Variability and climate change trend in vegetation phenology of recent decades in the Greater Khingan Mountain area, Northeastern China

Effect of Altitude and Topography on Vegetation Phenological Changes in the Niubeiliang Nature Reserve of Qinling Mountains, China

Due to the fragility of the habitats in mountain nature reserves, the vegetation is extremely sensitive to climate change, and its phenological changes are more specific. Therefore, it is of great significance to study the effects of topography and climate on the vegetation phenology in mountain nature reserves. Based on the vegetation phenology data retrieved from MODIS EVI2 during 2000 to 2017, combined with temperature data, spatial trend analysis and correlation analysis methods were used to study the effects of topographic and climatic factors on vegetation phenology in the Niubeiliang Nature Reserve of the Qinling Mountains. The results showed that the GSS (growing season start) was advanced with a rate of 4.24 days/10a, and the rates in the northern and southern slopes were almost the same; the GSE (growing season end) was delayed with a rate of 3.29 days/10a, and the rate in the northern slope was faster; and the GSL (growing season length) was prolonged. There were seasonal differences and north–south differences in the effects of topography on the phenophase. The phenophase changed regularly with the increase in altitude. The higher the altitude, the more significant the effect. The steeper the slope, the later the GSS, the earlier the GSE, and the more significant its effect on the GSE. The aspect had little effect on GSS but a more significant effect on GSE, which was the latest on the sunny slope and the earliest on the zero slope. Temperature affected both the GSS advance and the GSE delay, and both had a time-lag effect of approximately 2–3 months. Its effect was more significant in the GSE, in the southern slopes, and in the high-altitude areas.

Spatiotemporal Characteristics and Heterogeneity of Vegetation Phenology in the Yangtze River Delta

Vegetation phenology and its spatiotemporal driving factors are essential to reflect global climate change, the surface carbon cycle and regional ecology, and further quantitative studies on spatiotemporal heterogeneity and its two-way driving are needed. Based on MODIS phenology, meteorology, land cover and other data from 2001 to 2019, this paper analyzes the phenology change characteristics of the Yangtze River Delta from three dimensions: time, plane space and elevation. Then, the spatiotemporal heterogeneity of phenology and its driving factors are explored with random forest and geographic detector methods. The results show that (1) the advance of start of season (SOS) is insignificant—with 0.17 days per year; the end of season (EOS) shows a significant delay—0.48 days per year. The preseason temperature has a greater contribution to SOS, while preseason precipitation is main factor in determining EOS. (2) Spatial differences of the phenological index do not strictly obey the change rules of latitude at a provincial scale. The SOS of Jiangsu and Anhui is earlier than that of Zhejiang and Shanghai, and EOS shows an obvious double-clustering phenomenon. In addition, a divergent response of EOS with elevation grades is found; the most significant changes are observed at grades below 100 m. (3) Land cover (LC) type is a major factor of the spatial heterogeneity of phenology, and its change may also be one of the insignificant factors driving the interannual change of phenology. Furthermore, nighttime land surface temperature (NLST) has a relatively larger contribution to the spatial heterogeneity in non-core urban areas, but population density (PD) contributes little. These findings could provide a new perspective on phenology and its complex interactions between natural or anthropogenic factors.

Spatiotemporal Variations of Forest Vegetation Phenology and Its Response to Climate Change in Northeast China

Vegetation phenology is an important indicator of vegetation dynamics. The boreal forest ecosystem is the main part of terrestrial ecosystem in the Northern Hemisphere and plays an important role in global carbon balance. In this study, the dynamic threshold method combined with the ground-based phenology observation data was applied to extract the forest phenological parameters from MODIS NDVI time-series. Then, the spatiotemporal variation of forest phenology is discussed and the relationship between phenological change and climatic factors was concluded in the northeast China from 2011 to 2020. The results indicated that the distribution of the optimal extraction threshold has spatial heterogeneity, and the changing rate was 3% and 2% with 1° increase in latitude for SOS (the start of the growing season) and EOS (the end of the growing season). This research also notes that the SOS had an advanced trend at a rate of 0.29 d/a while the EOS was delayed by 0.47 d/a. This variation of phenology varied from different forest types. We also found that the preseason temperature played a major role in effecting the forest phenology. The temperature in winter of the previous year had a significant effect on SOS in current year. Temperature in autumn of the current year had a significant effect on EOS.

Remote Sensing in Studies of the Growing Season: A Bibliometric Analysis

Analyses of climate change based on point observations indicate an extension of the plant growing season, which may have an impact on plant production and functioning of natural ecosystems. Analyses involving remote sensing methods, which have added more detail to results obtained in the traditional way, have been carried out only since the 1980s. The paper presents the results of a bibliometric analysis of papers related to the growing season published from 2000–2021 included in the Web of Science database. Through filtering, 285 publications were selected and subjected to statistical processing and analysis of their content. This resulted in the identification of author teams that mostly focused their research on vegetation growth and in the selection of the most common keywords describing the beginning, end, and duration of the growing season. It was found that most studies on the growing season were reported from Asia, Europe, and North America (i.e., 32%, 28%, and 28%, respectively). The analyzed articles show the advantage of satellite data over low-altitude and ground-based data in providing information on plant vegetation. Over three quarters of the analyzed publications focused on natural plant communities. In the case of crops, wheat and rice were the most frequently studied plants (i.e., they were analyzed in over 30% and over 20% of publications, respectively).

Monitoring Cropland Phenology on Google Earth Engine Using Gaussian Process Regression

Monitoring cropland phenology from optical satellite data remains a challenging task due to the influence of clouds and atmospheric artifacts. Therefore, measures need to be taken to overcome these challenges and gain better knowledge of crop dynamics. The arrival of cloud computing platforms such as Google Earth Engine (GEE) has enabled us to propose a Sentinel-2 (S2) phenology end-to-end processing chain. To achieve this, the following pipeline was implemented: (1) the building of hybrid Gaussian Process Regression (GPR) retrieval models of crop traits optimized with active learning, (2) implementation of these models on GEE (3) generation of spatiotemporally continuous maps and time series of these crop traits with the use of gap-filling through GPR fitting, and finally, (4) calculation of land surface phenology (LSP) metrics such as the start of season (SOS) or end of season (EOS). Overall, from good to high performance was achieved, in particular for the estimation of canopy-level traits such as leaf area index (LAI) and canopy chlorophyll content, with normalized root mean square errors (NRMSE) of 9% and 10%, respectively. By means of the GPR gap-filling time series of S2, entire tiles were reconstructed, and resulting maps were demonstrated over an agricultural area in Castile and Leon, Spain, where crop calendar data were available to assess the validity of LSP metrics derived from crop traits. In addition, phenology derived from the normalized difference vegetation index (NDVI) was used as reference. NDVI not only proved to be a robust indicator for the calculation of LSP metrics, but also served to demonstrate the good phenology quality of the quantitative trait products. Thanks to the GEE framework, the proposed workflow can be realized anywhere in the world and for any time window, thus representing a shift in the satellite data processing paradigm. We anticipate that the produced LSP metrics can provide meaningful insights into crop seasonal patterns in a changing environment that demands adaptive agricultural production.

Exploring the Applicability and Scaling Effects of Satellite-Observed Spring and Autumn Phenology in Complex Terrain Regions Using Four Different Spatial Resolution Products

The information on land surface phenology (LSP) was extracted from remote sensing data in many studies. However, few studies have evaluated the impacts of satellite products with different spatial resolutions on LSP extraction over regions with a heterogeneous topography. To bridge this knowledge gap, this study took the Loess Plateau as an example region and employed four types of satellite data with different spatial resolutions (250, 500, and 1000 m MODIS NDVI during the period 2001–2020 and ~10 km GIMMS3g during the period 1982–2015) to investigate the LSP changes that took place. We used the correlation coefficient (r) and root mean square error (RMSE) to evaluate the performances of various satellite products and further analyzed the applicability of the four satellite products. Our results showed that the MODIS-based start of the growing season (SOS) and end of the growing season (EOS) were highly correlated with the ground-observed data with r values of 0.82 and 0.79, respectively (p < 0.01), while the GIMMS3g-based phenology signal performed badly (r < 0.50 and p > 0.05). Spatially, the LSP that was derived from the MODIS products produced more reasonable spatial distributions. The inter-annual averaged MODIS SOS and EOS presented overall advanced and delayed trends during the period 2001–2020, respectively. More than two-thirds of the SOS advances and EOS delays occurred in grasslands, which determined the overall phenological changes across the entire Loess Plateau. However, both inter-annual trends of SOS and EOS derived from the GIMMS3g data were opposite to those seen in the MODIS results. There were no significant differences among the three MODIS datasets (250, 500, and 1000 m) with regard to a bias lower than 2 days, RMSE lower than 1 day, and correlation coefficient greater than 0.95 (p < 0.01). Furthermore, it was found that the phenology that was derived from the data with a 1000 m spatial resolution in the heterogeneous topography regions was feasible. Yet, in forest ecosystems and areas with an accumulated temperature ≥10 °C, the differences in phenological phase between the MODIS products could be amplified.

Mapping Spatiotemporal Changes in Vegetation Growth Peak and the Response to Climate and Spring Phenology over Northeast China

Global climate change has led to significant changes in seasonal rhythm events of vegetation growth, such as spring onset and autumn senescence. Spatiotemporal shifts in these vegetation phenological metrics have been widely reported over the globe. Vegetation growth peak represents plant photosynthesis capacity and responds to climate change. At present, spatiotemporal changes in vegetation growth peak characteristics (timing and maximum growth magnitude) and their underlying governing mechanisms remain unclear at regional scales. In this study, the spatiotemporal dynamics of vegetation growth peak in northeast China (NEC) was investigated using long-term NDVI time series. Then, the effects of climatic factors and spring phenology on vegetation growth peak were examined. Finally, the contribution of growth peak to vegetation production variability was estimated. The results of the phenological analysis indicate that the date of vegetation green up in spring and growth peak in summer generally present a delayed trend, while the amplitude of growth peak shows an increasing trend. There is an underlying cycle of 11 years in the vegetation growth peak of the entire study area. Air temperature and precipitation before the growing season have a small impact on vegetation growth peak amplitude both in its spatial extent and magnitude (mainly over grasslands) but have a significant influence on the date of the growth peak in the forests of the northern area. Spring green-up onset has a more significant impact on growth peak than air temperature and precipitation. Although green-up date plays a more pronounced role in controlling the amplitude of the growth peak in forests and grasslands, it also affects the date of growth peak in croplands. The amplitude of the growth peak has a significant effect on the inter-annual variability of vegetation production. The discrepant patterns of growth peak response to climate and phenology reflect the distinct adaptability of the vegetation growth peak to climate change, and result in different carbon sink patterns over the study area. The study of growth peak could improve our understanding of vegetation photosynthesis activity over various land covers and its contribution to carbon uptake.

Classification of Urban Area Using Multispectral Indices for Urban Planning

An accelerating trend of global urbanization accompanying population growth makes frequently updated land use and land cover (LULC) maps critical. LULC maps have been widely created through the classification of remotely sensed imagery. Maps of urban areas have been both dichotomous (urban or non-urban) and entailing of discrete urban types. This study incorporated multispectral built-up indices, designed to enhance satellite imagery, for introducing new urban classification schemes. The indices examined are the new built-up index (NBI), the built-up area extraction index (BAEI), and the normalized difference concrete condition index (NDCCI). Landsat Level-2 data covering the city of Miami, FL, USA was leveraged with geographic data from the Florida Geospatial Data Library and Florida Department of Environmental Protection to develop and validate new methods of supervised and unsupervised classification of urban area. NBI was used to extract discrete urban features through object-oriented image analysis. BAEI was found to possess properties for visualizing and tracking urban development as a low-high gradient. NDCCI was composited with NBI and BAEI as the basis for a robust urban intensity classification scheme superior to that of the United States Geological Survey National Land Cover Database 2016. BAEI, implemented as a shadow index, was incorporated in a novel infill geosimulation of high-rise construction. The findings suggest that the proposed classification schemes are advantageous to the process of creating more detailed cartography in response to the increasing global demand.

DATimeS: A machine learning time series GUI toolbox for gap-filling and vegetation phenology trends detection

Optical remotely sensed data are typically discontinuous, with missing values due to cloud cover. Consequently, gap-filling solutions are needed for accurate crop phenology characterization. The here presented Decomposition and Analysis of Time Series software (DATimeS) expands established time series interpolation methods with a diversity of advanced machine learning fitting algorithms (e.g., Gaussian Process Regression: GPR) particularly effective for the reconstruction of multiple-seasons vegetation temporal patterns. DATimeS is freely available as a powerful image time series software that generates cloud-free composite maps and captures seasonal vegetation dynamics from regular or irregular satellite time series. This work describes the main features of DATimeS, and provides a demonstration case using Sentinel-2 Leaf Area Index time series data over a Spanish site. GPR resulted as an optimum fitting algorithm with most accurate gap-filling performance and associated uncertainties. DATimeS further quantified LAI fluctuations among multiple crop seasons and provided phenological indicators for specific crop types.

Estimating Grassland Carbon Stocks in Hulunber China, Using Landsat8 Oli Imagery and Regression Kriging

Accurately estimating grassland carbon stocks is important in assessing grassland productivity and the global carbon balance. This study used the regression kriging (RK) method to estimate grassland carbon stocks in Northeast China based on Landsat8 operational land imager (OLI) images and five remote sensing variables. The normalized difference vegetation index (NDVI), the wide dynamic range vegetation index (WDRVI), the chlorophyll index (CI), Band6 and Band7 were used to build the RK models separately and to explore their capabilities for modeling spatial distributions of grassland carbon stocks. To explore the different model performances for typical grassland and meadow grassland, the models were validated separately using the typical steppe, meadow steppe or all-steppe ground measurements based on leave-one-out crossvalidation (LOOCV). When the results were validated against typical steppe samples, the Band6 model showed the best performance (coefficient of determination (R²) = 0.46, mean average error (MAE) = 8.47%, and root mean square error (RMSE) = 10.34 gC/m²) via the linear regression (LR) method, while for the RK method, the NDVI model showed the best performance (R² = 0.63, MAE = 7.04 gC/m², and RMSE = 8.51 gC/m²), which were much higher than the values of the best LR model. When the results were validated against the meadow steppe samples, the CI model achieved the best estimation accuracy, and the accuracy of the RK method (R² = 0.72, MAE = 8.09 gC/m², and RMSE = 9.89 gC/m²) was higher than that of the LR method (R² = 0.70, MAE = 8.99 gC/m², and RMSE = 10.69 gC/m²). Upon combining the results of the most accurate models of the typical steppe and meadow steppe, the RK method reaches the highest model accuracy of R² = 0.69, MAE = 7.40 gC/m², and RMSE = 9.01 gC/m², while the LR method reaches the highest model accuracy of R² = 0.53, MAE = 9.20 gC/m², and RMSE = 11.10 gC/m². The results showed an improved performance of the RK method compared to the LR method, and the improvement in the accuracy of the model is mainly attributed to the enhancement of the estimation accuracy of the typical steppe. In the study region, the carbon stocks showed an increasing trend from west to east, the total amount of grassland carbon stock was 79.77 × 10⁴ Mg C, and the mean carbon stock density was 47.44 gC/m². The density decreased in the order of temperate meadow steppe, lowland meadow steppe, temperate typical steppe, and sandy steppe. The methodology proposed in this study is particularly beneficial for carbon stock estimates at the regional scale, especially for countries such as China with many grassland types.

Forest Phenology Dynamics to Climate Change and Topography in a Geographic and Climate Transition Zone: The Qinling Mountains in Central China

Forest ecosystems in an ecotone and their dynamics to climate change are growing ecological and environmental concerns. Phenology is one of the most critical biological indicators of climate change impacts on forest dynamics. In this study, we estimated and visualized the spatiotemporal patterns of forest phenology from 2001 to 2017 in the Qinling Mountains (QMs) based on the enhanced vegetation index (EVI) from MODerate-resolution Imaging Spectroradiometer (MODIS). We further analyzed this data to reveal the impacts of climate change and topography on the start of the growing season (SOS), end of the growing season (EOS), and the length of growing season (LOS). Our results showed that forest phenology metrics were very sensitive to changes in elevation, with a 2.4 days delayed SOS, 1.4 days advanced EOS, and 3.8 days shortened LOS for every 100 m increase in altitude. During the study period, on average, SOS advanced by 0.13 days year−1, EOS was delayed by 0.22 days year−1, and LOS increased by 0.35 day year−1. The phenological advanced and delayed speed across different elevation is not consistent. The speed of elevation-induced advanced SOS increased slightly with elevation, and the speed of elevation-induced delayed EOS shift reached a maximum value of 1500 m from 2001 to 2017. The sensitivity of SOS and EOS to preseason temperature displays that an increase of 1 °C in the regionally averaged preseason temperature would advance the average SOS by 1.23 days and delay the average EOS by 0.72 days, respectively. This study improved our understanding of the recent variability of forest phenology in mountain ecotones and explored the correlation between forest phenology and climate variables in the context of the ongoing climate warming.

Spatiotemporal variation in vegetation spring phenology and its response to climate change in freshwater marshes of Northeast China

Understanding wetland vegetation phenology and its response to climate change is important to predict the changes of wetland vegetation in wetland regions. Using the NDVI and climate data, this work studied the spatiotemporal change of start date of vegetation growing season (SOS) and explored the possible effects of climate change on the SOS over freshwater marshes of Northeast China. The results showed that the SOS significantly advanced by 0.52 day per year throughout the freshwater marshes of Northeast China during 2001 to 2016. The significant advancing of SOS was mainly concentrated in freshwater marshes of the Khingan Mountains (the Greater Khingan Mountains and the Lesser Khingan Mountains) and central arid or semi-arid regions (Songnen plain and Liaohe plain) in Northeast China. By contrast, there were weak delay trends of SOS in freshwater marshes of Eastern Inner Mongolia region, and Sanjiang plain. We found that precipitation was a dominant factor determining the SOS in arid or semi-arid regions (Songnen plain and Liaohe plain), while temperature played a bigger role in determining the SOS in Sanjiang plain and three cold mountains of the Northeast China. During the study period, increasing precipitation in the winter and spring contributed to advancing SOS in Songnen plain and Liaohe plain; the decrease of temperature from December to April explain the delaying SOS in freshwater marshes of Sanjiang Plain; the weak warming of temperature between November and May account for the advancing SOS of freshwater marshes in three cold mountains. In freshwater marshes of cold and the most arid region of Northeast China (Eastern Inner Mongolia), the SOS was influenced by both precipitation and temperature. Decreasing precipitation between January and April, as well as temperature decreases in March and April explain the delay of SOS in freshwater marshes of Eastern Inner Mongolia region.

Mangrove Phenology and Environmental Drivers Derived from Remote Sensing in Southern Thailand

Vegetation phenology is the annual cycle timing of vegetation growth. Mangrove phenology is a vital component to assess mangrove viability and includes start of season (SOS), end of season (EOS), peak of season (POS), and length of season (LOS). Potential environmental drivers include air temperature (Ta), surface temperature (Ts), sea surface temperature (SST), rainfall, sea surface salinity (SSS), and radiation flux (Ra). The Enhanced vegetation index (EVI) was calculated from Moderate Resolution Imaging Spectroradiometer (MODIS, MOD13Q1) data over five study sites between 2003 and 2012. Four of the mangrove study sites were located on the Malay Peninsula on the Andaman Sea and one site located on the Gulf of Thailand. The goals of this study were to characterize phenology patterns across equatorial Thailand Indo-Malay mangrove forests, identify climatic and aquatic drivers of mangrove seasonality, and compare mangrove phenologies with surrounding upland tropical forests. Our results show the seasonality of mangrove growth was distinctly different from the surrounding land-based tropical forests. The mangrove growth season was approximately 8–9 months duration, starting in April to June, peaking in August to October and ending in January to February of the following year. The 10-year trend analysis revealed significant delaying trends in SOS, POS, and EOS for the Andaman Sea sites but only for EOS at the Gulf of Thailand site. The cumulative rainfall is likely to be the main factor driving later mangrove phenologies.

Urbanization Impacts on Vegetation Phenology in China

Urbanization can affect the ecological processes, local climate and human health in urban areas by changing the vegetation phenology. In the past 20 years, China has experienced rapid urbanization. Thus, it is imperative to understand the impact of urbanization on vegetation phenology in China. In this study, we quantitatively analyzed the impact of urbanization on vegetation phenology at the national and climate zone scales using remotely sensed data. We found that the start of the growing season (SOS) was advanced by approximately 2.4 days (P < 0.01), and the end of the growing season (EOS) was delayed by approximately 0.7 days (P < 0.01) in the urban areas compared to the rural areas. As a result, the growing season length (GSL) was extended by approximately 3.1 days (P < 0.01). The difference in the SOS and GSL between the urban and rural areas increased from 2001 to 2014, with an annual rate of 0.2 days (R2 = 0.39, P < 0.05) and 0.2 days (R2 = 0.31, P < 0.05), respectively. We also found that the impact of urbanization on vegetation phenology varied among different vegetation types at the national and climate zone levels (P < 0.05). The SOS was negatively correlated with land surface temperature (LST), with a correlation coefficient of −0.24 (P < 0.01), and EOS and GSL were positively correlated with LST, with correlation coefficients of 0.56 and 0.44 (P < 0.01), respectively. The improved understanding of the impact of urbanization on vegetation phenology from this study will be of great help for policy-makers in terms of developing relevant strategies to mitigate the negative environmental effects of urbanization in China.

Spatiotemporal Dynamics in Vegetation GPP over the Great Khingan Mountains Using GLASS Products from 1982 to 2015

Gross primary productivity (GPP) is an important parameter that represents the productivity of vegetation and responses to various ecological environments. The Greater Khingan Mountain (GKM) is one of the most important state-owned forest bases, and boreal forests, including the largest primeval cold-temperature bright coniferous forest in China, are widely distributed in the GKM. This study aimed to reveal spatiotemporal vegetation variations in the GKM on the basis of GPP products that were generated by the Global LAnd Surface Satellite (GLASS) program from 1982 to 2015. First, we explored the spatiotemporal distribution of vegetation across the GKM. Then we analyzed the relationships between GPP variation and driving factors, including meteorological elements, growing season length (GSL), and Fraction of Photosynthetically Active Radiation (FPAR), to investigate the dominant factor for GPP dynamics. Results demonstrated that (1) the spatial distribution of accumulated GPP (AG) in spring, summer, autumn, and the growing season varied due to three main reasons: understory vegetation, altitude, and land cover; (2) interannual AG in summer, autumn, and the growing season significantly increased at the regional scale during the past 34 years under climate warming and drying; (3) interannual changes of accumulated GPP in the growing season (AGG) at the pixel scale displayed a rapid expansion in areas with a significant increasing trend (p < 0.05) during the period of 1982–2015 and this trend was caused by the natural forest protection project launched in 1998; and finally, (4) an analysis of driving factors showed that daily sunshine duration in summer was the most important factor for GPP in the GKM and this is different from previous studies, which reported that the GSL plays a crucial role in other areas.

Monitoring the long term vegetation phenology change in Northeast China from 1982 to 2015

Global warming has contributed to the extension of the growing season in North Hemisphere. In this paper, we investigated the spatial characteristics of the date of the start of the season (SOS), the date of the end of the season (EOS) and the length of the season (LOS) and their change trends from 1982 to 2015 in Northeast China. Our results showed that there was a significant advance of SOS and a significant delay of EOS, especially in the north part of Northeast China. For the average change slope of EOS in the study area, the delay trend was 0.25 d/y, which was more obvious than the advance trend of −0.13 d/y from the SOS. In particular, the LOS of deciduous needleleaf forest (DNF) and grassland increased with a trend of 0.63 d/y and 0.66 d/y from 1982 to 2015, indicating the growth season increased 21.42 and 22.44 days in a 34-year period, respectively. However, few negative signals were detected nearby Hulun Lake, suggesting that the continuous climate warming in the future may bring no longer growing periods for the grass in the semiarid areas as the drought caused by climate warming may limit the vegetation growth.