Characterising maize and intercropped maize spectral signatures for cropping pattern classification

Intercropping – the planting of more than one crop in the same plot of land – is a prevalent agricultural management practice which can be used for risk reduction. Despite its widespread use, intercropping is not commonly reported in agricultural statistics, resulting to very limited spatially disaggregated information about its prevalence. Remote sensing-based approaches to detect and estimate the area of cropping patterns like intercropping require good understanding of the spectral response of (intercropped) crops at different crop growth phases. This study integrates field surveys, farmer interviews and temporal Sentinel-2 data from four crop growth phases and the post-harvest period of maize and intercropped maize (imaize). The goal is to identify the optimal crop growth phases, spectral regions and vegetation indices (VIs) that can accurately discriminate the two cropping patterns. We computed p-values for the spectral bands using Mann-Whitney U test and identified critical crop growth phases. Classification of maize and imaize cropping patterns was performed using random forest classifier. Our spectral analysis revealed effective discrimination between maize and imaize cropping patterns during the vegetative (in all spectral bands) and flowering-yield phases (in Blue, Green, Red, RE704, RE783, NIR833, NIR865). The most suitable VIs contained red-edge and near-infrared spectal bands. Utilizing spectral data and VIs from vegetative and flowering-yield phases, we achieved optimal discrimination during the vegetative phase (user’s accuracy of 100 % and producer’s accuracy of 100 %). However, accuracy decreased during the flowering yield phase (overall accuracy of 87 % for all spectral bands). The highest classification results using all spectral bands at the flowering yield phase resulted in 80 % producer’s accuracy for maize and 100 % for imaize. This study illustrates the utility of temporal Sentinel-2 spectral data for identifying the critical crop growth phase, spectral regions and VIs for cropping patterns classification, particularly for intercropping

Single and multi-temporal assessment approach of natural resources using remote sensing

The study area of this project is located in Makhado Municipality, Limpopo, South Africa. The Limpopo Province is commonly known for being rich in the country?s natural resources. It has a number of villages that are characterized by rich natural resources and a well-known nature reserve, Soutpansberg Mountains. Natural resources such as water, plantations, woodlands and grasslands are commonly found in these villages and are commonly used for alleviating poverty. Rural communities in this municipality are still highly dependent on natural resources. The high dependence on these natural resources subsequently affects negatively the natural environment, e.g. processes such as land degradation. Villages in this region have limited infrastructure development that influence people?s livelihood. Infrastructure developments are commonly known for contributing to growing the economy and it will be no different if such developments are built in these villages. Therefore, it is imperative to find innovative and scientific techniques that provide information which can assist in finding ways of balancing the interaction between the environment and its people. In order to successfully do so, ways of managing and monitoring of natural resources in villages such as Makhado becomes a necessity. Land cover information is required to adequately understand the extent and status of the natural resources of the Makhado region. This information is required for effective monitoring of natural resources. With the aid of remote sensing applications, land cover studies are possible. The applications always aim to provide efficient methods using low cost or freely available data. The main objective of this study was to innovatively and accurately map the land cover classes of Makhado Municipality using Landsat imagery. The study investigated the performance of single and multi-temporal assessment approach. The study found that the results of the multi-temporal approach were more accurate compared to the single-date approach for both periods. The overall accuracy of single-date classifications were 78.1% with Kc of 0.74 and 54.3% with Kc of 0.46 respectively. The classification map results of the multi-temporal approach were 72.9% with Kc of 0.68 and 79.0% and a Kc of 0.76 respectively. The multi-temporal classification maps were used for post-classification change detection. The results of these methods illustrated the major decrease in grasslands from 2006-2009 and 2013-2015 respectively. These results assisted in making further inferences of how the drastic and severe drought that occurred in 2015 till recently had a significant impact on the land cover.Dissertation (MSc)--University of Pretoria, 2017.Geography, Geoinformatics and MeteorologyMScUnrestricte