Urban classification of the built-up and seasonal variations in vegetation: A framework integrating multisource datasets

García-Pardo, Karina Angélica; Moreno-Rangel, David; Domínguez-Amarillo, Samuel; García-Chávez, José Roberto

doi:10.1016/j.ufug.2023.128114

Cited by 3 publications

(8 citation statements)

References 28 publications

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“…The classifications of the biophysical variables, derived from a previous framework employed in this study found in García Pardo et al [22], show four distinct built-up clusters further delineated based on the nuances of their general vegetation cover and across different vegetation levels. The methods employed for the classifications include statistical analyses and spatial autocorrelations assessments, using two distinct approaches: the k-means clustering method and categorisation based on standardised values.…”

Section: Previous Classification Of Biophysical Variablesmentioning

confidence: 61%

“…Moreover, vegetation cover was considered both in a general sense and across various levels (high, medium, and low) from a Normalised Difference Vegetation Index (NDVI) threshold. Elaborate methodologies for calculating urban density and vegetation variables are outlined in detail in García Pardo et al [22].…”

Section: Data Retrievalmentioning

confidence: 99%

“…To enhance these classifications, variables associated with the variance in broadband albedo for each polygon, denoted BBAlvar, were introduced, and categorised based on the standard deviations to the overall set of values. BBAlvar categorisation follows the same approach applied to the vegetation variables in a previous study [22]. Land Surface Temperature…”

Section: Previous Classification Of Biophysical Variablesmentioning

confidence: 99%

“…To analyse the influence of the variables in LST using the classification made prior to the study [22], four multiple linear regressions for each LST were carried out by separating the polygon data by built-up clusters (a, b, c, and d). The intention was to ascertain variations in variable influence within these clusters, identify better fitting models, and highlight any residual unusual polygons in each cluster.…”

Section: Identitification Of Morphological Patterns From Multiple Reg...mentioning

confidence: 99%

“…Three methods concerning statistical analysis and observational techniques were employed to characterise these patterns, drawing upon pre-existing classifications of biophysical variables detailed in a prior study [22] within the Canillas neighbourhood, in Madrid, Spain. The basis of this characterisation rests upon the distribution of LST across winter and summer seasons.…”

Section: Introductionmentioning

confidence: 99%

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Characterisation of Morphological Patterns for Land Surface Temperature Distribution in Urban Environments: An Approach to Identify Priority Areas

García-Pardo,

Moreno-Rangel,

Domínguez-Amarillo

et al. 2023

Climate

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The validated influence of urban biophysical structure on environmental processes within urban areas has heightened the emphasis on studies examining morphological patterns to determine precise locations and underlying causes of urban climate conditions. The present study aims to characterise morphological patterns describing the distribution of Land Surface Temperature (LST) based on a prior classification of biophysical variables, including urban density (building intensity and average height), surface characteristics, shortwave solar radiation (broadband albedo), and seasonal variations in vegetation cover (high, medium, and low levels), retrieved from multisource datasets. To describe the distribution of LST, the variables were calculated, classified, and subsequently, analysed individually and collectively concerning winter and summer LST values applied in an urban neighbourhood in Madrid, Spain. The results from the analytical approaches (observation, correlations, and multiple regressions) were compared to define the morphological patterns. The selection of areas resulting from the morphological patterns with the most unfavourable LST values showed agreement of up to 89% in summer and up to 70% for winter, demonstrating the feasibility of the methods applied to identify priority areas for intervention by season. Notably, low and high vegetation levels emerged as pivotal biophysical characteristics influencing LST distribution compared to the other characteristics, emphasising the significance of integrating detailed seasonal vegetation variations in urban analyses.

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Section: Previous Classification Of Biophysical Variablesmentioning

confidence: 61%

Section: Data Retrievalmentioning

confidence: 99%

Section: Previous Classification Of Biophysical Variablesmentioning

confidence: 99%

Section: Identitification Of Morphological Patterns From Multiple Reg...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Characterisation of Morphological Patterns for Land Surface Temperature Distribution in Urban Environments: An Approach to Identify Priority Areas

García-Pardo,

Moreno-Rangel,

Domínguez-Amarillo

et al. 2023

Climate

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Spatial and Temporal Change Characteristics and Climatic Drivers of Vegetation Productivity and Greenness during the 2001–2020 Growing Seasons on the Qinghai–Tibet Plateau

Liang,

Marino,

2024

Land

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Exploring NDVI variation and what drives it on the Qinghai–Tibet Plateau can strategically inform environmental protection efforts in light of global climate change. For this analysis, we obtained MODIS NDVI data collected during the vegetative growing season, vegetation types for the region, and meteorological data for the same period from 2001 to 2020. We performed Theil–Sen trend analysis, Mann–Kendall significance testing, spatial autocorrelation analysis, and Hurst index calculation to review the spatiotemporal changes in NDVI characteristics on the plateau for various vegetation types. We used the correlation coefficients from these analyses to investigate how the NDVI responds to temperature and precipitation. We found the following: (1) Overall, the Qinghai–Tibet Plateau NDVI increased throughout the multi-year growing season, with a much larger area of improvement (65.68%) than of degradation (8.83%). (2) The four main vegetation types were all characterized by improvement, with meadows (72.13%) comprising the largest portion of the improved area and shrubs (18.17%) comprising the largest portion of the degraded area. (3) The spatial distribution of the NDVI had a strong positive correlation and clustering effect and was stable overall. The local clustering patterns were primarily low–low and high–high clustering. (4) The Hurst index had an average value of 0.46, indicating that the sustainability of vegetation is poor; that is, the trend of vegetation change in the growing season in a large part of the Qinghai–Tibet Plateau in the future is opposite to that in the past. (5) The plateau NDVI correlated positively with air temperature and precipitation. However, the correlations varied geographically: air temperature had a wide influence, whereas precipitation mainly influenced meadows and grassland in the northern arid zone. The overall temperature-driven effect was stronger than that of precipitation. This finding is consistent with the current research conclusion that global warming and humidification promote vegetation growth in high-altitude areas and further emphasizes the uniqueness of the Qinghai–Tibet Plateau as a climate-change-sensitive area. This study also offers a technical foundation for understanding how climate change impacts high-altitude ecosystems, as well as for formulating ecological protection strategies for the plateau.

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A Dynamic Prediction Framework for Urban Public Space Vitality: From Hypothesis to Algorithm and Verification

Liu,

Guo

2024

Sustainability

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Predicting and assessing the vitality of public urban spaces is crucial for effective urban design, aiming to prevent issues such as “ghost streets” and minimize resource wastage. However, existing assessment methods often lack temporal dynamics or heavily rely on historical big data, limiting their ability to accurately predict outcomes for unbuilt projects. To address these challenges, this study integrates previous methodologies with observations of crowd characteristics in public spaces. It introduces the crowd-frequency hypothesis and develops an algorithm to establish a time-dimensional urban vitality dynamic prediction model. Through a case study of the Rundle Mall neighborhood in Adelaide, Australia, the effectiveness of the prediction model was validated using on-site observation sampling and comparative verification. The prediction model framework allows for the determination of urban vitality within specific time ranges by directly inputting basic information, providing valuable support to urban planners and government officials during the design and decision-making processes. It offers a cost-effective approach to achieve sustainable urban vitality construction. Furthermore, machine learning techniques, specifically the decision tree model, were applied to case data to develop a set of preliminary algorithm tools, which enable output of reference urban vitality levels (high-medium-low).

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Urban classification of the built-up and seasonal variations in vegetation: A framework integrating multisource datasets

Cited by 3 publications

References 28 publications

Characterisation of Morphological Patterns for Land Surface Temperature Distribution in Urban Environments: An Approach to Identify Priority Areas

Characterisation of Morphological Patterns for Land Surface Temperature Distribution in Urban Environments: An Approach to Identify Priority Areas

Spatial and Temporal Change Characteristics and Climatic Drivers of Vegetation Productivity and Greenness during the 2001–2020 Growing Seasons on the Qinghai–Tibet Plateau

A Dynamic Prediction Framework for Urban Public Space Vitality: From Hypothesis to Algorithm and Verification

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