Assessment of Polsar and Insar Time-Series from the 2019 NASA AM-PM Campaign for Above-Ground Biomass Estimation

2020 ◽  
Author(s):  
Marco Lavalle ◽  
Unmesh Khati ◽  
Gustavo H.X. Shiroma ◽  
Bruce Chapman
Keyword(s):  
Time Series ◽  
Biomass Estimation ◽  
Above Ground Biomass ◽  
Ground Biomass

Above-ground biomass estimation based on NPP time-series − A novel approach for biomass estimation in semi-arid Kazakhstan

2017 ◽  
Vol 72 ◽  
pp. 13-22 ◽  
Author(s):  
Christina Eisfelder ◽  
Igor Klein ◽  
Aruzhan Bekkuliyeva ◽  
Claudia Kuenzer ◽  
Manfred F. Buchroithner ◽  
...  
Keyword(s):  
Time Series ◽  
Biomass Estimation ◽  
Above Ground Biomass ◽  
Ground Biomass ◽  
Novel Approach ◽  
Semi Arid

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.

scholarly journals Assessment of Forest Above-Ground Biomass Estimation from PolInSAR in the Presence of Temporal Decorrelation

2018 ◽  
Vol 10 (6) ◽  
pp. 815 ◽  
Author(s):  
Nafiseh Ghasemi ◽  
Valentyn Tolpekin ◽  
Alfred Stein
Keyword(s):  
Biomass Estimation ◽  
Above Ground Biomass ◽  
Ground Biomass ◽  
Temporal Decorrelation

Combing Polarization coherence tomography and PoLInSAR segmentation for forest above ground biomass estimation

2012 ◽  
Author(s):  
Wenmei Li ◽  
Erxue Chen ◽  
Zengyuan Li ◽  
Huanmin Luo ◽  
Wei Zhou ◽  
...  
Keyword(s):  
Biomass Estimation ◽  
Above Ground Biomass ◽  
Ground Biomass

Forest above ground biomass estimation from P-band tomography data

2016 ◽  
Author(s):  
Lan Li ◽  
Erxue Chen ◽  
Zengyuan Li ◽  
Lei Zhao ◽  
Xinzhi Gu
Keyword(s):  
Biomass Estimation ◽  
Above Ground Biomass ◽  
Ground Biomass ◽  
Tomography Data
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