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Quantifying annual land-cover change and vegetation greenness variation in a coastal ecosystem using dense time-series Landsat data

机译:使用密集的时间系列LANDSAT数据量化沿海生态系统的年陆地覆盖变化和植被绿色变化

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摘要

Land-cover change may affect water and carbon cycles when transitioning from one land-cover category to another (land-cover conversion, LCC) or when the characteristics of the land-cover type are altered without changing its overall category (land-cover modification, LCM). Given the increasing availability of time-series remotely sensed data for earth monitoring, there has been increased recognition of the importance of accounting for both LCC and LCM to study annual land-cover changes. In this study, we integrated 1,513 time-series Landsat images and a change-updating method to identify annual LCC and LCM during 1986-2015 in the coastal area of Zhejiang Province, China. The purpose was to quantify their contributions to land-cover changes and impacts on the amount of vegetation. The results show that LCC and LCM can be successfully distinguished with an overall accuracy of 90.0%. LCM accounted for 22% and 40.5% of the detected land-cover changes in reclaimed and inland areas, respectively, during 1986-2015. In the reclaimed area, LCC occurred mostly in muddy tidal flats, construction land, aquaculture ponds, and freshwater herbaceous land, whereas LCM occurred mostly in freshwater herbaceous land, Spartina alterniflora, and muddy tidal flats. In the inland area, both LCC and LCM were concentrated in forest and dryland. Overall, LCC had a mean magnitude of normalized difference vegetation index (NDVI) change similar to that of LCM. However, LCC had a positive effect and LCM had a negative effect on NDVI change in the reclaimed area. Both LCC and LCM in the inland area had negative impacts on vegetation greenness, but LCC resulted in larger NDVI change magnitude. Impacts of LCC and LCM on vegetation greenness were quantified for each land-cover type. This study provided a methodological framework to take both LCC and LCM into account when analyzing land-cover changes and quantified their effects on coastal ecosystem vegetation.
机译:当从一个陆地覆盖类别转换到另一个(陆地覆盖转换,LCC)或改变陆地覆盖类型的特性而不改变其整体类别时(陆盖修改,LCM)。鉴于随着地球监测的遥感数据的越来越多的时间系列远程感测数据,已经增加了对LCC和LCM学习年度覆盖变化的重要性。在这项研究中,我们综合了1,513个时间序列的Landsat图像和改变更新方法,以识别1986 - 2015年在浙江省沿海地区的年度LCC和LCM。目的是量化其对土地覆盖变更和对植被数量的影响的贡献。结果表明,LCC和LCM可以成功区分,整体准确性为90.0%。 LCM在1986 - 2015年,分别占未检测到的土地覆盖的22%和40.5%,分别在接收和内陆地区发生了22%和40.5%。在再生地区,LCC主要发生在泥泞的潮汐公寓,建筑用地,水产养殖池和淡水草本土地上,而LCM主要发生在淡水草本陆地,斯巴塔基纳德林和泥泞的潮流。在内陆地区,LCC和LCM都集中在森林和旱地中。总体而言,LCC具有与LCM类似的归一化差异植被指数(NDVI)变化的平均幅度。然而,LCC具有积极效应,LCM对再生区域的NDVI变化产生负面影响。内陆地区的LCC和LCM都对植被绿色产生负面影响,但LCC导致更大的NDVI变化幅度。每种陆地覆盖型量化LCC和LCM对植被绿色的影响。本研究提供了一种方法论框架,在分析土地覆盖变化时考虑到LCC和LCM,并量化它们对沿海生态系统植被的影响。

著录项

  • 来源
    《GIScience & remote sensing》 |2019年第6期|769-793|共25页
  • 作者单位

    Zhejiang A&F Univ Sch Environm & Resource Sci Key Lab Carbon Cycling Forest Ecosyst & Carbon Se State Key Lab Subtrop Silviculture Hangzhou 311300 Zhejiang Peoples R China;

    Zhejiang A&F Univ Sch Environm & Resource Sci Key Lab Carbon Cycling Forest Ecosyst & Carbon Se State Key Lab Subtrop Silviculture Hangzhou 311300 Zhejiang Peoples R China;

    Nanjing Forestry Univ Coll Biol & Environm Nanjing 210037 Jiangsu Peoples R China;

    Chinese Acad Forestry Res Inst Subtrop Forestry Wetland Ecosyst Res Stn Hangzhou Bay Hangzhou 311400 Zhejiang Peoples R China;

    Chinese Acad Forestry Res Inst Subtrop Forestry Wetland Ecosyst Res Stn Hangzhou Bay Hangzhou 311400 Zhejiang Peoples R China;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    land-cover conversion; land-cover modification; abrupt change; time-series Landsat; vegetation greenness;

    机译:陆地覆盖转换;陆地修改;突然变化;时间序列Landsat;植被绿色;

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