Quantifying structural physical habitat attributes using LIDAR and hyperspectral imagery

Remote Sensing of Riparian Ecosystems

Riparian zones are dynamic ecosystems that form at the interface between the aquatic and terrestrial components of a landscape. They are shaped by complex interactions between the biophysical components of river systems, including hydrology, geomorphology, and vegetation. Remote sensing technology is a powerful tool useful for understanding riparian form, function, and change over time, as it allows for the continuous collection of geospatial data over large areas. This paper provides an overview of studies published from 1991 to 2021 that have used remote sensing techniques to map and understand the processes that shape riparian habitats and their ecological functions. In total, 257 articles were reviewed and organised into six main categories (physical channel properties; morphology and vegetation or field survey; canopy detection; application of vegetation and water indices; riparian vegetation; and fauna habitat assessment). The majority of studies used aerial RGB imagery for river reaches up to 100 km in length and Landsat satellite imagery for river reaches from 100 to 1000 km in length. During the recent decade, UAVs (unmanned aerial vehicles) have been widely used for low-cost monitoring and mapping of riverine and riparian environments. However, the transfer of RS data to managers and stakeholders for systematic monitoring as a source of decision making for and successful management of riparian zones remains one of the main challenges.

Regional Scale Dryland Vegetation Classification with an Integrated Lidar-Hyperspectral Approach

The sparse canopy cover and large contribution of bright background soil, along with the heterogeneous vegetation types in close proximity, are common challenges for mapping dryland vegetation with remote sensing. Consequently, the results of a single classification algorithm or one type of sensor to characterize dryland vegetation typically show low accuracy and lack robustness. In our study, we improved classification accuracy in a semi-arid ecosystem based on the use of vegetation optical (hyperspectral) and structural (lidar) information combined with the environmental characteristics of the landscape. To accomplish this goal, we used both spectral angle mapper (SAM) and multiple endmember spectral mixture analysis (MESMA) for optical vegetation classification. Lidar-derived maximum vegetation height and delineated riparian zones were then used to modify the optical classification. Incorporating the lidar information into the classification scheme increased the overall accuracy from 60% to 89%. Canopy structure can have a strong influence on spectral variability and the lidar provided complementary information for SAM’s sensitivity to shape but not magnitude of the spectra. Similar approaches to map large regions of drylands with low uncertainty may be readily implemented with unmixing algorithms applied to upcoming space-based imaging spectroscopy and lidar. This study advances our understanding of the nuances associated with mapping xeric and mesic regions, and highlights the importance of incorporating complementary algorithms and sensors to accurately characterize the heterogeneity of dryland ecosystems.

Mapping Riparian Vegetation Functions Using 3D Bispectral LiDAR Data

Riparian zones experience many anthropic pressures and are the subject of European legislation to encourage their monitoring and management, to attenuate these pressures. Assessing the effectiveness of management practices requires producing indicators of ecological functions. Laser Detection and Ranging (LiDAR) data can provide valuable information to assess the ecological status of riparian zones. The objective of this study was to evaluate the potential of LiDAR point clouds to produce indicators of riparian zone status. We used 3D bispectral LiDAR data to produce several indicators of a riparian zone of a dammed river in Normandy (France). The indicators were produced either directly from the 3D point clouds (e.g., biomass overhanging the channel, variation in canopy height) or indirectly, by applying the Random Forest classification algorithm to the point clouds. Results highlight the potential of 3D LiDAR point clouds to produce indicators with sufficient accuracy (ca. 80% for the number of trunks and 68% for species composition). Our results also reveal advantages of using metrics related to the internal structure of trees, such as penetration indexes. However, intensity metrics calculated using bispectral properties of LiDAR did not improve the quality of classifications. Longitudinal analysis of the indicators revealed a difference in attributes between the reservoir and areas downstream from it.

Ecological risk based assessment used to restore riparian physical functions to a fresh water Creek

The purpose of this study was to determine if an interdisciplinary team using a qualitative proper functioning condition (PFC) assessment protocol could identify and reverse significant detrimental ecological alterations which occurred within Gertie's Creek watershed, Ontario, Canada. At potential, Gertie's Creek supported a woody debris glacial outwash fine gravel substrate fish spawning habitat. The anthropogenic activities on Georgina Island caused a denuded anadromous fish population since the early-to mid-1990's in the Gertie's Creek watershed. The PFC assessment indicated that anthropogenic activities on Georgina Island negatively impacted stream flows in Gertie's Creek. Reduced stream flow resulted in the natural stream (lotic) riparian habitat not advancing out of an early seral silver maple and eastern hemlock vegetated swamp (forested wetland) habitat. The Gertie's Creek interdisciplinary team PFC assessment indicated that the entire watershed is not in balance with the water and sediment being supplied along with a lack of diverse riparian vegetation. Sediment was not being transported to the wetland and lake coastal areas because of chronic reduced flows. Further qualitative assessments by the authors of other smaller lentic and lotic ecosystems on Georgina Island indicate that reduced hydrologic flow is an issue for the entire island. Ecosystem function management planning works with the ecosystem to continually respond as the ecology changes in ways that enhance remarkable natural recovery.

Impacts of climate-change-driven sea level rise on intertidal rocky reef habitats will be variable and site specific

Intertidal rocky reefs are complex and rich ecosystems that are vulnerable to even the smallest fluctuations in sea level. We modelled habitat loss associated with sea level rise for intertidal rocky reefs using GIS, high-resolution digital imagery, and LIDAR technology at fine-scale resolution (0.1 m per pixel). We used projected sea levels of +0.3 m, +0.5 m and +1.0 m above current Mean Low Tide Level (0.4 m). Habitat loss and changes were analysed for each scenario for five headlands in the Solitary Islands Marine Park (SIMP), Australia. The results indicate that changes to habitat extent will be variable across different shores and will not necessarily result in net loss of area for some habitats. In addition, habitat modification will not follow a regular pattern over the projected sea levels. Two of the headlands included in the study currently have the maximum level of protection within the SIMP. However, these headlands are likely to lose much of the habitat known to support biodiverse assemblages and may not continue to be suitable sanctuaries into the future. The fine-scale approach taken in this study thus provides a protocol not only for modelling habitat modification but also for future proofing conservation measures under a scenario of changing sea levels.

Influence of vegetation structure on lidar-derived canopy height and fractional cover in forested riparian buffers during leaf-off and leaf-on conditions

Estimates of canopy height (H) and fractional canopy cover (FC) derived from lidar data collected during leaf-on and leaf-off conditions are compared with field measurements from 80 forested riparian buffer plots. The purpose is to determine if existing lidar data flown in leaf-off conditions for applications such as terrain mapping can effectively estimate forested riparian buffer H and FC within a range of riparian vegetation types. Results illustrate that: 1) leaf-off and leaf-on lidar percentile estimates are similar to measured heights in all plots except those dominated by deciduous compound-leaved trees where lidar underestimates H during leaf off periods; 2) canopy height models (CHMs) underestimate H by a larger margin compared to percentile methods and are influenced by vegetation type (conifer needle, deciduous simple leaf or deciduous compound leaf) and canopy height variability, 3) lidar estimates of FC are within 10% of plot measurements during leaf-on periods, but are underestimated during leaf-off periods except in mixed and conifer plots; and 4) depth of laser pulse penetration lower in the canopy is more variable compared to top of the canopy penetration which may influence within canopy vegetation structure estimates. This study demonstrates that leaf-off lidar data can be used to estimate forested riparian buffer canopy height within diverse vegetation conditions and fractional canopy cover within mixed and conifer forests when leaf-on lidar data are not available.