scholarly journals Land Cover Changes Based on Cellular Automata for Land Surface Temperature in Semarang Regency

2021 ◽  
Vol 6 (3) ◽  
pp. 301
Author(s):  
Fahrudin Hanafi ◽  
Dinda Putri Rahmadewi ◽  
Fajar Setiawan
Keyword(s):  
Markov Chain ◽  
Cellular Automata ◽  
Land Cover ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Land Cover Changes ◽  
Creative Commons ◽  
Landsat 7 ◽  
Land Cover Maps

Land cover changes based on cellular automata for surface temperature in Semarang Regency has increased significantly due to the continuous rise in its population. Therefore, this study aims to identify, analyze and predict multitemporal land cover changes and surface temperature distribution in 2028. Data on the land cover map were obtained from Landsat 7 and 8 based on supervised classification, while Land Surface Temperature (LST) was calculated from its thermal bands. The collected data were analyzed for accuracy through observation, while Cellular Automata - Markov Chain was used to predict the associated changes in 2028. The result showed that there are 4 land cover maps with 5-year intervals from 2003 to 2018 at an accuracy of more than 85%. Furthermore, the existing land covers were dominated by forest with decreasing trend, while the built-up area continuously increased. The existing Land surface temperature range from 20.6°C to 36.6°C, at an average of 28.2°C and a yearly increase of 0.07°C. The temperature changes are positively correlated with the occurrence of land conversion. Land cover predictions for 2028 show similar forest dominance, with a 23,4% built-up area at a surface temperature of 28.9°C. Keywords: Land cover change; Cellular Automata-Markov Chain; Land Surface Temperature Copyright (c) 2021 Geosfera Indonesia and Department of Geography Education, University of Jember     This work is licensed under a Creative Commons Attribution-Share A like 4.0 International License

scholarly journals CA-Markov Chain Analysis of Seasonal Land Surface Temperature and Land Use Land Cover Change Using Optical Multi-Temporal Satellite Data of Faisalabad, Pakistan

2020 ◽  
Vol 12 (20) ◽  
pp. 3402 ◽  
Author(s):  
Aqil Tariq ◽  
Hong Shu
Keyword(s):  
Land Use ◽  
Markov Chain ◽  
Cellular Automata ◽  
Land Cover ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Winter Temperature ◽  
Markov Chain Analysis ◽  
Chain Analysis

Cellular Automata models are used for simulating spatial distributions and Markov Chain models are used for simulating temporal changes. The main aim of this study is to investigate the effect of urban growth on Faisalabad. This research is aimed at predicting seasonal Land-Surface-Temperature (LST) as well as Land-Use and Land-cover (LULC) with a Cellular-Automata-Markov-Chain (CA-Markov-Chain). Landsat 5, 7 and 8 data were used for mapping seasonal LULC and LST distributions during the months of May and November for the years 1990, 1998, 2004, 2008, 2013 and 2018. A CA-Markov-Chain was developed for simulating long-term landscape changes at 10-year time steps from 2018 to 2048. Furthermore, surface temperature during summers and winters were predicted well by Urban Index (UI), a non-vegetation index, demonstrating the highest correlation of R2 = 0.8962 and R2 = 0.9212 with respect to retrieved summer and winter surface temperature. Through the CA-Markov Chain analysis, we can expect that high density and low-density residential areas will grow from 22.23 to 24.52 km2 and from 108.53 to 122.61 km2 in 2018 and 2048, as inferred from the changes occurred from 1990 to 2018. Considering UI as the predictor of seasonal LST, we predicted that the summer and winter temperature 24–28 °C and 14–16 °C and regions would decrease in coverage from 10.75 to 3.14% and from 8.81 to 3.47% between 2018 and 2048, while the summer and winter temperature 35–42 °C and winter 26–32 °C regions will increase in the proportion covered from 12.69 to 24.17% and 6.75–15.15% of city.

scholarly journals Assessing the impact of land cover changes on land surface temperature and the relation to urban heat island in Makassar City, South Sulawesi

2021 ◽  
Vol 879 (1) ◽  
pp. 012010
Author(s):  
A S Liong ◽  
N Nasrullah ◽  
B Sulistyantara
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Surface Temperature ◽  
Urban Heat Island ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Land Cover Changes ◽  
Heat Island ◽  
South Sulawesi ◽  
Urban Heat

Abstract Makassar City, the capital of South Sulawesi Province, is the largest metropolitan city in the eastern part of Indonesia, with a population development rate of 1.19% in 2019. An increase in population impacts city development and results in land use and land cover changes. Changes in land use and land cover pattern bring impact to Land Surface Temperature (LST). This study examines land cover’s influence on land surface temperature in Makassar City using multi-temporal satellite data. Land cover and LST data were extracted using Landsat 7 and Landsat 8 over the period of 1999, 2009, and 2019. The result shows that the highest increase in land cover changed was a built-up area of 13.1%, and vegetation decreased by 8.6%. The change in average LST value in the last 20 years was 0.39°C with the highest LST distribution areas was in 30-32°C and 32-34°C classes. The result of LST analysis in 2019 shows that the Urban Heat Island phenomenon has occurred in Makassar in the downtown area and several areas with the densely built-up area. With an overview of the UHI phenomenon in Makassar, the government is expected to raise public awareness of this phenomenon so that preventive actions can be taken, so the effects of UHI do not spread more widely.

scholarly journals Expansion of oil palm and other cash crops causes an increase of the land surface temperature in the Jambi province in Indonesia

2017 ◽  
Vol 14 (20) ◽  
pp. 4619-4635 ◽  
Author(s):  
Clifton R. Sabajo ◽  
Guerric le Maire ◽  
Tania June ◽  
Ana Meijide ◽  
Olivier Roupsard ◽  
...  
Keyword(s):  
Land Cover ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Oil Palm ◽  
Land Surface ◽  
Land Cover Changes ◽  
Land Transformation ◽  
Clear Cut ◽  
The Past ◽  
Cash Crops

Abstract. Indonesia is currently one of the regions with the highest transformation rate of land surface worldwide related to the expansion of oil palm plantations and other cash crops replacing forests on large scales. Land cover changes, which modify land surface properties, have a direct effect on the land surface temperature (LST), a key driver for many ecological functions. Despite the large historic land transformation in Indonesia toward oil palm and other cash crops and governmental plans for future expansion, this is the first study so far to quantify the impacts of land transformation on the LST in Indonesia. We analyze LST from the thermal band of a Landsat image and produce a high-resolution surface temperature map (30 m) for the lowlands of the Jambi province in Sumatra (Indonesia), a region which suffered large land transformation towards oil palm and other cash crops over the past decades. The comparison of LST, albedo, normalized differenced vegetation index (NDVI) and evapotranspiration (ET) between seven different land cover types (forest, urban areas, clear-cut land, young and mature oil palm plantations, acacia and rubber plantations) shows that forests have lower surface temperatures than the other land cover types, indicating a local warming effect after forest conversion. LST differences were up to 10.1 ± 2.6 °C (mean ± SD) between forest and clear-cut land. The differences in surface temperatures are explained by an evaporative cooling effect, which offsets the albedo warming effect. Our analysis of the LST trend of the past 16 years based on MODIS data shows that the average daytime surface temperature in the Jambi province increased by 1.05 °C, which followed the trend of observed land cover changes and exceeded the effects of climate warming. This study provides evidence that the expansion of oil palm plantations and other cash crops leads to changes in biophysical variables, warming the land surface and thus enhancing the increase of the air temperature because of climate change.

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