Menno Straatsma

Airborne Laser Scanning as a Tool for Lowland Floodplain Vegetation Monitoring

Monitoring of three-dimensional floodplain vegetation structure is essential for ecological studies, as well as for hydrodynamic modelling of rivers. Height and density of submerged vegetation and density of emergent vegetation are the key characteristics from which roughness parameters in hydraulic models are derived. Airborne laser scanning is a technique with broad applications in vegetation structure mapping, which therefore may be a promising tool in monitoring floodplain vegetation for river management applications. This paper first provides an introduction to the laser scanning technique, and reviews previous studies on the extraction of vegetation height and density of forests, low vegetation and meadows or unvegetated areas. Reliable predictions using laser scan data have been reported for forest height (R2=0.64–0.98), parameters related to forest density, such as stem number, stem diameter, biomass, timber volume or basal area (R2=0.42–0.93), and herbaceous vegetation height (summer condition; R2=0.75–0.89). No empirical relations have been reported on density of herbaceous vegetation. Laser data of meadows and unvegetated areas show too much noise to predict vegetation structure correctly. In a case study for the lower Rhine river, the potential of laser scan mapping of vegetation structure was further explored for winter conditions. Three laser-derived metrics that are often reported in the literature have been applied to characterize local vertical distributions of laser reflections. The laser data clearly show the large structural differences both between and within vegetation units that currently are the basis of floodplain vegetation and roughness mapping. The results indicate that airborne laser scanning is a promising technique for extraction of 3D-structure of floodplain vegetation in winter, except for meadows and unvegetated areas.

Two novel methods for field measurements of hydrodynamic density of floodplain vegetation using terrestrial laser scanning and digital parallel photography

Hydrodynamic vegetation density, the sum of the projected plant area per unit volume, is an important parameter for floodplain flow models. This paper compares two novel techniques to quantify this parameter in the field: terrestrial laser scanning (TLS) and digital parallel photography (PP). Separate field reference data were collected for the two methods, which consisted of (1) a stem map of 650 trees, aggregated into 23 plots in a single forest patch, (2) 17 manually measured forest plots in two floodplains. PP consists of a series of digital photographic images of vegetation against a contrasting background. The centre columns of all images were merged into a single composite parallel image. This mosaic was thresholded to determine the fractional coverage of the vegetation, which is converted to vegetation density using the optical point quadrat method. A sensitivity analysis proved that PP is insensitive to small errors on the selected number of centre columns, photograph spacing should not exceed 20 cm, photograph resolution is important and the plot depth should be measured accurately. TLS was carried out using a Leica HDS3000 time‐of‐flight laser scanner. Data processing of TLS data consisted of slicing the points around breast height. In a polar grid the vegetation density was predicted using the optical point quadrat method, corrected for missing points. Both methods were compared to the field reference data. PP (EF = 0.99; bias = 8.4×10−5 m−1) showed a higher modelling efficiency than the TLS method (EF = 0.63; bias = 0.015 m−1). An advantage of the TLS method is the ability to provide a detailed 2‐dimensional or even 3‐dimensional distribution of vegetation density. PP is cheaper, faster, and data processing is limited. We conclude that TLS and PP are two complementary techniques that show high accuracies for field measurements of vegetation density.

Extracting structural characteristics of herbaceous floodplain vegetation under leaf-off conditions using airborne laser scanner data

Hydrodynamic models of river flow need detailed and accurate friction values as input. Friction values of floodplain vegetation are based on vegetation height and density. To map spatial patterns of floodplain vegetation structure, airborne laser scanning is a promising tool. In a test for the lower Rhine floodplain, vegetation height and density of herbaceous vegetation were measured in the field at 42 georeferenced plots of 200 m2 each. Simultaneously, three airborne laser scanning (ALS) surveys were carried out in the same area resulting in three high resolution, first pulse, small‐footprint datasets. The laser data surveys differed in flying height, gain setting and laser diode age. Point density of the laser data varied between 10 and 75 points m−2. Point heights relative to the DTM derived from the ALS data were used in all analyses. Laser points were labelled as either vegetation or ground using three different methods: (1) a fixed threshold value; (2) a flexible threshold value based on the inflection point in the point height distribution; and (3) using a Gaussian distribution to separate noise in the ground surface points from vegetation. Twenty‐one statistics were computed for each of the resulting vegetation‐point distributions, which were subsequently compared with field observations of vegetation height. Additionally, the percentage index (PI) was computed to relate density of vegetation points to hydrodynamic vegetation density. The vegetation height was best predicted by using the inflection method for labelling and the 95 percentile as a regressor (R 2 = 0.74–0.88). Vegetation density was best predicted using the threshold method for labelling and the PI as a predictor (R 2 = 0.51). The results of vegetation height prediction were found to depend on the combined effect of flying height, gain setting or laser diode age. The quality of the estimation of vegetation height and density is also affected by point density, for densities lower than 15 points m−2. We conclude that high resolution ALS data allows to estimate vegetation height and density of herbaceous vegetation in winter condition, but field reference data remains necessary for calibration.

Characterizing tree growth anomaly induced by landslides using LiDAR

Disrupted vegetation is often used as an indicator for landslide activity in forested regions. The extraction of irregular trees from airborne laser scanning data remains problematic because of low quality of observed data and paucity of field data validation. We obtained high density airborne LiDAR (HDAL) data with 180 points m−2 for characterizing tree growth anomalies caused by landslides in the Barcelonnette region, the Southern French Alps. HDAL allowed the mapping of a complex landslide and its three kinematic zones. The TreeVaW method detecting trees from the HDAL data and determined their position and height, while the SkelTre-skeletonization method extracted the tree inclination. The tree growth anomalies are parameterized by tree height dissimilarities and tree inclinations. These parameters were successfully extracted from the HDAL and compared with field data. We revealed that the distribution of LiDAR-derived tree growth anomalies was statistically different for landslide areas as compared to stable areas.

Quantitative Mapping of Hydrodynamic Vegetation Density of Floodplain Forests Under Leaf-off Conditions Using Airborne Laser Scanning

In this paper a method is presented to extract hydrodynamic vegetation density from airborne laser scanner data, relevant for exceedance levels of embankments of lowland areas. Two indices to predict vegetation density from the laser data were considered: (a) Percentage Index (PI) of points in the height interval inundated by the water, and (b) the Vegetation Area Index (VAI) that corrects for occlusion from the crown area. A computer simulation, using a digital forest model, showed a sensitivity of the indices for laser pulses that were sent out, but not detected by the laser receiver. The locations of these invalid points were therefore reconstructed. Two different assumptions were tested to assign new coordinates to these so-called invalid points. Percentage Index, with the invalid points reconstructed by means of thresholding the point density ratio, proved the best predictor (R 2 � 0.66) of vegetation density of deciduous floodplain forests under winter conditions.

Biodiversity recovery following delta-wide measures for flood risk reduction

We show that biodiversity recovery was successfully combined with flood risk reduction interventions at the river delta scale. Biodiversity declined markedly over the past 150 years, with the biodiversity loss in fluvial ecosystems exceeding the global average. River restoration now aims at flood safety while enhancing biodiversity and has had success locally. However, at the scale of large river distributaries, the recovery remained elusive. We quantify changes in biodiversity of protected and endangered species over 15 years of river restoration in the embanked floodplains of an entire river delta. We distinguish seven taxonomic groups and four functional groups in more than 2 million field observations of species presence. Of all 179 fluvial floodplain sections examined, 137 showed an increase in biodiversity, particularly for fast-spreading species. Birds and mammals showed the largest increase, that is, +13 and +3 percentage point saturation of their potential based on habitat. This shows that flood risk interventions were successfully combined with enhancement of biodiversity, whereas flood stage decreased (−24 cm).

High density airborne lidar estimation of disrupted trees induced by landslides

Airborne laser scanning (ALS) data has revolutionized the landslide assessment in a rugged vegetated terrain. It enables the parameterization of morphology and vegetation of the instability slopes. Vegetation characteristics are by far less investigated because of the currently available accuracy and density ALS data and paucity of field data validation. We utilized a high density ALS (HDALS) data with 170 points m-2 for characterizing disrupted vegetation induced by landslides by means of a variable window filter and the SkelTre-skeletonisation. Tree analyses in landslide areas resulted in relatively low height, small crown and more irregularities, whereas these peculiarities are not so obvious in the healthy forests. The statistical tests unveiled the clear differences between the extracted parameters in landslide and non-landslide zones and supported the field evidences. We concluded that HDALS is a promising tool to geometrically retrieve disrupted woody vegetation structures and can be good bioindicator to landslide activity.

Multitemporal Classification of River Floodplain Vegetation Using Time Series of UAV Images

The functions of river floodplains often conflict spatially, for example, water conveyance during peak discharge and diverse riparian ecology. Such functions are often associated with floodplain vegetation. Frequent monitoring of floodplain land cover is necessary to capture the dynamics of this vegetation. However, low classification accuracies are found with existing methods, especially for relatively similar vegetation types, such as grassland and herbaceous vegetation. Unmanned aerial vehicle (UAV) imagery has great potential to improve the classification of these vegetation types owing to its high spatial resolution and flexibility in image acquisition timing. This study aimed to evaluate the increase in classification accuracy obtained using multitemporal UAV images versus single time step data on floodplain land cover classification and to assess the effect of varying the number and timing of imagery acquisition moments. We obtained a dataset of multitemporal UAV imagery and field reference observations and applied object-based Random Forest classification (RF) to data of different time step combinations. High overall accuracies (OA) exceeding 90% were found for the RF of floodplain land cover, with six vegetation classes and four non-vegetation classes. Using two or more time steps compared with a single time step increased the OA from 96.9% to 99.3%. The users accuracies of the classes with large similarity, such as natural grassland and herbaceous vegetation, also exceeded 90%. The combination of imagery from June and September resulted in the highest OA (98%) for two time steps. Our method is a practical and highly accurate solution for monitoring areas of a few square kilometres. For large-scale monitoring of floodplains, the same method can be used, but with data from airborne platforms covering larger extents.

Flood hazard reduction from automatically applied landscaping measures in RiverScape, a Python package coupled to a two-dimensional flow model

Abstract River managers of alluvial rivers often need to reconcile conflicting objectives, but stakeholder processes are prone to subjectivity, time consuming and therefore limited in scope. Here we present RiverScape, a modeling tool for numerical creation, positioning and implementation of seven common flood hazard reduction measures at any intensity in a 2D hydrodynamic model for a river with embanked floodplains. It evaluates the measures for (1) hydrodynamic effects with the 2D flow model Delft3D Flexible Mesh, and (2) the required landscaping work expressed as the displaced volume of material. The most effective flood hazard reduction in terms of transported material is vegetation roughness smoothing, followed by main embankment raising, groyne lowering, minor embankment lowering, side channel construction, floodplain lowering and relocating the main embankment. Implementation of this tool may speed up decision making considerably. Applications elsewhere could weigh in adverse downstream effects, degradation of the ecology and overly expensive choices.

River floodplain vegetation classification using multi-temporal high-resolution colour infrared UAV imagery.

To evaluate floodplain functioning, monitoring of its vegetation is essential. Although airborne imagery is widely applied for this purpose, classification accuracy (CA) remains low for grassland (< 88%) and herbaceous vegetation (<57%) due to the spectral and structural similarity of these vegetation types. Increased availability of Unmanned Aerial Vehicles (UAV) allows low-cost production of high-resolution orthophotos and digital surface models (DSMs). Multi-temporal DSMs and orthophotos may be used as input for an improved classification methodology, using differences in phenological changes between vegetation types. The aim of this study was (1) to evaluate the improvement of the CA when using multi-temporal UAV-derived imagery and (2) to determine which layers of a multi-temporal imagery and derived DSMs yield an optimal balance between CA and acquisition effort. During six field surveys with six to ten weeks intervals over one year, a floodplain section along the lower Rhine, the Netherlands, was recorded with true-colour and false-colour imagery with a UAV. In several segmentation-classification-evaluation loops we determined the most important set of variables and the data layers providing them. Our main conclusions are (1) Multi-temporal data input greatly improve CAs of grassland and herbaceous vegetation classes in floodplains: user’s accuracies exceed 90%, and (2) the input data contributing most to these high CAs are NDVI layers from winter, spring and summer, and nDSM layers from winter and end of summer.