Xiaoping Xin

Assessment of the MODIS LAI Product Using Ground Measurement Data and HJ-1A/1B Imagery in the Meadow Steppe of Hulunber, China

The leaf area index (LAI) is a crucial parameter of vegetation structure. It provides key information for earth surface process simulations and climate change research on the global and regional scales. Focusing on the meadow steppe in Hulunber, Inner Mongolia, China, the present study assessed the accuracy of the Moderate Resolution Imaging Spectroradiometer (MODIS) LAI product in the study area. First, seven field campaigns collecting ground-based measurements were conducted during the growing season in 2013, and 252 pairs of LAIs and spectra were collected. Then, seven scenes of high-resolution LAI maps were obtained from the corresponding 30 m Chinese HJ-1A/1B charge-coupled diode (CCD) images by employing a regression approach. Finally, comparisons between the MODIS LAI product and the high resolution LAI maps were made to determine the accuracy of the MODIS LAI product. Moreover, the corresponding 500 m MODIS LAI maps were derived from the daily MODIS surface reflectance product to support the findings using the 1 km HJ LAI product and the ground-based comparison. The results showed that, compared to the ground data, the MODIS LAI product followed a reasonable seasonal trajectory during the growing season. However, an anomaly existed at the beginning of the growing season. Also, a slight overestimation was found for the MODIS LAI product compared to the HJ-retrieved LAI maps. The average overestimation for the LAI was approximately 0.4 m 2 /m 2 , and the relative absolute errors of the product ranged from 10%-50%. The overestimation at the beginning and end of the growing

Nitrogen acquisition by plants and microorganisms in a temperate grassland.

Nitrogen (N) limitation is common in most terrestrial ecosystems, often leading to strong competition between microorganisms and plants. The mechanisms of niche differentiation to reduce this competition remain unclear. Short-term 15N experiments with NH4+, NO3−, and glycine were conducted in July, August and September in a temperate grassland to evaluate the chemical, spatial and temporal niche differentiation by competition between plants and microorganisms for N. Microorganisms preferred NH4+ and NO3−, while plants preferred NO3−. Both plants and microorganisms acquired more N in August and September than in July. The soil depth had no significant effects on microbial uptake, but significantly affected plant N uptake. Plants acquired 67% of their N from the 0–5 cm soil layer and 33% from the 5–15 cm layer. The amount of N taken up by microorganisms was at least seven times than plants. Although microorganisms efficiently compete for N with plants, the competition is alleviated through chemical partitioning mainly in deeper soil layer. In the upper soil layer, neither chemical nor temporal niche separation is realized leading to strong competition between plants and microorganisms that modifies N dynamics in grasslands.

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

Vegetation phenology has been used in studies as an indicator of an ecosystem’s responses to climate change. Satellite remote sensing techniques can capture changes in vegetation greenness, which can be used to estimate vegetation phenology. In this study, a long-term vegetation phenology study of the Greater Khingan Mountain area in Northeastern China was performed by using the Global Inventory Modeling and Mapping Studies (GIMMS) normalized difference vegetation index version 3 (NDVI3g) dataset from the years 1982–2012. After reconstructing the NDVI time series, the start date of the growing season (SOS), the end date of the growing season (EOS) and the length of the growing season (LOS) were extracted using a dynamic threshold method. The response of the variation in phenology with climatic factors was also analyzed. The results showed that the phenology in the study area changed significantly in the three decades between 1982 and 2012, including a 12.1-day increase in the entire region’s average LOS, a 3.3-day advance in the SOS and an 8.8-day delay in the EOS. However, differences existed between the steppe, forest and agricultural regions, with the LOSs of the steppe region, forest region and agricultural region increasing by 4.40 days, 10.42 days and 1.71 days, respectively, and a later EOS seemed to more strongly affect the extension of the growing season. Additionally, temperature and precipitation were closely correlated with the phenology variations. This study provides a useful understanding of the recent change in phenology and its variability in this high-latitude study area, and this study also details the responses of several ecosystems to climate change.

Impacts of Differing Grazing Rates on Canopy Structure and Species Composition in Hulunber Meadow Steppe

ABSTRACT In this study, the impacts of cattle grazing with differing grazing rates on species composition, canopy structural traits, standing crop of canopy biomass, and plant species diversity were examined in a meadow steppe of the Hulunber grasslands, Northeastern China. Six stocking-rate treatments (0, 0.23, 0.34, 0.46, 0.69, and 0.92 AU.ha-1) with three replicates were established, and observations were conducted from 2009 to 2011. Our findings demonstrate that short-term grazing substantially altered the species composition and relative dominance, standing crop of aboveground biomass, and canopy structural traits, whereas no significant changes in species diversity and evenness occurred in response to different-rated grazing in this meadow steppe, which has a long-term evolutionary grazing history and high-resources availabilities. We found that perennial graminoid significantly decreased, while forbs and annuals increased at the same time, with increasing grazing intensity and duration; canopy height and coverage decreased substantially with increasing stocking rates, whereas significant changes in plant density occurred only at heavy grazing in the second and third years; and significant negative linear relations were found between the standing crop of biomass and grazing intensity in each individual year or for 3 years on average. Significantly highest species richness and canopy dominance occurred only at the intermediate grazing rate in the third year, and intermediate grazing intensity also maintained a highly constant standing crop of canopy biomass in the 3 years, all being in accordance with the intermediate disturbance hypothesis. Our findings imply that monitoring changes in species composition, canopy traits, and standing crop of biomass in grassland communities can provide important references for assessing current grazing management scenarios and conducting timely adaptive practices to maintain the long-term ability of grassland systems to perform their ecological functions.

Evaluation and Intercomparison of MODIS and GEOV1 Global Leaf Area Index Products over Four Sites in North China

This study investigated the performances of the Moderate Resolution Imaging Spectroradiometer (MODIS) and GEOLAND2 Version 1 (GEOV1) Leaf Area Index (LAI) products using ground measurements and LAI reference maps over four sites in North China for 2011–2013. The Terra + Aqua MODIS and Terra MODIS LAI retrieved by the main algorithm and GEOV1 LAI within the valid range were evaluated and intercompared using LAI reference maps to assess their uncertainty and seasonal variability The results showed that GEOV1 LAI is the most similar product with the LAI reference maps (R2 = 0.78 and RMSE = 0.59). The MODIS products performed well for biomes with low LAI values, but considerable uncertainty arose when the LAI was larger than 3. Terra + Aqua MODIS (R2 = 0.72 and RMSE = 0.68) was slightly more accurate than Terra MODIS (R2 = 0.57 and RMSE = 0.90) for producing slightly more successful observations. Both MODIS and GEOV1 products effectively followed the seasonal trajectory of the reference maps, and GEOV1 exhibited a smoother seasonal trajectory than MODIS. MODIS anomalies mainly occurred during summer and likely occurred because of surface reflectance uncertainty, shorter temporal resolutions and inconsistency between simulated and MODIS surface reflectances. This study suggests that further improvements of the MODIS LAI products should focus on finer algorithm inputs and improved seasonal variation modeling of MODIS observations. Future field work considering finer biome maps and better generation of LAI reference maps is still needed.

Grazing intensity and driving factors affect soil nitrous oxide fluxes during the growing seasons in the Hulunber meadow steppe of China

In this study, the effects of cattle grazing intensity on soil nitrous oxide (N2O) fluxes were examined in the Hulunber meadow steppe of north-eastern China. Six stocking-rate treatments (0, 0.23, 0.34, 0.46, 0.69, and 0.92 AU ha−1) with three replicates were established, and observations were conducted from 2010 to 2014. Our results showed that substantial temporal fluctuations in N2O flux occurred amongst the different grazing intensities, with peak N2O fluxes after natural rainfall. Grazing had a long-term effect on the soil N2O flux in the grasslands. After 4–5 years of grazing, the N2O fluxes under increased levels of grazing intensity began to decrease significantly by 31.4%–60.2% in 2013 and 32.5%–50.5% in 2014 compared to the non-grazing treatment. We observed a significant negative linear relationship between the soil N2O fluxes and grazing intensity for the five-year mean. The soil N2O flux was significantly affected each year in all of the treatments. Over the five years, the temporal coefficient of variation (CVs) of the soil N2O flux generally declined significantly with increasing grazing intensity. The soil N2O emission rate was significantly positively correlated with soil moisture (SM), soil available phosphorus (SAP), soil soil above-ground biomass (AGB), plant ground cover and height and was negatively correlated with total soil nitrogen (TN). Stepwise regressions showed that the N2O flux was primarily explained by SM, plant height, TN, soil pH, and soil Using structural equation modelling, we show that grazing significantly directly influenced the plant community and the soil environment, which then influenced the soil N2O fluxes. Our findings provide an important reference for better understanding of the mechanisms and identifying the pathways of grazing effects on soil N2O emission rates, and the key drivers plant community and soil environment within the nitrogen cycle that are mostly likely to affect N2O emissions in the Inner Mongolian meadow steppes.

Comparison of two inversion methods for leaf area index using HJ-1 satellite data in a temperate meadow steppe

Leaf area index (LAI) is one of the most important parameters for determining grassland canopy conditions. LAI controls numerous biological and physical processes in grassland ecosystems. Remote-sensing techniques are effective for estimating grassland LAI at a regional scale. Comparison of LAI inversion methods based on remote sensing is significant for accurate estimation of LAI in particular areas. In this study, we developed and compared two inversion models to estimate the LAI of a temperate meadow steppe in Hulunbuir, Inner Mongolia, China, based on HJ-1 satellite data and field-measured LAI data. LAI was measured from early June to late August in 2013, obtained from 326 sampling data. The back propagation (BP) neural network method proved better than the statistical regression model for estimating grassland LAI, the accuracy of the former being 82.8%. We then explored the spatio-temporal distribution in LAI of Stipa baicalensis Roshev. in the meadow steppe of Hulunbuir, including cut, grazed, and fenced plots. The LAI in the cut and grazed plots reflected the growth variations in S. baicalensis Roshev. However, because of the obvious litter layer, the LAI in the fenced plots was underestimated.

The implications of serial correlation and time-lag effects for the impact study of climate change on vegetation dynamics – a case study with Hulunber meadow steppe, Inner Mongolia

Ecological time series data are widely used in ecological research thanks to the development of remote-sensing technologies and fixed ecological research stations. However, the serial correlation issue with time series, which violates the fundamental assumption of independence for traditional statistical models or analysis, is rarely considered by ecologists in vegetation–climate relationship research. In addition, the issue of time lags between climate change and vegetation response is also often ignored. Inadequate consideration of these issues produces misleading results in some cases. In this article, we propose an approach based on the Autoregressive Integrated Moving Average (ARIMA) model and the nonparametric test to address serial correlation issue and distribution requirements for the valid statistical analysis of time series data. With Hulunber meadow steppe as a case, we applied this approach to analyse the role of climate factors in vegetation dynamics based on leaf area index (LAI) data and climatic data. The results showed that the LAI dynamics of Hulunber meadow steppe were mainly related to temperature with the time lag of zero, whereas the impact of precipitation on LAI dynamics was not statistically obvious. The comparison of regression models that deal with serial correlation and residual normality to different extents showed that ignoring the serial correlation issue with time series data likely produces misleading results, highlighting the importance of serial correlation removal. The combination of nonparametric correlation tests with ARIMA-based cross-correlation analysis also proved quite useful in reducing the chance of spurious correlation and time lags resulting from outlier values in ARIMA-based cross-correlation.

Estimating grassland LAI using the Random Forests approach and Landsat imagery in the meadow steppe of Hulunber, China

Abstract Leaf area index (LAI) is a key parameter for describing vegetation structures and is closely associated with vegetative photosynthesis and energy balance. The accurate retrieval of LAI is important when modeling biophysical processes of vegetation and the productivity of earth systems. The Random Forests (RF) method aggregates an ensemble of decision trees to improve the prediction accuracy and demonstrates a more robust capacity than other regression methods. This study evaluated the RF method for predicting grassland LAI using ground measurements and remote sensing data. Parameter optimization and variable reduction were conducted before model prediction. Two variable reduction methods were examined: the Variable Importance Value method and the principal component analysis (PCA) method. Finally, the sensitivity of RF to highly correlated variables was tested. The results showed that the RF parameters have a small effect on the performance of RF, and a satisfactory prediction was acquired with a root mean square error (RMSE) of 0.1956. The two variable reduction methods for RF prediction produced different results; variable reduction based on the Variable Importance Value method achieved nearly the same prediction accuracy with no reduced prediction, whereas variable reduction using the PCA method had an obviously degraded result that may have been caused by the loss of subtle variations and the fusion of noise information. After removing highly correlated variables, the relative variable importance remained steady, and the use of variables selected based on the best-performing vegetation indices performed better than the variables with all vegetation indices or those selected based on the most important one. The results in this study demonstrate the practical and powerful ability of the RF method in predicting grassland LAI, which can also be applied to the estimation of other vegetation traits as an alternative to conventional empirical regression models and the selection of relevant variables used in ecological models.

Predicting Grassland Leaf Area Index in the Meadow Steppes of Northern China: A Comparative Study of Regression Approaches and Hybrid Geostatistical Methods

Leaf area index (LAI) is a key parameter used to describe vegetation structures and is widely used in ecosystem biophysical process and vegetation productivity models. Many algorithms have been developed for the estimation of LAI based on remote sensing images. Our goal was to produce accurate and timely predictions of grassland LAI for the meadow steppes of northern China. Here, we compare the predictive power of regression approaches and hybrid geostatistical methods using Chinese Huanjing (HJ) satellite charge coupled device (CCD) data. The regression methods evaluated include partial least squares regression (PLSR), artificial neural networks (ANNs) and random forests (RFs). The two hybrid geostatistical methods were regression kriging (RK) and random forests residuals kriging (RFRK). The predictions were validated for different grassland types and different growing stages, and their performances were also examined by adding several groups of vegetation indices (VIs). The two hybrid geostatistical models (RK and RFRK) yielded the most accurate predictions (root mean squared error (RMSE) = 0.21 m2/m2 and 0.23 m2/m2 for RK and RFRK, respectively), followed by the RF model (RMSE = 0.27 m2/m2), which was the most accurate among the regression models. These three models also exhibited the best temporal performance across the duration of the growing season. The PLSR and ANN models were less accurate (RMSE = 0.33 m2/m2 and 0.35 m2/m2 for ANN and PLSR, respectively), and the PLSR model performed the worst (exhibiting varied temporal performance and unreliable prediction accuracy that was susceptible to ground conditions). By adding VIs to the predictor variables, the predictions of the PLSR and ANN models were obviously improved (RMSE improved from 0.35 m2/m2 to 0.28 m2/m2 for PLSR and from 0.33 m2/m2 to 0.28 m2/m2 for ANN); the RF and RFRK models did not generate more accurate predictions and the performance of the RK model declined (RMSE decreased from 0.21 m2/m2 to 0.32 m2/m2).