A Novel Approach for Deriving LAI of Salt Marsh Vegetation Using Structure From Motion and Multiangular Spectra

Badura, Gregory; Bachmann, Charles M.; Tyler, Anna Christina; Goldsmith, Sarah; Eon, Rehman S.; Lapszynski, Christopher S.

doi:10.1109/jstars.2018.2889476

Cited by 14 publications

(9 citation statements)

References 47 publications

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“…The goniometer of the Rochester Institute of Technology-Two (GRIT-T) is a second-generation system designed to obtain BRDF measurements in both laboratory and field settings [3,41,58]. Figure 2 illustrates the different components of the GRIT-T system, deployed in various settings.…”

Section: Grit-t: Design and Instrumentationmentioning

confidence: 99%

“…The small-form factor sensor head assembly minimizes self-shading. GRIT-T produces digital elevation models using two different methods: from an onboard laser measurement unit [41] and at higher resolution from an on-board field-of-view camera, which provides stereo views of the measurement location as input to a structure-from-motion algorithm [58].…”

Section: Grit-t: Design and Instrumentationmentioning

confidence: 99%

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Retrieval of Sediment Fill Factor by Inversion of a Modified Hapke Model Applied to Sampled HCRF from Airborne and Satellite Imagery

2018

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The physical properties of a medium such as density, grain size and surface roughness all influence the angular dependence of spectral signatures. Radiative transfer models, such as the one developed by Hapke, can relate the angular dependence of the reflectance to these geophysical variables. This paper focuses on extracting geophysical parameters, fill factor (decreasing porosity) and the single scattering albedo (SSA), through the inversion of a modified version of the Hapke model of airborne and space-borne imagery. The inversion methodology was validated through controlled experiments within a laboratory setting, where a good correlation (R 2 = 0.72) between the retrieved fill factor and the measured density was obtained. Using the same approach, we also retrieved the sediment fill factor and SSA from airborne data collected by the NASA G-LiHT system, and space-borne data observed by the NOAA GOES imager. The results from these studies provide a mechanism to understand geophysical characteristics of the terrain and may potentially be used for long-term monitoring of the dynamic dunes system.

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Section: Grit-t: Design and Instrumentationmentioning

confidence: 99%

Section: Grit-t: Design and Instrumentationmentioning

confidence: 99%

Retrieval of Sediment Fill Factor by Inversion of a Modified Hapke Model Applied to Sampled HCRF from Airborne and Satellite Imagery

2018

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“…Therefore, UAV-SfM has gained popularity in recent years in the field of remote sensing. A considerable amount of research has been conducted on vegetation height and leaf area measurements, as well as biomass growth rates [19][20][21][22][23][24][25][26][27][28][29][30][31][32][33]. For example, Diaz-Varela et al (2015) conducted a research on tree height and crown diameter estimation for olive trees using UAV [19].…”

Section: Introductionmentioning

confidence: 99%

“…Mathews and Jensen (2013) performed research on the SfM based Leaf Area Index (LAI) estimation of a vineyard [23]. Badura et al (2019) used SfM for estimation of the LAI of salt marsh vegetation [24]. Li et al (2016) investigated the estimation of above ground biomass of maize using the UAV-SfM method [25].…”

Section: Introductionmentioning

confidence: 99%

Estimation of Ground Surface and Accuracy Assessments of Growth Parameters for a Sweet Potato Community in Ridge Cultivation

Teng

Ōno²,

Aono

et al. 2019

Remote Sensing

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There are only a few studies that have been made on accuracy assessments of Leaf Area Index (LAI) and biomass estimation using three-dimensional (3D) models generated by structure from motion (SfM) image processing. In this study, sweet potato was grown with different amounts of nitrogen fertilization in ridge cultivation at an experimental farm. Three-dimensional dense point cloud models were constructed from a series of two-dimensional (2D) color images measured by a small unmanned aerial vehicle (UAV) paired with SfM image processing. Although it was in the early stage of cultivation, a complex ground surface model for ridge cultivation with vegetation was generated, and the uneven ground surface could be estimated with an accuracy of 1.4 cm. Furthermore, in order to accurately estimate growth parameters from the early growth to the harvest period, a 3D model was constructed using a root mean square error (RMSE) of 3.3 cm for plant height estimation. By using a color index, voxel models were generated and LAIs were estimated using a regression model with an RMSE accuracy of 0.123. Further, regression models were used to estimate above-ground and below-ground biomass, or tuberous root weights, based on estimated LAIs.

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“…SAR has also been utilized to detect change in the extent and composition of saltmarshes [82][83][84]; measure topography [85]; monitor storm surge and flooding [86][87][88]; burn recovery [89]; hurricane recovery [90,91]; oil spill impacts [92][93][94][95]. Structure from motion has enabled digital imagery to provide equivalent structural representations to lidar, permitting the retrieval of leaf area index from Spartina alterniflora [96].…”

Section: Introductionmentioning

confidence: 99%

Characterizing a New England Saltmarsh with NASA G-LiHT Airborne Lidar

et al. 2019

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Airborne lidar can observe saltmarshes on a regional scale, targeting phenological and tidal states to provide the information to more effectively utilize frequent multispectral satellite observations to monitor change. Airborne lidar observations from NASA Goddard Lidar Hyperspectral and Thermal (G-LiHT) of a well-studied region of saltmarsh (Plum Island, Massachusetts, United States) were acquired in multiple years (2014, 2015 and 2016). These airborne lidar data provide characterizations of important saltmarsh components, as well as specifications for effective surveys. The invasive Phragmites australis was observed to increase in extent from 8374 m2 in 2014, to 8882 m2 in 2015 (+6.1%), and again to 13,819 m2 in 2016 (+55.6%). Validation with terrestrial lidar supported this increase, but suggested the total extent was still underestimated. Estimates of Spartina alterniflora extent from airborne lidar were within 7% of those from terrestrial lidar, but overestimation of height of Spartina alterniflora was found to occur at the edges of creeks (+83.9%). Capturing algae was found to require observations within ±15° of nadir, and capturing creek structure required observations within ±10° of nadir. In addition, 90.33% of creeks and ditches were successfully captured in the airborne lidar data (8206.3 m out of 9084.3 m found in aerial imagery).

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A Novel Approach for Deriving LAI of Salt Marsh Vegetation Using Structure From Motion and Multiangular Spectra

Cited by 14 publications

References 47 publications

Retrieval of Sediment Fill Factor by Inversion of a Modified Hapke Model Applied to Sampled HCRF from Airborne and Satellite Imagery

Retrieval of Sediment Fill Factor by Inversion of a Modified Hapke Model Applied to Sampled HCRF from Airborne and Satellite Imagery

Estimation of Ground Surface and Accuracy Assessments of Growth Parameters for a Sweet Potato Community in Ridge Cultivation

Characterizing a New England Saltmarsh with NASA G-LiHT Airborne Lidar

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