Tracking bamboo dynamics in Zhejiang, China, using time-series of Landsat data from 1990 to 2014

被引:23
|
作者
Li, Mengna [1 ]
Li, Congcong [2 ]
Jiang, Hong [3 ]
Fang, Chengyuan [3 ]
Yang, Jun [2 ]
Zhu, Zhiliang [4 ]
Shi, Lei [5 ]
Liu, Shirong [5 ]
Gong, Peng [1 ,2 ]
机构
[1] Chinese Acad Sci, Inst Remote Sensing & Digital Earth, State Key Lab Remote Sensing Sci, Beijing 100101, Peoples R China
[2] Tsinghua Univ, Minist Edu, Ctr Earth Syst Sci, Key Lab Earth Syst Modelling, Beijing 100084, Peoples R China
[3] Zhejiang Agr & Forest Univ, Nurturing Stn State Key Lab Subtrop Silviculture, Linan, Peoples R China
[4] US Geol Survey, 959 Natl Ctr, Reston, VA 22092 USA
[5] State Forestry Adm Peoples Republ China, Int Ctr Bamboo & Rattan, Beijing, Peoples R China
关键词
RANDOM FOREST CLASSIFICATION; ACCURACY; UNCERTAINTY; MAP; SEQUESTRATION; ELEVATION; BIOMASS; IMAGERY; IMPACT; AREA;
D O I
10.1080/01431161.2016.1165885
中图分类号
TP7 [遥感技术];
学科分类号
081102 ; 0816 ; 081602 ; 083002 ; 1404 ;
摘要
Bamboo is an important vegetation type and provides a number of critical ecosystem services. Reliable and consistent information on bamboo distribution is required to better estimate its effect on climate change mitigation and socio-economic development. However, such information is rare over a large spatial area. In this study, we evaluate the contribution of different features in the identification of bamboo stands and determine a more discriminative set of features. We propose a bamboo mapping system including feature extraction and feature selection and derive the long-term trends of bamboo distribution in Zhejiang Province, China, using time-series of Landsat data from 1990 to 2014, with an increment of 5 years (1990, 1995, 2000, 2005, 2010, and 2014). The resultant maps of bamboo in the six epochs were evaluated using independent validation samples. The overall accuracies (OAs) of all six epochs range from 85.9% to 90.7%. We found that bamboo distribution in Zhejiang substantially increased from 1990 to 2014, particularly during the 2000s. Based on the produced maps, the area of bamboo in this region increased from 5363 +/- 490 km(2) in 1990 to 11671 +/- 653 km(2) in 2014, which is consistent with the National Forest Resource Inventory (NFRI) data. Our study demonstrates the capability of time-series of Landsat data for continuous monitoring of bamboo at a large spatial scale.
引用
收藏
页码:1714 / 1729
页数:16
相关论文
共 50 条
  • [1] Examining Forest Disturbance and Recovery in the Subtropical Forest Region of Zhejiang Province Using Landsat Time-Series Data
    Liu, Shanshan
    Wei, Xinliang
    Li, Dengqiu
    Lu, Dengsheng
    [J]. REMOTE SENSING, 2017, 9 (05)
  • [2] Information Extracting and Spatiotemporal Evolution of Bamboo Forest Based on Landsat Time Series Data in Zhejiang Province
    Li Y.
    Du H.
    Mao F.
    Li X.
    Cui L.
    Han N.
    Xu X.
    [J]. Linye Kexue/Scientia Silvae Sinicae, 2019, 55 (03): : 88 - 96
  • [3] A spatial and temporal analysis of forest dynamics using Landsat time-series
    Nguyen, Trung H.
    Jones, Simon D.
    Soto-Berelov, Mariela
    Haywood, Andrew
    Hislop, Samuel
    [J]. REMOTE SENSING OF ENVIRONMENT, 2018, 217 : 461 - 475
  • [4] Long-term monitoring of citrus orchard dynamics using time-series Landsat data: a case study in southern China
    Xu, Hanzeyu
    Qi, Shuhua
    Gong, Peng
    Liu, Chong
    Wang, Junbang
    [J]. INTERNATIONAL JOURNAL OF REMOTE SENSING, 2018, 39 (22) : 8271 - 8292
  • [5] Mapping burned areas using dense time-series of Landsat data
    Hawbaker, Todd J.
    Vanderhoof, Melanie K.
    Beal, Yen-Ju
    Takacs, Joshua D.
    Schmidt, Gail L.
    Falgout, Jeff T.
    Williams, Brad
    Fairaux, Nicole M.
    Caldwell, Megan K.
    Picotte, Joshua J.
    Howard, Stephen M.
    Stitt, Susan
    Dwyer, John L.
    [J]. REMOTE SENSING OF ENVIRONMENT, 2017, 198 : 504 - 522
  • [6] Regional glacier mapping from time-series of Landsat type data
    Winsvold, Solveig H.
    Kaab, Andreas
    Nuth, Christopher
    [J]. 2015 8TH INTERNATIONAL WORKSHOP ON THE ANALYSIS OF MULTITEMPORAL REMOTE SENSING IMAGES (MULTI-TEMP), 2015,
  • [7] Land Cover Classification Using Features Generated From Annual Time-Series Landsat Data
    Xiao, Jingge
    Wu, Honggan
    Wang, Chengbo
    Xia, Hao
    [J]. IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, 2018, 15 (05) : 739 - 743
  • [8] Extracting parametric dynamics from time-series data
    Huimei Ma
    Xiaofan Lu
    Linan Zhang
    [J]. Nonlinear Dynamics, 2023, 111 : 15177 - 15199
  • [9] Extracting parametric dynamics from time-series data
    Ma, Huimei
    Lu, Xiaofan
    Zhang, Linan
    [J]. NONLINEAR DYNAMICS, 2023, 111 (16) : 15177 - 15199
  • [10] Mapping Fifty Global Cities Growth Using Time-Series Landsat Data
    Bagan, Hasi
    Yamagata, Yoshiki
    [J]. LAND SURFACE REMOTE SENSING, 2012, 8524