scholarly journals Spatio-Temporal Analysis of Impervious Surface Expansion in the Qinhuai River Basin, China, 1988–2017

2021 ◽  
Vol 13 (22) ◽  
pp. 4494
Author(s):  
Shanshan Wang ◽  
Yingxia Pu ◽  
Shengfeng Li ◽  
Runjie Li ◽  
Maohua Li

Impervious surfaces are key indicators for urbanization monitoring and watershed degradation assessment over space and time. However, most empirical studies only extracted impervious surface from spatial, temporal or spectral perspectives, paying less attention to integrating multiple dimensions in acquiring continuous changes in impervious surfaces. In this study, we proposed a neighborhood-based spatio-temporal filter (NSTF) to obtain the continuous change information of impervious surfaces from multi-temporal Landsat images in the Qinhuai River Basin (QRB), Jiangsu, China from 1988–2017, based on the results from semi-automatic decision tree classification. Moreover, we used the expansion intensity index (EII) and the landscape extension index (LEI) to further characterize the spatio-temporal characteristics of impervious surfaces on different spatial scales. The preliminary results showed that the overall accuracies of the final classification were about 95%, with the kappa coefficients ranging between 0.9 and 0.96. The QRB underwent rapid urbanization with the percentage of the impervious surfaces increasing from 2.72% in 1988 to 25.6% in 2017. Since 2006, the center of urbanization expansion was shaped from the urban built-up areas of Nanjing and Jiangning to non-urban built-up areas of the Jiangning, Lishui, and Jurong districts. The edge expansion occupied 73% on average among the different landscape expansion types, greatly beyond outlying (12%) and infilling (15%). The window size in the NSTF has a direct impact on the subsequent analysis. Our research could provide decision-making references for future urban planning and development in the similar basins.

2019 ◽  
Vol 12 (1) ◽  
pp. 83-93 ◽  
Author(s):  
Ebenezer Boakye ◽  
F. O. K. Anyemedu ◽  
Jonathan A. Quaye-Ballard ◽  
Emmanuel A. Donkor

Cities ◽  
2020 ◽  
Vol 107 ◽  
pp. 102876
Author(s):  
Neema Simon Sumari ◽  
Patrick Brandful Cobbinah ◽  
Fanan Ujoh ◽  
Gang Xu

2019 ◽  
Vol 12 (22) ◽  
Author(s):  
Sainath Aher ◽  
Sambhaji Shinde ◽  
Praveen Gawali ◽  
Pragati Deshmukh ◽  
Lakshmi B. Venkata

2020 ◽  
Vol 20 (7) ◽  
pp. 2471-2483
Author(s):  
Chun Kang Ng ◽  
Jing Lin Ng ◽  
Yuk Feng Huang ◽  
Yi Xun Tan ◽  
Majid Mirzaei

Abstract Climate change is most likely to cause changes to the temporal and spatial variability of rainfall. A trend analysis to investigate the rainfall pattern can detect changes over temporal and spatial scales for a rainfall series. In this study, trend analysis using the Mann–Kendall test and Sen's slope estimator was conducted in the Kelantan River Basin, Malaysia. The Kwiatkowski–Phillips–Schmidt–Shin (KPSS) test was applied to evaluate the stationarity of the rainfall series. This basin annually faces onslaughts of varying year-end flooding conditions. The trend analysis was applied for monthly, seasonal and yearly rainfall series between 1989 and 2018. The temporal analysis results showed that both increasing and decreasing trends were detected for all rainfall series. The spatial analysis results indicated that the northern region of the Kelantan River Basin showed an increasing trend, whilst the southwest region showed a decreasing trend. It was found that almost all the rainfall series were stationary except at two rainfall stations during the Inter Monsoon 1, Inter Monsoon 2 and yearly rainfall series. Results obtained from this study can be used as reference for water resources planning and climate change assessment.


2021 ◽  
Author(s):  
Jie Yang ◽  
Xin Huang

Abstract. Land cover (LC) determines the energy exchange, water and carbon cycle between Earth's spheres. Accurate LC information is a fundamental parameter for the environment and climate studies. Considering that the LC in China has been altered dramatically with the economic development in the past few decades, sequential and fine-scale LC monitoring is in urgent need. However, currently, fine-resolution annual LC dataset produced by the observational images is generally unavailable for China due to the lack of sufficient training samples and computational capabilities. To deal with this issue, we produced the first Landsat-derived annual China Land Cover Dataset (CLCD) on the Google Earth Engine (GEE) platform, which contains 30 m annual LC and its dynamics of China from 1990 to 2019. We first collected the training samples by combining stable samples extracted from China’s Land-Use/Cover Datasets (CLUD), and visually-interpreted samples from satellite time-series data, Google Earth and Google Map. Using 335,709 Landsat images on the GEE, several temporal metrics were constructed and fed to the random forest classifier to obtain classification results. We then proposed a post-processing method incorporating spatial-temporal filtering and logical reasoning to further improve the spatial-temporal consistency of CLCD. Finally, the overall accuracy of CLCD reached 79.31 % based on 5,463 visually-interpreted samples. A further assessment based on 5,131 third-party test samples showed that the overall accuracy of CLCD outperforms that of MCD12Q1, ESACCI_LC, FROM_GLC, and GlobaLand30. Besides, we intercompared the CLCD with several Landsat-derived thematic products, which exhibited good consistencies with the Global Forest Change, the Global Surface Water, and three impervious surface products. Based on the CLCD, the trends and patterns of China’s LC changes during 1985 and 2019 were revealed, such as expansion of impervious surface (+148.71 %) and water (+18.39 %), decrease of cropland (−4.85 %) and grassland (−3.29 %), increase of forest (+4.34 %). In general, CLCD reflected the rapid urbanization and a series of ecological projects (e.g., Gain for Green) in China and revealed the anthropogenic implications on LC under the condition of climate change, signifying its potential application in the global change research. The CLCD dataset introduced in this article is freely available at http://doi.org/10.5281/zenodo.4417810 (Yang and Huang, 2021).


Author(s):  
W. Zhang ◽  
X. Kong ◽  
G. Tan ◽  
S. Zheng

Urban lakes are important natural, scenic and pattern attractions of the city, and they are potential development resources as well. However, lots of urban lakes in China have been shrunk significantly or disappeared due to rapid urbanization. In this study, four Landsat images were used to perform a case study for lake change detection in downtown Wuhan, China, which were acquired on 1991, 2002, 2011 and 2017, respectively. Modified NDWI (MNDWI) was adopted to extract water bodies of urban areas from all these images, and OTSU was used to optimize the threshold selection. Furthermore, the variation of lake shrinkage was analysed in detail according to SVM classification and post-classification comparison, and the coverage of urban lakes in central area of Wuhan has decreased by 47.37&amp;thinsp;km<sup>2</sup> between 1991 and 2017. The experimental results revealed that there were significant changes in the surface area of urban lakes over the 27 years, and it also indicated that rapid urbanization has a strong impact on the losses of urban water resources.


AGROFOR ◽  
2021 ◽  
Vol 4 (2) ◽  
Author(s):  
Seyed Hamidreza SADEGHI ◽  
Fahimeh MIRCHOOLI ◽  
Abdulvahed KHALEDI DARVISHAN

Land degradation is the major issue which affect watershed sustainability and following social, economic and environmental of livelihood people. So, early detection of land degradation is necessary for policy-makers to make appropriate decision. In this way, remote sensing method is a candidate choice for assessments and monitoring. In this study, land degradation was assessed using Rain-Use Efficiency (RUE) in the Shazand Watershed, Iran in 1986, 1998, 2008 and 2016. Thus, annual rainfall was calculated using inverse distance weight (IDW), net primary productivity (NPP) were calculated using Landsat images. The results indicated that RUE had increasing and then decreasing trends which were 10.66, 33.77, 20.03 and 9.47 kg C ha-1 yr-1. The results also illustrate that the mean value of RUE in different land uses varied between the irrigated land and orchard that had the highest value and outcrop dominant areas and bareland had the lowest value of RUE among land use categories. It is also established that spatio-temporal analysis of RUE can provide valuable information about the trend of watershed’s sustainability over years.


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