Investigation of Urbanization Effects on Land Surface Phenology in Northeast China during 2001–2015

Abstract

Abstract:The urbanization effects on land surface phenology (LSP) have been investigated by many studies, but few studies have focused on the temporal variations of urbanization effects on LSP. In this study, we used the Moderate-resolution Imaging Spectroradiometer (MODIS) Enhanced Vegetation Index (EVI), MODIS Land Surface Temperature (LST) data and China's Land Use/Cover Datasets (CLUDs) to investigate the temporal variations of urban heat island intensity (UHII) and urbanization effects on LSP in Northeast China during 2001-2015. LST and phenology differences between urban and rural areas represented the urban heat island intensity and urbanization effects on LSP, respectively. A Mann-Kendall nonparametric test and Sen's slope were used to evaluate the trends of urbanization effects on LSP and urban heat island intensity. The results indicated that the average LSP during 2001-2015 was characterized by high spatial heterogeneity. The start of the growing season (SOS) in old urban areas had become earlier and earlier compared to rural areas, and the differences in SOS between urbanized areas and rural areas changed greatly during 2001-2015 (−0.79 days/year, p < 0.01). Meanwhile, the length of the growing season (LOS) in urban and adjacent areas had become increasingly longer than rural areas, especially in urbanized areas (0.92 days/year, p < 0.01), but the differences in the end of the growing season (EOS) between urban and adjacent areas did not change significantly. Next, the UHII increased in spring and autumn during the whole study period. Moreover, the correlation analysis indicated that the increasing urban heat island intensity in spring contributed greatly to the increases of urbanization effects on SOS, but the increasing urban heat island intensity in autumn did not lead to the increases of urbanization effects on EOS in Northeast China.

Urbanization Effects on Vegetation and Surface Urban Heat Islands in China’s Yangtze River Basin

“…The results showed that the urban area extracted using the method in this study was larger than that from Landsat data ( Table 2); however, it is appropriate for this study due to these reasons: (a) the footprint of SUHI was larger than the actual urban size according to previous studies [40,48,49]; (b) the urbanization primarily occurred in the suburban areas according to previous studies [26,36]; (c) the study period in this study was 2001-2016, the land cover maps used were during 2001-2013, and the urban area may expand during 2014-2016. Finally, we generated the buffer zone between 20 and 25 km from the urban areas, and we excluded the pixel with DN > 10 (SNLD) in the 20-25 km buffer and defined it as rural area [5,41]. The rural areas in this study were set larger than previous studies, since the footprint of UEs was much larger than the urban areas in previous studies [5,40,48,49].…”

“…The purpose was to compare the SUHII or ∆EVI in the same places across different years [5]. In addition, the urban area in the year 2001 was defined as old urban area (OUA), and the urbanized area (UA) was defined as the differences between the urban area in the year 2001 and 2013.…”

“…Finally, we generated the buffer zone between 20 and 25 km from the urban areas, and we excluded the pixel with DN > 10 (SNLD) in the 20-25 km buffer and defined it as rural area [5,41]. The rural areas in this study were set larger than previous studies, since the footprint of UEs was much larger than the urban areas in previous studies [5,40,48,49]. We did not select larger buffer zones for reducing the uncertainties due to different climate conditions and topography [48].…”

“…In addition, the increasing rate of global urban area is twice as fast as population growth [2]. Rapid urbanization can bring a series of problems, including urban heat island [3], reduction of cropland and vegetation coverage [4], changes of land surface phenology [5], and air pollution [6]. Thus, the magnitudes, the temporal trends and the relationships between the above-mentioned problems due to urbanization should be comprehensively investigated.…”

Study on Noise Extraction and Correction of Time Series Satellite Profile Data Using Parallel Distributed System

“…Time series NDVI is often used when investigating land cover or change using vegetation phenology. Methods based on line interpolation [13,14], fast Fourier transformation (FFT) [15,16], and wavelet [17][18][19] have been generally used to correct pixels containing clouds included in a time series NDVI profile. However, these correction methods have limitations in minimizing cloud effects because the input data for profile correction are also used by data affected by clouds.…”