Modeling the spatiotemporal heterogeneity and changes of slope stability in rainfall-induced landslide areas

Que, Xiang; Zhuang, Xinhan; Ma, Xiaogang; Lai, Yuting; Xie, Jianfang; Fei, Tingting; Wang, Hui; Yuming, W. U.

doi:10.1007/s12145-023-01165-7

Cited by 5 publications

(4 citation statements)

References 23 publications

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“…The optimization strategies based on slack variables do not consider influencing factors other than inputs and outputs, and the reliability of the results needs to be verified. One possible solution is to employ the Geographically Weighted Regression (GWR) [71,72] or Spatiotemporal Weighted Regression (STWR) [73,74] models to further explore the spatiotemporal heterogeneity affected by different driving factors after the safety assessments.…”

Section: Discussionmentioning

confidence: 99%

Empirical Analysis of a Super-SBM-Based Framework for Wetland Carbon Stock Safety Assessment

Chen,

Wang,

et al. 2024

Remote Sensing

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With climate change and urbanization expansion, wetlands, which are some of the largest carbon stocks in the world, are facing threats such as shrinking areas and declining carbon sequestration capacities. Wetland carbon stocks are at risk of being transformed into carbon sources, especially those of wetlands with strong land use–natural resource conservation conflict. Moreover, there is a lack of well-established indicators for evaluating the health of wetland carbon stocks. To address this issue, we proposed a novel framework for the safety assessment of wetland carbon stocks using the Super Slack-Based Measure (Super-SBM), and we then conducted an empirical study on the Quanzhou Bay Estuary Wetland (QBEW). This framework integrates the unexpected output indicator (i.e., carbon emissions), the expected output indicators, including the GDP per capita and carbon stock estimates calculated via machine learning (ML)-based remote sensing inversion, and the input indicators, such as environmental governance investigations, climate conditions, socio-economic activities, and resource utilization. The results show that the annual average safety assessment for carbon pools in the QBEW was a meager 0.29 in 2015, signaling a very poor state, likely due to inadequate inputs or excessive unexpected outputs. However, there has been a substantial improvement since then, as evidenced by the fact that all the safety assessments have exceeded the threshold of 1 from 2018 onwards, reflecting a transition to a “weakly effective” status within a safe and acceptable range. Moreover, our investigation employing the Super-SBM model to calculate the “slack variables” yielded valuable insights into optimization strategies. This research advances the field by establishing a safety measurement framework for wetland carbon pools that leverages efficiency assessment methods, thereby offering a quantitative safeguard mechanism that supports the achievement of the “3060” dual-carbon target.

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Section: Discussionmentioning

confidence: 99%

Empirical Analysis of a Super-SBM-Based Framework for Wetland Carbon Stock Safety Assessment

Chen,

Wang,

et al. 2024

Remote Sensing

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“…However, as the population of these locations grew, the corresponding TCEa also increased significantly in those years. In contrast to the GWR and geographically and temporally weighted regression (GTWR) [43] models, the STWR model integrated into the framework adopts a decay weighting strategy based on the numerical difference change rate of a certain time interval rather than the time interval itself, which enables exploring the heterogeneity among change rates at different locations [38]. In terms of the spatiotemporal heterogeneous response of TCEa to different land-surface parameters, several exciting findings were detected through the coefficients with |Z-score| greater than 1.96 SD:…”

Section: Discussionmentioning

confidence: 99%

“…STWR is a temporally extended model based on GWR, characterized using numerically different rate attenuation weighting strategies [37]. The basic formula of the STWR model can be defined as [38]:…”

Section: Stwrmentioning

confidence: 99%

Exploring Urban Heat Distribution and Thermal Comfort Exposure Using Spatiotemporal Weighted Regression (STWR)

Chen,

Wang,

Que

et al. 2024

Buildings

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With rapid urbanization, many cities have experienced significant changes in land use and land cover (LULC), triggered urban heat islands (UHI), and increased the health risks of citizens’ exposure to UHI. Some studies have recognized residents’ inequitable exposure to UHI intensity. However, few have discussed the spatiotemporal heterogeneity in environmental justice and countermeasures for mitigating the inequalities. This study proposed a novel framework that integrates the population-weighted exposure model for assessing adjusted thermal comfort exposure (TCEa) and the spatiotemporal weighted regression (STWR) model for analyzing countermeasures. This framework can facilitate capturing the spatiotemporal heterogeneities in the response of TCEa to three specified land-surface and built-environment parameters (i.e., enhanced vegetation index (EVI), normalized difference built-up index (NDBI), and modified normalized difference water index (MNDWI)). Using this framework, we conducted an empirical study in the urban area of Fuzhou, China. Results showed that high TCEa was mainly concentrated in locations with dense populations and industrial regions. Although the TCEa’s responses to various land-surface and built-environment parameters differed at locations over time, the TCEa illustrated overall negative correlations with EVI and MNDWI while positive correlations with NDBI. Many exciting spatial details can be detected from the generated coefficient surfaces: (1) The influences of NDBI on TCEa may be magnified, especially in rapidly urbanizing areas. Still, they diminish to some extent, which may be related to the reduction in building construction activities caused by the COVID-19 epidemic and the gradual improvement of urbanization. (2) The influences of EVI on TCEa decline, which may be correlated with the population increase. (3) Compared with NDBI, the MNDWI had more continuous and stable significant cooling effects on TCEa. Several mitigation strategies based on the spatiotemporal heterogeneous relationships also emanated. The effectiveness of the presented framework was verified. It can help analysts effectively evaluate local thermal comfort exposure inequality and prompt timely mitigation efforts.

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“…It is characterized by considering the numerical time-varying attenuation weighting strategy and can accurately describe the impact of nearby points in space and time on the regression point [52]. The basic formula of the STWR model can be defined as [53]:…”

Section: Landscape Division Index (Division)mentioning

confidence: 99%

Spatiotemporal Heterogeneous Responses of Ecosystem Services to Landscape Patterns in Urban–Suburban Areas

Zou,

Wang,

Que

et al. 2024

Sustainability

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With the acceleration of urbanization, the ecosystem around cities is facing severe challenges. The drastic changes in the landscape pattern, especially in urban–suburban areas, are usually regarded as one of the main drivers. However, the spatiotemporal heterogeneous impacts of landscape patterns on the ecosystem services in this region remain unclear. To address this issue, we propose a novel framework integrating the InVEST-based ecosystem service assessment and spatiotemporal weighted regression (STWR)-based analysis of the spatiotemporal heterogeneity in urban–suburban areas, and apply it to the empirical study of Fuzhou City from 2000 to 2020. It first utilized the InVEST model to build a comprehensive ecosystem service index (CES) from five aspects (i.e., habitat quality, carbon storage, water yield, soil retention, and water purification capacity). Then, four landscape pattern indices (LPIs) (i.e., patch density (PD), area-weighted mean fractal dimension (FRAC_AM), splitting (SPLIT), and Shannon’s diversity (SHDI) index) were selected to build the STWR model. We compared and analyzed the differences in the spatial coefficient surfaces and significance tests generated by the STWR model in urban, urban–suburban, and rural areas. Results show that the following: (1) The CES in Fuzhou shows an upward trend from the urban area to the urban–suburban and rural areas, with significant gradient differences. (2) Compared with other areas, the LPIs in urban–suburban areas show more fragmentation, discreteness, and diversity, indicating more socioeconomic activities. (3) Although LPIs’ impacts on CES change over time (increasing from 2005 to 2010 and 2020 but decreasing in 2015), their effects are relatively low in urban–suburban areas, significantly lower than in urban areas. (4) Interestingly, the LPI coefficients near the urban–suburban boundary seem more significant. (5) This framework can effectively reveal the spatiotemporal heterogeneous relationships between various LPIs and CES, thus guiding concrete policies and measures that support decision-making for improving the ecosystem services surrounding cities through shaping landscape patterns.

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Modeling the spatiotemporal heterogeneity and changes of slope stability in rainfall-induced landslide areas

Cited by 5 publications

References 23 publications

Empirical Analysis of a Super-SBM-Based Framework for Wetland Carbon Stock Safety Assessment

Empirical Analysis of a Super-SBM-Based Framework for Wetland Carbon Stock Safety Assessment

Exploring Urban Heat Distribution and Thermal Comfort Exposure Using Spatiotemporal Weighted Regression (STWR)

Spatiotemporal Heterogeneous Responses of Ecosystem Services to Landscape Patterns in Urban–Suburban Areas

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