CHAPTER 3: DEVELOPMENT OF AN AGE MASK FOR COFFEE CONDITION ASSESSMENT
3.2 Material and methods
The study was conducted in Ward 19 of Chipinge district, which consists of four large coffee estates and smallholder communal coffee farmers. The site is located in the Mossurize sub- catchment, South-east of Zimbabwe between latitude 32̊ 36’00E and 32̊ 48’00E, and longitude 20̊
20’00S and 20̊ 33’00S in Chipinge district (Figure 1). The study site represents the current largest coffee producers in Zimbabwe in terms of volume and area. The climate of the area is subtropical with two distinct seasons, divided almost equally between months of the year (October–March is the growing season while April to September is the dry season). Compared to other parts of Zimbabwe, the area receives relatively high mean annual rainfall totals (1200-1300 mm/year) with mean annual temperatures around 22.5°C (Lagerblad, 2010; Nicolin, 2011). Together with deep red clayey soils formed from mafic rocks, the climatic conditions make the area suitable for quality coffee production. As the area is dominated by large scale coffee farms, the coffee production
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system is sun-coffee, which means plantations are exclusively grown with coffee and not mixed with shade trees. In Chipinge district, the mean size of coffee farms is 25 ha and is dominated by Catimor varieties due to their resistance to coffee leaf rust (Chemura et al., 2015a).
Figure 3.1: Map of the study area showing general landscape and roads. The insert shows the location of the study area in eastern Zimbabwe and in Southern Africa.
3.2.2 Field data collection
The training and validation data was collected during a field campaign conducted from the 15th to the 16th of December 2014. A general landscape map of the area was produced from Google Earth® to guide the fieldwork. A stratified sampling scheme was designed from the Google Earth®
imagery to enable proportional sampling of reference points with respect to their sizes. Nine classes as described in Table 3.1 were sampled in the study area. The centre coordinates of the selected locations were recorded using a handheld GPS receiver with an accuracy of ~3m (Garmin® eTrex Vista). Only sample sites that were greater than 1000m2 were selected.
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Table 3.1: Description of the land cover classes used in the classification.
Class Description
Bare Areas with open soil mainly due to cultivation.
Forest Area with tree crown cover of more than 10% covering at least 0.5 ha.
Built-up Areas with human settlements in high concentrations.
Grassland Open areas predominantly covered by grass species.
Tea Plantations with tea plants (Camellia sinensis).
Water Water bodies exceeding 5000m2
Young coffee Areas with coffee plants aged between 1 and 4 years.
Mature coffee Areas with coffee plants aged between 5 and 8 years.
Old coffee Areas with coffee plants aged 9 years and above.
All coffee Sample areas with sites selected from young, mature and old coffee.
3.2.3 Image data and pre-processing
One Landsat OLI scene (path 168 and row 74) was obtained for the study area from the USGS- EROS Centre archive (www.earthexplorer.usgs.gov). The image acquisition date is the 4th of December 2014 at a sun-azimuth angle of 99.7°, a sun elevation angle of 64.38° and was cloud- free. In addition, one Landsat ETM+ image for the same area was acquired and used for comparative analysis. The Landsat ETM+ data was taken on the 11 of November 2014 at sun azimuth angle of 87.39° and sun elevation angle of 64.85° and was cloud free. These images were the available images taken on a date closest to the dates of field data collection. For the purposes of this study, only band 2-7 of Landsat 8 OLI were considered necessary and used for image classification. This is because they have a uniform spatial resolution of 30 m and matched the bands in the Landsat 7 ETM+ data (Table 3.2).
Table 3.2: Spectral and spatial characteristics of the Landsat 8 OLI and Landsat ETM+ data
Landsat 8 OLI Landsat ETM+
Band Name Bandwidth(µm) GSD
(m) Band Name Bandwidth(µm) GSD (m)
2 Blue 0.452–0.512 30 1 Blue 0.441–0.514 30
3 Green 0.533–0.590 30 2 Green 0.519–0.601 30
4 Red 0.636–0.673 30 3 Red 0.631–0.692 30
5 NIR 0.851–0.879 30 4 NIR 0.772–0.898 30
6 SW1 1.566–1.651 30 5 SW1 1.547 – 1.749 30
7 SW2 2.107–2.294 30 7 SW2 2.064 – 2.345 30
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The two Landsat (OLI and ETM+) scenes were obtained in digital number (DN), and were converted to reflectance values. All Landsat ETM+ images acquired from 2003 have scan-line errors (striping problem) and were corrected for scanline errors using the Landsat toolbox in ArcGIS 10.2 before being converted to reflectance (Walawender et al., 2012). The Landsat ETM+
bands were then converted to Top-Of-Atmosphere (TOA) spectral radiances and then to at-sensor reflectance using the reflectance rescaling coefficients provided in the image’s metadata files.
Atmospheric correction of Landsat ETM + and Landsat 8 OLI images to surface reflectance was performed using the Fast Line-of-sight Atmospheric Analysis of Spectral Hypercube (FLAASH) radiative transfer model in ENVI Environment. Ten ground control points collected using a GPS with ~3m accuracy at noticeable places such as road intersections and sharp turns were used for geometric correction. A first order polynomial (Affine) transformation was used for geometric correction of the Landsat imagery and the root mean square error (RMSE) obtained was considered acceptable for the classification process, as it was less than half the pixel dimensions for both Landsat OLI and ETM+.
3.2.4 Spectral analysis and characterization
The spectral profiles of the three coffee age groups were determined by extracting reflectance values from Landsat 8 OLI bands using sample points. To test for the significant differences in spectral reflectance of the three coffee age groups in each band, the non-parametric Kruskal–
Wallis one-way analysis of variance was used at α<0.05 significance level (McKight & Najab, 2010). The Kruskal-Wallis test was chosen because it is known that spectral data seldom follows the normal distribution (Srivastava et al., 2012) and also to deal with the un-balanced number of the samples between classes.
Digitized regions of interest (roi) were used for both classification and accuracy assessment. This is because the sample size used in classification training (n=79) and accuracy assessment (n=55) was small. Evaluation of the suitability of the training samples to perform classification was done by statistically testing for separability using the transformed divergence separability index. The transformed divergence separability index (TDSI) is calculated as the Jeffries-Matusita (JM) distance squared (Equation 3.1).
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𝑇𝐷𝑆𝐼 = (√2(1 − 𝑒−∝))2 (3.1)
where ∝ is the Bhattacharya distance (Equation 3.2).
∝ = 1
8(𝜇𝑖 − 𝜇𝑗)𝑇 (𝐶𝑖+𝐶𝑖
2 )−1(𝜇𝑖 − 𝜇𝑗) +1
2𝑙𝑛 (|(𝐶𝑖+𝐶𝑗)/2|
√|𝐶𝑖|x|𝐶𝑗|) (3.2)
where i and j are the two signature classes being compared, Ci is the covariance matrix of signature i, µi is the mean vector of vector i and |Ci| is the determinant of Ci (Asmala, 2012; Dian et al., 2014). In the JM distance, values greater than 1.9 show that spectral classes are highly separable while values less than 1.0 are not statistically different to enable a proper classification (Castillejo- González et al., 2014).
3.2.5 Classification schemes
The performance of the RF classifier on Landsat 8 OLI and Landsat 7 ETM+ for intra-species discrimination of coffee was evaluated by running classifications with three age-based classes for coffee and other classes in Scheme A. This classification procedure had nine classes. In Scheme B, the three age-based classes were combined into one class and all other classes remained constant as defined in Table 3.1. In this way, Scheme B classification procedure had seven classes. Scheme B was used as the standard control as it represented the generic approach to running classifications.
Both Scheme A and Scheme B were used for classification with the RF classifier on Landsat OLI and Landsat ETM+.
3.2.6 Random forest classification
Random forest (RF) is an ensemble machine learning algorithm developed by Breiman (2001) to solve classification and regression problems through a multitude of decision trees. RF employs an iterative bagging (bootstrap aggregation) operation where a number of trees (ntree) are independently built using a random subset of samples from the training samples. Each tree is then independently grown to a maximum size based on a bootstrap sample of about two-thirds the training dataset. Each node is then split using the best, among a subset of input variables (mtry).
The ensemble then classifies the data that are not in the trees as out-of-bag (OOB) data, and by averaging the OOB error rates from all trees, the RF algorithm gives an error rate called the OOB
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classification error for each input variable. This way, the RF algorithm assesses the importance of each input variable to the outcome by comparing how much the OOB error increases when a variable is removed, while all others are left unchanged (Gislason et al., 2004; Breiman & Cutler, 2007; Adelabu et al., 2013).
In many applications, this algorithm produces one of the best accuracies to date and has important advantages over other techniques in terms of ability to handle highly non-linear data, robustness to noise and tuning simplicity (Caruana & Niculescu-Mizil, 2006; Rodriguez-Galiano et al., 2012;
Lebedev et al., 2014; Lu & Weng, 2007). When running the RF, the parameters, mtry and ntree have to be optimized for improved accuracy (Breiman, 2001; Adelabu et al., 2014). The default number of trees (ntree) in EnMAP box is 100 while mtry is determined as the square root of the total number of variables used which in this study was seven spectral bands (Breiman, 2001; Liaw
& Wiener, 2002). To determine the optimal ntree, the random forest model was iteratively ran between 500 and 2500 while assessing the accuracy on training data. An optimal default ntree of 500 was used for subsequent analysis as confirmed in literature (Breiman & Cutler, 2007). In the present study, the RF classification algorithm was implemented in EnMAP Box 2.2 (Rabe et al., 2014). The algorithm was applied at the original spatial resolution of 30m after pre-processing.
3.2.7 Accuracy assessment
A confusion matrix was used for accuracy assessment of the thematic maps. A confusion matrix is an empirical estimate of the probabilistic association between remotely sensed and ancillary data versus reference data. To assess the accuracy, the overall accuracy (OA), user’s accuracy (UA), producer’s accuracy (PA) and Kappa coefficient (Kc) were used (Congalton, 1991; Liu et al., 2007). OA expresses as percentage, the probability that a pixel is classified correctly by the thematic map and is a measure of the overall classification accuracy (Equation 3.3). PA for a certain class expresses the percentage of a category on the ground that is correctly classified by the classifier, and measures proportion of pixels omitted from a reference class (omission error) (Equation 3.4). UA expresses the proportion of a category on the ground that is included erroneously in another category (commission error) (Equation 3.5) (Congalton, 1991; Foody, 2002). Kc is a statistical measure of the actual agreement between reference data and classified data versus the chance of agreement between the reference data and a random classifier (Equation 3.6) (Congalton & Green, 1999). Kc measures the amount of agreement between attributes and
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corrects for the expected amount of agreement. If Kc is one or close to one then there is perfect agreement between the classified map and the reference data. However, limitations of Kc have been reported especially in terms of giving misleading information and difficulties in interpretation (Pontius et al., 2004; Pontius & Millones, 2011). The quantity disagreement and allocation disagreement measures in addition to the above measures were also used. Quantity disagreement (QD) is the amount of pixels of a class in the training data that is different from the quantity in the test data. Allocation disagreement (AD) is the location of a class of pixels in the training data that is different from the location of the same class in the test data (Pontius & Millones, 2011; Safaa &
Pontius, 2012).
OA = 1
𝑁∑𝑟𝑖=1𝑛𝑖𝑖 (3.3)
𝑃𝐴 = 𝑛𝑖𝑖
𝑛𝑖𝑐𝑜𝑙 (3.4)
𝑈𝐴 = 𝑛𝑖𝑖
𝑛𝑖𝑟𝑜𝑤 (3.5)
𝐾𝑐 = 𝑁 ∑𝑟𝑖=1𝑛𝑖𝑖− ∑ 𝑛𝑖𝑐𝑜𝑙𝑛𝑖𝑟𝑜𝑤
𝑁2
𝑟𝑖=1 − ∑𝑟𝑖=1𝑛𝑖𝑐𝑜𝑙𝑛𝑖𝑟𝑜𝑤 (3.6)
where nii is the number of pixels correctly classified in a category, N is the total number of pixels in a confusion matrix, r is the number of rows, and nicol are the column totals representing reference data and nirow is row total representing the predicted classes (Congalton & Green, 1999; Liu et al., 2007; Petropoulos et al., 2012).
In order to determine the above accuracy matrices, at least 100 pixels from each class (a total of 1532 pixels for Scheme A and 1283 pixels for Scheme B) that were set aside as validation regions of interest developed from fieldwork were used. This represented less than 0.5% of the study area.
These validation points were selected from equally representative areas as the training data and away from the training pixels to avoid overlaps between training and validation data. To ensure a fair comparative evaluation of performance of the two schemes, the same set of validation points
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were used for classification with Scheme A and B and for Landsat 8 OLI and Landsat ETM+
(Table 3.3). The only differences were in the class ‘coffee’.
Table 3.3: The number of training regions of interest (ROIs) and validation ROIs used for the classification by the two schemes with Landsat 8 OLI and Landsat 7 ETM+. Each ROI represents a field collected sample point.
Class Training ROIs Validation ROIs Total ROIs
Bare 9 6 15
Forest 12 9 21
Built-up 10 7 17
Grassland 8 5 13
Tea 12 9 21
Water 8 5 13
Young coffee 7 5 12
Mature coffee 6 4 10
Old coffee 7 5 12
All coffee 15 12 27
3.2.8 Comparing performance of classifiers
The statistical significance of the difference in accuracy performance of the RF classifier on Landsat 8 OLI and Landsat ETM+ was evaluated using the McNemar’s test (Foody, 2004). The McNemar’s test is used in evaluating the superiority of one thematic map over another using the same validation data and when developed from the same training samples. This test is based on the chi-square (χ2) test computed from a two by two matrix F (Equation 3.7) based on correctly and incorrectly classified pixels in both classifications (Equation 3.8).
𝐹 = (𝑓11 𝑓12
𝑓21 𝑓22) [3.7]
χ2 = (𝑓12 − 𝑓21)2
𝑓12+ 𝑓21 [3.8]
where f11 is the number of cases correctly classified by both data one (Landsat 8 OLI) and data two (Landsat 7 ETM+), f12 is the number of cases that are correctly classified in data one (Landsat 8 OLI) but incorrectly classified in data two (Landsat 7 ETM+),). f21 is the number of cases that are correctly classified in data two (Landsat 7 ETM+), but wrongly classified in data one (Landsat 8
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OLI) while f22 is the number of cases wrongly classified in both datasets. In addition, farm records on area under each age category from three large scale coffee farms in the area were obtained. The area on the farm records was compared with the area estimated to be in each category for the farms to understand how well the RF classifier produce age-maps related to actual field data.