Nominal 30-m Cropland Extent Map of Continental Africa by Integrating Pixel-Based and Object-Based Algorithms Using Sentinel-2 and Landsat-8 Data on Google Earth Engine
Abstract
:1. Introduction
2. Materials
2.1. Study Area
2.2. Cloud-Free Satellite Imagery Composition at 30-m Resolution
2.3. Reference Training Samples
2.4. Reference Validation Sample Polygons
3. Methods
3.1. Overview of the Methodology
- 30-m mosaic (11 bands) was built using Sentinel-2 and Landsat-8 data (Section 2.2) for period 1 (January–June, 2016) and period 2 (July–December, 2015);
- Random Forest and Support Vector Machines (Section 3.1) were used to classify input bands for croplands versus non-croplands;
- Using same bands as inputs, recursive hierarchical segmentation (Section 3.2) was carried out in 1 by 1 grid units on NASA pleiades supercomputer;
- The pixel-based classification was integrated with object-based segmentation into cropland extent map (Section 3.3) for further assessment (Section 3.4)
- We compared derived cropland areas with country-wise statistics from other sources in Section 3.5 and explored the consistency between GFSAD30AFCE map and other reference maps in Section 3.6.
- 30-m Cropland extent product is released through the NASA Land Processes Distributed Active Archive Center (LP DAAC) at: https://doi.org/10.5067/MEaSUREs/GFSAD/GFSAD30AFCE.001 and can be viewed at: https://croplands.org.
3.2. Pixel-Based Classifier: Random Forest (RF) and Support Vector Machine (SVM)
3.3. Recursive Hierarchical Image Segmentation (RHSeg)
3.4. Integration of Pixel-Based Classification and Object-Based Segmentation
3.5. Accuracy Assessment
- Stratified, random and balanced sampling: The African continent has been divided into 7 refined agro-ecological zones or RAEZs (Figure 1) for stratified random sampling. Due to a large crop diversity across RAEZ’s (Figure 1) there is high variability in their growing periods and crop distribution. Therefore, to maintain balanced sampling for each zone, samples have been randomly distributed in each zone. The question of how many samples are sufficient to achieve statistically valid accuracy results is described in next point below.
- Sample Size: The sample size has been chosen based on the analysis of incrementing minimum number of samples. Initially, first 50 samples were chosen as minimum number for all the 7 RAE’s and then incremented in steps with another 50 more samples. A few RAEZ’s in Africa have little cropland distribution so that 50 samples were enough to achieve a valid assessment. However, other RAEZ’s needed up to 250 samples for their assessment. Beyond 250 samples, accuracies of all RAEZ’s become asymptotic. Overall, for Africa, total 1754 samples were used from 7 RAEZ’s.
- Sample unit: The sample unit for a given validation sample must be a group of pixels (at least 3 × 3 pixels of 30-m resolution) in order to minimize the impact of positional accuracy [88]. This sampling unit is a 3 × 3 homogeneous window containing one class. If a sample at this step was recognized to be a mixed patch of cropland and non-cropland, it had to be excluded from the validation dataset in the accuracy assessment since heterogeneous windows were not considered, however excluding them is the best practical choice for accuracy assessment.
- Sampling was balanced to keep the proportion of the cropland versus non-cropland samples close to the proportion of the cropland versus non-cropland area from the product layer to be validated.
- Validation samples are created independently from training samples described in Section 2.3, by a different team.
3.6. Calculation of Actual Cropland Areas and Comparison with Areas from Other Sources
3.7. Consistency between GFSAD30AFCE Product and Four Existing Crop Maps
- Global Land Cover Map for 2009 (GlobCover 2009) [39]. Class 11, 14 were reclassified as “croplands” and other land cover classes were reclassified as “non-croplands”;
- Global rainfed, irrigated, and paddy croplands map GRIPC [16]. All agricultural classes include rainfed, irrigated and paddy were combined as “croplands” and other classes were “non-croplands”;
- 30-m global land-cover map FROM-GLC [48]. Level 1 class 10 and Level 2 Bare-cropland 94 were combined as “croplands” and other classes were “non-croplands”; and
- Global land cover GLC30 [45]. Class 10 was combined as “croplands” and other classes were “non-croplands”.
4. Results
4.1. GFSAD30AFCE Product
4.2. GFSAD30AFCE Product Accuracies
4.3. Cropland Areas and Comparison with Statistics from Other Sources
- Different definition of “croplands” class: GFSAD30AFCE product as per definition, includes all agricultural annual standing croplands, cropland fallows, and permanent plantation crops whereas cropland areas reported in statistics may not include cropland fallows;
- Different time: GFSAD30AFCE incorporate the latest cultivated area in 2015–2016 as well as the croplands fallows whereas country reported cropland areas may happen in other years.
4.4. Consistency between GFSAD30AFCE Product and Four Existing Crop Maps
5. Discussion
6. Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
Abbreviations
GFSAD30: | Global Food Security Analysis-Support Data Project |
GEE: | Google Earth Engine |
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**ong, J.; Thenkabail, P.S.; Tilton, J.C.; Gumma, M.K.; Teluguntla, P.; Oliphant, A.; Congalton, R.G.; Yadav, K.; Gorelick, N. Nominal 30-m Cropland Extent Map of Continental Africa by Integrating Pixel-Based and Object-Based Algorithms Using Sentinel-2 and Landsat-8 Data on Google Earth Engine. Remote Sens. 2017, 9, 1065. https://doi.org/10.3390/rs9101065
**ong J, Thenkabail PS, Tilton JC, Gumma MK, Teluguntla P, Oliphant A, Congalton RG, Yadav K, Gorelick N. Nominal 30-m Cropland Extent Map of Continental Africa by Integrating Pixel-Based and Object-Based Algorithms Using Sentinel-2 and Landsat-8 Data on Google Earth Engine. Remote Sensing. 2017; 9(10):1065. https://doi.org/10.3390/rs9101065
Chicago/Turabian Style**ong, Jun, Prasad S. Thenkabail, James C. Tilton, Murali K. Gumma, Pardhasaradhi Teluguntla, Adam Oliphant, Russell G. Congalton, Kamini Yadav, and Noel Gorelick. 2017. "Nominal 30-m Cropland Extent Map of Continental Africa by Integrating Pixel-Based and Object-Based Algorithms Using Sentinel-2 and Landsat-8 Data on Google Earth Engine" Remote Sensing 9, no. 10: 1065. https://doi.org/10.3390/rs9101065