scholarly journals Integrating Airborne LiDAR and Optical Data to Estimate Forest Aboveground Biomass in Arid and Semi-Arid Regions of China

2018 ◽  
Vol 10 (4) ◽  
pp. 532 ◽  
Author(s):  
Luodan Cao ◽  
Jianjun Pan ◽  
Ruijuan Li ◽  
Jialin Li ◽  
Zhaofu Li
Keyword(s):  
Aboveground Biomass ◽  
Arid Regions ◽  
Airborne Lidar ◽  
Optical Data ◽  
Semi Arid ◽  
Forest Aboveground Biomass

scholarly journals Generalized Hierarchical Model-Based Estimation for Aboveground Biomass Assessment Using GEDI and Landsat Data

2018 ◽  
Vol 10 (11) ◽  
pp. 1832 ◽  
Author(s):  
Svetlana Saarela ◽  
Sören Holm ◽  
Sean Healey ◽  
Hans-Erik Andersen ◽  
Hans Petersson ◽  
...  
Keyword(s):  
Hierarchical Model ◽  
Aboveground Biomass ◽  
Field Data ◽  
Airborne Lidar ◽  
Ecosystem Dynamics ◽  
Alternative Methods ◽  
Optical Data ◽  
Strongly Correlated ◽  
Landsat Data ◽  
Model Based

Recent developments in remote sensing (RS) technology have made several sources of auxiliary data available to support forest inventories. Thus, a pertinent question is how different sources of RS data should be combined with field data to make inventories cost-efficient. Hierarchical model-based estimation has been proposed as a promising way of combining: (i) wall-to-wall optical data that are only weakly correlated with forest structure; (ii) a discontinuous sample of active RS data that are more strongly correlated with structure; and (iii) a sparse sample of field data. Model predictions based on the strongly correlated RS data source are used for estimating a model linking the target quantity with weakly correlated wall-to-wall RS data. Basing the inference on the latter model, uncertainties due to both modeling steps must be accounted for to obtain reliable variance estimates of estimated population parameters, such as totals or means. Here, we generalize previously existing estimators for hierarchical model-based estimation to cases with non-homogeneous error variance and cases with correlated errors, for example due to clustered sample data. This is an important generalization to take into account data from practical surveys. We apply the new estimation framework to case studies that mimic the data that will be available from the Global Ecosystem Dynamics Investigation (GEDI) mission and compare the proposed estimation framework with alternative methods. Aboveground biomass was the variable of interest, Landsat data were available wall-to-wall, and sample RS data were obtained from an airborne LiDAR campaign that produced simulated GEDI waveforms. The results show that generalized hierarchical model-based estimation has potential to yield more precise estimates than approaches utilizing only one source of RS data, such as conventional model-based and hybrid inferential approaches.

scholarly journals Stratification-Based Forest Aboveground Biomass Estimation in a Subtropical Region Using Airborne Lidar Data

2020 ◽  
Vol 12 (7) ◽  
pp. 1101 ◽  
Author(s):  
Xiandie Jiang ◽  
Guiying Li ◽  
Dengsheng Lu ◽  
Erxue Chen ◽  
Xinliang Wei
Keyword(s):  
Aboveground Biomass ◽  
Forest Type ◽  
Forest Stand ◽  
Airborne Lidar ◽  
Accurate Estimation ◽  
Biomass Estimation ◽  
Lidar Data ◽  
Forest Types ◽  
Estimation Model ◽  
Forest Aboveground Biomass

Species-rich subtropical forests have high carbon sequestration capacity and play important roles in regional and global carbon regulation and climate changes. A timely investigation of the spatial distribution characteristics of subtropical forest aboveground biomass (AGB) is essential to assess forest carbon stocks. Lidar (light detection and ranging) is regarded as the most reliable data source for accurate estimation of forest AGB. However, previous studies that have used lidar data have often beenbased on a single model developed from the relationships between lidar-derived variables and AGB, ignoring the variability of this relationship in different forest types. Although stratification of forest types has been proven to be effective for improving AGB estimation, how to stratify forest types and how many strata to use are still unclear. This research aims to improve forest AGB estimation through exploring suitable stratification approaches based on lidar and field survey data. Different stratification schemes including non-stratification and stratifications based on forest types and forest stand structures were examined. The AGB estimation models were developed using linear regression (LR) and random forest (RF) approaches. The results indicate the following: (1) Proper stratifications improved AGB estimation and reduced the effect of under- and overestimation problems; (2) the finer forest type strata generated higher accuracy of AGB estimation but required many more sample plots, which were often unavailable; (3) AGB estimation based on stratification of forest stand structures was similar to that based on five forest types, implying that proper stratification reduces the number of sample plots needed; (4) the optimal AGB estimation model and stratification scheme varied, depending on forest types; and (5) the RF algorithm provided better AGB estimation for non-stratification than the LR algorithm, but the LR approach provided better estimation with stratification. Results from this research provide new insights on how to properly conduct forest stratification for AGB estimation modeling, which is especially valuable in tropical and subtropical regions with complex forest types.

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 Fusion of Multiple Gridded Biomass Datasets for Generating a Global Forest Aboveground Biomass Map

2020 ◽  
Vol 12 (16) ◽  
pp. 2559 ◽  
Author(s):  
Yuzhen Zhang ◽  
Shunlin Liang
Keyword(s):  
Aboveground Biomass ◽  
Land Surface ◽  
Forest Canopy ◽  
Forest Cover ◽  
Airborne Lidar ◽  
Estimation Methods ◽  
Area Index ◽  
Temperature And Precipitation ◽  
Error Removal ◽  
Forest Aboveground Biomass

Many advanced satellite estimation methods have been developed, but global forest aboveground biomass (AGB) products remain largely uncertain. In this study, we explored data fusion techniques to generate a global forest AGB map for the 2000s at 0.01-degree resolution with improved accuracy by integrating ten existing local or global maps. The error removal and simple averaging algorithm, which is efficient and makes no assumption about the data and associated errors, was proposed to integrate these ten forest AGB maps. We first compiled the global reference AGB from in situ measurements and high-resolution AGB data that were originally derived from field data and airborne lidar data and determined the errors of each forest AGB map at the pixels with corresponding reference AGB values. Based on the errors determined from reference AGB data, the pixel-by-pixel errors associated with each of the ten AGB datasets were estimated from multiple predictors (e.g., leaf area index, forest canopy height, forest cover, land surface elevation, slope, temperature, and precipitation) using the random forest algorithm. The estimated pixel-by-pixel errors were then removed from the corresponding forest AGB datasets, and finally, global forest AGB maps were generated by combining the calibrated existing forest AGB datasets using the simple averaging algorithm. Cross-validation using reference AGB data showed that the accuracy of the fused global forest AGB map had an R-squared of 0.61 and a root mean square error (RMSE) of 53.68 Mg/ha, which is better than the reported accuracies (R-squared of 0.56 and RMSE larger than 80 Mg/ha) in the literature. Intercomparison with previous studies also suggested that the fused AGB estimates were much closer to the reference AGB values. This study attempted to integrate existing forest AGB datasets for generating a global forest AGB map with better accuracy and moved one step forward for our understanding of the global terrestrial carbon cycle by providing improved benchmarks of global forest carbon stocks.

scholarly journals Comparison of Statistical Modelling Approaches for Estimating Tropical Forest Aboveground Biomass Stock and Reporting Their Changes in Low-Intensity Logging Areas Using Multi-Temporal LiDAR Data

2020 ◽  
Vol 12 (9) ◽  
pp. 1498 ◽  
Author(s):  
Franciel Eduardo Rex ◽  
Carlos Alberto Silva ◽  
Ana Paula Dalla Corte ◽  
Carine Klauberg ◽  
Midhun Mohan ◽  
...  
Keyword(s):  
Tropical Forest ◽  
Aboveground Biomass ◽  
Ordinary Least Squares ◽  
Forest Growth ◽  
Selective Logging ◽  
Airborne Lidar ◽  
Support Vector ◽  
Lidar Data ◽  
Field Plots ◽  
Forest Aboveground Biomass

Accurately quantifying forest aboveground biomass (AGB) is one of the most significant challenges in remote sensing, and is critical for understanding global carbon sequestration. Here, we evaluate the effectiveness of airborne LiDAR (Light Detection and Ranging) for monitoring AGB stocks and change (ΔAGB) in a selectively logged tropical forest in eastern Amazonia. Specifically, we compare results from a suite of different modelling methods with extensive field data. The calibration AGB values were derived from 85 square field plots sized 50 × 50 m field plots established in 2014 and which were estimated using airborne LiDAR data acquired in 2012, 2014, and 2017. LiDAR-derived metrics were selected based upon Principal Component Analysis (PCA) and used to estimate AGB stock and change. The statistical approaches were: ordinary least squares regression (OLS), and nine machine learning approaches: random forest (RF), several variations of k-nearest neighbour (k-NN), support vector machine (SVM), and artificial neural networks (ANN). Leave-one-out cross-validation (LOOCV) was used to compare performance based upon root mean square error (RMSE) and mean difference (MD). The results show that OLS had the best performance with an RMSE of 46.94 Mg/ha (19.7%) and R² = 0.70. RF, SVM, and ANN were adequate, and all approaches showed RMSE ≤54.48 Mg/ha (22.89%). Models derived from k-NN variations all showed RMSE ≥64.61 Mg/ha (27.09%). The OLS model was thus selected to map AGB across the time-series. The mean (±sd—standard deviation) predicted AGB stock at the landscape level was 229.10 (±232.13) Mg/ha in 2012, 258.18 (±106.53) in 2014, and 240.34 (sd ± 177.00) Mg/ha in 2017, showing the effect of forest growth in the first period and logging in the second period. In most cases, unlogged areas showed higher AGB stocks than logged areas. Our methods showed an increase in AGB in unlogged areas and detected small changes from reduced-impact logging (RIL) activities occurring after 2012. We also detected that the AGB increase in areas logged before 2012 was higher than in unlogged areas. Based on our findings, we expect our study could serve as a basis for programs such as REDD+ and assist in detecting and understanding AGB changes caused by selective logging activities in tropical forests.

scholarly journals MAPPING THE SPATIAL DISTRIBUTION OF COLOMBIA’S FOREST ABOVEGROUND BIOMASS USING SAR AND OPTICAL DATA

2019 ◽  
Vol XLII-3/W7 ◽  
pp. 57-60 ◽  
Author(s):  
P. Rodríguez-Veiga ◽  
A. P. Barbosa-Herrera ◽  
J. S. Barreto-Silva ◽  
P. C. Bispo ◽  
E. Cabrera ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Aboveground Biomass ◽  
Forest Inventory ◽  
National Forest Inventory ◽  
Total Carbon ◽  
Optical Data ◽  
National Forest ◽  
Ppm Level ◽  
Forest Aboveground Biomass

<p><strong>Abstract.</strong> An assessment on the amount and spatial distribution of forest aboveground biomass (AGB) for the forests in Colombia was generated using in-situ national forest inventory data (IDEAM, 2018), in combination with multispectral optical data and synthetic aperture radar (SAR) satellite imagery. ALOS-2 PALSAR-2 gamma-0 backscatter annual mosaics (2015&amp;ndash;2017) provided by JAXA were normalised and corrected using previous ALOS PALSAR annual mosaics (2007&amp;ndash;2010) as reference. A multi-temporal Landsat 7 &amp;amp; 8 composite over the whole of Colombia was used for the year 2016&amp;thinsp;&amp;plusmn;&amp;thinsp;1. The national forest inventory in-situ plots used to train our model consisted of 5-subplots each and were collected during the period 2015&amp;ndash;2017 in the main biomes of the country. A sample of permanent 1ha plots (PPMs) were also measured. Nationally developed allometries (Alvarez et al., 2012) were used to estimate AGB. A non-parametric random forests (RF) algorithm was used within a k-fold framework to retrieve AGB at 30&amp;thinsp;m spatial resolution for the whole of Colombia. The algorithm was trained using forest inventory plots and validated at plot (0.35&amp;thinsp;ha) and PPM level (1&amp;thinsp;ha). The accuracy assessment found coefficients of determination (R<sup>2</sup>) of 0.68 and 0.61, and relative root mean square errors (Rel. RMSE) of 49% and 34% at plot and at PPM level, respectively. The results showed that the average AGB for the country was 118.1&amp;thinsp;t&amp;thinsp;ha<sup>&amp;minus;1</sup> (45.6&amp;thinsp;t&amp;thinsp;ha<sup>&amp;minus;1</sup> for Caribe, 75.4&amp;thinsp;t&amp;thinsp;ha<sup>&amp;minus;1</sup> Andes, 122.5&amp;thinsp;t&amp;thinsp;ha<sup>&amp;minus;1</sup> Pacifico, 32.7&amp;thinsp;t&amp;thinsp;ha<sup>&amp;minus;1</sup> Orinoquia, and 200.5&amp;thinsp;t&amp;thinsp;ha<sup>&amp;minus;1</sup> for the Amazonia, regionally), and that the total carbon stocks for the country were 6.7&amp;thinsp;Pg C for the period 2015&amp;ndash;2017.</p>

scholarly journals Filtering ground noise from LiDAR returns produces inferior models of forest aboveground biomass

2021 ◽  
Author(s):  
Michael Mahoney ◽  
Lucas Johnson ◽  
Eddie Bevilacqua ◽  
Colin Beier
Keyword(s):  
Land Cover ◽  
Aboveground Biomass ◽  
Large Scale ◽  
Point Clouds ◽  
Airborne Lidar ◽  
Noise Filtering ◽  
Useful Signal ◽  
Lower Accuracy ◽  
Ground Noise ◽  
Forest Aboveground Biomass

Airborne LiDAR has become an essential data source for large-scale, high-resolution modeling of forest biomass and carbon stocks, enabling predictions with much higher resolution and accuracy than can be achieved using optical imagery alone. Ground noise filtering -- that is, excluding returns from LiDAR point clouds based on simple height thresholds -- is a common practice meant to improve the 'signal' content of LiDAR returns by preventing ground returns from masking useful information about tree size and condition contained within canopy returns. Although this procedure originated in LiDAR-based estimation of mean tree and canopy height, ground noise filtering has remained prevalent in LiDAR pre-processing, even as modelers have shifted focus to forest aboveground biomass (AGB) and related characteristics for which ground returns may actually contain useful information about stand density and openness. In particular, ground returns may be helpful for making accurate biomass predictions in heterogeneous landscapes that include a patchy mosaic of vegetation heights and land cover types. In this paper, we applied several ground noise filtering thresholds while mapping two study areas in New York (USA), one a forest-dominated area and the other a mixed-use landscape. We observed that removing ground noise via any height threshold systematically biases many of the LiDAR-derived variables used in AGB modeling. By fitting random forest models to each of these predictor sets, we found that that ground noise filtering yields models of forest AGB with lower accuracy than models trained using predictors derived from unfiltered point clouds. The relative inferiority of AGB models based on filtered LiDAR returns was much greater for the mixed land-cover study area than for the contiguously forested study area. Our results suggest that ground filtering should be avoided when mapping biomass, particularly when mapping heterogeneous and highly patchy landscapes, as ground returns are more likely to represent useful 'signal' than extraneous 'noise' in these cases.

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