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Proceeding Paper

Remote Sensing of Ecohydrological, Ecohydraulic, and Ecohydrodynamic Phenomena in Vegetated Waterways: The Role of Leaf Area Index (LAI) †

by
Giuseppe Francesco Cesare Lama
1,2,* and
Mariano Crimaldi
2
1
Department of Civil, Architectural and Environmental Engineering (DICEA), University of Naples Federico II, 80125 Napoli (NA), Italy
2
Department of Agricultural Sciences, Water Resources Management and Biosystems Engineering Division, University of Naples Federico II, 80055 Portici (NA), Italy
*
Author to whom correspondence should be addressed.
†
Presented at the 1st International Electronic Conference on Agronomy, 3–17 May 2021; Available online: https://sciforum.net/conference/IECAG2021.
Biol. Life Sci. Forum 2021, 3(1), 54; https://doi.org/10.3390/IECAG2021-09728
Published: 1 May 2021
(This article belongs to the Proceedings of The 1st International Electronic Conference on Agronomy)
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Abstract

:
Aquatic plants have considerable effects on the hydraulic roughness and the qualitative status of vegetated flows at real scale. Defining the most suitable practice of riparian vegetation control in manmade and natural water flows represents a key point, in both environmental and river engineering, particularly considering the ongoing climate change trends. In detail, vegetation elements modify the main fluid dynamic features, with impacts on the transport of pollutants and mixing traits across vegetated flows. This study was carried out to provide deep knowledge of the ecohydrodynamic synergy between plants and water flow at field scale, within a ditch covered by rigid plants. It was possible, assessing the accuracy of drone-based imagery in computing Leaf Area Index (LAI), to further calibrate predictive models of vegetative flow resistance.

1. Introduction

Plants embody the physical borders between vegetated water bodies and surrounding ground, with important effects on natural habitats growing within them [1,2], as shown in Figure 1.
Figure 2 reports the ecohydraulic classification of riparian vegetation growing within vegetated watercourses: emergent, floating, and submerged. They occur when stems and leaves develop above, on, and below the water table, respectively.
The main ecohydrodynamic features of both manmade and natural vegetated channels are influenced by riparian plants’ morphometry and architecture, strongly affecting their structural and bio-mechanical properties [3,4]. The values of these real-scale properties are obtainable from other physically based vegetation indices, such as Leaf Area Index (LAI), Normalized Difference Vegetation Index (NDVI), and Plant Phenology Index (PPI). It was demonstrated that, among other indices, LAI represents a highly reliable predictor of plants’ canopy architecture and distribution inside vegetated water flows [3,4,5].
As demonstrated in many notable ecohydrodynamic studies and reviews, LAI is a crucial element in the prediction of the hydraulic roughness of vegetated flows covered by rigid plants, given the linear association existing between LAI and the so-defined vegetative global flow resistance [1,2,3,4]. Among other remote sensing techniques, drone-based imagery can be considered highly suitable for characterizing the full-scale ecohydrodynamic behavior of vegetated waterways derived from LAI trends, since it can be simply employed in almost all environmental and orographic contexts directly in the field [2,3,4,5,6].
The recent technological improvements in real-scale scans, based on portable gaming-type devices, are considered by environmental, forestry, and hydraulic engineers as a useful tool for the indirect estimation of rigid plants and stands’ LAI. In this study, the accuracy of this method in predicting LAI values associated with rigid riparian plants was compared to those derived by multispectral image analysis, retrieved by drone-based remote sensing [5,6], representing a very important starting point in the calibration of vegetative flow resistance formulas for predicting the main field-scale ecohydrological, ecohydraulic, and ecohydrodynamic features of vegetated water systems colonized by rigid aquatic and riverine plants and patches [7,8,9].

2. Methods

Twenty-five rigid plants randomly located within an Italian fully vegetated ditch were monitored in the present case study to compute LAI through real-scale scans derived by a commercial gaming-type portable device and drone-based HD images acquired through an embedded multispectral camera.

2.1. Gaming-Type Portable Device for Scanning

Based on the so-called “structured-light” phenomenon, the Microsoft® Kinect gaming-type portable device employed in this study can be considered very useful for LAI assessment since they measure the three-dimensional shape of the target object by combining optical camera systems and projected light patterns in the field [10,11].
Figure 3a,b displays frontal and above real-scale scans of the examined plants, respectively. Thus, each scan was processed by using free imagery software [9] for obtaining indirect LAI assessments of the twenty-five rigid plants (Salix species) examined in the present case study.
The scans were analyzed to compute the total leaf area of each plant through automated processing steps of the freeware Blender software. Figure 4 shows the results of the real-scale scans for one of the rigid plants analyzed here.
Since LAI is defined by Watson [13] as the total one-sided area of leaf tissue per unit ground surface, the 3D scans were employed for quantifying both areas of each woody plant (Salix species) at rigid, then mature, phenological stage.

2.2. UAV-Based Processing

Figure 5 illustrates an example of the commercial-type drone employed in this experimental study. After a preliminary calibration stage of the multispectral camera sensors to the environmental radiative sunlight conditions, the riparian plants’ stems and leaves were then monitored through drone-based multispectral acquisitions, aiming at obtaining filed-scale areal LAI datasets via raster-algebra algorithms [8,9].
First, in-situ LAI values of the twenty-five rigid plants were evaluated by processing the real-scale scans (hereinafter indicated as LAIω) and then compared to those computed by multispectral UAV-based orthomosaic analysis (hereinafter indicated as LAIδ) through the Agisoft® Metashape Pro v1.6 software to identify a convenient relation between the two parameters analyzed here. In detail, aiming at minimizing the influence of wind turbulence as well as obtaining an average overall equal to at least 80% between two consecutive multispectral acquisitions, both horizontally (orthogonal to the flight direction) and vertically (along the flight direction), the commercial 4-rotors FIMI® mi 4 k UAV employed in the present study was piloted by imposing a flight speed and altitude of 2.5 m s−1 and 10 m.
As shown in Figure 6, LAIδ values were computed from an experimental correlation retrieved between NDVI and ground-based LAI retrieved by Lama et al. [9], this latter was obtained by using a portable LI-COR® Plant Canopy Analyzer for emergent woody riparian stands in December 2020.
It is crucial to highlight here that this method is extremely suitable for the present case study since the examined plants constitute a sub-sample of the riparian stands analyzed in the previous experimental study carried out by Lama et al. [9].
In detail, the NDVI map was obtained by combining several drone-based multispectral orthophotos, which are geometrically corrected—multispectral images to achieve a uniform scale for the entire map [8,9].

3. Results and Discussion

Figure 7 reports the comparison of LAIδ and LAIω values derived in this study for the twenty-five examined rigid plants.
The linear association observed in the present case study between LAIω and LAIδ values, corresponding to twenty-five rigid plants colonizing the Italian fully vegetated abandoned ditch analyzed here, can be expressed as follows:
LAIω = 0.66 × LAIδ + 1.13.
As described in many previous ecohydraulic studies [14,15,16,17,18,19,20], LAI values retrieved from the field represent support for modeling the ecohydrodynamic behavior of constructed and natural vegetated open channels. Thus, given a coefficient of determination R2 of 0.75, a good correlation was identified by comparing LAIδ to LAIω of twenty-five rigid plants, covering the examined Italian fully vegetated abandoned ditch. It is important to highlight that LAI datasets retrieved from the drone-based multispectral imagery can certainly be affected by noise associated with the atmospheric environments. In this case, the chance of adapting signal filtering methods to LAI may be an effective method to improve the relationship obtained in this case study [21,22].
The results of the comparative analysis carried out in the present case study are useful for further ecohydraulic and slope stability research, especially for the continuous monitoring of the most relevant hydrodynamic phenomena affecting vegetated waterways, colonized by very stiff vegetation elements [23,24,25].
As pointed out by many notable modeling and experimental studies and reviews dealing with the prediction of the ecohydrological, ecohydraulic, and ecohydrodynamic parameters characterizing complex aquatic and terrestrial ecosystems, the analysis of the behavior of riverine plants at full scale is a key factor in the environmental engineering management of vegetated water systems and natural resources in general [26,27,28,29,30,31,32], especially from the perspective of imminent severe rainfall and flooding events due to extreme weather conditions, related to current and future climate change patterns over time [33,34,35,36,37,38].
It can be stated that drone-based LAI values associated with hardly rigid riparian vegetation can be effectively considered as good predictors of those obtained from the real-scale scan imagery, based on portable gaming-type scan devices. This is a very promising research finding, to be taken properly into account for future research, dealing with both experimental and modeling ecohydrology and ecohydraulics [39,40,41,42,43,44,45,46], based on the application of high-resolution machine learning approaches [47,48,49,50,51,52], artificial intelligence, and soft-computing models in detail [53,54,55,56,57].

4. Conclusions

According to both forestry engineering models and botanical expectations, the linear relation retrieved in the present case study, for obtaining accurate, drone-based LAI predictions, allows one to reproduce the actual full-scale morphometric trends of riparian stands to be employed in flume laboratory and field experiments, involving various riparian vegetation species covering the bed and banks of vegetated waterways [58,59,60,61,62,63,64,65].
Considerable advances to the predictive performance of the methodology proposed in this case study can be reached, focusing on the use of physically—based models of vegetation patches and stands over time [66,67,68,69,70], affecting both fluid dynamic average patterns and chaotic fluctuations [71,72,73,74,75,76,77,78], generated by highly three-dimensional vortex shedding [79,80,81,82,83,84,85,86,87,88,89] and Ecohydrodynamic wake turbulent structures [90,91,92,93,94,95,96].

Author Contributions

Conceptualization, G.F.C.L. and M.C.; methodology, G.F.C.L. and M.C.; validation, G.F.C.L. and M.C.; investigation, M.C.; data curation, G.F.C.L.; writing—original draft preparation, G.F.C.L. and M.C.; writing—review and editing, G.F.C.L. and M.C. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data presented in this study are available on request from the corresponding author.

Acknowledgments

The authors would like to thank Vittorio Pasquino, Antonio Mautone, and Rossella Piscopo, for their useful support during the field campaign activities and cruising imagery carried out in this case study. Special thanks go to Eng. Slabbrar Macholato for her precious help in the choice of both drone and gaming-type scan devices employed in the present ecohydraulic experimental research.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Scheme of terrestrial and aquatic ecosystems developing in vegetated wetlands and lowlands. Adapted from: http://www.vewh.vic.gov.au/water-for-the-environment/environmental-benefits (accessed on 3 January 2021).
Figure 1. Scheme of terrestrial and aquatic ecosystems developing in vegetated wetlands and lowlands. Adapted from: http://www.vewh.vic.gov.au/water-for-the-environment/environmental-benefits (accessed on 3 January 2021).
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Figure 2. Detailed overview of field-scale ecohydraulic classification of riverine and aquatic plants and stands in both natural and manmade vegetated waterways: (A) emergent plants (black circle), (B) floating plants (blue ellipse), and (C) submerged plants (orange ellipse). Adapted from Lama et al. [1].
Figure 2. Detailed overview of field-scale ecohydraulic classification of riverine and aquatic plants and stands in both natural and manmade vegetated waterways: (A) emergent plants (black circle), (B) floating plants (blue ellipse), and (C) submerged plants (orange ellipse). Adapted from Lama et al. [1].
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Figure 3. Real-scale scans of rigid plants colonizing an Italian vegetated ditch: (a) above and (b) frontal scans (Salix species). The red ellipse indicates the gaming-type device. Adapted from Lama and Crimaldi [12].
Figure 3. Real-scale scans of rigid plants colonizing an Italian vegetated ditch: (a) above and (b) frontal scans (Salix species). The red ellipse indicates the gaming-type device. Adapted from Lama and Crimaldi [12].
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Figure 4. Digital processing of real-scale scan for a single rigid plant (Salix species), with the ground surface indicated by the red dashed lines. Adapted from Lama and Crimaldi [12].
Figure 4. Digital processing of real-scale scan for a single rigid plant (Salix species), with the ground surface indicated by the red dashed lines. Adapted from Lama and Crimaldi [12].
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Figure 5. Detailed overview of the commercial-type drone employed in the present case study: the yellow and the red arrows indicate the drone equipped with a multispectral camera and its remote controller, respectively. Adapted from Lama et al. [9].
Figure 5. Detailed overview of the commercial-type drone employed in the present case study: the yellow and the red arrows indicate the drone equipped with a multispectral camera and its remote controller, respectively. Adapted from Lama et al. [9].
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Figure 6. Drone-based NDVI for a fully vegetated abandoned ditch. Adapted from Lama et al. [9].
Figure 6. Drone-based NDVI for a fully vegetated abandoned ditch. Adapted from Lama et al. [9].
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Figure 7. Drone-based NDVI for a fully vegetated abandoned ditch.
Figure 7. Drone-based NDVI for a fully vegetated abandoned ditch.
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Lama, G.F.C.; Crimaldi, M. Remote Sensing of Ecohydrological, Ecohydraulic, and Ecohydrodynamic Phenomena in Vegetated Waterways: The Role of Leaf Area Index (LAI). Biol. Life Sci. Forum 2021, 3, 54. https://doi.org/10.3390/IECAG2021-09728

AMA Style

Lama GFC, Crimaldi M. Remote Sensing of Ecohydrological, Ecohydraulic, and Ecohydrodynamic Phenomena in Vegetated Waterways: The Role of Leaf Area Index (LAI). Biology and Life Sciences Forum. 2021; 3(1):54. https://doi.org/10.3390/IECAG2021-09728

Chicago/Turabian Style

Lama, Giuseppe Francesco Cesare, and Mariano Crimaldi. 2021. "Remote Sensing of Ecohydrological, Ecohydraulic, and Ecohydrodynamic Phenomena in Vegetated Waterways: The Role of Leaf Area Index (LAI)" Biology and Life Sciences Forum 3, no. 1: 54. https://doi.org/10.3390/IECAG2021-09728

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