scholarly journals Analysis of the Spatial and Temporal Evolution of Land Cover and Heat Island Effects in Six Districts of Chongqing’s Main City

Sensors ◽  
2019 ◽  
Vol 19 (23) ◽  
pp. 5239
Author(s):  
Qin Lang ◽  
Wenping Yu ◽  
Mingguo Ma ◽  
Jianguang Wen
Keyword(s):  
Land Cover ◽  
Land Surface ◽  
Impervious Surface ◽  
Land Cover Type ◽  
Heat Island ◽  
Landsat Images ◽  
Cover Type ◽  
Island Effect ◽  
The Mean ◽  
Main City

The urban heat island effect has always been one of the hottest issues in urban development. In this study, Landsat images from the summers of 2001, 2004, 2009, 2014 and 2018 were used to identify land cover type in six districts of Chongqing’s main city. Land cover was categorized as water, vegetation or impervious surface with the object-oriented method. Land surface temperature (LST) data was calculated with the atmospheric radiation transfer equation method, and was then divided into different heat island intensity grades. Next, the spatial and temporal changes in land cover type and heat island effect were analyzed in the six districts. Center migration analysis and heat island coefficients were used to quantitatively reflect the spatiotemporal evolution relationship between land cover and heat island effect. All six districts exhibited a trend of expanding impervious surface, with a 419.38% increase from 2001 to 2018, and shrinking vegetation, with a 17.81% decrease from 2001 to 2018. Also from 2001 to 2018, Yuzhong District had the most significant heat island effect, with a heat island coefficient 0.35 higher than the mean value of the whole study area. The impervious surface center migrated in different directions in each district. Both the direction and the corresponding velocity of the impervious surface and heat island centers were tightly correlated, with a correlation coefficient of 0.53. Relative heat island coefficients (the difference from the mean) of water ranged from −2.08 to −1.17 in different districts. That of impervious surface ranged from 1.60 to 1.93, and that of vegetation ranged from −0.22 to 1.09. The internal heterogeneity of land cover and heat island effect in Chongqing’s main city was huge. This study quantitatively analyzed the evolution of the heat island effect in the study area to help provide each district with some targeted suggestions for future urban planning.

scholarly journals Cooling Effect of Different Land Cover Types: A Case Study in Xi’an and Xianyang, China

2021 ◽  
Vol 13 (3) ◽  
pp. 1099
Author(s):  
Yuhe Ma ◽  
Mudan Zhao ◽  
Jianbo Li ◽  
Jian Wang ◽  
Lifa Hu
Keyword(s):  
Land Cover ◽  
Surface Temperature ◽  
Urban Heat Island ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Cooling Effect ◽  
Heat Island ◽  
Land Cover Types ◽  
Island Effect ◽  
Urban Heat

One of the climate problems caused by rapid urbanization is the urban heat island effect, which directly threatens the human survival environment. In general, some land cover types, such as vegetation and water, are generally considered to alleviate the urban heat island effect, because these landscapes can significantly reduce the temperature of the surrounding environment, known as the cold island effect. However, this phenomenon varies over different geographical locations, climates, and other environmental factors. Therefore, how to reasonably configure these land cover types with the cooling effect from the perspective of urban planning is a great challenge, and it is necessary to find the regularity of this effect by designing experiments in more cities. In this study, land cover (LC) classification and land surface temperature (LST) of Xi’an, Xianyang and its surrounding areas were obtained by Landsat-8 images. The land types with cooling effect were identified and their ideal configuration was discussed through grid analysis, distance analysis, landscape index analysis and correlation analysis. The results showed that an obvious cooling effect occurred in both woodland and water at different spatial scales. The cooling distance of woodland is 330 m, much more than that of water (180 m), but the land surface temperature around water decreased more than that around the woodland within the cooling distance. In the specific urban planning cases, woodland can be designed with a complex shape, high tree planting density and large planting areas while water bodies with large patch areas to cool the densely built-up areas. The results of this study have utility for researchers, urban planners and urban designers seeking how to efficiently and reasonably rearrange landscapes with cooling effect and in urban land design, which is of great significance to improve urban heat island problem.

scholarly journals Machine Learning Simulation of Land Cover Impact on Surface Urban Heat Island Surrounding Park Areas

2021 ◽  
Vol 13 (22) ◽  
pp. 12678
Author(s):  
Dakota McCarty ◽  
Jaekyung Lee ◽  
Hyun Woo Kim
Keyword(s):  
Machine Learning ◽  
Land Cover ◽  
Urban Heat Island ◽  
Land Surface ◽  
Heat Island ◽  
Land Use Land Cover ◽  
Island Effect ◽  
Land Surface Temperatures ◽  
Urban Heat ◽  
The City

The urban heat island effect has been studied extensively by many researchers around the world with the process of urbanization coming about as one of the major culprits of the increasing urban land surface temperatures. Over the past 20 years, the city of Dallas, Texas, has consistently been one of the fastest growing cities in the United States and has faced rapid urbanization and great amounts of urban sprawl, leading to an increase in built-up surface area. In this study, we utilize Landsat 8 satellite images, Geographic Information System (GIS) technologies, land use/land cover (LULC) data, and a state-of-the-art methodology combining machine learning algorithms (eXtreme Gradient Boosted models, or XGBoost) and a modern game theoretic-based approach (Shapley Additive exPlanation, or SHAP values) to investigate how different land use/land cover classifications impact the land surface temperature and park cooling effects in the city of Dallas. We conclude that green spaces, residential, and commercial/office spaces have the largest impacts on Land Surface Temperatures (LST) as well as the Park’s Cooling Intensity (PCI). Additionally, we have found that the extent and direction of influence of these categories depends heavily on the surrounding area. By using SHAP values we can describe these interactions in greater detail than previous studies. These results will provide an important reference for future urban and park placement planning to minimize the urban heat island effect, especially in sprawling cities.

scholarly journals Extraction and Analysis of Finer Impervious Surface Classes in Urban Area

2021 ◽  
Vol 13 (3) ◽  
pp. 459
Author(s):  
Wenyue Liao ◽  
Yingbin Deng ◽  
Miao Li ◽  
Meiwei Sun ◽  
Ji Yang ◽  
...  
Keyword(s):  
Land Cover ◽  
Urban Areas ◽  
Landsat Imagery ◽  
Impervious Surface ◽  
Land Cover Type ◽  
Limited Information ◽  
Guangzhou City ◽  
Cover Type ◽  
Quantitative Analyses ◽  
Mean Square Errors

Impervious surfaces (IS), the most common land cover in urban areas, not only provide convenience to the city, but also exert significant negative environmental impacts, thereby affecting the ecological environment carrying capacity of urban agglomerations. Most of the current research considers IS as a single land-cover type, yet this does not fully reflect the complex physical characteristics of various IS types. Therefore, limited information for urban micro-ecology and urban fine management can be provided through one IS land-cover type. This study proposed a finer IS classification scheme and mapped the detailed IS fraction in Guangzhou City, China using Landsat imagery. The IS type was divided into seven finer classes, including blue steel, cement, asphalt, other impervious surface, and other metal, brick, and plastic. Classification results demonstrate that finer IS can be well extracted from the Landsat imagery as all root mean square errors (RMSE) are less than 15%. Specially, the accuracies of asphalt, plastic, and cement are better than other finer IS types with the RMSEs of 7.99%, 8.48%, and 9.92%, respectively. Quantitative analyses illustrate that asphalt, other impervious surface, and brick are the dominant IS types in the study area with the percentages of 9.68%, 6.27%, and 4.45%, respectively, and they are mainly located in Yuexiu, Liwan, Haizhu, and Panyu districts. These results are valuable for research into urban fine management and can support the detailed analysis of urban micro-ecology.

scholarly journals The Use of Land Cover Indices for Rapid Surface Urban Heat Island Detection from Multi-Temporal Landsat Imageries

2021 ◽  
Vol 10 (6) ◽  
pp. 416
Author(s):  
Nagihan Aslan ◽  
Dilek Koc-San
Keyword(s):  
Land Cover ◽  
Urban Heat Island ◽  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Heat Island ◽  
Landsat Images ◽  
Multi Temporal ◽  
Urban Heat ◽  
Surface Urban Heat Island

The aims of this study were to determine surface urban heat island (SUHI) effects and to analyze the land use/land cover (LULC) and land surface temperature (LST) changes for 11 time periods from the years 2002 to 2020 using Landsat time series images. Bursa, which is the fourth largest metropolitan city in Turkey, was selected as the study area, and Landsat multi-temporal images of the summer season were used. Firstly, the normalized difference vegetation index (NDVI), soil-adjusted vegetation index (SAVI), modified normalized difference water index (MNDWI) and index-based built-up index (IBI) were created using the bands of Landsat images, and LULC classes were determined by applying automatic thresholding. The LST values were calculated using thermal images and SUHI effects were determined. The results show that NDVI, SAVI, MNDWI and IBI indices can be used effectively for the determination of the urban, vegetation and water LULC classes for SUHI studies, with overall classification accuracies between 89.60% and 95.90% for the used images. According to the obtained results, generally the LST values increased for almost all land cover areas between the years 2002 and 2020. The SUHI magnitudes were computed by using two methods, and it was found that there was an important increase in the 18-year time period.

scholarly journals CONTRIBUTION INDEX OF LAND COVER AND LAND SURFACE TEMPERATURE CHANGES IN UPPER HILL NAIROBI, KENYA

2021 ◽  
Vol V-3-2021 ◽  
pp. 141-149
Author(s):  
P. W. Mwangi ◽  
F. N. Karanja ◽  
P. K. Kamau ◽  
S. C. Letema
Keyword(s):  
Land Cover ◽  
Urban Heat Island ◽  
Land Surface ◽  
Heat Island ◽  
Forest Land ◽  
Urban Heat ◽  
The Mean ◽  
Forest Land Cover ◽  
The Impact ◽  
Contribution Index

Abstract. Urban heat island is the difference in thermal temperature between rural and urban areas. The urbanization process alters the material type with impervious surfaces being absorbers of incoming radiation during the day and emitting it at night. The research involved the use of time-series satellite imagery from Sentinel, Landsat, ASTER and MODIS for the period 1986, 1995, 2000, 2005, 2011, 2015 and 2017 over the Upper Hill, Nairobi. Morning, afternoon and night land surface temperatures (LST) were calculated for each of these years and analyzed together with the land cover. The mean albedo was calculated to determine the relationship between each land cover and mean LST. The contribution index was calculated to determine whether a land contributed positively or negatively to the mean LST in Upper Hill. Results indicated that built-up land cover had increased from 1986 to 2017 by 0.86% per annum while forest land cover had decreased by 0.99% per annum. Sparse grassland had higher albedo and LST values of 0.81 and 27.9 °C respectively, whereas water had lower albedo and LST values of 0.09 and 25.1 °C. Water had the lowest mean LST during the day but highest mean LST in the afternoon and night in each of the years due to its high thermal capacity. Bare ground tends to have a higher contribution index compared to other land covers, while forest land cover has a negative contribution index, indicating the impact land cover types have on LST and the urban heat island effect.

scholarly journals Land Use and Land Cover Change Assessment in the Context of Flood Hazard in Lagos State, Nigeria

Water ◽  
2021 ◽  
Vol 13 (8) ◽  
pp. 1105
Author(s):  
Dorcas Idowu ◽  
Wendy Zhou
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Flood Hazard ◽  
The State ◽  
Land Cover Type ◽  
Lulc Change ◽  
Cover Type ◽  
Lulc Changes ◽  
Lagos State ◽  
Very High

Incessant flooding is a major hazard in Lagos State, Nigeria, occurring concurrently with increased urbanization and urban expansion rate. Consequently, there is a need for an assessment of Land Use and Land Cover (LULC) changes over time in the context of flood hazard mapping to evaluate the possible causes of flood increment in the State. Four major land cover types (water, wetland, vegetation, and developed) were mapped and analyzed over 35 years in the study area. We introduced a map-matrix-based, post-classification LULC change detection method to estimate multi-year land cover changes between 1986 and 2000, 2000 and 2016, 2016 and 2020, and 1986 and 2020. Seven criteria were identified as potential causative factors responsible for the increasing flood hazards in the study area. Their weights were estimated using a combined (hybrid) Analytical Hierarchy Process (AHP) and Shannon Entropy weighting method. The resulting flood hazard categories were very high, high, moderate, low, and very low hazard levels. Analysis of the LULC change in the context of flood hazard suggests that most changes in LULC result in the conversion of wetland areas into developed areas and unplanned development in very high to moderate flood hazard zones. There was a 69% decrease in wetland and 94% increase in the developed area during the 35 years. While wetland was a primary land cover type in 1986, it became the least land cover type in 2020. These LULC changes could be responsible for the rise in flooding in the State.

Land Cover Type and Fire in Portugal: Do Fires Burn Land Cover Selectively?

2005 ◽  
Vol 20 (6) ◽  
pp. 661-673 ◽  
Author(s):  
Maria C.S. Nunes ◽  
Maria J. Vasconcelos ◽  
José M.C. Pereira ◽  
Nairanjana Dasgupta ◽  
Richard J. Alldredge ◽  
...  
Keyword(s):  
Land Cover ◽  
Land Cover Type ◽  
Cover Type
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