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Vegetation coverage is very important in terrestrial ecosystems and climate systems. However, the observational record of the normalized difference vegetation index (NDVI), which started in the 1980s when satellites became widely used, is too short to investigate the history of variation in vegetation coverage beyond the modern observation period. Here, we present a 189 y vegetation coverage series based on a total of 349 Mongolian pine (Pinus sylvestris var. mongolica Litv) cores from seven locations from the central–western Da Hinggan Mountains (CW–DHM), northeastern China. We found a significant relationship between tree-ring width and the regional cumulative normalized difference vegetation index (CNDVI). The correlation between the ring-width chronology and the regional June–July CNDVI (CNDVIJJ ) was significant, with r = 0.68 (n = 32, p < 0.001) and an explained variance of 45.8% (44.0% after the adjustment for the loss of the degree of freedom). On this basis, we designed a transfer function to reconstruct the CNDVIJJ for the CW–DHM region from 1825 to 2013 CE (Common Era). During the last 189 years, there were 28 years with high CNDVIJJ values, and another 28 years with low values. We also observed CNDVIJJ fluctuations at the inter-annual and decadal time scales, including eight low value periods and nine high value periods. Based on our analysis, the variation in CNDVI is associated with climatic factors, such as temperature, precipitation and the Palmer Drought Severity Index (PDSI), which combines both temperature and precipitation. From 1950 to 2002 CE, the CNDVI showed a noticeable decreasing trend in the CW–DHM region, whereas after 2003 CE, the CNDVI exhibited an apparent increase, which has also been observed in southern Central Siberia, eastern Mongolia and northeastern and eastern China, indicating that the CNDVI change in the CW–DHM is related to climate change in the local region and in some parts of Asia.
Ruoshi Liu; Yi Song; Yu Liu; Xuxiang Li; Huiming Song; Changfeng Sun; Qiang Li; Qiufang Cai; Meng Ren; Lu Wang. Changes in the Tree-Ring Width-Derived Cumulative Normalized Difference Vegetation Index over Northeast China during 1825 to 2013 CE. Forests 2021, 12, 241 .
AMA StyleRuoshi Liu, Yi Song, Yu Liu, Xuxiang Li, Huiming Song, Changfeng Sun, Qiang Li, Qiufang Cai, Meng Ren, Lu Wang. Changes in the Tree-Ring Width-Derived Cumulative Normalized Difference Vegetation Index over Northeast China during 1825 to 2013 CE. Forests. 2021; 12 (2):241.
Chicago/Turabian StyleRuoshi Liu; Yi Song; Yu Liu; Xuxiang Li; Huiming Song; Changfeng Sun; Qiang Li; Qiufang Cai; Meng Ren; Lu Wang. 2021. "Changes in the Tree-Ring Width-Derived Cumulative Normalized Difference Vegetation Index over Northeast China during 1825 to 2013 CE." Forests 12, no. 2: 241.
As important node cities in the Belt and Road region, Shenzhen and Bangkok are faced with similar environmental threats posed by the high‐speed social development process. Rapid urbanization leads to changes in vegetation growth and land cover types and then affects ecosystem services. In the current study, we used a time‐series normalized difference vegetation index dataset from 2000 to 2019 and two land cover type datasets from 2000 to 2018 to investigate and compare the spatiotemporal characteristics of the changes in vegetation and land cover types of the two cities. We found that the trend of vegetation change was mainly affected by the change in land cover types, while the interannual fluctuation of vegetation change was likely related to the extreme climate events caused by El Niño‐Southern Oscillation events. However, different urbanization strategies led to opposite vegetation change trends in Bangkok and Shenzhen after 2005. With urbanization, the vegetation coverage (Pv) of Shenzhen increased from 48% in 2000 to 62% in 2018. The total urban green spaces (except croplands) of Shenzhen have remained above 33% of the total area since 2006. However, the total urban green space in Bangkok accounted for only 8% of the total area in 2018, which was even lower than the area percentage of Shenzhen’s forests in the same year. Rapid urbanization without adequate urban green spaces caused a decreasing trend of Pv in Bangkok. Green development under the Belt and Road Initiative requires serious considerations of environmental quality and urban livability during the rapid urbanization. This article is protected by copyright. All rights reserved.
Yi Song; Jagannath Aryal; Liangcheng Tan; Long Jin; Zhihua Gao; Yunqiang Wang. Comparison of changes in vegetation and land cover types between Shenzhen and Bangkok. Land Degradation & Development 2020, 32, 1192 -1204.
AMA StyleYi Song, Jagannath Aryal, Liangcheng Tan, Long Jin, Zhihua Gao, Yunqiang Wang. Comparison of changes in vegetation and land cover types between Shenzhen and Bangkok. Land Degradation & Development. 2020; 32 (3):1192-1204.
Chicago/Turabian StyleYi Song; Jagannath Aryal; Liangcheng Tan; Long Jin; Zhihua Gao; Yunqiang Wang. 2020. "Comparison of changes in vegetation and land cover types between Shenzhen and Bangkok." Land Degradation & Development 32, no. 3: 1192-1204.
Soil nitrogen (N) is critical to ecosystem services and environmental quality. Hotspots of soil N in areas with high soil moisture have been widely studied, however, their spatial distribution and their linkage with soil N variation have seldom been examined at a catchment scale in areas with low soil water content. We investigated the spatial variation of soil N and its hotspots in a mixed land cover catchment on the Chinese Loess Plateau and used multiple statistical methods to evaluate the effects of the critical environmental factors on soil N variation and potential hotspots. The results demonstrated that land cover, soil moisture, elevation, plan curvature and flow accumulation were the dominant factors affecting the spatial variation of soil nitrate (NN), while land cover and slope aspect were the most important factors impacting the spatial distribution of soil ammonium (AN) and total nitrogen (TN). In the studied catchment, the forestland, gully land and grassland were found to be the potential hotspots of soil NN, AN and TN accumulation, respectively. We concluded that land cover and slope aspect could be proxies to determine the potential hotspots of soil N at the catchment scale. Overall, land cover was the most important factor that resulted in the spatial variations of soil N. The findings may help us to better understand the environmental factors affecting soil N hotspots and their spatial variation at the catchment scale in terrestrial ecosystems.
Yun-Long Yu; Zhao Jin; Henry Lin; Yun-Qiang Wang; Ya-Li Zhao; Guang-Chen Chu; Jing Zhang; Yi Song; Han Zheng. Spatial variation and soil nitrogen potential hotspots in a mixed land cover catchment on the Chinese Loess Plateau. Journal of Mountain Science 2019, 16, 1353 -1366.
AMA StyleYun-Long Yu, Zhao Jin, Henry Lin, Yun-Qiang Wang, Ya-Li Zhao, Guang-Chen Chu, Jing Zhang, Yi Song, Han Zheng. Spatial variation and soil nitrogen potential hotspots in a mixed land cover catchment on the Chinese Loess Plateau. Journal of Mountain Science. 2019; 16 (6):1353-1366.
Chicago/Turabian StyleYun-Long Yu; Zhao Jin; Henry Lin; Yun-Qiang Wang; Ya-Li Zhao; Guang-Chen Chu; Jing Zhang; Yi Song; Han Zheng. 2019. "Spatial variation and soil nitrogen potential hotspots in a mixed land cover catchment on the Chinese Loess Plateau." Journal of Mountain Science 16, no. 6: 1353-1366.
The Qinghai-Tibet (QT) Plateau Engineering Corridor is located in the hinterland of the QT Plateau, which is highly sensitive to global climate change. Climate change causes permafrost degradation, which subsequently affects vegetation growth. This study focused on the vegetation dynamics and their relationships with climate change and human activities in the region surrounding the QT Plateau Engineering Corridor. The vegetation changes were inferred by applying trend analysis, the Mann-Kendall trend test and abrupt change analysis. Six key regions, each containing 40 nested quadrats that ranged in size from 500 × 500 m to 20 × 20 km, were selected to determine the spatial scales of the impacts from different factors. Cumulative growing season integrated enhanced vegetation index (CGSIEVI) values were calculated for each of the nested quadrats of different sizes to indicate the overall vegetation state over the entire year at different spatial scales. The impacts from human activities, a sudden increase in precipitation and permafrost degradation were quantified at different spatial scales using the CGSIEVI values and meteorological data based on the double mass curve method. Three conclusions were derived. First, the vegetation displayed a significant increasing trend over 23.6% of the study area. The areas displaying increases were mainly distributed in the Hoh Xil. Of the area where the vegetation displayed a significant decreasing trend, 72.4% was made up of alpine meadows. Second, more vegetation, especially the alpine meadows, has begun to degenerate or experience more rapid degradation since 2007 due to permafrost degradation and overgrazing. Finally, an active layer depth of 3 m to 3.2 m represents a limiting depth for alpine meadows.
Yi Song; Long Jin; Haibo Wang. Vegetation Changes along the Qinghai-Tibet Plateau Engineering Corridor Since 2000 Induced by Climate Change and Human Activities. Remote Sensing 2018, 10, 95 .
AMA StyleYi Song, Long Jin, Haibo Wang. Vegetation Changes along the Qinghai-Tibet Plateau Engineering Corridor Since 2000 Induced by Climate Change and Human Activities. Remote Sensing. 2018; 10 (2):95.
Chicago/Turabian StyleYi Song; Long Jin; Haibo Wang. 2018. "Vegetation Changes along the Qinghai-Tibet Plateau Engineering Corridor Since 2000 Induced by Climate Change and Human Activities." Remote Sensing 10, no. 2: 95.
Highlights•5 Key parameters of the ET model were well estimated using the Bayesian inference.•The separated scheme is more applicable for cropland and grassland ecosystems.•The cluster analysis was used to classify the fields with the highest similarities.•The parameter values were applied at regional scale based on the cluster analysis.•The RMSE of the remotely sensed λET estimates was less than 20 W m−2. AbstractA simple two-source evapotranspiration (ET) model was applied to the Yingke and Daman irrigation districts of the Zhangye Oasis, which is located in the middle reaches of the Heihe River, China. The ET model was composed of two parts, including an evaporation (E) sub-model and a transpiration (T) sub-model. A separated parameter estimation scheme was conducted using Bayesian inference. First, an empirical multiplier was estimated for an E sub-model using observations that were collected after crop harvests. The empirical multiplier was then assigned to the most-likely value in the simple two-source ET model. Second, a global sensitivity analysis was performed to identify the key parameters that were responsible for most of the variability in the λET results within the T sub-model. To avoid equifinality or over-parameterization, Bayesian inference was applied to estimate the key parameters that induced the most variability in the first set. A second set of Bayesian inference was then performed by fixing the most-likely values of these parameters, and the other parameters were defined one-by-one as Bayesian parameters. These parameters were estimated for seven sites. The coefficient of determination for the modeled λET and the observed values exceeded 0.9. Next, a cluster analysis was conducted using the canopy height, leaf area index (LAI) and soil moisture content to classify the fields with the highest similarities and then to distribute the same parameter values to similar fields. Finally, λET was estimated using the most-likely values of the parameters at the regional scale. The root-mean-square error of the remotely sensed estimates was less than 20 W m−2, the mean absolute percent error did not exceed 4%, and the correlation coefficient was greater than 0.97. The validation was conducted for both the modeled λET at the point scale and for the remotely sensed λET at the satellite pixel scale. The results demonstrate that the separated parameter estimation scheme using Bayesian inference yields reasonable parameter values; using cluster analysis, the most-likely values of the parameters can be effectively applied to estimate remotely sensed λET. Graphical abstract
Yi Song; Long Jin; Gaofeng Zhu; Mingguo Ma. Parameter estimation for a simple two-source evapotranspiration model using Bayesian inference and its application to remotely sensed estimations of latent heat flux at the regional scale. Agricultural and Forest Meteorology 2016, 230-231, 20 -32.
AMA StyleYi Song, Long Jin, Gaofeng Zhu, Mingguo Ma. Parameter estimation for a simple two-source evapotranspiration model using Bayesian inference and its application to remotely sensed estimations of latent heat flux at the regional scale. Agricultural and Forest Meteorology. 2016; 230-231 ():20-32.
Chicago/Turabian StyleYi Song; Long Jin; Gaofeng Zhu; Mingguo Ma. 2016. "Parameter estimation for a simple two-source evapotranspiration model using Bayesian inference and its application to remotely sensed estimations of latent heat flux at the regional scale." Agricultural and Forest Meteorology 230-231, no. : 20-32.
This study presents a revised temporal scaling method based on a detection algorithm for the temporal stability of the evaporative fraction (EF) to estimate total daytime evapotranspiration (ET) at a regional scale. The study area is located in the Heihe River Basin, which is the second largest inland river basin in China. The remote sensing data and field observations used in this study were obtained from the Heihe Watershed Allied Telemetry Experimental Research (HiWATER) project. The half-hourly EF values (EFEC) calculated using meteorological observations from an eddy covariance (EC) system and an automatic meteorological station (AMS) represented the diurnal pattern of the EF across the majority of the study area. The remotely sensed instantaneous midday EF (EFASTER), which indicates the spatial distribution of the midday EF over the entire study area, was calculated from an Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) image. The temporal stability of EFEC was examined using a detection algorithm. Intervals with inconsistent EFEC values were distinguished from those with consistent EFEC values; the total daytime ET (from 9:00 to 19:00) within these interval types was integrated separately. Validation of the total daytime ET at the satellite pixel scale was conducted using measurements from17 EC towers. Using the detection algorithm for the temporal stability of the EF and dynamic adjustment, the revised temporal scaling method resulted in a root-mean-square error (RMSE) of 0.54 (mm·d−1), a mean relative error (MRE) of 7.26% and a correlation coefficient (Corr.) of 0.81; all of these values were superior to those of the two other methods (i.e., the constant EF and variable EF methods). The revised method easily extends to other areas and exhibits a superior performance in flat and regularly-irrigated farmlands at the regional scale.
Yi Song; Mingguo Ma; Long Jin; Xufeng Wang. A Revised Temporal Scaling Method to Yield Better ET Estimates at a Regional Scale. Remote Sensing 2015, 7, 6433 -6453.
AMA StyleYi Song, Mingguo Ma, Long Jin, Xufeng Wang. A Revised Temporal Scaling Method to Yield Better ET Estimates at a Regional Scale. Remote Sensing. 2015; 7 (5):6433-6453.
Chicago/Turabian StyleYi Song; Mingguo Ma; Long Jin; Xufeng Wang. 2015. "A Revised Temporal Scaling Method to Yield Better ET Estimates at a Regional Scale." Remote Sensing 7, no. 5: 6433-6453.