{"title":"不受控制的工业化对环境参数的影响:使用机器学习方法的勐拉出口加工区案例研究","authors":"Faishal Ahmed , Md Shihab Uddin , Ovi Ranjan Saha","doi":"10.1016/j.rsase.2024.101307","DOIUrl":null,"url":null,"abstract":"<div><p>Unplanned and uncontrolled industrialization leads to environmental pollution, which ends up impacting human life and destroying the economy. Especially in the era of global warming, coastal regions worldwide are the most vulnerable and hold significant ecological importance for human habitation. In 1998, the establishment of the Mongla Export Processing Zone (MEPZ) in the coastal town of Mongla Thana, which is already famous for its seaport, led the area to the challenges of salinity intrusion and the shrinking of agricultural land and its fertility. Unplanned industrialization in the area causes vegetation loss, severe droughts, and other environmental challenges, threatening local biodiversity and agricultural sustainability. In this paper, the effects of unplanned industrialization inside the Mongla EPZ on the area land surface temperature (LST), normalized difference vegetation index (NDVI), normalized difference water index (NDWI), and urban heat island (UHI) spanning from 2007 to 2023 have been investigated. Along with that, a machine-learning-based artificial neural network (ANN) model was employed to forecast the situation in 2027 and 2031. Our industrial settlement analysis reveals that a substantial rise in industrial building was seen in 2015 in the EPZ area, whereas the EPZ area was almost settlement-free before 2011. With this increase in 2015, above 2% of the total municipal area faced drought, which will become over 30% by 2023. The NDVI values are decreasing year-wise, which reveals that the area is becoming less vegetation-rich. Also, the increasing industrial activities in the EPZ led to an LST increment. Our CA-ANN algorithm-based future prediction shows that about 30% of the whole municipality will face LST 27 °C by 2031. Along with that, the area's UHI value, over 2 °C higher than the rural surrounding area, will reach 6.5% by 2031. Our findings indicate that the municipal area will face a devastating future, including vegetation loss, a high probability of severe drought, and ultimately, environmental degradation. This study will help raising awareness and decision-making process to mitigate the environmental risks and supporting sustainable development.</p></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"36 ","pages":"Article 101307"},"PeriodicalIF":3.8000,"publicationDate":"2024-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Effect of uncontrolled industrialization on environmental parameter: A case study of Mongla EPZ using machine learning approach\",\"authors\":\"Faishal Ahmed , Md Shihab Uddin , Ovi Ranjan Saha\",\"doi\":\"10.1016/j.rsase.2024.101307\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Unplanned and uncontrolled industrialization leads to environmental pollution, which ends up impacting human life and destroying the economy. Especially in the era of global warming, coastal regions worldwide are the most vulnerable and hold significant ecological importance for human habitation. In 1998, the establishment of the Mongla Export Processing Zone (MEPZ) in the coastal town of Mongla Thana, which is already famous for its seaport, led the area to the challenges of salinity intrusion and the shrinking of agricultural land and its fertility. Unplanned industrialization in the area causes vegetation loss, severe droughts, and other environmental challenges, threatening local biodiversity and agricultural sustainability. In this paper, the effects of unplanned industrialization inside the Mongla EPZ on the area land surface temperature (LST), normalized difference vegetation index (NDVI), normalized difference water index (NDWI), and urban heat island (UHI) spanning from 2007 to 2023 have been investigated. Along with that, a machine-learning-based artificial neural network (ANN) model was employed to forecast the situation in 2027 and 2031. Our industrial settlement analysis reveals that a substantial rise in industrial building was seen in 2015 in the EPZ area, whereas the EPZ area was almost settlement-free before 2011. With this increase in 2015, above 2% of the total municipal area faced drought, which will become over 30% by 2023. The NDVI values are decreasing year-wise, which reveals that the area is becoming less vegetation-rich. Also, the increasing industrial activities in the EPZ led to an LST increment. Our CA-ANN algorithm-based future prediction shows that about 30% of the whole municipality will face LST 27 °C by 2031. Along with that, the area's UHI value, over 2 °C higher than the rural surrounding area, will reach 6.5% by 2031. Our findings indicate that the municipal area will face a devastating future, including vegetation loss, a high probability of severe drought, and ultimately, environmental degradation. This study will help raising awareness and decision-making process to mitigate the environmental risks and supporting sustainable development.</p></div>\",\"PeriodicalId\":53227,\"journal\":{\"name\":\"Remote Sensing Applications-Society and Environment\",\"volume\":\"36 \",\"pages\":\"Article 101307\"},\"PeriodicalIF\":3.8000,\"publicationDate\":\"2024-07-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Remote Sensing Applications-Society and Environment\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S235293852400171X\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENVIRONMENTAL SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Remote Sensing Applications-Society and Environment","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S235293852400171X","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENVIRONMENTAL SCIENCES","Score":null,"Total":0}
Effect of uncontrolled industrialization on environmental parameter: A case study of Mongla EPZ using machine learning approach
Unplanned and uncontrolled industrialization leads to environmental pollution, which ends up impacting human life and destroying the economy. Especially in the era of global warming, coastal regions worldwide are the most vulnerable and hold significant ecological importance for human habitation. In 1998, the establishment of the Mongla Export Processing Zone (MEPZ) in the coastal town of Mongla Thana, which is already famous for its seaport, led the area to the challenges of salinity intrusion and the shrinking of agricultural land and its fertility. Unplanned industrialization in the area causes vegetation loss, severe droughts, and other environmental challenges, threatening local biodiversity and agricultural sustainability. In this paper, the effects of unplanned industrialization inside the Mongla EPZ on the area land surface temperature (LST), normalized difference vegetation index (NDVI), normalized difference water index (NDWI), and urban heat island (UHI) spanning from 2007 to 2023 have been investigated. Along with that, a machine-learning-based artificial neural network (ANN) model was employed to forecast the situation in 2027 and 2031. Our industrial settlement analysis reveals that a substantial rise in industrial building was seen in 2015 in the EPZ area, whereas the EPZ area was almost settlement-free before 2011. With this increase in 2015, above 2% of the total municipal area faced drought, which will become over 30% by 2023. The NDVI values are decreasing year-wise, which reveals that the area is becoming less vegetation-rich. Also, the increasing industrial activities in the EPZ led to an LST increment. Our CA-ANN algorithm-based future prediction shows that about 30% of the whole municipality will face LST 27 °C by 2031. Along with that, the area's UHI value, over 2 °C higher than the rural surrounding area, will reach 6.5% by 2031. Our findings indicate that the municipal area will face a devastating future, including vegetation loss, a high probability of severe drought, and ultimately, environmental degradation. This study will help raising awareness and decision-making process to mitigate the environmental risks and supporting sustainable development.
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