What is the Value of a Good Map? An Example Using High Spatial Resolution Imagery to Aid Riparian Restoration

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

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.

Assessing the Connectivity of Riparian Forests across a Gradient of Human Disturbance: The Potential of Copernicus “Riparian Zones” in Two Hydroregions

The connectivity of riparian forests can be used as a proxy for the capacity of riparian zones to provide ecological functions, goods and services. In this study, we aim to test the potential of the freely available Copernicus “Riparian Zones” dataset to characterize the connectivity of riparian forests located in two European bioclimatic regions—the Mediterranean and the Central Baltic hydroregions—when subject to a gradient of human disturbance characterized by land-use/land-cover and hydromorphological pressures. We extracted riparian patches using the Copernicus “Actual Riparian Zone” (ARZ) layer and calculated connectivity using the Integral Index of Connectivity (IIC). We then compared the results with a “Manual Riparian Zone” (MRZ) layer, produced by manually digitizing riparian vegetation patches over a very high-resolution World Imagery layer. Our research evidenced reduced forest connectivity in both hydroregions, with the exception of Least Disturbed sites in the Central Baltic hydroregion. The ARZ layer exhibited overall suitability to assess the connectivity of riparian forests in the Central Baltic hydroregion, while the Mediterranean hydroregion displayed a consistent pattern of connectivity overestimation in all levels of human disturbance. To address this, we recommend some improvements in the spatial resolution and thematic accuracy of the Copernicus ARZ layer.

Vegetation Properties in Human-Impacted Riparian Zones Based on Unmanned Aerial Vehicle (UAV) Imagery: An Analysis of River Reaches in the Yongding River Basin

Riparian zones, transitional areas between aquatic and terrestrial ecosystems, have high plant species diversities. However, they are extremely vulnerable to natural factors, such as changes in river hydrological conditions (floods, droughts) and disturbances from human activities (dams, farmland encroachment, etc.). The distribution of plant life forms and variations in the degree of vegetation coverage in a riparian zone can reflect changes in the environmental conditions. In this study, we analyzed eight reaches from the four main tributaries (Dongyang River, Yang River, Sanggan River, and Yongding River) of the Yongding River Basin, which were selected based on their climate, terrain, and degree of human disturbance. One reach was located on the Dongyang River (DYR), two reaches on the Yang River (YR1 and YR2), three on the Sanggan River (SGR1, 2, and 3), and two on the Yongding River (YDR1 and YDR2). Unmanned aerial vehicle (UAV) technology was used to obtain high-resolution, true-color, multispectral images. The distributions of the plant life forms and the differences in the vegetation coverage were analyzed in the eight selected riparian zones. The results showed that grasses dominated the riparian zone and shrubs and trees were sparsely distributed along both banks of all streams, excluding SGR2 and YDR1. The areas with an extremely high vegetation coverage classification accounted for the highest proportion in the DYR (29.3%), YR2 (48.1%), SGR1 (32.9%), SGR2 (39.9%), SGR3 (85.1%), YDR1 (36.7%), and YDR2 (51.1%) reaches. Extremely low vegetation coverage accounted for the highest proportion in the YR2 reach, reaching 37.4%. This study indicated that natural factors and human activities have a serious impact on the distribution of different plants life forms and vegetation coverage classifications in the riparian zones of the Yongding River Basin. We hope that this research can provide practical assistance in the efforts of ecological restoration and the management of riparian vegetation in the Yongding River Basin.

Using remote sensing to characterize riparian vegetation: A review of available tools and perspectives for managers

Riparian vegetation is a central component of the hydrosystem. As such, it is often subject to management practices that aim to influence its ecological, hydraulic or hydrological functions. Remote sensing has the potential to improve knowledge and management of riparian vegetation by providing cost-effective and spatially continuous data over wide extents. The objectives of this review were twofold: to provide an overview of the use of remote sensing in riparian vegetation studies and to discuss the transferability of remote sensing tools from scientists to managers. We systematically reviewed the scientific literature (428 articles) to identify the objectives and remote sensing data used to characterize riparian vegetation. Overall, results highlight a strong relationship between the tools used, the features of riparian vegetation extracted and the mapping extent. Very high-resolution data are rarely used for rivers longer than 100 km, especially when mapping species composition. Multi-temporality is central in remote sensing riparian studies, but authors use only aerial photographs and relatively coarse resolution satellite images for diachronic analyses. Some remote sensing approaches have reached an operational level and are now used for management purposes. Overall, new opportunities will arise with the increased availability of very high-resolution data in understudied or data-scarce regions, for large extents and as time series. To transfer remote sensing approaches to riparian managers, we suggest mutualizing achievements by producting open-access and robust tools. These tools will then have to be adapted to each specific project, in collaboration with managers.

UAV Monitoring of Stream Restorations

This study examines the potential and limits of the unmanned aerial vehicles (UAVs) applicability for the monitoring of stream restoration in an urban environment. UAV imaging was used for long-term post-restoration monitoring of an urban stream. The monitoring was aimed to track the stream changes significant for the assessment of the restoration success, such as the compliance of the restoration to the plan, stability and evolution of the stream channel, or changes in stream and riparian habitats. The recurrent imaging campaigns in the restored segment of Hostavicky brook in Prague, The Czech Republic, were undertaken for three years since the restoration using the DJI Inspire 1 Pro platform. The UAV monitoring revealed that the new stream pattern substantially differs from the proposed restoration plan. Despite this, the new channel has proved stability, supported by intense grassing of the floodplain, resulting in only marginal evolution of the restored channel. The new channel proved the ability to mitigate the course of a significant flood event without significant flood spills outside the riparian zone. The UAV monitoring also revealed intense eutrophication in newly created shallow ponds with insufficient drainage. The research proved that UAV imaging is a unique source of spatial data, providing reliable information for quantitative and qualitative assessment of the stream restoration progress and success.

A novel index for assessment of riparian strip efficiency in agricultural landscapes using high spatial resolution satellite imagery

Riparian strips are used worldwide to protect riverbanks and water quality in agricultural zones because of their numerous environmental benefits. A metric called Riparian Strip Quality Index, which is based on the percentage area of riparian vegetation, is used to evaluate their ecological condition. This index measures the potential capacity of riparian strips to filter sediments, retain pollutants, and provide shelter for terrestrial and aquatic species. This research aims to improve this metric by integrating the ability of riparian strips to intercept surface runoff, which is the major cause of water pollution and erosion in productive areas. In Canada and the Nordic countries, rapid surface drainage from snow melt and spring rains is often practiced to avoid production delays and losses. This reduces the efficiency of riparian buffer strips by promoting soil erosion due to concentrated runoff. A new proposed metric called Riparian Strip Efficiency Index (RSEI), incorporates not only land cover information, but topographic and hydrologic variables to model the intensity and spatial distribution of runoff streamflow, and the capability of riparian strips to retain sediments and pollutants. The research is performed over the La Chevrotière River Basin in the Portneuf municipality in Québec (Canada) using hydrological modeling, land cover and topographic data extracted from very high spatial resolution WorldView-2 imagery as a unique source of inputs. The results show that RSEI provides a better characterization of the ecosystem services of riparian strips in terms of pollutants filtration and prevention of soil erosion in agricultural areas. RSEI will allow a better management of agricultural practices such as drainage and land leveling. Further, it will provide to land managers information to monitor environmental changes and to prioritize intervention areas, which ultimately targets to ensure optimal allocation of private or public funds toward the most inefficient and threatened riparian strips.

Characterizing spatial uncertainty when integrating social data in conservation planning

Recent conservation planning studies have presented approaches for integrating spatially referenced social (SRS) data with a view to improving the feasibility of conservation action. We reviewed the growing conservation literature on SRS data, focusing on elicited or stated preferences derived through social survey methods such as choice experiments and public participation geographic information systems. Elicited SRS data includes the spatial distribution of willingness to sell, willingness to pay, willingness to act, and assessments of social and cultural values. We developed a typology for assessing elicited SRS data uncertainty which describes how social survey uncertainty propagates when projected spatially and the importance of accounting for spatial uncertainty such as scale effects and data quality. These uncertainties will propagate when elicited SRS data is integrated with biophysical data for conservation planning and may have important consequences for assessing the feasibility of conservation actions. To explore this issue further, we conducted a systematic review of the elicited SRS data literature. We found that social survey uncertainty was commonly tested for, but that these uncertainties were ignored when projected spatially. Based on these results we developed a framework which will help researchers and practitioners estimate social survey uncertainty and use these quantitative estimates to systematically address uncertainty within an analysis. This is important when using SRS data in conservation applications because decisions need to be made irrespective of data quality and well characterized uncertainty can be incorporated into decision theoretic approaches.

Effects of riparian buffers on nitrate concentrations in watershed discharges: new models and management implications

Watershed analyses of nutrient removal in riparian buffers have been limited by the geographic methods used to map buffers and by the statistical models used to test and quantify buffer effects on stream nutrient levels. We combined geographic methods that account for buffer prevalence along flow paths connecting croplands to streams with improved statistical models to test for buffer effects on stream nitrate concentrations from 321 tributary watersheds to the Chesapeake Bay, USA. We developed statistical models that predict stream nitrate concentration from watershed land cover and physiographic province. We used information theoretic methods (AIC(c)) to compare models with and without buffer terms, and we demonstrate that models accounting for riparian buffers better explain stream nitrate concentrations than models using only land cover proportions. We analyzed the buffer model parameters to quantify differences within and among physiographic provinces in the potentials for nitrate loss from croplands and nitrate removal in buffers. On average, buffers in Coastal Plain study watersheds had a higher relative nitrate removal potential (95% of the inputs from cropland) than Piedmont buffers (35% of inputs). Buffers in Appalachian Mountain study watersheds were intermediate (retaining 39% of cropland inputs), but that percentage was uncertain. The absolute potential to reduce nitrate concentration was highest in the Piedmont study watersheds because of higher nitrate inputs from cropland. Model predictions for the study watersheds provided estimates of nitrate removals achieved with the existing cropland and buffer distributions. Compared to expected nitrate concentrations if buffers were removed, current buffers reduced average nitrate concentrations by 0.73 mg N/L (50% of their inputs from cropland) in the Coastal Plain study watersheds, 0.40 mg N/L (11%) in the Piedmont, and 0.08 mg N/L (5%) in the Appalachian Mountains. Restoration to close all buffer gaps downhill from croplands would further reduce nitrate concentrations by 0.66 mg N/L, 0.83 mg N/L, and 0.51 mg N/L, respectively, in the Coastal Plain, Piedmont, and Appalachian Mountain study watersheds. Aggregate nitrate removal by riparian buffers was less than suggested by many studies of field-to-stream transects, but buffer nitrate removal is significant, and restoration could achieve substantial additional removal.