scholarly journals Models of stand volume and biomass estimation based on LiDAR data for the main forest types in Calabria (southern Italy)

2017 ◽  
Vol 14 (3) ◽  
pp. 175-187 ◽  
Author(s):  
G Scrinzi ◽  
A Floris ◽  
F Clementel ◽  
V Bernardini ◽  
F Chianucci ◽  
...  
Keyword(s):  
Southern Italy ◽  
Biomass Estimation ◽  
Lidar Data ◽  
Forest Types ◽  
Stand Volume

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 Evaluation of Stand Biomass Estimation Methods for Major Forest Types in the Eastern Da Xing’an Mountains, Northeast China

Forests ◽  
2019 ◽  
Vol 10 (9) ◽  
pp. 715
Author(s):  
Dong ◽  
Zhang ◽  
Li
Keyword(s):  
Model Fitting ◽  
Prediction Performance ◽  
Model Systems ◽  
Estimation Methods ◽  
Biomass Estimation ◽  
Forest Types ◽  
Stand Volume ◽  
Additive System ◽  
Biomass Models ◽  
Biomass Equations

Currently, forest biomass estimation methods at the regional scale have attracted the greatest attention from researchers, and the development of stand biomass models has become popular a trend. In this study, a total of 5074 measurements on 1053 permanent sample plots were obtained in the Eastern Da Xing’an Mountains, and three additive systems of stand biomass equations were developed. The first additive system (M-1) used stand variables as the predictors (i.e., stand basal area and average height), the second additive system (M-2) utilized stand volume as the sole predictor, and the third additive system (M-3) included both stand volume and biomass expansion and conversion factors (BCEFs) as the predictors. The coefficients of the three model systems were estimated with nonlinear seemingly unrelated regression (NSUR), while the heteroscedasticity of the model residuals was solved with the weight function. The jackknifing technique was used on the residuals, and several statistics were used to assess the prediction performance of each model. We comprehensively evaluated four stand biomass estimation methods (i.e., M-1, M-2, M-3 and a constant BCEF (M-4)). Here, we showed that the (1) three additive systems of stand biomass equations showed good model fitting and prediction performance, (2) M-3 significantly improved the model fitting and performance and provided the most accurate predictions for most stand biomass components, and (3) the ranking of the four stand biomass estimation methods followed the order of M-3 > M-2 > M-4 > M-1. Our results demonstrated these additive stand biomass models could be used to estimate the stand aboveground and belowground biomass for the major forest types in the Eastern Da Xing’an Mountains, although the most appropriate method depends on the available data and forest type.

scholarly journals SUBTROPICAL FOREST BIOMASS ESTIMATION USING AIRBORNE LiDAR AND HYPERSPECTRAL DATA

2016 ◽  
Vol XLI-B8 ◽  
pp. 747-749
Author(s):  
Yong Pang ◽  
Zengyuan Li
Keyword(s):  
Forest Ecosystem ◽  
Forest Biomass ◽  
Subtropical Forest ◽  
Hyperspectral Data ◽  
Airborne Lidar ◽  
Observation System ◽  
Biomass Estimation ◽  
Lidar Data ◽  
Forest Types ◽  
Waveform Lidar

Forests have complex vertical structure and spatial mosaic pattern. Subtropical forest ecosystem consists of vast vegetation species and these species are always in a dynamic succession stages. It is very challenging to characterize the complexity of subtropical forest ecosystem. In this paper, CAF’s (The Chinese Academy of Forestry) LiCHy (LiDAR, CCD and Hyperspectral) Airborne Observation System was used to collect waveform Lidar and hyperspectral data in Puer forest region, Yunnan province in the Southwest of China. The study site contains typical subtropical species of coniferous forest, evergreen broadleaf forest, and some other mixed forests. The hypersectral images were orthorectified and corrected into surface reflectance with support of Lidar DTM product. The fusion of Lidar and hyperspectral can classify dominate forest types. The lidar metrics improved the classification accuracy. Then forest biomass estimation was carried out for each dominate forest types using waveform Lidar data, which get improved than single Lidar data source.

Forest biomass estimation over three distinct forest types using TanDEM-X InSAR data and simulated GEDI lidar data

2019 ◽  
Vol 232 ◽  
pp. 111283 ◽  
Author(s):  
Wenlu Qi ◽  
Svetlana Saarela ◽  
John Armston ◽  
Göran Ståhl ◽  
Ralph Dubayah
Keyword(s):  
Forest Biomass ◽  
Biomass Estimation ◽  
Lidar Data ◽  
Forest Types

scholarly journals SUBTROPICAL FOREST BIOMASS ESTIMATION USING AIRBORNE LiDAR AND HYPERSPECTRAL DATA

2016 ◽  
Vol XLI-B8 ◽  
pp. 747-749
Author(s):  
Yong Pang ◽  
Zengyuan Li
Keyword(s):  
Forest Ecosystem ◽  
Forest Biomass ◽  
Subtropical Forest ◽  
Hyperspectral Data ◽  
Airborne Lidar ◽  
Observation System ◽  
Biomass Estimation ◽  
Lidar Data ◽  
Forest Types ◽  
Waveform Lidar

Forests have complex vertical structure and spatial mosaic pattern. Subtropical forest ecosystem consists of vast vegetation species and these species are always in a dynamic succession stages. It is very challenging to characterize the complexity of subtropical forest ecosystem. In this paper, CAF’s (The Chinese Academy of Forestry) LiCHy (LiDAR, CCD and Hyperspectral) Airborne Observation System was used to collect waveform Lidar and hyperspectral data in Puer forest region, Yunnan province in the Southwest of China. The study site contains typical subtropical species of coniferous forest, evergreen broadleaf forest, and some other mixed forests. The hypersectral images were orthorectified and corrected into surface reflectance with support of Lidar DTM product. The fusion of Lidar and hyperspectral can classify dominate forest types. The lidar metrics improved the classification accuracy. Then forest biomass estimation was carried out for each dominate forest types using waveform Lidar data, which get improved than single Lidar data source.

scholarly journals Aboveground Forest Biomass Estimation with Landsat and LiDAR Data and Uncertainty Analysis of the Estimates

2012 ◽  
Vol 2012 ◽  
pp. 1-16 ◽  
Author(s):  
Dengsheng Lu ◽  
Qi Chen ◽  
Guangxing Wang ◽  
Emilio Moran ◽  
Mateus Batistella ◽  
...  
Keyword(s):  
Uncertainty Analysis ◽  
Optical Sensors ◽  
Forest Biomass ◽  
Structural Data ◽  
Data Availability ◽  
Estimation Methods ◽  
Biomass Estimation ◽  
Lidar Data ◽  
Availability Constraints ◽  
Data Saturation

Landsat Thematic mapper (TM) image has long been the dominate data source, and recently LiDAR has offered an important new structural data stream for forest biomass estimations. On the other hand, forest biomass uncertainty analysis research has only recently obtained sufficient attention due to the difficulty in collecting reference data. This paper provides a brief overview of current forest biomass estimation methods using both TM and LiDAR data. A case study is then presented that demonstrates the forest biomass estimation methods and uncertainty analysis. Results indicate that Landsat TM data can provide adequate biomass estimates for secondary succession but are not suitable for mature forest biomass estimates due to data saturation problems. LiDAR can overcome TM’s shortcoming providing better biomass estimation performance but has not been extensively applied in practice due to data availability constraints. The uncertainty analysis indicates that various sources affect the performance of forest biomass/carbon estimation. With that said, the clear dominate sources of uncertainty are the variation of input sample plot data and data saturation problem related to optical sensors. A possible solution to increasing the confidence in forest biomass estimates is to integrate the strengths of multisensor data.

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