The assessment of synergetic effects of airborne LiDAR, CCD and hyperspectral data for above-ground biomass estimation

2018 ◽  
Author(s):  
Xin Shen ◽  
Lin Cao ◽  
Zhengnan Zhang ◽  
Guibin Wang ◽  
Fuliang Cao
Keyword(s):  
Hyperspectral Data ◽  
Airborne Lidar ◽  
Biomass Estimation ◽  
Above Ground Biomass ◽  
Synergetic Effects ◽  
Ground Biomass

Above ground biomass estimation in an African tropical forest with lidar and hyperspectral data

2014 ◽  
Vol 89 ◽  
pp. 49-58 ◽  
Author(s):  
Gaia Vaglio Laurin ◽  
Qi Chen ◽  
Jeremy A. Lindsell ◽  
David A. Coomes ◽  
Fabio Del Frate ◽  
...  
Keyword(s):  
Tropical Forest ◽  
Hyperspectral Data ◽  
Biomass Estimation ◽  
Above Ground Biomass ◽  
Ground Biomass

scholarly journals Synergistic use of Landsat 8 OLI image and airborne LiDAR data for above-ground biomass estimation in tropical lowland rainforests

2017 ◽  
Vol 406 ◽  
pp. 163-171 ◽  
Author(s):  
Mui-How Phua ◽  
Shazrul Azwan Johari ◽  
Ong Cieh Wong ◽  
Keiko Ioki ◽  
Maznah Mahali ◽  
...  
Keyword(s):  
Airborne Lidar ◽  
Biomass Estimation ◽  
Lidar Data ◽  
Landsat 8 ◽  
Above Ground Biomass ◽  
Landsat 8 Oli ◽  
Tropical Lowland ◽  
Ground Biomass ◽  
Airborne Lidar Data

scholarly journals A COMPARISON OF DECISION TREE-BASED MODELS FOR FOREST ABOVE-GROUND BIOMASS ESTIMATION USING A COMBINATION OF AIRBORNE LIDAR AND LANDSAT DATA

2021 ◽  
Vol V-3-2021 ◽  
pp. 235-241
Author(s):  
H. Tamiminia ◽  
B. Salehi ◽  
M. Mahdianpari ◽  
C. M. Beier ◽  
L. Johnson ◽  
...  
Keyword(s):  
Decision Tree ◽  
Regression Tree ◽  
Forest Degradation ◽  
Airborne Lidar ◽  
Forest Monitoring ◽  
Biomass Estimation ◽  
Above Ground Biomass ◽  
Ground Biomass ◽  
Deep Forest ◽  
Landsat 5 Tm

Abstract. Forest is one of the most crucial Earth’s resources. Forest above-ground biomass (AGB) mapping has been research endeavors for a long time in many applications since it provides valuable information for carbon cycle monitoring, deforestation, and forest degradation monitoring. A methodology to rapidly and accurately estimate AGB is essential for forest monitoring purposes. Thus, the main objective of this paper was to investigate the performance of decision tree-based models to predict AGB at a site in Huntington Wild Forest (HWF) in Essex County, NY using continuous forest inventory (CFI) plots. The results of decision tree, random forest, and deep forest regression models were compared using light detection and ranging (LiDAR), Landsat 5 TM, and a combination of them. The results illustrated the importance of integration of Landsat 5 TM and LiDAR data, which benefits from both vertical forest structure and spectral information reflected by canopy cover. In addition, the deep forest model with a root mean square error (RMSE) of 51.63 Mg/ha and R-squared (R2) of 0.45 outperformed other regression tree-based models, regardless of the dataset.

Above-ground biomass estimation of Indian tropical forests using X band Pol-InSAR and Random Forest

2021 ◽  
Vol 21 ◽  
pp. 100462
Author(s):  
Sadhana Yadav ◽  
Hitendra Padalia ◽  
Sanjiv K. Sinha ◽  
Ritika Srinet ◽  
Prakash Chauhan
Keyword(s):  
Random Forest ◽  
Tropical Forests ◽  
Biomass Estimation ◽  
Above Ground Biomass ◽  
Ground Biomass ◽  
X Band

Using photography to estimate above-ground biomass of small trees

2020 ◽  
pp. 1-7
Author(s):  
Brandon R. Hays ◽  
Corinna Riginos ◽  
Todd M. Palmer ◽  
Benard C. Gituku ◽  
Jacob R. Goheen
Keyword(s):  
Cost Effective ◽  
Allometric Equation ◽  
Biomass Estimation ◽  
Above Ground Biomass ◽  
Tree Biomass ◽  
East African ◽  
Ground Biomass ◽  
Herbivore Exclusion ◽  
Significant Difference ◽  
Allometric Relation

Abstract Quantifying tree biomass is an important research and management goal across many disciplines. For species that exhibit predictable relationships between structural metrics (e.g. diameter, height, crown breadth) and total weight, allometric calculations produce accurate estimates of above-ground biomass. However, such methods may be insufficient where inter-individual variation is large relative to individual biomass and is itself of interest (for example, variation due to herbivory). In an East African savanna bushland, we analysed photographs of small (<5 m) trees from perpendicular angles and fixed distances to estimate above-ground biomass. Pixel area of trees in photos and diameter were more strongly related to measured, above-ground biomass of destructively sampled trees than biomass estimated using a published allometric relation based on diameter alone (R2 = 0.86 versus R2 = 0.68). When tested on trees in herbivore-exclusion plots versus unfenced (open) plots, our predictive equation based on photos confirmed higher above-ground biomass in the exclusion plots than in unfenced (open) plots (P < 0.001), in contrast to no significant difference based on the allometric equation (P = 0.43). As such, our new technique based on photographs offers an accurate and cost-effective complement to existing methods for tree biomass estimation at small scales with potential application across a wide variety of settings.

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