Abstract

The evolution of the coastal fringe is closely linked to the impact of climate change, specifically increases in sea level and storm intensity. The anthropic pressure that is inflicted on these fragile environments strengthens the risk. Therefore, numerous research projects look into the possibility of monitoring and understanding the coastal environment in order to better identify its dynamics and adaptation to the major changes that are currently taking place in the landscape. This new study aims to improve the habitat mapping/classification at Very High Resolution (VHR) using Pleiades–1–derived topography, its morphometric by–products, and Pleiades–1–derived imageries. A tri–stereo dataset was acquired and processed by image pairing to obtain nine digital surface models (DSM) that were 0.50 m pixel size using the free software RSP (RPC Stereo Processor) and that were calibrated and validated with the 2018–LiDAR dataset that was available for the study area: the Emerald Coast in Brittany (France). Four morphometric predictors that were derived from the best of the nine generated DSMs were calculated via a freely available software (SAGA GIS): slope, aspect, topographic position index (TPI), and TPI–based landform classification (TPILC). A maximum likelihood classification of the area was calculated using nine classes: the salt marsh, dune, rock, urban, field, forest, beach, road, and seawater classes. With an RMSE of 4 m, the DSM#2–3_1 (from images #2 and #3 with one ground control point) outperformed the other DSMs. The classification results that were computed from the DSM#2–3_1 demonstrate the importance of the contribution of the morphometric predictors that were added to the reference Red–Green–Blue (RGB, 76.37% in overall accuracy, OA). The best combination of TPILC that was added to the RGB + DSM provided a gain of 13% in the OA, reaching 89.37%. These findings will help scientists and managers who are tasked with coastal risks at VHR.

Highlights

  • A maximum likelihood (ML) algorithm was applied to this digital surface models (DSM) using nine representative landscape classes from the study site

  • The contribution of each derived topographic band was evaluated at the landscape (OA) and class (PA) level

  • The best DSM was derived from images #2 and #3 (DSM#2–3_1), which featured, reThis research study on satellite photogrammetry with tri–stereo images is spectively, with incidence angles of

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Summary

Introduction

2022, 14, 219 to cope with and adapt to rapid climate changes that influence ocean currents, winds, precipitations, temperatures, and strongly re–shaped landscapes [2]. UAVs are cost–efficient and deployable for shoreline detection [4] and for the identification of seasonal variations in saltmarsh meadows [5]. They are, not well suited for monitoring areas at the landscape scale (several km2 ) due to the restrictions that are imposed by legislation and the technical limitations that are enforced by the number of flight times that are permitted by the battery capacity. In coastal areas, the meteorological and marine conditions require a maximum time of presence on the site due to the tides (±one hour after low water slack)

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