基于谷歌地球引擎SAR和光学图像分类的Qeshm红树林时空监测

IF 3.1 Q2 ENGINEERING, GEOLOGICAL International Journal of Engineering and Geosciences Pub Date : 2023-04-19 DOI:10.26833/ijeg.1118542
Mostafa Mahdavi̇fard, Sara KAVİANİ AHANGAR, B. Feizizadeh, Khalil Valizadeh Kamran, S. Karimzadeh
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引用次数: 0

摘要

红树林被认为是最复杂和动态的生态系统之一,面临着人为干扰和气候变化带来的各种挑战。红树林生态系统覆盖地区的过度采伐和土地利用变化对这些森林构成严重威胁。因此,对这些森林进行持续和定期的监测至关重要。幸运的是,遥感数据使定期和频繁监测这类森林成为可能。这项研究有两个目标。首先,将Landsat- 8和Sentinel-2卫星的光学数据与Sentinel-1雷达数据相结合,提高地表覆盖制图精度;其次,评估谷歌Earth Engine系统中SVM机器学习算法和随机森林对森林覆盖与其他土地类型的检测和区分。结果表明,支持向量机算法在S2 + S1数据集上的kappa系数为0.94,明显优于在L8 + S1组合数据集上的kappa系数为0.88。另一方面,随机森林算法在S2 + S1和L8 + S1数据集上的kappa系数分别为0.89和0.85。这再次表明了Sentinel-2和Sentinel-1数据集优于Landsat- 8和Sentinel-1数据集。总的来说,支持向量机(SVM)算法在光学和雷达数据集上的效果优于射频随机森林算法。结果表明,谷歌地球引擎系统和机器学习算法的使用加速了红树林制图甚至变化检测的过程。
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Spatio-Temporal monitoring of Qeshm mangrove forests through machine learning classification of SAR and Optical images on Google Earth Engine
Mangrove forests are considered one of the most complex and dynamic ecosystems facing various challenges due to anthropogenic disturbance and climate change. The excessive harvesting and land-use change in areas covered by mangrove ecosystems are critical threats for these forests. Therefore, the continuous and regular monitoring of these forests is essential. Fortunately, remote sensing data has made it possible to regularly and frequently monitor this type of forest. This study has two goals. Firstly, it combines optical data of Landsat- 8 and Sentinel-2 with Sentinel-1 radar data to improve land cover mapping accuracy. Secondly, it aims to evaluate the SVM machine learning algorithms and random forest to detection and differentiate forest cover from other land types in the Google Earth Engine system. The results show that the support vector machine (SVM) algorithm in the S2 + S1 dataset with a kappa coefficient of 0.94 performs significantly better than when used in the L8 + S1 combination dataset with a kappa coefficient of 0.88. On the other hand, the kappa coefficients of 0.89 and 0.85 were estimated for the random forest algorithm in S2 + S1 and L8 + S1 datasets. This again indicates the superiority of Sentinel-2 and Sentinel-1 datasets over Landsat- 8 and Sentinel-1 datasets. In general, the support vector machine (SVM) algorithm yielded better results than the RF random forest algorithm in optical and radar datasets. The results showed that the use of the Google Earth engine system and machine learning algorithms accelerates the process of mapping mangrove forests and even change detection.
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CiteScore
4.00
自引率
0.00%
发文量
12
审稿时长
30 weeks
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