Jinya Li

Remote Sensing-Based Biomass Estimation and Its Spatio-Temporal Variations in Temperate Grassland, Northern China

Grassland biomass is essential for maintaining grassland ecosystems. Moreover, biomass is an important characteristic of grassland. In this study, we combined field sampling with remote sensing data and calculated five vegetation indices (VIs). Using this combined information, we quantified a remote sensing estimation model and estimated biomass in a temperate grassland of northern China. We also explored the dynamic spatio-temporal variation of biomass from 2006 to 2012. Our results indicated that all VIs investigated in the study were strongly correlated with biomass (α < 0.01). The precision of the model for estimating biomass based on ground data and remote sensing was greater than 73%. Additionally, the results of our analysis indicated that the annual average biomass was 11.86 million tons and that the average yield was 604.5 kg/ha. The distribution of biomass exhibited substantial spatial heterogeneity, and the biomass decreased from the eastern portion of the study area to the western portion. The interannual biomass exhibited strong fluctuations during 2006–2012, with a coefficient of variation of 26.95%. The coefficient of variation of biomass differed among the grassland types. The highest coefficient of variation was found for the desert steppe, followed by the typical steppe and the meadow steppe.

Using MODIS time series data to estimate aboveground biomass and its spatio-temporal variation in Inner Mongolia’s grassland between 2001 and 2011

It is critical to understanding grassland biomass and its dynamics to study regional carbon cycles and the sustainable use of grassland resources. In this study, we estimated aboveground biomass (AGB) and its spatio-temporal pattern for Inner Mongolia’s grassland between 2001 and 2011 using field samples, Moderate Resolution Imaging Spectroradiometer normalized difference vegetation index (MODIS-NDVI) time series data, and statistical models based on the relationship between NDVI and AGB. We also explored possible relationships between the spatio-temporal pattern of AGB and climatic factors. The following results were obtained: (1) AGB averaged 19.1 Tg C (1 Tg = 1012 g) over a total area of 66.01 × 104 km2 between 2001 and 2011 and experienced a general fluctuation (coefficient of variation = 9.43%), with no significant trend over time (R2 = 0.05, p > 0.05). (2) The mean AGB density was 28.9 g C m−2 over the whole study area during the 11 year period, and it decreased from the northeastern part of the grassland to the southwestern part, exhibiting large spatial heterogeneity. (3) The AGB variation over the 11 year period was closely coupled with the pattern of precipitation from January to July, but we did not find a significant relationship between AGB and the corresponding temperature changes. Precipitation was also an important factor in the spatial pattern of AGB over the study area (R2 = 0.41, p < 0.001), while temperature seemed to be a minor factor (R2 = 0.14, p < 0.001). A moisture index that combined the effects of precipitation and temperature explained more variation in AGB than did precipitation alone (R2 = 0.45, p < 0.001). Our findings suggest that establishing separate statistical models for different vegetation conditions may reduce the uncertainty of AGB estimation on a large spatial scale. This study provides support for grassland administration for livestock production and the assessment of carbon storage in Inner Mongolia.

Remote Sensing Estimates of Grassland Aboveground Biomass Based on MODIS Net Primary Productivity (NPP): A Case Study in the Xilingol Grassland of Northern China

The precise and rapid estimation of grassland biomass is an important scientific issue in grassland ecosystem research. In this study, based on a field survey of 1205 sites together with biomass data of the Xilingol grassland for the years 2005–2012 and the “accumulated” MODIS productivity starting from the beginning of growing season, we built regression models to estimate the aboveground biomass of the Xilingol grassland during the growing season, then further analyzed the overall condition of the grassland and the spatial and temporal distribution of the aboveground biomass. The results are summarized as follows: (1) The unitary linear model based on the field survey data and “accumulated” MODIS productivity data is the optimum model for estimating the aboveground biomass of the Xilingol grassland during the growing period, with the model accuracy reaching 69%; (2) The average aboveground biomass in the Xilingol grassland for the years 2005–2012 was estimated to be 14.35 Tg, and the average aboveground biomass density was estimated to be 71.32 g∙m−2; (3) The overall variation in the aboveground biomass showed a decreasing trend from the eastern meadow grassland to the western desert grassland; (4) There were obvious fluctuations in the aboveground biomass of the Xilingol grassland for the years 2005–2012, ranging from 10.56–17.54 Tg. Additionally, several differences in the interannual changes in aboveground biomass were observed among the various types of grassland. Large variations occurred in the temperate meadow-steppe and the typical grassland; whereas there was little change in the temperate desert-steppe and temperate steppe-desert.

Spatio-Temporal Variation in Vegetation Biomass and Its Relationships with Climate Factors in the Xilingol Grasslands, Northern China

Knowledge about grassland biomass and its dynamics is critical for studying regional carbon cycles and for the sustainable use of grassland resources. In this study, we investigated the spatio-temporal variation of biomass in the Xilingol grasslands of northern China. Field-based biomass samples and MODIS time series data sets were used to establish two empirical models based on the relationship of the normalized difference vegetation index (NDVI) with above-ground biomass (AGB) as well as that of AGB with below-ground biomass (BGB). We further explored the climatic controls of these variations. Our results showed that the biomass averaged 99.01 Tg (1 Tg=1012 g) over a total area of 19.6×104 km2 and fluctuated with no significant trend from 2001 to 2012. The mean biomass density was 505.4 g/m2, with 62.6 g/m2 in AGB and 442.8 g/m2 in BGB, which generally decreased from northeast to southwest and exhibited a large spatial heterogeneity. The year-to-year AGB pattern was generally consistent with the inter-annual variation in the growing season precipitation (GSP), showing a robust positive correlation (R2=0.82, P<0.001), but an opposite coupled pattern was observed with the growing season temperature (GST) (R2=0.61, P=0.003). Climatic factors also affected the spatial distribution of AGB, which increased progressively with the GSP gradient (R2=0.76, P<0.0001) but decreased with an increasing GST (R2=0.70, P<0.0001). An improved moisture index that combined the effects of GST and GSP explained more variation in AGB than did precipitation alone (R2=0.81, P<0.0001). The relationship between AGB and GSP could be fit by a power function. This increasing slope of the GSP–AGB relationships along the GSP gradient may be partly explained by the GST–GSP spatial pattern in Xilingol. Our findings suggest that the relationships between climatic factors and AGB may be scale-dependent and that multi-scale studies and sufficient long-term field data are needed to examine the relationships between AGB and climatic factors.

MODIS-based remote-sensing monitoring of the spatiotemporal patterns of China's grassland vegetation growth

China has abundant grassland resources (approximately 400 million ha of natural grasslands), which account for 41.7% of Chinas total area. Grasslands are an important base for boosting the development of Chinas livestock husbandry economy and maintaining Chinas ecological security. Using Moderate Resolution Imaging Spectroradiometer (MODIS) remotely sensed data, this study developed a grassland vegetation growth index that ranked the magnitude of grassland vegetation growth indices across a wide variety of field experiments. This study applied the grassland vegetation growth index to conduct remote-sensing monitoring of the spatiotemporal status of Chinas grassland vegetation growth in 2008. We found that the vegetation growth of Chinas grassland was classified as ‘good’ in 2008. The areas of grassland with desirable vegetation growth accounted for 38.47% of Chinas monitored grassland areas, and the areas with less desirable vegetation growth accounted for 22.85%. Additionally, the good vegetation growth was stable within each 10 day study period in 2008. The vegetation growth reached a balance in early June. After early September, the proportion of grasslands with desirable vegetation growth declined, and the proportion of grasslands with balanced and less desirable growth increased. The regions with less desirable vegetation growth mainly included the middle and eastern regions of Inner Mongolia, the northern region of Xinjiang, and most parts of Heilongjiang. The regions with desirable vegetation growth were mainly distributed in the north of Tibet, the southwest of Qinghai, the west of Inner Mongolia, Gansu, Ningxia, Shanxi, and the northwest of Liaoning. The remote-sensing monitoring of the spatiotemporal patterns of Chinas grassland vegetation growth in the present study revealed the overall vegetation growth status of Chinas grassland on a broad scale. These findings could provide a helpful scientific basis for understanding Chinas grassland vegetation conditions and the management and regulation of grassland livestock husbandry.

Spatiotemporal Variations in Grassland Desertification Based on Landsat Images and Spectral Mixture Analysis in Yanchi County of Ningxia, China

Facing the worsening degradation of grasslands, state and local governments in China have implemented a series of ecological protection projects. Ningxia Hui Autonomous Region was the first province in China to implement a region-wide grazing ban, the effect of which has become contentious at all levels of government and a topic of public concern. The change of grassland desertification is the most direct indicator of the grazing bans effect. This paper chooses Yanchi County in Ningxia as the study area to analyze the grassland desertification situation. Based on a series of Landsat TM/ETM+ images, field observations and expert review, a Ningxia grassland desertification classification and grading system was constructed. Then, spectral mixture analysis (SMA) and a decision-tree method were used to interpret images of the study area from 4 years: 1993, 2000, 2006, and 2011. The following results were obtained: the area of desertified grassland decreased gradually from 4142 km2 in 1993 to 3695 km2 in 2011, a decrease of 10.80%. The severity of desertification also declined as the area of severely desertified grassland gradually decreased to 324 km2 in 2011 from 1093 km2 in 1993, a decrease of 70.34%. The annual reduction rate of the desertified grassland area reached its peak during the period 2000-2006. In particular, the areas of severely desertified grassland declined by 8.86% annually. In a situation of declining rainfall, human factors (such as ecological protection policy) have become a major cause of ongoing grassland desertification reversal and restoration of grassland vegetation.

The AMSL LST algorithm validated for the Xinjiang Autonomous Region in China

The split-window land surface brightness temperature (LSTb) algorithm of Coll and Caselles (1994) is one of the first approaches to estimate LSTb applied for large surface areas. In this article, we describe a calibrated and validated version of the Coll and Caselles (1994) algorithm applied for the retrieval of land surface air temperature (LSTa) – equivalent to standard WMO (World Meteorological Organization) temperature measurements – for the province of Xinjiang (PR of China). Locally received MODIS (Moderate Resolution Imaging Spectroradiometer) imagery (Fukang receiving station) is used as the input data stream for the so-called AMSL (Aqua MODIS SWA LSTa) algorithm. The objective to develop this algorithm is that it is an input for a distributed hydrological model as well as a soil moisture content retrieval algorithm. In the Xinjiang province with an abundance of arid to semi-arid regions, a highly continental climate, irrigated crop fields and mountain ranges of 6000 m and higher, one typically deals with the spatio-temporally complex conditions, making a high-accuracy retrieval of LSTa quite a challenge. The calibration and validation of the AMSL LSTa product (LSTa,amsl) – using the Jackknife method – is performed using LSTa measurements (LSTa,tmb) from 49 meteorological stations managed by the Tarim Meteorological Bureau (TMB). These stations are distributed relatively homogeneously over the province. The TMB stations’ temperature data are split into 40 calibration LSTa,tmb data sets and 9 validation LSTa,tmb data sets. We can observe that when validated, the LSTa,amsl versus LSTa,tmb validation relationship elicits a high correlation, a slope very close to 1 and an intercept very close to 0. The validated LSTa,amsl estimates demonstrate an estimation accuracy of 0.5 K. The results presented in this article suggest that the LSTa,amsl product is suitable to estimate the land surface air temperature spatio-temporal fields for the arid and semi-arid regions of the Xinjiang province accurately.

Characterizing changes in grassland desertification based on Landsat images of the Ongniud and Naiman Banners, Inner Mongolia

Desertification is treated as an important and critical environmental hazard. In the face of increasingly serious grassland desertification, China has made great efforts to combat desertification and a series of key national ecological projects has been launched. However, accurate, timely, and effective monitoring and assessment are required to determine whether these projects work well. The Horqin sandy land represents the largest area of sandy land in China. In this article, the Naiman and Ongniud Banners were studied because these contain the main desertified grassland in Horqin. Next, a desertification classification and grading system was designed for the Horqin sandy land after conducting fieldwork. Based on spectral mixture analysis (SMA) and decision-tree methods, we interpreted Landsat Thematic Mapper/Enhanced Thematic Mapper Plus/Operational Land Imager (TM/ETM+/OLI) images of the study area from four years: 1985, 1992, 2001, and 2013. Overall, the following results were obtained. The total area of desertified grassland in the Naiman and Ongniud Banners increased from 5979 km2 in 1985 to 9195 km2 in 1992 (an increase of 53.79%) and then decreased to 7828 km2 in 2001 and to 6023 km2 in 2013. The changes in the areas of desertified grassland with various degrees of desertification displayed the same trends as that of the total desertified grassland area. The severely desertified grassland expanded from 1872 km2 in 1985 to 3723 km2 in 1992 before shrinking to 2189 km2 in 2013. The annual rates of expansion of desertified grassland during the periods 1985–1992, 1992–2001, and 2001–2013 were 7.68%, −1.65%, and −1.92%, respectively, and the corresponding expansion rates of severely desertified grassland were 14.12%, −3.48%, and −1.19%, respectively. Both the desertified grasslands and the areas with various degrees of desertification displayed significant expansion during the period 1985–1992. Since 1992, this trend has reduced. During the study period, the changes in temperature and precipitation did not benefit the reversal of grassland sandy desertification. Furthermore, the growing population and expansion of livestock production and farming inhibited such reversal. However, the results presented in this article suggest that a reversal in grassland sandy desertification has been occurring since 1992. The results indicate that ecological engineering measures have helped reverse desertification and have promoted the restoration of grassland vegetation.

MODIS-based vegetation growth of temperate grassland and its correlation with meteorological factors in northern China

Vegetation dynamics, particularly vegetation growth, are often used as indicators of potential grassland degradation. Grassland vegetation growth can be monitored using remotely sensed data, which has rapid and broad coverage. Grassland ecosystems are an important component of the regional landscape. In this study, we developed an applicable method for monitoring grassland growth. The dynamic variation in the grassland was analysed using Moderate Resolution Imaging Spectroradiometer (MODIS) data. The normalized difference vegetation index (NDVI) was calculated from 2001 to 2010 during the grassland growing season. To evaluate the grassland growth, the use of the growth index (GI) was proposed. According to the GI values, five growth grades were identified: worse, slightly worse, balanced, slightly better, and better. We explored the spatial-temporal variation of grassland growth and the relationship between grassland growth and meteorological factors (i.e. precipitation and temperature factors). Our results indicated that, compared with the multi-year average, the spatial-temporal variation of grassland growth was significantly different between 2001 and 2010. The vegetation growth was worse in 2009 compared with the multi-year average. A GI of ‘worse’ accounted for 66.73% of the area. The vegetation growth in 2003 was the best of the years between 2001 and 2010, and a better GI accounted for 58.08% of the area in 2003. The GI from 2004 to 2008 exhibited significant fluctuations. The correlation coefficient between the GI and precipitation or temperature indicated that meteorological factors likely affected the inter-annual variations in the grassland growth. The peak of the grassland growth season was positively correlated with the spatial patterns of precipitation and negatively correlated with those of temperature. Precipitation during the growing season was the main influence in the arid and semi-arid regions. Monitoring grassland growth using remote sensing can accurately reveal the grassland growth status at the macro-scale in a timely manner. This research proposes an effective method for monitoring grassland growth and provides a reference for the sustainable development of grassland ecosystems.

Historical grassland desertification changes in the Horqin Sandy Land, Northern China (1985–2013)

Since rural reforms in the 1980s, both the state and local governments of China have devoted great efforts to combating desertification through a number of eco-environmental restoration campaigns, resulting in burgeoning contention at all levels of government and sparking public concern. Monitoring and accurately assessing the statuses and trends of grassland desertification are important for developing effective restoration strategies. The Horqin Sandy Land (HSL), a very typical desertified grassland (DG) with better hydrothermal conditions among sandy lands in north China, was recently selected (1985–2013) to assess the spatiotemporal dynamic performances of grassland desertification before and after implementing restoration projects. Landsat images (TM/ETM+/OLI), field investigations and expert review were integrated to form a classification scheme for the HSL. Then, spectral mixture analysis and the decision-tree method were used to extract bare-sand ratios and vegetation cover fraction dynamics. A favourable phenomenon of DG was seen to be reversed in an accelerated pace during 2001–2013, despite challenge from both climatic and anthropogenic factors. However, overexploitation of grassland (especially for farming) and ground water for irrigation has led to remarkable decreases in the ground water level in recent decades, which should be highly concerning regarding the formulation of restoration campaigns in the sandy land.