LUCC and landscape pattern variation of wetlands in warm-rainy Southern China over two decades

Wang

et al. 2018

Sensors

Short-term characteristics of urban land cover change have been observed and reported from satellite images, although urban landscapes are mainly influenced by anthropogenic factors. These short-term changes in urban areas are caused by rapid urbanization, seasonal climate changes, and phenological ecological changes. Quantifying and understanding these short-term characteristics of changes in various land cover types is important for numerous urban studies, such as urbanization assessments and management. Many previous studies mainly investigated one study area with insufficient datasets. To more reliably and confidently investigate temporal variation patterns, this study employed Fourier series to quantify the seasonal changes in different urban land cover types using all available Landsat images over four different cities, Melbourne, Sao Paulo, Hamburg, and Chicago, within a five-year period (2011–2015). The overall accuracy was greater than 86% and the kappa coefficient was greater than 0.80. The R-squared value was greater than 0.80 and the root mean square error was less than 7.2% for each city. The results indicated that (1) the changing periods for water classes were generally from half a year to one and a half years in different areas; and, (2) urban impervious surfaces changed over periods of approximately 700 days in Melbourne, Sao Paulo, and Hamburg, and a period of approximately 215 days in Chicago, which was actually caused by the unavoidable misclassification from confusions between various land cover types using satellite data. Finally, the uncertainties of these quantification results were analyzed and discussed. These short-term characteristics provided important information for the monitoring and assessment of urban areas using satellite remote sensing technology.

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Quantifying Short-Term Urban Land Cover Change with Time Series Landsat Data: A Comparison of Four Different Cities

Wang

et al. 2018

Sensors

“…In this paper, the environments in April and July are selected for simulation, corresponding to the spring and summer seasons of the year, respectively (Fan et al 2015, Gohain et al 2020, Kowsalya et al 2020, Li et al 2016, Shen et al 2020, Song et al 2010). In the simulation process for each season, 2 different sets of urban subsurface simulations are conducted: the rst set of experiments uses the 1980 LULC information, as shown in Figure 3a, in which the urban subsurface type of Xilinhot during this period was mainly grass, and the area of urban construction land was small because it was before the urbanization process; the second set of experiments uses the 2018 LULC information (Figure 3b), in which the urban area in this period signi cantly increased, re ecting the characteristics of postrapid urban development.…”

Section: Design Of the Experimentsmentioning

confidence: 99%

Modeling Impacts of Mining Activity-induced Landscape Change on Local Climate

Chen

et al. 2022

Preprint

As a major energy source, coal has been mined on an increasingly larger scale as the social economy has continuously developed, resulting in drastic land type changes. These changes in turn cause changes in the local climate and affect the local ecological environment. Therefore, for coal cities, mining activities are an important factor influencing the local climate, and clarifying the impact of mining activities on the ecological environment is important for guiding regional development. In this paper, the impact of land use/cover changes (LUCCs) on local temperature in the spring and summer seasons from 1980 to 2018 was simulated using the Weather Research and Forecasting (WRF) model with Xilinhot city as the study area, and the regional distribution of local surface energy was analyzed in conjunction with the ground-air energy transfer process. The results show that the grassland area in Xilinhot remained above 85% from 1980 to 2018, so mining activities had a small impact on the average temperature of the whole region. However, in the mining area, the warming effect caused by mining activities was more obvious, with an average temperature increase of 0.822 K. Among other land transformation types, the conversion to water bodies had a very obvious cooling effect, lowering the temperature by an average of 2.405 K. By comparing the latent heat flux (LH), sensible heat flux (SH) and ground heat flux (GRD) under different land use types, it was found that in 2018, the LH decreased by 0.487 W/m2, the SH decreased by 0.616 W/m2 and the GRD decreased by 0.753 W/m2. The conversion to built-up urban land caused a significant decrease in the LH in the corresponding area, allowing more energy to be used to increase SH values, which resulted in significantly higher urban temperatures than in other areas.

“…VNP landscape patterns in the study were analyzed using Fragstats 4.2, which was also used to calculate landscape indices for the respective periods. Landscape fragmentations, shape, and diversity were the selected landscape metrics because they aligned with the study's objectives (Ouyang et al, 2009;Song et al, 2010).…”

Section: Fragmentation Analysismentioning

confidence: 99%

Reconciling Landscape Fragmentation and Hippopotamuses Population Decrease in Virunga National Park Wetland

Udahogora¹,

Yang²,

Mindje³

et al. 2020

GEP