Extraction of High-Precision Urban Impervious Surfaces from Sentinel-2 Multispectral Imagery via Modified Linear Spectral Mixture Analysis.

This study explores the performance of Sentinel-2A Multispectral Instrument (MSI) imagery for extracting urban impervious surface using a modified linear spectral mixture analysis (MLSMA) method. Sentinel-2A MSI provided 10 m red, green, blue, and near-infrared spectral bands, and 20 m shortwave infrared spectral bands, which were used to extract impervious surfaces. We aimed to extract urban impervious surfaces at a spatial resolution of 10 m in the main urban area of Guangzhou, China. In MLSMA, a built-up image was first extracted from the normalized difference built-up index (NDBI) using the Otsu's method; the high-albedo, low-albedo, vegetation, and soil fractions were then estimated using conventional linear spectral mixture analysis (LSMA). The LSMA results were post-processed to extract high-precision impervious surface, vegetation, and soil fractions by integrating the built-up image and the normalized difference vegetation index (NDVI). The performance of MLSMA was evaluated using Landsat 8 Operational Land Imager (OLI) imagery. Experimental results revealed that MLSMA can extract the high-precision impervious surface fraction at 10 m with Sentinel-2A imagery. The 10 m impervious surface map of Sentinel-2A is capable of recovering more detail than the 30 m map of Landsat 8. In the Sentinel-2A impervious surface map, continuous roads and the boundaries of buildings in urban environments were clearly identified.

Assessment of land use/land cover changes and its effect on land surface temperature using remote sensing techniques in Southern Punjab, Pakistan.

Land surface temperature (LST) is defined as a phenomenon which shows that microclimate of an urban system gets heated much faster than its surrounding rural climates. The expansion of buildings has a noteworthy influence on land use/land cover (LULC) due to conversion of vegetation land into commercial and residential areas and their associated infrastructure by which LST is accelerated. The objective of the research was to study the impact of changes in LULC on LST of Southern Punjab (Pakistan) through remote sensing (RS) data. Landsat images of 30-year duration (1987, 1997, 2007 and 2017) were employed for identifying vegetation indices and LST in the study region. These images also helped to work out normalized difference water index (NDWI) and normalized difference built-up index (NDBI) maps. There was an increase from 29620 (3.63 %) to 88038 ha (10.8 %) in built-up area over the 30 years. LST values were found in the range 12-42 °C, 11-44 °C, 11-45 °C and 11-47 °C in the years 1987, 1997, 2007 and 2017, respectively. Regression coefficients (R2) 0.81, 0.78, 0.84 and 0.76 were observed between NDVI and LST in the corresponding years respectively. Our study showed that NDVI and NDWI were negatively correlated with less LST; however, NDBI showed positive correlation with high LST. Our study gives critical information of LULC and LST and will be a helpful tool for policy makers for developing effective policies in managing land resources.

The effects of climate on land use/cover: a case study in Turkey by using remote sensing data.

Economic and industrial development results in worldwide population concentration in cities, leading to increases in impervious surfaces. Thus, the surface temperatures increase and cities are exposed to the urban heat island effect. This study analyzed the changes in the urban heat island effect in the 30 years (from 1990 to 2021) in the central district of Bartin. In this sense, there were two primary goals. Firstly, land use/land cover change, land surface temperature (LST), normalized difference built-up index (NDBI), and normalized difference vegetation index (NDVI) were analyzed by using remote sensing methods between 1990 and 2021. Secondly, a linear regression analysis was conducted to determine the factors associated with LST, NDVI, and NDBI. The study results revealed increases in urban surfaces and the average land surface temperature values in the past 30 years and showed a decline in the vegetation. Regression analysis results indicated a strong negative relationship between LST and NDVI and a strong positive relationship between LST and NDBI. It was also found a robust negative relationship between NDBI and NDVI. In light of the findings, it was stated that the amount of open and green areas should be increased in order to prevent the negative effects of the urban heat island in the central district of Bartin. For this purpose, it has been proposed to encourage green roof systems throughout the city, to create city parks and to create a green belt system. In addition, as a result of the study, the importance of preventing forest destruction caused by over settlement in the Mountains, which is one of the rare habitats of the world with different plant species, was emphasized. In this sense, legal sanctions should be employed to protect those areas and prevent construction.

Using GIS tools to detect the land use/land cover changes during forty years in Lodhran District of Pakistan.

Land use/land cover (LULC) change has serious implications for environment as LULC is directly related to land degradation over a period of time and results in many changes in the environment. Monitoring the locations and distributions of LULC changes is important for establishing links between regulatory actions, policy decisions, and subsequent LULC activities. The normalized difference vegetation index (NDVI) has the potential ability to identify the vegetation features of various eco-regions and provides valuable information as a remote sensing tool in studying vegetation phenology cycles. Similarly, the normalized difference built-up index (NDBI) may be used for quoting built-up land. This study aims to detect the pattern of LULC, NDBI, and NDVI change in Lodhran district, Pakistan, from the Landsat images taken over 40 years, considering four major LULC types as follows: water bodies, built-up area, bare soil, and vegetation. Supervised classification was applied to detect LULC changes observed over Lodhran district as it explains the maximum likelihood algorithm in software ERDAS imagine 15. Most farmers (46.6%) perceived that there have been extreme changes of onset of temperature, planting season, and less precipitation amount in Lodhran district in the last few years. In 2017, building areas increased (4.3%) as compared to 1977. NDVI values for Lodhran district were highest in 1977 (up to + 0.86) and lowest in 1997 (up to - 0.33). Overall accuracy for classification was 86% for 1977, 85% for 1987, 86% for 1997, 88% for 2007, and 95% for 2017. LULC change with soil types, temperature, and NDVI, NDBI, and slope classes was common in the study area, and the conversions of bare soil into vegetation area and built-up area were major changes in the past 40 years in Lodhran district. Lodhran district faces rising temperatures, less irrigation water, and low rainfall. Farmers are aware of these climatic changes and are adapting strategies to cope with the effects but require support from government.