Hong Wang

A study of the seasonal dynamics of grassland growth rates in Inner Mongolia based on AVHRR data and a light‐use efficiency model

Growth rate data for different pastures could provide important reference data for developing rotation grazing plans, for hay production, and for forage replenishment. Based on AVHRR NDVI data and a light‐use efficiency (LUE) model, we estimated absorbed photosynthetically active radiation and LUE (ε) by integrating air and soil temperature, precipitation and total solar radiation time series data from 1986 to 1999, and calculated the absolute growth rate (AGR) and cumulative absolute growth rate (CAGR) of aboveground biomass in each growing season in Chinas Inner Mongolia region. AGR and CAGR estimated by the LUE model were validated using monthly growth data obtained for the vegetation in desert steppe, typical steppe, and meadow steppe ecosystems from 1986 to 1995. The LUE model provided sufficiently good simulation accuracy that its use should permit improved livestock feed management in the study area. From 1986 to 1999, average CAGR of steppe vegetation during the growing season increased quickly in June and July, reached a maximum in July and August, and declined in September. In 1999, AGR reflected the pattern of seasonal vegetation dynamics during the growing season.

Land Cover Classification Based on Fused Data from GF-1 and MODIS NDVI Time Series

Accurate regional and global information on land cover and its changes over time is crucial for environmental monitoring, land management, and planning. In this study, we selected Fengning County, in China’s Hebei Province, as a case study area. Using satellite data, we generated fused normalized-difference vegetation index (NDVI) data with high spatial and temporal resolution by utilizing the STARFM algorithm to produce a fused GF-1 and MODIS NDVI dataset. We extracted seven phenological parameters (including the start, end, and length of the growing season, base value, mid-season date, maximum NDVI, seasonal NDVI amplitude) from a fused NDVI time-series after reconstruction using the TIMESAT software. We developed four classification scenarios based on different combinations of GF-1 spectral features, the fused NDVI time-series, and the phenological parameters. We then classified the land cover using a support vector machine and analyzed the classification accuracies. We found that the proposed method achieved satisfactory classification results, and that the combination of the fused NDVI data with the extracted phenological parameters significantly improved classification accuracy. The classification accuracy based on the composited GF-1 multi-spectral bands combined with the phenological parameters was the highest among the four scenarios, with an overall classification accuracy of 88.8% and a Kappa coefficient of 0.8714, which represent increases of 9.3 percentage points and 0.1073, respectively, compared with GF-1 spectral data alone. The producer’s and user’s accuracy for different land cover types improved, with a few exceptions, and cropland and broadleaf forest had the largest increase.

Evaluation of changes in ecological security in China’s Qinghai Lake Basin from 2000 to 2013 and the relationship to land use and climate change

Ecological security evaluation is an important way to identify the need for improvement in a watershed and to assess the degree of regional sustainable development. Using a driver–pressure–state–exposure–response model, a comprehensive system of ecological security indicators was developed, and it was demonstrated in a case study of the main ecological problems facing the Qinghai Lake Basin. Indicators of the status of the natural ecological environment, socioeconomic pressure, and the degree of environmental damage were chosen to develop the model, and comprehensively evaluated the basin’s ecological security in 2000, 2004, 2009, and 2013 to reveal changes in the ecological security in response to changing climate and land use. The overall ecological security of the basin improved from 2000 to 2013, with considerable restoration and reconstruction of the ecosystem. From 2000 to 2004, environmental deterioration increased slightly as a result of pollution caused by human activities, excess land reclamation for agriculture, land desertification, and grassland degeneration. However, the obvious effect of ecological protection policies, such as conversion of farmland into grassland and stall feeding of livestock instead of grazing, led to improvement of the ecological environment from 2004 to 2013. Ecological security in the basin increased with increasing precipitation during the study period.

Influence of meadow changes on net primary productivity: a case study in a typical steppe area of XilinGol of Inner Mongolia in China

In this research, we monitored the change (degeneration or improvement) in meadow vegetation over an approximately 12-year timespan in the typical steppe area of Inner Mongolia in China. Linear trend analysis (LTA) and the MOD13Q1-NDVI time series data were used to evaluate the changes in the net primary productivity (NPP) during the vegetation growing seasons between 2000 and 2011. The Carnegie Ames Stanford Approach (CASA) model was used, and the relationship between the vegetation change and meadow NPP was analyzed and validated with field data collected in 2011. The results indicate the following: (1) the growth status and NPP of the meadow vegetation in the typical steppe area of Inner Mongolia varied greatly for each year without an obvious linear trend between the change of meadow vegetation and NPP; (2) additional analysis with field measured data, collected in 2011, revealed that the average dry weight of the above-ground biomass in the area where the NPP had increased was less than that in the area where it had decreased; the dry weight of the above-ground biomass of the meadow vegetation that showed degeneration was greater than that of the meadow vegetation that showed improvement; (3) a possible reason for the phenomenon mentioned in (2) was that the government protected the degenerated meadows with less biomass, which led to vegetation growth and increased NPP, whereas the meadows that had not been degenerated or showed only minor degeneration and still received rich biomass were over-grazed, causing the NPP to decline.

A model for the estimation of fractional vegetation cover based on the relationship between vegetation and soil moisture

Based on surface temperature and the normalized difference vegetation index (NDVI), we calculated the temperature vegetation dryness index (TVDI). Using the relationship between TVDI and NDVI, we established a vegetation–soil moisture response model that captures the sensitivity of NDVIs response to changes in TVDI using a linear unmixing approach, and validated the model using Landsat Thematic Mapper (TM) images acquired in 1997, 2004 and 2006 and a Landsat Enhanced Thematic Mapper Plus (ETM+) image acquired in 2000. We determined the correlations between TVDI and field-measured soil moisture in 2006. TVDI was correlated significantly with soil moisture at depths of 0 to 10 cm and 10 to 20 cm, so TVDI can be used as an index that captures changes in soil moisture at these depths. By using fractional vegetation cover (FVC) data measured in the field to validate the estimated values, we estimated mean absolute errors of 0.043 and 0.137 for shrub and grassland vegetation coverage, respectively, demonstrating acceptable estimation accuracy. Based on these results, it is possible to estimate a regions FVC using the linear unmixing model. The results show bare land coverage values distributed similarly to TVDI values. In mountain areas, grassland coverage mostly ranged from 0.4 to 0.6. Shrub coverage mostly ranged from 0.4 to 0.6. Forest coverage was zero in most parts of the study area.

Fractional Vegetation Cover Estimation Based on an Improved Selective Endmember Spectral Mixture Model

Vegetation is an important part of ecosystem and estimation of fractional vegetation cover is of significant meaning to monitoring of vegetation growth in a certain region. With Landsat TM images and HJ-1B images as data source, an improved selective endmember linear spectral mixture model (SELSMM) was put forward in this research to estimate the fractional vegetation cover in Huangfuchuan watershed in China. We compared the result with the vegetation coverage estimated with linear spectral mixture model (LSMM) and conducted accuracy test on the two results with field survey data to study the effectiveness of different models in estimation of vegetation coverage. Results indicated that: (1) the RMSE of the estimation result of SELSMM based on TM images is the lowest, which is 0.044. The RMSEs of the estimation results of LSMM based on TM images, SELSMM based on HJ-1B images and LSMM based on HJ-1B images are respectively 0.052, 0.077 and 0.082, which are all higher than that of SELSMM based on TM images; (2) the R2 of SELSMM based on TM images, LSMM based on TM images, SELSMM based on HJ-1B images and LSMM based on HJ-1B images are respectively 0.668, 0.531, 0.342 and 0.336. Among these models, SELSMM based on TM images has the highest estimation accuracy and also the highest correlation with measured vegetation coverage. Of the two methods tested, SELSMM is superior to LSMM in estimation of vegetation coverage and it is also better at unmixing mixed pixels of TM images than pixels of HJ-1B images. So, the SELSMM based on TM images is comparatively accurate and reliable in the research of regional fractional vegetation cover estimation.

Cross-comparison between China HJ1A-CCD and Landsat TM data

Considering there is great similarity between the wave-band design and spatial resolution of the China HJ1A/1B satellite CCD Camera and Landsat TM data, this paper conducts comparison and analysis to the data from the aspects of track parameter, spectral response characteristics and imaging quality. The orbital parameters have little difference. But HJ satellite has two satellites constellations in A/B, and its width is bigger than that of Landsat, which enables it to conduct two-day earth coverage observation, so it is higher than Landsat 5 in time resolution. Radiation precision evaluation results show that in the 4 wavebands of blue, green, red and near-infrared, HJ1A-CCD1 is more sensitive to low radiance, and has a stronger capability to receive low radiance value than TM; in the blue and red wavebands, it also has a stronger capability to receive high radiance value than TM, from which we can conclude that HJ1A-CCD1 has a wider threshold value range than TM in the blue and red wavebands; it has a weaker capability to receive high radiance than TM in the green and near-infrared wavebands; from the perspective of imaging effect, the information amount, texture feature and definition of various wavebands of HJ1A-CCD1 data are not as good as Landsat TM data.

An analysis on the degradation pattern of the steppe grassland on different slope in North China

In this study, the degradation pattern of the steppe was taken as the study object. Based on Gutman model, four kinds of degraded grassland were extracted from TM image in 1991 and 2005. And the slope of the research area was computed from DEM. In addition, the degradation pattern of the grassland on different slope was analyzed. The result suggests that the study of degradation pattern of the steppe is helpful to control the grassland degradation, and to attain the aim of the sustainable grassland ecosystem management.

Estimation of winter wheat yield in hebei plain of China by improved CASA model

In this study, we use an improved and validated Carnegie Ames Stanford Approach (CASA) model to predict NPP. In this model, NPP can be estimated just using monthly meteorological data and monthly NDVI data. It is relatively easier to acquire data than other model and its application can be enhanced. And we build regression model between NPP and yield got from national statistical almanac, so we can go further to estimate yield. Results showed that the square of regression coefficients of the model between NPP and yield was 0.985, and significance level of this model passed 0.001. And there are clearly strong spatial variations in Hebei Plain winter wheat NPP, and the spatial changes of yield were notable. The total annual yield of winter wheat in Hebei Plain in 2004 was 12.3 Mt.

Estimation of chlorophyll content in the typical steppe vegetation of Inner Mongolia, China, using various red-edge position extraction techniques

Abstract The red-edge position (REP) was extracted from reflectance spectral data at canopy and leaf scale using six different methods in the temperate typical steppe of Inner Mongolia with relatively high species richness. The results suggested that the REPs varied with the extraction methods, sampling sites, plant species, and estimation scales. At the canopy scale, chlorophyll content (CC) was estimated with the linear extrapolation method, and the polynomial fitting technique had coefficients of determination ( R 2 > 0.4 ). The chlorophyll estimates at Leymus chinensis- and Stipa grandis-dominated sites were slightly better than those from the large sampling sites with multiple dominant plant species. At the leaf scale, the linear extrapolation method and the polynomial fitting technique presented high coefficients of determination ( R 2 > 0.6 ). CC estimated at L. chinensis-dominated sites was substantially higher than at S. grandis-dominated sites as well as the large sampling site. The results using the maximum first-derivative method and Lagrangian interpolation techniques revealed a discontinuity, whereas the REPs, as extracted by the linear interpolation method, were shifted toward longer wavelengths. The linear interpolation and inverted Gaussian method were easily saturated. The results obtained with the polynomial fitting technique and the linear extrapolation method had higher sensitivity and accuracy for estimation of CC.