Estimation of above-ground carbon density of arbor forest in Zhejiang Province of southern China based on ZY-3 satellite multispectral image

Zheng Dongmei; Wang Haibin; Xia Chaozong; Chen Jian; Hou Ruiping; Hao Yuelan; An Tianyu

doi:10.12171/j.1000-1522.20180351

Journal of Beijing Forestry University > 2020 > 42(1): 65-74. > DOI: 10.12171/j.1000-1522.20180351

Zheng Dongmei, Wang Haibin, Xia Chaozong, Chen Jian, Hou Ruiping, Hao Yuelan, An Tianyu. Estimation of above-ground carbon density of arbor forest in Zhejiang Province of southern China based on ZY-3 satellite multispectral image[J]. Journal of Beijing Forestry University, 2020, 42(1): 65-74. DOI: 10.12171/j.1000-1522.20180351

Citation:

PDF (1254 KB)

Estimation of above-ground carbon density of arbor forest in Zhejiang Province of southern China based on ZY-3 satellite multispectral image

1.
Academy of Inventory and Planning, National Forestry and Grassland Administration, Beijing 100714, China
2.
Planning & Design Institute of Forest Products Industry, National Forestry and Grassland Administration, Beijing 100010, China

More Information

Received Date: October 24, 2018
Revised Date: January 30, 2019
Available Online: November 12, 2019
Published Date: January 13, 2020

Graphical Abstract

Abstract

Abstract

ObjectiveBased on the ZY-3 satellite imagery and the LULUCF carbon sink monitoring plot data covering Zhejiang Province of southern China, the study attempted to construct a technical method for automatically extracting the above-ground carbon density of arbor forest in this area.
MethodTaking the carbon density of arbor forest in Zhejiang Province as the research object, relevant research tests were carried out in the aspects of vector sign constructing, extraction of spectral information, purification of interpretation sign, ZY-3 satellite image classification, optimization of independent variables, optimization of modeling methods, production of carbon density map, etc.
ResultThe results showed that the accuracy of classification of ZY-3 imagery after purification of interpretation signs was higher than that of image classification before purification. The accuracy of classification of ZY-3 images by kNN method (average total accuracy was 80.31%, average Kappa coefficient was 0.69, average user accuracy of arbor forest was 91.86%, and the average producer accuracy of arbor forest was 80.85%), which was higher than the maximum likelihood classification method (average total accuracy was 78.56%, average Kappa coefficient was 0.62, average user accuracy of arbor forest was 89.68%, and the average producer accuracy of arbor forest was 77.79%). Among the selected modeling methods, the model accuracy constructed by the kNN method (average RMSE was 15.64 t/ha, average RRMSE was 23.53%) was better than the robust estimation method (average RMSE was 17.63 t/ha, average RRMSE was 25.11%). Finally, the above-mentioned carbon density distribution map of arbor forest in Zhejiang Province was generated.
ConclusionThis study provides a new path for arbor forest or forest carbon density estimation at the provincial or larger scale, providing a reference for automated estimation of carbon density and other forest parameters.
- ZY-3 satellite imagery,
- LULUCF carbon sink monitoring plot,
- above-ground carbon density of arbor forest,
- estimation

FullText(HTML)

References (27)

References

[1]	王海宾, 侯瑞萍, 郑冬梅, 等. 基于地理加权回归模型的亚热带地区乔木林生物量估算[J]. 农业机械学报, 2018, 49(6):184−190. doi: 10.6041/j.issn.1000-1298.2018.06.021 Wang H B, Hou R P, Zheng D M, et al. Biomass estimation of arbor forest in subtropical region based on geographically weighted regression model[J]. Transactions of the Chinese Society for Agricultural Machinery, 2018, 49(6): 184−190. doi: 10.6041/j.issn.1000-1298.2018.06.021
[2]	Lu D, Chen Q, Wang G, et al. A survey of remote sensing-based aboveground biomass estimation methods in forest ecosystems[J]. International Journal of Digital Earth, 2014, 9(1): 63−105.
[3]	刘茜, 杨乐, 柳钦火, 等. 森林地上生物量遥感反演方法综述[J]. 遥感学报, 2015, 19(1):62−74. doi: 10.11834/jrs.20154108 Liu Q, Yang L, Liu Q H, et al. Review of forest above ground biomass inversion methods based on remote sensing technology[J]. Journal of Remote Sensing, 2015, 19(1): 62−74. doi: 10.11834/jrs.20154108
[4]	Du H Q, Zhou G M, Ge H L, et al. Satellite-based carbon stock estimation for bamboo forest with a non-linear partial least square regression technique[J]. International Journal of Remote Sensing, 2012, 33(6): 1917−1933. doi: 10.1080/01431161.2011.603379
[5]	王海宾, 彭道黎, 范应龙, 等. 基于辅助信息的森林蓄积量空间模拟[J]. 农业机械学报, 2016, 47(6):283−289. doi: 10.6041/j.issn.1000-1298.2016.06.037 Wang H B, Peng D L, Fan Y L, et al. Spatial modeling of forest stock volume based on auxiliary information[J]. Transactions of the Chinese Society for Agricultural Machinery, 2016, 47(6): 283−289. doi: 10.6041/j.issn.1000-1298.2016.06.037
[6]	Gleason C J, Im J. A review of remote sensing of forest biomass and biofuel: options for small-area applications[J]. Giscience & Remote Sensing, 2011, 48(2): 141−170.
[7]	戚玉娇. 大兴安岭森林地上碳储量遥感估算与分析[D]. 哈尔滨: 东北林业大学, 2014. Qi Y J. Estimation of forest above ground carbon storage using remote sensing in Daxing’an Mountains[D]. Harbin: Northeast Forestry University, 2014.
[8]	李德仁. 我国第一颗民用三线阵立体测图卫星:资源三号测绘卫星[J]. 测绘学报, 2012, 41(3):317−322. Li D R. First civilian three-line-array stereo mapping satellite: ZY-3[J]. Acta Geodaetica et Cartographica Sinica, 2012, 41(3): 317−322.
[9]	李芬. 资源三号卫星数据在土地利用遥感监测中的应用研究[D]. 长春: 吉林大学, 2013. Li F. Application study of ZY-3 satellite data to landuse dynamic monitoring[D]. Changchun: Jinlin University, 2013.
[10]	朱汤军, 沈楚楚, 季碧勇, 等. 基于LULUCF温室气体清单编制的浙江省杉木林生物量换算因子[J]. 生态学报, 2013, 33(13):3925−3932. Zhu T J, Shen C C, Ji B Y, et al. Research on biomass expansion factor of Chinese fir forest in Zhejiang Province based on LULUCF greenhouse gas inventory[J]. Acta Ecologica Sinica, 2013, 33(13): 3925−3932.
[11]	张煜星, 王祝雄. 遥感技术在森林资源清查中的应用研究[M]. 北京: 中国林业出版社, 2007. Zhang Y X, Wang Z X. Application research of remote sensing technology in forest resources inventory[M]. Beijing: China Forestry Publishing House, 2007.
[12]	Du P, Xia J, Zhang W, et al. Multiple classifier system for remote sensing image classification: a review[J]. Sensors, 2012, 12(4): 4764−4792. doi: 10.3390/s120404764
[13]	郑刚. 基于KNN法的森林蓄积量的遥感估计和反演[D]. 南京: 南京林业大学, 2009. Zheng G. Estimation and retrieval of forest volume by remote sensing based on KNN: a case study in Wengyuan County of Guangdong Province[D]. Nanjing: Nanjing Forestry University, 2009.
[14]	邓书斌. ENVI遥感图像处理方法[M]. 北京: 高等教育出版社, 2014. Deng S B. ENVI remote sensing image processing method[M]. Beijing: Higher Education Press, 2014.
[15]	琚存勇, 邸雪颖, 蔡体久. 变量筛选方法对郁闭度遥感估测模型的影响比较[J]. 林业科学, 2007, 43(12):33−38. doi: 10.3321/j.issn:1001-7488.2007.12.006 Ju C Y, Di X Y, Cai T J. Comparing impact of two selecting variables methods on canopy closure estimation[J]. Scientia Silvae Sinicae, 2007, 43(12): 33−38. doi: 10.3321/j.issn:1001-7488.2007.12.006
[16]	李崇贵, 赵宪文, 李春干. 森林蓄积量遥感估测理论与实现[M]. 北京: 科学出版社, 2006. Li C G, Zhao X W, Li C G. Theory and realization of estimating forest stock volume by remote sensing[M]. Beijing: Science Press, 2006.
[17]	Chirici G, Mura M, McInerney D, et al. A meta-analysis and review of the literature on the k-nearest neighbors technique for forestry applications that use remotely sensed data[J]. Remote Sensing of Environment, 2016, 176: 282−294. doi: 10.1016/j.rse.2016.02.001
[18]	Gjertsen A K. Accuracy of forest mapping based on Landsat TM data and a kNN-based method[J]. Remote Sensing of Environment, 2007, 110(4): 420−430. doi: 10.1016/j.rse.2006.08.018
[19]	McRoberts R E, Nelson M D, Wendt D G. Stratified estimation of forest area using satellite imagery, inventory data, and the k-Nearest Neighbors technique[J]. Remote Sensing of Environment, 2002, 82(2−3): 457−468. doi: 10.1016/S0034-4257(02)00064-0
[20]	Tomppo E, Katila M. Satellite image-based national forest inventory of finland for publication in the igarss’91 digest[C]//Proceedings of IGARSS’91. Remote sensing: global monitoring for earth management. Helsinki: IEEE, 1991: 1141−1144.
[21]	Tomppo E, Nilsson M, Rosengren M, et al. Simultaneous use of Landsat-TM and IRS-1C WiFS data in estimating large area tree stem volume and aboveground biomass[J]. Remote Sensing of Environment, 2002, 82(1): 156−171. doi: 10.1016/S0034-4257(02)00031-7
[22]	郑刚, 彭世揆, 戎慧, 等. 基于KNN方法的森林蓄积量遥感估计和反演概述[J]. 遥感技术与应用, 2010, 25(3):430−437. doi: 10.11873/j.issn.1004-0323.2010.3.430 Zheng G, Peng S K, Rong H, et al. A general introduction to estimation and retrieval of forest volume with remote sensing based on KNN[J]. Remote Sensing Technology and Application, 2010, 25(3): 430−437. doi: 10.11873/j.issn.1004-0323.2010.3.430
[23]	刘俊, 毕华兴, 朱沛林, 等. 基于ALOS遥感数据纹理及纹理指数的柞树蓄积量估测[J]. 农业机械学报, 2014, 45(7):245−254. doi: 10.6041/j.issn.1000-1298.2014.07.038 Liu J, Bi H X, Zhu P L, et al. Estimating stand volume of Xylosma racemosum forest based on texture parameters and derivative texture indices of ALOS imagery[J]. Transactions of the Chinese Society for Agricultural Machinery, 2014, 45(7): 245−254. doi: 10.6041/j.issn.1000-1298.2014.07.038
[24]	Beguet B, Guyon D, Boukir S, et al. Automated retrieval of forest structure variables based on multi-scale texture analysis of VHR satellite imagery[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2014, 96: 164−178. doi: 10.1016/j.isprsjprs.2014.07.008
[25]	Eckert S. Improved forest biomass and carbon estimations using texture measures from WorldView-2 satellite data[J]. Remote Sensing, 2012, 4(4): 810−829. doi: 10.3390/rs4040810
[26]	Jin X L, Ma J H, Wen Z D, et al. Estimation of maize residue cover using Landsat-8 OLI image spectral information and textural features[J]. Remote Sensing, 2015, 7(11): 14559−14575. doi: 10.3390/rs71114559
[27]	Meng J H, Li S M, Wang W, et al. Estimation of forest structural diversity using the spectral and textural information derived from SPOT-5 satellite images[J]. Remote Sensing, 2016, 8(2): 125−148. doi: 10.3390/rs8020125

[1]	Chen Ling, Chen Feng, Niu Shukui, Li Lianqiang, Tao Changsen. Correlation analysis between the spatial characteristics of landscape pattern and risk of forest fire in Jiufeng Forest Park of Beijing[J]. Journal of Beijing Forestry University, 2021, 43(6): 41-49. DOI: 10.12171/j.1000-1522.20180431
[2]	Li Lianqiang, Yang Huixia, Ding Guoquan, Li Chun. Precipitation redistribution characteristics and its correlation analysis of Pinus densiflora and Quercus mongolica forests in the Liaodong Peninsula of northeastern China[J]. Journal of Beijing Forestry University, 2020, 42(11): 47-55. DOI: 10.12171/j.1000-1522.20200009
[3]	Luo Guisheng, Ma Lüyi, Jia Zhongkui, Wu Danni, Chi Mingfeng, Zhang Shumin, Zhao Guijuan. Correlation analysis between natural regeneration and environment in canopy gap of Chinese pine (Pinus tabuliformis) plantation[J]. Journal of Beijing Forestry University, 2019, 41(9): 59-68. DOI: 10.13332/j.1000-1522.20180416
[4]	Li Lianqiang, Niu Shukui, Tao Changsen, Chen Ling, Chen Feng. Correlations between stand structure and surface potential fire behavior of Pinus tabuliformis forests in Miaofeng Mountain of Beijing[J]. Journal of Beijing Forestry University, 2019, 41(1): 73-81. DOI: 10.13332/j.1000-1522.20180304
[5]	WANG Xin, LIU Qin, HUANG Qin, ZHANG Hua-yu, LI Zong-feng, ZHANG Shi-qiang, DENG Hong-ping. Niche characteristics and CCA ordination of dominant species of Thuja sutchuenensis community[J]. Journal of Beijing Forestry University, 2017, 39(8): 60-67. DOI: 10.13332/j.1000-1522.20160172
[6]	CHEN Wu, KONG De-cang, CUI Yan-hong, CAO Ming, PANG Xiao-ming, LI Ying-yue. Phenotypic genetic diversity of a core collection of Ziziphus jujuba and correlation analysis of dehiscent characters[J]. Journal of Beijing Forestry University, 2017, 39(6): 78-84. DOI: 10.13332/j.1000-1522.20170024
[7]	MA Feng-feng, PAN Gao, LI Xi-quan, HAN Yun-juan. Interspecific relationship and canonical correspondence analysis within woody plant communities in the karst mountains of Southwest Guangxi, southern China[J]. Journal of Beijing Forestry University, 2017, 39(6): 32-44. DOI: 10.13332/j.1000-1522.20160379
[8]	WANG Dan, WANG Bing, DAI Wei, LI Ping. Sensitivity analysis of variables correlated to soil organic matter in Chinese fir plantations[J]. Journal of Beijing Forestry University, 2011, 33(1): 78-83.
[9]	LIU Chun-yan, , GU Jian-cai, LI Ji-yue, CHEN Ping, LU Gu i-qiao, TIAN Guo-heng. Correlated analysis between the growth of Larix principisrupprechtii and climatic factors in Saihanba Nature Reserve, northern Hebei Province．[J]. Journal of Beijing Forestry University, 2009, 31(4): 102-105.
[10]	ZHANG Qiu-hui, ZHAO Guang-jie, ZHONG Jie.. Liquefaction of waste CCA-treated wood in phenol and the technology of metal removing processing.[J]. Journal of Beijing Forestry University, 2009, 31(3): 111-115.

Cited By

Cited by

Periodical cited type(13)

1.	熊海贝，龙有为，陈琳，丁叶蔚. 木结构无损检测技术研究与应用综述. 结构工程师. 2023(01): 191-201 .
2.	王祺，冯鑫浩，史诗琪，杨兆哲，詹先旭，吴智慧. 机器视觉在木制品制造中的应用. 木材科学与技术. 2022(05): 17-24 .
3.	王锦亚，李振业，倪超. 基于机器视觉的实木地板在线分色识别算法. 林业工程学报. 2021(05): 135-139 .
4.	庄子龙，刘英，沈鹭翔，丁奉龙，王争光. 基于多层感知机的木材颜色分类. 林业机械与木工设备. 2020(06): 8-14 .
5.	陈威，刘艳，雷庆. 基于智能视觉的小差异行为特征分类. 计算机科学. 2019(03): 298-302 .
6.	孙建平，梁懿，蒋志林，柳婧如. 图像处理技术在竹木复合材料性能评估中的应用展望. 西北林学院学报. 2019(02): 246-249+256 .
7.	王明谦，王昆，许清风. 木结构无损检测技术研究进展. 施工技术. 2019(21): 85-90 .
8.	杜丽娟. 舰船导航系统超分辨率图像智能提取技术研究. 舰船科学技术. 2018(16): 82-84 .
9.	何波. 篮球投射过程中的角度智能视觉图像分解判断方法. 现代电子技术. 2018(10): 175-178 .
10.	马玉芳. 基于智能视觉的微型高精度图像采集系统设计. 现代电子技术. 2018(19): 67-70 .
11.	魏晓慧，马晓珍，刘亚秋. 基于蜂群单阈值分割的SRC板材缺陷分类方法. 沈阳工业大学学报. 2017(03): 292-298 .
12.	陈熔，刘杰. 基于智能视觉的特定人员检索平台设计与实现. 现代电子技术. 2017(14): 102-105 .
13.	李晓东. 视觉传达设计认识探讨. 鸭绿江(下半月版). 2016(12): 175 .

Other cited types(9)

Get Citation

PDF

XML

Article views PDF downloads Cited by(22)

Turn off MathJax

Article Contents

Abstract

References

Estimation of above-ground carbon density of arbor forest in Zhejiang Province of southern China based on ZY-3 satellite multispectral image