Analysing the Effects of Different Land Cover Types on Land Surface Temperature Using Satellite Data

Şekertekin, Aliihsan; Kutoğlu, Şenol Hakan; Kaya, Ş.; Marangoz, Aycan Murat

doi:10.5194/isprsarchives-xl-1-w5-665-2015

Cited by 11 publications

(3 citation statements)

References 8 publications

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“…In recent years, climate change has been one of the most important problems that the ecological system of the world has been encountered (Sekertekin, Kutoglu, Kaya, & Marangoz, 2015). Retrieving LST is crucial for climate change, especially for understanding urban heat islands and local climate changes (Kaya, Basar, Karaca, & Seker, 2012;Voogt & Oke, 1998).…”

Section: Land Surface Temperaturementioning

confidence: 99%

Land Use Analysis on Land Surface Temperature in Urban Areas Using a Geographically Weighted Regression and Landsat 8 Imagery, a Case Study: Tehran, Iran

Karimi

Pahlavani

Bigdeli

2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Due to urbanization and changes in the urban thermal environment and because the land surface temperature (LST) in urban areas are a few degrees higher than in surrounding non-urbanized areas, identifying spatial factors affecting on LST in urban areas is very important. In this regard, due to the unique properties of spatial data, in this study, a geographically weighted regression (GWR) was used to identify effective spatial factors. The GWR is a suitable method for spatial regression issues, because it is compatible with two unique properties of spatial data, i.e. the spatial autocorrelation and spatial non-stationarity. In this study, the Landsat 8 satellite data on 18 August 2014 and Tehran land use data in 2006 was used for determining the land surface temperature and its effective factors. As a result, R 2 value of 0.765983 was obtained by taking the Gaussian kernel. The results showed that the industrial, military, transportation, and roads areas have the highest surface temperature.

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Section: Land Surface Temperaturementioning

confidence: 99%

Land Use Analysis on Land Surface Temperature in Urban Areas Using a Geographically Weighted Regression and Landsat 8 Imagery, a Case Study: Tehran, Iran

Karimi

Pahlavani

Bigdeli

2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…For soil sand, the most effective parameter was found to be LST, which depends on the amount of solar energy absorbed by land cover types and local environmental conditions [67]. Sandy and agricultural areas absorb the highest temperatures due to the structure of the land cover [68]. The second parameter affecting sand was found to be B5, where areas with water dams had the highest amount of sand, and the amount of B5 reflection was the lowest.…”

Section: Spatial Prediction Of Soil Propertiesmentioning

confidence: 99%

Geospatial Artificial Intelligence (GeoAI) and Satellite Imagery Fusion for Soil Physical Property Predicting

Hosseini,

Seo,

Razavi-Termeh

et al. 2023

Sustainability

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This study aims to predict vital soil physical properties, including clay, sand, and silt, which are essential for agricultural management and environmental protection. Precision distribution of soil texture is crucial for effective land resource management and precision agriculture. To achieve this, we propose an innovative approach that combines Geospatial Artificial Intelligence (GeoAI) with the fusion of satellite imagery to predict soil physical properties. We collected 317 soil samples from Iran’s Golestan province for dependent data. The independent dataset encompasses 14 parameters from Landsat-8 satellite images, seven topographic parameters from the Shuttle Radar Topography Mission (SRTM) DEM, and two meteorological parameters. Using the Random Forest (RF) algorithm, we conducted feature importance analysis. We employed a Convolutional Neural Network (CNN), RF, and our hybrid CNN-RF model to predict soil properties, comparing their performance with various metrics. This hybrid CNN-RF network combines the strengths of CNN networks and the RF algorithm for improved soil texture prediction. The hybrid CNN-RF model demonstrated superior performance across metrics, excelling in predicting sand (MSE: 0.00003%, RMSE: 0.006%), silt (MSE: 0.00004%, RMSE: 0.006%), and clay (MSE: 0.00005%, RMSE: 0.007%). Moreover, the hybrid model exhibited improved precision in predicting clay (R2: 0.995), sand (R2: 0.992), and silt (R2: 0.987), as indicated by the R2 index. The RF algorithm identified MRVBF, LST, and B7 as the most influential parameters for clay, sand, and silt prediction, respectively, underscoring the significance of remote sensing, topography, and climate. Our integrated GeoAI-satellite imagery approach provides valuable tools for monitoring soil degradation, optimizing agricultural irrigation, and assessing soil quality. This methodology has significant potential to advance precision agriculture and land resource management practices.

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“…Land Surface Temperature (LST) plays a key role in various scientific studies. These include management of the ecological environment of plants (Setturu et al, 2013;Douffi et al, 2018), quantification of vegetation index (Sekertekin et al, 2015;Anbazhagan and Paramasivam, 2016). Agriculture and bushfires (Vlassova and Pérez-Cabello, 2016), urban vegetation management (Mobio et al, 2017;Odindi et al, 2020) and meteorological and climate studies (Tomlinson et al, 2011) use LST as a methodological support.…”

Section: Introductionmentioning

confidence: 99%

Afforestation in Response to Thermal Change in the Forest-Savannah Transition of the Lamto Scientific Reserve, Côte d'Ivoire

Guy-Casimir

Yao

Koffi

et al. 2021

European Journal of Forest Engineering

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In a context of deforestation and climate change, the land cover or use induced by human or natural transformation influences the climate from global to local scale. The aim of this study was to assess the vegetation dynamic between 1985 and 2020 within the Lamto Scientific Reserve (Côte d'Ivoire). After mapping the land cover by the neural network algorithm for the years 1988, 2002 and 2020, the climatic parameters including the Land Surface Temperature (LST) and the standardized precipitation index (SPI) were estimated since 1985. Then, the land cover response was estimated in the presence of the LST, with the average LST from 1985 to 2020 as a threshold. The forest dynamic showed an increase in area of 291.87 ha in 32 years. The area of forest has increased from 610.42 ha (21.42%) in 1988 to 902.29 ha (31.59%) in 2020. The practice of fire remains predominant in the reserve management. SPI shows three alternating seasons over the time. One wet season (1995 to 2010) framed by two dry seasons (1985 to 1994 and 2011 to 2020). The relation LST -NDVI shows covariation. LST indicates a variation from 30.46°C to 34.68°C, with an increase of 4.22°C between 1985 and 2020. The land cover response indicates that, with the exception of the LST of shrub savannahs and burnt areas, the land cover LST of 1988 and 2002 have remained below the LST threshold (32.70°C). The land cover LST of 2020 has risen above the threshold. Land-use changes have led to afforestation despite warming due to bushfires. In addition, forests could continue to act as a thermal moderator of Lamto reserve.

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Analysing the Effects of Different Land Cover Types on Land Surface Temperature Using Satellite Data

Cited by 11 publications

References 8 publications

Land Use Analysis on Land Surface Temperature in Urban Areas Using a Geographically Weighted Regression and Landsat 8 Imagery, a Case Study: Tehran, Iran

Land Use Analysis on Land Surface Temperature in Urban Areas Using a Geographically Weighted Regression and Landsat 8 Imagery, a Case Study: Tehran, Iran

Geospatial Artificial Intelligence (GeoAI) and Satellite Imagery Fusion for Soil Physical Property Predicting

Afforestation in Response to Thermal Change in the Forest-Savannah Transition of the Lamto Scientific Reserve, Côte d'Ivoire

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