Authors:
André Alves
1
;
Daniel Moraes
2
;
3
;
Bruno Barbosa
1
;
Hugo Costa
2
;
3
;
Francisco Moreira
3
;
Pedro Benevides
3
;
Mário Caetano
2
;
3
and
Manuel Campagnolo
1
Affiliations:
1
Forest Research Centre, Associate Laboratory TERRA, School of Agriculture, University of Lisbon, Tapada da Ajuda, 1349-017 Lisboa, Portugal
;
2
NOVA Information Management School (NOVA IMS), Universidade NOVA de Lisboa, Campus de Campolide, 1070-312 Lisboa, Portugal
;
3
Direção-Geral do Território, Rua Artilharia Um, 107, 1099-052 Lisboa, Portugal
Keyword(s):
Land Cover Change Classification, Thematic Map, Spectral Composites, NDVI, CCDC, COSc, Earth Observation.
Abstract:
Land use/land cover (LULC) change detection and classification in maps based on automated data processing are becoming increasingly sophisticated in Earth Observation (EO). There is a growing number of annual maps available, with diverse but related production structures consisting primarily of classification and post-classification phases, the latter of which deals with inaccuracies of the first. The methodology production of the “Carta de Ocupação do Solo conjuntural” (COSc), a thematic land cover map of continental Portugal produced by the Directorate-General for Territory (DGT) mostly based on Sentinel-2 images classification, includes a semi-automatic phase of correction that combines expert knowledge and ancillary data in if-then-else rules validated by photointerpretation. Although this approach reduces misclassifications from an initial Random Forest (RF) prediction map, improving consistency between years and compliance with ecological succession, requires a lot of time-cons
uming semi-automatic procedures. This work evaluates the relevance of exploring an additional set of variables for automatic classification over disturbance-prone areas. A multitemporal dataset with 124 variables was analysed using data dimensionality reduction techniques, resulting in the identification of 35 major explanatory indicators, which were then used as inputs for RF classification with cross-validation. The estimated importance of the explanatory variables shows that composites of spectral bands, which are already included in the current COSc workflow, in conjunction with the inclusion of additional data namely, historical land cover information and change detection coefficients, from the Continuous Change Detection and Classification (CCDC) algorithm, are relevant for predicting land cover classes after disturbance. Since map updating is a more challenging task for disturbed pixels, we focused our analysis on locations where COSc indicated potential land cover change. Nonetheless, the overall classification accuracy for our experiments was 72.34 % which is similar to the accuracy of COSc for this region of Portugal. The findings suggest new variables that could improve future COSc maps.
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