Land Surface Temperature Responses to Land Use Land Cover Dynamics

Setturu, Bharath; Ks, Rajan; Ramach,; Tv, ra

doi:10.4172/2327-4581.1000112

Cited by 20 publications

(3 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In these methods, different values are calculated as the membership grade of each pixel based on land cover variation, while in definitive categorization methods each image pixel is attributed to only one class. The accuracy of the Kappa coefficient for land use maps, derived from the satellite data classification by using the Fuzzy ARTMAP classification for 1990, 1995, 2000, 2005, 2010, 2015, and 2018 is approximately equal to 87, 92, 90, 90, 89, 94 Setturu et al (2013) in Uttara Kannada district in India, Pal and Ziaul (2017) in English Bazar urban center of West Bengal state of India, Fathizad et al (2017), in southwest of Iran and Choudhury et al (2019) in Asansol-Durgapur Development show that land use changes are directly related to the increase in surface temperature. By examining the trend of temperature changes in the study area during the period 1990-2018, we see an increase of 1.92 °C which is in line with the IPCC report 2018.…”

Section: Discussionmentioning

confidence: 99%

Spatial and temporal changes of land uses and its relationship with surface temperature in western Iran

Rostami

Fathizad

2022

Atm.

View full text Add to dashboard Cite

A Split-Window algorithm has been used in the Ilam Dam watershed to determine the relationship between Land Surface Temperature (LST) and types of land use. Landsat satellite images of TM sensor for 1990, 1995, 2000, 2005 and 2010 and Landsat 8 (OLI Sensor) for 2015 and 2018 are used. After geometric and radiometric corrections of satellite images, land use maps are extracted by using Fuzzy ARTMAP method. An accuracy assessment showed that the highest value of the Kappa coefficient was 94% with a total accuracy of 0.95 for 2015, and that the lowest Kappa coefficient value was 87% with a total accuracy of 0.9 for 1990. The high values of these coefficients indicate the acceptable accuracy of using Landsat's remote sensing data for land use detection. The most important land use change is related to dense forest and sparse forest land uses, with a decrease of 20.07 and 17.04 percent, respectively. The minimum LST measures in 1990, 2010, and 2018 in dense forest are 21.27, 30.55 and 33.82 °C respectively. The maximum LST for the sparse forest land use in 1990 and 2010 are 52.48, 56.09, and for the dense forest land use in 2018 is 56.10 °C. As a result, the average LST in agricultural lands was lower than in sparse forest and rangeland; this is mainly due to the high moisture content and the greater evapotranspiration rate. Land Use / Land Cover (LULC) variations from 1990 to 2018 show that all land uses have experienced an increase in LST.

show abstract

Section: Discussionmentioning

confidence: 99%

Spatial and temporal changes of land uses and its relationship with surface temperature in western Iran

Rostami

Fathizad

2022

Atm.

View full text Add to dashboard Cite

show abstract

“…1c (adapted from Abiodun et al [52]) was used to derive spatial distribution of surface emissivity ( Fig. 1d) using the values previously obtained with remotesensing technology over the region [67] and similar vegetation types elsewhere [68,69].…”

Section: Methods and Data Processingmentioning

confidence: 99%

Impacts of cloudiness on near surface radiation and temperature in Nigeria, West Africa

et al. 2020

View full text Add to dashboard Cite

This study investigates impacts of cloudiness on surface radiation budget and temperature from 1983 to 2018 in Nigeria, West Africa. The National Aeronautics and Space Administration meteorological datasets were used to achieve this objective. Net radiation (R) increased latitudinally from the coast to the inland areas. The least value of R was obtained in wet season which coincided with the period when cloud cover was at its optimal. We obtained significant decreasing trends (− 0.110 to − 0.536 Wm −2 year −1) in R but significant increasing trends in cloud cover in most parts of the region. Under clear-sky conditions, however, significant increasing trends (0.256-0.807 Wm −2 year −1) in R were obtained. Annual mean temperature showed significant upward trends under all-sky (0.009-0.059 °C year −1) and clear-sky (0.019-0.072 °C year −1) conditions. Furthermore, significant negative correlations (− 0.70 ≤ r ≤ − 0.95) were obtained between monthly values of cloud cover and R over the country. However, r values for cloud cover and temperature were positive over Sahel (r ~ 0.65) but negative over Guinea (r ~ 0.45); both significant at p < 0.05. Changes in R were accounted for by 21 to 26% variations in cloud cover, while fluctuation in temperature was explained by about 14% variation in cloud cover over Sahel region at p < 0.05. Our results revealed that the surface radiation budget was significantly perturbed by variations in cloudiness which resulted in net cloud-radiative cooling (− 30 Wm −2) over Guinea but warming over Savannah (25 Wm −2) and Sahel (50 Wm −2) zones.

show abstract

“…It plays a crucial role in determining the energy balance and climate change in terms of global warming and the greenhouse effect. It determines the earth's surface's effective blistering temperature and influences energy exchange, biogeochemical cycle, crop and wind patterns, rainfall, biodiversity, and ecology (Bharath, Rajan et al, 2013;Jin, Li et al, 2015;Nimish, Chandan et al, 2018). Estimation of LST depends upon surface albedo, moisture content in soil and atmosphere, health and density of vegetation, surface characteristics, season-of-year, and time-of-day.…”

Section: Introductionmentioning

confidence: 99%