Comparative analysis of forest biomass retrieval from water cloud model (WCM) of polarized SAR data with different wavelengths

Ji Yongjie; Xu Kunpeng; Zhang Wangfei; Shi Jianmin; Zhang Fuxiang

doi:10.12171/j.1000-1522.20220006

Journal of Beijing Forestry University > 2023 > 45(2): 24-33. > DOI: 10.12171/j.1000-1522.20220006

Ji Yongjie, Xu Kunpeng, Zhang Wangfei, Shi Jianmin, Zhang Fuxiang. Comparative analysis of forest biomass retrieval from water cloud model (WCM) of polarized SAR data with different wavelengths[J]. Journal of Beijing Forestry University, 2023, 45(2): 24-33. DOI: 10.12171/j.1000-1522.20220006

Citation:

PDF (13962 KB)

Comparative analysis of forest biomass retrieval from water cloud model (WCM) of polarized SAR data with different wavelengths

1.
School of Geography and Ecotourism, Southwest Forestry University, Kunming 650224, Yunnan, China
2.
Institute of Forest Resources Information Technique, Chinese Academy of Forestry, Beijing 100091, China
3.
College of Forestry, Southwest Forestry University, Kunming 650224, Yunnan, China

More Information

Received Date: January 02, 2021
Revised Date: February 05, 2022
Available Online: January 03, 2023
Published Date: February 24, 2023

Graphical Abstract

Abstract

Abstract

Objective Water cloud model (WCM) is a semi empirical model using SAR data to retrieve forest aboveground biomass (AGB). The objective of this study is to explore the capability of introducing different wavelengths at different polarization channels into WCM for forest AGB inversion. And through the exploration, it is expected to provide scientific reference for improving the accuracy of forest AGB retrieval.
Method In this paper, firstly, we applied WCM in forest AGB estimation at X-, C-, L- and P-band with HH, HV, and VV polarizations, respectively, and their results were compared and analyzed. Then a parameter named the ratio of surface scattering power and volume scattering power was constructed based on polarization decomposition components and embedded in WCM, here we named it PolWCM. The potential of PolWCM on forest AGB estimation was explored by X-, C-, L- and P-band polarimetric decomposition components.
Result (1) HV backscattering coefficients showed best performance in forest AGB estimation using WCM at C-, L- and P-band, among them, L- and P-band performed better than X- and C-band (R² = 0.46, RMSE = 18.0 t/ha for L-band and R² = 0.43, RMSE = 21.18 t/ha for P-band). (2) PolWCM performed better than WCM for forest AGB estimation at X-, C-, L- and P-band, respectively. Their RMSE values for X-, C-, L- and P-band were 24.90, 24.71, 17.70 and 18.08 t/ha, respectively.
Conclusion The forest AGB estimation of WCM shows obvious dependence on wavelength and polarization, HV backscatter coefficients at L-band perform best in forest AGB estimation. Polarimetric information embedded in WCM through the ratio of surface scattering power and volume scattering power can improve the forest AGB estimation accuracy.

FullText(HTML)

References (25)

References

[1]	罗环敏. 基于极化干涉SAR的森林结构信息提取模型与方法[D]. 成都: 电子科技大学, 2011. Luo H M. Models and methods of extracting forest structure information by polarmetric SAR interferometry[D]. Chengdu: University of Electronic Science and Technology of China, 2011.
[2]	West P W. Tree and forest measurement[M]. Berlin: Springer Nature, 2004.
[3]	Chen L, Wang Y, Ren C, et al. Assessment of multi-wavelength SAR and multispectral instrument data for forest aboveground biomass mapping using random forest kriging[J]. Forest Ecology and Management, 2019, 447: 12−25. doi: 10.1016/j.foreco.2019.05.057
[4]	Toan T L, Beaudoin A, Riom J, et al. Relating forest biomass to SAR data[J]. IEEE Transactions on Geoscience and Remote Sensing, 1992, 30(2): 403−411. doi: 10.1109/36.134089
[5]	陈尔学. 合成孔径雷达森林生物量估测研究进展[J]. 世界林业研究, 1999, 12(6): 18−23. Chen E X. Development of forest biomass estimation using SAR data[J]. World Forestry Research, 1999, 12(6): 18−23.
[6]	姬永杰, 张王菲. 森林地上生物量合成孔径雷达技术反演研究综述[J]. 世界林业研究, 2022, 35(3): 32−39. Ji Y J, Zhang W F. Review on retrieval of forest aboveground biomass by SAR technology[J]. World Forestry Research, 2022, 35(3): 32−39.
[7]	李云, 张王菲, 崔鋆波, 等. 参数优选支持的光学与SAR数据森林地上生物量反演研究[J]. 北京林业大学学报, 2020, 42(10): 11−19. Li Y, Zhang W F, Cui J B, et al. Inversion exploration on forest aboveground biomass of optical and SAR data supported by parameter optimization method[J]. Journal of Beijing Forestry University, 2020, 42(10): 11−19.
[8]	史建敏, 张王菲, 曾鹏, 等. 联合GF-1和GF-3影像的森林地上生物量反演[J]. 北京林业大学学报, 2022, 44(11): 70−81. Shi J M, Zhang W F, Zeng P, et al. Inversion of forest aboveground biomass from combined images of GF-1 and GF-3[J]. Journal of Beijing Forestry University, 2022, 44(11): 70−81.
[9]	Attema E, Ulaby F T. Vegetation modeled as a water cloud[J]. Radio Science, 1978, 13(2): 357−364. doi: 10.1029/RS013i002p00357
[10]	杨贵军, 岳继博, 李长春, 等. 基于改进水云模型和Radarsat-2数据的农田土壤含水量估算[J]. 农业工程学报, 2016, 32(22): 146−153. Yang G J, Yue J B, Li C H, et al. Estimation of soil moisture in farmland using improved water cloud model and Radarsat-2 data[J]. Transactions of the Chinese Society of Agricultural Engineering, 2016, 32(22): 146−153.
[11]	张智宏. 基于极化SAR的小麦倒伏灾害与长势监测研究[D]. 西安: 西安科技大学, 2017. Zhang Z H. Research on wheat condition and growth monitoring based on compact polarimetric SAR[D]. Xi’an: Xi’an University of Science and Technology, 2017.
[12]	Hosseini M, Mcnairn H, Mitchell S, et al. A comparison between support vector machine and water cloud model for estimating crop leaf area index[J/OL]. Remote Sensing, 2021, 13(7): 1348[2021−03−01]. https://doi.org/10.3390/rs13071348.
[13]	Santoro M, Askne J, Eriksson L, et al. Seasonal dynamics and stem volume retrieval in boreal forests using JERS-1 backscatter[C]. Proceedings of SPIE. Berlin: the International Society for Optical Engineering, 2003.
[14]	戴玉芳, 凌飞龙. 水云模型于L波段SAR和中国北方森林的适用性分析[J]. 遥感信息, 2013, 28(4): 44−49. Dai Y F, Ling F L. Suitability analysis of water cloud model for L-band synthetic aperture radar to forest in northeast China[J]. Remote Sensing Information, 2013, 28(4): 44−49.
[15]	Askne J I H, Dammert P B G, Ulander L M H, et al. C-band repeat-pass interferometric SAR observations of the forest[J]. IEEE Transactions on Geoscience and Remote Sensing, 1997, 35: 25−35. doi: 10.1109/36.551931
[16]	Cartus O, Santoro M, Kellndorfer J. Mapping forest aboveground biomass in the Northeastern United States with ALOS PALSAR dual-polarization L-band[J]. Remote Sensing of Environment, 2012, 124: 466−478. doi: 10.1016/j.rse.2012.05.029
[17]	Santoro M, Eriksson L E B, Fransson J E S. Reviewing ALOS PALSAR backscatter observations for stem volume retrieval in swedish forest[J]. Remote Sensing, 2015, 7(4): 4290−4317. doi: 10.3390/rs70404290
[18]	Santoro M, Cartus O. Research pathways of forest above-ground biomass estimation based on SAR backscatter and interferometric SAR observations[J/OL]. Remote Sensing, 2018, 10(4): 608[2018−04−01]. https://doi.org/10.3390/rs10040608.
[19]	Santoro M, Beer C, Cartus O, et al. Retrieval of growing stock volume in boreal forest using hyper-temporal series of Envisat ASAR ScanSAR backscatter measurements[J]. Remote Sensing of Environment, 2011, 115: 490−507. doi: 10.1016/j.rse.2010.09.018
[20]	张伟伦, 张延成, 范文义, 等. 干涉水云模型对不同极化方式哨兵数据估测森林生物量的精度比较[J]. 东北林业大学学报, 2020, 48(11): 27−32. Zhang W L, Zhang Y C, Fan W Y, et al. Comparison of biomass accuracy with different polarization data with interferometric water cloud model[J]. Journal of Northeast Forestry University, 2020, 48(11): 27−32.
[21]	穆喜云. 森林地上生物量遥感估测方法研究[D]. 呼和浩特: 内蒙古农业大学, 2015. Mu X Y. A study on the estimating method of forest above ground biomass based on remote sensing data[D]. Hohhot: Inner Mongolia Agricultural University, 2015.
[22]	冯琦, 陈尔学, 李增元, 等. 基于机载P波段全极化SAR数据的复杂地形森林地上生物量估测方法[J]. 林业科学, 2016, 52(3): 10−22. Feng Q, Chen E X, Li Z Y, et al. Forest above-ground biomass estimation method of rugged terrain based on airborne P-band PolSAR data[J]. Scientia Silvae Sinicae, 2016, 52(3): 10−22.
[23]	Pulliainen J, Heiska K, Hyyppae J, et al. Backscattering properties of boreal forests at C and X-band[J]. Ieee Transactions on Geoscience and Remote Sensing, 1994, 32(5): 1041−1050. doi: 10.1109/36.312892
[24]	Cartus O, Santoro M. Exploring combinations of multi-temporal and multi-frequency radar backscatter observations to estimate above-ground biomass of tropical forest[J/OL]. Remote Sensing of Environment, 2019, 232: 111313[2022−07−01]. https://doi.org/10.1016/j.rse.2019.111313.
[25]	Kumar S, Pandey U, Kushwaha S P, et al. Aboveground biomass estimation of tropical forest from Envisat advanced synthetic aperture radar data using modeling approach[J/OL]. Journal of Applied Remote Sensing, 2012, 6(1): 063588[2021−10−12]. https://doi.org/10.1117/1.JRS.6.063588.

[1]	Zhou Lai, Cheng Xiaofang, Zhang Mengtao. Model construction of Larix principis-rupprechtii canopy volume and surface area based on BP neural network[J]. Journal of Beijing Forestry University, 2024, 46(8): 94-100. DOI: 10.12171/j.1000-1522.20230166
[2]	Shi Jianmin, Zhang Wangfei, Zeng Peng, Zhao Lixian, Wang Mengjin. Inversion of forest aboveground biomass from combined images of GF-1 and GF-3[J]. Journal of Beijing Forestry University, 2022, 44(11): 70-81. DOI: 10.12171/j.1000-1522.20220029
[3]	Tian Xiaomin, Zhang Xiaoli. Estimation of forest aboveground biomass by remote sensing[J]. Journal of Beijing Forestry University, 2021, 43(8): 137-148. DOI: 10.12171/j.1000-1522.20200166
[4]	Li Yun, Zhang Wangfei, Cui Junbo, Li Chunmei, Ji Yongjie. Inversion exploration on forest aboveground biomass of optical and SAR data supported by parameter optimization method[J]. Journal of Beijing Forestry University, 2020, 42(10): 11-19. DOI: 10.12171/j.1000-1522.20190389
[5]	Li Ming-ze, Yu Xin-tong, Gao Yuan-ke, Fan Wen-yi. Remote sensing quantification on forest biomass based on SAR polarization decomposition and Landsat data[J]. Journal of Beijing Forestry University, 2018, 40(2): 1-10. DOI: 10.13332/j.1000-1522.20170284
[6]	LOU Xin, GU Yan, ZHANG Jun-hui, HAN Shi-jie. Effects of snow cover and freeze-thaw cycles on stability of surface soil aggregates in forest[J]. Journal of Beijing Forestry University, 2016, 38(4): 63-70. DOI: 10.13332/j.1000-1522.20150435
[7]	LI Chun-mei, ZHANG Wang-fei, LI Zeng-yuan, CHEN Er-xue, TIAN Xin. Retrieval of forest above-ground biomass using multi-source data in Genhe, Inner Mongolia[J]. Journal of Beijing Forestry University, 2016, 38(3): 64-72. DOI: 10.13332/j.1000-1522.20150209
[8]	ZHANG Zhi, TIAN Xin, CHEN Er-xue, HE Qi-sheng. Review of methods on estimating forest above ground biomass[J]. Journal of Beijing Forestry University, 2011, 33(5): 144-150.
[9]	LIU Zeng-wen, DUAN Er-jun, LIU Zhuo-majie, FENG Shun-yu.. Soil polarization under pure stands of planted forests in the semiarid, windblown sand region of northern Shaanxi Province, northwestern China[J]. Journal of Beijing Forestry University, 2009, 31(2): 1-9.
[10]	ZHANG Jun-mei, LI Wen-bin, SA Chao, WANG De-ming. Power control system of remote control pruning robot for standing trees[J]. Journal of Beijing Forestry University, 2007, 29(4): 22-26. DOI: 10.13332/j.1000-1522.2007.04.006

Cited By

Cited by

Periodical cited type(11)

1.	赵钰婷，陈冬瑶，杨柳，李晶楠，宁广亮，姜静. 白桦四倍体×紫雨桦二倍体杂交种子活力及杂种子代生长特性分析. 温带林业研究. 2025(01): 1-8 .
2.	任亚超，张军，王进茂，杨敏生. 科研反哺教学在林木育种学教学中的探索与实践. 安徽农业科学. 2024(10): 278-282 .
3.	杨琦，王湘莹，王晓明，乔中全，唐丽. 大花紫薇×‘丹红紫叶’紫薇杂交F_1代不育株转录组测序. 东北林业大学学报. 2024(09): 25-29 .
4.	赵一帆，孔博，程雪桐，李亮，凌傲宇，李智群，康向阳，张平冬. 赤霉素喷洒处理诱导新疆杨2n花粉产生及其对微管骨架的影响. 北京林业大学学报. 2023(01): 40-50 . 本站查看
5.	李智群，孔博，程雪桐，李亮，张平冬. 高温诱导银灰杨花粉败育的细胞学机理研究. 北京林业大学学报. 2023(05): 25-34 . 本站查看
6.	刘春洋，彭朝凤，程世平，姚鹏强，耿喜宁，谢丽华. 高温诱导‘凤丹’牡丹2n雌配子创制三倍体. 园艺学报. 2023(07): 1455-1466 .
7.	刘宣晨，刘彩霞，张世凯，李开隆，曲冠证. 大青杨×小黑杨异源三倍体新种质创制. 东北林业大学学报. 2023(10): 19-27 .
8.	周炳秀，刘勇，彭玉信，张劲，赵建松，朱轶超，赵巧玲，王硕，陶靖，孟路. 雄性毛白杨无性系苗期表型和生理变异的早期综合评价. 东北林业大学学报. 2023(11): 1-9 .
9.	吴婷，贾瑞冬，杨树华，赵鑫，于晓南，国圆，葛红. 蝴蝶兰多倍体育种研究进展与展望. 园艺学报. 2022(02): 448-462 .
10.	张新宇，董阳，王梦蕾，孙照斌. 银腺杨解剖及理化性能研究. 林业科技. 2022(05): 33-36 .
11.	陈赢男，韦素云，曲冠正，胡建军，王军辉，尹佟明，潘惠新，卢孟柱，康向阳，李来庚，黄敏仁，王明庥. 现代林木育种关键核心技术研究现状与展望. 南京林业大学学报(自然科学版). 2022(06): 1-9 .

Other cited types(14)

Get Citation

PDF

XML

Article views (1161) PDF downloads (151) Cited by(25)

Turn off MathJax

Article Contents

Abstract

References

Comparative analysis of forest biomass retrieval from water cloud model (WCM) of polarized SAR data with different wavelengths