Urban sprawl and the shortage of proper sanitary infrastructures significantly jeopardize public health and urban sustainability. The problem is further aggravated as a result of the rapid urbanization and urban sprawl. This study investigated the relationship between urban sprawl and sanitation risk conditions in a rapidly growing city in India. This was accomplished by investigating changes in urban sprawl areas between the periods 2000–2020 using multispectral satellite images and Shanon's entropy model and studying the pattern of spatial variations in basic sanitation services derived from the 100 household‐based surveyed WASH (water availability, sanitation, and hygiene) data collected in 2018 before COVID‐19 from 45 sprawl regions. Spatial statistical techniques, namely, the inverse distance weighted (IDW) interpolation and the multicriteria decision technique, were employed for neighborhood analysis and assessing sanitation risks inside the sprawl region. Results showed that Raipur exhibited urban sprawl and around 93.68% of the sprawl area was classified between high (6.47%)‐ and medium (80.52%)‐risk zones.
{"title":"Understanding impact of urban sprawl over sanitation risks using GIS‐based multicriteria decision‐making approach","authors":"Debrupa Chatterjee, Dharmaveer Singh, Diganta Bhushan Das, Pushpendra Kumar Singh","doi":"10.1111/tgis.13220","DOIUrl":"https://doi.org/10.1111/tgis.13220","url":null,"abstract":"Urban sprawl and the shortage of proper sanitary infrastructures significantly jeopardize public health and urban sustainability. The problem is further aggravated as a result of the rapid urbanization and urban sprawl. This study investigated the relationship between urban sprawl and sanitation risk conditions in a rapidly growing city in India. This was accomplished by investigating changes in urban sprawl areas between the periods 2000–2020 using multispectral satellite images and Shanon's entropy model and studying the pattern of spatial variations in basic sanitation services derived from the 100 household‐based surveyed WASH (water availability, sanitation, and hygiene) data collected in 2018 before COVID‐19 from 45 sprawl regions. Spatial statistical techniques, namely, the inverse distance weighted (IDW) interpolation and the multicriteria decision technique, were employed for neighborhood analysis and assessing sanitation risks inside the sprawl region. Results showed that Raipur exhibited urban sprawl and around 93.68% of the sprawl area was classified between high (6.47%)‐ and medium (80.52%)‐risk zones.","PeriodicalId":47842,"journal":{"name":"Transactions in GIS","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2024-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141783359","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Accurate prediction of future urban land demand is essential for effective urban management and planning. However, existing studies often focus on predicting total demand within an administrative region, neglecting the spatiotemporal heterogeneities and interrelationships within its subregions, such as grids. This study introduces a dynamic spatiotemporal rolling prediction model (STRM) that integrates historical trends, neighborhood status, and spatial proximity for spatially explicit prediction of urban land demand at a grid level within an administrative region. STRM leverages historical urban land demand and proximity information from neighborhood grids to predict future demand of the foci grid. By integrating history and neighborhood information into a deep forest model, STRM provides an approach for rolling predictions of grid‐level urban land demand. Parameter sensitivity and structural sensitivity analyses of STRM reveal the impact of historical lags, neighborhood size, and spatial proximity on urban land demand predictions. Application of STRM in Wuhan demonstrated the performance of STRM over a 17‐year period (2000–2017), with an average adjusted R2 of 0.89, outperforming other urban land demand prediction models. By predicting demand on a year‐by‐year basis, STRM effectively captures spatiotemporal heterogeneity and enhances the resolution of urban land demand prediction. STRM represents a shift from static macroscopic to dynamic microscopic prediction of urban land demand, offering valuable insights for future urban development and planning decisions.
{"title":"Multidimensional effects of history, neighborhood, and proximity on urban land growth: A dynamic spatiotemporal rolling prediction model (STRM)","authors":"Yingjian Ren, Jianxin Yang, Yang Shen, Lizhou Wang, Zhong Zhang, Zibo Zhao","doi":"10.1111/tgis.13224","DOIUrl":"https://doi.org/10.1111/tgis.13224","url":null,"abstract":"Accurate prediction of future urban land demand is essential for effective urban management and planning. However, existing studies often focus on predicting total demand within an administrative region, neglecting the spatiotemporal heterogeneities and interrelationships within its subregions, such as grids. This study introduces a dynamic spatiotemporal rolling prediction model (STRM) that integrates historical trends, neighborhood status, and spatial proximity for spatially explicit prediction of urban land demand at a grid level within an administrative region. STRM leverages historical urban land demand and proximity information from neighborhood grids to predict future demand of the foci grid. By integrating history and neighborhood information into a deep forest model, STRM provides an approach for rolling predictions of grid‐level urban land demand. Parameter sensitivity and structural sensitivity analyses of STRM reveal the impact of historical lags, neighborhood size, and spatial proximity on urban land demand predictions. Application of STRM in Wuhan demonstrated the performance of STRM over a 17‐year period (2000–2017), with an average adjusted <jats:italic>R</jats:italic><jats:sup>2</jats:sup> of 0.89, outperforming other urban land demand prediction models. By predicting demand on a year‐by‐year basis, STRM effectively captures spatiotemporal heterogeneity and enhances the resolution of urban land demand prediction. STRM represents a shift from static macroscopic to dynamic microscopic prediction of urban land demand, offering valuable insights for future urban development and planning decisions.","PeriodicalId":47842,"journal":{"name":"Transactions in GIS","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2024-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141740954","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Belén Pedregal, Gabriel Orozco, Joaquin Osorio, Pilar Díaz‐Cuevas
In this article, we compile and characterize a total of 43 collaborative web map projects by a set of parameters that enable the understanding and comparability of current and future projects. We then develop a comprehensive methodological framework to explore volunteered geographic information (VGI) and spatial data infrastructure (SDI) convergence based on this review. The main results show the dominance of citizen science projects, followed by initiatives promoting sustainability values, local development, and governance. Although values remain low, the potential to achieve convergence in VGI–SDI features is very high in citizen science projects, where the presence of experts and the funding of these projects by governments and decision‐making entities enable quality standards in the collection and distribution of the contributed information. The work concludes by addressing two major challenges facing current VGI projects: firstly, accessing affordable technological solutions that allow the creation of collaborative web maps with SDI‐like functions. Secondly, guaranteeing the project's sustainability and the preservation of the information gathered.
{"title":"Characterizing collaborative mapping projects. A methodological framework for analyzing volunteered geographic information and spatial data infrastructure convergence","authors":"Belén Pedregal, Gabriel Orozco, Joaquin Osorio, Pilar Díaz‐Cuevas","doi":"10.1111/tgis.13210","DOIUrl":"https://doi.org/10.1111/tgis.13210","url":null,"abstract":"In this article, we compile and characterize a total of 43 collaborative web map projects by a set of parameters that enable the understanding and comparability of current and future projects. We then develop a comprehensive methodological framework to explore volunteered geographic information (VGI) and spatial data infrastructure (SDI) convergence based on this review. The main results show the dominance of citizen science projects, followed by initiatives promoting sustainability values, local development, and governance. Although values remain low, the potential to achieve convergence in VGI–SDI features is very high in citizen science projects, where the presence of experts and the funding of these projects by governments and decision‐making entities enable quality standards in the collection and distribution of the contributed information. The work concludes by addressing two major challenges facing current VGI projects: firstly, accessing affordable technological solutions that allow the creation of collaborative web maps with SDI‐like functions. Secondly, guaranteeing the project's sustainability and the preservation of the information gathered.","PeriodicalId":47842,"journal":{"name":"Transactions in GIS","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141746078","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Yilan Liao, Yuanhao Shi, Zhirui Fan, Zhiyu Zhu, Binghu Huang, Wei Du, Jinfeng Wang, Liping Wang
Syndromic surveillance is a type of public health surveillance that utilizes nonspecific indicators or symptoms associated with a particular disease or condition to detect and track disease outbreaks early. However, data completeness has been a significant challenge for syndromic surveillance systems in many countries. Incomplete data may make it difficult to accurately identify anomalies or trends in surveillance data. In this study, a new disease mapping method based on a high‐accuracy, low‐rank tensor completion (HaLRTC) algorithm is proposed to estimate the quarterly positivity rate of the human influenza virus (IFV) based on highly insufficient 2010–2015 respiratory syndromic surveillance data from the subtropical monsoon region of China. The HaLRTC algorithm is a spatiotemporal interpolation method applied to fill in missing or incomplete data using a low‐rank tensor structure. The results show that the accuracy (R2 = 0.880, RMSE = 0.037) of the proposed method is much higher than that of three traditional disease mapping methods: Cokriging, hierarchical Bayesian, and sandwich estimation methods. This study provides a new disease mapping approach to improve the quality and completeness of data in syndrome surveillance or other familiar systems with a large proportion of missing data.
{"title":"A new disease mapping method for improving data completeness of syndromic surveillance with high missing rates","authors":"Yilan Liao, Yuanhao Shi, Zhirui Fan, Zhiyu Zhu, Binghu Huang, Wei Du, Jinfeng Wang, Liping Wang","doi":"10.1111/tgis.13200","DOIUrl":"https://doi.org/10.1111/tgis.13200","url":null,"abstract":"Syndromic surveillance is a type of public health surveillance that utilizes nonspecific indicators or symptoms associated with a particular disease or condition to detect and track disease outbreaks early. However, data completeness has been a significant challenge for syndromic surveillance systems in many countries. Incomplete data may make it difficult to accurately identify anomalies or trends in surveillance data. In this study, a new disease mapping method based on a high‐accuracy, low‐rank tensor completion (HaLRTC) algorithm is proposed to estimate the quarterly positivity rate of the human influenza virus (IFV) based on highly insufficient 2010–2015 respiratory syndromic surveillance data from the subtropical monsoon region of China. The HaLRTC algorithm is a spatiotemporal interpolation method applied to fill in missing or incomplete data using a low‐rank tensor structure. The results show that the accuracy (<jats:italic>R</jats:italic><jats:sup>2</jats:sup> = 0.880, RMSE = 0.037) of the proposed method is much higher than that of three traditional disease mapping methods: Cokriging, hierarchical Bayesian, and sandwich estimation methods. This study provides a new disease mapping approach to improve the quality and completeness of data in syndrome surveillance or other familiar systems with a large proportion of missing data.","PeriodicalId":47842,"journal":{"name":"Transactions in GIS","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2024-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141740955","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Ya Zhang, Jiping Liu, Yong Wang, Yungang Cao, Shenghua Xu, An Luo
Building shape cognition is essential for tasks, such as map generalization, urban modeling, and building semantics and distribution pattern recognition. Traditional geometric and statistical methods rely on human‐defined shape indicators, and spectral‐based graph neural networks (GNNs) require Laplacian eigendecomposition, resulting in high algorithmic complexity. Therefore, we proposed a low‐complexity and simple‐to‐use spatial‐domain GNN for differentiating building shapes. To examine the influence of the building vertices on their shape, we treated each building as a graph and proposed a graph isomorphic network with weighted multi‐aggregators (GIN‐WMA) by analyzing the node connectivity of a building graph. The GIN‐WMA utilizes a novel aggregator that combines the sum and max aggregators, enhancing its recognition and differentiation capabilities. This approach can effectively differentiate nodes that have identical features after aggregation by the sum aggregator. We extracted features considering both local node and global shape features, drawing inspiration from Gestalt cognitive psychology and GNN's “node–graph” differentiation strategy. In addition, we compared the performance of GIN‐WMA with existing methods, studying the effect of various node features and their combinations on classification accuracy. The results demonstrated that GIN‐WMA outperforms other methods in discriminating building shapes, demonstrating superior capabilities in shape classification and enabling end‐to‐end extraction and classification of building shapes.
{"title":"Graph isomorphism network with weighted multi‐aggregators for building shape classification","authors":"Ya Zhang, Jiping Liu, Yong Wang, Yungang Cao, Shenghua Xu, An Luo","doi":"10.1111/tgis.13201","DOIUrl":"https://doi.org/10.1111/tgis.13201","url":null,"abstract":"Building shape cognition is essential for tasks, such as map generalization, urban modeling, and building semantics and distribution pattern recognition. Traditional geometric and statistical methods rely on human‐defined shape indicators, and spectral‐based graph neural networks (GNNs) require Laplacian eigendecomposition, resulting in high algorithmic complexity. Therefore, we proposed a low‐complexity and simple‐to‐use spatial‐domain GNN for differentiating building shapes. To examine the influence of the building vertices on their shape, we treated each building as a graph and proposed a graph isomorphic network with weighted multi‐aggregators (GIN‐WMA) by analyzing the node connectivity of a building graph. The GIN‐WMA utilizes a novel aggregator that combines the sum and max aggregators, enhancing its recognition and differentiation capabilities. This approach can effectively differentiate nodes that have identical features after aggregation by the sum aggregator. We extracted features considering both local node and global shape features, drawing inspiration from Gestalt cognitive psychology and GNN's “node–graph” differentiation strategy. In addition, we compared the performance of GIN‐WMA with existing methods, studying the effect of various node features and their combinations on classification accuracy. The results demonstrated that GIN‐WMA outperforms other methods in discriminating building shapes, demonstrating superior capabilities in shape classification and enabling end‐to‐end extraction and classification of building shapes.","PeriodicalId":47842,"journal":{"name":"Transactions in GIS","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2024-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141640495","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Land surface phenology encompasses variations in the life cycle events of plants induced by seasonal changes in environmental factors, primarily meteorological conditions. This study leverages Google Earth Engine to extract a comprehensive time series of two‐band Enhanced Vegetation Index (EVI 2) from Landsat images. Utilizing relatively sparse data spanning from 2001 to 2020, a Bayesian hierarchical model is applied at a 30 m resolution to capture the continuous temporal evolution of phenology. The fitting results of this study demonstrate excellent performance, with annual correlation coefficients consistently exceeding 0.89. The findings indicate that between 2001 and 2020, the Start of Season in Shanxi advanced by an average of 0.79 days per year, the End of Season was delayed by an average of 0.83 days per year, and the Length of Season (LOS) extended by an average of 0.80 days per year. Spatial disparities in phenological periods in Shanxi are evident, with an average LOS of 192 days on 35–36° N and only 122 days on 40–41° N. Below 1200 m, phenological periods exhibit significant changes influenced by human activities, while between 1200 m and 2600 m, LOS shows a weak trend of shortening. Above 2600 m, there is a noticeable reduction in LOS. With an increasing slope, LOS increases from an average of 175 days to 187 days (>25°). This study, utilizing Shanxi as a case study, explores the spatiotemporal evolution characteristics of vegetation phenology, aiming to support fine land management and enhance agricultural productivity.
{"title":"Spatial and temporal heterogeneity of land surface phenology in Shanxi Province from 2001 to 2020","authors":"Haipeng Zhao, Xiangzheng Deng, Zehao Wang","doi":"10.1111/tgis.13219","DOIUrl":"https://doi.org/10.1111/tgis.13219","url":null,"abstract":"Land surface phenology encompasses variations in the life cycle events of plants induced by seasonal changes in environmental factors, primarily meteorological conditions. This study leverages Google Earth Engine to extract a comprehensive time series of two‐band Enhanced Vegetation Index (EVI 2) from Landsat images. Utilizing relatively sparse data spanning from 2001 to 2020, a Bayesian hierarchical model is applied at a 30 m resolution to capture the continuous temporal evolution of phenology. The fitting results of this study demonstrate excellent performance, with annual correlation coefficients consistently exceeding 0.89. The findings indicate that between 2001 and 2020, the Start of Season in Shanxi advanced by an average of 0.79 days per year, the End of Season was delayed by an average of 0.83 days per year, and the Length of Season (LOS) extended by an average of 0.80 days per year. Spatial disparities in phenological periods in Shanxi are evident, with an average LOS of 192 days on 35–36° N and only 122 days on 40–41° N. Below 1200 m, phenological periods exhibit significant changes influenced by human activities, while between 1200 m and 2600 m, LOS shows a weak trend of shortening. Above 2600 m, there is a noticeable reduction in LOS. With an increasing slope, LOS increases from an average of 175 days to 187 days (>25°). This study, utilizing Shanxi as a case study, explores the spatiotemporal evolution characteristics of vegetation phenology, aiming to support fine land management and enhance agricultural productivity.","PeriodicalId":47842,"journal":{"name":"Transactions in GIS","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2024-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141645714","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The misallocation of land resources is an important factor restricting the high‐quality development of China's economy. Based on the perspective of supply and demand matching, this study proposed a measurement method for the spatial misallocation of land resources and constructed two models for the testing and decomposition of factors affecting land resource spatial misallocation. We used this measurement method and these two models to explore the spatiotemporal characteristics and determinants of the spatial misallocation of land resources in China from 2000 to 2018 with the aim of providing policy recommendations for the correction of land resource misallocation in China and other developing countries. The results showed that the spatial misallocation of land resources in China showed an upward trend with evident spatial differentiation and the proportion of cities with high and severe misallocation increased. Industrial isomorphism and market misallocation are the main driving factors of land misallocation. Government misallocation and factor market abnormal development aggravate land resource misallocation. Extensive economic development and excessive factor agglomeration have a small effect on land resource spatial misallocation. Therefore, strengthening the land supply‐side reform, and implementing differentiated land allocation policies are effective pathways to control land resource misallocation in China.
{"title":"Investigating spatiotemporal patterns and determinants of land resource misallocation in prefecture‐level China","authors":"Junfeng Zhang, Sanwei He, Yuwei Weng, Jiancheng Ding","doi":"10.1111/tgis.13213","DOIUrl":"https://doi.org/10.1111/tgis.13213","url":null,"abstract":"The misallocation of land resources is an important factor restricting the high‐quality development of China's economy. Based on the perspective of supply and demand matching, this study proposed a measurement method for the spatial misallocation of land resources and constructed two models for the testing and decomposition of factors affecting land resource spatial misallocation. We used this measurement method and these two models to explore the spatiotemporal characteristics and determinants of the spatial misallocation of land resources in China from 2000 to 2018 with the aim of providing policy recommendations for the correction of land resource misallocation in China and other developing countries. The results showed that the spatial misallocation of land resources in China showed an upward trend with evident spatial differentiation and the proportion of cities with high and severe misallocation increased. Industrial isomorphism and market misallocation are the main driving factors of land misallocation. Government misallocation and factor market abnormal development aggravate land resource misallocation. Extensive economic development and excessive factor agglomeration have a small effect on land resource spatial misallocation. Therefore, strengthening the land supply‐side reform, and implementing differentiated land allocation policies are effective pathways to control land resource misallocation in China.","PeriodicalId":47842,"journal":{"name":"Transactions in GIS","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2024-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141649375","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Landslide‐dammed lakes are potentially hazardous and catastrophic for their possible failures and outburst floods (OFs) that will cause disastrous damage and life‐threatening losses, especially in the alpine areas where seismicity is strong and frequent, such as the eastern margin of the Tibetan Plateau. This study focused on spreading an effective numerical model to reconstruct downstream hazards induced by a giant ancient landslide‐dammed lake outburst flood (LLOF) in the upper Minjiang River valley, eastern Tibetan Plateau based on the integration of the hydraulic characteristics of the upstream dammed lake, dam failure and erosion process, and downstream OF dynamics. The peak discharge levels and paleohydraulics of the LLOF were reconstructed using single‐embankment dam‐break program and one‐dimensional steady hydraulic numerical model. The results reveal that the maximum peak discharge of the Diexi paleo LLOF was 73,060–82,235 m3/s, with an uncertainty bound of 73,000–90,000 m3/s (mean value: 81,500 m3/s). Which inferred that the Diexi paleo LLOF was one of the largest known LLOFs in the view of worldwide scope comparing with other types of floods. Then, the hydraulic characteristics and route evolution of the LLOF were simulated in one‐dimensional unsteady numerical model. The results showed that the Diexi paleo LLOF took 7.47 h to transport from Diexi to Wenchuan within the simulated section of 91.23 km, with an average propagation velocity of 3.39 m/s. At the time of 15.57 h, the simulating section (between Diexi and Wenchuan) reached the maximum extent of inundation which was 664.91 km2, with an average value of 7.29 km2/km. Our modeling supports that the numerical model can be used successfully to reconstruct the hydraulics of a paleo LLOF in deep confined gorge environment. The reconstructed paleo LLOF data are of great significance to enrich the regional megaflood records and provide valuable information for geological hazard controls and OF risk assessment within the upper catchment of Minjiang River at the eastern margin of the Tibetan Plateau.
滑坡堰塞湖具有潜在的危险性和灾难性，其可能发生的溃决和溃决洪水将造成灾难性破坏和生命损失，尤其是在青藏高原东缘等地震活动频繁的高寒地区。本研究在综合考虑上游堰塞湖水力特征、溃坝和侵蚀过程以及下游 OF 动力的基础上，建立了重建青藏高原东部岷江上游流域巨型古滑坡堰塞湖溃决洪水（LLOF）下游危害的有效数值模型。利用单堤溃坝程序和一维稳定水力数值模型重建了泸沽湖的泄洪峰值和古水力学特征。结果表明，蝶溪古河床的最大泄洪峰值为 73,060-82,235 m3/s，不确定边界为 73,000-90,000 m3/s（平均值为 81,500 m3/s）。由此推断，与其他类型的洪水相比，迭溪古大洪水是已知世界范围内最大的大洪水之一。随后，在一维非稳态数值模型中模拟了蝶溪古溃决洪水的水力特征和路线演化过程。结果表明，蝶溪古LLOF在91.23 km的模拟河段内，从蝶溪到汶川需要7.47 h，平均传播速度为3.39 m/s。在 15.57 h 时，模拟断面（蝶溪至汶川）达到最大淹没范围 664.91 km2，平均值为 7.29 km2/km。我们的建模结果表明，数值模型可成功用于重建深部封闭峡谷环境中的古河套水力学。重建的古LLOF数据对丰富区域特大洪水记录具有重要意义，并为青藏高原东缘岷江上游流域地质灾害防治和OF风险评估提供了宝贵资料。
{"title":"Hydraulic reconstruction of giant paleolandslide‐dammed lake outburst floods in high‐mountain region, eastern Tibetan Plateau: A case study of the Upper Minjiang River valley","authors":"Junxue Ma, Jian Chen, Chong Xu","doi":"10.1111/tgis.13218","DOIUrl":"https://doi.org/10.1111/tgis.13218","url":null,"abstract":"Landslide‐dammed lakes are potentially hazardous and catastrophic for their possible failures and outburst floods (OFs) that will cause disastrous damage and life‐threatening losses, especially in the alpine areas where seismicity is strong and frequent, such as the eastern margin of the Tibetan Plateau. This study focused on spreading an effective numerical model to reconstruct downstream hazards induced by a giant ancient landslide‐dammed lake outburst flood (LLOF) in the upper Minjiang River valley, eastern Tibetan Plateau based on the integration of the hydraulic characteristics of the upstream dammed lake, dam failure and erosion process, and downstream OF dynamics. The peak discharge levels and paleohydraulics of the LLOF were reconstructed using single‐embankment dam‐break program and one‐dimensional steady hydraulic numerical model. The results reveal that the maximum peak discharge of the Diexi paleo LLOF was 73,060–82,235 m<jats:sup>3</jats:sup>/s, with an uncertainty bound of 73,000–90,000 m<jats:sup>3</jats:sup>/s (mean value: 81,500 m<jats:sup>3</jats:sup>/s). Which inferred that the Diexi paleo LLOF was one of the largest known LLOFs in the view of worldwide scope comparing with other types of floods. Then, the hydraulic characteristics and route evolution of the LLOF were simulated in one‐dimensional unsteady numerical model. The results showed that the Diexi paleo LLOF took 7.47 h to transport from Diexi to Wenchuan within the simulated section of 91.23 km, with an average propagation velocity of 3.39 m/s. At the time of 15.57 h, the simulating section (between Diexi and Wenchuan) reached the maximum extent of inundation which was 664.91 km<jats:sup>2</jats:sup>, with an average value of 7.29 km<jats:sup>2</jats:sup>/km. Our modeling supports that the numerical model can be used successfully to reconstruct the hydraulics of a paleo LLOF in deep confined gorge environment. The reconstructed paleo LLOF data are of great significance to enrich the regional megaflood records and provide valuable information for geological hazard controls and OF risk assessment within the upper catchment of Minjiang River at the eastern margin of the Tibetan Plateau.","PeriodicalId":47842,"journal":{"name":"Transactions in GIS","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2024-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141609147","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Nik Ahmad Faris Nik Effendi, Nurul Ain Mohd Zaki, Zulkiflee Abd Latif, Mohd Faisal Abdul Khanan
The increase in greenhouse gases in the atmosphere is due to carbon dioxide (CO2), which has affected climate change. Therefore, the forest plays an essential role in carbon storage which absorbs the CO2 and releases oxygen (O2) to stabilize the earth's ecosystem. This research aims to estimate aboveground biomass (AGB) using a combination of airborne hyperspectral and LiDAR data with field observation in a tropical forest. The objective of this study is to test the ability of vegetation indices and topographic features derived from hyperspectral and LiDAR data using machine learning for AGB estimation and to identify the best machine learning algorithms for estimating AGB in tropical forest. In this research, artificial neural network (ANN) and random forest (RF) algorithm were used to predict the AGB using different models with different combinations of variables. During model selection, the best model fit was selected by calculating statistical parameters such as the residual of the coefficient of determination (R2) and root mean square error (RMSE). Based on the statistical indicators, the most suitable model is Model 4 using anRF algorithm with mtry = p, and a combination of field observation, LiDAR, hyperspectral, vegetation indices (VIs), and topography. This model produced R2 = 0.997 and RMSE = 30.653 kg/tree. Therefore, using a combination of field observation and remote sensing data with machine learning techniques is reliable in forest management to estimate AGB in tropical forest.
{"title":"Combination of hyperspectral and LiDAR for aboveground biomass estimation using machine learning","authors":"Nik Ahmad Faris Nik Effendi, Nurul Ain Mohd Zaki, Zulkiflee Abd Latif, Mohd Faisal Abdul Khanan","doi":"10.1111/tgis.13214","DOIUrl":"https://doi.org/10.1111/tgis.13214","url":null,"abstract":"The increase in greenhouse gases in the atmosphere is due to carbon dioxide (CO<jats:sub>2</jats:sub>), which has affected climate change. Therefore, the forest plays an essential role in carbon storage which absorbs the CO<jats:sub>2</jats:sub> and releases oxygen (O<jats:sub>2</jats:sub>) to stabilize the earth's ecosystem. This research aims to estimate aboveground biomass (AGB) using a combination of airborne hyperspectral and LiDAR data with field observation in a tropical forest. The objective of this study is to test the ability of vegetation indices and topographic features derived from hyperspectral and LiDAR data using machine learning for AGB estimation and to identify the best machine learning algorithms for estimating AGB in tropical forest. In this research, artificial neural network (ANN) and random forest (RF) algorithm were used to predict the AGB using different models with different combinations of variables. During model selection, the best model fit was selected by calculating statistical parameters such as the residual of the coefficient of determination (<jats:italic>R</jats:italic><jats:sup>2</jats:sup>) and root mean square error (RMSE). Based on the statistical indicators, the most suitable model is Model 4 using anRF algorithm with <jats:italic>mtry</jats:italic> = p, and a combination of field observation, LiDAR, hyperspectral, vegetation indices (VIs), and topography. This model produced <jats:italic>R</jats:italic><jats:sup>2</jats:sup> = 0.997 and RMSE = 30.653 kg/tree. Therefore, using a combination of field observation and remote sensing data with machine learning techniques is reliable in forest management to estimate AGB in tropical forest.","PeriodicalId":47842,"journal":{"name":"Transactions in GIS","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2024-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141609146","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
With the widespread application of multisource data, the identification of urban polycenters faces the challenge of increasing data costs. This study developed a cost‐effective model for identifying urban polycenters by employing a combination of the Random Forest algorithm and Local Moran's I index. Using point‐of‐interest data from Amap, our model was benchmarked against a multisource data model to verify its effectiveness and accuracy. The results indicate that the single‐source model possesses an accuracy comparable to that of the multisource model in determining the centrality and spatial distribution of urban centers, thus offering a substantial capability to reduce reliance on multisource data. The random forest method exhibits a significant accuracy advantage over traditional ordinary least squares regression methods. However, it also exhibited susceptibility to overfitting and variations in data sampling. This suggests that while the model is highly effective for large‐scale urban studies, it requires careful handling of data inputs. This model can be applied to actual urban planning and research, providing a useful instrument for investigating urban polycentric structures at different spatial scales. This will increase the usefulness of the model in real‐world scenarios and lower the expenses related to analyzing urban data.
随着多源数据的广泛应用，城市多中心的识别面临着数据成本增加的挑战。本研究采用随机森林算法和本地莫兰 I 指数相结合的方法，开发了一种经济高效的城市多中心识别模型。利用 Amap 的兴趣点数据，我们的模型与多源数据模型进行了基准测试，以验证其有效性和准确性。结果表明，单源模型在确定城市中心的中心性和空间分布方面具有与多源模型相当的准确性，从而大大减少了对多源数据的依赖。与传统的普通最小二乘回归方法相比，随机森林方法在准确性方面具有显著优势。不过，它也表现出易受过度拟合和数据采样变化的影响。这表明，虽然该模型在大规模城市研究中非常有效，但需要谨慎处理数据输入。该模型可应用于实际的城市规划和研究，为研究不同空间尺度的城市多中心结构提供有用的工具。这将提高模型在现实世界场景中的实用性，并降低与分析城市数据相关的费用。
{"title":"A global polycenter identification method with single‐source data: The integration of local multisource data recognition","authors":"Yichen Ruan, Xiaoyi Zhang, Qiuxiao Chen, Mingyu Zhang","doi":"10.1111/tgis.13211","DOIUrl":"https://doi.org/10.1111/tgis.13211","url":null,"abstract":"With the widespread application of multisource data, the identification of urban polycenters faces the challenge of increasing data costs. This study developed a cost‐effective model for identifying urban polycenters by employing a combination of the Random Forest algorithm and Local Moran's <jats:italic>I</jats:italic> index. Using point‐of‐interest data from Amap, our model was benchmarked against a multisource data model to verify its effectiveness and accuracy. The results indicate that the single‐source model possesses an accuracy comparable to that of the multisource model in determining the centrality and spatial distribution of urban centers, thus offering a substantial capability to reduce reliance on multisource data. The random forest method exhibits a significant accuracy advantage over traditional ordinary least squares regression methods. However, it also exhibited susceptibility to overfitting and variations in data sampling. This suggests that while the model is highly effective for large‐scale urban studies, it requires careful handling of data inputs. This model can be applied to actual urban planning and research, providing a useful instrument for investigating urban polycentric structures at different spatial scales. This will increase the usefulness of the model in real‐world scenarios and lower the expenses related to analyzing urban data.","PeriodicalId":47842,"journal":{"name":"Transactions in GIS","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2024-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141609145","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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