Zhengguo Li

Exploring the Use of Google Earth Imagery and Object-Based Methods in Land Use/Cover Mapping

Google Earth (GE) releases free images in high spatial resolution that may provide some potential for regional land use/cover mapping, especially for those regions with high heterogeneous landscapes. In order to test such practicability, the GE imagery was selected for a case study in Wuhan City to perform an object-based land use/cover classification. The classification accuracy was assessed by using 570 validation points generated by a random sampling scheme and compared with a parallel classification of QuickBird (QB) imagery based on an object-based classification method. The results showed that GE has an overall classification accuracy of 78.07%, which is slightly lower than that of QB. No significant difference was found between these two classification results by the adoption of Z-test, which strongly proved the potentials of GE in land use/cover mapping. Moreover, GE has different discriminating capacity for specific land use/cover types. It possesses some advantages for mapping those types with good spatial characteristics in terms of geometric, shape and context. The object-based method is recommended for imagery classification when using GE imagery for mapping land use/cover. However, GE has some limitations for those types classified by using only spectral characteristics largely due to its poor spectral characteristics.

Change analysis of rice area and production in China during the past three decades

Rice’s spatial-temporal distributions, which are critical for agricultural, environmental and food security research, are affected by natural conditions as well as socio-economic developments. Based on multi-source data, an effective model named the Spatial Production Allocation Model (SPAM) which integrates arable land distribution, administrative unit statistics of crop data, agricultural irrigation data and crop suitability data, was used to get a series of spatial distributions of rice area and production with 10-km pixels at a national scale — it was applied from the early 1980s onwards and used to analyze the pattern of spatial and temporal changes. The results show that significant changes occurred in rice in China during 1980–2010. Overall, more than 50% of the rice area decreased, while nearly 70% of rice production increased in the change region during 1980–2010. Spatially, most of the increased area and production were in Northeast China, especially, in Jilin and Heilongjiang; most of the decreased area and production were located in Southeast China, especially, in regions of rapidly urbanization in Guangdong, Fujian and Zhejiang. Thus, the centroid of rice area was moved northeast approximately 230 km since 1980, and rice production about 320 km, which means rice production moved northeastward faster than rice area because of the significant rice yield increase in Northeast China. The results also show that rice area change had a decisive impact on rice production change. About 54.5% of the increase in rice production is due to the expansion of sown area, while around 83.2% of the decrease in rice production is due to contraction of rice area. This implies that rice production increase may be due to area expansion and other non-area factors, but reduced rice production could largely be attributed to rice area decrease.

Spatio-temporal responses of cropland phenophases to climate change in Northeast China

We investigated the responses of cropland phenophases to changes of agricultural thermal conditions in Northeast China using the SPOT-VGT Normalized Difference Vegetation Index (NDVI) ten-day-composed time-series data, observed crop phenophases and the climate data collected from 1990 to 2010. First, the phenological parameters, such as the dates of onset-of-growth, peak-of-growth and end-of-growth as well as the length of the growing season, were extracted from the smoothed NVDI time-series dataset and showed an obvious correlation with the observed crop phenophases, including the stages of seedling, heading, maturity and the length of the growth period. Secondly, the spatio-temporal trends of the major thermal conditions (the first date of ⩾ 10°C, the first frost date, the length of the temperature-allowing growth period and the accumulated temperature (AT) of ⩾ 10°C) in Northeast China were illustrated and analyzed over the past 20 years. Thirdly, we focused on the responses of cropland phenophases to the thermal conditions changes. The results showed that the onset-of-growth date had an obvious positive correlation with the first date of ⩾ 10°C (P < 0.01), especially in the northern part of the Songnen Plain, the eastern part of the Sanjiang Plain and the middle and eastern parts of Jilin Province. For the extracted length of growing season and the observed growth period, notable correlations were found in almost same regions (P < 0.05). However, there was no obvious correlation between the end-of-growth date and the first frost date in the study area. Opposite correlations were observed between the length of the growing season and the AT of ⩾ 10°C. In the northern part of the Songnen Plain, the eastern part of the Sanjiang Plain and the middle part of Jilin and Liaoning Provinces, the positive correlation coefficients were higher than the critical value of 0.05, whereas the negative correlation coefficients reached a level of 0.55 (P < 0.05) in the middle and southern parts of Heilongjiang Province and some parts of the Sanjiang Plain. This finding indicated that the crop growth periods were shortened because of the elevated temperature; in contrast, the extended growth period usually meant a crop transformation from early- or middle-maturing varieties into middle or late ones.

Chinese rice production area adaptations to climate changes, 1949-2010.

Climate change has great impact on cropping system. Understanding how the rice production system has historically responded to external forces, both natural and anthropogenic, will provide critical insights into how the system is likely to respond in the future. The observed historic rice movement provides insights into the capability of the rice production system to adapt to climate changes. Using province-level rice production data and historic climate records, here we show that the centroid of Chinese rice production shifted northeastward over 370 km (2.98°N in latitude and 1.88°E in longitude) from 1949 to 2010. Using a linear regression model, we examined the driving factors, in particular climate, behind such rice production movement. While the major driving forces of the rice relocation are such social economic factors as urbanization, irrigation investment, and agricultural or land use policy changes, climate plays a significant role as well. We found that temperature has been a significant and coherent influence on moving the rice center in China and precipitation has had a significant but less spatially coherent influence.

Spatio-temporal dynamics of maize cropping system in Northeast China between 1980 and 2010 by using spatial production allocation model

Understanding crop patterns and their changes on regional scale is a critical requirement for projecting agro-ecosystem dynamics. However, tools and methods for mapping the distribution of crop area and yield are still lacking. Based on the cross-entropy theory, a spatial production allocation model (SPAM) has been developed for presenting spatiotemporal dynamics of maize cropping system in Northeast China during 1980–2010. The simulated results indicated that (1) maize sown area expanded northwards to 48°N before 2000, after that the increased sown area mainly occurred in the central and southern parts of Northeast China. Meanwhile, maize also expanded eastwards to 127°E and lower elevation (less than 100 m) as well as higher elevation (mainly distributed between 200 m and 350 m); (2) maize yield has been greatly promoted for most planted area of Northeast China, especially in the planted zone between 42°N and 48°N, while the yield increase was relatively homogeneous without obvious longitudinal variations for whole region; (3) maize planting density increased gradually to a moderately high level over the investigated period, which reflected the trend of aggregation of maize cultivation driven by market demand.

Selecting the optimal NDVI time-series reconstruction technique for crop phenology detection

AbstractA new scored method has been proposed in this study to evaluate the performances of different NDVI time-series reconstruction techniques. By giving a synthetic score to each of the candidates techniques based on two quantified criteria the optimal one is selected for the purpose of phenology detection. Three widely used techniques including Asymmetric Gaussian function fitting (AG), Double Logistic function fitting (DL) and Savitzky-Golay filtering (SG) are compared using NDVI time-series products from Moderate Resolution Imaging Spectroradiometer (MODIS) on Terra satellite over cropland of Northeast China. The results show that AG approach outperforms the two others in our study area. Cropland NDVI values have been improved obviously after the reconstruction by AG. Spatial patterns of the crop phenology detected from the AG reconstructed NDVI time-series are reasonable. The errors of the derived crop phenology metrics are within an acceptable limit.

Spatial evaluation of crop maps by the spatial production allocation model in China

Spatial Production Allocation Model (SPAM), developed by International Food Policy Research Institute (IFPRI), is one of broadest spatial models that applied a cross-entropy method to downscale the area and yield for each crop with a resolution of 5 arc minute globally for the year 2000 and 2005. To evaluate the accuracy of three staple crops (rice, wheat and maize) in China allocated by SPAM, we compared these crop maps with remote sensed cropland derived from national land cover datasets. This is done through a comparison scheme that accounts for spatial difference at the pixel level. Four types (no-existing, mis-allocated, over-estimated and reasonable) were formulated in this scheme that was used to evaluate the per-pixel area accuracy of each of the three crops on national and provincial scales. Overall, the map of maize has the highest area accuracy with a 64% percentage of reasonable pixels that covers 96% of the total maize area, in contrast, 57% (90%) and 44% (81%) for the wheat and rice map respectively. Further, crop area consistency in rain-fed cropland is better than that in irrigated cropland. Through the evaluations, we can provide decision makers with information on the SPAM products exist as well as the strengths and weaknesses. Meanwhile, some recommendations can be concluded on priorities for further work on the improvement of the reliability, utility and periodic repeatability of crop distribution products.

Spatial distribution of maize in response to climate change in northeast China during 1980-2010

Based on county-level crop statistics and other ancillary information, spatial distribution of maize in the major maize-growing areas (latitudes 39°–48°N) was modelled for the period 1980–2010 by using a cross-entropy-based spatial allocation model. Maize extended as far north as the northern part of the Lesser Khingan Mountains during the period, and the area sown to maize increased by about 5 million ha. More than half of the increase occurred before 2000, and more than 80% of it in the climate transitional zone, where the annual accumulated temperature (AAT) was 2800–3400 °C·d. Regions with AAT of 3800–4000 °C·d became more important, accounting for more than 25% of the increase after 2000. The expansion of maize was thus closely related to warming, although some variation in the distribution was noticed across zones in relation to the warming, indicating that maize in northeast China may have adapted successfully to the warming by adjusting its spatial distribution to match the changed climate.