Regional forest above-ground biomass retrieval by optimized k-NN algorithm in Northeast China

Xin Tian; Erxue Chen; Zengyuan Li; Z. Bob Su; Lina Bai; C. Van Der Tol

doi:10.1109/IGARSS.2013.6721326

Regional forest above-ground biomass retrieval by optimized k-NN algorithm in Northeast China

Xin Tian^*, Erxue Chen, Zengyuan Li, Z. Bob Su, Lina Bai, C. Van Der Tol

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

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Abstract

This study explores retrieval of wall-to-wall forest above-ground biomass (AGB) over Jilin province in Northeast China, using the optimized non-parametric k-NN method, the 7^th National Forest Inventory (NFI) data, and the remote sensing data: Landsat-TM/ETM+ images. For pixel-based validation, the estimated result was compared to the NFI data by leave-one-out process and R² = 0.40 and RMSE = 54.29 tons/hm². For county-scale validation, the result was verified by the intensive forest sub-compartment data of eight county and R² = 0.80 and RMSE = 34.26 tons/hm².

Original language	English
Title of host publication	2013 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2013 - Proceedings
Place of Publication	Piscataway, NJ
Publisher	IEEE
Pages	979-982
Number of pages	4
ISBN (Electronic)	978-1-4799-1114-1
DOIs	https://doi.org/10.1109/IGARSS.2013.6721326
Publication status	Published - 1 Dec 2013
Event	33rd IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2013: Building a Sustainable Earth through Remote Sensing - Melbourne, Australia Duration: 21 Jul 2013 → 26 Jul 2013 Conference number: 33 http://www.igarss2013.org/

Publication series

Name	IEEE International Geoscience and Remote Sensing Symposium - IGARSS
Publisher	IEEE
Volume	2013
ISSN (Print)	2153-6996
ISSN (Electronic)	2153-7003

Conference

Conference	33rd IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2013
Abbreviated title	IGARSS
Country/Territory	Australia
City	Melbourne
Period	21/07/13 → 26/07/13
Internet address	http://www.igarss2013.org/

Keywords

Forest above-ground biomass
k-NN method
Optimized configuration

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10.1109/IGARSS.2013.6721326

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Tian, X., Chen, E., Li, Z., Su, Z. B., Bai, L., & Van Der Tol, C. (2013). Regional forest above-ground biomass retrieval by optimized k-NN algorithm in Northeast China. In 2013 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2013 - Proceedings (pp. 979-982). [6721326] (IEEE International Geoscience and Remote Sensing Symposium - IGARSS; Vol. 2013). IEEE. https://doi.org/10.1109/IGARSS.2013.6721326

@inproceedings{69612870b3824c78a51b75f1b276fcbe,

title = "Regional forest above-ground biomass retrieval by optimized k-NN algorithm in Northeast China",

abstract = "This study explores retrieval of wall-to-wall forest above-ground biomass (AGB) over Jilin province in Northeast China, using the optimized non-parametric k-NN method, the 7th National Forest Inventory (NFI) data, and the remote sensing data: Landsat-TM/ETM+ images. For pixel-based validation, the estimated result was compared to the NFI data by leave-one-out process and R2 = 0.40 and RMSE = 54.29 tons/hm2. For county-scale validation, the result was verified by the intensive forest sub-compartment data of eight county and R2 = 0.80 and RMSE = 34.26 tons/hm2.",

keywords = "Forest above-ground biomass, k-NN method, Optimized configuration",

author = "Xin Tian and Erxue Chen and Zengyuan Li and Su, {Z. Bob} and Lina Bai and {Van Der Tol}, C.",

year = "2013",

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doi = "10.1109/IGARSS.2013.6721326",

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note = "33rd IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2013 : Building a Sustainable Earth through Remote Sensing, IGARSS ; Conference date: 21-07-2013 Through 26-07-2013",

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Tian, X, Chen, E, Li, Z, Su, ZB, Bai, L & Van Der Tol, C 2013, Regional forest above-ground biomass retrieval by optimized k-NN algorithm in Northeast China. in 2013 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2013 - Proceedings., 6721326, IEEE International Geoscience and Remote Sensing Symposium - IGARSS, vol. 2013, IEEE, Piscataway, NJ, pp. 979-982, 33rd IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2013, Melbourne, Victoria, Australia, 21/07/13. https://doi.org/10.1109/IGARSS.2013.6721326

Regional forest above-ground biomass retrieval by optimized k-NN algorithm in Northeast China. / Tian, Xin; Chen, Erxue; Li, Zengyuan et al.
2013 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2013 - Proceedings. Piscataway, NJ: IEEE, 2013. p. 979-982 6721326 (IEEE International Geoscience and Remote Sensing Symposium - IGARSS; Vol. 2013).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

TY - GEN

T1 - Regional forest above-ground biomass retrieval by optimized k-NN algorithm in Northeast China

AU - Tian, Xin

AU - Chen, Erxue

AU - Li, Zengyuan

AU - Su, Z. Bob

AU - Bai, Lina

AU - Van Der Tol, C.

N1 - Conference code: 33

PY - 2013/12/1

Y1 - 2013/12/1

N2 - This study explores retrieval of wall-to-wall forest above-ground biomass (AGB) over Jilin province in Northeast China, using the optimized non-parametric k-NN method, the 7th National Forest Inventory (NFI) data, and the remote sensing data: Landsat-TM/ETM+ images. For pixel-based validation, the estimated result was compared to the NFI data by leave-one-out process and R2 = 0.40 and RMSE = 54.29 tons/hm2. For county-scale validation, the result was verified by the intensive forest sub-compartment data of eight county and R2 = 0.80 and RMSE = 34.26 tons/hm2.

AB - This study explores retrieval of wall-to-wall forest above-ground biomass (AGB) over Jilin province in Northeast China, using the optimized non-parametric k-NN method, the 7th National Forest Inventory (NFI) data, and the remote sensing data: Landsat-TM/ETM+ images. For pixel-based validation, the estimated result was compared to the NFI data by leave-one-out process and R2 = 0.40 and RMSE = 54.29 tons/hm2. For county-scale validation, the result was verified by the intensive forest sub-compartment data of eight county and R2 = 0.80 and RMSE = 34.26 tons/hm2.

KW - Forest above-ground biomass

KW - k-NN method

KW - Optimized configuration

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Tian X, Chen E, Li Z, Su ZB, Bai L, Van Der Tol C. Regional forest above-ground biomass retrieval by optimized k-NN algorithm in Northeast China. In 2013 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2013 - Proceedings. Piscataway, NJ: IEEE. 2013. p. 979-982. 6721326. (IEEE International Geoscience and Remote Sensing Symposium - IGARSS). doi: 10.1109/IGARSS.2013.6721326

Regional forest above-ground biomass retrieval by optimized k-NN algorithm in Northeast China

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