通过谷歌地球引擎、机器学习和 QGIS-MOLUSCE 评估土地利用-土地覆被动态及其未来预测:印度奥迪沙贾格津普尔地区的案例研究

IF 1.3 4区 地球科学 Q3 GEOSCIENCES, MULTIDISCIPLINARY
Kavita Devanand Bathe, Nita Sanjay Patil
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引用次数: 0

摘要

准确的土地利用--土地覆被绘图对于决策者进行未来规划至关重要。本研究旨在评估印度奥迪沙邦 Jagatsinghpur 地区的土地利用-土地覆被动态,并估计其未来预测。近年来,谷歌地球引擎等云平台和机器学习等领域吸引了研究人员的极大关注。本研究在谷歌地球引擎的多时态哨兵-1 C 波段数据集上实验了五种机器学习算法,如分类和回归树、Naive Bayes、支持向量机、梯度树提升和随机森林。实验结果根据总体准确率和 Kappa 统计量等指标进行评估。性能指标表明,有 60 棵树的随机森林的性能优于其他方法。接下来,使用随机森林分类器生成了研究区域 2017 年和 2021 年的土地利用-土地覆盖图。结果与 ESRI 土地覆被图和 ESA 世界覆被图进行了比较。2017 年和 2021 年的地图被导出到 QGIS,这些地图被用来生成 2021 年的模拟地图。2021 年土地利用-土地覆被模拟地图显示出良好的结果,总体 Kappa 值为 0.97,正确率为 98.21%。模拟地图与实际地图进行了验证。最后,使用 QGIS-MOLUSCE 对 2030 年和 2050 年的土地利用变化进行了预测。预测地图显示,农业区和建筑区将大幅增加。这些发现将有助于决策者进行未来规划。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Assessment of land use-land cover dynamics and its future projection through Google Earth Engine, machine learning and QGIS-MOLUSCE: A case study in Jagatsinghpur district, Odisha, India

Assessment of land use-land cover dynamics and its future projection through Google Earth Engine, machine learning and QGIS-MOLUSCE: A case study in Jagatsinghpur district, Odisha, India

Accurate land use-land cover mapping is essential to policymakers for future planning. This study aims to assess the land use-land cover dynamics and estimate its future projection in the Jagatsinghpur district of Odisha state from India. In recent years, cloud-based platforms like Google Earth Engine and domains like machine learning have attracted considerable attention from researchers. In this study, five machine learning algorithms, such as Classification and Regression Tree, Naive Bayes, Support Vector Machine, Gradient Tree Boost and Random Forest are experimented on the multitemporal Sentinel-1 C-band dataset from Google Earth Engine. The results are evaluated based on metrics like overall accuracy and Kappa statistics. The performance metrics indicate that Random Forest with 60 trees outperforms others. Next, the land use-land cover maps of the study area are generated with Random Forest classifier for the years 2017 and 2021. The results are compared to ESRI land cover maps and ESA world cover maps. The 2017 and 2021 maps are exported to QGIS, and these maps are used to generate a simulation map for 2021. The simulated land use-land cover map for 2021 indicates promising results with an overall Kappa value of 0.97 and a percentage of correctness of 98.21%. The simulated map is validated against a factual map. Finally, future projections of land-use changes are forecasted for the years 2030 and 2050 using QGIS-MOLUSCE. The predicted maps project a significant rise in agricultural and built-up areas. These findings will assist policymakers in future planning.

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来源期刊
Journal of Earth System Science
Journal of Earth System Science Earth and Planetary Sciences-General Earth and Planetary Sciences
CiteScore
3.20
自引率
5.30%
发文量
226
期刊介绍: The Journal of Earth System Science, an International Journal, was earlier a part of the Proceedings of the Indian Academy of Sciences – Section A begun in 1934, and later split in 1978 into theme journals. This journal was published as Proceedings – Earth and Planetary Sciences since 1978, and in 2005 was renamed ‘Journal of Earth System Science’. The journal is highly inter-disciplinary and publishes scholarly research – new data, ideas, and conceptual advances – in Earth System Science. The focus is on the evolution of the Earth as a system: manuscripts describing changes of anthropogenic origin in a limited region are not considered unless they go beyond describing the changes to include an analysis of earth-system processes. The journal''s scope includes the solid earth (geosphere), the atmosphere, the hydrosphere (including cryosphere), and the biosphere; it also addresses related aspects of planetary and space sciences. Contributions pertaining to the Indian sub- continent and the surrounding Indian-Ocean region are particularly welcome. Given that a large number of manuscripts report either observations or model results for a limited domain, manuscripts intended for publication in JESS are expected to fulfill at least one of the following three criteria. The data should be of relevance and should be of statistically significant size and from a region from where such data are sparse. If the data are from a well-sampled region, the data size should be considerable and advance our knowledge of the region. A model study is carried out to explain observations reported either in the same manuscript or in the literature. The analysis, whether of data or with models, is novel and the inferences advance the current knowledge.
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