Indications of Dynamic Effects on Scaling Relationships Between Channel Sinuosity and Vegetation Patch Size Across a Salt Marsh Platform

Abstract

Salt marshes are important coastal areas that consist of a vegetated intertidal marsh platform and a drainage network of tidal channels. How salt marshes and their drainage networks develop is not fully understood, but it has been shown that the biogeomorphic interactions and feedbacks between vegetation development and channel formation play an important role. We examined the relationships among tidal channel sinuosity, marsh roughness, vegetation type (pioneer, Elymus athericus or Phragmites australis), and patch size at different spatial scales using a high‐resolution vegetation map (derived from aerial photography) and lower‐resolution satellite imagery processed with linear spectral mixture analysis. The patch‐size distribution in all vegetation types corresponded to a power law, suggesting the presence of self‐organizational processes. While small vegetation patches are more dominant in pioneer vegetation, they were present in all vegetation types. The largest patch size is restricted to E. athericus. We observed an inverse logarithmic relationship between channel sinuosity and vegetation patch size in all vegetation types. The fact that this relationship is observed in both pioneer and later successional stages suggests that after the establishment of a drainage network in the dynamic pioneer stages of salt marsh development, the later stages of salt marsh succession largely inherit the meandering pattern of the early successional stages. Our study confirms recent evidence that no significant changes in the specific features of tidal channel networks (e.g., channel width, drainage density, and efficiency) take place during the later stages of salt marsh development.

Review and Evaluation of Deep Learning Architectures for Efficient Land Cover Mapping with UAS Hyper-Spatial Imagery: A Case Study Over a Wetland

“…Lastly, coastal wetlands are among some of the most dynamic and complex ecosystems on the planet. Many different factors, such as seasonal and climate changes, water temperature, altered flooding and salinity patterns, sea-level rise, topography, etc., [11,13], contribute to the current state of the land cover and its physical properties at the time of recording the remote sensing observations. Thus, the authors emphasize that the classification results shown here, based on the classes chosen to be examined, are valid for the specific data set acquired at a certain time over the study site.…”

“…Coastal wetland classification is challenging because vegetation and other land cover objects modulate with water level fluctuation and other environmental stressors, leading to sometimes rapid and frequent changes in the type and spatial distribution of a certain land cover [9,10]. The ability to accurately and quickly monitor and predict land cover undergoing rapid and seasonal variations in response to changing environmental factors, including seasonal and climate changes, topography, sea-level rise, water temperature, altered flooding and salinity patterns, etc., [11][12][13], is crucial for updated and/or continuous land cover monitoring systems. Wetland land cover processes as well as other dynamic landscapes are further complicated by the need for frequent data collection methods, and the subsequent demands for faster and automatic algorithms analyzing very high spatial, temporal, and spectral resolution imagery by the monitoring system with the lowest level of human intervention.…”

“…Exploiting sophisticated techniques and algorithms along with some level of field operations for ground truthing and results validation are often a few required components for accurate monitoring of the wetland or other natural environments through remote sensing image classification [12,13,19]. In traditional RS classification techniques, pixel-wise classification methods assume each pixel is pure and typically labeled to the most likely land cover category.…”

Exploring the Dunes: The Correlations between Vegetation Cover Pattern and Morphology for Sediment Retention Assessment Using Airborne Multisensor Acquisition

“…These validation areas were appositely selected for the visual estimation of cover percentage and a quick classification of the main vegetation typologies like sand-partially vegetated, shrub and herbaceous vegetation, shrubs, and trees. The field observations dataset was integrated by in situ spectral measurements ( Figure 3) with a ViewSpecPRO ® ASD (Boulder, CO, USA) instrument [31,[35][36][37].…”

“…where l is the along-foot line length and L is the straight-foot line distance between the starting and ending points [35]. In addition, the profile underlying the surface, a proxy for the sediment volume of the dune, was calculated with the following equation:…”