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Abstract Wood researchers increasingly rely on remote‐sensing products to augment field information about wood deposits in river corridors. The availability of very high‐resolution (<1 m) satellite imagery makes capturing wood over greater spatial extents possible, but previous studies have found difficulty in automatically extracting wood deposits due to the challenge in distinguishing wood from spectrally similar corridor features such as sand. We also lack knowledge on the spectral properties of different wood deposit types in multiple depositional environments. In this work, we explore image classification work‐flows for four wood deposit types in three North American environments: in‐channel jams deposited in the Tatshenshini River in Alaska, USA; a wood raft on the Slave River in Northwest Territories, Canada; and wood deposited along a lakeshore and coastal embayment in the Mackenzie River Delta in Northwest Territories, Canada. We compare classification results of object‐based and pixel‐based image analysis with supervised [support vector machine (SVM)] and unsupervised (ISO clustering) classifiers. We evaluate several accuracy assessment parameters and achieve overall classification accuracies of 65–99%, showing automated image classification is a possible approach for analysing wood across larger areas. We also find that wood sensitivity in the classification ranged from 0 to 95%, indicating that some techniques are better suited to wood capture than others. We find that supervised classification produced more accurate wood maps, though there is large variation in classification outcomes across environments related to spatial arrangement of wood in the landscape. We discuss the influence of depositional environment on classification and provide recommendations for designing a wood classification work‐flow. 

Abstract Coastal river deltas are centers of surface water nitrate processing, yet the mechanisms controlling spatio‐temporal patterns in nutrient variability are still little understood. Nitrate fluctuations in these systems are controlled by complex interactions between hydrological and biogeochemical drivers, which act together to transport and transform inorganic nutrients. Distinguishing the contributions of these drivers and identifying wetland zones where nitrate processing is occurring can be difficult, yet is critical to make assessments of nutrient removal capacity in deltaic wetlands. To address these issues, we analyze relationships among regional “external” (river discharge, tides, wind) and local “internal” (water level, temperature, turbidity, and nitrate) variables in a deltaic wetland in coastal Louisiana by coupling a process connectivity framework with information theory measures. We classify variable interactions according to whether they work uniquely, redundantly, or synergistically to influence nitrate dynamics and identify timescales of interaction. We find that external drivers work together to influence nitrate transport. Patterns of hydrological and sediment connectivity change over time due to tidal flushing and discharge variation. This connectivity influences the emergence of functional zones where local nitrate fluctuations and temperature and water level process couplings are strong controls on nitrate variability. High vegetation density decreases hydrological process connectivity, even during periods of high river discharge, but it also increases biogeochemical process connections, due to the lengthening of the hydraulic residence time. Based on these results we make recommendations for monitoring nitrate in a wetland.