scholarly journals Analisis Heat Island pada Perkebunan Kelapa Sawit: Studi Kasus di Kabupaten Kayong Utara, Kalimantan Barat

2020 ◽  
Vol 8 (2) ◽  
pp. 106-115
Author(s):  
Nurul Ihsan Fawzi ◽  
Marindah Yulia Iswari
Keyword(s):  
Remote Sensing ◽  
Surface Temperature ◽  
Palm Oil ◽  
Secondary Forest ◽  
Remote Sensing Data ◽  
Primary Forest ◽  
Landsat 8 ◽  
Heat Island ◽  
Local Climate ◽  
Island Effect

Between 2000 – 2017, 3.06 million hectares of primary forest in Kalimantan have been converted into palm oil plantation. This change impacts local climate changes. This study aims is to analyze the heat island in palm oil plantation. The analytical method used surface temperature estimation through remote sensing and zonal statistics. The remote sensing data that are used is Landsat 8 images acquired on 15 July 2018 and 3 August 2019. From this research, we found that young palm oil plantations have an average IHI value of 2.1 ± 1.7oC in 2018 and 1.7 ± 1.4oC in 2019. The IHI value is close to the heat island in a built-up area. IHI for mature palm oil plantation (11-12 years) created a cool island with an intensity close to secondary forest. The decreasing value of IHI for 2018 and 2019 in palm oil plantations is due to the growth of palm oil trees, which decreases surface temperature. The implication of this research is to know heat island effect due to deforestation or land cover changes, especially change into palm oil plantations.

scholarly journals Detecting the Cool Island Effect of Urban Parks in Wuhan: A City on Rivers

2020 ◽  
Vol 18 (1) ◽  
pp. 132
Author(s):  
Qijiao Xie ◽  
Jing Li
Keyword(s):  
Remote Sensing ◽  
Negative Impact ◽  
Remote Sensing Data ◽  
Urban Parks ◽  
High Water ◽  
Water Bodies ◽  
Green Spaces ◽  
Heat Island ◽  
Island Effect ◽  
Urban Heat

As a nature-based solution, development of urban blue-green spaces is widely accepted for mitigating the urban heat island (UHI) effect. It is of great significance to determine the main driving factors of the park cool island (PCI) effect for optimizing park layout and achieving a maximum cooling benefit of urban parks. However, there have been obviously controversial conclusions in previous studies due to varied case contexts. This study was conducted in Wuhan, a city with high water coverage, which has significant differences in context with the previous case cities. The PCI intensity and its correlation with park characteristics were investigated based on remote sensing data. The results indicated that 36 out of 40 urban parks expressed a PCI effect, with a PCI intensity of 0.08~7.29 °C. As expected, larger parks with enough width had stronger PCI intensity. An increased density of hardened elements in a park could significantly weaken PCI effect. Noticeably, in this study, water bodies in a park contributed the most to the PCI effect of urban parks, while the vegetated areas showed a negative impact on the PCI intensity. It implied that in a context with higher water coverage, the cooling effect of vegetation was weakened or even masked by water bodies, due to the interaction effect of different variables on PCI intensity.

scholarly journals Data and techniques for studying the urban heat island effect in Johannesburg

2015 ◽  
Vol XL-7/W3 ◽  
pp. 203-206 ◽  
Author(s):  
C. H. Hardy ◽  
A. L. Nel
Keyword(s):  
Remote Sensing ◽  
Urban Heat Island ◽  
Urban Areas ◽  
Remote Sensing Data ◽  
Urban Heat Island Effect ◽  
Heat Island ◽  
Sensing Data ◽  
Island Effect ◽  
Urban Heat ◽  
The City

The city of Johannesburg contains over 10 million trees and is often referred to as an urban forest. The intra-urban spatial variability of the levels of vegetation across Johannesburg’s residential regions has an influence on the urban heat island effect within the city. Residential areas with high levels of vegetation benefit from cooling due to evapo-transpirative processes and thus exhibit weaker heat island effects; while their impoverished counterparts are not so fortunate. The urban heat island effect describes a phenomenon where some urban areas exhibit temperatures that are warmer than that of surrounding areas. The factors influencing the urban heat island effect include the high density of people and buildings and low levels of vegetative cover within populated urban areas. This paper describes the remote sensing data sets and the processing techniques employed to study the heat island effect within Johannesburg. In particular we consider the use of multi-sensorial multi-temporal remote sensing data towards a predictive model, based on the analysis of influencing factors.

scholarly journals THE EFFECT OF ENVIRONMENTAL CONDITION CHANGES ON DISTRIBUTION OF URBAN HEAT ISLAND IN JAKARTA BASED ON REMOTE SENSING DATA

2017 ◽  
Vol 12 (1) ◽  
pp. 27
Author(s):  
Indah Prasasti ◽  
. Suwarsono ◽  
Nurwita Mustika Sari
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Urban Heat Island ◽  
Environmental Conditions ◽  
Remote Sensing Data ◽  
Parameter Extraction ◽  
Landsat 8 ◽  
Heat Island ◽  
Sensing Data ◽  
Urban Heat

Anthropogenic activities of urban growth and development in the area of Jakarta has caused increasingly uncomfortable climatic conditions and tended to be warmer and potentially cause the urban heat island (UHI). This phenomenon can be monitored by observing the air temperature measured by climatological station, but the scope is relatively limited. Therefore, the utilization of remote sensing data is very important in monitoring the UHI with wider coverage and effective. In addition, the remote sensing data can also be used to map the pattern of changes in environmental conditions (microclimate). This study aimed to analyze the effect of changes in environmental conditions (land use/cover, Normalized Difference Vegetation Index (NDVI) and Normalized Difference Build-up Index (NDBI)) toward the spread of the urban heat island (UHI). In this case, the UHI was identified from pattern changes of Land Surface Temperature (LST) in Jakarta based on data from remote sensing. The data used was Landsat 7 in 2007 and Landsat 8 in 2013 for parameter extraction environmental conditions, namely: land use cover, NDVI, NDBI, and LST. The analysis showed that during the period 2007 to 2013, there has been a change in the condition of the land use/cover, impairment NDVI, and expansion NDBI that trigger an increase in LST and the formation of heat islands in Jakarta, especially in the area of business centers, main street and surrounding area, as well as in residential areas.

scholarly journals Analysis of Urban Heat Island Phenomenon as A Global Warming Control Based on Remote Sensing in Jember Urban, Indonesia

2021 ◽  
Vol 887 (1) ◽  
pp. 012002
Author(s):  
R. N. Listyawati ◽  
P. Prasetiyo
Keyword(s):  
Remote Sensing ◽  
Global Warming ◽  
Population Growth ◽  
Surface Temperature ◽  
Urban Heat Island ◽  
Landsat 8 ◽  
Heat Island ◽  
Surface Temperatures ◽  
Study Results ◽  
Urban Heat

Abstract Global warming is a world problem because it has a significant impact on the survival of the earth, which is the Urban Heat Island (UHI) phenomenon. Jember Regency is a district with a relatively high population growth rate. Total population growth per year is 0.55% in 2020. In general, increasing the urban population increases the need for built-up land to support human activities and can affect surface temperatures, producing the urban heat island phenomenon. This study analyses the UHI phenomenon in 3 sub-districts in urban Jember using the Landsat 8 OLI remote sensing image processing method and TIRS Multispectral Imagery to obtain surface temperature values and high-resolution aerial photos from 2013 to 2021, which will be used to identify surface temperatures through several methods. Extraction is the use of supervised classification (supervised), NDVI (Normalized Difference Vegetation Index), and LST (Land Surface Temperature). The study results illustrate that the UHI value in 2013-2021 tends to fluctuate with decreasing temperatures. The Non-UHI classification dominates the urban area of Jember, where the majority are in the suburbs. Meanwhile, the highest UHI class value spreads in the downtown area.

scholarly journals The use of Landsat 8 remote sensing data for assessing the temperature conditions of the Larus ridibundus L. habitat

2020 ◽  
Vol 9 (4) ◽  
pp. 184-191
Author(s):  
Sergey Arkadyevich Shurakov ◽  
Aleksey Nikolaevich Chashchin
Keyword(s):  
Remote Sensing ◽  
Surface Temperature ◽  
Land Surface ◽  
Satellite Image ◽  
Remote Sensing Data ◽  
Close Correlation ◽  
Primary Data ◽  
Landsat 8 ◽  
Sensing Data ◽  
Temperature Conditions

This paper discusses the possibilities of using Landsat 8 remote sensing data for assessing the temperature conditions of aquatic landscapes when studying the abundance and density of gulls. The study of the ornithological situation was carried out on the territory of the Perm international airport of the Perm Region, where the black-headed gull is an unfavorable factor in the safety of passenger aircraft flights. Within the boundaries of the region, 5 reservoirs were identified. A method for calculating the surface temperature from a multispectral satellite image of the Landsat 8 series is described in detail with the presentation of primary data sources, atmospheric parameters and obtaining raster coverage with a resolution of 30 meters per pixel. The tool used for the calculation is the Land Surface Temperature module of the QGIS software. The paper presents maps of temperature within the area of conducted ornithological surveys and the density of gulls. The densities of birds for individual bodies of water are calculated using the Spatial Analyst module of the ArcGIS program with the kernel density tool. According to the research results, a close correlation was established between the attractiveness of reservoirs for gulls and water temperature. The correlation coefficients were 0,83 and 0,71, respectively, with the abundance and density of gulls.

scholarly journals Downscale MODIS Land Surface Temperature Based on Three Different Models to Analyze Surface Urban Heat Island: A Case Study of Hangzhou

2020 ◽  
Vol 12 (13) ◽  
pp. 2134 ◽  
Author(s):  
Rui Wang ◽  
Weijun Gao ◽  
Wangchongyu Peng
Keyword(s):  
Remote Sensing ◽  
Surface Temperature ◽  
Urban Heat Island ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Landsat 8 ◽  
Heat Island ◽  
Time Dimension ◽  
Urban Heat ◽  
Surface Urban Heat Island

Remote sensing technology plays an increasingly important role in land surface temperature (LST) research. However, various remote sensing data have spatial–temporal scales contradictions. In order to address this problem in LST research, the current study downscaled LST based on three different models (multiple linear regression (MLR), thermal sharpen (TsHARP) and random forest (RF)) from 1 km to 100 m to analyze surface urban heat island (SUHI) in daytime (10:30 a.m.) and nighttime (10:30 p.m.) of four seasons, based on Moderate Resolution Imaging Spectroradiometer (MODIS)/LST products and Landsat 8 Operational Land Imager (OLI). This research used an area (25 × 25 km) of Hangzhou with high spatial heterogeneity as the study area. R2 and RMSE were introduced to evaluate the conversion accuracy. Finally, we compared with similarly retrieved LST to verify the feasibility of the method. The results indicated the following. (1) The RF model was the most suitable to downscale MODIS/LST. The MLR model and the TsHARP model were not applicable for downscaling studies in highly heterogeneous regions. (2) From the time dimension, the prediction precision in summer and winter was clearly higher than that in spring and autumn, and that at night was generally higher than during the day. (3) The SUHI range at night was smaller than that during the day, and was mainly concentrated in the urban center. The SUHI of the research region was strongest in autumn and weakest in winter. (4) The validation results of the error distribution histogram indicated that the MODIS/LST downscaling method based on the RF model is feasible in highly heterogeneous regions.

Sign in / Sign up

Export Citation Format

Share Document