scholarly journals Modeling and Mapping Agroforestry Aboveground Biomass in the Brazilian Amazon Using Airborne Lidar Data

2015 ◽  
Vol 8 (1) ◽  
pp. 21 ◽  
Author(s):  
Qi Chen ◽  
Dengsheng Lu ◽  
Michael Keller ◽  
Maiza dos-Santos ◽  
Edson Bolfe ◽  
...  
Keyword(s):  
Aboveground Biomass ◽  
Brazilian Amazon ◽  
Airborne Lidar ◽  
Lidar Data ◽  
Airborne Lidar Data

scholarly journals Regional Tropical Aboveground Biomass Mapping with L-Band Repeat-Pass Interferometric Radar, Sparse Lidar, and Multiscale Superpixels

2020 ◽  
Vol 12 (12) ◽  
pp. 2048
Author(s):  
Charlie Marshak ◽  
Marc Simard ◽  
Laura Duncanson ◽  
Carlos Alberto Silva ◽  
Michael Denbina ◽  
...  
Keyword(s):  
Aboveground Biomass ◽  
Airborne Lidar ◽  
Lidar Data ◽  
Single Model ◽  
Tropical Regions ◽  
Full Waveform ◽  
Applicable Range ◽  
L Band ◽  
Airborne Lidar Data ◽  
Synergistic Relationships

We introduce a multiscale superpixel approach that leverages repeat-pass interferometric coherence and sparse AGB estimates from a simulated spaceborne lidar in order to extend the NISAR mission’s applicable range of aboveground biomass (AGB) in tropical forests. Airborne and spaceborne L-band radar and full-waveform airborne lidar data are used to simulate the NISAR and GEDI mission, respectively. In addition to UAVSAR data, we use spaceborne ALOS-2/PALSAR-2 imagery with 14-day temporal baseline, which is comparable to NISAR’s 12-day baseline. Our reference AGB maps are derived from the airborne LVIS data during the AfriSAR campaign for three sites (Mondah, Ogooue, and Lope). Each tropical site has mean AGB of at least 125 Mg/ha in addition to areas with AGB exceeding 700 Mg/ha. Spatially sampling from these LVIS-derived AGB reference maps, we approximate GEDI AGB estimates. To evaluate our methodology, we perform several different analyses. First, we partition each study site into low (≤100 Mg/ha) and high (>100 Mg/ha) AGB areas, in conformity with the NISAR mission requirement to provide AGB estimates for forests between 0 and 100 Mg/ha with a RMSE below 20 Mg/ha. In the low AGB areas, this RMSE requirement is satisfied in Lope and Mondah and it fell short of the requirement in Ogooue by less 3 Mg/ha with UAVSAR and 6 Mg/ha with PALSAR-2. We note that our maps have finer spatial resolution (50 m) than NISAR requires (1 hectare). In the high AGB areas, the normalized RMSE increases to 51% (i.e., <90 Mg/ha), but with negligible bias for all three sites. Second, we train a single model to estimate AGB across both high and low AGB regimes simultaneously and obtain a normalized RMSE that is <60% (or <100 Mg/ha). Lastly, we show the use of both (a) multiscale superpixels and (b) interferometric coherence significantly improves the accuracy of the AGB estimates. The InSAR coherence improved the RMSE by approximately 8% at Mondah with both sensors, lowering the RMSE from 59 Mg/ha to 47.4 Mg/h with UAVSAR and from 57.1 Mg/ha to 46 Mg/ha. This work illustrates one of the numerous synergistic relationships between the spaceborne lidars, such as GEDI, with L-band SAR, such as PALSAR-2 and NISAR, in order to produce robust regional AGB in high biomass tropical regions.

scholarly journals Modeling Forest Aboveground Carbon Density in the Brazilian Amazon with Integration of MODIS and Airborne LiDAR Data

2020 ◽  
Vol 12 (20) ◽  
pp. 3330
Author(s):  
Xiandie Jiang ◽  
Guiying Li ◽  
Dengsheng Lu ◽  
Emilio Moran ◽  
Mateus Batistella
Keyword(s):  
Carbon Stock ◽  
Brazilian Amazon ◽  
Airborne Lidar ◽  
Coefficient Of Determination ◽  
Lidar Data ◽  
Carbon Density ◽  
Modis Data ◽  
Aboveground Carbon ◽  
Aboveground Carbon Density ◽  
Airborne Lidar Data

Timely updates of carbon stock distribution are needed to better understand the impacts of deforestation and degradation on forest carbon stock dynamics. This research aimed to explore an approach for estimating aboveground carbon density (ACD) in the Brazilian Amazon through integration of MODIS (moderate resolution imaging spectroradiometer) and a limited number of light detection and ranging (Lidar) data samples using linear regression (LR) and random forest (RF) algorithms, respectively. Airborne LiDAR data at 23 sites across the Brazilian Amazon were collected and used to calculate ACD. The ACD estimation model, which was developed by Longo et al. in the same study area, was used to map ACD distribution in the 23 sites. The LR and RF methods were used to develop ACD models, in which the samples extracted from LiDAR-estimated ACD were used as dependent variables and MODIS-derived variables were used as independent variables. The evaluation of modeling results indicated that ACD can be successfully estimated with a coefficient of determination of 0.67 and root mean square error of 4.18 kg C/m2 using RF based on spectral indices. The mixed pixel problem in MODIS data is a major factor in ACD overestimation, while cloud contamination and data saturation are major factors in ACD underestimation. These uncertainties in ACD estimation using MODIS data make it difficult to examine annual ACD dynamics of degradation and growth, however this method can be used to examine the deforestation-induced ACD loss.

Aboveground Biomass Estimates of Sagebrush Using Terrestrial and Airborne LiDAR Data in a Dryland Ecosystem (Dataset)

2018 ◽  
Author(s):  
Aihua Li ◽  
Nancy F. Glenn ◽  
Peter J. Olsoy ◽  
Jessica J. Mitchell ◽  
Rupesh Shrestha
Keyword(s):  
Aboveground Biomass ◽  
Airborne Lidar ◽  
Lidar Data ◽  
Airborne Lidar Data

scholarly journals Airborne LIDAR-Derived Aboveground Biomass Estimates Using a Hierarchical Bayesian Approach

2019 ◽  
Vol 11 (9) ◽  
pp. 1050
Author(s):  
Mengxi Wang ◽  
Qingwang Liu ◽  
Liyong Fu ◽  
Guangxing Wang ◽  
Xiongqing Zhang
Keyword(s):  
Aboveground Biomass ◽  
Bayesian Model ◽  
Bayesian Method ◽  
Airborne Lidar ◽  
Projection Area ◽  
Lidar Data ◽  
Hierarchical Bayesian ◽  
Hierarchical Bayesian Model ◽  
Hierarchical Bayesian Method ◽  
Airborne Lidar Data

Conventional ground survey data are very accurate, but expensive. Airborne lidar data can reduce the costs and effort required to conduct large-scale forest surveys. It is critical to improve biomass estimation and evaluate carbon stock when we use lidar data. Bayesian methods integrate prior information about unknown parameters, reduce the parameter estimation uncertainty, and improve model performance. This study focused on predicting the independent tree aboveground biomass (AGB) with a hierarchical Bayesian model using airborne LIDAR data and comparing the hierarchical Bayesian model with classical methods (nonlinear mixed effect model, NLME). Firstly, we chose the best diameter at breast height (DBH) model from several widely used models through a hierarchical Bayesian method. Secondly, we used the DBH predictions together with the tree height (LH) and canopy projection area (CPA) derived by airborne lidar as independent variables to develop the AGB model through a hierarchical Bayesian method with parameter priors from the NLME method. We then compared the hierarchical Bayesian method with the NLME method. The results showed that the two methods performed similarly when pooling the data, while for small sample sizes, the Bayesian method was much better than the classical method. The results of this study imply that the Bayesian method has the potential to improve the estimations of both DBH and AGB using LIDAR data, which reduces costs compared with conventional measurements.

scholarly journals Aboveground biomass estimation with airborne hyperspectral and LiDAR data in Tesinske Beskydy Mountains

Beskydy ◽  
2015 ◽  
Vol 8 (1) ◽  
pp. 35-46 ◽  
Author(s):  
Olga Brovkina ◽  
František Zemek ◽  
Tomáš Fabiánek
Keyword(s):  
Aboveground Biomass ◽  
Field Measurements ◽  
Airborne Lidar ◽  
Biomass Estimation ◽  
Lidar Data ◽  
Predicting Model ◽  
The Third ◽  
Study Results ◽  
Airborne Lidar Data ◽  
Forest Aboveground Biomass

The study presents three models for estimation of forest aboveground biomass (AGB) for plot level using different categories of airborne data. The first and the second models estimate AGB from metrics of airborne LiDAR data. The third model estimates AGB from integration of metrics of airborne hyperspectral and LiDAR data. The results are compared with plot level biomass estimated from field measurements. The results show that the best AGB estimate is obtained from the model utilizing a fusion of hyperspectral and LiDAR metrics. Study results expand existing research on the applicability of airborne hyperspectral and LiDAR datasets for AGB assessment. It evidences the efficiency of using a predicting model based on hyperspectral and LiDAR data for study area.

scholarly journals Estimating mangrove aboveground biomass from airborne LiDAR data: a case study from the Zambezi River delta

2018 ◽  
Vol 13 (2) ◽  
pp. 025012 ◽  
Author(s):  
Temilola Fatoyinbo ◽  
Emanuelle A Feliciano ◽  
David Lagomasino ◽  
Seung Kuk Lee ◽  
Carl Trettin
Keyword(s):  
Aboveground Biomass ◽  
River Delta ◽  
Airborne Lidar ◽  
Lidar Data ◽  
Airborne Lidar Data ◽  
Zambezi River Delta ◽  
Zambezi River

scholarly journals Estimating Logged-Over Lowland Rainforest Aboveground Biomass in Sabah, Malaysia Using Airborne LiDAR Data

2016 ◽  
Vol 27 (4) ◽  
pp. 481 ◽  
Author(s):  
Mui-How Phua ◽  
Su Wah Hue ◽  
Keiko Ioki ◽  
Mazlan Hashim ◽  
Kawi Bidin ◽  
...  
Keyword(s):  
Aboveground Biomass ◽  
Airborne Lidar ◽  
Lidar Data ◽  
Lowland Rainforest ◽  
Airborne Lidar Data
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