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Remote Sensing of Riparian Ecosystems

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Ecological Remote Sensing".

Deadline for manuscript submissions: closed (31 December 2022) | Viewed by 25452

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Special Issue Editors

Dr. Miloš Rusnák

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Department of Physical Geography, Geomorphology and Natural Hazards, Institute of Geography, Slovak Academy of Sciences, 81473 Bratislava, Slovakia
Interests: UAV; aerial images; fluvial geomorphology; gravel bars; GIS; river; connectivity

Dr. Monika Šulc Michalková

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Guest Editor

Department of Geography, Faculty of Science, Masaryk University, 60177 Brno, Czech Republic
Interests: hydrology; fluvial geomorphology; remote sensing; historical analysis of river channels

Dr. Anna Kidová

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Guest Editor

Department of Physical Geography, Geomorphology and Natural Hazards, Institute of Geography, Slovak Academy of Sciences, 81473 Bratislava, Slovakia
Interests: fluvial geomorphology; remote sensing datasets; GIS; riparian vegetation; river management

Dr. Zdeněk Máčka

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Guest Editor

Department of Geography, Faculty of Science, Masaryk University, 60177 Brno, Czech Republic
Interests: fluvial geomorphology; large wood in rivers; (dis)connectivity in fluvial systems

Dr. László Bertalan

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Guest Editor

Department of Physical Geography and Geoinformatics, University of Debrecen, Debrecen, H-4032 Egyetem tér 1, Hungary
Interests: UAS; hydromorphology; bank erosion; hydrology; agricultural mapping
Special Issues, Collections and Topics in MDPI journals

Dr. Maciej Liro

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Institute of Nature Conservation, Polish Academy of Sciences, 31-120 Krakow, Poland
Interests: UAV; aerial images; fluvial geomorphology and biogeomorphology; dams’ effects on mountain rivers; riverine macroplastic

Dr. Malia A. Volke

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Guest Editor

Washington Department of Natural Resources, Spokane, WA 99201, USA
Interests: riparian vegetation; dams and flow regulation; reservoir backwaters; mapping and temporal analysis of river floodplains; fluvial geomorphology

Special Issue Information

Dear Colleagues,

Riparian (streamside) zones are dynamic ecosystems that form at the interface of aquatic and terrestrial components of the landscape. They are shaped by underlying physical processes associated with river flow, including the erosion and deposition of sediment, periodic inundation, and groundwater–surface water exchanges. In their natural state, riparian ecosystems are characterized by high spatial and temporal heterogeneity, which supports a diversity of species, habitats, and ecological processes. Today, across much of the world, rivers and their riparian zones have been profoundly modified by human activities associated with river management (e.g., dams and flow regulation) and land use pressures (e.g., agricultural conversion and irrigation withdrawals), altering the patterns and processes that sustain riparian functions and biodiversity. Monitoring and the assessment of riparian ecosystems is challenging. Recent advances in remote sensing methods enable effective mapping, monitoring, and improved understanding of riparian systems and management outcomes. High-resolution imagery (satellite, aerial, and UAV) and digital elevation models (DEMs) constructed from LiDAR and UAVs are powerful tools for assessing the biophysical dynamics of riparian zones (e.g., hydrology, geomorphology, and vegetation) over time and three-dimensional space. Machine learning techniques can provide important insights about the long-term spatiotemporal dynamics of riparian systems (e.g., vegetation succession, habitat conditions, the extent and turnover of geomorphic surfaces) and their associated ecological functions.

Dr. Miloš Rusnák
Dr. Monika Šulc Michalková
Dr. Anna Kidová
Dr. Zdeněk Máčka
Dr. László Bertalan
Dr. Maciej Liro
Dr. Malia A. Volke
Guest Editors

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Keywords

Aerial images
Satellite
UAV
LiDAR
Riparian zone
Vegetation succession
River management

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Published Papers (7 papers)

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Research

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26 pages, 15778 KiB

Open AccessArticle

A Comparative Assessment of Multi-Source Generation of Digital Elevation Models for Fluvial Landscapes Characterization and Monitoring

by Paweł Sudra, Luca Demarchi, Grzegorz Wierzbicki and Jarosław Chormański

Remote Sens. 2023, 15(7), 1949; https://doi.org/10.3390/rs15071949 - 6 Apr 2023

Cited by 5 | Viewed by 2663

Abstract

Imaging and measuring the Earth’s relief with sensors mounted upon unmanned aerial vehicles is an increasingly frequently used and promising method of remote sensing. In the context of fluvial geomorphology and its applications, e.g., landform mapping or flood modelling, the reliable representation of the land surface on digital elevation models is crucial. The main objective of the study was to assess and compare the accuracy of state-of-the-art remote sensing technologies in generating DEMs for riverscape characterization and fluvial monitoring applications. In particular, we were interested in DAP and LiDAR techniques comparison, and UAV applicability. We carried out field surveys, i.e., GNSS-RTK measurements, UAV and aircraft flights, on islands and sandbars within a nature reserve on a braided section of the Vistula River downstream from the city of Warsaw, Poland. We then processed the data into DSMs and DTMs based on four sources: ULS (laser scanning from UAV), UAV-DAP (digital aerial photogrammetry), ALS (airborne laser scanning), and satellite Pléiades imagery processed with DAP. The magnitudes of errors are represented by the cross-reference of values generated on DEMs with GNSS-RTK measurements. Results are presented for exposed sediment bars, riverine islands covered by low vegetation and shrubs, or covered by riparian forest. While the average absolute height error of the laser scanning DTMs oscillates around 8–11 cm for most surfaces, photogrammetric DTMs from UAV and satellite data gave errors averaging more than 30 cm. Airborne and UAV LiDAR measurements brought almost the perfect match. We showed that the UAV-based LiDAR sensors prove to be useful for geomorphological mapping, especially for geomorphic analysis of the river channel at a large scale, because they reach similar accuracies to ALS and better than DAP-based image processing. Full article

(This article belongs to the Special Issue Remote Sensing of Riparian Ecosystems)

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37 pages, 10190 KiB

Open AccessArticle

Riparian Plant Evapotranspiration and Consumptive Use for Selected Areas of the Little Colorado River Watershed on the Navajo Nation

by Pamela L. Nagler, Armando Barreto-Muñoz, Ibrahima Sall, Matthew R. Lurtz and Kamel Didan

Remote Sens. 2023, 15(1), 52; https://doi.org/10.3390/rs15010052 - 22 Dec 2022

Cited by 6 | Viewed by 3017

Abstract

Estimates of riparian vegetation water use are important for hydromorphological assessment, partitioning within human and natural environments, and informing environmental policy decisions. The objectives of this study were to calculate the actual evapotranspiration (ETa) (mm/day and mm/year) and derive riparian vegetation annual consumptive use (CU) in acre-feet (AF) for select riparian areas of the Little Colorado River watershed within the Navajo Nation, in northeastern Arizona, USA. This was accomplished by first estimating the riparian land cover area for trees and shrubs using a 2019 summer scene from National Agricultural Imagery Program (NAIP) (1 m resolution), and then fusing the riparian delineation with Landsat-8 OLI (30-m) to estimate ETa for 2014–2020. We used indirect remote sensing methods based on gridded weather data, Daymet (1 km) and PRISM (4 km), and Landsat measurements of vegetation activity using the two-band Enhanced Vegetation Index (EVI2). Estimates of potential ET were calculated using Blaney-Criddle. Riparian ETa was quantified using the Nagler ET(EVI2) approach. Using both vector and raster estimates of tree, shrub, and total riparian area, we produced the first CU measurements for this region. Our best estimate of annual CU is 36,983 AF with a range between 31,648–41,585 AF and refines earlier projections of 25,387–46,397 AF. Full article

(This article belongs to the Special Issue Remote Sensing of Riparian Ecosystems)

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32 pages, 12805 KiB

Open AccessArticle

Combining Hyperspectral, LiDAR, and Forestry Data to Characterize Riparian Forests along Age and Hydrological Gradients

by Julien Godfroy, Jérôme Lejot, Luca Demarchi, Simone Bizzi, Kristell Michel and Hervé Piégay

Remote Sens. 2023, 15(1), 17; https://doi.org/10.3390/rs15010017 - 21 Dec 2022

Cited by 7 | Viewed by 3235

Abstract

Riparian forests are complex ecosystems shaped by their connectivity to a river system, which produces a mosaic of ages and species. Because of increasing anthropic pressure from factors such as damming or climate change, they are often endangered and suffer from a drop in groundwater accessibility and increased water stress. By combining hyperspectral, LiDAR, and forestry datasets along a 20 km corridor of the Ain River, this paper assesses the ability of remote sensing to characterize and monitor such environments. These datasets are used to investigate changes in site conditions and forest characteristics, such as height and canopy water content, along a gradient of ecosystem ages and for reaches under distinct geomorphic conditions (shifting, sediment-starved, incised). The data show that, over time, forest patches aggrade, and the forest grows and becomes more post-pioneer. However, forest patches that are located in the incised reach aggrade more and appear to be less developed in height, more stressed, and feature species compositions reflecting dryer conditions, in comparison with better-connected patches of the same age. Random forest analysis was applied to predict the indicators of forest connectivity with remotely sensed LIDAR and hyperspectral data, in order to identify the spatial trends at the reach scale and compare them with the geomorphic segmentation of the river. The random forest classifications achieved an accuracy between 80% and 90% and resulted in spatial trends that highlighted the differences in hydrological connectivity between differing geomorphic conditions. Overall, remote sensing appears to be a good tool for characterizing the impact of channel incisions and adjustments on riparian forest conditions by identifying the locations of dryer forest patches. In addition, good accuracy was achieved when attempting to classify these forest patches, even when using hyperspectral data alone, which suggests that satellite data could become a powerful tool for monitoring the health of riparian forests, in the context of increasing anthropic pressures. Full article

(This article belongs to the Special Issue Remote Sensing of Riparian Ecosystems)

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22 pages, 10369 KiB

Open AccessArticle

Using Structure-from-Motion Photogrammetry to Improve Roughness Estimates for Headwater Dryland Streams in the Pilbara, Western Australia

by Alissa Flatley, Ian Rutherfurd and Alexander Sims

Remote Sens. 2022, 14(3), 454; https://doi.org/10.3390/rs14030454 - 18 Jan 2022

Cited by 2 | Viewed by 2477

Abstract

There are numerous situations where engineers and managers need to estimate flow resistance (roughness) in natural channels. Most estimates of roughness in small streams come from humid areas. Ephemeral streams in arid and semi-arid areas have different morphology and vegetation that leads to different roughness characteristics, but roughness in this class of stream has seldom been studied. A lack of high-resolution spatial data hinders our understanding of channel form and vegetation composition. High resolution structure-from-motion (SfM)-derived point clouds allow us to estimate channel boundary roughness and quantify the influence of vegetation during bankfull flows. These point clouds show individual plants at centimetre accuracy. Firstly, a semi-supervised machine learning procedure called CANUPO was used to identify and map key geomorphic features within a series of natural channels in the Pilbara region of Western Australia. Secondly, we described the variation within these reaches and the contribution of geomorphic forms and vegetation to the overall in-channel roughness. Channel types are divided into five reach types based on presence and absence of geomorphic forms: bedrock; alluvial single channel (≥cobble or sand dominated); alluvial multithread; composed of either nascent barforms or more established; stable alluvial islands. Using this reach classification as a guide, we present estimates of Manning’s roughness within these channels drawing on an examination of 650 cross sections. The contribution of in-channel vegetation toward increasing channel roughness was investigated at bankfull flow conditions for a subset of reaches. Roughness within these channels is highly variable and established in-channel vegetation can provide between a 35–55% increase in total channel roughness across all channel types. This contribution is likely higher in shallow flows and identifies the importance of integrating vegetation and geomorphic features into restorative practices for these headwater channels. These results also guide Manning’s selection for these semi-arid river systems and contribute to the vegetation-roughness literature within a relatively understudied region. Full article

(This article belongs to the Special Issue Remote Sensing of Riparian Ecosystems)

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26 pages, 5095 KiB

Open AccessArticle

Reconstruction of Nineteenth-Century Channel Patterns of Polish Carpathians Rivers from the Galicia and Bucovina Map (1861–1864)

by Karol Witkowski

Remote Sens. 2021, 13(24), 5147; https://doi.org/10.3390/rs13245147 - 18 Dec 2021

Cited by 6 | Viewed by 2917

Abstract

Historical maps are often the only source of information allowing for the regional reconstructions of river channel patterns in the past. In the Polish Carpathians, analyses of historical channel patterns were performed mostly in river reaches scale. In this paper, the Galicia and Bucovina map (1861–1864) (the Second military survey of the Habsburg Empire) was used to reconstruct and map the historical channel patterns of seven rivers from the Polish Carpathians. It was found that, in the nineteenth century, rivers in the western part of the study area (Soła, Skawa, Raba, Dunajec) supported a multi-thread channel pattern, whereas rivers in the eastern part (Wisłoka, San, Wisłok) present a mostly single-thread channel pattern. These differences probably result from the higher relief energy and precipitation, lower proportions of forests in the catchments, and more frequent floods favouring high sediment supply to the fluvial system, and thus the formation of multi-thread reaches in the western part of the study area. At the local scale, the most important factor supporting multi-thread channel pattern development was the availability of gravel sediments in the wide valley floor sections. The formation of anabranching reaches with a single mid-channel form was probably associated with the channel avulsion process. There is no clear evidence linking the change in the channel pattern type with an abrupt change in the river channel slope. This study confirms the usefulness of the second military survey map of the Habsburg Empire for the regional reconstruction of river channel pattern types. Full article

(This article belongs to the Special Issue Remote Sensing of Riparian Ecosystems)

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15 pages, 3599 KiB

Open AccessArticle

Identifying Factors That Influence Accuracy of Riparian Vegetation Classification and River Channel Delineation Mapped Using 1 m Data

by Ge Pu, Lindi J. Quackenbush and Stephen V. Stehman

Remote Sens. 2021, 13(22), 4645; https://doi.org/10.3390/rs13224645 - 18 Nov 2021

Cited by 4 | Viewed by 2953

Abstract

Riparian vegetation delineation includes both the process of delineating the riparian zone and classifying vegetation within that zone. We developed a holistic framework to assess riparian vegetation delineation that includes evaluating channel boundary delineation accuracy using a combination of pixel- and object-based metrics. We also identified how stream order, riparian zone width, riparian land use, and image shadow influenced the accuracy of delineation and classification. We tested the framework by evaluating vegetation vs. non-vegetation riparian zone maps produced by applying random forest classification to aerial photographs with a 1 m pixel size. We assessed accuracy of the riparian vegetation classification and channel boundary delineation for two rivers in the northeastern United States. Overall accuracy for the channel boundary delineation was generally above 80% for both sites, while object-based accuracy revealed that 50% of delineated channel was less than 5 m away from the reference channel. Stream order affected channel boundary delineation accuracy while land use and image shadows influenced riparian vegetation classification accuracy; riparian zone width had little impact on observed accuracy. The holistic approach to quantification of accuracy that considers both channel boundary delineation and vegetation classification developed in this study provides an important tool to inform riparian management. Full article

(This article belongs to the Special Issue Remote Sensing of Riparian Ecosystems)

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Review

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32 pages, 24343 KiB

Open AccessReview

Remote Sensing of Riparian Ecosystems

by Miloš Rusnák, Tomáš Goga, Lukáš Michaleje, Monika Šulc Michalková, Zdeněk Máčka, László Bertalan and Anna Kidová

Remote Sens. 2022, 14(11), 2645; https://doi.org/10.3390/rs14112645 - 31 May 2022

Cited by 25 | Viewed by 5985

Abstract

Riparian zones are dynamic ecosystems that form at the interface between the aquatic and terrestrial components of a landscape. They are shaped by complex interactions between the biophysical components of river systems, including hydrology, geomorphology, and vegetation. Remote sensing technology is a powerful tool useful for understanding riparian form, function, and change over time, as it allows for the continuous collection of geospatial data over large areas. This paper provides an overview of studies published from 1991 to 2021 that have used remote sensing techniques to map and understand the processes that shape riparian habitats and their ecological functions. In total, 257 articles were reviewed and organised into six main categories (physical channel properties; morphology and vegetation or field survey; canopy detection; application of vegetation and water indices; riparian vegetation; and fauna habitat assessment). The majority of studies used aerial RGB imagery for river reaches up to 100 km in length and Landsat satellite imagery for river reaches from 100 to 1000 km in length. During the recent decade, UAVs (unmanned aerial vehicles) have been widely used for low-cost monitoring and mapping of riverine and riparian environments. However, the transfer of RS data to managers and stakeholders for systematic monitoring as a source of decision making for and successful management of riparian zones remains one of the main challenges. Full article

(This article belongs to the Special Issue Remote Sensing of Riparian Ecosystems)

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