Dynamic analysis and modeling of Forest above-ground biomass

Xin Tian, Zengyuan Li, Yun Guo, Min Yan, Erxue Chen, Zhongbo Su, Christiaan Van Der Tol, Feilong Ling

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

2 Citations (Scopus)

Abstract

Estimating forest above-ground biomass (AGB) and monitoring its variation are relevant for sustainable forest management, monitoring global change, carbon accounting, particularly for the Qilian Mountains (QMs), a water resource protection zone. In this work, the results of above-ground biomass (AGB) estimates from Landsat Thematic Mapper 5 (TM) images and field data from the fragmented landscape of the upper reaches of the Heihe River Basin (HRB), located in the Qilian Mountains of Gansu province in northwest China, are presented. An optimized k-Nearest Neighbor (k-NN) method was determined by varying both the mathematical formulation of the algorithm and remote sensing data input which resulted in 3,000 different model configurations. Following the sun-canopy-sensor plus C (SCS+C) topographic correction, performance of the optimized k-NN method was satisfied (R2=0.59, RMSE=24.92 ton/ha) which indicated that the optimized k-NN is capable of operational applications of forest AGB estimates in regions where only a few inventory data are available. Afterwards, the calibrated BIOME-BGC was applied to simulate the carbon fluxes over QMs forests with satisfactory accuracy. Finally, the dynamic analysis and modeling of forest AGB was conducted based on the remotely sensed estimation of forest AGB and the annual forest AGB increment from the ecological process model.

Original languageEnglish
Title of host publication 2014 IEEE Geoscience and Remote Sensing Symposium
PublisherIEEE
Pages729-732
Number of pages4
ISBN (Electronic)9781479957750
DOIs
Publication statusPublished - 1 Jan 2014
EventJoint International Geoscience and Remote Sensing Symposium (IGARSS 2014) / 35th Canadian Symposium on Remote Sensing (35th CSRS): Energy and our Changing Planet - Quebec City Convention Centre, Quebec, Canada
Duration: 13 Jul 201418 Jul 2014

Conference

ConferenceJoint International Geoscience and Remote Sensing Symposium (IGARSS 2014) / 35th Canadian Symposium on Remote Sensing (35th CSRS)
Abbreviated titleIGARSS 2014
CountryCanada
CityQuebec
Period13/07/1418/07/14

Fingerprint

dynamic analysis
aboveground biomass
Dynamic analysis
Biomass
modeling
Carbon
mountain
Monitoring
Forestry
montane forest
monitoring
carbon flux
Water resources
Landsat thematic mapper
Sun
Catchments
global change
forest management
Remote sensing
river basin

Keywords

  • Biome-BGC model
  • Dynamic analysis and modeling
  • forest carbon
  • remote sensing

Cite this

Tian, X., Li, Z., Guo, Y., Yan, M., Chen, E., Su, Z., ... Ling, F. (2014). Dynamic analysis and modeling of Forest above-ground biomass. In 2014 IEEE Geoscience and Remote Sensing Symposium (pp. 729-732). [6946527] IEEE. https://doi.org/10.1109/IGARSS.2014.6946527
Tian, Xin ; Li, Zengyuan ; Guo, Yun ; Yan, Min ; Chen, Erxue ; Su, Zhongbo ; Van Der Tol, Christiaan ; Ling, Feilong. / Dynamic analysis and modeling of Forest above-ground biomass. 2014 IEEE Geoscience and Remote Sensing Symposium. IEEE, 2014. pp. 729-732
@inproceedings{89d8770601f947a9ab6e49ce3469ef1d,
title = "Dynamic analysis and modeling of Forest above-ground biomass",
abstract = "Estimating forest above-ground biomass (AGB) and monitoring its variation are relevant for sustainable forest management, monitoring global change, carbon accounting, particularly for the Qilian Mountains (QMs), a water resource protection zone. In this work, the results of above-ground biomass (AGB) estimates from Landsat Thematic Mapper 5 (TM) images and field data from the fragmented landscape of the upper reaches of the Heihe River Basin (HRB), located in the Qilian Mountains of Gansu province in northwest China, are presented. An optimized k-Nearest Neighbor (k-NN) method was determined by varying both the mathematical formulation of the algorithm and remote sensing data input which resulted in 3,000 different model configurations. Following the sun-canopy-sensor plus C (SCS+C) topographic correction, performance of the optimized k-NN method was satisfied (R2=0.59, RMSE=24.92 ton/ha) which indicated that the optimized k-NN is capable of operational applications of forest AGB estimates in regions where only a few inventory data are available. Afterwards, the calibrated BIOME-BGC was applied to simulate the carbon fluxes over QMs forests with satisfactory accuracy. Finally, the dynamic analysis and modeling of forest AGB was conducted based on the remotely sensed estimation of forest AGB and the annual forest AGB increment from the ecological process model.",
keywords = "Biome-BGC model, Dynamic analysis and modeling, forest carbon, remote sensing",
author = "Xin Tian and Zengyuan Li and Yun Guo and Min Yan and Erxue Chen and Zhongbo Su and {Van Der Tol}, Christiaan and Feilong Ling",
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Tian, X, Li, Z, Guo, Y, Yan, M, Chen, E, Su, Z, Van Der Tol, C & Ling, F 2014, Dynamic analysis and modeling of Forest above-ground biomass. in 2014 IEEE Geoscience and Remote Sensing Symposium., 6946527, IEEE, pp. 729-732, Joint International Geoscience and Remote Sensing Symposium (IGARSS 2014) / 35th Canadian Symposium on Remote Sensing (35th CSRS), Quebec, Canada, 13/07/14. https://doi.org/10.1109/IGARSS.2014.6946527

Dynamic analysis and modeling of Forest above-ground biomass. / Tian, Xin; Li, Zengyuan; Guo, Yun; Yan, Min; Chen, Erxue; Su, Zhongbo; Van Der Tol, Christiaan; Ling, Feilong.

2014 IEEE Geoscience and Remote Sensing Symposium. IEEE, 2014. p. 729-732 6946527.

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

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AU - Van Der Tol, Christiaan

AU - Ling, Feilong

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AB - Estimating forest above-ground biomass (AGB) and monitoring its variation are relevant for sustainable forest management, monitoring global change, carbon accounting, particularly for the Qilian Mountains (QMs), a water resource protection zone. In this work, the results of above-ground biomass (AGB) estimates from Landsat Thematic Mapper 5 (TM) images and field data from the fragmented landscape of the upper reaches of the Heihe River Basin (HRB), located in the Qilian Mountains of Gansu province in northwest China, are presented. An optimized k-Nearest Neighbor (k-NN) method was determined by varying both the mathematical formulation of the algorithm and remote sensing data input which resulted in 3,000 different model configurations. Following the sun-canopy-sensor plus C (SCS+C) topographic correction, performance of the optimized k-NN method was satisfied (R2=0.59, RMSE=24.92 ton/ha) which indicated that the optimized k-NN is capable of operational applications of forest AGB estimates in regions where only a few inventory data are available. Afterwards, the calibrated BIOME-BGC was applied to simulate the carbon fluxes over QMs forests with satisfactory accuracy. Finally, the dynamic analysis and modeling of forest AGB was conducted based on the remotely sensed estimation of forest AGB and the annual forest AGB increment from the ecological process model.

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BT - 2014 IEEE Geoscience and Remote Sensing Symposium

PB - IEEE

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Tian X, Li Z, Guo Y, Yan M, Chen E, Su Z et al. Dynamic analysis and modeling of Forest above-ground biomass. In 2014 IEEE Geoscience and Remote Sensing Symposium. IEEE. 2014. p. 729-732. 6946527 https://doi.org/10.1109/IGARSS.2014.6946527