Effectiveness of the BFAST algorithm for detecting vegetation response patterns in a semi-arid region
Effectiveness of the BFAST algorithm for detecting vegetation response patterns in a semi-arid region
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DOI：
10.1016/j.rse.2014.08.023
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发表时间：
2014-11
影响因子：
13.5
通讯作者：
L. Watts;S. Laffan
中科院分区：
文献类型：
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作者：
L. Watts;S. Laffan
有效地利用卫星图像时间序列检查跨区域范围的植被响应模式，需要一种方法，占在季节尺度上的变化，同时检测任何长期趋势的突变。添加季节性和趋势中断（BFAST）算法已开发完成。然而，其有效性在半干旱地区，植被响应通常是季节性的，还有待assessed.In这项研究中的BFAST算法进行了评估，在澳大利亚新南威尔士州西北部的Paroo集水区的半干旱研究区。中分辨率成像光谱仪（MODIS）EVI时间序列分解使用BFAST为270个样本像素，以评估该算法的能力来检测已知的火灾和洪水引起的植被响应的突然变化。该算法也适用于整个研究区域的突变的时间和幅度和方向和幅度的长期trends.The的时间的区域模式BFAST检测到的中断对应于已知的洪水在研究区域的时间为68%和79%之间的样本像素检测到的中断，这取决于在分解中使用的参数。然而，BFAST无法准确地探测到Paroo地区的火灾，只有3%的火灾发生时间与火灾发生时间一致。这很可能反映了火灾发生前的低经济脆弱性指数值，这是半干旱地区的典型情况。在整个研究区域的突变和绿化和布朗宁趋势的时间的空间格局是土地覆盖和植被类型的函数。这些结果表明，BFAST是能够检测在半干旱地区的已知洪水造成的植被绿化的突然变化。空间格局的存在，在结果中也表明，该算法是敏感的植被覆盖类型。因此，BFAST能够探测到植被响应预计不会显示强烈季节性模式的区域的突然趋势变化，并可用于进一步的应用，如半干旱环境中的分类或区域植被建模。
The effective use of satellite image time series for examining vegetation response patterns across regional extents requires a method which accounts for variation at the seasonal scale while simultaneously detecting abrupt changes in any long term trends. The Breaks for Additive Seasonal and Trend (BFAST) algorithm has been developed to do this. However, its effectiveness in semi-arid regions, where vegetation response is typically aseasonal, has yet to be assessed.In this research the BFAST algorithm was assessed for a semi-arid study area in the Paroo catchment of far north-western New South Wales, Australia. Moderate Resolution Imaging Spectroradiometer (MODIS) EVI time series were decomposed using BFAST for 270 sample pixels to assess the algorithm's ability to detect abrupt changes in vegetation response caused by known fires and floods. The algorithm was also applied across the study area to examine regional patterns in the timing and magnitude of abrupt changes and the direction and magnitude of the long term trends.The timing of breaks detected by BFAST corresponded with the timing of known floods in the study region for between 68% and 79% of breaks detected across the sample pixels, depending on the parameters used in the decomposition. BFAST was not, however, able to accurately detect fires in the Paroo region, with agreement between the timing of breaks and fires occurring in only 3% of breaks detected. This most likely reflects the low EVI values present before a fire event, which would be typical of semi-arid zones. Spatial patterns in the timing of abrupt changes and greening and browning trends across the study area were a function of land cover and vegetation type. These results indicate that BFAST is able to detect abrupt changes in vegetation greening caused by known floods in semi-arid regions. The presence of spatial patterns in the results also indicates that the algorithm is sensitive to vegetation cover type. BFAST is therefore able to detect abrupt trend changes in regions where vegetation response is not expected to show strong seasonal patterns and could be used in further applications such as classification or regional vegetation modelling in semi-arid environments.