CHAPTER 7: LANDSCAPE SCALE MULTISPECTRAL REMOTE SENSING OF COFFEE TOTAL
7.2 Materials and methods
The study was conducted at Jersey Tea and Coffee Estates in Chipinge district, Zimbabwe. The site is located at longitude 32̊ 41’00E and 32̊ 42’00E, and latitude 20̊ 28’00S and 20̊ 31’00S (Figure 7.1). The area is characterised by a subtropical with two distinct dry and wet seasons, divided almost equally between months of the year i.e. October to March – rainy and April to September - dry season. The topography is undulating with a relief difference of over 100m. The area receives relatively high mean annual rainfall totals for a subtropical area(1200-1300 mm/year) with mostly warm temperatures, around 22.5°C (Lagerblad, 2010; Nicolin, 2011).
With deep red clayey soils formed from mafic rocks, climatic conditions in the area make it suitable for good quality coffee production. Sun-coffee production, i.e. coffee plantations without tree shading is practiced in the area (Chemura et al., 2015a).
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Figure 7.1: The study area showing the distribution of the coffee fields and stand ages.
7.2.2 Field data
Field data on various biophysical (coffee height, variety, canopy width, age since planting) and biochemical (Chl) characteristics of coffee that were deemed to be related to reflectance was carried out during the first week of January 2017. Sampling points were randomly selected across the coffee fields and a high-resolution imagery sampling map was generated for the identification of sampling sites. The total size of the sampled fields was 216 hectares with 64% under mature coffee. Sampling points were selected to cover all coffee age groups (mature and young) and to capture the varieties produced in the plantations (Catimor 129 and CR95). The sampling plots on which data was collected consisted of six coffee plants obtained from two rows of three adjacent plants each. The coffee trees had a sowing arrangement of 2.0 m x 2.5 m giving a sampling area of 15m2 for each sampling point. The height (in cm) of each plant was measured using a graduated stick and averaged per plot.
Canopy area was determined by measuring canopy diameter (in cm) of each planting station in the plot, which was then used to calculate canopy area, assuming that the coffee canopy
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approximates a circle. A SPAD-502 (Konica Minolta Sensing Inc., Japan) was used to measure absorbance that was converted into total coffee leaf Chl content. The SPAD reading of each coffee leaf was taken at approximately the same spot on selected leaves. The average of SPAD measurements from five leaf discs of each of the six coffee trees was used to determine the average SPAD readings of the plot. To convert the SPAD readings to total Chl, 34 coffee leaves collected from the same fields as SPAD measurements were frozen and their Chl a and b determined in the laboratory. The N,N-dimethylformamide (DMSO) solvent method was used for Chl extraction and a curvilinear function relating SPAD readings and total Chl (µg/cm2) was established and used.
7.2.3 Image acquisition and pre-processing
Sentinel-2 Level-1C (L1C) MSI data were downloaded from Remote Pixel, a Sentinel-2 Data Hosting portal (https://remotepixel.ca/). The Sentinel-2 data for the study area was taken on the 30th of November 2016, which was the closet day to field work with usable cloud-free images.
The Sentinel-2 L1C product used in this study is characterized by individual bands of 100 km2 tiles (ortho-images in UTM/WGS84 projection). The image had 0.02% cloud cover and the spectral bands, center wavelengths and band width of the Sentinel-2 MSI data used are shown in Table 7.1.
Table 7.1: Specifications of the Sentinel-2 Multispectral Instrument (MSI)
Spectral band Centre wavelength (nm) Band width (nm)
B2 490 65
B3 560 35
B4 665 30
B5 705 15
B6 740 15
B7 783 20
B8 842 115
B11 1610 90
B12 2190 180
Individual bands were stacked in four different ways according to the different spatial resolutions. To obtain a stack with all 9 bands at 20m resolution, the 10m bands were resampled to 20m using nearest neighbour in Sentinel Application Platform (SNAP) version 4.0. To obtain
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all 9 bands at 10m resolution the 20m bands were pan sharpened in ENVI 5.3 (Exelis Visual Information Solutions, Boulder, CO, USA) using Gram-Schmidt pan-sharpening. The Gram- Schmidt method is generally recommended for most pan-sharpening applications as it is typically more accurate than other methods. This accuracy is attributed to its use of spectral response function of a given sensor to estimate what the higher resolution band would be.
All resampling and pan-sharpening were done after converting reflectance to top of the canopy reflectance (Level-2A) using the Sen2cor atmospheric correction module in SNAP (Muller-Wilm et al., 2013). The dark-object subtraction method was applied in QGIS 2.6 using Semi-Automatic Classification Plugin for QGIS as the reflectance values were low after atmospheric correction in SNAP. To model the influence of age on performance, data analysis was done firstly on all the data, then on mature coffee stands only, with the mature fields obtained from an age mask.
The band combination, number of variables and spatial resolutions are shown in Table 7.2.
Table 7.2: Specifications of the Sentinel-2 Multispectral Instrument (MSI) band settings showing spatial/spectral combinations, number of bands and spatial resolution.
Set Spatial/Spectral Combinations
# of bands
Sentinel-2 MSI bands
Spatial Resolutio
n
Description
A All MSI bands at 20m
9 B2, B3, B4, B5, B6, B7, B8a, B11, B12
20m 10m bands resampled to 20m
B 10m bands only
4 B2, B3, B4, B8a 10m Original 10m bands only
C 20m bands only
5 B5, B6, B7, B11, B12
20m Original 20m bands only
D All MSI bands at 10m
9 B2, B3, B4, B5, B6, B7, B8a, B11, B12
10m 20m bands pan-sharpened to 10m resolution
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The Random Forest (RF) algorithm was used for modelling total coffee chlorophyll from different band combinations and spatial resolutions. RF is an ensemble machine learning algorithm developed by Breiman (2001) to solve regression problems through a multitude of decision trees. RF employs an iterative bagging (bootstrap aggregation) operation where the number of trees (ntree) are independently built using a random subset of samples from the training samples. Each node is then split using the best, among a subset of input variables (mtry).
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 (Rodriguez-Galiano et al., 2012; Lebedev et al., 2014). The default number of trees (ntree) of 500 was used while mtry is automatically determined as the square root of the total number of variables used (Breiman, 2001). Coffee chlorophyll modelling was done in R (R Core Team, 2013) using the R package randomForest to run the RF modelling (Liaw et al., 2009) for both training and prediction.
7.2.5 Accuracy assessment and performance comparison
In order to assess the performance of all models in coffee leaf chlorophyll estimation, the field data was split into 60% for training and 40% for evaluation (43:29 for all stands and 36:24 for mature coffee stands). The correlation coefficient (r) and coefficient of determination (R2) were used to assess the goodness of fit of the predicted and measured coffee leaf chlorophyll. In addition, Mean Absolute Error (MAE, Equation 7.1) Root Mean Square Error (RMSE, Equation 7.2), and percent bias (pBias, Equation 7.3) were used to determine the errors of the model in predicting coffee leaf chlorophyll from variables.
1
| ˆ |MAE yi yi
n [7.1]
)2
( ˆ
RMSE 1n
yiy [7.2]pBias = (∑(𝑦𝑖−𝑦̂∑ 𝑦𝑖)∗100
𝑖 ) [7.3]
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where for all cases n is the number of data points, yi is the measured coffee leaf chlorophyll content at that data point and ŷi is the model predicted coffee leaf chlorophyll content at that data point (Moriasi et al., 2007; DeJonge et al., 2016).