Xiufang Zhu

Spatiotemporal changes in vegetation coverage and its driving factors in the Three-River Headwaters Region during 2000–2011

The Three-River Headwaters Region (TRHR), which is the source area of the Yangtze River, Yellow River, and Lancang River, is of key importance to the ecological security of China. Because of climate changes and human activities, ecological degradation occurred in this region. Therefore, “The nature reserve of Three-River Source Regions” was established, and “The project of ecological protection and construction for the Three-River Headwaters Nature Reserve” was implemented by the Chinese government. This study, based on MODIS-NDVI and climate data, aims to analyze the spatiotemporal changes in vegetation coverage and its driving factors in the TRHR between 2000 and 2011, from three dimensions. Linear regression, Hurst index analysis, and partial correlation analysis were employed. The results showed the following: (1) In the past 12 years (2000–2011), the NDVI of the study area increased, with a linear tendency being 1.2%/10a, of which the Yangtze and Yellow River source regions presented an increasing trend, while the Lancang River source region showed a decreasing trend. (2) Vegetation coverage presented an obvious spatial difference in the TRHR, and the NDVI frequency was featured by a bimodal structure. (3) The area with improved vegetation coverage was larger than the degraded area, being 64.06% and 35.94%, respectively during the study period, and presented an increasing trend in the north and a decreasing trend in the south. (4) The reverse characteristics of vegetation coverage change are significant. In the future, degradation trends will be mainly found in the Yangtze River Basin and to the north of the Yellow River, while areas with improving trends are mainly distributed in the Lancang River Basin. (5) The response of vegetation coverage to precipitation and potential evapotranspiration has a time lag, while there is no such lag in the case of temperature. (6) The increased vegetation coverage is mainly attributed to the warm-wet climate change and the implementation of the ecological protection project.

Agricultural irrigation in China

As the most populous country in the world, China always faces challenges for food security. The country must feed its 1.3 billion people with less than 10% of the worlds arable land (Wu et al. 2010). Over the last 60 years, the population of China has increased from 0.5 to 1.3 billion, the total irrigated area has increased almost monotonically from 15.9 million ha (39.3 million ac) to 61.7 million ha (152.5 million ac), and grain output has increased from 113.2 billion kg (249.6 billion lb) to 571.2 billion kg (1,259.5 billion lb) (figure 1). Arable land and available water resources are distributed unevenly in China. To realize self-sufficiency in food production, the Chinese have undertaken large-scale programs to increase agricultural production. Efforts include using chemical pesticides and fertilizers, developing new strains of genetically modified crops, and investing in irrigation infrastructure. Among those measures, agricultural irrigation has made the largest contribution to crop yield increase and poverty reduction in rural areas (Huang et al. 2006). Irrigation stabilizes crop production, improves crop quality, reduces rural poverty, and allows for diversification in farm production. Approximately half of the national cropland is irrigated and produces 75% of the nations food, 80% of its…

An Improved STARFM with Help of an Unmixing-Based Method to Generate High Spatial and Temporal Resolution Remote Sensing Data in Complex Heterogeneous Regions

Remote sensing technology plays an important role in monitoring rapid changes of the Earths surface. However, sensors that can simultaneously provide satellite images with both high temporal and spatial resolution haven’t been designed yet. This paper proposes an improved spatial and temporal adaptive reflectance fusion model (STARFM) with the help of an Unmixing-based method (USTARFM) to generate the high spatial and temporal data needed for the study of heterogeneous areas. The results showed that the USTARFM had higher accuracy than STARFM methods in two aspects of analysis: individual bands and of heterogeneity analysis. Taking the predicted NIR band as an example, the correlation coefficients (r) for the USTARFM, STARFM and unmixing methods were 0.96, 0.95, 0.90, respectively (p-value < 0.001); Root Mean Square Error (RMSE) values were 0.0245, 0.0300, 0.0401, respectively; and ERGAS values were 0.5416, 0.6507, 0.8737, respectively. The USTARM showed consistently higher performance than STARM when the degree of heterogeneity ranged from 2 to 10, highlighting that the use of this method provides the capacity to solve the data fusion problems faced when using STARFM. Additionally, the USTARFM method could help researchers achieve better performance than STARFM at a smaller window size from its heterogeneous land surface quantitative representation.

Mapping Cropland Distributions Using a Hard and Soft Classification Model

Accurate and timely information regarding the location and area of major crop types has significant economic, food, policy, and environmental implications. Both hard and soft classification methods are used throughout the growing season to generate cropland distribution maps using multiple remotely sensed data. Hard classification models (HCMs) yield good results in large homogeneous areas where pure pixels are dominant, but they fail in fragmented areas where mixed pixels are dominant. Conversely, soft classification models (SCMs) are thought to have greater accuracy in fragmented areas than in regions with pure pixels. To take advantage of both methods, we develop a hard and SCM (HSCM) based on existing HCMs and SCMs, and test it using data from simulated images as well as actual satellite data from southeast Beijing, China. The model assessment was performed using three statistical metrics at scales ranging from 1×1 to 10×10 pixels. The results reveal that the HSCM has the highest classification accuracy and produces more reasonable cropland distribution maps than those produced by either HCMs or SCMs. Moreover, the theory and methods employed in developing the HSCM provide a unifying framework for mapping land cover types, and they can be applied to different HCMs and SCMs beyond those currently in use.

Changes in Growing Season Vegetation and Their Associated Driving Forces in China during 2001–2012

In recent decades, the monitoring of vegetation dynamics has become crucial because of its important role in terrestrial ecosystems. In this study, a satellite-derived normalized difference vegetation index (NDVI) was combined with climate factors to explore the spatiotemporal patterns of vegetation change during the growing season, as well as their driving forces in China from 2001 to 2012. Our results showed that the growing season NDVI increased continuously during 2001–2012, with a linear trend of 1.4%/10 years (p < 0.01). The NDVI in north China mainly exhibited an increasing spatial trend, but this trend was generally decreasing in south China. The vegetation dynamics were mainly at a moderate intensity level in both the increasing and decreasing areas. The significantly increasing trend in the NDVI for arid and semi-arid areas of northwest China was attributed mainly to an increasing trend in the NDVI during the spring, whereas that for the north and northeast of China was due to an increasing trend in the NDVI during the summer and autumn. Different vegetation types exhibited great variation in their trends, where the grass-forb community had the highest linear trend of 2%/10 years (p < 0.05), followed by meadow, and needle-leaf forest with the lowest increasing trend, i.e., a linear trend of 0.3%/10 years. Our results also suggested that the cumulative precipitation during the growing season had a dominant effect on the vegetation dynamics compared with temperature for all six vegetation types. In addition, the response of different vegetation types to climate variability exhibited considerable differences. In terms of anthropological activity, our statistical analyses showed that there was a strong correlation between the cumulative afforestation area and NDVI during the study period, especially in a pilot region for ecological restoration, thereby suggesting the important role of ecological restoration programs in ecological recovery throughout China in the last decade.

Agricultural drought monitoring: Progress, challenges, and prospects

In this paper, we compared the concept of agricultural drought and its relationship with other types of droughts and reviewed the progress of research on agricultural drought monitoring indices on the basis of station data and remote sensing. Applicability and limitations of different drought monitoring indices were also compared. Meanwhile, development history and the latest progress in agricultural drought monitoring were evaluated through statistics and document comparison, suggesting a transformation in agricultural drought monitoring from traditional single meteorological monitoring indices to meteorology and remote sensing-integrated monitoring indices. Finally, an analysis of current challenges in agricultural drought monitoring revealed future research prospects for agricultural drought monitoring, such as investigating the mechanism underlying agricultural drought, identifying factors that influence agricultural drought, developing multi-spatiotemporal scales models for agricultural drought monitoring, coupling qualitative and quantitative agricultural drought evaluation models, and improving the application levels of remote sensing data in agricultural drought monitoring.

Spatiotemporal changes of cold surges in Inner Mongolia between 1960 and 2012

In this study, we analyzed the spatiotemporal variation of cold surges in Inner Mongolia between 1960 and 2012 and their possible driving factors using daily minimum temperature data from 121 meteorological stations in Inner Mongolia and the surrounding areas. These data were analyzed utilizing a piecewise regression model, a Sen+Mann-Kendall model, and a correlation analysis. Results demonstrated that (1) the frequency of single-station cold surges decreased in Inner Mongolia during the study period, with a linear tendency of −0.5 times/10a (−2.4 to 1.2 times/10a). Prior to 1991, a significant decreasing trend of −1.1 times/10a (−3.3 to 2.5 times/10a) was detected, while an increasing trend of 0.45 times/10a (−4.4 to 4.2 times/10a) was found after 1991. On a seasonal scale, the trend in spring cold surges was consistent with annual values, and the most obvious change in cold surges occurred during spring. Monthly cold surge frequency displayed a bimodal structure, and November witnessed the highest incidence of cold surge. (2) Spatially, the high incidence of cold surge is mainly observed in the northern and central parts of Inner Mongolia, with a higher occurrence observed in the northern than in the central part. Inter-decadal characteristic also revealed that high frequency and low frequency regions presented decreasing and increasing trends, respectively, between 1960 and 1990. High frequency regions expanded after the 1990s, and regions exhibiting high cold surge frequency were mainly distributed in Tulihe, Xiao’ergou, and Xi Ujimqin Banner. (3) On an annual scale, the cold surge was dominated by AO, NAO, CA, APVII, and CQ. However, seasonal differences in the driving forces of cold surges were detected. Winter cold surges were significantly correlated with AO, NAO, SHI, CA, TPI, APVII, CW, and IZ, indicating they were caused by multiple factors. Autumn cold surges were mainly affected by CA and IM, while spring cold surges were significantly correlated with CA and APVII.

Changes in Spring Phenology in the Three-Rivers Headwater Region from 1999 to 2013

Abstract: Vegetation phenology is considered a sensitive indicator of terrestrial ecosystem response to global climate change. We used a satellite-derived normalized difference vegetation index to investigate the spatiotemporal changes in the green-up date over the Three-Rivers Headwater Region (TRHR) from 1999 to 2013 and characterized their driving forces using climatic data sets. A significant advancement trend was observed throughout the entire study area from 1999 to 2013 with a linear tendency of 6.3 days/decade ( p < 0.01); the largest advancement trend was over the Yellow River source region (8.6 days/decade, p < 0.01). Spatially, the green-up date increased from the southeast to the northwest, and the green-up date of 87.4% of pixels fell between the 130th and 150th Julian day. Additionally, about 91.5% of the study area experienced advancement in the green-up date, of which 80.2%, mainly distributed in areas of vegetation coverage increase, experienced a significant advance. Moreover, it was found that the green-up date and its trend were significantly correlated with altitude. Statistical analyses showed that a 1-°C increase in spring temperature would induce an advancement in the green-up date of 4.2 days. We suggest that the advancement of the green-up date in the TRHR might be attributable principally to warmer and wetter springs.

Mapping Irrigated Areas in China From Remote Sensing and Statistical Data

Spatial information on irrigation is needed for a variety of applications, such as studies on water exchange between the land surface and atmosphere, climate change, and irrigation water requirements, water resources management, hydrological modeling, and agricultural planning. However, it is hard to map irrigated areas automatically by traditional image classification methods because of the high spectral similarity between the same crops with and without irrigation. In this study, we developed three irrigation potential indices by using the time series normalized difference vegetation index (NDVI) and precipitation data. Using these indices and a spatial allocation model, we downscaled the census data on irrigation from administrative units to individual pixels and produced a new irrigation map of China around the year 2000. We collected 614 reference samples (262 irrigated samples and 352 nonirrigated) in mainland China to validate our new irrigation map and also two global irrigation maps: one is produced by the Food and Agriculture Organization of the United Nations and the University of Frankfurt (FAO/UF map), whereas the other is produced by the International Water Management Institute (IWMI map). The overall accuracies of IWMI map (0.0089282°) and the new map (1 km) are 60.91% and 68.40%, respectively. We also resampled the IWMI map and the new map to match the spatial resolution of FAO/UF map (0.0833333°), and calculated the producer accuracies of FAO/UF map, resampled IWMI map, and resampled new irrigation map. The accuracies are 83.2%, 83.2%, and 87.0%, respectively. We further compared the three maps using cluster and outlier analysis and spot analysis. Comparison results suggest that our new map agrees very well with the patterns of irrigated area distribution from the FAO/UF map, but differs greatly from the IWMI map. Results from this study suggest that our method is a promising tool for mapping irrigated areas. It has several advantages. First, its inputs are quite simple, and no training samples are needed. Second, our model is general and repeatable. Third, it can be used to map historical irrigated areas. The limitations of our method are also discussed.

Vegetation dynamics in Qinling-Daba Mountains in relation to climate factors between 2000 and 2014

Using the Moderate Resolution Imaging Spectroradiometer-normalized difference vegetation index (NDVI) dataset, we investigated the patterns of spatiotemporal variation in vegetation coverage and its associated driving forces in the Qinling-Daba (Qinba) Mountains in 2000–2014. The Sen and Mann–Kendall models and partial correlation analysis were used to analyze the data, followed by calculation of the Hurst index to analyze future trends in vegetation coverage. The results of the study showed that (1) NDVI of the study area exhibited a significant increase in 2000–2014 (linear tendency, 2.8%/10a). During this period, a stable increase was detected before 2010 (linear tendency, 4.32%/10a), followed by a sharp decline after 2010 (linear tendency,–6.59%/10a). (2) Spatially, vegetation cover showed a “high in the middle and a low in the surroundings” pattern. High values of vegetation coverage were mainly found in the Qinba Mountains of Shaanxi Province. (3) The area with improved vegetation coverage was larger than the degraded area, being 81.32% and 18.68%, respectively, during the study period. Piecewise analysis revealed that 71.61% of the total study area showed a decreasing trend in vegetation coverage in 2010–2014. (4) Reverse characteristics of vegetation coverage change were stronger than the same characteristics on the Qinba Mountains. About 46.89% of the entire study area is predicted to decrease in the future, while 34.44% of the total area will follow a continuously increasing trend. (5) The change of vegetation coverage was mainly attributed to the deficit in precipitation. Moreover, vegetation coverage during La Nina years was higher than that during El Nino years. (6) Human activities can induce ambiguous effects on vegetation coverage: both positive effects (through implementation of ecological restoration projects) and negative effects (through urbanization) were observed.