通过大地遥感卫星图像分析巴伦西亚社区的土地覆盖变化:从 1984 年到 2022 年

Land Pub Date : 2024-07-17 DOI:10.3390/land13071072
José A. Sobrino, Sergio Gimeno, Virginia Crisafulli, Álvaro Sobrino-Gómez
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引用次数: 0

摘要

土地覆被变化是全球最重要的变化之一,对生态系统、生物多样性和当前的气候危机有着深远的影响。在这项研究中,我们的目标是分析巴伦西亚大区过去四十年的土地覆被变化。利用大地遥感卫星 5 号、8 号和 9 号夏季图像,我们采用了以能够处理大型数据集和复杂变量而著称的随机森林算法来生成土地覆被分类,包括五个类别:"城市地区"、"茂密植被"、"稀疏植被"、"水体 "和 "其他"。通过与已有产品进行现场测量比较,并利用混淆矩阵对结果进行了验证。在研究期间,城市面积几乎翻了一番,从约 482 平方公里增至 940 平方公里。这一扩张主要集中在现有城市区域附近,主要发生在 1985 年至 1990 年间。植被茂密类和植被稀疏类多年来波动较大,其累计值呈微妙的下降趋势。水体和其他类别在过去几年中没有发生重大变化。随机森林算法的总体准确率(OA)为 95%,Kappa 值为 93%,与实地测量值(OA 为 88%)、欧空局世界植被图(OA 为 80%)和哥白尼全球土地服务土地植被图(OA 为 73%)显示出良好的一致性,证实了该方法在生成土地植被分类方面的有效性。
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Analysing Land Cover Change in the Valencian Community through Landsat Imagery: From 1984 to 2022
Land cover change represents one of the most significant global transformations, which has profound impacts on ecosystems, biological diversity, and the ongoing climate crisis. In this study, our objective was to analyse land cover transformation in the Valencian Community over the last four decades. Utilising Landsat 5, 8, and 9 summer images, a Random Forest algorithm renowned for its ability to handle large datasets and complex variables, was employed to produce land cover classifications consisting of five categories: ‘Urban Areas’, ‘Dense Vegetation’, ‘Sparse Vegetation’, ‘Water Bodies’, and Other’. The results were validated through in situ measurements comparing with pre-existing products and utilising a confusion matrix. Over the study period, the urban area practically doubled, increasing from approximately 482 to 940 square kilometres. This expansion was concentrated mainly in the proximity of the already existing urban zone and occurred primarily between 1985 and 1990. The Dense and Sparse Vegetation classes exhibit substantial fluctuations over the years, displaying a subtle trend towards a decrease in their cumulative value. Water bodies and Other classes do not show substantial changes over the years. The Random Forest algorithm showed a high Overall Accuracy (OA) of 95% and Kappa values of 93%, showing good agreement with field measurements (88% OA), ESA World Cover (80% OA), and the Copernicus Global Land Service Land Cover Map (73% OA), confirming the effectiveness of this methodology in generating land cover classifications.
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