Unmanned Aerial Vehicles (UAVs) in environmental biology: A Review

Acquiring information about the environment is a key step during each study in the field of environmental biology at different levels, from an individual species to community and biome. However, obtaining information about the environment is frequently difficult because of, for example, the phenological timing, spatial distribution of a species or limited accessibility of a particular area for the field survey. Moreover, remote sensing technology, which enables the observation of the Earth’s surface and is currently very common in environmental research, has many limitations such as insufficient spatial, spectral and temporal resolution and a high cost of data acquisition. Since the 1990s, researchers have been exploring the potential of different types of unmanned aerial vehicles (UAVs) for monitoring Earth’s surface. The present study reviews recent scientific literature dealing with the use of UAV in environmental biology. Amongst numerous papers, short communications and conference abstracts, we selected 110 original studies of how UAVs can be used in environmental biology and which organisms can be studied in this manner. Most of these studies concerned the use of UAV to measure the vegetation parameters such as crown height, volume, number of individuals (14 studies) and quantification of the spatio-temporal dynamics of vegetation changes (12 studies). UAVs were also frequently applied to count birds and mammals, especially those living in the water. Generally, the analytical part of the present study was divided into following sections: (1) detecting, assessing and predicting threats on vegetation, (2) measuring the biophysical parameters of vegetation, (3) quantifying the dynamics of changes in plants and habitats and (4) population and behaviour studies of animals. At the end, we also synthesised all the information showing, amongst others, the advances in environmental biology because of UAV application. Considering that 33% of studies found and included in this review were published in 2017 and 2018, it is expected that the number and variety of applications of UAVs in environmental biology will increase in the future

FINE SCALE MAPPING OF LAURENTIAN MIXED FOREST NATURAL HABITAT COMMUNITIES USING MULTISPECTRAL NAIP AND UAV DATASETS COMBINED WITH MACHINE LEARNING METHODS

Natural habitat communities are an important element of any forest ecosystem. Mapping and monitoring Laurentian Mixed Forest natural communities using high spatial resolution imagery is vital for management and conservation purposes. This study developed integrated spatial, spectral and Machine Learning (ML) approaches for mapping complex vegetation communities. The study utilized ultra-high and high spatial resolution National Agriculture Imagery Program (NAIP) and Unmanned Aerial Vehicle (UAV) datasets, and Digital Elevation Model (DEM). Complex natural vegetation community habitats in the Laurentian Mixed Forest of the Upper Midwest. A detailed workflow is presented to effectively process UAV imageries in a dense forest environment where the acquisition of ground control points (GCPs) is extremely difficult. Statistical feature selection methods such as Joint Mutual Information Maximization (JMIM) which is not that widely used in the natural resource field and variable importance (varImp) were used to discriminate spectrally similar habitat communities. A comprehensive approach to training set delineation was implemented including the use of Principal Components Analysis (PCA), Independent Components Analysis (ICA), soils data, and expert image interpretation. The developed approach resulted in robust training sets to delineate and accurately map natural community habitats. Three ML algorithms were implemented Random Forest (RF), Support Vector Machine (SVM), and Averaged Neural Network (avNNet). RF outperformed SVM and avNNet. Overall RF accuracies across the three study sites ranged from 79.45-87.74% for NAIP and 87.31-93.74% for the UAV datasets. Different ancillary datasets including spectral enhancement and image transformation techniques (PCA and ICA), GLCM-Texture, spectral indices, and topography features (elevation, slope, and aspect) were evaluated using the JMIM and varImp feature selection methods, overall accuracy assessment, and kappa calculations. The robustness of the workflow was evaluated with three study sites which are geomorphologically unique and contain different natural habitat communities. This integrated approach is recommended for accurate natural habitat community classification in ecologically complex landscapes

Classifying and Mapping Aquatic Vegetation in Heterogeneous Stream Ecosystems Using Visible and Multispectral UAV Imagery

The need for assessment and management of aquatic vegetation in stream ecosystems is recognized given the importance in impacting water quality, hydrodynamics, and aquatic biota. However, existing approaches to monitor are laborious and its currently not feasible to track spatial and temporal differences at broad scales. The objective of this study was therefore to map and classify aquatic vegetation of a shallow stream with heterogenous mixtures of emergent and submerged aquatic vegetation. Data was collected in the Camden Creek watershed within the Inner Bluegrass Region of central Kentucky. The use of unmanned aerial vehicles (UAVs) was employed and both visible (RGB) and multispectral imagery were collected. Machine learning techniques were applied in an off-the-shelf software (QGIS environment) to develop visible and multispectral classification land-cover maps following an effective object-based image analysis workflow. Visible images were additionally coupled with high frequency water quality data to examine the spatial and temporal behavior of the aquatic vegetation. Results showed high overall classification accuracies (OA=83.5% for the training dataset and OA=83.73% for the validation dataset) for the visible imagery, with excellent user’s and producer’s accuracies for duckweed, both for training and validation. Surprisingly, multispectral overall accuracies were substantial (OA=77.8% for the training dataset and OA=70.2% for the validation dataset) but were inferior to the visible classification results. User’s and producer’s accuracies were lower for almost all classes. However, this approach was unsuccessful in detecting, segmenting and classifying submerged aquatic vegetation (algae) for both datasets. Finally, a change detection algorithm was applied to the visible classified maps and the changes in duckweed areal coverage were successfully estimated

Multiscale collection and analysis of submerged aquatic vegetation spectral profiles for Eurasian watermilfoil detection

The ability to differentiate a non-native aquatic plant, Myriophyllum spicatum (Eurasian watermilfoil or EWM), from other submerged aquatic vegetation (SAV) using spectral data collected at multiple scales was investigated as a precursor to mapping of EWM. Spectral data were collected using spectroradiometers for SAV taken out of the water, from the side of a boat directly over areas of SAV and from a lightweight portable radiometer system flown from an unmanned aerial system (UAS). EWM was spectrally different from other SAV when using 651 spectral bands collected in ultraviolet to near-infrared range of 350 to 1000 nm but does not provide a practical system for EWM mapping because this exceeds the capabilities of available airborne hyperspectral imaging systems. Using only six spectral bands corresponding to an available multispectral camera or eight wetlands-centric bands did not reliably differentiate EWM from other SAV and assemblages. However, a modified version of the normalized difference vegetation index (mNDVI), using a ratio of red-edge to red light, was significantly different among dominant vegetation groups. Also, averaging the full range of spectral to 65 10-nm wide bands, similar to available hyperspectral imaging systems, provided the ability to identify EWM separately from other SAV. The UAS-collected spectral data had the lowest remote sensing reflectance versus the out-of-water and boatside data, emphasizing the need to collect optimized data. The spectral data collected for this study support that with relatively clear and calm water, hyperspectral data, and mNDVI, it is likely that UAS-based imaging can help with mapping and monitoring of EWM

Land cover classification of treeline ecotones along a 1100 km latitudinal transect using spectral- and three-dimensional information from UAV-based aerial imagery

The alpine treeline ecotone is expected to move upwards in elevation with global warming. Thus, mapping treeline ecotones is crucial in monitoring potential changes. Previous remote sensing studies have focused on the usage of satellites and aircrafts for mapping the treeline ecotone. However, treeline ecotones can be highly heterogenous, and thus the use of imagery with higher spatial resolution should be investigated. We evaluate the potential of using unmanned aerial vehicles (UAVs) for the collection of ultra-high spatial resolution imagery for mapping treeline ecotone land covers. We acquired imagery and field reference data from 32 treeline ecotone sites along a 1100 km latitudinal gradient in Norway (60–69°N). Before classification, we performed a superpixel segmentation of the UAV-derived orthomosaics and assigned land cover classes to segments: rock, water, snow, shadow, wetland, tree-covered area and five classes within the ridge-snowbed gradient. We calculated features providing spectral, textural, three-dimensional vegetation structure, topographical and shape information for the classification. To evaluate the influence of acquisition time during the growing season and geographical variations, we performed four sets of classifications: global, seasonal-based, geographical regional-based and seasonal-regional-based. We found no differences in overall accuracy (OA) between the different classifications, and the global model with observations irrespective of data acquisition timing and geographical region had an OA of 73%. When accounting for similarities between closely related classes along the ridge-snowbed gradient, the accuracy increased to 92.6%. We found spectral features related to visible, red-edge and near-infrared bands to be the most important to predict treeline ecotone land cover classes. Our results show that the use of UAVs is efficient in mapping treeline ecotones, and that data can be acquired irrespective of timing within a growing season and geographical region to get accurate land cover maps. This can overcome constraints of a short field-season or low-resolution remote sensing data.publishedVersio

DETECTION AND CLASSIFICATION OF EURASIAN WATERMILFOIL WITH MULTISPECTRAL DRONE-ENABLED SENSING

Littoral zones support growth of submerged aquatic vegetation, creating productive areas that provide food and habitat for fish, amphibians, macroinvertebrates, and other parts of the food web. Understanding macrophyte dynamics requires the identification of submerged aquatic vegetation (SAV) taxa, which can be possible if taxa of interest are spectrally distinct with data collected at appropriate scales. Eurasian watermilfoil, Myriophyllum spicatum or EWM, is a non-native SAV species that forms thick, often monotypic beds that reduce benthic species richness, restrict recreation, reduce property values, clog water intakes, and lower dissolved oxygen concentrations. Remote sensing of SAV species has to address the presence of lake color constituents that reduce lake clarity, making identification of species of interest more challenging. To address this challenge, I first investigated how to collect spectral data of SAV from boatside and drone platforms to determine the number and types of bands needed to identify EWM. Hyperspectral numbers of bands such as 65 10-nm wide bands between 350 and 1000nm reliably identified EWM, while use of a modified normalized difference vegetation (NDVI) index provided significant differences among SAV vs. other dominant aquatic vegetation groups. We demonstrated this for classifications at five sites over three years in the littoral areas of the Les Cheneaux Islands in northwestern Lake Huron, Michigan, USA, with 78.7% average producer’s accuracy and 76.7% average user’s accuracy, higher than most previous efforts at remote sensing of SAV. Finally, we applied these mapping capabilities to two areas in the Les Cheneaux Islands and one area in the Keweenaw Peninsula in Michigan’s northwestern Upper Peninsula that received treatments to reduce EWM. One site underwent mechanical harvesting, a second had a native fungus applied as a method of biological control, and a third site had diver-assisted suction harvesting completed. Classifications before and after treatment showed that it was possible to quantify the reductions of 63-89% in EWM extent due to these efforts. These results help demonstrate that UAS-enabled multispectral sensing can produce useful quantitative data on the presence and extent of SAV taxa of interest, providing a tool for monitoring treatment effects and improving understanding of aquatic ecology

Comparison of high-resolution NAIP and unmanned aerial vehicle (UAV) imagery for natural vegetation communities classification using machine learning approaches

To map and manage forest vegetation including wetland communities, remote sensing technology has been shown to be a valid and widely employed technology. In this paper, two ecologically different study areas were evaluated using free and widely available high-resolution multispectral National Agriculture Imagery Program (NAIP) and ultra-high-resolution multispectral unmanned aerial vehicle (UAV) imagery located in the Upper Great Lakes Laurentian Mixed Forest. Three different machine learning algorithms, random forest (RF), support vector machine (SVM), and averaged neural network (avNNet), were evaluated to classify complex natural habitat communities as defined by the Michigan Natural Features Inventory. Accurate training sets were developed using both spectral enhancement and transformation techniques, field collected data, soil data, texture, spectral indices, and expert knowledge. The utility of the various ancillary datasets significantly improved classification results. Using the RF classifier, overall accuracies (OA) between 83.8% and 87.7% with kappa (k) values between 0.79 and 0.85 for the NAIP imagery and between 87.3% and 93.7% OA with k values between 0.83 and 0.92 for the UAV dataset were achieved. Based on the results, we concluded RF to be a robust choice for classifying complex forest vegetation including surrounding wetland communities

sUAS and Deep Learning for High-Resolution Monitoring of Tidal Marshes in Coastal South Carolina

Tidal marshes are dynamic environments, now more than ever threatened by both natural and anthropogenic forces. Best practices for monitoring tidal marshes, as well as the environmental factors that affect them, have been studied for more than 40 years. With recent technological advances in remote sensing, new capabilities for monitoring tidal marshes have emerged. One of these new opportunities and challenges is hyper-spatial resolution imagery (\u3c10 \u3ecm) that can be captured by small unmanned aerial systems (sUAS). Aside from enhanced visualization, structure-from-motion (SfM) technology can derive dense point clouds from overlapped sUAS images for high resolution digital elevation models (DEMs). Furthermore, Deep Learning (DL) algorithms, patterned after the brain’s neural networks, provide effective and efficient analysis of mass amounts of pixels in high-resolution images. In this dissertation, I seek to apply these developing geospatial technologies—sUAS and DL—to map, monitor, and model marsh vegetation. First, sUAS and coastal vegetation related literature was extensively reviewed to provide a secure foundation to build upon. Second, an above ground biomass (AGB) model of the tidal marsh vegetation Spartina Alterniflora was developed using high resolution sUAS imagery to assess marsh distribution and healthiness in the estuary. We determined that the best RGB-based index for mapping S. Alterniflora biomass was the Excess Green Index (ExG), and using a quadratic relationship we achieved an R2 of 0.376. Third, with a time series of sUAS missions, tidal marsh wrack was monitored before and after a hurricane event to map and monitor its short- and long-term effects of tidal wrack deposition on vegetation. sUAS proved to be an exceptionally capable tool for this study, revealing that 55% of wrack stayed within 10 m of a water body and wrack may persist for only 3-4 months over the same location after a hurricane event. Finally, deep learning remote sensing techniques were applied to county-wide NAIP aerial imagery to map Land Use/ Land Cover (LULC) changes of Beaufort County, South Carolina from 2009 to 2019, and to assess if and why marsh losses or gains may have occurred around the county from coastal development. We discovered that the DL U-net classifier performed the best (92.4% overall accuracy) and the largest changes in the county have come by way of forest loss for urban growth, which will impact the marshes over time. This dissertation advances the theoretical and application-based use of sUAS and DL to benefit application driven GIScientists and coastal managers in the coastal marsh realm to mitigate future negative impacts and expand our understanding of how we can protect such majestic environments

Detecting peatland vegetation patterns with multi-temporal field spectroscopy

doi: 10.1080/15481603.2022.2152303Peatlands are one of the most significant terrestrial carbon pools, and the processes behind the carbon cycle in peatlands are strongly associated with different vegetation patterns. Handheld spectroradiometer data has been widely applied in ecological research, but there is a lack of studies on peatlands assessing how the temporal and spectral resolution affect the detectability of vegetation patterns. We collected field spectroscopy and vegetation inventory data at two northern boreal peatlands, Lompolojankka and Halssiaapa, between late May and August 2019. We conducted multivariate random forest regressions to examine the appropriate periods, benefits of multi-temporal data, and optimal spectral bandwidth and sampling interval for detecting plant communities and the two-dimensional (2D) %-cover, above-ground biomass (AGB) and leaf area index (LAI) of seven plant functional types (PFTs). In the best cross-site regression models for detecting plant community clusters (PCCs), R-2 was 42.6-48.0% (root mean square error (RMSE) 0.153-0.193), and for PFT 2D %-cover 53.9-69.8% (RMSE 8.2-17.6%), AGB 43.1-61.5% (RMSE 86.2-165.5 g/m(2)) and LAI 46.3-51.3% (RMSE 0.220-0.464 m(2)/m(2)). The multi-temporal data of the whole season increased R-2 by 13.7-24.6%-points and 10.2-33.0%-points for the PCC and PFT regressions, respectively. There was no single optimal temporal window for vegetation pattern detection for the two sites; in Lompolojankka the early growing season between late May and mid-June had the highest regression performance, while in Halssiaapa, the optimal period was during the peak season, from July to early August. In general, the spectral sampling interval between 1 to 10 nm yielded the best regression performance for most of the vegetation characteristics in Lompolojankka, whereas the optimal range extended to 20 nm in Halssiaapa. Our findings underscore the importance of fieldwork timing and the use of multi-temporal and hyperspectral data in detecting vegetation in spatially heterogeneous landscapes.Peer reviewe