scholarly journals Land Cover Change Dynamics and their Impacts on Thermal Environment of Dadri Block, Gautam Budh Nagar, India

2020 ◽  
Vol 13 (2) ◽  
pp. 1-13
Author(s):  
Sushma Shastri ◽  
Prafull Singh ◽  
Pradipika Verma ◽  
Praveen Kumar Rai ◽  
A. P. Singh
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Land Surface ◽  
Large Scale ◽  
Thermal Environment ◽  
Negative Impact ◽  
Information Source ◽  
Physical Parameters ◽  
Change Analysis ◽  
Landsat Satellite

AbstractLand use / land cover (LULC) has been considered as one of the important bio-physical parameters and have significant affect on local environmental change, particularly increasing anthropogenic temperature. Remote sensing images from Landsat series satellites are a major information source for LULC change analysis. In the present investigation, long term changes in LULC and its negative impact on land surface temperature (LST) were analyzed using multi-temporal Landsat satellite images between 2000 to 2016. firstly LULC of the study area has been classified and temporal changes in land use classes were quantify, and observed that in most of the land use classes such as vegetation (-1.28 %), water bodies (-1.65 %), agriculture (-3.52) and open land (-2.43 %) have shown negative change, however large scale positive changes in built-up area (+8.87 %) has been observed during the analysis, which is mainly due to continuous urbanization and growth of population in the area. The classified thermal images from the same period also show mean temperature of the area has increased by 1.60 °C since last 16 years. The observation from the present study reveals that due to the large-scale land use change practices in urban and peri-urban area witnessed for the rising temperature due to loss natural vegetation and other natural resources.

scholarly journals Assessment and simulation of land use and land cover change impacts on the land surface temperature of Chaoyang District in Beijing, China

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e9115 ◽  
Author(s):  
Muhammad Amir Siddique ◽  
Liu Dongyun ◽  
Pengli Li ◽  
Umair Rasool ◽  
Tauheed Ullah Khan ◽  
...  
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Surface Temperature ◽  
Land Cover Change ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Rapid Urbanization ◽  
K Value ◽  
Vegetation Water ◽  
Landsat Satellite

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. The present study is focused on estimating current and simulating future LULC and LST trends in the urban environment of Chaoyang District, Beijing. Past patterns of LULC and LST were identified through the maximum likelihood classification (MLC) method and multispectral Landsat satellite images during the 1990–2018 data period. The cellular automata (CA) and stochastic transition matrix of the Markov model were applied to simulate future (2025) LULC and LST changes, respectively, using their past patterns. The CA model was validated for the simulated and estimated LULC for 1990–2018, with an overall Kappa (K) value of 0.83, using validation modules in IDRISI software. Our results indicated that the cumulative changes in built-up to vegetation area were 74.61 km2 (16.08%) and 113.13 km2 (24.38%) from 1990 to 2018. The correlation coefficient of land use and land cover change (LULCC), including vegetation, water bodies and built-up area, had values of r =  − 0.155 (p > 0.005), −0.809 (p = 0.000), and 0.519 (p > 0.005), respectively. The results of future analysis revealed that there will be an estimated 164.92 km2 (−12%) decrease in vegetation area, while an expansion of approximately 283.04 km2 (6% change) will occur in built-up areas from 1990 to 2025. This decrease in vegetation cover and expansion of settlements would likely cause a rise of approximately ∼10.74 °C and ∼12.66 °C in future temperature, which would cause a rise in temperature (2025). The analyses could open an avenue regarding how to manage urban land cover patterns to enhance the resilience of cities to climate warming. This study provides scientific insights for environmental development and sustainability through efficient and effective urban planning and management in Beijing and will also help strengthen other research related to the UHI phenomenon in other parts of the world.

scholarly journals The Potential of Open Geodata for Automated Large-Scale Land Use and Land Cover Classification

2019 ◽  
Vol 11 (19) ◽  
pp. 2249 ◽  
Author(s):  
Patrick Leinenkugel ◽  
Ramona Deck ◽  
Juliane Huth ◽  
Marco Ottinger ◽  
Benjamin Mack
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Large Scale ◽  
Natura 2000 ◽  
High Potential ◽  
Training Data ◽  
Data Generation ◽  
Data Set ◽  
Similar Accuracy ◽  
Landsat Satellite

This study examines the potential of open geodata sets and multitemporal Landsat satellite data as the basis for the automated generation of land use and land cover (LU/LC) information at large scales. In total, six openly available pan-European geodata sets, i.e., CORINE, Natura 2000, Riparian Zones, Urban Atlas, OpenStreetMap, and LUCAS in combination with about 1500 Landsat-7/8 scenes were used to generate land use and land cover information for three large-scale focus regions in Europe using the TimeTools processing framework. This fully automated preprocessing chain integrates data acquisition, radiometric, atmospheric and topographic correction, spectral–temporal feature extraction, as well as supervised classification based on a random forest classifier. In addition to the evaluation of the six different geodata sets and their combinations for automated training data generation, aspects such as spatial sampling strategies, inter and intraclass homogeneity of training data, as well as the effects of additional features, such as topography and texture metrics are evaluated. In particular, the CORINE data set showed, with up to 70% overall accuracy, high potential as a source for deriving dominant LU/LC information with minimal manual effort. The intraclass homogeneity within the training data set was of central relevance for improving the quality of the results. The high potential of the proposed approach was corroborated through a comparison with two similar LU/LC data sets, i.e., GlobeLand30 and the Copernicus High Resolution Layers. While similar accuracy levels could be observed for the latter, for the former, accuracy was considerable lower by about 12–24%.

scholarly journals Land Use-Land Cover Changed Analysis in Ogan Watershed, South SumatraDuring 2014-2019 Period

2021 ◽  
Vol 5 (2) ◽  
Author(s):  
Utari Hikmah Pratiwi ◽  
Eddy brahim ◽  
Edward Saleh
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Management Practices ◽  
Negative Impact ◽  
Image Interpretation ◽  
Visual Image ◽  
Infrastructure Development ◽  
Land Use Land Cover ◽  
Sensing Technology ◽  
Landsat Satellite

Land use-land cover (LULC) is one of the indicators commonly used in monitoring the quality of natural resources. Mostof the Ogan watershed is a peat ecosystem that plays an important role in maintaining the balance of the ecosystem andwater supply. During the 2014-2019 period the Ogan watershed experienced several wildfires and infrastructure development,particularly freeways. This study aims to analyze changes in the LULC in the Ogan watershed during the 2014-2019 period.LULC analysis uses remote sensing technology by utilizing Sentinel and Landsat satellite imagery data. LULC identificationused the visual image interpretation method, while LULC changes were analyzed using the GIS technique with the spatialoverlay method. The results showed that changes in LULC led to the LULC managed class, where the increase in areaoccurred in the rubber and oil palm plantation classes. Meanwhile, the highest reduction in area occurred in the dry landforest class. Changes in LULC that occurred during the observation period had a negative impact on the watershed in theform of land degradation, decreased levels of biodiversity and increased fire vulnerability. Based on these results, land use inthe Ogan watershed needs to be controlled and land management practices must pay attention to environmental sustainabilityaspects.

scholarly journals Spatiotemporal Influence of Land Use/Land Cover Change Dynamics on Surface Urban Heat Island: A Case Study of Abuja Metropolis, Nigeria

2021 ◽  
Vol 10 (5) ◽  
pp. 272
Author(s):  
Auwalu Faisal Koko ◽  
Wu Yue ◽  
Ghali Abdullahi Abubakar ◽  
Akram Ahmed Noman Alabsi ◽  
Roknisadeh Hamed
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Land Surface ◽  
Urban Expansion ◽  
Urban Heat Islands ◽  
Sustainable Land Use ◽  
Land Use Land Cover ◽  
Urban Centers ◽  
Rapid Urbanization ◽  
Urban Heat

Rapid urbanization in cities and urban centers has recently contributed to notable land use/land cover (LULC) changes, affecting both the climate and environment. Therefore, this study seeks to analyze changes in LULC and its spatiotemporal influence on the surface urban heat islands (UHI) in Abuja metropolis, Nigeria. To achieve this, we employed Multi-temporal Landsat data to monitor the study area’s LULC pattern and land surface temperature (LST) over the last 29 years. The study then analyzed the relationship between LULC, LST, and other vital spectral indices comprising NDVI and NDBI using correlation analysis. The results revealed a significant urban expansion with the transformation of 358.3 sq. km of natural surface into built-up areas. It further showed a considerable increase in the mean LST of Abuja metropolis from 30.65 °C in 1990 to 32.69 °C in 2019, with a notable increase of 2.53 °C between 2009 and 2019. The results also indicated an inverse relationship between LST and NDVI and a positive connection between LST and NDBI. This implies that urban expansion and vegetation decrease influences the development of surface UHI through increased LST. Therefore, the study’s findings will significantly help urban-planners and decision-makers implement sustainable land-use strategies and management for the city.

scholarly journals Effects of Cropland Expansion on Temperature Extremes in Western India from 1982 to 2015

Land ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 489
Author(s):  
Jinxiu Liu ◽  
Weihao Shen ◽  
Yaqian He
Keyword(s):  
Land Use ◽  
Heat Flux ◽  
Land Cover ◽  
Minimum Temperature ◽  
Negative Impact ◽  
Maximum Temperature ◽  
Daily Minimum Temperature ◽  
Daily Maximum ◽  
Temperature Extremes ◽  
Western India

India has experienced extensive land cover and land use change (LCLUC). However, there is still limited empirical research regarding the impact of LCLUC on climate extremes in India. Here, we applied statistical methods to assess how cropland expansion has influenced temperature extremes in India from 1982 to 2015 using a new land cover and land use dataset and ECMWF Reanalysis V5 (ERA5) climate data. Our results show that during the last 34 years, croplands in western India increased by ~33.7 percentage points. This cropland expansion shows a significantly negative impact on the maxima of daily maximum temperature (TXx), while its impacts on the maxima of daily minimum temperature and the minima of daily maximum and minimum temperature are limited. It is estimated that if cropland expansion had not taken place in western India over the 1982 to 2015 period, TXx would likely have increased by 0.74 (±0.64) °C. The negative impact of croplands on reducing the TXx extreme is likely due to evaporative cooling from intensified evapotranspiration associated with croplands, resulting in increased latent heat flux and decreased sensible heat flux. This study underscores the important influences of cropland expansion on temperature extremes and can be applicable to other geographic regions experiencing LCLUC.

Modelling future land use land cover changes and their impacts on land surface temperatures in Rajshahi, Bangladesh

2020 ◽  
Vol 18 ◽  
pp. 100314 ◽  
Author(s):  
Abdulla - Al Kafy ◽  
Md. Shahinoor Rahman ◽  
Abdullah-Al- Faisal ◽  
Mohammad Mahmudul Hasan ◽  
Muhaiminul Islam
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Land Surface ◽  
Land Cover Changes ◽  
Land Use Land Cover ◽  
Surface Temperatures ◽  
Land Surface Temperatures
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