Scale effects in mangrove mapping from ultra-high-resolution remote sensing imagery

Abstract

Mangroves, critical for ecological sustainability, are challenging to map accurately due to their fragmented nature and difficult accessibility. Existing datasets, often constrained to 10 m or above resolutions, could misrepresent fragmented mangrove regions and suffer from sampling biases, limiting their regional applicability. Furthermore, scale conversion’s spatial and statistical implications on mangrove mapping accuracy and area estimation remain largely unexplored. This study proposes a novel framework that leverages UHR (0.2 m) aerial photos and the DeepLabV3+ model for fine-scale mapping and systematically simulates and quantifies scale-induced effects. The resultant 20 cm-resolution mangrove map of Hong Kong achieved an overall accuracy (OA) of 92.1 %, with up to 53 % improvement compared to various existing datasets. It delineates complex boundaries in diverse coastal settings while preserving the structural integrity of fragmented patches. The total mangrove area in Hong Kong is estimated at ∼720 ha, with Deep Bay comprising 77.5 %. The scale effects analysis revealed pronounced sensitivity in fragmented habitats, where each 1 m increase in resolution could result in an average area underestimation of 5000 m2 and up to 25 % OA degradation when transitioning from 0.2 m to 30 m. Moreover, integrating patch geometry and scale responses indicated that 6 m is the optimal scale for monitoring. Beyond this, OA could sharply decline to below 82 % at the commonly used 10 m resolution and drop as low as 66 % at 30 m. These findings highlight the critical importance of fine-scale mapping using UHR images for effective mangrove conservation and management.