Modeling the Effects of the Urban Built-Up Environment on Plant Phenology Using Fused Satellite Data

Gervais, Norman; Buyantuev, Alexander; Gao, Feng

doi:10.3390/rs9010099

Cited by 13 publications

(9 citation statements)

References 49 publications

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“…Data fusion is also a promising way to acquire high spatial-temporal resolution data, regardless of the weather condition. The derived multitemporal Landsat-like NDVI data used in this study have high spatial resolution and a medium temporal resolution that can be used to identify paddy rice and capture the phenology of vegetation [39,[95][96][97]. Moreover, using the fused time series can produce accurate vegetation (especially crops) distributions.…”

Section: Discussionmentioning

confidence: 99%

“…Regarding complex heterogeneous areas, high temporal and spatial variations might lead to greater prediction errors than in homogeneous areas. Different temporal growing patterns of vegetation and spatial heterogeneity in the study area might account for this [97]. Concerning a CNN, the proposed method does not consider the spatial pattern of the study area, which is also a key factor for discriminating vegetation types.…”

Section: Discussionmentioning

confidence: 99%

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Mapping Paddy Rice Using a Convolutional Neural Network (CNN) with Landsat 8 Datasets in the Dongting Lake Area, China

et al. 2018

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Rice is one of the world’s major staple foods, especially in China. Highly accurate monitoring on rice-producing land is, therefore, crucial for assessing food supplies and productivity. Recently, the deep-learning convolutional neural network (CNN) has achieved considerable success in remote-sensing data analysis. A CNN-based paddy-rice mapping method using the multitemporal Landsat 8, phenology data, and land-surface temperature (LST) was developed during this study. First, the spatial–temporal adaptive reflectance fusion model (STARFM) was used to blend the moderate-resolution imaging spectroradiometer (MODIS) and Landsat data for obtaining multitemporal Landsat-like data. Subsequently, the threshold method is applied to derive the phenological variables from the Landsat-like (Normalized difference vegetation index) NDVI time series. Then, a generalized single-channel algorithm was employed to derive LST from the Landsat 8. Finally, multitemporal Landsat 8 spectral images, combined with phenology and LST data, were employed to extract paddy-rice information using a patch-based deep-learning CNN algorithm. The results show that the proposed method achieved an overall accuracy of 97.06% and a Kappa coefficient of 0.91, which are 6.43% and 0.07 higher than that of the support vector machine method, and 7.68% and 0.09 higher than that of the random forest method, respectively. Moreover, the Landsat-derived rice area is strongly correlated (R2 = 0.9945) with government statistical data, demonstrating that the proposed method has potential in large-scale paddy-rice mapping using moderate spatial resolution images.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Mapping Paddy Rice Using a Convolutional Neural Network (CNN) with Landsat 8 Datasets in the Dongting Lake Area, China

et al. 2018

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“…In Shanghai, China, for example, the average rural-urban difference was approximately 5-10 days using Landsat EVI (30 m) during 2001-2010 [26] compared to 7-15 days using SPOT NDVI (1 km) during 2002-2009 [27]. Similarly, in Salt Lake City, United States, the average rural-urban difference was greater than 15 days using MODIS EVI (500 m) during 2003-2012 [24] while the average rural-urban difference was less than 3.56 days using fused NDVI (30 m) during 2000-2011 [28]. From these examples, it is noticeable that urbanization effects on vegetation spring phenology using images with coarser resolutions is larger than finer resolutions, which implies large uncertainties when using satellite images to study these effects.…”

Section: Introductionmentioning

confidence: 88%

“…Knowledge of these reason(s) can improve the accuracy and reliability of the future studies on this topic. (30 m ) TIMESAT Salt Lake City, United States Less than 3.56 [28] 1 D means average difference of vegetation spring phenology between rural and urban areas; 2 LPA means Landsat phenology algorithm; 3 Fused NDVI means NDVI time series derived from Landsat and MODIS fusion.…”

Section: Introductionmentioning

confidence: 99%

Coarse-Resolution Satellite Images Overestimate Urbanization Effects on Vegetation Spring Phenology

Tian

Zhu

et al. 2020

Remote Sensing

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Numerous investigations of urbanization effects on vegetation spring phenology using satellite images have reached a consensus that vegetation spring phenology in urban areas occurs earlier than in surrounding rural areas. Nevertheless, the magnitude of this rural–urban difference is quite different among these studies, especially for studies over the same areas, which implies large uncertainties. One possible reason is that the satellite images used in these studies have different spatial resolutions from 30 m to 1 km. In this study, we investigated the impact of spatial resolution on the rural–urban difference of vegetation spring phenology using satellite images at different spatial resolutions. To be exact, we first generated a dense 10 m NDVI time series through harmonizing Sentinel-2 and Landsat-8 images by data fusion method, and then resampled the 10 m time series to coarser resolutions from 30 m to 8 km to simulate images at different resolutions. Afterwards, to quantify urbanization effects, vegetation spring phenology at each resolution was extracted by a widely used tool, TIMESAT. Last, we calculated the difference between rural and urban areas using an urban extent map derived from NPP VIIRS nighttime light data. Our results reveal: (1) vegetation spring phenology in urban areas happen earlier than rural areas no matter which spatial resolution from 10 m to 8 km is used, (2) the rural–urban difference in vegetation spring phenology is amplified with spatial resolution, i.e., coarse satellite images overestimate the urbanization effects on vegetation spring phenology, and (3) the underlying reason of this overestimation is that the majority of urban pixels in coarser images have higher diversity in terms of spring phenology dates, which leads to spring phenology detected from coarser NDVI time series earlier than the actual dates. This study indicates that spatial resolution is an important factor that affects the accuracy of the assessment of urbanization effects on vegetation spring phenology. For future studies, we suggest that satellite images with a fine spatial resolution are more appropriate to explore urbanization effects on vegetation spring phenology if vegetation species in urban areas is very diverse.

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“…Even more recently, a study fused MODIS and Landsat reflectance data in addition to LST derived from Landsat thermal data and found that in general, Ogden, UT experienced earlier start of season, later end of season, and consequently longer length of season than the surrounding exurban areas [26].…”

Section: Introductionmentioning

confidence: 99%

Impacts of Thermal Time on Land Surface Phenology in Urban Areas

2017

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Urban areas alter local atmospheric conditions by modifying surface albedo and consequently the surface radiation and energy balances, releasing waste heat from anthropogenic uses, and increasing atmospheric aerosols, all of which combine to increase temperatures in cities, especially overnight, compared with surrounding rural areas, resulting in a phenomenon called the "urban heat island" effect. Recent rapid urbanization of the planet has generated calls for remote sensing research related to the impacts of urban areas and urbanization on the natural environment. Spatially extensive, high spatial resolution data products are needed to capture phenological patterns in regions with heterogeneous land cover and external drivers such as cities, which are comprised of a mixture of land cover/land uses and experience microclimatic influences. Here we use the 30 m normalized difference vegetation index (NDVI) product from the Web-Enabled Landsat Data (WELD) project to analyze the impacts of urban areas and their surface heat islands on the seasonal development of the vegetated land surface along an urban-rural gradient for 19 cities located in the Upper Midwest of the United States. We fit NDVI observations from 2003-2012 as a quadratic function of thermal time as accumulated growing degree-days (AGDD) calculated from the Moderate-resolution Imaging Spectroradiometer (MODIS) 1 km land surface temperature product to model decadal land surface phenology metrics at 30 m spatial resolution. In general, duration of growing season (measured in AGDD) in green core areas is equivalent to duration of growing season in urban extent areas, but significantly longer than duration of growing season in areas outside of the urban extent. We found an exponential relationship in the difference of duration of growing season between urban and surrounding rural areas as a function of distance from urban core areas for perennial vegetation, with an average magnitude of 669 AGDD (base 0 • C) and the influence of urban areas extending greater than 11 km from urban core areas. At the regional scale, relative change in duration of growing season does not appear to be significantly related to total area of urban extent, population, or latitude. The distance and magnitude that urban areas exert influence on vegetation in and near cities is relatively uniform.

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Modeling the Effects of the Urban Built-Up Environment on Plant Phenology Using Fused Satellite Data

Cited by 13 publications

References 49 publications

Mapping Paddy Rice Using a Convolutional Neural Network (CNN) with Landsat 8 Datasets in the Dongting Lake Area, China

Mapping Paddy Rice Using a Convolutional Neural Network (CNN) with Landsat 8 Datasets in the Dongting Lake Area, China

Coarse-Resolution Satellite Images Overestimate Urbanization Effects on Vegetation Spring Phenology

Impacts of Thermal Time on Land Surface Phenology in Urban Areas

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