30 m global impervious surface area dynamics and urban expansion pattern observed by Landsat satellites: From 1972 to 2019

Huang, Xin; Li, Jiayi; Yang, Jie; Zhang, Zhen; Li, Dongrui; Liu, Xiaoping

doi:10.1007/s11430-020-9797-9

Cited by 112 publications

(45 citation statements)

References 29 publications

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“…Specifically, with respect to some dynamic LCs (i.e. impervious surfaces, forests and surface water), we intercompared the CLCD with the Global Forest Change (GFC) (Hansen et al, 2013), Global Impervious Surface Area (GISA) (Xin et al, 2021), Global Artificial Impervious Area (GAIA) (Gong et al, 2020), Global Annual Urban Dynamics (GAUD) (Liu et al, 2020b) and the Global Surface Water (GSW) (Pekel et al, 2016) datasets.…”

Section: Datasets Inter-comparisonmentioning

confidence: 99%

“…The barren class increased slightly by 0.80 % from 1985 to 2000 but decreased by 2.62 % from 2000 to 2019. The decrease in the barren class may be related to the ecological project Returning Grazing Land to Grassland after 2003 (Xiong et al, 2016). In contrast, grassland decreased by 2.15 % (6.23 million ha) before 2000 but only 1.16 % (3.28 million ha) from 2000 to 2019 thanks to the implementation of grassland conservation policies such as the Gain for Green after 2000 (Li et al, 2017).…”

Section: General Temporal Trendmentioning

confidence: 99%

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The 30 m annual land cover dataset and its dynamics in China from 1990 to 2019

Yang

Huang

2021

Earth Syst. Sci. Data

Self Cite

1,422

251

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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 in China from 1990 to 2019. We first collected the training samples by combining stable samples extracted from China's land-use/cover datasets (CLUDs) and visually interpreted samples from satellite time-series data, Google Earth and Google Maps. 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 5463 visually interpreted samples. A further assessment based on 5131 third-party test samples showed that the overall accuracy of CLCD outperforms that of MCD12Q1, ESACCI_LC, FROM_GLC and GlobeLand30. 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 in cropland (−4.85 %) and grassland (−3.29 %), and increase in 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 https://doi.org/10.5281/zenodo.4417810 (Yang and Huang, 2021).

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Section: Datasets Inter-comparisonmentioning

confidence: 99%

Section: General Temporal Trendmentioning

confidence: 99%

The 30 m annual land cover dataset and its dynamics in China from 1990 to 2019

Yang

Huang

2021

Earth Syst. Sci. Data

Self Cite

1,422

251

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“…All image resolutions are 30 m. The band characteristics of Landsat 5 TM and Landsat 8 OLI sensors are basically the same, and there is a high degree of consistency in spectral resolution. There is a high degree of consistency in the spectral resolution of these products [26]. In this study, four images with serial number 117,027 at four different points in time were assessed to find the image least influenced by cloud cover.…”

Section: Picea Asperatamentioning

confidence: 99%

Research on the Temporal and Spatial Distributions of Standing Wood Carbon Storage Based on Remote Sensing Images and Local Models

Zhang

Sun

Jia

et al. 2022

Forests

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Background and Objectives: It is important to understand the temporal and spatial distributions of standing wood carbon storage in forests to maintain ecological balance and forest dynamics. Such information can provide technical and data support for promoting ecological construction, formulating different afforestation policies, and implementing forest management strategies. Long-term series of Landsat 5 (Thematic Mapper, TM) and Landsat 8 (Operational Land Imager, OLI) remote sensing images and digital elevation models (DEM), as well as multiphase survey data, provide new opportunities for research on the temporal and spatial distributions of standing wood carbon storage in forests. Methods: The extracted remote sensing factors, terrain factors, and forest stand factors were analyzed with stepwise regression in relation to standing wood carbon storage to identify significant influential factors, build a global ordinary least squares (OLS) model and a linear mixed model (LMM), and construct a local geographically weighted regression (GWR), multiscale geographically weighted regression model (MGWR), temporally weighted regression (TWR), and geographically and temporally weighted regression (GTWR). Model evaluation indicators were used to calculate residual Moran’s I values, and the optimal model was selected to explore the spatiotemporal dynamics of standing wood carbon storage in the Liangshui Nature Reserve. Results: Remote sensing factors, topographic factors (Slope), and stand factors (Age and DBH) were significantly correlated with standing wood carbon storage, and the constructed global models exhibited fitting effects inferior to those of the established local models. LMM is also used as a global model to add random effects on the basis of OLS, and R2 is increased to 0.52 compared with OLS. The local models based on geographically weighted regression, namely, GWR, MGWR, TWR, and GTWR, all have good performance. Compared with OLS, the R2 is increased to 0.572, 0.589, 0.643, and 0.734, and the fitting effect of GTWR is the best. GTWR can overcome spatial autocorrelation and temporal autocorrelation problems, with a higher R2 (0.734) and a more ideal model residual than other models. This study develops a model for carbon storage (CS) considering various influential factors in the Liangshui area and provides a possible solution for the estimation of long-term carbon storage distribution.

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“…Urban expansion is the most intuitive activity in the process of urban spatial-form transformation [11,12]. Due to regional differences in natural, social, and economic conditions, the pattern of spatial expansion varies by city and can have different driving forces behind the expansion [13,14].…”

Section: Introductionmentioning

confidence: 99%

Analysis of the Spatio-Temporal Characteristics of Nanjing’s Urban Expansion and Its Driving Mechanisms

Tao

2022

IJGI

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The expansion and evolution of urban areas are the most perceptible manifestations of the transformation of the urban spatial form. This study uses remote sensing images of Nanjing from 2001, 2006, 2011, 2016, and 2021, along with socio-economic data to analyse the spatio-temporal characteristics of the city’s urban expansion. Furthermore, we utilize a binary logistic regression to quantitatively analyse the driving forces in each stage. We find that from 2001 to 2021, Nanjing’s urban area expanded approximately 3.97 times. Notably, the city started moving from a stage of medium-speed development to rapid development in 2006, and then slowed down and returned to medium-speed development in 2011. The urban land mainly expanded in the north, northeast, southeast, and southwest directions in a lopsided cross-shape roughly along the northwest-southeast direction; meanwhile, the city’s centre of gravity continuously moved towards the southeast. Among the driving factors, neighbourhood (distance from planned commercial centres, railways, and highways), topography, and geolocation (distance from the Yangtze River, and elevation) had a greater, albeit inhibitory effect on urban expansion. However, the effects of different socio-economic factors (GDP per capita, resident population, secondary and tertiary industry, etc.) varied across different time periods.

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30 m global impervious surface area dynamics and urban expansion pattern observed by Landsat satellites: From 1972 to 2019

Cited by 112 publications

References 29 publications

The 30 m annual land cover dataset and its dynamics in China from 1990 to 2019

The 30 m annual land cover dataset and its dynamics in China from 1990 to 2019

Research on the Temporal and Spatial Distributions of Standing Wood Carbon Storage Based on Remote Sensing Images and Local Models

Analysis of the Spatio-Temporal Characteristics of Nanjing’s Urban Expansion and Its Driving Mechanisms

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