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Intra-Annual land cover mapping: Automatic training sample extraction from old maps for intra-annual land cover mapping at central of Portugal
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Resumo(s)
Making operational e cient the production of Land Use Land cover (LULC)
mapping over large areas as the consistency and accuracy keep a high quality
is an essential condition for the implementation of applications that require periodic
information, such as forest re propagation, crop monitoring or climate
models. The increasing spatial and temporal resolution satellite images, such
as those provided by Sentinel 2, open new opportunities for producing accurate
datasets that can improve the lack of production of global and regional LULC
maps with ne scale and up-to-date information. In this context, while this thesis
aimed to make automatic the generation of intra-annual maps implementing
a work
ow that consists of supervised classi cation in synergy with automatic
extraction of training samples from an old map, it also aimed to use singular and
BAP composites. Therefore, after a preliminary selection and preprocessing of
the implemented spectral bands in the classi cation both from single and BAP
composites of Sentinel 2 images of 2017, a random selection of training points is
extracted from an old reference map; national LULC map of Portugal, COS 2015.
We performed a classi cation scheme using support vector machine (SVM) and
Random forest (RF) classi ers with two datasets of six and nine di erent number
of land cover classes. The out-of-date information derived from the old map led
us to evaluate the viability of implementing two re ning procedures over the data to improve accuracy; one based on margins of NDVI signals and another based on
an iterative learning procedure. Since the proposed methodologies did not lead
to improving OA on the classi cation of any of the images of 2017, we questioned
for robustness of the classi ers RF and SVM by injecting di erent levels of noise
during the modeling. Finally, the free cloud and phenological maximization of
the BAP composites become in a consistent and e cient input for the production
of seasonal LULC mapping.
Descrição
Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial Technologies
Palavras-chave
Best available pixel Intra-annual Land Use Land Cover Support vector machine and random forest Geographical information systems