Extraction and classification of channels from LiDAR in plains by channel tracking

Abstract

Accurate and detailed maps of irrigation and drainage networks in plains are imperative for efficient water management. Conducting field surveys of these networks proves to be expensive and labor-intensive. Besides, manual extraction of this information from remote sensing data lacks the necessary speed and accuracy. While LiDAR data offers highly detailed insights into terrain morphology, extracting water channels from it is not a straightforward task. The commonly employed slope-based algorithms, primarily reliant on elevation gradients, falter in plains due to minimal height variations such as local depressions or sinks, which can lead to misinterpretations. The primary goal of this study is to automate the extraction of the network using high-resolution Digital Terrain Models (DTMs) derived from LiDAR. Though the number of parameters is limited, users have the flexibility to finely adjust hydrologically relevant and intuitive parameters based on their specific channel preferences. The algorithm commences with a curvature analysis of the surface. Random seed points are strategically chosen from locations where the local curvature is categorized as 'valley.' A cross-section of the DTM is then extracted aligned with the direction of maximum curvature. The section's morphology is thoroughly evaluated to determine its validity as a water channel section. If deemed valid, points on both sides of the thalweg point along the zero-curvature direction undergo testing. The channel is traced in both directions, as long as the cross-sections maintain their validity. Upon the cessation of tracking, new hypotheses are examined until all 'valley' type points have been exhausted. The study demonstrates that the algorithm can also calculate channel capacity or perform channel classification using the acquired channel thalweg points and cross-sections. To validate the algorithm's effectiveness, tests are conducted across three distinct areas within the Bergama Plain in Turkey. Manual markings establish the ground truth for the channels. The precision and recall values achieve 100%, with the exception of Test Area 2 (77% precision). Additionally, the outcomes surpass those of flow-based algorithm implemented in the same areas. The proposed method proves its utility in plains and remains open to further refinement and development.