Combined Use of Airborne Lidar and DBInSAR Data to Estimate LAI in Temperate Mixed Forests

Peduzzi, Alicia; Wynne, Randolph H.; Thomas, Valerie A.; Nelson, Ross; Reis, James J.; Sanford, Mark

doi:10.3390/rs4061758

Cited by 18 publications

(12 citation statements)

References 40 publications

(54 reference statements)

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“…Although not specifically applied to viticulture, airborne lidar datasets can confidently predict LAI and other biophysical characteristics of tree vegetation by calculating several height-based metrics [16][17][18][19]. Yet another method, that of statistically-based modeling, was implemented by [20] to look at single vine canopy and explore potential light interception for different grapevine varietals.…”

Section: Introductionmentioning

confidence: 99%

Visualizing and Quantifying Vineyard Canopy LAI Using an Unmanned Aerial Vehicle (UAV) Collected High Density Structure from Motion Point Cloud

2013

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Abstract:This study explores the use of structure from motion (SfM), a computer vision technique, to model vine canopy structure at a study vineyard in the Texas Hill Country. Using an unmanned aerial vehicle (UAV) and a digital camera, 201 aerial images (nadir and oblique) were collected and used to create a SfM point cloud. All points were classified as ground or non-ground points. Non-ground points, presumably representing vegetation and other above ground objects, were used to create visualizations of the study vineyard blocks. Further, the relationship between non-ground points in close proximity to 67 sample vines and collected leaf area index (LAI) measurements for those same vines was also explored. Points near sampled vines were extracted from which several metrics were calculated and input into a stepwise regression model to attempt to predict LAI. This analysis resulted in a moderate R 2 value of 0.567, accounting for 57 percent of the variation of LAI SQRT using six predictor variables. These results provide further justification for SfM datasets to provide three-dimensional datasets necessary for vegetation structure visualization and biophysical modeling over areas of smaller extent. Additionally, SfM datasets can provide an increased temporal resolution compared to traditional three-dimensional datasets like those captured by light detection and ranging (lidar).

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Section: Introductionmentioning

confidence: 99%

Visualizing and Quantifying Vineyard Canopy LAI Using an Unmanned Aerial Vehicle (UAV) Collected High Density Structure from Motion Point Cloud

2013

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“…For instance, Straatsma et al [12] and Forzieri et al [20] used a fusion of airborne spectral and altimetry data sets to estimate roughness input parameters such as vegetation height and vegetation density, and subsequently used these data as input into a hydrodynamic model to compute flow resistance values of a local river floodplain. It is foreseen that in the forthcoming years it will become possible to upscale these approaches using spaceborne instruments, e.g., in combination with SAR data [21,76,77]. The advantages of relying on spaceborne optical data are ample; it offers a standardized, spatially-explicit and repeatable monitoring scheme that can cover complete river catchments with high spatial detail.…”

Section: Towards Spaceborne River Floodplain Monitoringmentioning

confidence: 99%

Mapping Vegetation Density in a Heterogeneous River Floodplain Ecosystem Using Pointable CHRIS/PROBA Data

2012

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River floodplains in the Netherlands serve as water storage areas, while they also have the function of nature rehabilitation areas. Floodplain vegetation is therefore subject to natural processes of vegetation succession. At the same time, vegetation encroachment obstructs the water flow into the floodplains and increases the flood risk for the hinterland. Spaceborne pointable imaging spectroscopy has the potential to quantify vegetation density on the basis of leaf area index (LAI) from a desired view zenith angle. In this respect, hyperspectral pointable CHRIS data were linked to the ray tracing canopy reflectance model FLIGHT to retrieve vegetation density estimates over a heterogeneous river floodplain. FLIGHT enables simulating top-of-canopy reflectance of vegetated surfaces either in turbid (e.g., grasslands) or in 3D (e.g., forests) mode. By inverting FLIGHT against CHRIS data, LAI was computed for three main classified vegetation types, 'herbaceous', 'shrubs' and 'forest', and for the CHRIS view zenith angles in nadir, backward (−36°) and forward (+36°) scatter direction. The −36° direction showed most LAI variability within the vegetation types and was best validated, closely followed by the nadir direction. The +36° direction led to poorest LAI retrievals. The class-based inversion process has been implemented into a GUI toolbox which would enable the river manager to generate LAI maps in a semiautomatic way.

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“…In addition, the leave-one-out cross-validation (LOOCV) is used to evaluate the generalization capability of regression models because there is no additional data for model validation [50,51]. Specifically, the root mean square error from the cross validation analysis (RMSE CV ), calculated from the predicted residual sum of squares (PRESS statistic) and the number of observations, was used to validate the accuracies of biomass estimation models.…”

Section: Regression Analysismentioning

confidence: 99%

Estimating the Biomass of Maize with Hyperspectral and LiDAR Data

Wang

Nie

et al. 2016

Remote Sensing

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Abstract:The accurate estimation of crop biomass during the growing season is very important for crop growth monitoring and yield estimation. The objective of this paper was to explore the potential of hyperspectral and light detection and ranging (LiDAR) data for better estimation of the biomass of maize. First, we investigated the relationship between field-observed biomass with each metric, including vegetation indices (VIs) derived from hyperspectral data and LiDAR-derived metrics. Second, the partial least squares (PLS) regression was used to estimate the biomass of maize using VIs (only) and LiDAR-derived metrics (only), respectively. Third, the fusion of hyperspectral and LiDAR data was evaluated in estimating the biomass of maize. Finally, the biomass estimates were validated by a leave-one-out cross-validation (LOOCV) method. Results indicated that all VIs showed weak correlation with field-observed biomass and the highest correlation occurred when using the red edge-modified simple ratio index (ReMSR). Among all LiDAR-derived metrics, the strongest relationship was observed between coefficient of variation (H CV ) of digital terrain model (DTM) normalized point elevations with field-observed biomass. The combination of VIs through PLS regression could not improve the biomass estimation accuracy of maize due to the high correlation between VIs. In contrast, the H CV combined with H mean performed better than one LiDAR-derived metric alone in biomass estimation (R 2 = 0.835, RMSE = 374.655 g/m 2 , RMSE CV = 393.573 g/m 2 ). Additionally, our findings indicated that the fusion of hyperspectral and LiDAR data can provide better biomass estimates of maize (R 2 = 0.883, RMSE = 321.092 g/m 2 , RMSE CV = 337.653 g/m 2 ) compared with LiDAR or hyperspectral data alone.

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Combined Use of Airborne Lidar and DBInSAR Data to Estimate LAI in Temperate Mixed Forests

Cited by 18 publications

References 40 publications

Visualizing and Quantifying Vineyard Canopy LAI Using an Unmanned Aerial Vehicle (UAV) Collected High Density Structure from Motion Point Cloud

Visualizing and Quantifying Vineyard Canopy LAI Using an Unmanned Aerial Vehicle (UAV) Collected High Density Structure from Motion Point Cloud

Mapping Vegetation Density in a Heterogeneous River Floodplain Ecosystem Using Pointable CHRIS/PROBA Data

Estimating the Biomass of Maize with Hyperspectral and LiDAR Data

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