Mapping built-up area expansion in landslide susceptible zones using automatic land use/land cover classification

IF 1.3 4区地球科学 Q3 GEOSCIENCES, MULTIDISCIPLINARY Journal of Earth System Science Pub Date : 2024-07-06 DOI:10.1007/s12040-024-02345-9

Lekshmi S Sunil, Minu Treesa Abraham, Neelima Satyam

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引用次数: 0

Abstract

The information on land use/land cover (LULC) is indispensable in regional planning, policy formulation and tracking land use/land cover changes (LULCC). The rapid urbanization of hilly terrains, driven by population growth, has significant implications for landslide risk reduction. Recognizing the need for an innovative approach for extracting LULC information, the present study uses a random forest (RF) classifier to develop a novel, pre-trained and universal tool that automatically generates LULC classification maps based on natural colour satellite imagery without any training input from the end-user. The proposed framework with an overall accuracy of 0.75 and an area under the curve (AUC) score of 0.95 in the receiver operating characteristic curve (ROC) approach was used for mapping built-up area expansion in regions susceptible to rainfall-induced landslides in Idukki block panchayat (administrative division), Kerala, India. By comparing the LULC information for the years 2012 and 2022, it was understood that the built-up area in the location has increased from 12.76% of the total area in 2012 to 26.48% in 2022. It is important to consider the rapid increase in built-up area expansion in the ‘very high’ landslide susceptibility zones in the study area. This clearly demonstrates the need for hazard inclusive planning and tracking of LULCC, for disaster risk reduction.

Research Highlights

A pre-trained Land Use/Land Cover (LULC) classification tool is developed using the Random Forest (RF) classifier.
Based on natural colour satellite imagery, the tool automatically generates LULC maps for various landscapes worldwide.
The tool demonstrates a satisfactory performance, achieving an overall accuracy of 0.75 and an overall ROC AUC score of 0.95.
The tool was used to understand the LULC changes in Idukki block panchayat between 2012 and 2022.

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利用土地利用/土地覆被自动分类绘制滑坡易发区建成区扩展图

摘要土地利用/土地覆被信息在区域规划、政策制定和跟踪土地利用/土地覆被变化（LULCC）方面不可或缺。在人口增长的推动下，丘陵地带的快速城市化对降低滑坡风险具有重大影响。认识到需要一种创新的方法来提取 LULC 信息，本研究使用随机森林（RF）分类器开发了一种新颖的、预先训练好的通用工具，它可以根据自然彩色卫星图像自动生成 LULC 分类图，而无需最终用户提供任何训练输入。所提出的框架在接收器工作特征曲线（ROC）方法中的总体准确率为 0.75，曲线下面积（AUC）得分为 0.95，被用于绘制印度喀拉拉邦 Idukki 区县（行政区划）易受降雨引起的山体滑坡影响地区的建筑面积扩张图。通过比较 2012 年和 2022 年的 LULC 信息，可以发现该地区的建成区面积已从 2012 年占总面积的 12.76% 增加到 2022 年的 26.48%。考虑到研究区域内 "极高 "滑坡易发区的建成区迅速扩大，这一点非常重要。这清楚地表明，为了减少灾害风险，有必要对土地利用/土地覆被进行灾害包容性规划和跟踪。研究亮点使用随机森林（RF）分类器开发了一种预先训练的土地利用/土地覆被分类工具。该工具的性能令人满意，总体准确率达到 0.75，总体 ROC AUC 得分为 0.95。

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来源期刊

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.