Effective monitoring for stream habitat health is essential for sustainable restoration and management to conserve ecosystem services. Traditional in situ surveying with multi-metric indices is widely used by state and federal agencies but is often labor-intensive and spatially limited. To address these challenges, this study introduces a suite of cost-effective drone-based monitoring toolkits that integrate automated flight-route design, Structure-from-Motion (SfM) image reconstruction, and multi-metric index (MMI) computation. The toolkits form an end-to-end workflow for quantitative habitat evaluation, enabling centimeter-scale mapping and standardized MMI scoring based on Wisconsin DNR and EPA guidelines. The toolkits were examined at Black Earth Creek, Wisconsin, where high- and low-elevation drone images were collected and processed to generate high-resolution (< 1 cm) ortho-photos and terrain maps. The reconstructed terrain achieved horizontal and vertical accuracies of 0.04 m and 0.17 m, respectively, and the UAV-derived MMI results showed strong agreement with field measurements. The entire process—from flight planning to assessment—can be completed by one operator within three hours using an off-the-shelf drone (< USD 1000) and standard photogrammetry software. Beyond improving efficiency, the integrated toolkits provide spatially continuous, repeatable assessments that capture habitat variability and degradation hotspots often missed by conventional transect-based methods. By significantly reducing labor and costs associated with data collection, processing, and assessment, these UAV-based toolkits offer a cost-effective, scalable, and reproducible solution for routine stream habitat monitoring and long-term conservation planning.

Shoreline change detection from remote sensing imagery remains challenging in environments subject to water level fluctuations, as remotely sensed shoreline positions reflect instantaneous hydrodynamic states rather than true geomorphic change. In the Great Lakes, seasonal and short-term water level variations can produce apparent shoreline shifts unrelated to sediment dynamics. Reliable calibration with bathymetry and water level data can mitigate this effect, but such data are often unavailable or difficult to obtain for many coastal and lacustrine systems worldwide. To address this limitation, we proposed a morphology-based framework that quantifies geometric change between successive shoreline curves using a discrete Fréchet distance, a modified Euclidean distance and a Union distance metric. Rather than relying solely on cross-shore displacements, the approach leverages shape similarity to differentiate water-level-driven shifts from true morphological change. We evaluated the framework across three spatial scales (100 m, 500 m, and 1000 m) along 125 km of southwestern Lake Michigan coastline using 2010 and 2020 aerial imagery, benchmarking against water-level-calibrated DSAS erosion hotspots. The Fréchet distance improved monotonically with scale, achieving strong agreement at 1000 m (F1 = 0.84, Spearman $\rho$ = 0.79) but limited reliability at 100 m. While individual morphology-based metrics appeared competitive with or inferior to uncalibrated DSAS at each scale, the union of both distances substantially outperformed uncalibrated DSAS at management-relevant scales (F1 of 0.64 vs. 0.50 at 500 m and 0.79 vs. 0.42 at 1000 m), reflecting the complementary nature of shape-based and displacement-based detection. The Patient Rule Induction Method (PRIM) further identified gentle nearshore slopes and moderate separation from engineered structures as the geomorphic conditions under which the morphology-based and calibrated erosion indicators converged most closely (in-box F1 = 0.92 at 1000 m and 0.72 at 500 m). These results suggest that the proposed framework, particularly the complementary union of both metrics, provides a practical, calibration-free alternative for multiscale shoreline change screening in lacustrine and microtidal, data-limited environments, while local-scale applications still benefit from explicit water-level correction.

Accurate shoreline positioning is critical for coastal monitoring and management, yet deep learning shoreline products are often evaluated using conventional waterbody segmentation metrics that do not explicitly measure boundary alignment. Using 20,689 NAIP aerial images covering the Great Lakes shoreline from the Coastal Aerial Imagery Dataset (CAID), we benchmark five semantic segmentation models and quantify the inconsistency between image-level segmentation accuracy (pixel accuracy, IoU) and shoreline positioning accuracy measured by the Shoreline Intersection Ratio (SIR) and Average Eulerian Distance (AED). Although segmentation performance is consistently high (pixel accuracy typically >98% and IoU often >90%), shoreline agreement is substantially lower and strongly landscape-dependent, with the poorest results in wetlands and urban scenes. Correlation analyses across coastal types and water-surface conditions show that the correspondence between segmentation metrics and SIR varies with shoreline morphology. Multivariate regressions confirm the shoreline-to-water ratio (SWR) as the dominant predictor of both SIR and AED, while shoreline complexity (SCI) and mean water hue (MWH) have weaker, context-dependent effects. These results demonstrate that high segmentation accuracy does not guarantee precise shoreline delineation and motivate shoreline-aware evaluation protocols.

Shoreline segmentation is a specialized water-land segmentation task that detects the position of the shoreline. Existing datasets for segmentation either do not focus on coastal environments (e.g., DeepGlobe) or are not based on high-resolution imagery (e.g., Sen1Floods11). To fill this gap, we built Coastal Aerial Imagery Dataset (CAID), a high-resolution, manually labeled benchmark dataset built on aerial imagery for shoreline segmentation. CAID consists of 20 689 images of 500 $\times$ 500 pixels across 850 National Agriculture Imagery Program (NAIP) sites across the Great Lakes region. The dataset covers a variety of coastal landscapes, and its representativeness of coastal environments was evaluated using three criteria: 1) water body area; 2) shoreline length ratio; and 3) mean water hue. We also assessed the benchmark performance of existing segmentation models on CAID. While these models achieved high mean intersection over union (mIoU) and mean accuracy (mAcc) scores, accurately positioning shorelines remains a challenge, a limitation that is not fully reflected by existing evaluation metrics. To address this, we introduced mean shoreline covering ratio (mSCR), a novel metric that quantifies each model’s ability to align with ground-truth shorelines. The CAID dataset, along with the associated code for training and evaluating deep learning models, will be publicly released under the CC-BY-4.0 license via GitHub and Zenodo to ensure open-access availability. IEEE SOCIETY/COUNCIL Geoscience and Remote Sensing Society DATA TYPE/LOCATION Aerial Imagery, Segmentation; The Great Lakes region, USA DATA DOI/PID 10.5281/zenodo.15330911