scholarly journals Ecological Change Analysis of Lanzhou City Based on Remote Sensing Ecological Index

2019 ◽  
Vol 1 ◽  
pp. 1-2
Author(s):  
Yuhan Huang ◽  
Haowen Yan
Keyword(s):  
Remote Sensing ◽  
Urban Areas ◽  
Yellow River ◽  
Gansu Province ◽  
Ecological Environment ◽  
Ecological Change ◽  
Change Analysis ◽  
Ecological Index ◽  
Lanzhou City ◽  
The City

<p><strong>Abstract.</strong> Lanzhou City is the capital of Gansu Province and located in the semi-arid of northwest China. The Yellow River passing through the inner city from west to east, which has formed a special ecological environment. In recent years, the economic level of Lanzhou City has continued to develop, and the degree of urbanization has been continuously improved, which has had a certain impact on the ecological environment of the city. This paper used the Remote Sensing Ecology Index (RSEI) model (Xu, H.Q.,2013) to assess the ecological changes of the four major urban areas Chengguan District, Qilihe District, Anning District and Xigu District of Lanzhou from 2013 to 2017, and evaluate the current ecological environment of the city to provide a basis for the sustainable development of the city.</p>

scholarly journals REMOTE SENSING EVALUATION OF ECOLOGICAL ENVIRONMENT OF ANQING CITY BASED ON REMOTE SENSING ECOLOGICAL INDEX

2020 ◽  
Vol XLIII-B3-2020 ◽  
pp. 733-737
Author(s):  
X. Niu ◽  
Y. Li
Keyword(s):  
Remote Sensing ◽  
Urban Area ◽  
Environmental Quality ◽  
Ecological Environment ◽  
Mountainous Area ◽  
Ecological Change ◽  
Ecological Quality ◽  
Ecological Index ◽  
Use Of Resources

Abstract. Figuring out the regional ecological environment quality and ecological change is critical for ecological environment monitoring and management and urban construction planning. Based on the remote sensing ecological index (RESI), we evaluate the ecological quality and ecological change from 1999 to 2019 of Anqing city. Multi-temporal Landsat images are used to extract the four indicators of humidity, vegetation, heat and dryness, respectively. Then the RSEI is calculated by principal component analysis. The results show that the ecological quality of Anqing city declined from 1999 to 2019 and then grew slowly from 2009 to 2019. The eco-environmental quality of Anqing city dropped slightly from 1999 to 2019, and the regions with worse quality grades exceeded those becoming better. Particularly, we find that from 1999 to 2009, the area where the ecological quality became better made up 18.31% of the urban area, while the worse ecological area accounted for 29.68% of the urban area; from 2009 to 2019, the area of improved ecological environment reached 24.35%, while the area of degraded quality constituted 41.36%. Land-use changes dominated eco-environmental quality. The areas of poor eco-environmental quality expanded in residential regions and eco-environmental quality of mountainous area improved since returning cultivated land in steep hills into forest. The RSEI results are expected to provide a quantitative foundation for planning sustainable development and the rational use of resources in Anqing city.

scholarly journals Identificação de Ilhas de Calor na Cidade de Campina Grande-PB Utilizando Sensoriamento Remoto (Identification of Heat Islands in Campina Grande City-PB Using Remote Sensing)

2016 ◽  
Vol 9 (2) ◽  
pp. 614 ◽  
Author(s):  
Elânia Daniele Silva Araújo
Keyword(s):  
Remote Sensing ◽  
Surface Temperature ◽  
Rural Areas ◽  
Urban Areas ◽  
Spatial And Temporal Variability ◽  
Heat Islands ◽  
Social Nature ◽  
Spatial Changes ◽  
Remote Sensing Techniques ◽  
The City

A intensa urbanização causa diversos problemas de natureza ambiental, climática e social. O crescimento não planejado da população urbana e a remoção da vegetação são fatores que intensificam estes problemas. As temperaturas na cidade são significativamente mais quentes do que as suas zonas rurais circundantes devido às atividades humanas. As intensas mudanças espaciais em áreas urbanas, promovem significativo aumento na temperatura, causando o chamado efeito de Ilha de Calor Urbano (ICU). Campina Grande é uma cidade de tamanho médio que experimentou um crescimento desordenado, desde o tempo do comércio de algodão e, como qualquer cidade de grande ou médio porte, sofre alterações em seu espaço. Dessa forma, este estudo teve por objetivo analisar a variabilidade espaço-temporal da temperatura da superfície (Ts) e detectar ICU, através de técnicas de sensoriamento remoto. Para o efeito, foram utilizadas imagens dos satélites Landsat 5 e 8, dos anos de 1995, 2007 e 2014. Aumentos da Ts foram bem evidentes e foram detectadas duas ICU. Campina Grande mostra um padrão de tendência: o crescimento urbano não planejado é responsável por mudanças no ambiente físico e na forma e estrutura espacial da cidade, o que se reflete sobre o microclima e, em última análise, na qualidade de vida das pessoas.   ABSTRACT The intense urbanization causes several problems of environmental, climate and social nature. The unplanned growth of urban population and the vegetation removal are factors that deepen these problems. Temperatures in the city are significantly warmer than its surrounding rural areas due to human activities. Large spatial changes in urban areas promote significant increase in temperature, causing the so-called Urban Heat Island effect (UHI). Campina Grande is a medium-sized town that experienced an uncontrolled growth since the time of the cotton trade and like any large or medium-sized city, undergoes changes in its space. Therefore, this study aimed to analyze surface temperature spatial and temporal variability and to detect potential UHI, through remote sensing techniques. Spectral images from Landsat 5 and 8 satellites were used. Using images from years 1995, 2007 and 2014, considerable increases in temperature were identified and two UHI were recognize. Campina Grande shows a trend pattern: the urban unplanned growth is responsible for changes in the physical environment and in the form and spatial structure of the city, reflecting on people quality of life. Keywords: change detection, surface temperature, heat islands, urbanization.   

Simple Discussion on Creating a Historical and Cultural Concept on City Square Design — Exemplified by Hundred Miles of the Yellow River in Lanzhou

2011 ◽  
Vol 368-373 ◽  
pp. 3435-3439
Author(s):  
Ming Hui Ye ◽  
Xiang Wu Meng ◽  
Han Zhang
Keyword(s):  
Urban Space ◽  
Cultural Context ◽  
Urban Culture ◽  
Yellow River ◽  
The Yellow River ◽  
Lanzhou City ◽  
Spiritual Civilization ◽  
Local Residents ◽  
Line Design ◽  
The City

City square, as a major public urban space. By a sense of spiritual civilization, it should be a window of the city and essential building space to daily life of local residents; it also bears an important heritage city in cultural context responsibility. Based on the Yellow River in Lanzhou City, Barry style line design concept of the study, analyzed and summarized, presented the concept of square designs to create a historical and cultural context of urban culture, the importance of heritage and modern artistic expression should be on urban history and culture diversity to interpretation, to make people re-establish the cultural identity for the city to gain ownership of the spirit.

scholarly journals Mapping Urban Areas Using a Combination of Remote Sensing and Geolocation Data

2019 ◽  
Vol 11 (12) ◽  
pp. 1470 ◽  
Author(s):  
Nan Xia ◽  
Liang Cheng ◽  
ManChun Li
Keyword(s):  
Remote Sensing ◽  
Land Surface ◽  
Urban Areas ◽  
Large Scale ◽  
High Spatial Resolution ◽  
Human Life ◽  
Human Movement ◽  
Night Time ◽  
Urban Mapping ◽  
The City

Urban areas are essential to daily human life; however, the urbanization process also brings about problems, especially in China. Urban mapping at large scales relies heavily on remote sensing (RS) data, which cannot capture socioeconomic features well. Geolocation datasets contain patterns of human movement, which are closely related to the extent of urbanization. However, the integration of RS and geolocation data for urban mapping is performed mostly at the city level or finer scales due to the limitations of geolocation datasets. Tencent provides a large-scale location request density (LRD) dataset with a finer temporal resolution, and makes large-scale urban mapping possible. The objective of this study is to combine multi-source features from RS and geolocation datasets to extract information on urban areas at large scales, including night-time lights, vegetation cover, land surface temperature, population density, LRD, accessibility, and road networks. The random forest (RF) classifier is introduced to deal with these high-dimension features on a 0.01 degree grid. High spatial resolution land cover (LC) products and the normalized difference built-up index from Landsat are used to label all of the samples. The RF prediction results are evaluated using validation samples and compared with LC products for four typical cities. The results show that night-time lights and LRD features contributed the most to the urban prediction results. A total of 176,266 km2 of urban areas in China were extracted using the RF classifier, with an overall accuracy of 90.79% and a kappa coefficient of 0.790. Compared with existing LC products, our results are more consistent with the manually interpreted urban boundaries in the four selected cities. Our results reveal the potential of Tencent LRD data for the extraction of large-scale urban areas, and the reliability of the RF classifier based on a combination of RS and geolocation data.

scholarly journals A Multi-Criteria Evaluation of the Urban Ecological Environment in Shanghai Based on Remote Sensing

2021 ◽  
Vol 10 (10) ◽  
pp. 688
Author(s):  
Yuxiang Yan ◽  
Xianwen Yu ◽  
Fengyang Long ◽  
Yanfeng Dong
Keyword(s):  
Remote Sensing ◽  
Evaluation Method ◽  
Projection Pursuit ◽  
Detection Methods ◽  
Ecological Environment ◽  
Ecological Index ◽  
Environment Quality ◽  
Shanghai City ◽  
Projection Pursuit Model ◽  
The Impact

The urban ecological environment is related to human health and is one of the most concerned issues nowadays. Hence, it is essential to detect and then evaluate the urban ecological environment. However, the conventional manual detection methods have many limitations, such as the high cost of labor, time, and capital. The aim of this paper is to evaluate the urban ecological environment more conveniently and reasonably, thus this paper proposed an ecological environment evaluation method based on remote sensing and a projection pursuit model. Firstly, a series of criteria for the urban ecological environment in Shanghai City are obtained through remote sensing technology. Then, the ecological environment is comprehensively evaluated using the projection pursuit model. Lastly, the ecological environment changes of Shanghai City are analyzed. The results show that the average remote sensing ecological index of Shanghai in 2020 increased obviously compared with that in 2016. In addition, Jinshan District, Songjiang District, and Qingpu District have higher ecological environment quality, while Hongkou District, Jingan District, and Huangpu District have lower ecological environment quality. In addition, the ecological environment of all districts has a significant positive spatial autocorrelation. These findings suggest that the ecological environment of Shanghai has improved overall in the past five years. In addition, Hongkou District, Jingan District, and Huangpu District should put more effort into improving the ecological environment in future, and the improvement of ecological environment should consider the impact of surrounding districts. Moreover, the proposed weight setting method is more reasonable, and the proposed evaluation method is convenient and practical.

scholarly journals Data and techniques for studying the urban heat island effect in Johannesburg

2015 ◽  
Vol XL-7/W3 ◽  
pp. 203-206 ◽  
Author(s):  
C. H. Hardy ◽  
A. L. Nel
Keyword(s):  
Remote Sensing ◽  
Urban Heat Island ◽  
Urban Areas ◽  
Remote Sensing Data ◽  
Urban Heat Island Effect ◽  
Heat Island ◽  
Sensing Data ◽  
Island Effect ◽  
Urban Heat ◽  
The City

The city of Johannesburg contains over 10 million trees and is often referred to as an urban forest. The intra-urban spatial variability of the levels of vegetation across Johannesburg’s residential regions has an influence on the urban heat island effect within the city. Residential areas with high levels of vegetation benefit from cooling due to evapo-transpirative processes and thus exhibit weaker heat island effects; while their impoverished counterparts are not so fortunate. The urban heat island effect describes a phenomenon where some urban areas exhibit temperatures that are warmer than that of surrounding areas. The factors influencing the urban heat island effect include the high density of people and buildings and low levels of vegetative cover within populated urban areas. This paper describes the remote sensing data sets and the processing techniques employed to study the heat island effect within Johannesburg. In particular we consider the use of multi-sensorial multi-temporal remote sensing data towards a predictive model, based on the analysis of influencing factors.

scholarly journals Research on Temporal and Spatial Resolution and the Driving Forces of Ecological Environment Quality in Coal Mining Areas Considering Topographic Correction

2021 ◽  
Vol 13 (14) ◽  
pp. 2815
Author(s):  
Xinran Nie ◽  
Zhenqi Hu ◽  
Qi Zhu ◽  
Mengying Ruan
Keyword(s):  
Remote Sensing ◽  
Coal Mining ◽  
Coal Mine ◽  
Ecological Environment ◽  
Topographic Correction ◽  
Ecological Index ◽  
Mining Areas ◽  
Environment Quality ◽  
Temporal And Spatial

Over the last few years, under the combined effects of climate change and human factors, the ecological environment of coal mining areas has undergone tremendous changes. Therefore, the rapid and accurate quantitative assessments of the temporal and spatial evolution of the ecological environment quality is of great significance for the ecological restoration and development planning of coal mining areas. This study applied the ecological environment index after topographic correction to improve the remote sensing ecological index (RSEI). Based on a series of Landsat images, the ecological environment quality of Yangquan Coal Mine in Shanxi Province from 1987 to 2020 was monitored and evaluated by an improved remote sensing ecological index. The results show that after topographic correction, the topographic effect of the remote sensing ecological index was greatly reduced, and its practicability was improved. From 1987 to 2020, the ecological environment quality of Yangquan Coal Mine was improved, and the mean of the RSEI increased from 0.4294 to 0.6379. The ecological environment quality of the six coal mines in the study area was improved. Among the six coal gangue dumps, the ecological environmental quality of D1, D2, D3, and D4 has improved, and the ecological environment quality of D5 and D6 worsened. The percentages of improved, unchanged, and degraded ecological environment quality in the entire coal mining area were 77.08%, 0.99%, and 21.93%, respectively. The global Moran’s index was between 0.7929 and 0.9057, and it was shown that there was a strong positive correlation between the ecological environmental qualities of the study area, and that its spatial distribution was clustered rather than random. The LISA cluster map showed that the aggregation and dispersion degree of ecological environment quality was mainly high–high clustering and low–low clustering over the whole stage. During the study period, temperature and precipitation had limited impacts on the ecological environment quality of Yangquan Coal Mine, while the coal mining activities and urbanization construction seriously affected the local ecological environment quality and the implementation of ecological restoration policies, regulations, and measures was the main reason for the improvement of the ecological environment quality.

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