Wenquan Zhu

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.

A Comparative Analysis between GIMSS NDVIg and NDVI3g for Monitoring Vegetation Activity Change in the Northern Hemisphere during 1982–2008

The long-term Normalized Difference Vegetation Index (NDVI) time-series data set generated from the Advanced Very High Resolution Radiometers (AVHRR) has been widely used to monitor vegetation activity change. The third version of NDVI (NDVI3g) produced by the Global Inventory Modeling and Mapping Studies (GIMMS) group was released recently. The comparisons between the new and old versions should be conducted for linking existing studies with future applications of NDVI3g in monitoring vegetation activity change. Based on simple and piecewise linear regression methods, this study made a comparative analysis between NDVIg and NDVI3g for monitoring vegetation activity change and its responses to climate change in the middle and high latitudes of the Northern Hemisphere during 1982–2008. Our results indicated that there were large differences between NDVIg and NDVI3g in the spatial patterns for both the overall changing trends and the timing of Turning Points (TP) in NDVI time series, which spread over almost the entire study region. The average NDVI trend from NDVI3g was almost twice as great as that from NDVIg and the detected average timing of TP from NDVI3g was about one year later. Although the general spatial patterns were consistent between two data sets for detecting the responses of growing-season NDVI to temperature and precipitation changes, there were large differences in the response magnitude, with a higher response magnitude to temperature in NDVI3g and an opposite response to precipitation change for the two data sets. These results demonstrated that the NDVIg data set may underestimate the vegetation activity change trend and its response to climate change in the middle and high latitudes of the Northern Hemisphere during the past three decades.

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

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.

Measurement of terrestrial ecosystem service value in China based on remote sensing

With the measurement of net primary productivity and vegetation coverage fraction based on remote sensing data,the terrestrial ecosystem service value of China in 2000 was quantitatively estimated as 9.17 x 10(12) yuan (RMB). The spatial distribution of the ecological service value showed a decreasing trend from southeast China to northwest China, which was consistent with the regional distribution of vegetation types. The service value varied with different vegetations, e. g., forests had the highest service value of 18 789 yuan x hm(-2), accounting for 40.80% of the total terrestrial ecosystem service value, and bushes and farmlands had a higher service value of 13 789 yuan x hm(-2) and 13054 yuan x hm(-2), which was 10.79% and 24.23% of total value, respectively. The service value was also varied with different ecosystem functions, i.e., gas regulation contributed the highest value of 45.16% to the total service value, and the contribution of soil conservation and water conservation was 28.83% and 14.44%, respectively. The integrated approach coupling ecology and remote sensing data provided a new method to measure the ecological service value, which could estimate the value objectively and spatial-explicitly. However, some uncertainties still existed in this approach, which should be improved in the future studies.

Remote-Sensed Monitoring of Dominant Plant Species Distribution and Dynamics at Jiuduansha Wetland in Shanghai, China

Spartina alterniflora is one of the most hazardous invasive plant species in China. Monitoring the changes in dominant plant species can help identify the invasion mechanisms of S. alterniflora, thereby providing scientific guidelines on managing or controlling the spreading of this invasive species at Jiuduansha Wetland in Shanghai, China. However, because of the complex terrain and the inaccessibility of tidal wetlands, it is very difficult to conduct field experiments on a large scale in this wetland. Hence, remote sensing plays an important role in monitoring the dynamics of plant species and its distribution on both spatial and temporal scales. In this study, based on multi-spectral and high resolution (<10 m) remote sensing images and field observational data, we analyzed spectral characteristics of four dominant plant species at different green-up phenophases. Based on the difference in spectral characteristics, a decision tree classification was built for identifying the distribution of these plant species. The results indicated that the overall classification accuracy for plant species was 87.17%, and the Kappa Coefficient was 0.81, implying that our classification method could effectively identify the four plant species. We found that the area of Phragmites australi showed an increasing trend from 1997 to 2004 and from 2004 to 2012, with an annual spreading rate of 33.77% and 31.92%, respectively. The area of Scirpus mariqueter displayed an increasing trend from 1997 to 2004 (12.16% per year) and a decreasing trend from 2004 to 2012 (−7.05% per year). S. alterniflora has the biggest area (3302.20 ha) as compared to other species, accounting for 51% of total vegetated area at the study region in 2012. It showed an increasing trend from 1997 to 2004 and from 2004 to 2012, with an annual spreading rate of 130.63% and 28.11%, respectively. As a result, the native species P. australi was surrounded and the habitats of S. mariqueter were occupied by S. alterniflora. The high proliferation ability and competitive advantage for S. alterniflora inhibited the growth of other plant species and we anticipate a continuous expansion of this invasive species at Jiuduansha Wetland. Effective measures should be taken to control the invasion of S. alterniflora.

Using phenological metrics and the multiple classifier fusion method to map land cover types

Abstract Feature selection and multiple classifier fusion (MCF) are effective approaches to improve land cover classification accuracy. In this study, we combined phenological metrics and the MCF method to map land cover types in Jiangsu province of China during the second crop growing season using moderate resolution imaging spectroradiometer time-series data. Eight phenological metrics were developed and calculated, and a MCF scheme was proposed by combining a simple majority vote and the measurement of posterior probabilities. The four base classifiers (i.e., the maximum likelihood classifier, the Mahalanobis distance classifier, the support vector machine classifier, and the neural networks classifier) and the MCF method were used in classifications using two spectral indices from the original satellite data (direct classification) and the computed metric data (metrics-based classification). Accuracy assessments indicated that the overall accuracies and kappa coefficients of the metrics-based classifications were all higher than those of direct classifications. The average overall accuracy and kappa coefficient of metrics-based classifications were 8.36% and 0.1 higher than that of direct classifications, respectively. Similarly, the overall accuracy and kappa coefficient of MCF generally were close to or exceeded the highest accuracy among all the base classifiers. The highest overall accuracy and kappa coefficient was achieved by classification with the MCF method based on phenological metrics (m-MCF), which were 88% and 0.85, respectively. Our results suggested that combining phenological metrics and MCF in classification is a promising method for land cover mapping in regions where strong phenological signals can be detected.

Spatio-temporal distribution of net primary productivity along the northeast china transect and its response to climatic change from 1982 to 2000

An improved Carnegie Ames Stanford Approach model (CASA) model was used to estimate the net primary productivity (NPP) of the Northeast China Transect (NECT) from 1982 to 2000. The spatial-temporal distribution of NPP along NECT and its response to climatic change were also analyzed. Results showed that: (1) The spatial distribution of NPP in NECT is quite similar with that of precipitation and their spatial correlation coefficient is up to 0.93 (P<0.01). (2) The interannual variation of NPP in NECT is mainly affected by the change of the aestival NPP of each year. It accounts for 67.6% of the inter-annual increase in NPP and their spatial correlation coefficient is 0.95 (P<0.01). (3) The NPP in NECT is mainly cumulated between May and September, which accounts for 89.8% of the annual NPP. Summer (June to September) accounts for 65.9% of the annual NPP and winter has the lowest NPP. (4) The mean NPP in NECT was 392.4 gC/m/yr in these 19 years, ranging from 333.8 to 448.4 gC/m/year. NPP increased 14.3% from 1980s to 1990s. The inter-annual linear trend of NPP is 4.6 gC/m/yr/yr, and the relative trend 1.17%/yr owning mainly to the increasing temperature. Keywords-Northeast China Transect; remote sensing; NPP; climatic change; spatio-temporal distribution