İnci Güneralp

Process–form linkages in meander morphodynamics Bridging theoretical modeling and real world complexity

Meandering rivers are one of the most dynamic earth-surface systems. They play an important role in terrestrial-sediment fluxes, landscape evolution, and the dynamics of riverine ecosystems. Meandering rivers have been of fundamental interest to researchers across a wide range of disciplines, from fluvial geomorphology to fluid mechanics, from river engineering to landscape ecology, owing to the intriguing complexity of meander morphodynamics. This interest also comes from the socio-economic concerns due to the river hazards caused by bank erosion, channel change, and flooding, as well as the adverse responses of meandering rivers to human- and climate-induced changes in the environmental conditions. An in-depth, process-based understanding of the dynamics of meandering river–floodplain systems is critical in order to investigate the responses of these systems to the changes in environmental conditions. Over the last few decades, there have been significant advances in river meandering research, with contributions from both theoretical modeling and experimental and field-based research. This paper presents a detailed overview of river meandering research, particularly focusing on the advances in the process-based understanding of meander morphodynamics. It also discusses the standing challenges in addressing the dynamics of real meandering rivers and their floodplain patterns and processes, and potential future directions in river meandering research. The paper advocates the crucial need for bridging theoretical modeling with field- and laboratory-based research in order to inform accurate assessments of river-hazard risks and facilitate ecologically sound river-management and restoration practices with the aim of supporting healthy ecosystems.

River-flow boundary delineation from digital aerial photography and ancillary images using Support Vector Machines

Delineation of river-flow boundaries constitutes an important step in various river-related studies, including river hydraulic modeling, flow-width estimations, and river and floodplain habitat mapping and assessment. Increasing the level of automation of delineation of flow boundaries from synoptic remote-sensing images provides great potential, by reducing the labor cost, especially for studies focusing on long river reaches and those examining flow changes over time. This article investigates the boundary delineation of river channel flow from aerial photographs using Support Vector Machine (SVM) and image-derived ancillary data layers. It also includes a quantitative evaluation of delineation accuracy. The findings show that SVM performs satisfactory delineations of the boundaries, and the ancillary data layers generated using edge detectors and spatial domain texture statistics particularly increase delineation accuracy. Moreover, a multiscale evaluation scheme allows for examining the performance of SVM for the whole river reach, as well as that for the subriver sections with varied geomorphic and environmental conditions.

Hyperspectral remote sensing of aboveground biomass on a river meander bend using multivariate adaptive regression splines and stochastic gradient boosting

Research on aboveground biomass (AGB) retrieval via remote sensing in floodplain forests, in particular, is urgently needed for improved understanding of carbon cycling in such areas. AGB estimation is particularly challenging in floodplain forests, which are characterized by high spatial variability in AGB resulting from biogeomorphodynamic processes. In this study, we perform remote AGB retrieval for a deciduous riparian forest on a river meander bend based on hyperspectral/high-dimensional Hyperion bands and other input variables. We compare multivariate adaptive regression splines (MARS)-, stochastic gradient boosting (SGB)- and Cubist-based AGB estimates. Results show that MARS- and SGB-derived estimates are significantly more accurate than Cubist-based AGB. The most accurate MARS and SGB estimates have a coefficient of determination, R2, of 0.97 and 0.95, respectively, whereas the Cubist estimate with the lowest error has an R2 of 0.85. MARS and SGB AGB are not significantly different, however. These modelling approaches are applicable across scales and environments.

Spatial and temporal changes in biodiversity and ecosystem services in the San Antonio River Basin, Texas, from 1984 to 2010

A fundamental premise of the Millennium Ecosystem Assessment is that biodiversity and ecosystem services are key determinants of long-term sustainability of social-ecological systems. With a continuing decline in local and global biodiversity and ecosystem services, it is crucial to understand how biodiversity and various ecosystem services interact and how land change may modify these interactions over time. However, few studies have been conducted to quantify these relationships. In this study, we present the first empirical comparative results to analyze how spatial associations between biodiversity and ecosystem services (BES) changed at multiple scales between 1984 and 2010 in the rapidly urbanizing San Antonio River Basin (SARB), Texas, USA. We found statistically significant positive spatial associations among biodiversity, carbon storage, and sediment retention both in the entire SARB and the urban watersheds in Bexar County. Overall, biodiversity and carbon storage declined across the SARB, while sediment retention remained relatively stable. Moreover, the rates of biodiversity loss and carbon storage degradation were negatively related to the urban expansion and have accelerated since the inception of the North American Free Trade Agreement (NAFTA) in 1994. During the pre- and post-NAFTA periods (1984-1995 and 1995-2010, respectively) the rates of biodiversity loss increased from 0.7% to 0.9%, and the rates of carbon-storage loss increased from 0.1% to 1.4% per annum in the urban watersheds. Our hotspot analyses indicate that the upstream watersheds in the Basin, which supply water to the critically important Edwards Aquifer, should be targeted for priority conservation to mitigate the adverse impacts of land change on BES. Our results suggest the strong need for green infrastructure policies that integrate biodiversity conservation and sustainable use of multiple ecosystem services to address the environmentally deleterious impacts of the extensive land change under the NAFTA and to ensure the long-term social-ecological sustainability of the rapidly urbanizing SARB.

Hyperspectral Aquatic Radiative Transfer Modeling Using a High-Performance Cluster Computing-Based Approach

For aquatic studies, radiative transfer (RT) modeling can be used to compute hyperspectral above-surface remote sensing reflectance that can be utilized for inverse model development. Inverse models can provide bathymetry and inherent-and bottom-optical property estimation. Because measured oceanic field/organic datasets are often spatio-temporally sparse, synthetic data generation is useful in yielding sufficiently large datasets for inversion model development; however, these forward-modeled data are computationally expensive and time-consuming to generate. This study establishes the magnitude of wall-clock-time savings achieved for performing large, aquatic RT batch-runs using parallel computing versus a sequential approach. Given 2,600 simulations and identical compute-node characteristics, sequential architecture required ~100 hours until termination, whereas a parallel approach required only ~2.5 hours (42 compute nodes)—a 40x speed-up. Tools developed for this parallel execution are discussed.

Influence of shadow removal on image classification in riverine environments

Shadows in remote-sensor images can yield marked errors in classification of riverine environments. We propose use of a modified shadow-removal algorithm as a preprocessing step for remote-sensing image classification of riverine landscapes. To accommodate characterization of spatially complex river features in the image, we investigate an illumination suppression-based shadow-removal algorithm, modified to include a user-defined tiling approach. We quantitatively evaluate the influence of shadow removal from aerial photography on classification accuracy as such studies are currently lacking. Experimental results demonstrate that this modified shadow-removal method significantly increases classification accuracy and improves detection of small river channels partially obscured by shadow.

Automated extraction of meandering river morphodynamics from multitemporal remotely sensed data

Abstract We introduce P y RIS, an automated, process-based software for extracting extensive meandering and anabranching river morphodynamics from multitemporal satellite imagery, including a unique ability to quantify river bars dynamics. P y RIS provides three main computations: (i) detection of planform centerline including complex river patterns, (ii) computation of migration vectors between subsequent centerlines, and (iii) analysis of sediment bars dynamics. P y RIS was validated against several test cases in the Amazon River basin, specifically i) main channel extraction from the anabranching Amazon river, ii) migration analysis following a large cutoff on the Ucayali River and iii) detection of sediment bar migration on the Xingu River. Tests prove the capability of P y RIS to detect the main channel in anabranching structures and chute cutoffs. P y RIS can extract extensive morphodynamic information with unprecedented automation levels and reasonable computational effort (5 h for 28 Landsat images of a 240 km reach of the Xingu River on a 3.20 GHz Intel).

What is the Direction of Land Change? A New Approach to Land-Change Analysis

Accurate characterization of the direction of land change is a neglected aspect of land dynamics. Knowledge on direction of historical land change can be useful information when understanding relative influence of different land-change drivers is of interest. In this study, we present a novel perspective on land-change analysis by focusing on directionality of change. To this end, we employed Maximum Cross-Correlation (MCC) approach to estimate the directional change in land cover in a dynamic river floodplain environment using Landsat 5 Thematic Mapper (TM) images. This approach has previously been used for detecting and measuring fluid and ice motions but not to study directional changes in land cover. We applied the MCC approach on land-cover class membership layers derived from fuzzy remote-sensing image classification. We tested the sensitivity of the resulting displacement vectors to three user-defined parameters—template size, search window size, and a threshold parameter to determine valid (non-noisy) displacement vectors—that directly affect the generation of change, or displacement, vectors; this has not previously been thoroughly investigated in any application domain. The results demonstrate that it is possible to quantitatively measure the rate of directional change in land cover in this floodplain environment using this particular approach. Sensitivity analyses indicate that template size and MCC threshold parameter are more influential on the displacement vectors than search window size. The results vary by land-cover class, suggesting that spatial configuration of land-cover classes should be taken into consideration in the implementation of the method.