Using multilevel remote sensing and ground data to estimate forest biomass resources in remote regions: a case study in the boreal forests of interior Alaska

2011 ◽  
Vol 37 (6) ◽  
pp. 596-611 ◽  
Author(s):  
Hans-Erik Andersen ◽  
Jacob Strunk ◽  
Hailemariam Temesgen ◽  
Donald Atwood ◽  
Ken Winterberger
Keyword(s):  
Remote Sensing ◽  
Boreal Forests ◽  
Forest Biomass ◽  
Interior Alaska ◽  
Biomass Resources

scholarly journals The Utilization of ALOS PALSAR Image to Estimate Natural Forest Biomass: Case Study at Bogani Nani Wartabone National Park

Jurnal Wasian ◽  
2014 ◽  
Vol 1 (1) ◽  
pp. 15
Author(s):  
Nurlita Indah Wahyuni
Keyword(s):  
Remote Sensing ◽  
National Park ◽  
Satellite Image ◽  
Forest Biomass ◽  
Research Area ◽  
Natural Forest ◽  
Alos Palsar ◽  
Remote Sensing Technology ◽  
Sensing Technology

The development of remote sensing technology makes it possible to utilize its data in many sectors including forestry. Remote sensing image has been used to map land cover and monitor deforestation. This paper presents utilization of ALOS PALSAR image to estimate and map aboveground biomass at natural forest of Bogani Nani Wartabone National Park especially SPTN II Doloduo and SPTN III Maelang. We used modeling method between biomass value from direct measurement and digital number of satellite image. There are two maps which present the distribution of biomass and carbon from ALOS PALSAR image with 50 m spatial resolution. These maps were built based on backscatter polarization of HH and HV bands. The maps indicate most research area dominated with biomass stock 0-5.000 ton/ha.

scholarly journals Using Airborne Light Detection and Ranging as a Sampling Tool for Estimating Forest Biomass Resources in the Upper Tanana Valley of Interior Alaska

2011 ◽  
Vol 26 (4) ◽  
pp. 157-164 ◽  
Author(s):  
Hans-Erik Andersen ◽  
Jacob Strunk ◽  
Hailemariam Temesgen
Keyword(s):  
Sampling Error ◽  
Forest Biomass ◽  
Light Detection And Ranging ◽  
Total Biomass ◽  
Probability Sample ◽  
Interior Alaska ◽  
Biomass Resources ◽  
Field Plots ◽  
Bootstrap Distribution ◽  
Plot Location

Abstract Airborne laser scanning, collected in a sampling mode, has the potential to be a valuable tool for estimating the biomass resources available to support bioenergy production in rural communities of interior Alaska. In this study, we present a methodology for estimating forest biomass over a 201,226-ha area (of which 163,913 ha are forested) in the upper Tanana valley of interior Alaska using a combination of 79 field plots and high-density airborne light detection and ranging (LiDAR) collected in a sampling mode along 27 single strips (swaths) spaced approximately 2.5 km apart. A model-based approach to estimating total aboveground biomass for the area is presented. Although a design-based sampling approach (based on a probability sample of field plots) would allow for stronger inference, a model-based approach is justified when the cost of obtaining a probability sample is prohibitive. Using a simulation-based approach, the proportion of the variability associated with sampling error and modeling error was assessed. Results indicate that LiDAR sampling can be used to obtain estimates of total biomass with an acceptable level of precision (8.1 ± 0.7 [8%] teragrams [total ± SD]), with sampling error accounting for 58% of the SD of the bootstrap distribution. In addition, we investigated the influence of plot location (i.e., GPS) error, plot size, and field-measured diameter threshold on the variability of the total biomass estimate. We found that using a larger plot (1/30 ha versus 1/59 ha) and a lower diameter threshold (7.6 versus 12.5 cm) significantly reduced the SD of the bootstrap distribution (by approximately 20%), whereas larger plot location error (over a range from 0 to 20 m root mean square error) steadily increased variability at both plot sizes.

Forest biomass estimation using remote sensing and field inventory: a case study of Tripura, India

2019 ◽  
Vol 191 (9) ◽  
Author(s):  
Prem Chandra Pandey ◽  
Prashant K. Srivastava ◽  
Tilok Chetri ◽  
Bal Krishan Choudhary ◽  
Pavan Kumar
Keyword(s):  
Remote Sensing ◽  
Forest Biomass ◽  
Biomass Estimation
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