Using Landsat observations (1988–2017) and Google Earth Engine to detect vegetation cover changes in rangelands - A first step towards identifying degraded lands for conservation
Using Landsat observations (1988–2017) and Google Earth Engine to detect vegetation cover changes in rangelands - A first step towards identifying degraded lands for conservation
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使用 Landsat 观测数据（1988 年至 2017 年）和 Google Earth Engine 检测牧场植被覆盖变化 - 识别退化土地以进行保护的第一步
DOI：
10.1016/j.rse.2019.111317
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发表时间：
2019-10
影响因子：
13.5
通讯作者：
McDonald-Madden Eve
中科院分区：
文献类型：
--
作者：
Xie Zunyi;Phinn Stuart;Game Edward;Pannell David;Hobbs Richard;Briggs Peter;McDonald-Madden Eve
在全球范围内，农业用地面积正在缩小，部分原因是环境退化。收购和恢复不再用于农业的退化土地可能是一个重要的保护机会，社会和政治反对最小。从区域到全球范围内有效准确地识别这些土地的能力将有助于保护管理，最终增强实现可持续发展目标（SDG）的全球前景。遥感提供了一种潜在的工具，可用来查明哪些地区的地表特性变化可绘制成图并与土地退化联系起来。在这项研究中，我们开始，以解决这一挑战的一小部分，提出新的方法来映射植被覆盖量的变化，在像素级（30米），使用谷歌地球引擎（GEE）。我们说明了我们的方法在澳大利亚昆士兰州的大规模牧场，使用三十年的Landsat卫星图像（1988年至2017年）沿着与土地条件分数进行验证的实地观察。该方法利用现有的动态参考覆盖方法去除降雨量的变化，重点研究人为管理对植被覆盖变化的影响，结果表明，与一组参考水平相比，所识别的植被覆盖变化可分为减少、增加和稳定覆盖5类，所述参考水平是从所有干旱年份最持久的地面覆盖位置获得的。总的来说，在我们的研究区域中，20%的植被覆盖减少，类似的土地恢复部分，其余（~60%）保持稳定。植被覆盖减少的土地面积约为2 × 105 km 2，其抗旱能力明显降低。准确度评估得出的总体分类准确度为82.6%（±3.32标准误差），生产者和用户的准确度分别为75.0%（±5.16%）和70.0%（±4.13%），植被覆盖显著减少的地区。确定退化土地的面积将需要多阶段的空间数据分析，这项工作为确定大规模牧场环境中植被覆盖变化提供了第一阶段，并为今后的研究和开发提供了一个平台，以确定退化土地及其对实现保护努力的效用。
Globally, the area of agricultural land is shrinking in part due to environmental degradation. Acquisition and restoration of degraded lands no longer used for agriculture may present a major conservation opportunity with minimal social and political opposition. The ability to efficiently and accurately identify these lands from regional to global scales will aid conservation management, ultimately enhancing the global prospects of achieving the Sustainable Development Goals (SDGs). Remote Sensing provides a potential tool to identify areas where surface property changes can be mapped and linked with land degradation. In this study, we begin to tackle a small section of this challenge by presenting novel approach to mapping changes in vegetation cover amounts at the pixel level (30 m), using Google Earth Engine (GEE). We illustrate our approach across large-scale rangelands in Queensland Australia, using three decades of Landsat satellite imagery (1988–2017) along with field observations of land condition scores for validation. The approach used an existing method for dynamic reference cover to remove the rainfall variability and focused on the human management effects on the vegetation cover changes.Results showed the identified vegetation cover changes could be categorized into five classes of decrease, increase or stable cover compared with a set reference level, which was obtained from locations of the most persistent ground cover across all dry years. In total, vegetation cover decrease was observed in 20% of our study area, with similar portion of lands recovering and the rest (~60%) staying stable. The lands with decrease in vegetation cover, covering a considerable area of ~2 × 105km2, exhibited a markedly reduced resilience to droughts. The accuracy assessment yielded an overall classification accuracy of 82.6% (±3.32 standard error) with 75.0% (±5.16%) and 70.0% (±4.13%) producer's and user's accuracy for areas experiencing a significant decrease in vegetation cover, respectively. Identifying areas of degraded land will require multiple stages of spatial data analysis and this work provided the first stage for identifying vegetation cover changes in large-scale rangeland environment, and provides a platform for future research and development to identify degraded lands and their utility for achieving conservation endeavours.
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影响因子：
3.2
作者：
R. Hobbs
通讯作者：
R. Hobbs
影响因子：
4.7
作者：
J. Vogt;U. Safriel;G. V. Maltitz;Y. Sokona;R. Zougmoré;G. Bastin;J. Hill
通讯作者：
J. Vogt;U. Safriel;G. V. Maltitz;Y. Sokona;R. Zougmoré;G. Bastin;J. Hill
影响因子：
4.7
作者：
S. Sommer;C. Zucca;A. Grainger;M. Cherlet;R. Zougmoré;Y. Sokona;J. Hill;R. D. Peruta;J. Roehrig;G. Wang
通讯作者：
S. Sommer;C. Zucca;A. Grainger;M. Cherlet;R. Zougmoré;Y. Sokona;J. Hill;R. D. Peruta;J. Roehrig;G. Wang
影响因子：
8.5
作者：
Butchart, Stuart H. M.;Clarke, Martin;Burgess, Neil D.
通讯作者：
Burgess, Neil D.
影响因子：
13.5
作者：
A. Tewkesbury;A. Comber;N. Tate;A. Lamb;P. Fisher
通讯作者：
A. Tewkesbury;A. Comber;N. Tate;A. Lamb;P. Fisher