Urban vegetation phenology analysis using high spatio-temporal NDVI time series

Li, Feng; Guo, Song; Zhu, Liujun; Yinqing, Zhou; Lu, Di

doi:10.1016/j.ufug.2017.05.001

Cited by 50 publications

(19 citation statements)

References 34 publications

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“…Theoretically, the air temperature should follow a relatively well-defined spatial pattern radiating from the urban center to the suburbs [17,47]. Although the LST would show a certain fluctuation because of the surface properties, including geometric, radiative, thermal, moisture and aerodynamic, during the radiation process (see the blue points in Figure 2), the overall performance of the fitted logistic curve (see the red line in Figure 2) could reflect the similar decline trend compared to air temperature [61][62][63]. When these regions are far enough away from the urban center, the LST should remain relatively stable as it is no longer affected by the SUHI effect.…”

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confidence: 95%

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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confidence: 95%

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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“…Previous studies characterizing the phenological dynamics of vegetation with the use of VIs derived from satellite imagery focus on natural (woodlands and wild areas) or agricultural environments [35,45,[50][51][52], with urban environments remaining scarcely investigated [55][56][57]. In addition, they are mainly based on NDVI phenology curves [35,[50][51][52] leaving the rest of VIs only slightly analyzed [45], and so, the spectral richness of new satellites is not fully taken into account.…”

Section: Introductionmentioning

confidence: 99%

Phenological Dynamics Characterization of Alignment Trees with Sentinel-2 Imagery: A Vegetation Indices Time Series Reconstruction Methodology Adapted to Urban Areas

et al. 2020

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This article presents a novel methodology for the characterization of tree vegetation phenology, based on vegetation indices time series reconstruction and adapted to urban areas. The methodology is based on a pixel by pixel curve fitting classification, together with a subsequent Savitzky–Golay filtering of raw phenological curves from pixels classified as vegetation. Moreover, the new method is conceived to face specificities of urban environments such as: the high heterogeneity of impervious/natural elements, the 3D structure of the city inducing shadows, the restricted spatial extent of individual tree crowns and the strong biodiversity of urban vegetation. Three vegetation indices have been studied: Normalized Difference Vegetation Index (NDVI) and Normalized Difference Red Edge Index 1 (NDRE1), which are mainly linked to chlorophyll content and leaf density and Normalized Burn Ratio (NBR) mostly correlated to water content and leaf density. The methodology has been designed to allow the analysis of annual and intra-annual vegetation phenological dynamics. Then, different annual and intra-annual criteria for phenology characterization are proposed and criticized. To show the applicability of the methodology, this article focuses on Sentinel-2 (S-2) imagery covering 2018 and the study of groups of London planes in an alignment structure in the French city of Toulouse. Results showed that the new method allows the ability to 1) describe the heterogeneity of phenologies from London planes exposed to different environmental conditions (urban canyons, proximity with a source of water) and 2) to detect intra-annual phenological dynamics linked to changes in meteorological conditions.

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“…In Mexico [8], Italy [9], Philippines [10], China [11], France [12] and other countries, work has been done with the same satellite (NDVI) for analysis of urban vegetation, for soil classification, for crop analysis, etc. Direct models center the data at a single point, while satellite images (with correct processing of the image and dates as close as possible to the sampling dates) have shown to improve the accuracy of forest biomass estimations [13].…”

Section: Introductionmentioning

confidence: 99%

Modeling Potential C, N, H Content in Aboveground Biomass with Spectral Data from Sentinel 2a

Reyes-Zurita¹,

Ja²,

Rodríguez-Ortiz³

et al. 2020

Preprint

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Nutrient estimation in forest ecosystems through satellite images allows us to obtain accurate data, starting with data transformation from forest stands and the existing relationship with the spectral information of the image through modeling. The objective of the study was to quantify and validate the content of C, N, H in aboveground tree biomass in managed stands using spatial modeling and satellite images. This study was conducted during 2017-2018 in managed forest stands in San Juan Lachao, Oaxaca, Mexico. Fifteen 400 m2 experimental sites were selectively established, using a completely randomized experimental design of five silvicultural treatments with three replications. As part of data preprocessing, normality and homogeneity of variances assumptions were checked using the Shapiro-Wilk and Bartlett tests, respectively. From the pixels, data of the average of Normalized Difference Vegetation Index (NDVI) that surrounded the sampling sites were contrasted against the data obtained from forest inventory and the regression models to estimate C, N, H and biomass were generated. Models were validated by NDVI. With the models we estimated 0.95 t ha-1 biomass, which contains between 0.61 and 0.63 of C, 0.44-0.46 of N and 0.24 of H. The models generated had coefficients of determination (R2) of 0.85 to 0.87, which are significant parameters (p ≤ 0.0001). These results confirm that the use of Sentinel satellite images in the estimation of these elements in forest ecosystems based on the relationship between data inventory and the NDVI is highly reliable.

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Urban vegetation phenology analysis using high spatio-temporal NDVI time series

Cited by 50 publications

References 34 publications

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

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

Phenological Dynamics Characterization of Alignment Trees with Sentinel-2 Imagery: A Vegetation Indices Time Series Reconstruction Methodology Adapted to Urban Areas

Modeling Potential C, N, H Content in Aboveground Biomass with Spectral Data from Sentinel 2a

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