Predicting changes in land use/land cover and seasonal land surface temperature using multi-temporal landsat images in the northwest region of Bangladesh.

Land use/land cover (LULC) variations are accelerated by rapid urbanization and significantly impacted global Land Surface Temperature (LST). The dynamic increase in LST results in the Urban Heat Island (UHI) effect. In this study, future LULC change scenarios, seasonal (summer & winter) LST variations, and LST distribution over different LULC classes were predicted using Landsat satellite images for 1999, 2009, and 2019 in Rajshahi District, Bangladesh. Cellular Automata (CA) and Artificial Neural Network (ANN) procedures were used to predict the LULC changes and seasonal LST variations for 2029 and 2039. In addition, Focus Group Discussions (FGDs) and Key Informants Interviews (KIIs) were conducted to identify the possible impacts of LULC change, LST shifts, and climate change on agricultural productivity and developed a sustainable land use management plan for the study area. Validation of the CA model demonstrated an excellent accuracy with a kappa value of 0.82. Similarly, the ANN model's validation using Mean Square Error (0.523 and 0.796 for summer) and Correlation coefficient (0.6023 and 0.831 for winter) values demonstrated a good prediction accuracy. The LULC prediction result indicated that the built-up area will be expanded by 58.03 km2 and 79.90 km2, respectively, from 2019 to 2029 and 2039. The predicted seasonal LST indicated that in 2029 and 2039, more than 23.30 % and 50.46 % of the summer and 3.02 % and 13.02 % of the winter seasons will likely be experienced LSTs greater than 35 °C. The results of public participation exposed that changes in LULC classes, variations in LST, and climate change significantly impact the regional biodiversity (loss of farmland and water bodies), reduce agricultural productivity, and increase extreme weather events (flood, heavy rainfall, and cold/warm temperature). This study provides the useful guidelines for agricultural officers, urban planners, and environmental engineers to understand the spatial configurations of built-up area enlargement and provide effective policy measures to conserve farming lands to ensure environmental sustainability.

Kafy AA ，Faisal AA ，Al Rakib A ，Roy S ，Ferdousi J ，Raikwar V ，Kona MA ，Fatin SMAA ... -  《Heliyon》

Analysis and simulation of land cover changes and their impacts on land surface temperature in a lower Himalayan region.

Rapid urbanization is changing the existing patterns of Land Use Land Cover (LULC) globally which is consequently increasing the Land Surface Temperature (LST) in many regions. Present study was focused on estimating the current and simulating the future LULC and LST trends in the alpine environment of lower Himalayan region of Pakistan. Past patterns of LULC and LST were identified through the Support Vector Machine (SVM) and multi-spectral Landsat satellite images during 1987-2017 data period. The Cellular automata (CA) model and Artificial Neural Network (ANN) were applied to simulate future (years 2032 and 2047) LULC and LST changes, respectively, using their past patterns. CA model was validated for the simulated and the estimated LULC for the year 2017 with an overall Kappa (K) value of 0.77 using validation modules in QGIS and IDRISI software. ANN method was validated by correlating the observed and simulated LST for the year 2017 with correlation coefficient (R) and Mean Square Error (MSE) values of 0.81 and 0.51, respectively. Results indicated a change in the LULC and LST for instance the built-up area was increased by 4.43% while agricultural area and bare soil were reduced by 2.74% and 4.42%, respectively, from 1987 to 2017. The analysis of LST for different LULC classes indicated that built-up area has highest temperature followed by barren, agriculture and vegetation surfaces. Simulation of future LULC and LST showed that the built-up area will be increased by 2.27% (in 2032) and 4.13% (in 2047) which led 42% (in 2032) and 60% (in 2047) of the study area as compared to 26% area (in 2017) to experience LST greater than 27 °C. A strong correlation between built-up area changes and LST was thus found signifying major challenge to urban planners mitigating the consequent of Urban Heat Island (UHI) phenomenon. It is suggested that future urban planning should focus on urban plantation to counter UHI phenomena in the region of lower Himalayas.

Ullah S ，Ahmad K ，Sajjad RU ，Abbasi AM ，Nazeer A ，Tahir AA ... -  《-》

Use of cellular automata-based artificial neural networks for detection and prediction of land use changes in North-Western Dhaka City.

The purpose of this study was to analyze the trend of change in land use land cover (LULC) and land surface temperature (LST) in Mirpur and its surrounding area over the last 30 years using Landsat satellite images and remote sensing indices, and to develop relationships between LULC types and LST, as well as to analyze their impact on local warming. Using this analyzed data, a further projection of LULC and LST change over the next two decades was made. From 1989 to 2019, 5-year intervals of Landsat 4-5 TM and Landsat 8 OLI images were utilized to track the relationship between LULC changes and LST. The modeled LST was validated with MODIS-derived LST within the study area. Cellular automata-based artificial neural network (CA-ANN) algorithm was used to model the LULC and LST maps for the year 2039. The Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Built-up Index (NDBI) were analyzed to determine their link with LST. The relation between LST and LULC types indicates that built-up area raises LST by substituting non-evaporating surfaces for natural vegetation. The average surface temperature was increasing steadily for the last 30 years. For the year 2019, it was determined that roughly 86% of total land area has been converted to built-up area and that 89% of land area had an LST greater than 28 °C. According to the study, if the current trend continues, 72% of the Mirpur area is predicted to see temperatures near 32 °C in 2039. Additionally, LST had a significant positive association with NDBI and a negative correlation with NDVI. The overall accuracy of LULC was greater than 90%, with a kappa coefficient of 0.83. The study may assist urban planners and environmental engineers in comprehending and recommending effective policy measures and plans to mitigate the consequences of LULC.

Rahman F ，Rahman MTU 《-》

Trends and projections of land use land cover and land surface temperature using an integrated weighted evidence-cellular automata (WE-CA) model.

Land use land cover (LULC) change has become a major concern for biodiversity, ecosystem alteration, and modifying the climatic pattern especially land surface temperature (LST). The present study assessed past and predicted future LULC and LST change in the Swabi District of Pakistan. LULC maps were generated from satellite data for years 1987, 2002, and 2017 using supervised classification. Mean LST and its areal change were estimated for different LULC classes from thermal bands of satellite images. LULC and LST were projected for the year 2047 using the integrated weighted evidence-cellular automata (WE-CA) model and a regression equation developed in this study, respectively. LULC change revealed an increase of > 5% in the built-up while a decrease in the agricultural area by ~ 9%. There was an increase of ~ 63% area in the LST class ≥ 27 °C which may create urban heat island (UHI). Simulation results indicated that the built-up area will further be increased by ~ 3% until 2047. Area associated with LST class > 30 °C indicated a further increase of ~ 38% till 2047 with reference to year 2017. Findings of this study suggested proper utilization of LULC in order to mitigate the creation of UHIs associated with urbanization and built-up areas.

Khan M ，Tahir AA ，Ullah S ，Khan R ，Ahmad K ，Shahid SU ，Nazir A ... -  《-》

Modelling future land use land cover changes and their impacts on urban heat island intensity in Guangzhou, China.

During rapid urbanization in developing countries, changes in land use and land cover (LULC) can significantly alter urban land surface temperatures (LST), exacerbating the urban heat island (UHI) effect and degrading the outdoor environment. In this study, taking Guangzhou, China, as an example, we used Landsat series satellite data from 1992 to 2022, classified the LULC of the study area by the Support Vector Machine (SVM) method, estimated the LST of the area by the mono-window algorithm, and classified the LST of the study area into five UHI intensity classes based on the normalized values of the LST, and explored the influence of the LULC on the distribution of the UHI intensity. The CA-ANN (cellular automata-artificial neural network) model in QGIS software was employed to forecast the distribution of LULC and UHI intensity in Guangzhou for 2032. The findings reveal a strong correlation between UHI intensity and LULC, with water bodies and vegetation primarily exhibiting low and sub-low temperatures, while urban areas exhibit sub-high and high temperatures. The prediction results show that, according to the current development trend, compared with 1992, the water body and vegetation cover in 2032 will decrease by 46.97% and 34.24%, the building land will increase by 263.71%, and the sub-high and high temperature areas will increase by 127.76% and 375.92%. By analysing the spatial and temporal changes in LULC and its relationship with the distribution of UHI intensity during urbanization, this study assists government administrations and urban planners in devising sensible urban development strategies and implementing effective measures to plan LULC rationally. This approach aims to mitigate the impacts of the urban heat island and foster sustainable urbanization.

Xiang X ，Zhai Z ，Fan C ，Ding Y ，Ye L ，Li J ... -  《-》