Mapping Spartina alterniflora Biomass Using LiDAR and Hyperspectral Data

Wang, Jing; Liu, Zhengjun; Yu, Haiying; Li, Fangfang

doi:10.3390/rs9060589

Cited by 25 publications

(19 citation statements)

References 33 publications

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“…Airborne LiDAR had also been coupled with other remote sensing datasets, which lack tridimensionality and adequate spatial resolution to supply this information. Satellite data were associated with LiDAR to map wetland flooding [46], vernal pools [47], and coastal wetlands landscape and vegetation characteristics [48][49][50][51][52]. Optical images were also coupled with LiDAR point clouds for vegetation classification purposes [53][54][55].…”

Section: Introductionmentioning

confidence: 99%

Estimating Ground Elevation and Vegetation Characteristics in Coastal Salt Marshes Using UAV-Based LiDAR and Digital Aerial Photogrammetry

et al. 2021

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This study evaluates the skills of two types of drone-based point clouds, derived from LiDAR and photogrammetric techniques, in estimating ground elevation, vegetation height, and vegetation density on a highly vegetated salt marsh. The proposed formulation is calibrated and tested using data measured on a Spartina alterniflora-dominated salt marsh in Little Sapelo Island, USA. The method produces high-resolution (ground sampling distance = 0.40 m) maps of ground elevation and vegetation characteristics and captures the large gradients in the proximity of tidal creeks. Our results show that LiDAR-based techniques provide more accurate reconstructions of marsh vegetation (height: MAEVH = 12.6 cm and RMSEVH = 17.5 cm; density: MAEVD = 6.9 stems m−2 and RMSEVD = 9.4 stems m−2) and morphology (MAEM = 4.2 cm; RMSEM = 5.9 cm) than Digital Aerial Photogrammetry (DAP) (MAEVH = 31.1 cm; RMSEVH = 38.1 cm; MAEVD = 12.7 stems m−2; RMSEVD = 16.6 stems m−2; MAEM = 11.3 cm; RMSEM = 17.2 cm). The accuracy of the classification procedure for vegetation calculation negligibly improves when RGB images are used as input parameters together with the LiDAR-UAV point cloud (MAEVH = 6.9 cm; RMSEVH = 9.4 cm; MAEVD = 10.0 stems m−2; RMSEVD = 14.0 stems m−2). However, it improves when used together with the DAP-UAV point cloud (MAEVH = 21.7 cm; RMSEVH = 25.8 cm; MAEVD = 15.2 stems m−2; RMSEVD = 18.7 stems m−2). Thus, we discourage using DAP-UAV-derived point clouds for high-resolution vegetation mapping of coastal areas, if not coupled with other data sources.

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Section: Introductionmentioning

confidence: 99%

Estimating Ground Elevation and Vegetation Characteristics in Coastal Salt Marshes Using UAV-Based LiDAR and Digital Aerial Photogrammetry

et al. 2021

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“…From LiDAR data, a number of elevation-and structure-related features of wetlands can be captured. Notably, LiDAR can penetrate the vegetation canopy and allows for the characterization of wetland vegetation structure, which is much less feasible using optical satellite imagery or when using SAR in high-biomass areas [211]. Several studies have examined and applied LiDAR data to wetland classification [123,127,146,[212][213][214][215].…”

Section: Elevation Data For Wetland Classificationmentioning

confidence: 99%

Meta-Analysis of Wetland Classification Using Remote Sensing: A Systematic Review of a 40-Year Trend in North America

et al. 2020

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North America is covered in 2.5 million km2 of wetlands, which is the remainder of an estimated 56% of wetlands lost since the 1700s. This loss has resulted in a decrease in important habitat and services of great ecological, economic, and recreational benefits to humankind. To better manage these ecosystems, since the 1970s, wetlands in North America have been classified with increasing regularity using remote sensing technology. Since then, optimal methods for wetland classification by numerous researchers have been examined, assessed, modified, and established. Over the past several decades, a large number of studies have investigated the effects of different remote sensing factors, such as data type, spatial resolution, feature selection, classification methods, and other parameters of interest on wetland classification in North America. However, the results of these studies have not yet been synthesized to determine best practices and to establish avenues for future research. This paper reviews the last 40 years of research and development on North American wetland classification through remote sensing methods. A meta-analysis of 157 relevant articles published since 1980 summarizes trends in 23 parameters, including publication, year, study location, application of specific sensors, and classification methods. This paper also examines is the relationship between several remote sensing parameters (e.g., spatial resolution and type of data) and resulting overall accuracies. Finally, this paper discusses the future of remote sensing of wetlands in North America with regard to upcoming technologies and sensors. Given the increasing importance and vulnerability of wetland ecosystems under the climate change influences, this paper aims to provide a comprehensive review in support of the continued, improved, and novel applications of remote sensing for wetland mapping across North America and to provide a fundamental knowledge base for future studies in this field.

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“…Several studies have coupled point clouds with hyperspectral images (Hirano et al ., 2003), advanced spaceborne thermal emission and reflection radiometer (ASTER) datasets (Pavri et al ., 2011), multispectral data (Rapinel et al ., 2015) and normalized difference vegetation index (NDVI) data (O'Neil et al ., 2020) to produce vegetation and habitat maps in wetlands. Hyperspectral images have also been used to estimate wetland vegetation biomass (Wang et al ., 2017b) . Moreover, Landsat data were coupled with LiDAR for mapping wetland inundation changes (Huang et al ., 2014).…”

Section: Introductionmentioning

confidence: 99%

A new algorithm for estimating ground elevation and vegetation characteristics in coastal salt marshes from high‐resolution UAV‐based LiDAR point clouds

Pinton

Canestrelli

Wilkinson

et al. 2020

Earth Surf Processes Landf

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Salt marshes are transitional zones between ocean and land, which act as natural buffers against coastal hazards. The survival of salt marshes is governed by the rate of organic and inorganic deposition, which strongly depends on vegetation characteristics, such as height and density. Vegetation also favours the dissipation of wind waves and storm surges. For these reasons, an accurate description of both ground elevation and vegetation characteristics in salt marshes is critical for their management and conservation. For this purpose, airborne LiDAR (light detection and ranging) laser scanning has become an accessible and cost-effective tool to map salt marshes quickly. However, the limited horizontal resolution (~1m) of airborne-derived point clouds prevents the direct extraction of ground elevation, vegetation height and vegetation density without the coupling with imagery datasets. Instead, due to the lower flight altitude, UAV (unmanned aerial vehicle)-borne laser scanners provide point clouds with much higher resolution (~5cm). Although methods for estimating ground level and vegetation characteristics from UAV LiDAR have been proposed for flat ground, we demonstrate that a sloping ground increases prediction errors. Here we derive a new formulation that improves the estimation by employing a correction based on a LiDAR-derived estimate of local ground slope. Our method directly converts the 3D distribution of UAV LiDAR-derived points into vegetation density and height, as well as ground elevation, without the support of additional datasets. The proposed formulation is calibrated by using measured density and height of Spartina alterniflora in a marsh in Sapelo Island, Georgia, USA, and successfully tested on an independent dataset. Our method produces high-resolution (40×40cm 2) maps of ground elevation and vegetation characteristics, thus capturing the large gradients in the proximity of tidal creeks.

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Mapping Spartina alterniflora Biomass Using LiDAR and Hyperspectral Data

Cited by 25 publications

References 33 publications

Estimating Ground Elevation and Vegetation Characteristics in Coastal Salt Marshes Using UAV-Based LiDAR and Digital Aerial Photogrammetry

Estimating Ground Elevation and Vegetation Characteristics in Coastal Salt Marshes Using UAV-Based LiDAR and Digital Aerial Photogrammetry

Meta-Analysis of Wetland Classification Using Remote Sensing: A Systematic Review of a 40-Year Trend in North America

A new algorithm for estimating ground elevation and vegetation characteristics in coastal salt marshes from high‐resolution UAV‐based LiDAR point clouds

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