Potential of Multiscale Texture Polarization Ratio of C-band SAR for Forest Biomass Estimation

2013 ◽  
pp. 69-83 ◽  
Author(s):  
Latifur Rahman Sarker ◽  
Janet Nichol ◽  
Ahmad Mubin
Keyword(s):  
Forest Biomass ◽  
Biomass Estimation ◽  
Polarization Ratio

scholarly journals Tropical-Forest Biomass Estimation at X-Band From the Spaceborne TanDEM-X Interferometer

2015 ◽  
Vol 12 (2) ◽  
pp. 239-243 ◽  
Author(s):  
Robert Treuhaft ◽  
Fabio Gonzalves ◽  
Joao Roberto dos Santos ◽  
Michael Keller ◽  
Michael Palace ◽  
...  
Keyword(s):  
Tropical Forest ◽  
Forest Biomass ◽  
Biomass Estimation ◽  
X Band

Forest Biomass Estimation Based on Forest Inventory Data in Middle and Last Scales

2011 ◽  
Vol 183-185 ◽  
pp. 220-224
Author(s):  
Ming Ze Li ◽  
Wen Yi Fan ◽  
Ying Yu
Keyword(s):  
Tree Species ◽  
Forest Inventory ◽  
Forest Biomass ◽  
Biomass Estimation ◽  
Total Biomass ◽  
Inventory Data ◽  
Biomass Equation ◽  
Tree Species Composition ◽  
Forest Inventory Data ◽  
Ecosystem Carbon

The forest biomass (which is referred to the arbor aboveground biomass in this research) is one of the most primary factors to determine the forest ecosystem carbon storages. There are many kinds of estimating methods adapted to various scales. It is a suitable method to estimate forest biomass of the farm or the forestry bureau in middle and last scales. First each subcompartment forest biomass should be estimated, and then the farm or the forestry bureau forest biomass was estimated. In this research, based on maoershan farm region, first the single tree biomass equation of main tree species was established or collected. The biomass of each specie was calculated according to the materials of tally, such as height, diameter and so on in the forest inventory data. Secondly, each specie’s biomass and total biomass in subcompartment were calculated according to the tree species composition in forest management investigation data. Thus the forest biomass spatial distribution was obtained by taking subcompartment as a unit. And last the forest total biomass was estimated.

scholarly journals Forest Biomass Estimation Using SAR and Optical Images

2015 ◽  
Vol 3 (1) ◽  
pp. 15-26
Author(s):  
Mohamad Reza Ramezani ◽  
Mahmood Reza Sahebi ◽  
◽  
Keyword(s):  
Forest Biomass ◽  
Biomass Estimation ◽  
Optical Images

Using synthetic data to evaluate the benefits of large field plots for forest biomass estimation with LiDAR

2018 ◽  
Vol 213 ◽  
pp. 115-128 ◽  
Author(s):  
Fabian Ewald Fassnacht ◽  
Hooman Latifi ◽  
Florian Hartig
Keyword(s):  
Forest Biomass ◽  
Synthetic Data ◽  
Large Field ◽  
Biomass Estimation ◽  
Field Plots

scholarly journals Influence of Variable Selection and Forest Type on Forest Aboveground Biomass Estimation Using Machine Learning Algorithms

Forests ◽  
2019 ◽  
Vol 10 (12) ◽  
pp. 1073 ◽  
Author(s):  
Li ◽  
Li ◽  
Li ◽  
Liu
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Variable Selection ◽  
Aboveground Biomass ◽  
Forest Type ◽  
Learning Algorithms ◽  
Forest Biomass ◽  
Machine Learning Algorithms ◽  
Biomass Estimation ◽  
Landsat 8

Forest biomass is a major store of carbon and plays a crucial role in the regional and global carbon cycle. Accurate forest biomass assessment is important for monitoring and mapping the status of and changes in forests. However, while remote sensing-based forest biomass estimation in general is well developed and extensively used, improving the accuracy of biomass estimation remains challenging. In this paper, we used China’s National Forest Continuous Inventory data and Landsat 8 Operational Land Imager data in combination with three algorithms, either the linear regression (LR), random forest (RF), or extreme gradient boosting (XGBoost), to establish biomass estimation models based on forest type. In the modeling process, two methods of variable selection, e.g., stepwise regression and variable importance-base method, were used to select optimal variable subsets for LR and machine learning algorithms (e.g., RF and XGBoost), respectively. Comfortingly, the accuracy of models was significantly improved, and thus the following conclusions were drawn: (1) Variable selection is very important for improving the performance of models, especially for machine learning algorithms, and the influence of variable selection on XGBoost is significantly greater than that of RF. (2) Machine learning algorithms have advantages in aboveground biomass (AGB) estimation, and the XGBoost and RF models significantly improved the estimation accuracy compared with the LR models. Despite that the problems of overestimation and underestimation were not fully eliminated, the XGBoost algorithm worked well and reduced these problems to a certain extent. (3) The approach of AGB modeling based on forest type is a very advantageous method for improving the performance at the lower and higher values of AGB. Some conclusions in this paper were probably different as the study area changed. The methods used in this paper provide an optional and useful approach for improving the accuracy of AGB estimation based on remote sensing data, and the estimation of AGB was a reference basis for monitoring the forest ecosystem of the study area.

scholarly journals Removing the Scaling Error Caused by Allometric Modelling in Forest Biomass Estimation at Large Scales

Forests ◽  
2019 ◽  
Vol 10 (7) ◽  
pp. 602
Author(s):  
Carl Zhou ◽  
Xiaolu Zhou
Keyword(s):  
Large Scale ◽  
Forest Ecosystems ◽  
Forest Biomass ◽  
Field Measurements ◽  
Allometric Equation ◽  
Biomass Estimation ◽  
Allometric Relationships ◽  
Data Errors ◽  
Pinus Yunnanensis

To estimate the responses of forest ecosystems, most relationships in biological systems are described by allometric relationships, the parameters of which are determined based on field measurements. The use of existing observed data errors may occur during the scaling of fine-scale relationships to describe ecosystem properties at a larger ecosystem scale. Here, we analyzed the scaling error in the estimation of forest ecosystem biomass based on the measurement of plots (biomass or volume per hectare) using an improved allometric equation with a scaling error compensator. The efficiency of the compensator on reducing the scaling error was tested by simulating the forest stand populations using pseudo-observation. Our experiments indicate that, on average, approximately 94.8% of the scaling error can be reduced, and for a case study, an overestimation of 3.6% can be removed in practice from a large-scale estimation for the biomass of Pinus yunnanensis Franch.

Pol-InSAR for Forest Biomass Estimation with the Transformation of the Polarization Basis

2019 ◽  
Vol 47 (7) ◽  
pp. 1097-1109
Author(s):  
Samira Hosseini ◽  
Hamid Ebadi ◽  
Yasser Maghsoudi ◽  
Franck Garestier
Keyword(s):  
Forest Biomass ◽  
Biomass Estimation

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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