Yaozhong Pan

An Integrative Classification of Vegetation in China Based on NOAA AVHRR and Vegetation-Climate Indices of the Holdridge Life Zone

We developed a method for integrated analysis of multi-source data for vegetation classification at the continental scale, and applied it to China. Multi-temporal 1 km NOAA Advanced Very High Resolution Radiometer (AVHRR) Holdridges life zone system and its vegetation-climate classification indices such as bio-temperature (BT), potential evapotranspiration rate (PER) and precipitation ( P ) correspond better with undisturbed vegetation types all over the world. We generated 1 km images of BT, PER and P using the quantitative model of Holdridges life zone system with climate data of China. They were processed with principal component analysis (PCA) to produce an ancillary image. This image and 12 monthly images of maximum Normalized Difference Vegetation Index (NDVI) values at 1 km resolution were input into an ISODATA clustering algorithm to carry out a vegetation classification. As a result, 47 information classes were obtained. Seasonal NDVI parameters derived through time series analysis (TSA) of the NDVI temporal profile and a set of quantitative vegetation-climate parameters of Holdriges life zone model were synthetically utilized to label information classes. In this method, climate, terrain and spectral data were integrated; separability between vegetation types and classification accuracy were improved. A total of 47 land cover classes were obtained. Validation data collected in the field using GPS indicated that an overall classification accuracy of 71.4% was reached, an 8.1% improvement to the map derived only from multi-temporal NDVI images. To compare our results with the International Geosphere-Biosphere Programme (IGBP) DISCover land cover dataset, we aggregated our land cover classes according to the IGBP classification system. The overall classification accuracy for the aggregated vegetation map from our classification results improved IGBP land cover map from 75.5% to 86.3%.

Modelling net primary productivity of terrestrial ecosystems in East Asia based on an improved CASA ecosystem model

By using a land cover map, normalized difference vegetation index (NDVI) data sets, monthly meteorological data and observed net primary productivity (NPP) data, we have improved the method of estimating light use efficiency (LUE) for different biomes and soil moisture coefficients in the Carnegie–Ames–Stanford Approach (CASA) ecosystem model. Based on this improved model we produced an annual NPP map (in 1999) for the East Asia region located at 10–70° N, 70–170° E (about 19.66% of the terrestrial surface of the Earth). The results show that the mean NPP for the study area in 1999 was 374.12 g carbon (C) m−2 year−1 and the total NPP was 1.096 × 1014 kg C year−1, making up 17.51–18.39% of the global NPP. Comparison between the estimated NPP obtained from this improved CASA ecosystem model and the observed NPP obtained from two NPP databases indicates that the estimated NPP is close to the observed NPP, with an average error of 5.15% for the study region. We used two different land cover maps of China to drive the improved CASA model by keeping other inputs unchanged to determine how the classification accuracy of the land cover map affects the estimated NPP, and the results indicate that an accurate land cover map is important for obtaining an accurate and reliable estimate of NPP for some regions, especially for a particular biome.

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.

A Changing-Weight Filter Method for Reconstructing a High-Quality NDVI Time Series to Preserve the Integrity of Vegetation Phenology

Time-series data of normalized difference vegetation index (NDVI), derived from satellite sensors, can be used to support land-cover change detection and phenological interpretations, but further analysis and applications are hindered by residual noise in the data. As an alternative to a number of existing algorithms developed to compensate for such noise, we develop a simple but computationally efficient method (which we call the changing-weight filter method) to reconstruct a high-quality NDVI time series. The new algorithm consists of two major procedures: (1) detecting the local maximum/minimum points in a growth cycle along an NDVI temporal profile based on a mathematical morphology algorithm and a rule-based decision process and (2) filtering an NDVI time series with a three-point changing-weight filter. This method is tested at 470 test points for 55 vegetation types and a test region in China using a 250-m 16-day Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI product. Comparing our results to those of three other well-known methods-asymmetric Gaussian function fitting, double logistic function fitting, and Savitzky-Golay filtering-the new method has many of the advantages of existing methods, while in some cases, the changing-weight filter method more effectively preserves the curve shape as well as the timing and the amplitude of the local maxima/minima in the NDVI time series for a broad range of phenologies. Moreover, the response of the filtering algorithm is relatively insensitive to the exact values of its design parameters, making the new method more flexible and effective in adjusting to fit a variety of classes of NDVI time series.

Estimating net primary productivity of terrestrial vegetation based on remote sensing: a case study in Inner Mongolia, China

Some vegetation primary production models have been developed in recent years as research issues related to food security and biotic response to climate warming have become more compelling. An estimation model of net primary productivity (NPP), based on geographic information system (GIS) and remote sensing (RS) technology, is presented. The model, driven with ground meteorological data and remote sensing data, moves beyond simple correlative models to a more mechanistic basis and avoids the need for a full suite of eco-physiological process algorithms that require explicit parameterization. Therefore, it is relatively easier to acquire data. Application and validation of this model in Inner Mongolia, China, was conducted. After the validation with observed data and the comparison with other NPP models, the results showed that the predicted NPP was in good agreement with field measurement, and the remote sensing method can more actually reflect the forest NPP than Chikugo model. These results illustrated the utility of the model for terrestrial primary production over regional scales

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…

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.