Quantifying Vegetation and Landscape Metrics with Hyperspatial Unmanned Aircraft System Imagery in a Coastal Oligohaline Marsh

Broussard, Whitney P.; Visser, Jenneke M.; Brooks, Robert

doi:10.1007/s12237-020-00828-8

Cited by 10 publications

(19 citation statements)

References 54 publications

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“…Image classification techniques often involved using Esri or AgiSoft software, with occasional classifications made in Python [56], Google Earth Engine [36]), eCognition [10,14,24,27,29,42,63,[69][70][71][72], and ENVI [13,14,73] software. Vegetation classification often used Random Forest algorithms [23,27,55,69,[74][75][76], and there was a significant cluster of studies using object-based image analysis (OBIA) [23,24,29,69,70,77,78]. A recent study used deep learning algorithms such as convolutional neural network architectures to classify coastal wetland land cover [79].…”

Section: Geographic and Technical Characteristics Of The Reviewed Uav Applicationsmentioning

confidence: 99%

“…Shang et al [77] used an OBIA method to classify coastal wetland community types in China. Durgan et al [29] and Broussard et al [24] included the DSM product in their OBIA classification of floodplain and coastal marsh vegetation, respectively. Both found the OBIA method improved classification accuracy, and each report accuracies up to 85%.…”

Section: Vegetation Inventoriesmentioning

confidence: 99%

“…A riparian study in the UK [54] mentioned UAV flight regulations as a reason for dividing the study area into sections and acquiring their images separately. In addition to common protocols related to weather, airspace regulations, and proximity to specific land uses [24,40], wetlands may invoke additional regulatory constraints due to presence of sensitive species and habitats [101]. For instance, wildlife inventories may often require permitting for both field surveys of animal populations and on-site UAV use and may demand that researchers and managers formally assess and minimize the disturbance during field sessions [63,101,103].…”

Section: Field Operations In Wetland Settingmentioning

confidence: 99%

“…For instance, wildlife inventories may often require permitting for both field surveys of animal populations and on-site UAV use and may demand that researchers and managers formally assess and minimize the disturbance during field sessions [63,101,103]. Such restrictions not only can become direct obstacles to data collection in certain contexts [5,24], but also affect user perceptions about the adoption of UAVs as a monitoring tool [161].…”

Section: Field Operations In Wetland Settingmentioning

confidence: 99%

“…Globally threatened and disappearing at alarming rates, wetlands provide critical ecosystem services such as hydrological regulation, sequestration of carbon and mitigation of sea level rise, and support of biodiversity and critical habitats at land-water interfaces [20][21][22]. Protecting and amplifying these services via restoration, conservation and management measures often requires studying and monitoring wetlands at a landscape scale; however, the scope of wetland field surveys is frequently limited by difficult access, hazardous field conditions and the risk of disturbing sensitive plant and animal species [23][24][25]. These challenges are amplified in diverse and heterogeneous wetlands spanning complex topographic and hydrological gradients, which may require intensive surveys with large numbers of sampling locations [26][27][28][29].…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

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

Dronova

Kislik

Dinh

et al. 2021

Drones

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Recent developments in technology and data processing for Unoccupied Aerial Vehicles (UAVs) have revolutionized the scope of ecosystem monitoring, providing novel pathways to fill the critical gap between limited-scope field surveys and limited-customization satellite and piloted aerial platforms. These advances are especially ground-breaking for supporting management, restoration, and conservation of landscapes with limited field access and vulnerable ecological systems, particularly wetlands. This study presents a scoping review of the current status and emerging opportunities in wetland UAV applications, with particular emphasis on ecosystem management goals and remaining research, technology, and data needs to even better support these goals in the future. Using 122 case studies from 29 countries, we discuss which wetland monitoring and management objectives are most served by this rapidly developing technology, and what workflows were employed to analyze these data. This review showcases many ways in which UAVs may help reduce or replace logistically demanding field surveys and can help improve the efficiency of UAV-based workflows to support longer-term monitoring in the face of wetland environmental challenges and management constraints. We also highlight several emerging trends in applications, technology, and data and offer insights into future needs.

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Section: Geographic and Technical Characteristics Of The Reviewed Uav Applicationsmentioning

confidence: 99%

Section: Vegetation Inventoriesmentioning

confidence: 99%

Section: Field Operations In Wetland Settingmentioning

confidence: 99%

Section: Field Operations In Wetland Settingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Dronova

Kislik

Dinh

et al. 2021

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show abstract

Modeling approach for coastal dune habitat detection on coastal ecosystems combining very high‐resolution UAV imagery and field survey

Agrillo

Filipponi

Salvati³

et al. 2023

Remote Sens Ecol Conserv

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Earth observation (EO) data, derived from remote sensing and unmanned aerial vehicle (UAV), have been recently demonstrated to be essential tools for the ecosystem monitoring and habitat mapping, combining high technological and methodological procedures for applied ecology. However, research based on EO data analyses often tend to focus on image processing techniques, neglecting the development of a detailed sampling design scheme needed for an exhaustive habitat detection. This paper shows the results of a novel approach for mapping coastal dune habitats at a fine scale, using a supervised machine learning model, through the combination of vegetation plot sampling scheme, synergic use of multi-sensor spectral imagery (UAV-VHR) and environmental predictors (e.g., LiDAR), object-based image analysis, and landscape metrics analysis. Proposed approach was tested in a protected area, established to preserve notable habitats along the Italian Tyrrhenian coast. A detailed sampling scheme was designed and carried out during spring and summer of 2019, combining simultaneously UAV flight acquisition and field vegetation survey data, collected at high precision positioning. The calibrated classification model achieved an overall accuracy of 78.6% (standard error 4.33), allowing us to accurately classify and map five coastal habitats, according to EUNIS (European Nature Information System) classification, which were further verified through a fully independent validation field survey. Results demonstrate that VHR imageries, combined with specific field survey schemes, can be exploited to train classification models used for the detection of plant communities (i.e., meso-habitat) and plant species at local scale. Our findings demonstrate that UAV-VHR data is a valid tool to produce high spatial resolution information in sand beach ecosystems, giving ecology research a new way for responsive, timely, and cost-effective ecosystem monitoring.

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Simple Machine Learning with Aerial Imagery Reveals Severe Loss of a Salt Marsh Foundation Species

Rippel

Minsavage-Davis

Shirey

et al. 2023

Estuaries and Coasts

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Salt marshes are globally important ecosystems, but many have been lost or transformed due to the impacts of global change. There have been attempts to broadly quantify salt marsh communities, especially the ubiquitous grasses which serve as foundation species such as Spartina alterniflora and Spartina patens, the latter of which is being lost due to sea level rise. However, few researchers have used high-resolution geospatial imagery to quantify fine-scale changes in the distribution of grasses or to track losses of S. patens. To address this issue, we utilized a simple and rapid method of classifying geospatial marsh imagery with cloud-based machine learning in Google Earth Engine (>92% accuracy for S. patens regardless of imagery age). Our methods allowed us to characterize full landscapes (two geospatially proximal areas, >7,000 ha each) of critical salt marshes on the New Jersey coast and to evaluate fine-scale (1-m) community transformations in response to global change with imagery from 2006 to 2019. Notably, one marsh experienced very little change while the other experienced an 81.17% (1,087 ha) loss of S. patens, illuminating disparate patterns of change for two geographically proximal ecosystems. Further exploration revealed an association in the loss of S. patens with increases in streamflow and total nitrogen content in the rivers that run through each marsh. These results signify the importance of broad-scale ecological studies that evaluate fine-scale community transformations and for management strategies that do not generalize across landscapes of an ecosystem-type.

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Quantifying Vegetation and Landscape Metrics with Hyperspatial Unmanned Aircraft System Imagery in a Coastal Oligohaline Marsh

Cited by 10 publications

References 54 publications

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

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

Modeling approach for coastal dune habitat detection on coastal ecosystems combining very high‐resolution UAV imagery and field survey

Simple Machine Learning with Aerial Imagery Reveals Severe Loss of a Salt Marsh Foundation Species

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