Land cover mapping with random forest using intra-annual sentinel 2 data in central Portugal : a comparative analysis

Dissertation submitted in partial fulfilment of the requirements for the degree of Master of Science in Geospatial TechnologiesIn recent years, data mining algorithms are increasingly applied to optimise the classification process of remotely sensed imagery. Random Forest algorithms have shown high potential for land cover mapping problems yet have not been sufficiently tested on their ability to process and classify multi-temporal data within one classification process. Additionally, a growing amount of geospatial data is freely available online without having their usability assessed, such as EUROSTAT´s LUCAS land use land cover dataset. This study provides a comparative analysis of two land cover classification approaches using Random Forest on open-access multi-spectral, multi-temporal Sentinel-2A/B data. A classification system composed of six classes (sealed surfaces, non-vegetated unsealed surfaces, water, woody, herbaceous permanent, herbaceous periodic) was designed for this study. Ten images of ten bands plus NDVI each, taken between November 2016 and October 2017 in Central Portugal, were processed in R using a pixel-based approach. Ten maps based on single month data were produced. These were then used as input data for the classifier to create a final map. This map was compared with a map using all 100 bands at once as training for the classifier. This study concluded that the approach using all bands produced maps with 11% higher, yet overall low accuracy of 58%. It was also less time-consuming with about 5 hours to over 15 hours of work for the multi-temporal predictions. The main causes for the low accuracy identified by this thesis are uncertainties with EUROSTAT´s Land Use/Cover Area Statistical Survey (LUCAS) training data and issues with the accompanying nomenclature definition. Additional to the comparison of the classification approaches, the usability of LUCAS (2015) is tested by comparing four different variations of it as training data for the classification based on 100 bands. This research indicates high potential of using Sentinel-2 imagery and multi-temporal stacks of bands to achieve an averaged land cover classification of the investigated time span. Moreover, the research points out lower potential of the multi-map approach and issues regarding the suitability of using LUCAS open-access data as sole input for training a classifier for this study. Issues include inaccurate surveying and a partially long distance between the marked point and the actual observation point reached by the surveyors of up to 1.5 km. Review of the database, additional sampling and ancillary data appears to be necessary for achieving accurate results

Discrimination and biomass estimation of co-existing C3 and C4 grass functional types over time : a view from space.

Doctor of Philosophy in Environmental Sciences. University of KwaZulu-Natal, Pietermaritzburg, 2018.The co-existence of C3 and C4 grass species significantly influence their spatio-temporal variations of biochemical cycling, productivity (i.e. biomass) and role in provision of ecosystem goods and services. Consequently, the discrimination of the two species is critical in understanding their spatial distribution and productivity. Such discrimination is particularly valuable for accounting for their socio-economic and environmental contributions, as well as decisions related to climate change mitigation. Due to the growing popularity of remotely sensed approaches, this study sought to discriminate the two grass species and determine their AGB using new generation sensors. Specifically, the potential of Landsat 8, Sentinel 2 and Worldview 2, with improved sensing characteristics were tested in achieving the above objectives. Generally, the results demonstrate the suitability of the adopted sensors in the discrimination and determination of C3 and C4 AGB using Discriminant Analysis and Sparse Partial Least Squares Regression models. Using multi-date Sentinel 2 data, the study established that winter period (May) was the most suitable for discriminating the two grass species. On the other hand, the winter fall (August) was found to be the least optimal period for the two grass species discrimination. The study also established that the amount of AGB for C3 and C4 were higher in winter and summer, respectively; a variability attributed to elevation and rainfall. The study concludes that Sentinel 2 dataset, although had weaker performance than Worldview 2; it offers a valuable opportunity in understanding the C3 and C4 spatial distribution within a landscape; hence useful in understanding both temporal and multi-temporal distribution of the two grass species. Successful seasonal characterization of C3 and C4 AGB allows for inferences on their contribution to forage availability and fire regimes; therefore, this contributes to the development of well-informed conservation strategies, which can lead to sustainable utilization of rangelands, especially in relation to the changing climate