | Description |
---|---|
Maximum value | |
Minimum value | |
Mean value | |
Standard deviation value | |
Median value |
Notation | Description |
---|---|
Maximum value | |
Minimum value | |
Mean value | |
Standard deviation value | |
Median value |
3.1.3. Supervised Classification: Random Forest
3.2. Object-Based Supervised Classification with a Pre-Filtering Task (P-OB)
Image Segmentation
3.3. Object-Based Supervised Classification with Post-Filtering Task (OB-P)
4. Validation Approach
5. Experimental Results and Discussion
5.1. Cropland Area Validation
5.2. No-Crop Area Validation
5.3. Global Evaluation
6. Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
References
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Valero, S.; Morin, D.; Inglada, J.; Sepulcre, G.; Arias, M.; Hagolle, O.; Dedieu, G.; Bontemps, S.; Defourny, P.; Koetz, B. Production of a Dynamic Cropland Mask by Processing Remote Sensing Image Series at High Temporal and Spatial Resolutions. Remote Sens. 2016, 8, 55. https://doi.org/10.3390/rs8010055
Valero S, Morin D, Inglada J, Sepulcre G, Arias M, Hagolle O, Dedieu G, Bontemps S, Defourny P, Koetz B. Production of a Dynamic Cropland Mask by Processing Remote Sensing Image Series at High Temporal and Spatial Resolutions. Remote Sensing. 2016; 8(1):55. https://doi.org/10.3390/rs8010055
Chicago/Turabian StyleValero, Silvia, David Morin, Jordi Inglada, Guadalupe Sepulcre, Marcela Arias, Olivier Hagolle, Gérard Dedieu, Sophie Bontemps, Pierre Defourny, and Benjamin Koetz. 2016. "Production of a Dynamic Cropland Mask by Processing Remote Sensing Image Series at High Temporal and Spatial Resolutions" Remote Sensing 8, no. 1: 55. https://doi.org/10.3390/rs8010055