Scalable Surface Water Mapping up to Fine-scale using Geometric Features of Water from Topographic Airborne LiDAR Data

• URL: http://arxiv.org/abs/2301.06567v2
• Date: Wed, 16 Aug 2023 03:45:46 GMT
• Title: Scalable Surface Water Mapping up to Fine-scale using Geometric Features of Water from Topographic Airborne LiDAR Data
• Authors: Hunsoo Song, Jinha Jung
• Abstract summary: We propose a unique method that focuses on the geometric characteristics of water instead of its variable reflectance properties. By harnessing this natural law in conjunction with connectivity, our method can accurately and scalably identify small water bodies.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Despite substantial technological advancements, the comprehensive mapping of surface water, particularly smaller bodies (<1ha), continues to be a challenge due to a lack of robust, scalable methods. Standard methods require either training labels or site-specific parameter tuning, which complicates automated mapping and introduces biases related to training data and parameters. The reliance on water's reflectance properties, including LiDAR intensity, further complicates the matter, as higher-resolution images inherently produce more noise. To mitigate these difficulties, we propose a unique method that focuses on the geometric characteristics of water instead of its variable reflectance properties. Unlike preceding approaches, our approach relies entirely on 3D coordinate observations from airborne LiDAR data, taking advantage of the principle that connected surface water remains flat due to gravity. By harnessing this natural law in conjunction with connectivity, our method can accurately and scalably identify small water bodies, eliminating the need for training labels or repetitive parameter tuning. Consequently, our approach enables the creation of comprehensive 3D topographic maps that include both water and terrain, all performed in an unsupervised manner using only airborne laser scanning data, potentially enhancing the process of generating reliable 3D topographic maps. We validated our method across extensive and diverse landscapes, while comparing it to highly competitive Normalized Difference Water Index (NDWI)-based methods and assessing it using a reference surface water map. In conclusion, our method offers a new approach to address persistent difficulties in robust, scalable surface water mapping and 3D topographic mapping, using solely airborne LiDAR data.

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