Modelling land use/land cover (LULC) change dynamics, future prospects, and its environmental impacts based on geospatial data models and remote sensing data

Ahmad, Muhammad Nasar; Shao, Zhenfeng; Javed, Akib

doi:10.1007/s11356-022-24442-2

Cited by 17 publications

(3 citation statements)

References 76 publications

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“…Similar indices may develop for better LULC classification. Urban trends or prediction studies can increase accuracy using this soil indices by minimizing false urban ISA signature (Ahmad et al, 2023). Besides, fusion studies are also compatible with NSAI indices because of their simple band mathematics.…”

Section: Future Research Directionsmentioning

confidence: 99%

Razvoj normaliziranog indeksa tla za urbane studije upotrebom podataka daljinskih mjerenja

et al. 2023

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This paper presents two novel spectral soil area indices to identify bare soil area and distinguish it more accurately from the urban impervious surface area (ISA). This study designs these indices based on medium spatial resolution remote sensing data from Landsat 8 OLI dataset. Extracting bare soil or urban ISA is more challenging than extracting water bodies or vegetation in multispectral Remote Sensing (RS). Bare soil and the urban ISA area often were mixed because of their spectral similarity in multispectral sensors. This study proposes Normalized Soil Area Index 1 (NSAI1) and Normalized Soil Area Index 2 (NSAI2) using typical multispectral bands. Experiments show that these two indices have an overall accuracy of around 90%. The spectral similarity index (SDI) shows these two indices have higher separability between soil area and ISA than previous indices. The result shows that percentile thresholds can effectively classify bare soil areas from the background. The combined use of both indices measured the soil area of the study area over 71 km2. Most importantly, proposed soil indices can refine urban ISA measurement accuracy in spatiotemporal studies.

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Section: Future Research Directionsmentioning

confidence: 99%

Razvoj normaliziranog indeksa tla za urbane studije upotrebom podataka daljinskih mjerenja

et al. 2023

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“…This effect is thought to result from the increased release of bioisoprene from plants, which subsequently leads to higher ozone concentrations [42,43]. Furthermore, the DEM, LULC, and POPU have been identified as influential factors in surface ozone pollution [42,44,45].…”

Section: The Relative Importance Of Explanatory Variables To O 3 Poll...mentioning

confidence: 99%

Quantitative Analysis of Spatiotemporal Patterns and Factor Contributions of Surface Ozone in the North China Plain

Li,

Liu,

et al. 2024

Applied Sciences

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Over the past decade, surface ozone has emerged as a significant air pollutant in China, especially in the North China Plain (NCP). For effective ozone management in the NCP, it is crucial to accurately estimate the surface ozone levels and identify the primary influencing factors for ozone pollution in this region. This study utilized ozone precursors such as volatile organic compounds (VOCs) and nitrogen oxides (NOX), meteorological data, land cover, normalized difference vegetation index (NDVI), terrain, and population data to build an extreme gradient boosting (XGBoost)-based ozone estimation model in the NCP during 2019 to 2021. Four ozone estimation models were developed using different NO2 and formaldehyde (HCHO) datasets from the Sentinel-5 TROPOMI observations and Copernicus Atmosphere Monitoring Service (CAMS) reanalysis data. Site-based validation results of these four models showed high accuracy with R2 values above 0.86. Among these four models, two models with higher accuracy and higher spatial coverage ratio were selected, and their results were averaged to produce the final ozone estimation products. The results indicated that VOCs and NOX were the two main pollutants causing ozone pollution in the NCP, and their relative contributions accounted for more than 23.34% and 10.23%, respectively, while HCHO also played a significant role, contributing over 5.64%. Additionally, meteorological factors also had a notable impact, contributing 28.63% to ozone pollution, with each individual factor contributing more than 2.38%. The spatial distribution of ozone pollution identified the Hebei–Shandong–Henan junction as a pollution hotspot, with the peak occurring in summer, particularly in June. Therefore, for this hotspot region in the NCP, promoting the reduction in VOCs and NOx can play an important role in the mitigation of O3 pollution and the improvement in air quality in this region.

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“…Accurate and timely mapping of urban land use and land cover [1] is crucial, and relying on a single satellite dataset may have limitations [2]. Therefore, integrating multiple data sources in remote sensing can significantly enhance the accuracy and value of the extracted information [3].…”

Section: Introductionmentioning

confidence: 99%

Optical–SAR Data Fusion Based on Simple Layer Stacking and the XGBoost Algorithm to Extract Urban Impervious Surfaces in Global Alpha Cities

Ahmad,

Shao,

Javed

et al. 2024

Remote Sensing

Self Cite

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This study proposes a fusion approach to enhancing urban remote sensing applications by integrating SAR (Sentinel-1) and optical (Landsat-8) satellite datasets. The fusion technique combines feature-based fusion and simple layer stacking (SLS) to improve the accuracy of urban impervious surface (UIS) extraction. SAR textures and modified indices are used for feature extraction, and classification is performed using the XGBoost machine learning algorithm in Python and Google Earth Engine. The study focuses on four global cities (New York, Paris, Tokyo, and London) with heterogeneous climatic zones and urban dynamics. The proposed method showed significant results. The accuracy assessment using random validation points shows an overall accuracy of 86% for UIS classification with the SLS method, outperforming single-data classification. The proposed approach achieves higher accuracy (86%) compared to three global products (ESA, ESRI, and Dynamic World). New York exhibits the highest overall accuracy at 88%. This fusion approach with the XGBoost classifier holds potential for new applications and insights into UIS mapping, with implications for environmental factors such as land surface temperature, the urban heat island effect, and urban pluvial flooding.

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Modelling land use/land cover (LULC) change dynamics, future prospects, and its environmental impacts based on geospatial data models and remote sensing data

Cited by 17 publications

References 76 publications

Razvoj normaliziranog indeksa tla za urbane studije upotrebom podataka daljinskih mjerenja

Razvoj normaliziranog indeksa tla za urbane studije upotrebom podataka daljinskih mjerenja

Quantitative Analysis of Spatiotemporal Patterns and Factor Contributions of Surface Ozone in the North China Plain

Optical–SAR Data Fusion Based on Simple Layer Stacking and the XGBoost Algorithm to Extract Urban Impervious Surfaces in Global Alpha Cities

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