REMOTE SENSING DATA: A SYNTHESIS
Photo credits: A. Chemura (2017)
174 9.1 Introduction
Coffee is an important commodity in international agricultural trade especially for producer countries that depend on it for off-setting balance of payments, local taxes and livelihoods. The major players in the coffee sector are producers who struggle with climatic, edaphic and other biotic factors to produce coffee of sufficient quantity and quality to satisfy a demand of over 2 billion coffee cups consumed daily (Waston & Achinelli, 2008). Biotic and abiotic calamities are increasingly affecting the coffee sector, with these explaining fluctuations in world coffee supplies and market prices (Van der Vossen, 2001). Given the importance of coffee in developing producer economies, consumer countries and ecosystems, there is a need for systematic landscape-scale monitoring of coffee crop condition to increase productivity through reducing impacts of potential stressors. These monitoring mechanisms are required for assisting on farm decisions especially for large farms, for insurance and risk services and national coffee sector planning. Monitoring coffee condition is also important in precision farming in which case agrochemicals are efficiently applied to reduce costs and associated environmental impacts from over-application of fertilizers and pesticides. However, current condition assessment methods in the coffee sector are based on sampling that assumes the selected sites represent the whole field and also on subjective visual assessments that are laborious and conclusive once economic damage has been inflicted.
Developments in remote sensing data acquisition, storage and processing provide an opportunity for spatially explicit and objective vegetation condition assessments. However, there are a number of challenges in applications of remote sensing coffee for crop area mapping and condition assessment that render traditional remote sensing approaches limited in coffee producing areas. These challenges include the fact that coffee is a perennial crop and as such requires long-term monitoring beyond one season. Secondly, at landscape scale, producers plant coffee of different ages, with age of the coffee plants influencing percentage canopy cover and its resultant effect on background effect. There is also a systematic planting arrangement in hedgerows that ensures gaps between rows for efficient farm management operations but also results in background spectral effect (Brunsell et al., 2009). In addition, the LAI of coffee follow biennial cycle that is influenced by fruiting sequence and this influences applications of remote sensing applications (Bernardes et al., 2012). Coffee stressors can also concurrently occur, making it difficult to separate them spectrally or to understand the root cause of the problem.
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(Logan & Biscoe, 1987; Kutywayo et al., 2013). Given the need for crop condition assessment in resource-constrained coffee producer countries and the challenges from the nature of the crop and the production system, the objectives of this work were to:
1. Develop detailed age-specific thematic maps for coffee for heterogeneous agricultural landscapes for use as age-masks in condition assessments.
2. Integrate age in the identifying and mapping of incongruous patches in coffee plantations using multi-temporal multispectral level anomalies.
3. Evaluate the potential of multispectral remote sensing data for quantifying leaf water stress in coffee using only the secondary effects of water absorption.
4. Assess the potential of multispectral remote sensing bands and vegetation indices for discriminating and predicting coffee leaf rust infection levels at leaf level.
5. Determine the effects of spectral settings, spatial resolution and crop canopy cover on ability of multispectral level data to predict leaf chlorophyll content as an indicator of crop condition in coffee plantations.
6. Model the spatial variability in foliar nitrogen in coffee plantations with multispectral level data for agronomic decision making.
9.2 Mapping coffee plantations and development of age-masks
The fact that coffee plants have different age groups presents challenges not only in separating coffee from other land use/cover types with remote sensing data but also in coffee condition assessments. In addition, remote sensing-based age-specific thematic maps for coffee areas are important for coffee farm planning such as the rate of application of fertilisers and pest control chemicals are applied, which are age-dependent. The objective of this study was to capitalize on sensor improvements in Landsat 8 OLI data and machine learning algorithms in developing age- specific thematic maps for coffee producing areas. The overall outcome is that it is possible to develop age-specific thematic maps for coffee in a heterogeneous agricultural landscape using Landsat 8 OLI and the RF algorithm. The ability to achieve reliable classification accuracy for coffee age-groups (young, mature and old) is based on the significance of the differences in spectral reflectance among coffee classes of different ages and between coffee and other land use/cover types that is captured by higher sensor fidelity of the Landsat 8 OLI. For example, Landsat 8 OLI uses numerous elongated sets of detectors for each waveband, which increases
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the sensitivity of the sensor to the age-based biophysical characteristics that determine vegetation reflectance (Dube & Mutanga, 2015).
This study confirmed the importance of the NIR in remote sensing coffee plantations. For example, this study observed that the spectral difference between mature coffee and old coffee was only discernible in the NIR band. It was however clear from this study that, splitting coffee into three age classes reduces the classification accuracy (for example by a 4.1% decrease in overall accuracy). However, the reduction in classification accuracy that occur when coffee is split into three classes is small, indicating that it could be worthwhile to trade-off some accuracy to obtain a more functional thematic map with age-specific classes in mapping complex agricultural landscapes. The success of development of age-specific thematic maps for coffee is premised on the improvements in spectral characteristics of the Landsat 8 OLI sensor, coupled by an intelligent and robust non-parametric classifier, the RF. The developed age-specific thematic maps are useful not only in separating coffee from other land use/cover types, but among different age categories which deals with the challenge of age in coffee area mapping and condition assessment. Development of age masks has been a remote sensing challenge for other perennial agricultural crops (McMorrow, 2001; Tan, 2013; Chemura et al., 2015b). It would be interesting to assess if the same approach can work in coffee under shade or in robusta coffee that has more canopy cover than arabica coffee. The produced age-masks were used in subsequent condition assessment studies.
9.3 Identification of anomalous patches in coffee plantations
As a perennial crop, coffee requires a consistent monitoring framework to monitor biotic and abiotic stressors and their effect on inter and intra-annual variations in crop conditions, but these are currently largely unavailable. Vegetation indices such as NDVI and LSWI can be used to monitor crop conditions over a large area and to detect any significant changes over time. When the “normal” or expected index value is known, then any departure from this indicates an anomaly. The objective of this study was to use the age-specific thematic maps from the preceding chapter to identify areas in coffee fields that are anomalous and therefore requiring attention. An approach using deviation from age-specific mean NDVI and LSWI was developed using Landsat 8 OLI data and applied for identifying, quantifying and mapping between and within field crop stand variations. The outcome of this study was that age has a significant
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influence on coffee NDVI and LSWI values associated with physiological development, photosynthetic efficiency, LAI and canopy cover of the coffee plants (Logan & Biscoe, 1987).
Landsat 8 OLI derived NDVI performed better than LSWI in identifying of anomalous areas in coffee fields. This is because NDVI associates well with plant biophysical characteristics that are affected by age, condition and other factors (Ke et al., 2015). Age-adjusted NDVI deviations from the mean were even more accurate in showing areas that are growing either below or above their expected growth stages. It was therefore clear that age-adjustment of NDVI anomalies improved performance of mapping incongruous areas in coffee plantations. In addition, multi- temporal analysis clearly showed how some coffee areas moved from being healthy to being incongruous over the months. This provides a spatial, quantitative and temporal idea of crop performance for every Landsat 8 pixel per scene date that can be used for farm level decision making. The presented age-adjusted anomaly detection identifies where in the coffee plantations condition is deteriorating either through biotic or abiotic stress or improving through management intervention. Although this approach does not identify the exact cause of the anomaly that is important in decision making, this is a challenge dealt with in subsequent studies in this project. Identifying these anomalous areas is important for identifying specific areas requiring attention in large plantation and for more targeted application of remote sensing methods to determine causes at particular sites
Three important advancements in remote sensing were presented in this study; (i) age-adjustment is important in anomaly detection in coffee, (ii) scene-based anomaly detection is more applicable in tropical areas where coffee is produced because of problems of clouds and seasonality and (iii) multi-temporal remote sensing is a good basis for understanding crop dynamics in coffee condition assessment, which is possible with accessible multispectral data such as Landsat 8 OLI and/or Sentinel-2 MSI data compared to many studies that are dependent on only a single scene. Although this method is very promising for long-term monitoring of perennial tree crops such as coffee which are in the field for decades, it forms a basis for more specific studies on specific causes of anomalies in coffee plantations.
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9.4 Predicting plant water content in coffee plants using multispectral remote sensing Having developed an approach for identifying anomalous patches in coffee plantations, it was imperative to identify, with remote sensing, the specific cause of the anomalies. Experimental evaluation of the possibility to quantify plant water content using secondary water absorption bands in the VIS/NIR region was done. Wavebands that are selected by reflectance sensitivity were the best in predicting PWC with relatively high correlations between measured and predicted PWC on test data and lower error metrics. Reflectance sensitivity identified bands mainly in the red-edge and the visible, particularly the blue region of the spectrum, with none in the NIR region. However, reflectance in this region is influenced mostly by leaf structure, chlorophyll, biomass and stand age. Since this is a single species study using same age healthy plants, these factors are not significant. This also underscores the importance of separating coffee from other land use/cover types and age categories (Chapter 3). The ability of wavebands in the visible and NIR range to predict PWC is very attractive for application of multispectral scanners, in coffee condition assessments. This is so because multispectral scanners such as Sentinel-2 can be focused on specific crop fields and super-spectral bands strategically positioned across important regions of the VIS/NIR bands applied in PWC estimation. It is important to note that this study relied on healthy coffee leaves and under field conditions; other factors such as diseases and pests (Mutanga & Ismail, 2010; Oumar & Mutanga, 2014) may influence potential for PWC estimation. Overall, this study provides a basis for application of remote sensing in identifying and quantifying PWC, which alone can explain anomalies in coffee fields or indirectly as PWC is related to other plant conditions such as pest and disease attacks.
9.5 Modelling coffee leaf rust using multispectral remote sensing
PWC is an abiotic factor contributing to coffee stress and this study extended the application of remote sensing in biotic stress by experimentally evaluating multispectral remote sensing of coffee leaf rust caused by the fungus H. vastatrix. This study has for the first time demonstrated the utility of the new generation Sentinel-2 MSI sensor in discriminating disease infections. The most important Sentinel-2 MSI’s spectral bands (i.e. B4, B6 and B5) for CLR discrimination and modelling were identified. The finding that the red band (B4) was the most significant band in CLR discrimination is explained by the fact that CLR is different from other diseases and plant stress conditions in that it is not necrotic (Belan et al., 2015) and also result in distinctive asymptomatic rustic pustules on the underside of the leaf. As expected, vegetation indices
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produced better results in CLR discrimination, when compared to the use of raw spectral bands, which was expected. In CLR severity modelling, the proportion of leaf diseased area was not significantly correlated to most spectral bands because of the non-linear influence of disease infection on spectral reflectance (Zhang et al., 2012; Mahlein et al., 2013).
Using the RBF-PLS showed a high accuracy in CLR severity modelling with resampled data.
This is an achievement considering that much of the reported work on disease modelling is based on vegetation indices or at least a combination of vegetation indices and spectral bands, even with hyperspectral data. This is attributed to the Sentinel-2 sensor settings as Ramoelo et al.
(2015) also used only spectral bands to model nitrogen in rangelands and obtained a high accuracy, confirming that the spectral settings of Sentinel-2 MSI spectral settings are good for modelling vegetation condition. Findings of this study showed higher accuracy for severe levels, but from an operation perspective, this is not good as economic damage would have already been inflicted on the crop. This study underpins the application of Sentinel-2 MSI data in coffee condition assessment that can improve management of croplands and stewardship of the environment through reduced unnecessary use of crop protection chemical for disease control.
Although the results are positive in indicating potential application as sensors in disease levels modelling, these results are based on field spectra at leaf level. There is need for more field studies to apply the RF and RBF-PLS in modelling the biophysical (e.g. LAI reduced to defoliation) and biochemical (e.g. chlorophyll and foliar nitrogen) parameters of coffee that are affected by the disease.
9.6 Multispectral remote sensing of coffee chlorophyll content
Chl is important in agricultural crop management as it is a reliable indicator of photosynthetic rates and leaf health as majority of the plant stressors affect this variable. A field level application of multispectral remote sensing data in mapping and quantifying the spatial variability of chlorophyll in coffee fields that can be related to the abiotic (Chapter 5) and biotic (Chapter 6) factors was presented. The aim of this study was to evaluate the influence of Sentinel-2 MSI spectral settings and spatial resolution and coffee stand age in empirical landscape scale coffee Chl estimation. As expected, coffee biophysical attributes (height and canopy area) were strongly related to coffee age while biochemical parameters (PWC and Chl) were age invariant, and thus more influenced by site conditions such as fertility, insects or pests or other local factors.