Integrated approaches for monitoring and modeling vegetation in riparian and coastal environments

L'abstract è presente nell'allegato / the abstract is in the attachmen

Role of stochastic forcing in coastal dune vegetation

The relevance and fragility of coastal dune systems are widely recognized. Various conceptual and numerical models have been formulated so far to cope with the threats that affect coastal systems worldwide. These models acknowledge the fundamental influence of vegetation in controlling coastal dunes stability but usually disregard some of the factors affecting coastal dynamics, such as the randomness of the driving forces. In agreement with these observations, a new model for the coastal dune vegetation is here briefly described, and one simplified version is applied to estimate the natural beach width

Density-Based Individual Tree Detection from Three-Dimensional Point Clouds

The use of three-dimensional point clouds in forestry is steadily increasing. Numerous algorithms to detect individual trees from point clouds and derive some fundamental inventory parameters have been proposed so far, but they usually provide higher accuracy in coniferous stands than in deciduous one. In the latter kind of stands, indeed, the tree identification is hampered by the geometrical round shape of the crowns, the interlacing branches of adjacent trees and the usual presence of understory vegetation. In an attempt to overcome these limitations, we developed an algorithm that is innovatively based on the areal point density of the three-dimensional cloud and that provides the height and coordinates of all the trees within a region of interest. In this work, we apply the algorithm to different situations, ranging from the regularly-arranged plantations to the very interlaced crowns of the naturally established stands, demonstrating how it is able to correctly detect most of the trees and recreate a map of their spatial distribution. We also test its capability to deal with relatively low point density and explore the possibility to use it to recreate time series of vegetation biomass. Finally, we discuss the algorithm’s limitations and potentialities, particularly focusing on its coupling to other existing tools to deal with a wider range of applications in forestry and land management

Regional-scale analysis of dune-beach systems using Google Earth Engine

Coastal sand dunes provide a large variety of ecosystem services, among which the inland protection from marine floods. Nowadays, this protection is fundamental, and its importance will further increase in the future due to the rise of the sea level and storm violence induced by climate change. Despite the crucial role of coastal dunes and their potential application in mitigation strategies, the phenomenon of the coastal squeeze, which is mainly caused by the urban sprawl, is progressively reducing the extents of the areas where dune can freely undergo their dynamics, thus dramatically impairing their capability of providing ecosystem services. Aiming to embed the use of satellite images in the study of coastal foredune and beach dynamics, we developed a classification algorithm that uses the satellite images and server-side functions of Google Earth Engine (GEE). The algorithm runs on the GEE Python API and allows the user to retrieve all the available images for the study site and the chosen time period from the selected sensor collection. The algorithm also filters the cloudy and saturated pixels and creates a percentile-composite image over which it applies a random forest classification algorithm. The classification is finally refined by defining a mask for land pixels only. According to the provided training data and sensor selection, the algorithm can give different outcomes, ranging from sand and vegetation maps, beach width measurements, and shoreline time evolution visualization. This very versatile tool that can be used in a great variety of applications within the monitoring and understanding of the dune-beach systems and associated coastal ecosystem services. For instance, we show how this algorithm, combined with machine learning techniques and the assimilation of real data, can support the calibration of a coastal model that gives the natural extent of the beach width and that can be, therefore, used to plan restoration activities

A Density-Based Algorithm for the Detection of Individual Trees from LiDAR Data

Nowadays, LiDAR is widely used for individual tree detection, usually providing higher accuracy in coniferous stands than in deciduous ones, where the rounded-crown, the presence of understory vegetation, and the random spatial tree distribution may affect the identification algorithms. In this work, we propose a novel algorithm that aims to overcome these difficulties and yield the coordinates and the height of the individual trees on the basis of the point density features of the input point cloud. The algorithm was tested on twelve deciduous areas, assessing its performance on both regular-patterned plantations and stands with randomly distributed trees. For all cases, the algorithm provides high accuracy tree count (F-score > 0.7) and satisfying stem locations (position error around 1.0 m). In comparison to other common tools, the algorithm is weakly sensitive to the parameter setup and can be applied with little knowledge of the study site, thus reducing the effort and cost of field campaigns. Furthermore, it demonstrates to require just 2 points·m^−2 as minimum point density, allowing for the analysis of low-density point clouds. Despite its simplicity, it may set the basis for more complex tools, such as those for crown segmentation or biomass computation, with potential applications in forest modeling and management

An integrated methodology for the riparian vegetation modelling

We propose a methodology to calibrate a stochastic model for riparian vegetation dynamics that is based on real data. The methodology integrates various tools that are often used individually in fluvial investigations and it is here applied to the case of the Cinca river (Spain), aiming to explore how its riparian vegetation responds to changing climate conditions

Calibration of a stochastic model for riparian vegetation dynamics from LiDAR acquisitions

The distribution of phreatophyte riparian vegetation can be described by a stochastic model for vegetation growth. According to this, vegetation dynamics are influenced by the topography of the riparian transect and the randomness of hydrological fluctuations, acting as a dichotomous Markov noise. Also, the response of vegetation to this forcing, i.e. its rate of growth and decay, depends of its intrinsic biological features, which are represented in the model by specific input parameters. Although most of these parameters has already been set and literature values provided for the most common tree species in riparian environments, the one representing the vegetation decay still needs to be properly calibrated. To this purpose, a segment of Cinca River (Spain) is here modelled, aiming to obtain a calibration of the decay rate of riparian vegetation in temperate climate. The choice of the study river was done according to the availability of hydrological and LiDAR data. The processing of LiDAR raw data allowed to define the digital terrain model of the study area, providing the geometrical input data of the model. Moreover, LiDAR acquisitions returned a measure of vegetation height and its spatial density, thus leading to the estimation of riparian above-ground biomass, which represents the model output. As the decay rate was the sole unknown parameter for the modelling of the study river, its calibration was possible. Furthermore, as LiDAR provided a highly detailed geometry, the outcome of calibration was not a single value of decay rate for the entire riparian corridor, but a set of values for increasing altitude bands, thus allowing the investigation of its relation with topographic positio

Short‐term biogeomorphology of a gravel‐bed river: Integrating remote sensing with hydraulic modelling and field analysis

In recent decades, fluvial geomorphology and ecohydraulic research have extensively used field observations, remote sensing or hydrodynamic modelling to understand river systems. This study presents an innovative approach that combines field surveys, Light Detection and Ranging (LiDAR)-based topographical and biomass analyses and model-derived hydro-morphodynamic geostatistics to examine short-term bio-geomorphological changes in the wandering gravel-bed Orco River in Italy. Our primary hypothesis is that hydro-morphological variables can be robust descriptors for riparian vegetation distribution. From a geomorphological perspective, our study con-firms the prevalent wandering behaviour of the Orco River. Moreover, we identified a widening trend in braiding and anabranching sections, particularly downstream.This is evident because of hotspots of flood-induced morphological reactivation and the redistribution of sediments from the riverbed to lateral bars, resulting in a multi-thread pattern. Our analysis reveals a net increase in biomass during the observation period despite frequent flood disturbances. We attributed it to two opposing bio-geomorphological dynamics: the reduced flow disturbance in some regions due to flood-induced geomorphological changes and the self-healing of lateral connectivity through river wandering. Such a net increase indicates that transitional rivers store carbon in the form of vegetation biomass due to their short-term morphological instability and the different timescales between vegetation and morphological adjustments. Finally, we supported our initial hypothesis with three key findings: (i) a signature of vegetation not just on topography but also on hydro-morphological conditions, summarised by inundation probability; (ii) the lower variance in vertical topographical changes in vegetated areas compared with bare ones; and (iii) the introduction of a new parameter, named inundation viscosity, derived from the product of mean bed shear stress and average inundation duration, as a discriminating factor for colonisation conditions. These results underscore the value of our comprehensive approach

Satellite Image Processing for the Coarse-Scale Investigation of Sandy Coastal Areas

In recent years, satellite imagery has shown its potential to support the sustainable management of land, water, and natural resources. In particular, it can provide key information about the properties and behavior of sandy beaches and the surrounding vegetation, improving the ecomorphological understanding and modeling of coastal dynamics. Although satellite image processing usually demands high memory and computational resources, free online platforms such as Google Earth Engine (GEE) have recently enabled their users to leverage cloud-based tools and handle big satellite data. In this technical note, we describe an algorithm to classify the coastal land cover and retrieve relevant information from Sentinel-2 and Landsat image collections at specific times or in a multitemporal way: the extent of the beach and vegetation strips, the statistics of the grass cover, and the position of the shoreline and the vegetation–sand interface. Furthermore, we validate the algorithm through both quantitative and qualitative methods, demonstrating the goodness of the derived classification (accuracy of approximately 90%) and showing some examples about the use of the algorithm’s output to study coastal physical and ecological dynamics. Finally, we discuss the algorithm’s limitations and potentialities in light of its scaling for global analyses