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伊拉克穆萨纳省基于集成三维卷积神经网络和元胞自动机的荒漠化预测

Desertification prediction with an integrated 3D convolutional neural network and cellular automata in Al-Muthanna, Iraq.

作者信息

Aldabbagh Yasir Abdulameer Nayyef, Shafri Helmi Zulhaidi Mohd, Mansor Shattri, Ismail Mohd Hasmadi

机构信息

Department of Civil Engineering, Faculty of Engineering, Universiti Putra Malaysia (UPM), Serdang, Malaysia.

Geospatial Information Science Research Centre (GISRC), Faculty of Engineering, Universiti Putra Malaysia (UPM), Serdang, Malaysia.

出版信息

Environ Monit Assess. 2022 Oct;194(10):715. doi: 10.1007/s10661-022-10379-z. Epub 2022 Aug 31.

Abstract

Desertification is a major environmental issue all over the world, and Al-Khidhir district, Al-Muthanna, in the south of Iraq is no exception. In mapping, assessing, and predicting desertification, remote sensing and geospatial solutions (spatial analysis, machine learning) are crucial. During 1998-2018, this study employed satellite images from Landsat TM, ETM + , and OLI to map and predict desertification in the Al-Khidhir district. The year 2028 was chosen as the target date. Prediction models were constructed using a 3D convolutional neural network (3D CNN) and cellular automata (CA) techniques. In addition to the historical land cover maps, the model incorporated desertification indicators identified as important in the study, including geology, soil type, distance from waterways, elevation, population density, and Normalized Difference Vegetation Index (NDVI). Several accuracy metrics were used to evaluate the models, including overall accuracy (OA), average accuracy (AA), and the Kappa index (K). The simulated and actual land cover maps from 1998 and 2008 were used to evaluate the desertification prediction models. The 3D CNN model outperforms the typical 2D CNN for both the 2008 and 2018 images, according to the results. For the 2008 image, the 3D CNN model achieved 89.675 OA, 69.946 AA, and 0.781 K, while the 2018 image achieved 91.494 OA, 75.138 AA, and 0.770 K. The 2D CNN model performed a little worse than the 3D CNN model. The results of the change assessment showed that between 1998 and 2008, agricultural land was the dominant class (39%, 47.4%, respectively). The bare land, however, was the most dominant class in 2018, accounting for 46.6% of the total, compared to 26.2% for agricultural land. The spatial distribution characteristics of desertification in the Al-Khidhir, in the year 1998, were prevalent in the area's south (25.9%). In the following 10 years, desertification has spread to the surrounding territories. In the year 2008, desertification increased in the north of the study area (50.8%). Unless the local administration of Al-Khidhir district establishes desertification control strategies, this study suggests that the extent of bare land could expand in 2028 (54.1%).

摘要

荒漠化是全球主要的环境问题,伊拉克南部穆萨纳省的希迪尔区也不例外。在荒漠化的测绘、评估和预测中,遥感和地理空间解决方案(空间分析、机器学习)至关重要。在1998年至2018年期间,本研究利用陆地卫星TM、ETM + 和OLI的卫星图像来绘制和预测希迪尔区的荒漠化情况。选择2028年作为目标日期。使用三维卷积神经网络(3D CNN)和细胞自动机(CA)技术构建预测模型。除了历史土地覆盖图外,该模型还纳入了在研究中被确定为重要的荒漠化指标,包括地质、土壤类型、与水道的距离、海拔、人口密度和归一化植被指数(NDVI)。使用了几个精度指标来评估模型,包括总体精度(OA)、平均精度(AA)和卡帕指数(K)。利用1998年和2008年的模拟和实际土地覆盖图来评估荒漠化预测模型。结果表明,对于2008年和2018年的图像,3D CNN模型的表现优于典型的二维CNN模型。对于2008年的图像,3D CNN模型的OA为89.675、AA为69.946、K为0.781,而2018年图像的OA为91.494、AA为75.138、K为0.770。二维CNN模型的表现比3D CNN模型稍差。变化评估结果表明,在1998年至2008年期间,农业用地是主要类别(分别为39%、47.4%)。然而,裸地是2018年最主要的类别,占总面积的46.6%,而农业用地占26.2%。1998年希迪尔区荒漠化的空间分布特征在该地区南部最为普遍(25.9%)。在接下来的10年里,荒漠化蔓延到了周边地区。2008年,研究区域北部的荒漠化有所增加(50.8%)。除非希迪尔区地方政府制定荒漠化控制策略,本研究表明,到2028年裸地面积可能会扩大(54.1%)。

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