Peng Yang

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.

Evaluation of MODIS Land Cover and LAI Products in Cropland of North China Plain Using In Situ Measurements and Landsat TM Images

Global maps of land cover and leaf area index (LAI) from the moderate resolution imaging spectroradiometer (MODIS) are available from the earth resources observation system data center. Validation of these MODIS terrestrial products is crucial for scientific research. Numerous pre- and postlaunch validation activities have been completed during the last several years. However, such work was mainly located in North America, Europe, and Africa. The validated vegetation types were principally focused on woodland and savanna. In this paper, we evaluated the Collection 3 MODIS land cover (MOD12Q1) and the Collection 4 MODIS LAI (MOD15A2) products in the North China Plain (NCP). We placed the emphasis on croplands by: (1) developing an accurate land cover map, based on the MODIS LAI/fraction of photosynthetically active radiation land cover classification scheme; (2) deriving empirical LAI validation maps, based on in situ measurement and landsat thematic mapper imagery; and (3) conducting an initial MODIS validation exercise to assess the quality of MODIS products. The results indicate that an apparent misclassification exists between grasses/cereal crop and broadleaf crop biomes in the MODIS land cover product. Although MODIS LAI could resemble the general pattern of the LAI seasonal vegetation variation in the NCP, the MODIS LAI algorithm markedly underestimated LAI values (down to 2-3 m2/m2) for the April 2004 winter wheat cropland. Biome misclassification and the atmospheric effect of cloud or aerosol were proven to decrease the value of MODIS LAI, but these impacts cannot fully explain the significant underestimation. Additional studies are needed to understand the main reasons for this underestimation in the study area.

Characterizing spatial patterns of phenology in cropland of China based on remotely sensed data.

Abstract This study used time-series of global inventory modeling and mapping studies (GIMMS) normalized difference vegetation index (NDVI) datasets at a spatial resolution of 8 km and 15-d interval to investigate the spatial patterns of cropland phenology in China. A smoothing algorithm based on an asymmetric Gaussian function was first performed on NDVI dataset to minimize the effects of anomalous values caused by atmospheric haze and cloud contamination. Subsequent processing for identifying cropping systems and extracting phenological parameters, the starting date of growing season (SGS) and the ending date of growing season (EGS) was based on the smoothed NVDI time-series data. The results showed that the cropping systems in China became complex as moving from north to south of China. Under these cropping systems, the SGS and EGS for the first growing season varied largely over space, and those regions with multiple cropping systems generally presented a significant advanced SGS and EGS than the regions with single cropping patterns. On the contrary, the phenological events of the second growing season including both the SGS and EGS showed little difference between regions. The spatial patterns of cropping systems and phenology in Chinese cropland were highly related to the geophysical environmental factors. Several anthropogenic factors, such as crop variety, cultivation levels, irrigation, and fertilizers, could profoundly influence crop phenological status. How to discriminate the impacts of biophysical forces and anthropogenic drivers on phenological events of cultivation remains a great challenge for further studies.

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.

How Could Agricultural Land Systems Contribute to Raise Food Production Under Global Change

To feed the increasing world population, more food needs to be produced from agricultural land systems. Solutions to produce more food with fewer resources while minimizing adverse environmental and ecological consequences require sustainable agricultural land use practices as supplementary to advanced biotechnology and agronomy. This review paper, from a land system perspective, systematically proposed and analyzed three interactive strategies that could possibly raise future food production under global change. By reviewing the current literatures, we suggest that cropland expansion is less possible amid fierce land competition, and it is likely to do less in increasing food production. Moreover, properly allocating crops in space and time is a practical way to ensure food production. Climate change, dietary shifts, and other socio-economic drivers, which would shape the demand and supply side of food systems, should be taken into consideration during the decision-making on rational land management in respect of sustainable crop choice and allocation. And finally, crop-specific agricultural intensification would play a bigger role in raising future food production either by increasing the yield per unit area of individual crops or by increasing the number of crops sown on a particular area of land. Yet, only when it is done sustainably is this a much more effective strategy to maximize food production by closing yield and harvest gaps.

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.

Scenario-based assessment of future food security

This paper presents a scenario-based assessment of global future food security. To do that, the socio-economic and climate change scenarios were defined for the future and were linked to an integrated modeling framework. The crop yields simulated by the GIS-based Environmental Policy Integrated Climate (EPIC) model and crop areas simulated by the crop choice decision model were combined to calculate the total food production and per capita food availability, which was used to represent the status of food availability and stability. The per capita Gross Domestic Product (GDP) simulated by IFPSIM model was used to reflect the situation of food accessibility and affordability. Based on these two indicators, the future food security status was assessed at a global scale over a period of approximately 20 years, starting from the year 2000. The results show that certain regions such as South Asia and most African countries will likely remain hotspots of food insecurity in the future as both the per capita food availability and the capacity of being able to import food will decrease between 2000 and 2020. Low food production associated with poverty is the determining factor to starvation in these regions, and more efforts are needed to combat hunger in terms of future actions. Other regions such as China, most Eastern European countries and most South American countries where there is an increase in per capita food availability or an increase in the capacity to import food between 2000 and 2020 might be able to improve their food security situation.

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.

Mapping Spatial and Temporal Variations of Leaf Area Index for Winter Wheat in North China

Leaf area index (LAI) is an important parameter in a number of models related to ecosystem functioning, carbon budgets, climate, hydrology, and crop growth simulation. Mapping and monitoring the spatial and temporal variations of LAI are necessary for understanding crop growth and development at regional level. In this study, the relationships between LAI of winter wheat and Landsat TM spectral vegetation indices (SVIs) were analyzed by using the curve estimation procedure in North China Plain. The series of LAI maps retrieved by the best regression model were used to assess the spatial and temporal variations of winter wheat LAI. The results indicated that the general relationships between LAI and SVIs were curvilinear, and that the exponential model gave a better fit than the linear model or other nonlinear models for most SVIs. The best regression model was constructed using an exponential model between surface-reflectance-derived difference vegetation index (DVI) and LAI, with the adjusted R^2 (0.82) and the RMSE (0.77). The TM LAI maps retrieved from DVILAI model showed the significant spatial and temporal variations. The mean TM LAI value (30 m) for winter wheat of the study area increased from 1.29 (March 7, 2004) to 3.43 (April 8, 2004), with standard deviations of 0.22 and 1.17, respectively. In conclusion, spectral vegetation indices from multi-temporal Landsat TM images can be used to produce fine-resolution LAI maps for winter wheat in North China Plain.