An efficient approach to capture continuous impervious surface dynamics using spatial-temporal rules and dense Landsat time series stacks

Liu, Chong; Zhang, Qi; Luo, Hui; Qi, Shanshan; Shen, Tao; Xu, Hualan; Yao, Yuan

doi:10.1016/j.rse.2019.04.025

Cited by 83 publications

(74 citation statements)

References 74 publications

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“…China has witnessed rapid economic development in the past decades, accompanied by population growth, industrialization and urban expansion [1,[48][49][50]. Such development, however, is compromised by environmental problems, among which atmospheric pollution raises one of the most critical issues [3].…”

Section: Discussionmentioning

confidence: 99%

Mechanism of Spatiotemporal Air Quality Response to Meteorological Parameters: A National-Scale Analysis in China

Qiao

et al. 2019

Sustainability

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The air quality over China exhibits seasonal and regional variation, resulting from heterogeneity in industrialization, and is highly affected by variability in meteorological conditions. We performed the first national-scale exploration of the relationship between the Air Pollution Index (API) and multiple meteorological parameters in China, using partial correlation and hierarchical cluster analyses. Relative humidity, wind speed, and temperature were the dominant factors influencing air quality year-round, due to their significant effects on pollutant dispersion and/or transformation of pollutants. The response of the API to single or multiple meteorological factors varied among cities and seasons, and a regional clustering of response mechanisms was observed, particularly in winter. Clear north–south differentiation was detected in the mechanisms of API response to relative humidity and wind speed. These findings provide insight into the spatiotemporal variation in air quality sensitivity to meteorological conditions, which will be useful for implementing regional air pollution control strategies.

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Section: Discussionmentioning

confidence: 99%

Mechanism of Spatiotemporal Air Quality Response to Meteorological Parameters: A National-Scale Analysis in China

Qiao

et al. 2019

Sustainability

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“…Analytics solutions based on MapReduce paradigm (Hadook‐based and Spark‐based), Deep Learning and Fog Computing, among others, allow researchers to deal with this huge amount of data and to harness their temporal dimension, though the use of these solutions also introduces new challenges such as maintaining data security and privacy. Some proposals for mining TARs from Big Data scenarios have appeared recently, but further developments for big DM are still expected. Some recent proposals also incorporate the spatial information into the mining process to obtain spatio‐temporal association rules (STARs) since the occurrence of some events is often associated with a particular temporal period and location (C. Liu et al, 2019). However, several authors have noticed problematic issues arising from this spatial association rule mining.…”

Section: Critical Considerations and Further Directionsmentioning

confidence: 99%

Temporal association rule mining: An overview considering the time variable as an integral or implied component

Segura‐Delgado

Gacto

Alcalá

et al. 2020

WIREs Data Min & Knowl

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Association rules are commonly used to provide decision‐makers with knowledge that helps them to make good decisions. Most of the published proposals mine association rules without paying particular attention to temporal information. However, in real‐life applications data usually change over time or presenting different temporal situations. Therefore, the extracted knowledge may not be useful, since we may not know whether the rules are currently applicable or whether they will be applicable in the future. For this reason, in recent years, many methods have been proposed in the literature for mining temporal association rules, which introduce a greater predictive and descriptive power providing an additional degree of interestingness. One of the main problems in this research field is the lack of visibility most works suffer since there is no standard terminology to refer to it, making it difficult to find and compare proposals and studies in the field. This contribution attempts to offer a well‐defined framework that allows researchers both to easily locate the previous proposals and to propose well‐grounded methods in the future. To accomplish both objectives, a two‐level taxonomy is proposed according to whether the time variable is considered to provide order to the data collection and to locate some temporal constraints, or whether it is considered as an attribute within the learning process. Some recent applications, available software tools, and a bibliographical analysis in accordance with the Web of Science are also shown. Finally, some critical considerations and potential further directions are discussed. This article is categorized under: Technologies > Association Rules Algorithmic Development > Association Rules

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“…With rapid economic development and population flow [64,65], many places in the world are experiencing rapid urbanization [66], catalyzing the formation of the urban heat island (UHI) [3]. To capture the UHI FP is of critical importance in understanding the process of urbanization and promoting sustainable urban development.…”

Section: Elimination Of Artificial Bias In Background Temperaturesmentioning

confidence: 99%

Determining the Boundary and Probability of Surface Urban Heat Island Footprint Based on a Logistic Model

Qiao

Dongqi

et al. 2019

Remote Sensing

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Studies of the spatial extent of surface urban heat island (SUHI or UHISurf) effects require precise determination of the footprint (FP) boundary. Currently available methods overestimate or underestimate the SUHI FP boundary, and can even alter its morphology, due to theoretical limitations on the ability of their algorithms to accurately determine the impacts of the shape, topography, and landscape heterogeneity of the city. The key to determining the FP boundary is identifying background temperatures in reference rural regions. Due to the instability of remote sensing data, these background temperatures should be determined automatically rather than manually, to eliminate artificial bias. To address this need, we developed an algorithm that adequately represents the decay of land surface temperature (LST) from the urban center to surrounding rural regions, and automatically calculates thresholds for reference rural LSTs in all directions based on a logistic curve. In this study, we applied this algorithm with data from the Aqua Moderate Resolution Imaging Spectroradiometer (Aqua/MODIS) 8-day level 3 (L3) LST global grid product to delineate precise SUHI FPs for the Beijing metropolitan area during the summers of 2004–2018 and determine the interannual and diurnal variations in FP boundaries and their relationship with SUHI intensity.

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An efficient approach to capture continuous impervious surface dynamics using spatial-temporal rules and dense Landsat time series stacks

Cited by 83 publications

References 74 publications

Mechanism of Spatiotemporal Air Quality Response to Meteorological Parameters: A National-Scale Analysis in China

Mechanism of Spatiotemporal Air Quality Response to Meteorological Parameters: A National-Scale Analysis in China

Temporal association rule mining: An overview considering the time variable as an integral or implied component

Determining the Boundary and Probability of Surface Urban Heat Island Footprint Based on a Logistic Model

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