Study of coastal wetland classification based on decision rules using ALOS AVNIR-2 images and ancillary geospatial data

Abstract

Wetlands play irreplaceable key roles in ecological and environmental procedures. To make effective conservation and management, it is essential to understand the wetlands' distribution and changes. In this study, an approach based on decision rules algorithm in conjunction with maximum likelihood classification is proposed for coastal wetland mapping using multi-temporal remotely sensed imagery and ancillary geospatial data. As a case study, Multi-temporal Advanced Visible and Near Infrared Radiometer type 2 images acquired by Japanese Advanced Land Observation Satellite are analysed to investigate the seasonal change pattern of coastal wetlands in Washington State, USA. Geospatial data, including Digital Elevation Model and spatial neighbourhood knowledge, are further integrated to characterize wetland features and discriminate classes within a certain elevation ranges. The final result is a refined coastal wetland map with 15 land cover categories. Preliminary evaluation of the final result shows that the proposed approach is effective in coastal wetland mapping.

Coastal Wetland Mapping with Sentinel-2 MSI Imagery Based on Gravitational Optimized Multilayer Perceptron and Morphological Attribute Profiles

“…As one of the most biologically productive ecosystems on earth, wetlands are of significant importance for hydrological and ecological processes [1]. They play a vital role in flood storage, water-quality improvement, shoreline protection, carbon sequestration, and, more importantly, a desirable habitat for both animals and plants [2][3][4]. Unfortunately, worldwide wetlands, especially in coastal areas, are vulnerable to threats from nature and human influence, including shoreline erosion because of tides and storms, reservoir construction, and increasing urbanization [3,5,6].…”