通过整合遥感和机器学习算法对土地利用/土地覆盖进行环境监测

Firas Aljanabi, M. Dedeoğlu, C. Şeker
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引用次数: 0

摘要

对大面积区域的土地利用/土地覆被情况进行评估在多个领域都非常重要,包括土壤、水等自然资源以及气候变化风险,而土地利用/土地覆被变化已成为环境的高度焦虑。因此,我们测试并比较了三种分类算法的性能:支持向量机 (SVM)、随机树 (RT) 和最大似然法 (MaxL) 三种分类算法的性能进行了测试和比较,以得出并提取萨拉约努/科尼亚地区的土地利用、土地利用变化(LULC)信息,包括五个不同的类别:水域、种植园、草地、建筑用地和裸露土地。归一化差异植被指数(NDVI)和归一化差异水指数(NDWI)这两个遥感指数被用作 LULC 分类的补充输入。为了评估算法的性能,采用了混淆矩阵。结果发现,支持向量机、随机树和最大似然算法的平均总体准确率分别为 85.60%、79.20% 和 74.80%,Kappa 系数分别为 82.00%、74.00% 和 68.50%。这些结果表明,支持向量机算法在准确性方面优于其他算法。研究结果表明,在土地利用、土地利用变化监测/判定中结合遥感技术的分类算法可以生成准确的分类图,并可用作基础数据。这是由于机器学习算法能够学习复杂的模式,适应不同的数据,并不断改进,使其与传统分类器相比获得更高的准确性。因此,建议决策者使用这些算法。
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Environmental Monitoring of Land Use/ Land Cover by Integrating Remote Sensing and Machine Learning Algorithms
Evaluation of the land use/ land cover (LULC) case over large regions is very important in a variety of domains, including natural resources such as soil, water, etc., and climate change risks and LULC change has emerged as a high anxiety for the environment. Therefore, we tested and compared the performance of three classification algorithms: Support Vector Machines (SVM), Random Trees (RT), and Maximum Likelihood (MaxL) to derive and extract LULC information for the district of Sarayönü/ Konya across five distinct classes: water, plantation, grassland, built-up, and bare land. Two remote sensing indices, the normalized difference vegetation index (NDVI) and the normalized difference water index (NDWI), were used as supplementary inputs for the classification of LULC. To evaluate the performance of the algorithms, a confusion matrix was employed. The average overall accuracy of support vector machines, random trees, and maximum likelihood algorithms was found 85.60%, 79.20%, and 74.80%, respectively, and 82.00%, 74.00%, and 68.50% for the Kappa coefficient. These results indicate that the support vector machines algorithm outperforms other algorithms in terms of accuracy. As a result of the research, it was determined that classification algorithms integrated with remote sensing in LULC change monitoring/determination could produce accurate classification maps that can be used as base data. This is due to the ability of machine learning algorithms to learn complex patterns, adapt to diverse data, and continuously improve, making them achieve higher accuracy compared to traditional classifiers. Therefore, their use was recommended for decision-makers.
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来源期刊
CiteScore
0.70
自引率
0.00%
发文量
74
审稿时长
50 weeks
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