Please wait a minute...
 
Remote Sensing for Land & Resources    2021, Vol. 33 Issue (1) : 214-220     DOI: 10.6046/gtzyyg.2020178
|
Research on spatial change of the boreal forest cover in Siberia over the past 30 years based on TM images
TIAN Lei1,2(), FU Wenxue1(), SUN Yanwu1, JING Linhai1, QIU Yubao1, LI Xinwu1
1. Key Laboratory of Digital Earth, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
2. School of Geomatics, Anhui University of Science and Technology, Huainan 232001, China
Download: PDF(2702 KB)   HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks    
Abstract  

In the context of global warming, the study of the long-term spatial change characteristics of the boreal forest cover not only is important for global climate change and sustainable development research but also can provide the support for the further research on the response of the boreal forest changes to climate change. The data sources were Landsat TM/OLI images with 2 temporal series in summer season from 1985 and 2015, respectively. The Krasnoyarsk region in Russia was selected as the typical research area of the boreal forest in Siberia. The forest cover in 1985 and 2015 was classified based on the decision tree method and verification with random sample points of GF-2 satellite images, and the classification accuracy was 94.53%. The information of the dynamic spatial distribution of forest cover was quantified through latitude zones with 2° interval in the range of N51°~69° and the spatial overlay analysis for the dynamic forest cover maps of the two periods. The results show that, in the past 30 years, the boreal forest cover in Siberia changed significantly, and the overall forest cover changed from 75.42% in 1985 to 80.53% in 2015, increasing by 5.11 percentage points. Simultaneously, the changes of forest land area were different with each latitude zones: the highest change rate occurred in the latitude zone N65°~67°, followed by the latitude zone N67°~69° and the lowest was in N57°~59°. Overall, the forest cover increased in all latitude zones, the most significant increase was in N63°~67°; the change of forest cover was relatively stable in N57°~63° and the increase of forest cover decreased with the latitude zone in N51°~57°.

Keywords TM images      boreal forest      forest coverage      Siberia      spatial change     
ZTFLH:  TP79  
Corresponding Authors: FU Wenxue     E-mail: richard_dada@outlook.com;fuwx@aircas.ac.cn
Issue Date: 18 March 2021
Service
E-mail this article
E-mail Alert
RSS
Articles by authors
Lei TIAN
Wenxue FU
Yanwu SUN
Linhai JING
Yubao QIU
Xinwu LI
Cite this article:   
Lei TIAN,Wenxue FU,Yanwu SUN, et al. Research on spatial change of the boreal forest cover in Siberia over the past 30 years based on TM images[J]. Remote Sensing for Land & Resources, 2021, 33(1): 214-220.
URL:  
https://www.gtzyyg.com/EN/10.6046/gtzyyg.2020178     OR     https://www.gtzyyg.com/EN/Y2021/V33/I1/214
Fig.1  Location of the study area
Fig.2  Preprocessing flowchart of TM/OLI scenes
Fig.3  TM/OLI scenes classification based on decision tree classification model
Fig.4  Forest cover in 1985 and 2015
纬度 总面积/km2 1985年 2015年 1985—2015年
面积/km2 覆盖度/% 面积/km2 覆盖度/% 面积增加
量/km2
面积变
化率/%
覆盖度变
化百分点
N 67°~69° 19 596.45 5 838.93 29.80 6 731.24 34.35 892.31 15.28 4.55
N 65°~67° 58 813.18 32 255.77 54.84 40 192.99 68.34 7 937.23 24.61 13.50
N 63°~65° 58 364.09 40 083.99 68.68 45 243.34 77.52 5 159.35 12.87 8.84
N 61°~63° 66 364.15 55 911.37 84.25 56 952.28 85.82 1 040.92 1.86 1.57
N 59°~61° 130 507.24 106 668.66 81.73 110 896.36 84.97 4 227.70 3.96 3.24
N 57°~59° 155 232.73 139 694.20 89.99 141 250.67 90.99 1 556.47 1.11 1.00
N 55°~57° 114 456.67 79 057.51 69.07 88 167.88 77.03 9 110.38 11.52 7.96
N 53°~55° 77 579.58 53 261.04 68.65 58 131.19 74.93 4 870.15 9.14 6.28
N 51°~53° 33 564.27 26 059.58 77.64 27 744.80 82.66 1 685.21 6.47 5.02
总计 714 478.36 538 831.05 75.42 575 310.77 80.53 36 479.72 6.77 5.11
Tab.1  Statistics of forest cover information in 1985 and 2015
Fig.5  Monitoring results of dynamic changes of forest cover based on classification results
[1] Edenhofer O, Madruga P R, Sokona Y, et al. Intergovernmental panel on climate change[C]// Climate change 2014:Mitigation of climate change.Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. New York: Cambridge University Press, 2014.
[2] Food and Agriculture Organization of the United Nations. Global forest resources assessment 2015:How are the world’s forests changing[M]. Rome:Food and Agriculture Organization of the United Nations, 2015.
[3] Corinne L Q, Andrew R M, Canadell J G, et al. Global carbon budget 2016[J]. Earth System Science Data, 2016,7(1):47-85.
[4] Grassi G, House J, Dentener F, et al. The key role of forests in meeting climate targets requires science for credible mitigation[J]. Nature Climate Change, 2017,7(3):220-226.
[5] Pan Y, Birdsey R A, Fang J. A large and persistent carbon sink in the world’s forests[J]. Science, 2011,333:988-993.
doi: 10.1126/science.1201609 pmid: 21764754 url: https://www.ncbi.nlm.nih.gov/pubmed/21764754
[6] 李剑泉, 李智勇, 易浩若. 森林与全球气候变化的关系[J]. 西北林学院学报, 2010,25(4):23-28.
[6] Li J Q, Li Z Y, Yi H R. Interaetion relation between forestand global climate change[J]. Journal of Northwest Forestry University, 2010,25(4):23-28.
[7] Stocks B J, Lynham T J. Fire weather climatology in Canada and Russia[M]. Boston:Kluwer Academic Publishers, 1996.
[8] Larsen J A. The boreal ecosystem[M]. New York: Academic Press, 1980.
[9] 吴雪琼, 覃先林, 周汝良, 等. 森林覆盖变化遥感监测方法研究进展[J]. 林业资源管理, 2010(4):82-87.
[9] Wu X Q, Qin X L, Zhou R L, et al. Progress of study on forest cover change detection by using remote sensing technique[J]. Forest Resources Management, 2010(4):82-87.
[10] 覃先林, 陈尔学, 李增元, 等. 基于MODIS数据的森林覆盖变化监测方法研究[J]. 遥感技术与应用, 2006,21(3):178-183.
[10] Qin X L, Chen E X, Li Z Y, et al. Forest cover change monitoring using MODIS data[J]. Remote Sensing Technology and Application, 2006,21(3):178-183.
[11] 王荣, 江东, 韩惠, 等. 高分辨率遥感影像天然林与人工林植被覆盖信息提取[J]. 资源科学, 2013,35(4):868-874.
[11] Wang R, Jiang D, Han H, et al. Extracting natural and artificial forest information based on high resolution remote sensing data[J]. Resources Science, 2013,35(4):868-874.
[12] Wilson E H, Sader S A. Detection of forest harvest type using multiple dates of Landsat TM imagery[J]. Remote Sensing of Environment, 2002,80(3):385-396.
[13] 任冲, 鞠洪波, 张怀清, 等. 天水市近30年林地动态变化遥感监测研究[J]. 林业科学研究, 2017,30(1):25-33.
[13] Ren C, Ju H B, Zhang H Q, et al. Research on remote sensing monitoring technology of forest land dynamic change in Tianshui in recent 30 years[J]. Forest Research, 2017,30(1):25-33.
[14] 姜洋, 李艳. 浙江省森林信息提取及其变化的空间分布[J]. 生态学报, 2014,34(24):7261-7270.
[14] Jiang Y, Li Y. The extraction of forest information and the spatial distribution of its change in Zhejiang Province[J]. Acta Ecologica Sinica, 2014,34(24):7261-7270.
[15] Serreze M C, Walsh J E, Chapin F S, et al. Observational evidence of recent change in the northern high-latitude environment[J]. Climatic Change, 2000,46(1-2):159-207.
[16] Stocker T F, Qin D, Plattner G K, et al. Climate change:The physical science basis[M]. Cambridge: Cambridge University Press, 2013.
[17] 李存军, 刘良云, 王纪华, 等. 基于Landsat影像自身特征的薄云自动探测与去除[J]. 浙江大学学报(工学版), 2006,40(1):10-13.
[17] Li C J, Liu L Y, Wang J H, et al. Automatic detection and removal of thin haze based on own features of Landsat image[J]. Journal of Zhejiang University(Engineering Science), 2006,40(1):10-13.
url: http://www.journals.zju.edu.cn/eng//CN/abstract/abstract9433.shtml
[18] 李德仁, 王树良, 李德毅, 等. 论空间数据挖掘和知识发现的理论与方法[J]. 武汉大学学报(信息科学版), 2002,27(3):221-233.
[18] Li D R, Wang S L, Li D Y, et al. Theories and technologies of spatial data mining and knowledge discovery[J]. Geomatics and Information Science of Wuhan University, 2002,27(3):221-223.
[19] 胡德勇, 邓磊, 林文鹏. 遥感图像处理原理和方法[M]. 北京: 测绘出版社, 2014.
[19] Hu D Y, Deng L, Lin W P. Principle and methods of remote sensing image processing[M]. Beijing: Surveying and Mapping Publishing House, 2014.
[20] 刘淼, 胡远满, 布仁仓, 等. 基于RS和GIS的松潘县景观变化研究[J]. 辽宁工程技术大学学报, 2007,26(3):351-353.
[20] Liu M, Hu Y M, Bu R C, et al. Study on landscape change based on RS and GIS in Songpan County[J]. Journal of Liaoning Technical University, 2007,26(3):351-353.
[21] Myneni R, Keeling C, Tucker C, et al. Increased plant growth in the northern high latitudes from 1981 to 1991[J]. Nature, 1997,386:698-702.
[22] Keeling C, Chin J, Whorf T. Increased activity of northern vegetation inferred from atmospheric CO2 measurements[J]. Nature, 1996,382(6587):146-149.
[23] 尹凌宇, 覃先林, 孙桂芬, 等. 利用KPCA法检测高分一号影像中的森林覆盖变化[J]. 国土资源遥感, 2018,30(1):95-101.doi: 10.6046/gtzyyg.2018.01.13.
[23] Yi L Y, Qin X L, Sun G F, et al. The method for detecting forest cover change in GF-1 images by using KPCA[J]. Remote Sensing for Land and Resources, 2018,30(1):95-101.doi: 10.6046/gtzyyg.2018.01.13.
[1] MAO Kebiao, YAN Yibo, CAO Mengmeng, YUAN Zijin, QIN Zhihao. Reconstruction of surface temperature data and analysis of spatial and temporal changes in North America[J]. Remote Sensing for Natural Resources, 2022, 34(4): 203-215.
[2] ZHAO Lianjie, WU Mengquan, ZHENG Longxiao, LUAN Shaopeng, ZHAO Xianfeng, XUE Mingyue, LIU Jiayan, LIU Chenxi. Temporal-spatial changes and driving analysis of the northern shorelines of Jiaodong Peninsula[J]. Remote Sensing for Natural Resources, 2022, 34(4): 87-96.
[3] YANG Yunxue, ZHANG Yanfang. Temporal-spatial evolutionary characteristics of ecological sensitivity in Yanhe River basin based on spatial distance index[J]. Remote Sensing for Natural Resources, 2021, 33(3): 229-237.
[4] CHEN Hong, GUO Zhaocheng, HE Peng. Spatial and temporal change characteristics of vegetation coverage in Erhai Lake basin during 1988—2018[J]. Remote Sensing for Land & Resources, 2021, 33(2): 116-123.
[5] Guanbing HU, Fang LIU, Wei DANG, Kun YANG, Qingsong CHEN. Application of remote sensing technology to geological mapping in the vegetation covered area of southwest Yunnan[J]. Remote Sensing for Land & Resources, 2019, 31(2): 224-230.
[6] QI Shuai, ZHANG Yonghong, WANG Huiqin. Analysis of fire disturbed forests scattering characteristics using polarimetric SAR image[J]. REMOTE SENSING FOR LAND & RESOURCES, 2016, 28(2): 48-53.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
京ICP备05055290号-2
Copyright © 2017 Remote Sensing for Natural Resources
Support by Beijing Magtech