Comparison of digital elevation models and relevant derived attributes

Li, Xinchuan; Zhang, Youjing; Jin, Xiuliang; He, Qian; Zhang, Xiuping

doi:10.1117/1.jrs.11.046027

Cited by 14 publications

(16 citation statements)

References 48 publications

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“…To eliminate the influence of the elevation factor on the calculation for the SUHI intensity, we selected NASA Shuttle Radar Topography Mission (SRTM) data provided by the United States Geological Survey (USGS). Previous research has shown that these data have better vertical accuracy than the Global Multiresolution Terrain Elevation Data from 2010 (GMTED2010) and the advanced spaceborne thermal emission and reflection radiometer (ASTER) elevation data [42].…”

Section: Datamentioning

confidence: 99%

“…To compare the differences in the temporal trends between the AB method and the OSUE method SUHI, we combine the Theil-Sen median trend analysis and the Mann-Kendall test to analyze the calculated results. Theil-Sen median trend analysis is a robust statistical method for trend analysis [42], and it calculates the median slopes between all of the possible n(n − 1)/2 pairwise combinations throughout the time-series data. The method is based on nonparametric statistics and is particularly effective when the estimation of trends in small series is needed [43].…”

Section: Calculation Of the Suhi Intensitymentioning

confidence: 99%

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Spatiotemporal Patterns and Drivers of the Surface Urban Heat Island in 36 Major Cities in China: A Comparison of Two Different Methods for Delineating Rural Areas

et al. 2020

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Urban heat islands (UHIs) are an important issue in urban sustainability, and the standardized calculation of surface urban heat island (SUHI) intensity has been a common concern of researchers in the past. In this study, we used the administrative borders (AB) method and an optimized simplified urban-extent (OSUE) algorithm to calculate the surface urban heat island intensity from 2001 to 2017 for 36 major cities in mainland China by using Moderate Resolution Imaging Spectroradiometer (MODIS) images. The spatiotemporal differences between these two methods were analyzed from the perspectives of the regional and national patterns and the daily, monthly, and annual trends. Regardless of the spatial or temporal scale, the calculation results of these two methods showed extremely similar patterns, especially for the daytime. However, when the calculated SUHI intensities were investigated through a regression analysis with multiple driving factors, we found that, although natural conditions were the main drivers for both methods, the anthropogenic factors obtained from statistical data (population and gross domestic product) were more correlated with the SUHI intensity from the AB method. This trend was probably caused by the spatial extent of the statistical data, which aligned more closely with the rural extent in the AB method. This study not only explores the standardization of the calculation of urban heat intensity but also provides insights into the relationship between urban development and the SUHI.

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Section: Datamentioning

confidence: 99%

Section: Calculation Of the Suhi Intensitymentioning

confidence: 99%

Spatiotemporal Patterns and Drivers of the Surface Urban Heat Island in 36 Major Cities in China: A Comparison of Two Different Methods for Delineating Rural Areas

et al. 2020

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“…DEM data, which are Global Multi-resolution Terrain Elevation Data 2010 with a spatial resolution of about 30 m, were acquired from GEE. The topographic slope was derived from DEM on GEE [44]. The terrain characteristics may affect vegetation cover and thus should be examined in terms of their impact on urban vegetation cover.…”

Section: Data Sourcesmentioning

confidence: 99%

Exploring the Dynamics of Urban Greenness Space and Their Driving Factors Using Geographically Weighted Regression: A Case Study in Wuhan Metropolis, China

Yang

Sha

2020

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Urban greenness plays a vital role in supporting the ecosystem services of a city. Exploring the dynamics of urban greenness space and their driving forces can provide valuable information for making solid urban planning policies. This study aims to investigate the dynamics of urban greenness space patterns through landscape indices and to apply geographically weighted regression (GWR) to map the spatially varied impact on the indices from economic and environmental factors. Two typical landscape indices, i.e., percentage of landscape (PLAND) and aggregation index (AI), which measure the abundance and fragmentation of urban greenness coverage, respectively, were taken to map the changes in urban greenness. As a case study, the metropolis of Wuhan, China was selected, where time-series of urban greenness space were extracted at an annual step from the Landsat collections from Google Earth Engine during 2000–2018. The study shows that the urban greenness space not only decreased significantly, but also tended to be more fragmented over the years. Road network density, normalized difference built-up index (NDBI), terrain elevation and slope, and precipitation were found to significantly correlate to the landscape indices. GWR modeling successfully captures the spatially varied impact from the considered factors and the results from GWR modeling provide a critical reference for making location-specific urban planning.

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“…Diversos fatores influenciam a precisão do MDE, incluindo: a fonte dos dados, o algoritmo usado para calcular diferentes atributos, elevação e resolução espacial (LI et al, 2017). É preciso considerar, também, a necessidade de diversos estudos de validação, seja eles locais e/ou considerando bases de dados diferentes.…”

Section: Conclusõesunclassified

“…O Modelo Digital de Elevação -MDE -é uma das bases de dados utilizadas no Sistema de Informações Geográficas -SIG. O MDE é fundamental para os métodos de análise do terreno (LI et al, 2017) e a sua precisão é crucial (ERSKINE et al, 2007). Essa base de dados, da qual se extrai informações, deve apresentar resolução (REDDY; REDDY, 2015) e precisão, adequadas para se extrair informações precisas sobre determinado aspecto.…”

Section: Introductionunclassified

PRECISÃO DO MODELO DIGITAL DE ELEVAÇÃO (SRTM-Topodata) COM BASE EM DADOS DE PROJETOS DE APROVEITAMENTOS HIDRELÉTRICOS

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Silva³

et al. 2019

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A precisão altimétrica do Modelo Digital de Elevação – MDE tem sido tema de diversos estudos. Essa precisão exerce forte influência sobre as informações extraídas desses dados. Nesse contexto, o presente estudo compara dados observados em projetos de aproveitamentos hidrelétricos com dados extraídos do MDE. A comparação de dados altimétricos de informações extraídas pelo MDE com as contidas no Projeto Básico Ambiental – PBA desses empreendimentos, assumido como informação verdadeira, foi feita com o intuito de analisar o erro das informações extraídas do MDE em relação aos dados contidos no PBA e assim verificar a confiança nesse tipo de estimativa. Foram calculados o erro e o coeficiente de determinação de Pearson entre a altura da barragem (determinada com base no MDE) em relação à altura apresentada no PBA. Também foi comparada uma seção topobatimétrica do PBA com a mesma seção extraída pelo MDE. O erro relativo médio e o coeficiente de determinação entre as cinco alturas (estimadas e de projeto) foi de 11% e 0,874, respectivamente. O coeficiente de determinação, o erro médio quadrático e o erro médio entre as seções foram de 0,98, 1,56 e -0,02, respectivamente. A análise evidenciou que há erros em relação às informações extraídas do MDE. Entretanto, considerando a escala utilizada nesse estudo, os erros foram menores que os observados na literatura.Palavras-chave: MDE validação, Altura de barragem, Seção topobatimétrica. ALTIMETRY ACCURACY OF THE DIGITAL ELEVATION MODEL (SRTM-Topodata) BASED ON DESIGNS OF HYDROPOWER PLANTS DATA ABSTRACT: The altimetry accuracy measurements of the Digital Elevation Model - DEM have been the subject of several studies. This accuracy plays an important role on information extracted from these data. In this context, this study compares data observed in designs of hydroelectric power plants with data extracted from the DEM. The comparison between the altimetry of data extracted from the DEM with those contained in the Basic Environmental Project - BEP of these facilities, assumed as the true information, was done with the purpose of analyzing the error of the information extracted from the DEM in relation to the data contained in the BEP and thus verifying the confidence in this type of estimate. The error and the coefficient of determination between the dam height (determined based on the DEM) and the dam height presented in the BEP were calculated. Moreover, a river cross section published in the BEP was contrasted with the same cross section extracted from the DEM. The mean relative error and the coefficient of determination between the five heights (estimated and projected) was 11% and 0.874, respectively. The coefficient of determination, mean square error and mean error between sections were 0.98, 1.56 and -0,02, respectively. The analysis evidenced that there are errors in relation to the information extracted from the DEM. However, considering the scale used in this study, the errors were smaller than those observed in the literature.Keywords: DEM validation, dam height, cross section.

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Comparison of digital elevation models and relevant derived attributes

Cited by 14 publications

References 48 publications

Spatiotemporal Patterns and Drivers of the Surface Urban Heat Island in 36 Major Cities in China: A Comparison of Two Different Methods for Delineating Rural Areas

Spatiotemporal Patterns and Drivers of the Surface Urban Heat Island in 36 Major Cities in China: A Comparison of Two Different Methods for Delineating Rural Areas

Exploring the Dynamics of Urban Greenness Space and Their Driving Factors Using Geographically Weighted Regression: A Case Study in Wuhan Metropolis, China

PRECISÃO DO MODELO DIGITAL DE ELEVAÇÃO (SRTM-Topodata) COM BASE EM DADOS DE PROJETOS DE APROVEITAMENTOS HIDRELÉTRICOS

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