Spatial Analysis of Land Use and Land Cover Changes Using Spectral Indices in the Tsunami Affected Areas in Kerala, India

Sarun, S.; Vineetha, P.; Kumar, Rishi; Jayalekshmi, V

doi:10.9734/jgeesi/2018/41927

Cited by 3 publications

(1 citation statement)

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“…The research novelty is development a tsunami high vulnerability area identification software framework using the DEM, LULC, and VI indicators as part of tsunami mitigation [10]. This software framework was developed with the following concepts: 1) the LULC temporal dynamics which are identified using VI indicators consist of normalized difference vegetation index (NDVI), modified soil adjusted vegetation index (MSAVI), normalized difference water index (NDWI), modified normalized difference water index (MNDWI), and normalized difference built-up index (NDBI) [11], [12]; 2) The spatial dynamics of LULC which are predicted using ML consist of Classification and Regression Tree (CART), multivariate adaptive regression spline (MARS), random forest (RF), and k-nearest neighbors (k-nn) [13], [14]; 3) Slopes analysis of DEM SRTM data in areas with a high risk of tsunami waves [15], [16]; 4) Create algortihms for computer modeling framework to classify tsunami high vulnerability areas using the DEM, LULC, and VI indicators.…”

Section: Introductionmentioning

confidence: 99%

Computer model for tsunami vulnerability using sentinel 2A and SRTM images optimized by machine learning

et al. 2021

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This study aims to develop a software framework for modeling of tsunami vulnerability using DEM and Sentinel 2 images. The stages of study, are: 1) extraction Sentinel 2 images using algorithms NDVI, NDBI, NDWI, MSAVI, and MNDWI; 2) prediction vegetation indices using machine learning algorithms. 3) accuracy testing using the MSE, ME, RMSE, MAE, MPE, and MAPE; 4) spatial prediction using Kriging function and 5) modeling tsunami vulnerability indicators. The results show that in 2021 the area was dominated by vegetation density between (-0.1-0.3) with moderate to high vulnerability and risk of land use tsunami as a result of the decreasing of vegetation. The prediction results for 2021 show a low canopy density of vegetation and a high degree of land surface slope. Based on the prediction results in 2021, the study area mostly shows the existence of built-up lands with a high tsunami vulnerability risk (more than 0.1). Vegetation population had decreased to 67% from the original areas in 2017 with an area of 135 km2. Forest vegetation had decreased by 45% from 116 km2 in 2017. Land use for fisheries had increased to the area of 86 km2 from 2017 with an area of 24 km2.

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Section: Introductionmentioning

confidence: 99%

Computer model for tsunami vulnerability using sentinel 2A and SRTM images optimized by machine learning

et al. 2021

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Cost-effective disaster-induced land cover analysis: a semi-automatic methodology Using machine learning and satellite imagery

I. Volke,

Abarca-Del-Rio,

A. Warner

2024

International Journal of Remote Sensing

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Evaluation of Urban Land Cover Indices Affecting Urban Heat Island Formation Using Advanced Geo-Spatial Techniques

YILMAZ,

OZTURK

2024

Preprint

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Evaluation of land surface temperature during the climate change process is important in urban planning. The urban thermal environment is closely related to land surface characteristics. The relationship between land surface properties and land surface temperature (LST) is among the current research topics. As a result of advances in geospatial and remote sensing fields, remote sensing-based spectral indices have been developed to investigate land use/land cover (LULC) effects on the urban thermal environment. The study aims to investigate the impact of remote sensing-based LULC indices on LST in Ankara metropolitan city. LST values, which are an important representation of the urban heat island, were calculated from Landsat 8 OLI/TIRS data for 2013, 2018, and 2023. Urban Thermal Field Variance Index (UTFVI) was used to define the urban heat island effect from a thermal perspective. Additionally, to define the urban heat island effect in terms of land cover characteristics, temporal-spatial changes of the LULC indices which are Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Normalized Difference Built-Up Index (NDBI), Normalized Difference Impervious Index (NDISI) has been evaluated. Linear regression analysis was performed to determine the effect of LULC indices on LST. As a result of the analysis, it was determined that NDVI and NDWI had a negative correlation with LST, while NDBI and NDISI had a positive correlation. The highest correlation values belong to 2023. NDVI (R²=0.4944) and NDWI (R²=0.2666) affect the 2023 LST negatively, while NDBI (R²=0.3664) and NDISI (R²=0.6010) affect it positively. While the results show the importance of green vegetation and water surfaces in reducing LST, they show that NDISI, which is a representation of impervious surfaces, has the most impact on increasing LST. The results of the study also reveal the impact and importance of spatial patterns of LULC indices on LST.

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Spatial Analysis of Land Use and Land Cover Changes Using Spectral Indices in the Tsunami Affected Areas in Kerala, India

Cited by 3 publications

References 0 publications

Computer model for tsunami vulnerability using sentinel 2A and SRTM images optimized by machine learning

Computer model for tsunami vulnerability using sentinel 2A and SRTM images optimized by machine learning

Cost-effective disaster-induced land cover analysis: a semi-automatic methodology Using machine learning and satellite imagery

Evaluation of Urban Land Cover Indices Affecting Urban Heat Island Formation Using Advanced Geo-Spatial Techniques

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