CHAPTER 4: IDENTIFICATION AND MAPPING OF ANOMALOUS PATCHES IN COFFEE
4.4 Discussion
The aim of the study was to demonstrate a robust method for evaluating and incorporating age class in identifying incongruent patches in large coffee plantations by using plantations in Eastern Zimbabwe as a case study. To achieve this objective, NDVI and LSWI anomalies with and without age-adjustments derived from the Landsat 8 OLI data were used because of their improved sensor characteristics. These sensor characteristics were perceived to have the capability to improve the identification of healthy and anomalous areas and therefore map spatial heterogeneity of a perennial tree crop by using between and within field NDVI and LSWI variations.
4.4.1 Effect of age on NDVI and LSWI values
It was observed from the study that age significantly influences coffee NDVI and LSWI values.
This indicates that there is a transition in terms of spectral signatures of coffee over time, although this is less pronounced between mature and old coffee. The age-classes used in this study have a known influence on various management and productivity aspects of coffee. This is because young coffee is mainly in the gestation period before reasonable yields are achieved, mature coffee represent the most productive and profitable stage and old coffee represent the
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drooping stages of the plant when productivity, LAI and photosynthetic efficiency are decreased (Logan & Biscoe, 1987). Moreover, younger coffee have a significant soil background influence, due to open canopies, which explains the observed significantly lower NDVI and LSWI values across all image scene dates (Chemura & Mutanga, 2016). Using high resolution QuickBird imagery, Campos et al. (2005) characterised the fractional components of different coffee fields to explain the observed significant effects of growth stage on NDVI and LSWI.
Landsat 8 NDVI performed better than LSWI in terms of distinguishing between age classes and in terms of accuracy of the mapping of incongruous areas. NDVI is known to correlate well with plant biophysical characteristics that are affected by age, condition and other factors (Ke et al., 2015). It was expected that NDVI will have problems in handling mature coffee which tend to have more biomass per unit area and canopy cover. However, this was not so possibly because coffee and other plantation crops are systematically planted in rows and therefore do not exhibit the dense canopies in natural forests and grasslands or in high density crops where the problems of saturation are reported (Mutanga & Skidmore, 2004; Wang et al., 2004). NDVI also performed better than LSWI for coffee possibly because LSWI is specific to water stress when the conditions of anomalous patches in this study were not only limited to water stress. This is despite the fact that some studies have linked water stress to crop stressors such as pests and diseases infestations, soil nutrient deficiency, and used water-sensitive vegetation indices to map these conditions (Peñuelas et al., 1994; Mutanga & Ismail, 2010;
Oumar & Mutanga, 2014).
4.4.2 Remote sensing-based identification of incongruent patches
Transforming coffee NDVI and LSWI values into age-based deviation proved to be able to show and emphasize extreme patches that are either below or above their expected growth stages. For example, it was very clear how some patches moved from being incongruent to be in the range of their age-expected means for young coffee, indicating growth. This shows that although the age classes are wide (four years), they are able to provide a spatial and quantitative idea of crop performance for every Landsat 8 pixel per scene date. In addition, the results demonstrate that the change in characteristics of a particular pixel either naturally or after management intervention can be monitored using this approach. Although at this stage it is not possible to provide an indication of what is causing particular areas to be incongruous, identifying them provides not only the opportunity for managers to identify areas requiring
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attention in large plantations, but provide opportunities for further application of remote sensing methods to determine causes at particular sites. Many hyperspectral, and high spatial and spectral resolution sensors can be focused on these specific areas to determine the cause of the below-average performance. For example, approaches demonstrated by Li et al. (2014) for citrus greening disease detection and Delalieux et al. (2009) for leaf biotic stress of apple plants can be targeted, making these more effective and cheaper. Methods such as linear discriminant analysis on hyperspectral data can then be used to detect levels of infection on specifically identified patches using satellite data, field spectroscopy UAVs or a combination of sensors (Zhang et al., 2012). However, not all pixels identified as incongruent are diseased or necessarily lagging in growth. This means that this method provides a first step in crop condition assessment, which can be followed up either by fieldwork or by other sensing approaches.
Relationships between NDVI and physical characteristics in croplands is confounded by many other factors to be directly determined. Venteris et al. (2015), correlated NDVI anomalies of field crops with cumulative crop moisture index (CCMI) and concluded that the relationships were complex. This is possibly because it is not only soil moisture that explains NDVI variation of a pixel and this explains why LSWI did not perfume better than NDVI in this study.
Therefore, a two-stage process maybe the most appropriate for practical field application of remote sensing crop stress where the first method identifies incongruent patches (as demonstrated in this study) and the other methods identifies and quantifies the causes of the observed incongruence. Higher spatial and spectral resolution data, such as Worldview 2 and Sentinel-2 data could be used in the second stage of identifying what exactly is responsible for the observed departures from age-expected vigour.
Scene-based anomaly detection is more applicable in tropical areas because of problems of clouds and other surface characteristics that are dependent on sharp variations in seasons. For instance, many factors such as dry weather conditions and their effects on LAI explain the lower NDVI during the winter. The natural phenological cycle of coffee and field operations such as harvesting may have contributed to this (Brunsell et al., 2009; Bernardes et al., 2012).
A general identification of anomalous areas could be more appropriate in plantation crops where crop conditions are a function of multiple stressors through opportunistic infections. For example, a short period of soil moisture stress could create opportunities for coffee white stem borer (Monochamus leuconotus) infestation which predisposes plants to pathogens, such as cercospora leaf spot (cercospora coffeicola) among others (Logan & Biscoe, 1987; Nelson,
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2008; Kutywayo et al., 2013). In addition, crop insurance companies can also use this approach to objectively determine compensation for farmers by setting a threshold of farm area determined incongruent.
It was expected that using age-adjusted anomalies would outperform use of the global mean in identification and mapping of incongruous patches in coffee. This is because Bausch (1993) pointed out that when the soil background effect is not factored in NDVI-based crop coefficient determination, there could be estimation errors of magnitudes exceeding 20%. In other studies, it was concluded that regression functions of plant biophysical characteristics with NDVI were significantly different at different growth stages (Sembiring et al., 2000; Freeman et al., 2007).
From these it was concluded therefore that growth specific calibration is required when using NDVI for crop condition assessments but then this is impossible with coarse data. This points to the increasing requirement for age-mapping agricultural plantations to feed the information into precision phenotyping and characterisation that are required for many applications (Thenkabail et al., 2004b; Bhojaraja et al., 2015). The challenge is therefore in having age- disaggregated spatial datasets to enable applications of approaches like this. Merely separating a crop from other land use/cover types is still a challenge and thus extending the separation into within class classification brings further challenges. However, with improvements in sensor characteristics and availability of robust methods such as random forests, objected- oriented classification and sub-pixel classification, this is becoming possible (Tan, 2013;
Chemura & Mutanga, 2016).
4.4.3 Potential limitations and future improvements
The approach presented in this study could have some limitations, which provide opportunities for future development. Despite promising results, there are still several limitations of the proposed method that need to be considered. The study is dependent on a pre-determined age- map also produced from Landsat 8 OLI data. An approach that could produce the age-map and concurrently identify anomalies could be easier to implement because not all areas have these age maps. The spatial resolution of the medium resolution Landsat 8 OLI data is large, meaning that it is not possible to identify specific individual plants that are anomalous due to infection, fertility and/or soil moisture deficit. Therefore, there is already considerable spread before the source of the anomalous plant is actually identified by this approach. However, it is known that causes of anomalous crop conditions, such as pest attacks, disease infections and fertility cause considerable damage to the plant system and even spread before any visual signs could be detected even by field methods (Carter & Miller, 1994; Eitel et al., 2011). This therefore means
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that an anomalous pixel could actually have a mixture of problems which individual stress identification methods may give the error of omission, making the approach presented here a better option. In addition, since the method is not dependent on field samples, once expected mean values have been established or sample positions to collect them are set, there is huge opportunity for development of automated online platforms and apps for use in coffee monitoring from this approach.