The impact of industrial activities on the surrounding environment based on hybrid filter and machine learning

IF 3.8 Q2 ENVIRONMENTAL SCIENCES

Remote Sensing Applications-Society and Environment Pub Date : 2025-04-01 DOI:10.1016/j.rsase.2025.101599

Agus Suprijanto , Yumin Tan , Rodolfo Domingo Moreno Santillan , Syed Mohammad Masum

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引用次数: 0

Abstract

Industrial development has emerged as a significant driver of environmental degradation and urban heat island (UHI) formation. However, studies explicitly addressing the long-term spatial impact of heavy industries—particularly in tropical, cloud-prone regions—remain limited due to persistent data gaps and noise in satellite observations. This study addresses that research gap by analyzing the environmental effects of industrial activities in Cilegon City, Indonesia—one of the nation's largest industrial zones—using monthly Landsat-8 time series data from 2014 to 2022. A hybrid filtering approach was applied to reconstruct high-quality data by removing cloud and cloud shadow interference. The reconstructed NDVI and LST were then used as multivariate input features to model Land Surface Temperature (LST) using the XGBoost algorithm, with 30-m spatial resolution. The predicted LST was subsequently analyzed alongside NDVI to examine spatio-temporal trends and quantify industrial heat island (IHI) effects. Results show that industrial heat extends up to 1.5 km from core industrial zones, with IHI intensity reaching 5.58 °C in 2022. Vegetation health showed severe decline, with NDVI values dropping by 81.36 % in industrial cores and 29.25 % in adjacent areas. LST exhibited a positive trend of 0.23 °C/month in highly industrialized subdistricts and maintained a strong negative correlation with NDVI (r = −0.95). These findings highlight the amplified environmental impact of industrial activities in cloud-prone tropical cities and emphasize the urgent need for sustainable land management and the implementation of green infrastructure to mitigate local warming and protect surrounding ecosystems.

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基于混合滤波和机器学习的工业活动对周围环境的影响

工业发展已成为环境退化和城市热岛形成的重要驱动因素。然而，由于持续存在的数据缺口和卫星观测中的噪声，明确解决重工业长期空间影响的研究——特别是在热带多云地区——仍然有限。本研究利用2014年至2022年的每月Landsat-8时间序列数据，分析了印尼最大的工业区之一——西里贡市的工业活动对环境的影响，从而弥补了这一研究空白。采用混合滤波方法去除云和云影干扰，重构高质量数据。然后将重建的NDVI和LST作为多变量输入特征，使用XGBoost算法模拟地表温度（LST），空间分辨率为30 m。随后，将预测的地表温度与NDVI一起进行分析，以检验时空趋势并量化工业热岛（IHI）效应。结果表明，2022年，工业热从核心工业区延伸至1.5 km， IHI强度达到5.58°C。植被健康度下降严重，工业核心区NDVI下降81.36%，周边区下降29.25%。在工业化程度较高的街道，地表温度呈上升趋势（0.23°C/月），与NDVI呈显著负相关（r = - 0.95）。这些发现强调了易云热带城市工业活动对环境的巨大影响，并强调了可持续土地管理和实施绿色基础设施以缓解当地变暖和保护周围生态系统的迫切需要。

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