Mapping Cropping Practices of a Sugarcane-Based Cropping System in Kenya Using Remote Sensing
"> Figure 1
<p>Location of Kibos-Miwani sugar zone (Data source: ©2015 Google, USGS/NASA Landsat).</p> "> Figure 2
<p>Sugarcane cropping calendar in western Kenya.</p> "> Figure 3
<p>Ground survey points collected in Kibos-Miwani. The field survey was conducted from 14 to 18 October 2013. Seventy-five percent of the points were used for land use classification training the other 25% were used for classification validation.</p> "> Figure 4
<p>Landsat 8 NDVI colored composite image (R: May 2013; G: September 2013; B: November 2013). The image is located at 34°30′E–35°E and 0°S–0°45′S.</p> "> Figure 5
<p>The classified Landsat image of Kibos-Miwani showing six land cover classes: five classes of “Sugarcane” based on different stages (represented by #) of the crop, and one class of “Other”.</p> "> Figure 6
<p>A zoom on the classified Landsat image of Kibos-Miwani sugar zone in area “A”.</p> "> Figure 7
<p>A classified Landsat image of Kibos-Miwani sugar zone after re-coding of all sugarcane and other pixels in two classes: sugarcane and other cover.</p> "> Figure 8
<p>The sugarcane image, obtained using a majority filter applied on the classified Landsat time series (<a href="#remotesensing-07-14428-f007" class="html-fig">Figure 7</a>).</p> "> Figure 9
<p>The bars correspond to the frequency distribution of NDWI differences between the mean field values measured before and after the harvest, for the green and burnt modes. The lines correspond to Gauss-fitted frequencies.</p> "> Figure 10
<p>Map of the sugarcane harvest mode and other cover in Kibos-Miwani.</p> ">
Abstract
:1. Introduction
2. Material and Methods
2.1. Study Area
2.2. Data
2.2.1. Ground Survey Data
- n0 = sample size;
- Z2 = abscissa of the normal curve that cuts off an area of desired confidence level at the tails;
- e = desired level of precision;
- p = maximum variability of farmers that will be studied;
- q = 1 − p.
- -
- The ground survey data were composed of 831 observations, where 530 fields were sugarcane and 301 fields were other land cover. These fields were encoded during a ground survey conducted between 14 and 18 October 2013. Figure 3 illustrates the location of the surveyed fields in Kibos-Miwani zone, showing location of those used for training (75%) and those used for validation (25%).
- -
- The Kibos Sugar Factory database was composed of 449 fields, where 270 fields were sugarcane fields and 179 fields were other land cover. These data were adopted from the existing land use data set compiled on 15 August 2013 by Kibos Sugar factory. Attributes for these fields (planting and harvesting date) were entered in our database in accordance with the factory office record. The factory data was relevant since it was collected within the study time frame of this research.
2.2.2. Satellite Data and Preprocessing
Bands | Wavelength (micrometers) | Resolution (m) |
---|---|---|
Band 4–Red | 0.64–0.67 | 30 |
Band 5-Near Infrared (NIR) | 0.85–0.88 | 30 |
Band 6-ShortWave InfraRed (SWIR 1) | 1.57–1.65 | 30 |
Band 8-Panchromatic | 0.50–0.68 | 15 |
2.3. Methods
2.3.1. Landsat 8 Image Analysis
2.3.2. Mapping Cropping Practices
Class Name | Age (Months) | Number of Points |
---|---|---|
Sugarcane #1 | 0–2 | 131 |
Sugarcane #2 | 3–5 | 129 |
Sugarcane #3 | 6–8 | 150 |
Sugarcane #4 | 9–11 | 100 |
Sugarcane #5 | Over 12 | 90 |
Other | -- | 360 |
All | -- | 960 |
- -
- For the sugarcane map, we evaluated accuracy of the classification by creating a confusion matrix based on the 25% of the unused ground data (320 points).
- -
- For the harvest mode map, we evaluated accuracy of the classification by creating a confusion matrix based on the 25% of the unused ground data (200 points).
3. Results and Discussion
3.1. Spatial Variability
3.2. Sugarcane Classification
-- | Classification | ||||||
---|---|---|---|---|---|---|---|
Sugarcane | Other | Unclassified | Line Total | Producer’s Accuracy | Omission Error | ||
Ground Truth | Sugarcane | 160 | 22 | 18 | 200 | 80.0% | 20.0% |
Other | 7 | 108 | 5 | 120 | 90.0% | 10.0% | |
Row total | 167 | 130 | 23 | 320 | -- | -- | |
User’s accuracy | 95.8% | 83.1% | -- | -- | 83.8% | -- | |
Commission error | 4.2% | 16.9% | -- | -- | -- | -- |
3.3. Sugarcane Harvest Mode Classification
-- | NDWI_Diff | NDVI_Diff |
---|---|---|
Mean Green harvest | 0.10 | 0.26 |
Std Green harvest | 0.06 | 0.08 |
Mean Burnt harvest | 0.41 | 0.24 |
Std Burnt harvest | 0.12 | 0.07 |
p-value (difference Green/Burnt harvest) | 0.000 | 0.345 |
Harvest Mode | Total (ha) | % Coverage |
---|---|---|
Green | 2284 | 25.5 |
Burnt | 6672 | 74.5 |
-- | Classification | |||||
---|---|---|---|---|---|---|
Green Harvest | Burnt Harvest | Line Total | Producer’s Accuracy | Omission Error | ||
Ground Truth | Green Harvest | 90 | 8 | 98 | 91.8% | 8.2% |
Burnt Harvest | 12 | 90 | 102 | 88.2% | 11.8% | |
Row total | 102 | 98 | 200 | -- | -- | |
User’s Accuracy | 88.2% | 91.8% | -- | 90.0% | -- | |
Commission error | 11.8% | 8.2% | -- | -- | -- |
4. Conclusions and Perspectives
Acknowledgments
Author Contributions
Conflicts of Interest
References
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Mulianga, B.; Bégué, A.; Clouvel, P.; Todoroff, P. Mapping Cropping Practices of a Sugarcane-Based Cropping System in Kenya Using Remote Sensing. Remote Sens. 2015, 7, 14428-14444. https://doi.org/10.3390/rs71114428
Mulianga B, Bégué A, Clouvel P, Todoroff P. Mapping Cropping Practices of a Sugarcane-Based Cropping System in Kenya Using Remote Sensing. Remote Sensing. 2015; 7(11):14428-14444. https://doi.org/10.3390/rs71114428
Chicago/Turabian StyleMulianga, Betty, Agnès Bégué, Pascal Clouvel, and Pierre Todoroff. 2015. "Mapping Cropping Practices of a Sugarcane-Based Cropping System in Kenya Using Remote Sensing" Remote Sensing 7, no. 11: 14428-14444. https://doi.org/10.3390/rs71114428