Exploring long term Impervious Surface Areas (ISA) dynamics using Landsat imagery, Μachine Learning and GEE: The case of Attica, Greece

IF 3.8 Q2 ENVIRONMENTAL SCIENCES

Remote Sensing Applications-Society and Environment Pub Date : 2024-09-16 DOI:10.1016/j.rsase.2024.101338

Aikaterini Dermosinoglou, George P. Petropoulos

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引用次数: 0

Abstract

Accurate data on Impervious Surface Areas (ISA) are essential for various studies concerning urban environments, as the constant proliferation of these surfaces is a noticeable result of urbanization, especially in metropolitan cities. The present study proposes a methodology approach in performing a long-term mapping of ISA changes in Attica Prefecture, Greece, from 1984 to 2022, exploiting the Landsat archive and contemporary machine learning (ML) methods of geospatial data processing, namely Support Vector Machines (SVM) and Random Forests (RF). Using Google Earth Engine cloud platform, the SVM and RF classifiers are developed and implemented for four single dates (in years 1984, 1999, 2013 and 2022). Accuracy assessment of the classification maps was based on the computation of a series of statistical metrics based on the confusion matrix, ans the McNemar's chi-square test which was used to evaluate the statistical significance of the difference in the classification maps, derived from SVM and RF classifiers. Both SVM and RF provided very accurate results, with Overall Accuracy (OA) higher than 90% and kappa coefficient (Kappa) higher than 0.8 for all classification maps, with SVM performing better in 1984 and 2022 and RF outperforming SVM in 2013. In addition, the McNemar's test confirmed the statistical significance of the research findings reported herein. Change detection results, highlighted the wide sprawl of the urban fabric, especially in sub-urban areas, surrounding the metropolitan center of Athens. The employed methodology represents a significant advancement in the application of GEE, beyond their general use, by integrating cutting-edge ML techniques with available remote sensing data to create an automated analysis process. This innovative fusion not only enhances the precision and efficiency of ISA mapping but also establishes the basis for a pioneering standard in the field by harnessing the power of advanced technologies and accessible data sources.

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利用大地遥感卫星图像、Μ机器学习和 GEE 探索长期不透水表面积 (ISA) 动态：希腊阿提卡案例

关于不透水表面积（ISA）的准确数据对于有关城市环境的各种研究至关重要，因为不透水表面积的不断增加是城市化的一个明显结果，尤其是在大都市。本研究提出了一种方法论，利用大地遥感卫星档案和当代地理空间数据处理的机器学习（ML）方法，即支持向量机（SVM）和随机森林（RF），对希腊阿提卡州从 1984 年到 2022 年的 ISA 变化进行长期测绘。利用谷歌地球引擎云平台，针对四个单一日期（1984 年、1999 年、2013 年和 2022 年）开发并实施了 SVM 和 RF 分类器。分类图的准确性评估基于一系列基于混淆矩阵的统计指标的计算，以及 McNemar's chi-square 检验，该检验用于评估 SVM 和 RF 分类器得出的分类图差异的统计意义。SVM 和 RF 都提供了非常准确的结果，所有分类图的总体准确率 (OA) 均高于 90%，卡帕系数 (Kappa) 均高于 0.8，其中 SVM 在 1984 年和 2022 年的表现更好，而 RF 在 2013 年的表现优于 SVM。此外，McNemar 检验证实了本文所报告研究结果的统计意义。变化检测结果凸显了雅典都市中心周围城市结构的广泛扩张，尤其是在郊区。所采用的方法将最前沿的 ML 技术与可用的遥感数据相结合，创建了一个自动分析流程，代表了 GEE 应用领域的重大进步，超越了其一般用途。这种创新的融合不仅提高了 ISA 测绘的精度和效率，还通过利用先进技术和可访问数据源的力量，为该领域的先锋标准奠定了基础。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Remote Sensing Applications-Society and Environment Multiple-

CiteScore

8.00

自引率

8.50%

发文量

204

审稿时长

65 days

期刊介绍： The journal ''Remote Sensing Applications: Society and Environment'' (RSASE) focuses on remote sensing studies that address specific topics with an emphasis on environmental and societal issues - regional / local studies with global significance. Subjects are encouraged to have an interdisciplinary approach and include, but are not limited by: " -Global and climate change studies addressing the impact of increasing concentrations of greenhouse gases, CO2 emission, carbon balance and carbon mitigation, energy system on social and environmental systems -Ecological and environmental issues including biodiversity, ecosystem dynamics, land degradation, atmospheric and water pollution, urban footprint, ecosystem management and natural hazards (e.g. earthquakes, typhoons, floods, landslides) -Natural resource studies including land-use in general, biomass estimation, forests, agricultural land, plantation, soils, coral reefs, wetland and water resources -Agriculture, food production systems and food security outcomes -Socio-economic issues including urban systems, urban growth, public health, epidemics, land-use transition and land use conflicts -Oceanography and coastal zone studies, including sea level rise projections, coastlines changes and the ocean-land interface -Regional challenges for remote sensing application techniques, monitoring and analysis, such as cloud screening and atmospheric correction for tropical regions -Interdisciplinary studies combining remote sensing, household survey data, field measurements and models to address environmental, societal and sustainability issues -Quantitative and qualitative analysis that documents the impact of using remote sensing studies in social, political, environmental or economic systems