UAV-Based Wetland Monitoring: Multispectral and Lidar Fusion with Random Forest Classification
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1. Introduction
Coastal wetlands and associated habitats provide many environmental services and economic benefits to coastal communities, including carbon sequestration, protection from storm surges and other erosive forces, and maintenance of biodiversity [1,2]. These valuable coastal environments are threatened by climate-induced changes to sea level, local climate, and storms. They require study and monitoring to help ensure their preservation. Coastal mangroves are common along tropical and subtropical coasts, ranging in stature from tall trees exceeding 50 m in the tropics to shrubs at the northern extent of their range [3]. Rising sea levels are contributing to their landward migration along waterways, while ocean warming is providing new habitats to the north [4,5]. Northward migration of storm tracks in the northern hemisphere combined with increasing storm intensity challenge this expansion [6]. Growth and dieback are a dynamic process, suggesting the need for monitoring. This migration will encounter anthropogenic and landscape barriers, causing a ‘coastal squeeze’, potentially reducing the extent of shoreline suitable for mangroves and requiring management in near real-time [7]. Effective monitoring of coastal forests will be key to this process.
Traditionally, wetland monitoring has relied on ground surveys that allow for a detailed understanding of the study site. However, this approach can be labor intensive, time consuming, and possibly dangerous. In response to these issues, studies taking place in the past 30 years have moved increasingly toward larger-scale airborne and satellite-based monitoring efforts. Primarily, these efforts have been directed at wetland classification and change detection [8]. These authors also found that, in its current state, satellite-based sensing has been most successful with both convolutional neural networks (CNN) for deep learning and random forest models for machine learning. These methods are not without their challenges. Some challenges to CNN are the minimum sampling area needed for mangrove detection and spectral confusion with species common to mangrove swamps [9,10]. For random forest methods, studies have shown better performance when optical- and radar-based data are used as model inputs [11,12]. Reference [13] describes wetlands as a ‘moving target’ based on large variability between environments. This variability exists between locations and even between seasons at a single location. These and related challenges are discussed by [14,15].
An alternative approach is the use of unoccupied aerial vehicles (UAVs), which can operate at a moderate cost, facilitating repeat surveys [16,17]. UAV systems can also generate accurate digital elevation models (DEMs), using LiDAR or structure from motion (SfM) [18], with higher spatial resolution compared to satellite-based data [19]. SfM is adequate for forest height and density monitoring in mangrove-dominant environments [20] but is challenged where bare earth elevation is required and vegetation is dense.
Reference [21] reviewed the current state of UAVs in wetland applications, reporting on strengths, weaknesses, and emerging trends. Among the strengths were the many ways a UAV survey can be customized. UAV-derived data have been used in biomass calculations, vegetation mapping, and four-dimensional (3D + time) change detection [22,23,24]. UAVs can be deployed at nearly any time, limited mainly by regulations and weather, and can be equipped with multiple sensor types for increased data gathering, with UAV-LiDAR becoming more common. However, some studies limited reporting on accuracy—for example, the number and quality of ground control points. In lieu of ground control points, real time kinematic (RTK) surveys are playing a larger role. However, processing the high-resolution data acquired by this instrumentation has proven challenging: the accuracy of RTK may be much less than the pixel resolution of the survey. At the pixel scale, heterogeneities in spectral signatures can make classification and delineation challenging. In response to this, some studies have preferred an object-based approach. This may then be complicated by the requirement to fine-tune the segmentation algorithms with appropriate variables. Reference [21] reviews these and related issues.
One emerging trend is to use UAV surveys to augment and scale up to satellite-based regional scales [25,26]. Hyperspectral data are also becoming more widely available [27,28,29,30], albeit with data processing challenges compared to simpler multispectral data with fewer bands. Machine learning has also become a tool used in many classification efforts. In part, these algorithms are employed to deal with the large number of descriptors that can be generated by UAV surveys, including UAV-LiDAR data [27,29,30,31].
In this study, we aim to use UAV-based methods with simplified data collection and processing protocols for wetland surveyance and classification. Our protocols highlight the utility of first-order data to monitoring efforts. We use high spatial-resolution multi-spectral data with five spectral bands, high-resolution ground elevation data derived from LiDAR, and a simple random forest machine learning classification algorithm.
5. Conclusions
The proliferation of hardware and easy-to-use software has simplified geodetic and ecological surveying. UAVs have lowered the cost of data collection, enabling higher-resolution surveys compared to space-based satellite and high-altitude aviation, albeit with much less spatial coverage.
Landscape classification based on machine learning has also improved to the point where non-specialists can apply the technique. We used a Python-based machine learning package providing all the algorithms needed for machine learning model generation, data preparation, and analysis. This allowed us to create a streamlined workflow where we generated models and reviewed these in minutes on low-cost workstations.
Combining topographic and multispectral raster layers allowed us to study and characterize a mixed-use subtropical lowland at the mouth of Tampa Bay, Florida. Simple on-site workflows for UAV deployment facilitated rapid and spatially accurate data collection. Several machine learning-based classification models were tested using the Random Forest algorithm. Despite the small training area, comparison with ground-based validation data indicates that the 2000-pixel smoothed models are 80% accurate. A smoothing process based on a consensus of neighboring pixels improved classification accuracy.
For a coastal wetland site, adding bare earth elevation data to multi-spectral optical imagery improves classification. With the growing interest in wetland habitats as markers of climate change, detailed mapping will be needed. Our approach offers a cost-effective solution, with sparse parameter input (five spectral bands plus LiDAR-based elevation) and minimal processing. These methods should work in other environments where habitats are partitioned particularly by ground elevation, but this will require additional research.
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