Characterizing spatial variability in coastal wetland biomass across multiple scales using UAV and satellite imagery

Abstract

Coastal wetland biomass is an important indicator of wetland productivity, carbon storage, health, and vulnerability to climate change. The ability to estimate aboveground biomass (AGB) in wetlands at ecologically relevant scales is complicated by the spatial variability inherent to patterns in wetland vegetation and the biogeomorphic processes that help create them. Remote sensing provides an approach for mapping wetland biomass, but the spatial resolutions of satellite and airborne imagery often constrain the types of ecological patterns and processes that can be detected. Unmanned Aerial Vehicles (UAVs) have previously been used to capture fine-scale (≤1 m) variability in AGB in coastal wetland settings. However, it remains unclear if a UAV approach to estimating wetland biomass is transferrable across diverse wetland sites or how it compares to commonly used satellite-based approaches. Here, we test the capabilities of UAVs in remotely quantifying AGB and compare biomass estimation using UAV and Landsat satellite imagery (30 m resolution) in several wetland sites in Southern California. Field surveys highlight significant spatial variability in wetland plant community AGB and height that influence remote biomass estimation. Relationships between UAV vegetation indices and AGB were site-specific and influenced by vegetation types. Biomass estimation using UAVs (r 2 = 0.40, RMSE = 534.6 g m −2 ) showed better correlation with NDVI than a Landsatbased approach (r 2 = 0.26, RMSE = 596.8 g m −2 ). We found combining highresolution UAV AGB maps and Landsat NDVI to develop AGB models showed the highest correlation (r 2 = 0.45, RMSE = 659.7 g m −2 ) and provided additional spatial information to aid scaling field data to satellite imagery. Overall, UAVs captured more spatial complexity in aboveground biomass at finer scales than is possible with moderate-resolution Landsat pixels, indicating that UAVs can be used to characterize patterns of within-marsh variability resulting from local-scale (≤ 100s of meters) ecological processes.

Machine Learning Classification and Accuracy Assessment from High-Resolution Images of Coastal Wetlands

“…The use of high-resolution images to classify plant communities in wetlands has been previously undertaken in several studies using either commercial satellites (<4 m resolution) [81][82][83] or UAVs images (<0.1 m resolution) for environmental assessment [10,84]. Classification of plant communities in coastal wetlands, where individual plants are less than 1 m wide (e.g., coastal grasslands, floodplain grasslands, saltmarshes and seagrasses) benefits from the use of very high resolution data to reveal as much spectral variability as possible [38,42].…”

“…The use of ML algorithms in remote sensing has provided a solution for complex classifications, using additional explanatory variables apart from spectral values to build classification models, such as vegetation indices or DEMs [22,42,84]. These algorithms can also classify categorical units in a more complex feature space, where the spectral separability is low due to the higher variability within classes [85].…”

A Review of Unoccupied Aerial Vehicle Use in Wetland Applications: Emerging Opportunities in Approach, Technology, and Data

“…Two studies focused on the use of SfM from RGB data [53,87] to estimate biomass. Others linked targeted UAV data with satellite imagery to map biomass, including combining NDVI from multispectral UAV imagery and Landsat imagery [33], UAV-LiDAR and Sentinel-2 imagery [86], and RGB UAV imagery with Sentinel-1 and Sentinel-2 imagery [62]. Two studies [54,67] used SfM with RGB imagery to capture detailed models of riparian vegetation in order to reconstruct physical models of structure and shading properties, while another riverine study [23] used RGB and multispectral orthophotos with an OBIA approach.…”

“…Another important emerging interest is applying UAV data towards scaling ecosystem characteristics from local to regional extents [33,145] and comparing the predictive performance of UAV-based versus satellite-based ecological indicators [134]. Although such applications are still relatively few, they are extremely promising for enhancing regionalscale modeling of ecosystem functions and services targeted by management at the new levels of robustness enabled by comprehensive UAV-based reference information.…”

“…UAVs are uniquely positioned to cost-effectively overcome these challenges at local observation scales via spatially comprehensive coverage, customizable flight schedules, and diverse sensor instruments for specific applications [18,[30][31][32][33]. The high spatial resolution of UAV-derived imagery makes them an especially valuable surrogate for field assessments by enabling visual recognition of landscape elements and evaluation of multiple indicators of wetland habitat and ecological status (e.g., [7,[34][35][36]).…”