Quantifying biomass consumption and carbon release from the California Rim fire by integrating airborne LiDAR and Landsat OLI data

Garcı́a, Mariano; Saatchi, Sassan; Casas, A.; Koltunov, Alexander; Ustin, Susan L.; Ramı́rez, C.; García-Gutiérrez, Jorge; Balzter, Heiko

doi:10.1002/2015jg003315

Cited by 43 publications

(31 citation statements)

References 69 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The analysis of the influence of point density on model predictions have been analyzed by several authors (e.g., [39][40][41]), who established that point density has little or no effect on predictions as statistical metrics remain stable [42]. Furthermore, Garcia et al [43], Singh et al [44], and Ruiz et al [45] pointed out that low-density datasets were a viable solution at regional scales. Furthermore, the use of multi-temporal, low-point density data has only been explored in boreal ecosystems [17,28] and in temperate forests [24,27] but not in other ecosystems, such as Mediterranean ones, which are characterized by a higher heterogeneity in terms of forest structure.…”

Section: Introductionmentioning

confidence: 99%

Temporal Transferability of Pine Forest Attributes Modeling Using Low-Density Airborne Laser Scanning Data

et al. 2019

View full text Add to dashboard Cite

This study assesses model temporal transferability using airborne laser scanning (ALS) data acquired over two different dates. Seven forest attributes (i.e. stand density, basal area, squared mean diameter, dominant diameter, tree dominant height, timber volume, and total tree biomass) were estimated using an area-based approach in Mediterranean Aleppo pine forests. Low-density ALS data were acquired in 2011 and 2016 while 147 forest inventory plots were measured in 2013, 2014, and 2016. Single-tree growth models were used to generate concomitant field data for 2011 and 2016. A comparison of five selection techniques and five regression methods were performed to regress field observations against ALS metrics. The selection of the best regression models fitted for each stand attribute, and separately for both 2011 and 2016, was performed following an indirect approach. Model performance and temporal transferability were analyzed by extrapolating the best fitted models from 2011 to 2016 and inversely from 2016 to 2011 using the direct approach. Non-parametric support vector machine with radial kernel was the best regression method with average relative % root mean square error differences of 2.13% for 2011 models and 1.58% for 2016 ones.

show abstract

Section: Introductionmentioning

confidence: 99%

Temporal Transferability of Pine Forest Attributes Modeling Using Low-Density Airborne Laser Scanning Data

et al. 2019

View full text Add to dashboard Cite

show abstract

“…The latter method selected only two variables that potentially described the 3D structural characteristics of the canopy, namely H 50 and AUCW. Further details on the feature selection and the LS-SVM approach used to estimate CFL can be found in García et al [53].…”

Section: Canopy Fuel Properties Estimation From Lidar Datamentioning

confidence: 99%

“…Feature selection was limited to a stepwise regression approach due to the unfeasibility of applying the evolutionary algorithm over this large dataset, as well as the lack of a priori knowledge of what spectral variables were related to the CFL, as these variables are not directly related to foliage biomass [53]. After applying stepwise regression using all Landsat metrics, the correlation between the selected variables was evaluated, and whenever a pair of variables showed the absolute value of the correlation coefficient >0.7 we selected the one more strongly correlated with the dependent variable.…”

Section: Canopy Fuel Properties Extrapolation Using Landsat Datamentioning

confidence: 99%

Extrapolating Forest Canopy Fuel Properties in the California Rim Fire by Combining Airborne LiDAR and Landsat OLI Data

et al. 2017

Self Cite

View full text Add to dashboard Cite

Accurate, spatially explicit information about forest canopy fuel properties is essential for ecosystem management strategies for reducing the severity of forest fires. Airborne LiDAR technology has demonstrated its ability to accurately map canopy fuels. However, its geographical and temporal coverage is limited, thus making it difficult to characterize fuel properties over large regions before catastrophic events occur. This study presents a two-step methodology for integrating post-fire airborne LiDAR and pre-fire Landsat OLI (Operational Land Imager) data to estimate important pre-fire canopy fuel properties for crown fire spread, namely canopy fuel load (CFL), canopy cover (CC), and canopy bulk density (CBD). This study focused on a fire prone area affected by the large 2013 Rim fire in the Sierra Nevada Mountains, California, USA. First, LiDAR data was used to estimate CFL, CC, and CBD across an unburned 2 km buffer with similar structural characteristics to the burned area. Second, the LiDAR-based canopy fuel properties were extrapolated over the whole area using Landsat OLI data, which yielded an R 2 of 0.8, 0.79, and 0.64 and RMSE of 3.76 Mg· ha −1 , 0.09, and 0.02 kg·m −3 for CFL, CC, and CBD, respectively. The uncertainty of the estimates was estimated for each pixel using a bootstrapping approach, and the 95% confidence intervals are reported. The proposed methodology provides a detailed spatial estimation of forest canopy fuel properties along with their uncertainty that can be readily integrated into fire behavior and fire effects models. The methodology could be also integrated into the LANDFIRE program to improve the information on canopy fuels.

show abstract

“…where AGB An uncertainty analysis of the AGB 2012 and AGB 2014 at landscape level for each pulse density target and DTM scenario was also performed by integrating the pixel level errors and accounting for spatial autocorrelation of the errors as follows [27][28][29]:…”

Section: Aboveground Biomass Change Estimation and Mappingmentioning

confidence: 99%

Impacts of Airborne Lidar Pulse Density on Estimating Biomass Stocks and Changes in a Selectively Logged Tropical Forest

et al. 2017

Self Cite

View full text Add to dashboard Cite

Airborne lidar is a technology well-suited for mapping many forest attributes, including aboveground biomass (AGB) stocks and changes in selective logging in tropical forests. However, trade-offs still exist between lidar pulse density and accuracy of AGB estimates. We assessed the impacts of lidar pulse density on the estimation of AGB stocks and changes using airborne lidar and field plot data in a selectively logged tropical forest located near Paragominas, Pará, Brazil. Field-derived AGB was computed at 85 square 50 × 50 m plots in 2014. Lidar data were acquired in 2012 and 2014, and for each dataset the pulse density was subsampled from its original density of 13.8 and 37.5 pulses·m −2 to lower densities of 12, 10, 8, 6, 4, 2, 0.8, 0.6, 0.4 and 0.2 pulses·m −2 . For each pulse density dataset, a power-law model was developed to estimate AGB stocks from lidar-derived mean height and corresponding changes between the years 2012 and 2014. We found that AGB change estimates at the plot level were only slightly affected by pulse density. However, at the landscape level we observed differences in estimated AGB change of >20 Mg·ha −1 when pulse density decreased from 12 to 0.2 pulses·m −2 . The effects of pulse density were more pronounced in areas of steep slope, especially when the digital terrain models (DTMs) used in the lidar derived forest height were created from reduced pulse density data. In particular, when the DTM from high pulse density in 2014 was used to derive the forest height from both years, the effects on forest height and the estimated AGB stock and changes did not exceed 20 Mg·ha −1 . The results suggest that AGB change can be monitored in selective logging in tropical forests with reasonable accuracy and low cost with low pulse density lidar surveys if a baseline high-quality DTM is available from at least one lidar survey. We recommend the results of this study to be considered in developing projects and national level MRV systems for REDD+ emission reduction programs for tropical forests.

show abstract

Quantifying biomass consumption and carbon release from the California Rim fire by integrating airborne LiDAR and Landsat OLI data

Cited by 43 publications

References 69 publications

Temporal Transferability of Pine Forest Attributes Modeling Using Low-Density Airborne Laser Scanning Data

Temporal Transferability of Pine Forest Attributes Modeling Using Low-Density Airborne Laser Scanning Data

Extrapolating Forest Canopy Fuel Properties in the California Rim Fire by Combining Airborne LiDAR and Landsat OLI Data

Impacts of Airborne Lidar Pulse Density on Estimating Biomass Stocks and Changes in a Selectively Logged Tropical Forest

Contact Info

Product

Resources

About