A comparative study on intra-annual classification of invasive saltcedar with Landsat 8 and Landsat 9
A comparative study on intra-annual classification of invasive saltcedar with Landsat 8 and Landsat 9
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DOI：
10.1080/01431161.2023.2195573
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发表时间：
2023-03
影响因子：
3.4
通讯作者：
Rui-Dong Li;Le Wang;Ying Lu
中科院分区：
文献类型：
--
作者：
Rui-Dong Li;Le Wang;Ying Lu
摘要：美国西部沿河岸走廊的外来盐杉迅速扩张，极大地改变了美国西部河岸生境的景观结构和生态功能。发展精确、可复制的遥感制图方法，对于及时监测盐渍化、重新评价盐渍化生态功能、制定有效的防治措施具有不可缺少的作用。实现这一目标的最大挑战表现为缺乏时间序列的遥感图像来充分捕捉盐杉木物候。为此，新获得的Landsat 9图像与Landsat 8相结合，为弥补时间图像的不足提供了宝贵的机会。为了更好地理解Landsat 9在盐杉树分类中的作用，并为其应用提供有用的信息，本研究首次尝试使用Landsat 8和Landsat 9年际图像对盐杉树进行分类。我们采用支持向量机（SVM）和随机森林（RF）两种机器学习算法，比较了Landsat 9和Landsat 8在年内盐杉分类中的性能。此外，我们还研究了每个光谱波段对整体性能的贡献，并确定了盐雪松分类的最佳时间窗。结果表明，Landsat 9与Landsat 8的分类性能差异不显著。Landsat 8和Landsat 9的短波红外波段对盐酸盐的识别贡献最大。7月、11月和12月获得的图像对盐杉木分类的效果优于其他月份。综上所述，Landsat 8和Landsat 9星座在更大的时空尺度上具有提高盐类分类精度的潜力。
ABSTRACT The rapid expansion of exotic saltcedar along riparian corridors has dramatically altered the landscape structure and ecological function of riparian habitats in the western United States. The development of accurate and reproducible mapping methods with remote sensing plays an indispensable role in the timely monitoring of saltcedar, re-evaluating its ecological functions, and establishing effective control measures. The utmost challenge for achieving this goal is manifested as the lack of time series of remote sensing images to capture the saltcedar phenology adequately. To this end, the newly available Landsat 9 images, combined with its counterpart of Landsat 8, offer a precious opportunity to compensate for the temporal image shortage. To understand Landsat 9 in the saltcedar classification and to discover helpful information for its application, this study presents the first attempt to classify saltcedar using intra-annual Landsat 8 and Landsat 9 images. We adopted two machine learning algorithms, support vector machine (SVM) and random forest (RF), to compare the performance of Landsat 9 and Landsat 8 for intra-annual saltcedar classification. In addition, we investigated the respective contribution of each spectral band to the overall performance and identified the optimal time window for saltcedar classification. The results indicated that the difference in classification performance between Landsat 9 and Landsat 8 was insignificant. The shortwave infrared bands associated with both Landsat 8 & 9 have contributed most to the process of saltcedar identification. Image acquired in July, November, and December yielded better results than other months for saltcedar classification. It is concluded that Landsat 8 & 9 constellation has the potential to refine saltcedar classification accuracy on larger spatial and temporal scales.