TITLE:
Semantic Segmentation of the Intertidal Zone of an Estuary—In Search of the Best Solution
AUTHORS:
Maria da Conceição Proença, Ricardo Nogueira Mendes, Ricardo Melo
KEYWORDS:
Estuary, Intertidal Zone, ArcGIS Pro, Segmentation, Global Changes
JOURNAL NAME:
Journal of Geoscience and Environment Protection,
Vol.12 No.7,
July
12,
2024
ABSTRACT: An aerial photographic coverage acquired on two consecutive days in October 2021 with a ground resolution of 20 cm and a spectral resolution of 4 bands (red, green, blue and near infrared), allowed to distinguish most of the classes of interest present in the intertidal zone of the Sado estuary. We explored the possibilities of thematic classification in the powerful and complex software ArcGIS Pro; we presented the methodology used in a detailed way that allows others with minimal knowledge of GIS to reproduce the classification process without having to decipher the specifics of the software. The classification implemented used ground truth from four classes related to the macro-occupations of the area. In a first phase we explore the standard algorithms with object-based capabilities, like K-Nearest Neighbor, Random Trees Forest and Support Vector Machine, and in a second phase we proceed to test three deep learning classifiers that provide semantic segmentation: a U-Net configuration, a Pyramid Scene Parsing Network and DeepLabV3. The resulting classifications were quantitatively evaluated with a set of 500 control points in a test area of 37,500 × 12,500 pixels, using confusion matrices and resorting to Cohen’s kappa statistic and the concept of global accuracy, achieving a Kappa in the range [0.72, 0.81] and a global accuracy between 88.9% and 92.9%; the option U-Net had the most interesting results. This work establishes a methodology to provide a baseline for assessing future changes in the distribution of Sado estuarine habitats, which can be replicated in other wetland ecosystems for conservation and management purposes.