Fusion of airborne LiDAR data and hyperspectral imagery for aboveground and belowground forest biomass estimation

Luo, Shuai; Wang, Cheng; Xi, Xiaohuan; Pan, Feifei; Peng, Dailiang; Zou, Jie; Nie, Sheng; Qin, Haiming

doi:10.1016/j.ecolind.2016.10.001

Cited by 106 publications

(60 citation statements)

References 84 publications

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“…However, the data fusion approach only reported a slight improvement for the uncertainly of biomass compared with the LiDAR-derived metrics alone, with RMSE values decreasing from 374.655 g/m 2 to 321.092 g/m 2 . Similar results were also reported in previous studies, which indicated the fusion of hyperspectral data could not significantly improve the estimation accuracy of forest biomass [25,49]. The main reason is that hyperspectral data could not provide accurate biomass estimates due to the saturation problem and LiDAR-derived metrics generally showed a strong relationship with field-observed biomass, thus, fused hyperspectral and LiDAR data only made a small improvement in biomass estimation accuracy.…”

Section: Discussionsupporting

confidence: 76%

“…However, our study found that VIs derived from CASI data had limited capability in estimating the biomass of maize. Although narrowband VIs were more sensitive to vegetation biomass than broadband Vis, according to the previous study [49], they still saturated over medium to high biomass area. Thus, VIs derived from hyperspectral data could not provide accurate biomass estimates because most of the biomass values are high in this experiment site.…”

Section: Discussionmentioning

confidence: 99%

“…Different from multiple linear regression, PLS regression is a multivariate statistical method which can effectively deal with the problem of multicollinearity among predictor variables [25,49]. It has been successfully employed in vegetation biomass estimation.…”

Section: Regression Analysismentioning

confidence: 99%

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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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Section: Discussionsupporting

confidence: 76%

Section: Discussionmentioning

confidence: 99%

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Estimating the Biomass of Maize with Hyperspectral and LiDAR Data

Wang

Nie

et al. 2016

Remote Sensing

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“…Moreover, hyperspectral variables were not only used for species classification, but they were also incorporated in the common ALS framework as they were selected as important variables for volume modeling. At the moment, hyperspectral data are the most powerful tool for species identification [6] and consequently they can improve the accuracy of the predicted biophysical attributes [31]. Forest inventories can be improved by the use of these combined data as they can increase the spatial detail, coverage, and accuracy of forest biophysical attributes.…”

Section: Discussionmentioning

confidence: 99%

Prediction of Species-Specific Volume Using Different Inventory Approaches by Fusing Airborne Laser Scanning and Hyperspectral Data

et al. 2017

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Abstract:Fusion of ALS and hyperspectral data can offer a powerful basis for the discrimination of tree species and enables an accurate prediction of species-specific attributes. In this study, the fused airborne laser scanning (ALS) data and hyperspectral images were used to model and predict the total and species-specific volumes based on three forest inventory approaches, namely the individual tree crown (ITC) approach, the semi-ITC approach, and the area-based approach (ABA). The performances of these inventory approaches were analyzed and compared at the plot level in a complex Alpine forest in Italy. For the ITC and semi-ITC approaches, an ITC delineation algorithm was applied. With the ITC approach, the species-specific volumes were predicted with allometric models for each crown segment and aggregated to the total volume. For the semi-ITC and ABA, a multivariate k-most similar neighbor method was applied to simultaneously predict the total and species-specific volumes using leave-one-out cross-validation at the plot level. In both methods, the ALS and hyperspectral variables were important for volume modeling. The total volume of the ITC, semi-ITC, and ABA resulted in relative root mean square errors (RMSEs) of 25.31%, 17.41%, 30.95% of the mean and systematic errors (mean differences) of 21.59%, −0.27%, and −2.69% of the mean, respectively. The ITC approach achieved high accuracies but large systematic errors for minority species. For majority species, the semi-ITC performed slightly better compared to the ABA, resulting in higher accuracies and smaller systematic errors. The results indicated that the semi-ITC outperformed the two other inventory approaches. To conclude, we suggest that the semi-ITC method is further tested and assessed with attention to its potential in operational forestry applications, especially in cases for which accurate species-specific forest biophysical attributes are needed.

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“…1 also called canopy point density [42], laser intercept index [78] Phenology was represented by the amplitude of the seasonal difference in the normalized difference vegetation index (NDVI). We measured amplitude using a harmonic regression time series analysis on Landsat images recorded between 2012 to 2015.…”

mentioning

confidence: 99%

Development of a Regional Lidar-Derived Forest Inventory Model with Bayesian Model Averaging for use in Ponderosa Pine and Mixed Conifer Forests in Arizona and New Mexico, USA

Tenneson¹,

Patterson²,

Mellin³

et al. 2018

Preprint

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USA, using previously collected field data. The selected regional model includes a mid and low 8 canopy height metric, a canopy cover, and height distribution term. It explains 72% of the variability 9 in field estimates of AGB, and the RMSE of the two independent validation data sets are 23.25 and 10 32.82 Mg/ha. The regional model developed is structured in accordance with previously described 11 models fit to local data, and performs equivalently to models designed for smaller scale application.

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Fusion of airborne LiDAR data and hyperspectral imagery for aboveground and belowground forest biomass estimation

Cited by 106 publications

References 84 publications

Estimating the Biomass of Maize with Hyperspectral and LiDAR Data

Estimating the Biomass of Maize with Hyperspectral and LiDAR Data

Prediction of Species-Specific Volume Using Different Inventory Approaches by Fusing Airborne Laser Scanning and Hyperspectral Data

Development of a Regional Lidar-Derived Forest Inventory Model with Bayesian Model Averaging for use in Ponderosa Pine and Mixed Conifer Forests in Arizona and New Mexico, USA

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