Effects of riparian vegetation on evapotranspiration processes and water quality of small plain streams

Dunea, Daniel; Brețcan, Petre; Purcoi, Larisa; Tanislav, Dănuț; Șerban, Gheorghe; Neagoe, Aurora; Iordache, Virgil; Iordache, Ştefania

doi:10.1016/j.ecohyd.2021.02.004

Cited by 12 publications

(2 citation statements)

References 40 publications

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“…Numerous studies involving UAS techniques have been dedicated to natural hazards and risks in general, and hydrological ones in particular, as these phenomena need to be monitored and managed under the existing conditions of amplified climate change effects [17][18][19][20][21][22][23][24].…”

Section: Introductionmentioning

confidence: 99%

Identification of Vegetation Surfaces and Volumes by Height Levels in Reservoir Deltas Using UAS Techniques—Case Study at Gilău Reservoir, Transylvania, Romania

Rus,

Șerban,

Brețcan

et al. 2024

Sustainability

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The hydrophilic vegetation from reservoir deltas sustains rapid expansions in surface and important increases in vegetal mass against a background of a significant influx of alluvium and nutrients from watercourses. It contributes to reservoir water quality degradation and reservoir silting due to organic residues. In this paper, we propose an evaluation method of two-dimensional and three-dimensional parameters (surfaces and volumes of vegetation), using the combined photogrammetric techniques from the UAS category. Raster and vector data—high-resolution orthophotoplan (2D), point cloud (pseudo-LIDAR) (3D), points that defined the topographic surface (DTM—Digital Terrain Model (3D) and DSM—Digital Surface Model (3D))—were the basis for the realization of grid products (a DTM and DSM, respectively). After the successive completion of the operations within the adopted workflow (data acquisition, processing, post-processing, and their integration into GIS), after the grid analysis, the two proposed variables (topics) of this research, respectively, the surface of vegetation and its volume, resulted. The data acquisition area (deriving grids with a centimeter resolution) under the conditions of some areas being inaccessible using classical topometric or bathymetric means (low depth, the presence of organic mud and aquatic vegetation, etc.) has an important role in the reservoirs’ depth dynamics and reservoir usage. After performing the calculations in the abovementioned direction, we arrived at results of practical and scientific interest: Cut Volume = 196,000.3 m3, Cut 2D Surface Area = 63,549 m2, Fill Volume = 16.59998 m3, Fill 2D Surface Area = 879.43 m2, Total Volume Between Surfaces = 196,016.9 m3. We specify that this approach does not aim to study the vegetation’s diversity but to determine its dimensional components (surface and volume), whose organic residues participate in mitigating the reservoir functions (water supply, hydropower production, flash flood attenuation capacity, etc.).

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Section: Introductionmentioning

confidence: 99%

Identification of Vegetation Surfaces and Volumes by Height Levels in Reservoir Deltas Using UAS Techniques—Case Study at Gilău Reservoir, Transylvania, Romania

Rus,

Șerban,

Brețcan

et al. 2024

Sustainability

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“…Furthermore, accurate detection of urban park vegetation plays a significant role in the sustainable assessment of planting designs and low-carbon engineering practices (Escobedo et al, 2010;Dunea et al, 2021). The precise detection of indicators such as the boundaries, scale, and growth of trees, shrubs, and ground covers is crucial for effective urban parkland management.…”

Section: Introductionmentioning

confidence: 99%

Multispectral remote sensing and DANet model improve the precision of urban park vegetation detection: an empirical study in Jinhai Park, Shanghai

Wei,

Cao,

Wang

et al. 2023

Front. Ecol. Evol.

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IntroductionThe precise detection of vegetation in urban parks is crucial for accurate carbon sink calculations and planting assessments, particularly in high-density cities. Unlike traditional methods designed for forest vegetation, the detection and classification of urban park vegetation face challenges such as unclear boundaries, multiple vegetation categories, low image resolution, labor-intensive manual calculations, and unreliable modeling results. However, by utilizing unmanned aerial vehicles (UAVs) equipped with high-resolution visible and multispectral (MS) remote sensing cameras, it becomes possible to label images with green normalized difference vegetation index (GNDVI) and full-spectral three-channel information.MethodsBy employing a dual attention convolutional neural network (DANet) model that incorporates image fusion, DANet, and feature decoding networks, the high-precision detection of urban park vegetation can be significantly improved.ResultsEmpirical validation carried out in Jinhai Park since 2021 has provided evidence of the effectiveness of the DANet model when utilizing early fusion and feature fusion techniques. This model achieves an accurate detection rate of 88.6% for trees, 92.0% for shrubs, 92.6% for ground cover, and 91.8% for overall vegetation. These detection rates surpass those achieved using only visible images (88.7%) or GNDVI images (86.6%).DiscussionThe enhanced performance can be attributed to the intelligent capabilities of the double-in network. This high-precision detection model provides more precise scientific and technical support for subsequent park carbon sink calculations, assessments of existing vegetation for planting designs, and evaluations of urban ecological impacts.

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Characterization of carbon, nitrogen, and phosphorus stoichiometry of plant leaves in the riparian zone of Dahuofang Reservoir

Chang,

Chen,

et al. 2024

Ecology and Evolution

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Carbon (C), nitrogen (N), and phosphorus (P) are essential nutrients that promote plant growth and development and maintain the stability of ecosystem structure and function. Analyzing the C, N, and P characteristics of plant leaves aids in understanding the plant's nutrient status and nutrient limitation. Seasonal water‐level fluctuations in riparian zones lead to various ecological problems, such as reduced biodiversity and decreased ecosystem stability. Therefore, comprehending the stoichiometric characteristics of riparian zone plants and their nutrient response to plant traits is important for a deeper insight into riparian zone forest ecosystems. This study analyzed the C, N, and P contents of the leaves of 44 woody plants in the riparian zone of the Dahuofang Reservoir to investigate the stoichiometric characteristics of C, N, and P of trees in the region. The results showed that the average C content of the leaves in woody plants was 446.9 g kg−1; the average N content was 28.42 g kg−1; and the average P content was 2.26 g kg−1. Compared to global and regional scales, woody plants in the riparian zone of the Dahuofang Reservoir exhibited higher N and P contents but lower N:P ratios. Compared to other riparian zones, woody plant leaves in the riparian zone of Dahuofang Reservoir had relatively high N content and N:P ratios. Variations in plant stoichiometric characteristics across different life forms were minimal, with only tree leaf P content significantly lower than its in shrubs. There was no significant correlation between leaf C, N, and P in woody plants, while specific leaf area showed a negative correlation with leaf C content. Trees in the riparian zone have high leaf N and P content and are primarily N‐limited during the growing season. This study reveals the stoichiometric characteristics of leaves of woody plants in the riparian zone, which can contribute to an in‐depth understanding of leaf stoichiometric patterns and the factors influencing them among plant life types in the riparian zone.

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Effects of riparian vegetation on evapotranspiration processes and water quality of small plain streams

Cited by 12 publications

References 40 publications

Identification of Vegetation Surfaces and Volumes by Height Levels in Reservoir Deltas Using UAS Techniques—Case Study at Gilău Reservoir, Transylvania, Romania

Identification of Vegetation Surfaces and Volumes by Height Levels in Reservoir Deltas Using UAS Techniques—Case Study at Gilău Reservoir, Transylvania, Romania

Multispectral remote sensing and DANet model improve the precision of urban park vegetation detection: an empirical study in Jinhai Park, Shanghai

Characterization of carbon, nitrogen, and phosphorus stoichiometry of plant leaves in the riparian zone of Dahuofang Reservoir

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