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Cold Damage Risk Assessment of Double Cropping Rice in Hunan, China
Journal of Integrative Agriculture
February 2013
2013, 12(2): 352-363
RESEARCH ARTICLE
Cold Damage Risk Assessment of Double Cropping Rice in Hunan, China CHENG Yong-xiang1, 2, 3, HUANG Jing-feng1, 2, 4, HAN Zhong-ling5, GUO Jian-ping6, ZHAO Yan-xia6, WANG Xiu-zhen7 and GUO Rui-fang1 Institute of Remote Sensing and Information Application, Zhejiang University, Hangzhou 310058, P.R.China Key Laboratory of Polluted Environment Remediation and Ecological Health, Ministry of Education/College of Natural Resources and Environmental Science, Zhejiang University, Hangzhou 310058, P.R.China 3 College of Life Science, Shihezi University, Shihezi 832000, P.R.China 4 The Provinical Key Laboratory of Agricultural Remote Sensing and Information System, Zhejiang Province, Hangzhou 310058, P.R. China 5 Institute of Information and Technology, Shihezi University, Shihezi 832000, P.R.China 6 Chinese Academy of Meteorological Science, Beijing 100875, P.R.China 7 Institute of Remote Sensing and Earth Sciences, Hangzhou Normal University, Hangzhou 311121, P.R.China 1 2
Abstract Combined with remote sensing data and meteorological data, cold damage risk was assessed for planting area of double cropping rice (DCR) in Hunan Province, China. A new methodology of cold damage risk assessment was built that apply to grid and have clear hazard-affected body. Each station cold damage annual frequency and average annual intensity of cold damage was calculated by using 1951-2010 station daily mean temperature and simple cold damage identification index. On this basis, average annual cold damage risk index was obtained by their product. The spatial analysis models of cold damage risk index about double-season early rice (DSER) and double-season later rice (DSLR) were established respectively by the relation of average annual cold damage risk index and its geographic factors. Critical threshold of level of average annual cold damage risk index for DSER and DSLR were respectively divided by the correlative equation of cold damage annual frequency and average annual intensity of cold damage. 2001-2010 planting area of DCR, acquired by time series analysis of MOD09A1 8-d composite land surface reflectance product, was as target of assessment. The results show average annual intensity of cold damage is exponential function of cold damage annual frequency, average annual cold damage risk index is directly proportional to cold damage cumulant and cold damage annual frequency, and is inversely proportional to happen times of cold damage and the square of statistical time sequence length. Cold damage risk of DSER is higher than DSLR in Hunan Province. In the 10-yr stacking map, DCR planting in low risk area accounted for 11.92% of total extraction area, in moderate risk area accounted for 69.62%, in high risk area accounted for 18.46%. According to the cold damage risk assessment result, DCR production can be guided to reduce cold damage losses. Key words: double cropping rice, cold damage, risk assessment
INTRODUCTION In the same patch of field, planting and harvesting rice twice a year is known as double cropping rice (DCR).
According to the different farming seasons, DCR has been subdivided into double-season early rice (DSER) and double-season later rice (DSLR). DCR is widely planted in Hunan Province, and the planting area ranks first in China for many years. Because of long planting
Received 5 April, 2012 Accepted 29 October, 2012 CHENG Yong-xiang, Mobile: 15088687895, E-mail: [email protected]; Correspondence HUANG Jing-feng, Mobile: 13957171636, E-mail: hjf@zju. edu.cn © 2013, CAAS. All rights reserved. Published by Elsevier Ltd. doi:10.1016/S2095-3119(13)60235-X
Cold Damage Risk Assessment of Double Cropping Rice in Hunan, China
time span, DCR production is often affected by cold damage, especially in hills and mountains (Huang et al. 2011; Lu et al. 2011). From 2001 to 2010 in Hunan Province, average annual disaster area covered for cold damage and freeze injury is 516.5×103 ha. Disaster area affected is 317.7×103 ha. No harvest area is 64.7×103 ha according to China Agriculture Yearbook (China Agriculture Press, 2002-2011). Therefore, in order to guide and arrange production, reduce the negative influence of cold damage, ensure food safety, it is necessary for DCR to assess cold damage risk. Risk assessment study on cold damage is far less than study on flood and drought in the risk assessment of agriculture meteorological disaster at home and abroad (Zhang 2004; Hsu et al. 2011; Wang et al. 2011; Zhang et al. 2011; Hao et al. 2012; Su et al. 2012). At present cold damage risk assessment methods mainly have probability method (Wang 1982; Zu and Zu 1999), comprehensive evaluation method (Ma et al. 2003; Xi et al. 2003; Wang and Zhang 2010) and information diffusion theory method (Li et al. 2009; Zhang et al. 2009; Zhou et al. 2010), and rarely involved in analytic hierarchy process (AHP) and weighted comprehensive method (WCM) that have been commonly used in other disaster risk assessment (Li et al. 2009). The above methods are all based on a single meteorological station or administrative region level. It does not apply to cold damage risk assessment of small grid level, and the objectivity of threshold dividing and weight assignment for comprehensive cold damage risk index and each construction factor needs to be further researched. With continuously increasing of spatial accuracy requirement for cold damage risk assessment, this paper attempts to establish new mode that is suitable for small grid cold damage risk assessment by using simple average annual cold damage risk index, to meet the increasing agricultural production requirements. At present cold damage risk assessment usually doesn’t combine with hazard-affected body, so assessment result lacks of strong pertinence. The method to extract rice field information by using remote sensing image classification or time series analysis has been more mature (Turner and Congalton 1998; Xiao et al. 2005; Chen and McNairn 2006; Sakamoto et al. 2007; Huang et al. 2011; Choudhury et al. 2012). Based on the results of previous studies, time series analysis equations
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to be used to early and later rice information extraction was combined to extract information of DCR, in order to provide more valuable base map for cold damage risk assessment. Finally, a new mode of cold damage risk assessment was established that make full use of 3S technology and meteorological station information.
RESULTS Based on analysis of MOD09A1 8-d composites, DCR in Hunan mostly distributes in Dongting Lake region (Fig. 1). Annual extraction area of DCR is consistent with statistical area in five years (2001, 2002, 2006, 2009 and 2010). For other five years (2003-2005, 2007-2008), the extraction area is lower than the statistical area. Analysis shows that growing seasons of DCR had appeared continuous cloudy days during 2005 to 2008 in Hunan Province, and a number of pixels cannot be interpolated but be excluded from the possible results and cause the extraction results lower than the statistical area. For this research, although annual rice planting area may be underestimated in some years, in some extent the stacking results of the 10-yr extraction area can reflect actual distribution of DCR in Hunan Province, and the result map is considered to have reference significance for cold damage risk assessment of DCR. Simulation precision of the cold damage risk model was validated by use of meteorological stations that did not participate in the modeling (Figs. 2 and 3). Cor-
Fig. 1 Stacking map of 10-yr planting region for DCR.
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So there is no significant difference between the statistical and simulation values, and the model simulation results can be used for cold damage risk assessment. Using Table 1, risk score was performed respectively for DSER and DSLR. Slight cold damage risk was graded as 1. Low cold damage risk was graded as 2. Moderate cold damage risk was further divided into moderately weak and moderately strong. Their score was 3 and 4, respectively. High cold damage risk was graded as 5. Extra-high cold damage risk was graded as 6. The results obtained are as follows: for DSER, low risk accounts for 11.92% of total extraction area, moderately weak risk accounts for 25.94%, moderately strong risk accounts for 43.68%, and high risk accounts for 18.46% (Fig. 4). For DSLR, slight risk accounts for 1.26% of total extraction area, low risk accounts for 42.19%, moderately weak risk accounts for 33.97%, moderately strong risk accounts for 17.79%, and high risk accounts for 4.78% (Fig. 5). Because cold damage risk of DSER was higher than that of DSLR, the map of cold damage risk of DCR can be denoted by that of DSER. In order to compensate for error of DCR area extraction and promote applicability of the results in agriculture production, risk level map in the range of physiological adaptation of DCR was given (Figs. 6 and 7). The results can be further referenced by agricultural production sector of Hunan Province. In order to better reflect changes in risk level of cold damage between DSER and DSLR, the change map of risk level between them was given (Figs. 8 and 9). The variation of risk which is 5-5, 5-4 or 5-3 has high cold damage risk in May and is not suitable for DCR cultivation, but this area is suitable for single season rice. The area of 4-4, when the temperature is low in May, generally should not plant DSLR, but the area of 4-3 or 4-2, when encounter the same situation, generally can consider continuing to plant DSLR, especially for 4-2 region. The area where the variation of risk is 3-3, 3-2, 3-1, 2-2 or 2-1 is beneficial to DCR production because of low cold damage risk.
Fig. 2 Validation of simulation results for DSER.
Fig. 3 Validation of simulation results for DSLR.
relation analysis shows that two data sets all have good correlation. Correlation coefficient is 0.93 and 0.89 respectively. From paired T-test results of DSER and DSLR, we can see that mean values of the difference sequence between statistical and simulation values were 0.36 and -0.19. Calculated T value were -1.47 and -1.91, respectively. Their companion probabilities were 0.15 and 0.07, and both significance levels were higher than 0.05. Table 1 The scenario analysis of latitude longitude and altitude change Change Latitude ascend Longitude ascend Altitude ascend
Tj Descend Ascend Descend
IY
j
Ascend Descend Ascend
The correlation with Tj from 5 to 9 mon Decreased Decreased Increased
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Fig. 4 Risk level map of DSER in 10-yr information extraction results of DCR.
Fig. 6 Risk level map of DSER in the range of physiological adaptation of DCR.
Fig. 5 Risk level map of DSLR in 10-yr information extraction results of DCR.
Fig. 7 Risk level map of DSLR in the range of physiological adaptation of DCR.
DISCUSSION
from the result in the year with heavy clouds. Therefore the method for cloud removal and interpolation should be explored more in the future research. Although some of rice field information may be omitted due to weather conditions or detection accuracy limit (500 m spatial resolution), large paddy field and clustered rice patches in Hunan Province can be detected by merging 10-yr planting information. Average annual cold damage risk index is directly proportional to cold damage cumulant and cold damage annual frequency, and is inversely proportional to happen times of cold damage and the square of statisti-
Cold damage risk assessment was affected by information extraction of double rice cropping. The method to extract the DCR was a beneficial experiment, the accuracy of result directly impact on percentage distribution of cold damage risk assessment grade. To minimize the effect cloud pixel on the results, the extraction window for rice growth information was expanded to 32 days, but a considerable number of pixel values contaminated by clouds were un-interpolatable and removed
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Fig. 9 Risk level change map in 10-yr information extraction results of DCR.
aged cell is easy to recover from cold injury, so that cold damage will not have much effect on future rice yield. But one or few higher-intensity cold damage may cause irreversible damage to plant cell. As a result rice yield will decline in the future. The results of this paper are basically same as that of Lu et al. (2011) for cold damage of DSER in May and Huang et al. (2011) for cold dew wing of DSLR in September, but have higher spatial precision than their results. Using 2010 daily mean temperature data of 296 meteorological stations of 13 DCR provinces and cities in South China (Hunan, Hubei, Jiangsu, Zhejiang, Jiangxi, Guangdong, Guangxi, Guizhou, Chongqing, Hainan, Fujian, Anhui and Shanghai), through correlation analysis with geographical factors, the correlation coefficient are obtained within DCR growth period (Figs. 10-14). We can see from these figures that the correlation between daily mean temperature and latitude and altitude is strong. The correlation with longitude is weak. There are no correlation between daily mean temperature and slope and aspect. Through scenario analysis of latitude longitude and altitude change (Table 1), it can help us understand correlation coefficient change between geographical factors and of stations in spatial analysis model of cold damage risk index (Table 2). While average annual cold damage risk index was affected by altitude and longitude, it decreased relatively in September, therefore, to the north and west, cold damage risk level was declined compared with May, especially in Changsha and sounth of Yueyang, risk significantly decreased. This law is embodied in the risk assessment results (Figs. 4-7). In this paper, the experiment result is affected by three factors (latitude longi-
cal time sequence length. When the other items are equal to each other in the equation of average annual cold damage risk index, to compare two stations, the more times of cold damage on the station has happened, the lower risk of cold damage on this station. This is related to the characteristics of cold damage, namely, harmfulness caused by successive several times with lower-intensity cold damage was not as large as that caused by one times with higher-intensity cold damage. Because there is temperature compensation between several times with lower-intensity cold damage, the dam-
Fig. 10 Correlation of latitude and daily mean temperature.
Fig. 8 Risk level change map in the range of physiological adaptation of DCR.
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Fig. 11 Correlation of longitude and daily mean temperature.
Fig. 13 Correlation of slope and daily mean temperature.
Fig. 12 Correlation of altitude and daily mean temperature.
Fig. 14 Correlation of aspect and daily mean temperature.
tude and altitude). The accuracy of altitude map is lower than latitude and longitude map compared to DSER, which directly lead to decline in accuracy of risk assessment of DSLR. This is embodied in accuracy verification. Namely, the companion probability of DSER is higher than that of DSLR. It has contributed to further improve evaluation accuracy of cold damage risk to use high-precision altitude data in the future research. Compared to traditional cold damage risk assessment based on meteorological station or administrative area, this paper make clear hazard-affected body, and effectively expand station risk index to each grid through index spatial model. This improved spatial precision of cold damage risk assessment and expanded information of single station to whole study area. The risk assessment of this paper is only from meteorology perspective to discuss that somewhere may happen what kind of meteorological grade of cold damage. This paper belongs to intensity risk assessment, does not involve losses risk assessment and disaster resistance assessment (Li et al. 2004). Therefore, the risk as-
Table 2 Correlations between geographical factors and IY of stations j used to establish fitting model Item IYj of DSER IYj of DSLR
Latitude
Longitude
0.70 ** 0.68 **
-0.59 ** -0.55 **
Altitude 0.63 ** 0.65 **
Slope 0.19 5.30×10 -2
Aspect -0.12 -0.70×10 -2
, significance at the 0.01 level (2-tailed).
**
sessment is not belong to a comprehensive cold damage risk assessment, it still needs to study how to integrate them into the comprehensive cold damage risk assessment of small grid level in the future.
CONCLUSION In the 10-year stacking map, DCR planting in low risk region accounted for 11.92% of total extraction area, in moderate risk region accounted for 69.62%, in high risk region accounted for 18.46%. Cold damage risk of DSER is higher than risk of DSLR in Hunan Province. The area where cold damage risk level of DSER is high level should be as far as possible to re-
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duce DCR cultivation and to increase single rice cultivation, especially for this area where both risk is high. The area where cold damage risk level of DSER and DSLR are all moderately strong level, if there is cold damage of DSER, should not continue to plant DSLR. The area where cold damage risk level of DSER is moderately strong and that of DSLR is lower than this level, if there is cold damage of DSER, usually could continue to plant DSLR. The area where cold damage risk level of DSER and DSLR are all moderately weak or lower than this level, will be helpful for planting DCR. Annual average intensity of cold damage is exponential function of cold damage annual frequency. Average annual cold damage risk index is directly proportional to cold damage cumulant and cold damage annual frequency, and is inversely proportional to happen times of cold damage and the square of statistical time sequence length. Precision influence of altitude to risk assessment of DSLR is higher than that of it to risk assessment of DSER, so it is necessary for cold damage risk assessment of DSLR to use more accurate altitude data.
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Data collecting and preprocessing Four types of data were used in this study. Remote sensing data, 2001-2010 MOD09A1 8-d composite land surface reflectance product with 500 m spatial resolution, are available freely from the Land Processes Distributed Active Archive Center (http://lpdaac.usgs.gov). DEM at spatial resolution of 30 m was obtained through the website (https://wist.echo.nasa.gov/~wist/api/imswelcome/). Administrative map (at the scale of 1/4 000 000) of Hunan Province was downloaded from the National Fundamental Geographic Information Systems (http://nfgis.nsdi.gov.cn/). Statistical data, including 1951-2010 daily mean temperature of Hunan and neighboring meteorological stations and rice growth period data, was collected from the China Meteorological Data Sharing Network (http://cdc.cma. gov.cn/satellite/). To cover the whole extent of Hunan Province for a year, 3 tiles of MOD09A1 images with 46 dates are needed. The tile numbers are H27V05, H27V06 and H28V06. The 3 individual tiles were respectively mosaicked into five large images according to different bands (band 1-3, band 6, QA) by MRT (MODIS Re-projection tools). Each band mosaic image was stacked according to time series by ENVI/ IDL. The enhanced vegetation index (EVI, eq. (1)) and land surface water index (LSWI, eq. (2)) were calculated by use of time-series image that had been stacked. (1)
MATERIALS AND METHODS Study area Hunan Province is located at the middle reach of Yangtze River, south of Dongting Lake (Lat. 24°39´-30°08´N, Long. 108°47´-114°15´E). The climate of the region is continental subtropical monsoon humid climate. The annual average temperature is around 17°C. Due to impact of monsoon and topography, the general trend of temperature distribution is the south higher than the north and the east higher than the west. Average annual precipitation is 1 300-1 600 mm. The province’s topography is the south higher than the north. Hunan is surrounded by mountains on the east, south and west. The north is plains of Dongting Lake. The central is mostly hills and basins. The total land area is about 211 800 km2, 51.25% of which is mountainous, 29.30% is hills and basins, plains account for 13.10%, and water area accounts for 6.40%. More than 18% of the total land area is arable (Fig. 15). DCR is a major grain crop in Hunan. It is mostly planted in this region where altitude is less than 500 m and slope less than 6° (Xie 1984; Shuai et al. 2005).
(2) Although the 8-d composite surface reflectance products of MODIS have been strictly preprocessed to reduce the effects of clouds, shadows and aerosols, obviously residual noises still exist due to atmospheric effects and lasting heavy clouds in Hunan Province. Therefore, it is essential to further remove the noises. There are some methods to remove noise in time-series analysis, such as moving average, best index slope extraction algorithm (BISE), Fourier smoothing algorithm, wavelet smoothing algorithm and conditional temporal interpolation filtering (CTIF). In order to maintain the cloud-free original pixel values and effectively repair the pixel values that are interfered by the clouds and cloud shadows, we used the CTIF methods same as previous research (Huang et al. 2009). The specific process is as follows: First, data quality and cloud state based on QA information were implemented to examine whether a pixel is clear and is produced at ideal quality in all bands. If it is true, keep its EVI value. Otherwise, replace the pixel EVI value with average value of the previous and the following images when both are cloudless. If only one of the EVI values of the previous and the following images is clear, then use the clear one as a substitute. If pixel EVI
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Cold Damage Risk Assessment of Double Cropping Rice in Hunan, China
values are all contaminated in the three adjacent images, then it is regarded as invalid pixel. However, all the smoothing methods are unsuitable for LSWI, because soil moisture normally changes suddenly due to irrigation or precipitation, and soil moisture may be reduced with the evaporation in a period of time, so this phenomenon is normal. LSWI is very sensitive to smoothing. A lot of useful information may be also removed after smoothing. Therefore, we did not perform smoothing on LSWI but kept the original data.
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The slope and aspect maps of Hunan Province were extracted with DEM data. Altitude and slop masks were built in accordance with condition, altitude less than 500 m and slope less than 6°. All of the data above has been projected and re-sampled according to MODIS data. Each growth period scope of DCR in Hunan Province was obtained by analyzing long-term statistical data (Fig. 16). Planting and seedling time of DSER is usually between March 21 and April 15, transplanting between April 22 and May 8, booting and heading between May 27 and July 4,
Fig. 15 Meteorological station location and altitude distribution in Hunan and neighboring provinces. The total number of modeling and validation stations are 41 and 43, respectively.
Fig. 16 Phenological calendar of DCR in Hunan Province.
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and ripening and harvesting between July 6 and August 3. Planting and seedling time of DSLR is between June 13 and July 9, transplanting between July 16 and August 8, booting and heading between August 26 and September 24, and ripening and harvesting between October 1 and October 29. All the preprocessing results above were used to subsequent information extraction of DCR or cold damage risk assessment.
Methodology Information extraction of DCR DCR production has special rhythm: LSWI ascended when DSER was transplanted in the spring, at this time EVI remained at low
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level. Along with the growth of DSER, EVI ascended gradually; LSWI ascended again when DSLR was transplanted, at this time EVI remained at relatively low level because of harvesting of DSER. Along with the growth of DSLR, EVI again ascended gradually. With reference to preprocessing result, we know that transplanting period of DSER in Hunan Province probably corresponds to the 14-16 dates of MOD09A1 8-d composites, and booting and heading periods probably correspond to 19-23 dates. Transplanting period of DSLR probably corresponds to 25-27 dates of MOD09A1 8-d composites, and booting and heading periods probably correspond to 30-34 dates. Finally, combined with altitude and slop masks, information extraction of DCR was performed by ENVI/IDL of eq. (3).
(3)
In the equation, x=(14, 15, 16) are dates of MOD09A1 8-d composites, when DSER was transplanted, y=(25, 26, 27) are dates of MOD09A1 8-d composites, when DSLR was transplanted. Through 9 different combinations of x and y, all kinds of farming variance caused by sowing periods and rice varieties can be mostly covered for each year. 10 years DCR maps were merged into one map. The result as hazardaffected body was used to cold damage risk assessment. Spatial analysis model construction of average annual cold damage risk index Cold damage of DCR in Hunan Province mainly includes late spring cold, low temperature in May, and cold dew wind (Ye 1986). This article chooses low temperature in May and cold dew wing as the research objects. According to national standard GB/ T 27959-2011 (http://standard.cma.gov.cn/) of low temperature disaster of southern rice, low temperature in May is that daily mean temperature less than or equal to 20°C and 5 consecutive days or more when DSER is at the period of tiller and booting. This standard was selected as cold damage identification index for DSER in Hunan. According to national standard GB/T 27959-2011 or Hunan local standard DB43/T 234-2004 (http://standard.cma.gov. cn/), cold dew wind is that daily mean temperature less than or equal to 20°C and 3 consecutive days when DSLR is at the period of heading. Considering the difference of cold dew wind index between japonica and indica rice (Xu 2004), for japonica rice, it is that daily mean temperature less than or equal to 18-20°C and 3 consecutive days or more, for indica rice, it is that daily mean temperature less than or equal to 20-22°C and 3 consecutive days or more, the paper finally chose 20°C and 3 consecutive days, which applies to both japonica and indica rice, as the critical threshold of cold dew wind for DSLR. We can see from Fig. 16 that heading period in Hunan generally is from September 6 to September 24. But according to
Hunan local standard, time range of cold dew wind is the whole month of September. And scholars have usually chosen September as study period of cold dew wind of Hunan, in order to facilitate the comparison with previous results, this article also chosen September as study period for cold dew wind. Happen times of cold damage and the times of years with cold damage at each meteorological station was calculated according to cold damage identification index above and the number of years recorded was counted for each station. On this basis, average annual cold damage risk index was calculated for each station. fj=
(4)
(5) (6) (7) (8)
(9) Where, tj is happen times of cold damage at j station for all years recorded. Yj is all years recorded at j station. fj is average times of cold damage in every year. is cold damage cumulant at j station
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Cold Damage Risk Assessment of Double Cropping Rice in Hunan, China
for all years recorded, in this item, Cp is cold damage idenis daily mean tification value of DCR, equal to 20°C, temperature of k d at i times cold damage for j station. dji is days for i times cold damage.
is average cold damage
is average cold damage in intensity for every times. tensity for every year. P j is frequency of cold damage year, in this item nj is times of year with cold damage. is average annual cold damage risk index at j station. Average annual cold damage risk index is directly related to the amount of solar radiation that meteorological station received. The amount of incoming solar radiation varies with latitude, longitude and altitude of the station. Therefore, the relational model of average annual cold damage risk index and geographical factors can be expressed as eq. (10): (10) In the equation,
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Average annual intensity of cold damage, corresponding to cold damage annual frequency (0.2, 0.4, 0.5, 0.6, 0.8, and 1), was calculated by relation equations of frequency and intensity (Table 4). Then the corresponding average annual cold damage risk index was calculated by eq. (9). The results were taken as threshold to rank risk index maps of DSER and DSLR (Table 5). Cold damage risk assessment was finally completed for DCR (Fig. 19). Table 3 Spatial analysis model of IYj Equations
R2 value
F value
DSER
Item
IYj=31.74+1.69j-0.67l+1.70×10-2h
0.87
81.20
DSLR
IYj=5.45+0.536j-0.17l+0.60×10-2h
0.87
78.52
Table 4 Relation equations of Pj and SYj Item
Equations
R2 value
is average annual cold damage risk
DSER
SYj=-1.22×10-9×exp(-Pj /-3.98×10-2)-3.46+30.74×Pj
0.98
index, j, l, h, represent latitude, longitude and altitude.
DSLR
SYj=0.16×exp(-Pj /-0.22)-0.08+5.38×Pj
0.97
Function F(j, l, h) is called climate expression of risk index,
e is the integrated geographic residuals eq. (11). (11) Using the method of part modeling and part validation, taking geographical factors (latitude, longitude, altitude) of some stations as independent variables, and taking average annual cold damage risk index of these stations as the dependent variables, spatial analysis model of of DSER and DSLR were established respectively with multiple linear regression analysis (Table 3). Tow model all passed the 0.01 significance test and can be used to calculate grid average annual cold damage index. By use of the 500 m×500 m grid maps of latitude, longitude and altitude generated in the preprocessing step, of DSER and DSLR were calculated according to the equations above. The residuals of risk index for each modeling station were calculated using eq. (11), and then the residuals were interpolated with GIS geostatistical analysis tools according to the Inverse Distance Weighting Method (IDW). Finally, fitting results and interpolation results were summing and obtained maps of DSER and DSLR respectively. The accuracy of the results was validated using the validation stations. Threshold division of
Fig. 17 Relation of cold damage annual frequency and average annual intensity of cold damage for DSER.
The higher annual frequency of
cold damage the station has, the higher average annual intensity of cold damage it has. Relation equations between frequency and intensity of cold damage are built by nonlinear regression analysis for DSER and DSLR respectively (Talbe 4), the two equations both complies with exponential form (Figs. 17 and 18).
Fig. 18 Relation of cold damage annual frequency and average annual intensity of cold damage for DSLR.
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Table 5 Risk grade table of cold damage of DCR Cold damage risk level
DSER
DSLR
Slight risk 1 Low risk 2 Moderate risk Moderately weak 3 Moderately strong 4 High risk 5
(Pj 0.2) (0.2
IYj 0.54 0.54
IYj 0.28 0.28
(0.4
3.53
1.23
Extra-high risk 6
(0.8
17.42
8.32
Fig. 19 Flowchart of cold damage risk assessment of DCR.
Acknowledgements This research was funded by the Key Technologies R&D Program of China during the 12th Five-Year Plan period (2011BAD32B01), the National Natural Science Foundation of China (40875070), the Research Fund for Doctoral Program of Higher Education, China (20100101110035). In addition, we thank Prof. Zhao Yanxia from Chinese Academy of Meteorological Science for her help with meteorological station data.
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Cold Damage Risk Assessment of Double Cropping Rice in Hunan, China
Province. Arid Land Geography, 32, 754-760. (in Chinese) Li W L, Zhang L J, Zhang D Y. 2009. Study on risk assessment and zoning of low temperature and cold damage in Heilongjiang Province. Journal of Arid Land Resources and Environment, 23, 149-153. (in Chinese) Lu K D, Luo B L, Huang W H, Cui W. 2011. Risk evaluation of the effects of chilling in May on early rice production in Hunan Province. Chinese Journal of Agrometeorology, 32, 283-289. (in Chinese) Ma S Q, Xi Z X, Wang Q. 2003. Risk evaluation of cold damage to corn in Northeast China. Journal of Natural Disasters, 12, 137-141. (in Chinese) Sakamoto T, Nguyen N V, Kotera A, Ohno H, Ishitsuka N, Yokozawa M. 2007. Detecting temporal changes in the extent of annual flooding within the Cambodia and the Vietnamese Mekong Delta from MODIS time-series imagery. Remote Sensing of Environment, 109, 295313. Shuai X Q, Wang K J, Huang W H. 2005. Adjustment analysis and layout proposals for Hunan agricultural structure. Journal of Guangxi Meteorology, 26, 103-109. (in Chinese) Su X H, Zhang X D, Yang S Q, Liu S C, Su W, Yan T L, Yang J Y, Huang J X. 2012. County-level flood risk level assessment in China using geographic information system. Sensor Letters, 10, 379-386. Turner M D, Congalton R G. 1998. Classification of multitemporal SPOT-XS satellite data for mapping rice fields on a West African floodplain. International Journal of Remote Sensing, 19, 21-41. Wang S Y. 1982. Cold damage zoning of crops in northeast China. Meteorology, 6, 26-28. (in Chinese) Wang X Q, Zhang Y. 2010. Risk evaluation of cold damage to rice in Heilongjiang. Journal of Anhui Agricultural Sciences, 40, 263-266. (in Chinese) Wang Y M, Li Z W, Tang Z H, Zeng G M. 2011. A GISbased spatial multi-criteria approach for flood risk
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assessment in the Dongting Lake region, Hunan, central China. Water Resources Management, 25, 3465-3484. Xi Z X, Ma S Q, Wang Q. 2003. Risk evaluation and zonation of the low tem perature and cold damage in Northeast China. Journal of Natural Disasters, 12, 98-102. (in Chinese) Xiao X M, Boles S, Liu J Y, Zhuang D F, Frolking S, Li C S, Salas W, Moore B. 2005. Mapping paddy rice agriculture in southern China using multi-temporal MODIS images. Remote Sensing of Environment, 95, 480-492. Xie T S. 1984. Land resources and rational useing in Hunan Province. Resources Science, 1, 58-64. (in Chinese) Xu C S. 2004. Complete Works of Ago-Meteorological Index. China Meteorological Press, China. pp. 1-4. (in Chinese) Ye Y Y. 1986. The relationship between temperature variation and low temperature in the growing season during the recent 70 years in Hunan Province. Geographical Research, 5, 81-89. (in Chinese) Zhang D, Wang G L, Zhou H C. 2011. Assessment on agricultural drought risk based on variable fuzzy sets model. Chinese Geographical Science, 21, 167-175. (in Chinese) Zhang J Q. 2004. Risk assessment of drought disaster in the maize-growing region of Songliao Plain, China. Agriculture Ecosystems & Environment, 102, 133-153. Zhang L J, Li W L, Zhang D Y. 2009. Meteorological disaster risk assessment method based on information diffusion theory. Scientia Geographica Sinica, 29, 250-254. (in Chinese) Zhou D Y, Zhang L J, Zhang L, Fan H X. 2010. Occurrence and risk assessment of meteorological disaster in Harbin. Chinese Agricultural Science Bulletin, 26, 332336. (in Chinese) Zu S H, Zu X M. 1999. Climatic zoning for cool injury to crops in Heilongjiang Province. Chinese Journal of Agrometeorology, 20, 1-8. (in Chinese) (Managing editor SUN Lu-juan)
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2013, 12(2): 352-363
RESEARCH ARTICLE
Cold Damage Risk Assessment of Double Cropping Rice in Hunan, China CHENG Yong-xiang1, 2, 3, HUANG Jing-feng1, 2, 4, HAN Zhong-ling5, GUO Jian-ping6, ZHAO Yan-xia6, WANG Xiu-zhen7 and GUO Rui-fang1 Institute of Remote Sensing and Information Application, Zhejiang University, Hangzhou 310058, P.R.China Key Laboratory of Polluted Environment Remediation and Ecological Health, Ministry of Education/College of Natural Resources and Environmental Science, Zhejiang University, Hangzhou 310058, P.R.China 3 College of Life Science, Shihezi University, Shihezi 832000, P.R.China 4 The Provinical Key Laboratory of Agricultural Remote Sensing and Information System, Zhejiang Province, Hangzhou 310058, P.R. China 5 Institute of Information and Technology, Shihezi University, Shihezi 832000, P.R.China 6 Chinese Academy of Meteorological Science, Beijing 100875, P.R.China 7 Institute of Remote Sensing and Earth Sciences, Hangzhou Normal University, Hangzhou 311121, P.R.China 1 2
Abstract Combined with remote sensing data and meteorological data, cold damage risk was assessed for planting area of double cropping rice (DCR) in Hunan Province, China. A new methodology of cold damage risk assessment was built that apply to grid and have clear hazard-affected body. Each station cold damage annual frequency and average annual intensity of cold damage was calculated by using 1951-2010 station daily mean temperature and simple cold damage identification index. On this basis, average annual cold damage risk index was obtained by their product. The spatial analysis models of cold damage risk index about double-season early rice (DSER) and double-season later rice (DSLR) were established respectively by the relation of average annual cold damage risk index and its geographic factors. Critical threshold of level of average annual cold damage risk index for DSER and DSLR were respectively divided by the correlative equation of cold damage annual frequency and average annual intensity of cold damage. 2001-2010 planting area of DCR, acquired by time series analysis of MOD09A1 8-d composite land surface reflectance product, was as target of assessment. The results show average annual intensity of cold damage is exponential function of cold damage annual frequency, average annual cold damage risk index is directly proportional to cold damage cumulant and cold damage annual frequency, and is inversely proportional to happen times of cold damage and the square of statistical time sequence length. Cold damage risk of DSER is higher than DSLR in Hunan Province. In the 10-yr stacking map, DCR planting in low risk area accounted for 11.92% of total extraction area, in moderate risk area accounted for 69.62%, in high risk area accounted for 18.46%. According to the cold damage risk assessment result, DCR production can be guided to reduce cold damage losses. Key words: double cropping rice, cold damage, risk assessment
INTRODUCTION In the same patch of field, planting and harvesting rice twice a year is known as double cropping rice (DCR).
According to the different farming seasons, DCR has been subdivided into double-season early rice (DSER) and double-season later rice (DSLR). DCR is widely planted in Hunan Province, and the planting area ranks first in China for many years. Because of long planting
Received 5 April, 2012 Accepted 29 October, 2012 CHENG Yong-xiang, Mobile: 15088687895, E-mail: [email protected]; Correspondence HUANG Jing-feng, Mobile: 13957171636, E-mail: hjf@zju. edu.cn © 2013, CAAS. All rights reserved. Published by Elsevier Ltd. doi:10.1016/S2095-3119(13)60235-X
Cold Damage Risk Assessment of Double Cropping Rice in Hunan, China
time span, DCR production is often affected by cold damage, especially in hills and mountains (Huang et al. 2011; Lu et al. 2011). From 2001 to 2010 in Hunan Province, average annual disaster area covered for cold damage and freeze injury is 516.5×103 ha. Disaster area affected is 317.7×103 ha. No harvest area is 64.7×103 ha according to China Agriculture Yearbook (China Agriculture Press, 2002-2011). Therefore, in order to guide and arrange production, reduce the negative influence of cold damage, ensure food safety, it is necessary for DCR to assess cold damage risk. Risk assessment study on cold damage is far less than study on flood and drought in the risk assessment of agriculture meteorological disaster at home and abroad (Zhang 2004; Hsu et al. 2011; Wang et al. 2011; Zhang et al. 2011; Hao et al. 2012; Su et al. 2012). At present cold damage risk assessment methods mainly have probability method (Wang 1982; Zu and Zu 1999), comprehensive evaluation method (Ma et al. 2003; Xi et al. 2003; Wang and Zhang 2010) and information diffusion theory method (Li et al. 2009; Zhang et al. 2009; Zhou et al. 2010), and rarely involved in analytic hierarchy process (AHP) and weighted comprehensive method (WCM) that have been commonly used in other disaster risk assessment (Li et al. 2009). The above methods are all based on a single meteorological station or administrative region level. It does not apply to cold damage risk assessment of small grid level, and the objectivity of threshold dividing and weight assignment for comprehensive cold damage risk index and each construction factor needs to be further researched. With continuously increasing of spatial accuracy requirement for cold damage risk assessment, this paper attempts to establish new mode that is suitable for small grid cold damage risk assessment by using simple average annual cold damage risk index, to meet the increasing agricultural production requirements. At present cold damage risk assessment usually doesn’t combine with hazard-affected body, so assessment result lacks of strong pertinence. The method to extract rice field information by using remote sensing image classification or time series analysis has been more mature (Turner and Congalton 1998; Xiao et al. 2005; Chen and McNairn 2006; Sakamoto et al. 2007; Huang et al. 2011; Choudhury et al. 2012). Based on the results of previous studies, time series analysis equations
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to be used to early and later rice information extraction was combined to extract information of DCR, in order to provide more valuable base map for cold damage risk assessment. Finally, a new mode of cold damage risk assessment was established that make full use of 3S technology and meteorological station information.
RESULTS Based on analysis of MOD09A1 8-d composites, DCR in Hunan mostly distributes in Dongting Lake region (Fig. 1). Annual extraction area of DCR is consistent with statistical area in five years (2001, 2002, 2006, 2009 and 2010). For other five years (2003-2005, 2007-2008), the extraction area is lower than the statistical area. Analysis shows that growing seasons of DCR had appeared continuous cloudy days during 2005 to 2008 in Hunan Province, and a number of pixels cannot be interpolated but be excluded from the possible results and cause the extraction results lower than the statistical area. For this research, although annual rice planting area may be underestimated in some years, in some extent the stacking results of the 10-yr extraction area can reflect actual distribution of DCR in Hunan Province, and the result map is considered to have reference significance for cold damage risk assessment of DCR. Simulation precision of the cold damage risk model was validated by use of meteorological stations that did not participate in the modeling (Figs. 2 and 3). Cor-
Fig. 1 Stacking map of 10-yr planting region for DCR.
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So there is no significant difference between the statistical and simulation values, and the model simulation results can be used for cold damage risk assessment. Using Table 1, risk score was performed respectively for DSER and DSLR. Slight cold damage risk was graded as 1. Low cold damage risk was graded as 2. Moderate cold damage risk was further divided into moderately weak and moderately strong. Their score was 3 and 4, respectively. High cold damage risk was graded as 5. Extra-high cold damage risk was graded as 6. The results obtained are as follows: for DSER, low risk accounts for 11.92% of total extraction area, moderately weak risk accounts for 25.94%, moderately strong risk accounts for 43.68%, and high risk accounts for 18.46% (Fig. 4). For DSLR, slight risk accounts for 1.26% of total extraction area, low risk accounts for 42.19%, moderately weak risk accounts for 33.97%, moderately strong risk accounts for 17.79%, and high risk accounts for 4.78% (Fig. 5). Because cold damage risk of DSER was higher than that of DSLR, the map of cold damage risk of DCR can be denoted by that of DSER. In order to compensate for error of DCR area extraction and promote applicability of the results in agriculture production, risk level map in the range of physiological adaptation of DCR was given (Figs. 6 and 7). The results can be further referenced by agricultural production sector of Hunan Province. In order to better reflect changes in risk level of cold damage between DSER and DSLR, the change map of risk level between them was given (Figs. 8 and 9). The variation of risk which is 5-5, 5-4 or 5-3 has high cold damage risk in May and is not suitable for DCR cultivation, but this area is suitable for single season rice. The area of 4-4, when the temperature is low in May, generally should not plant DSLR, but the area of 4-3 or 4-2, when encounter the same situation, generally can consider continuing to plant DSLR, especially for 4-2 region. The area where the variation of risk is 3-3, 3-2, 3-1, 2-2 or 2-1 is beneficial to DCR production because of low cold damage risk.
Fig. 2 Validation of simulation results for DSER.
Fig. 3 Validation of simulation results for DSLR.
relation analysis shows that two data sets all have good correlation. Correlation coefficient is 0.93 and 0.89 respectively. From paired T-test results of DSER and DSLR, we can see that mean values of the difference sequence between statistical and simulation values were 0.36 and -0.19. Calculated T value were -1.47 and -1.91, respectively. Their companion probabilities were 0.15 and 0.07, and both significance levels were higher than 0.05. Table 1 The scenario analysis of latitude longitude and altitude change Change Latitude ascend Longitude ascend Altitude ascend
Tj Descend Ascend Descend
IY
j
Ascend Descend Ascend
The correlation with Tj from 5 to 9 mon Decreased Decreased Increased
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Fig. 4 Risk level map of DSER in 10-yr information extraction results of DCR.
Fig. 6 Risk level map of DSER in the range of physiological adaptation of DCR.
Fig. 5 Risk level map of DSLR in 10-yr information extraction results of DCR.
Fig. 7 Risk level map of DSLR in the range of physiological adaptation of DCR.
DISCUSSION
from the result in the year with heavy clouds. Therefore the method for cloud removal and interpolation should be explored more in the future research. Although some of rice field information may be omitted due to weather conditions or detection accuracy limit (500 m spatial resolution), large paddy field and clustered rice patches in Hunan Province can be detected by merging 10-yr planting information. Average annual cold damage risk index is directly proportional to cold damage cumulant and cold damage annual frequency, and is inversely proportional to happen times of cold damage and the square of statisti-
Cold damage risk assessment was affected by information extraction of double rice cropping. The method to extract the DCR was a beneficial experiment, the accuracy of result directly impact on percentage distribution of cold damage risk assessment grade. To minimize the effect cloud pixel on the results, the extraction window for rice growth information was expanded to 32 days, but a considerable number of pixel values contaminated by clouds were un-interpolatable and removed
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Fig. 9 Risk level change map in 10-yr information extraction results of DCR.
aged cell is easy to recover from cold injury, so that cold damage will not have much effect on future rice yield. But one or few higher-intensity cold damage may cause irreversible damage to plant cell. As a result rice yield will decline in the future. The results of this paper are basically same as that of Lu et al. (2011) for cold damage of DSER in May and Huang et al. (2011) for cold dew wing of DSLR in September, but have higher spatial precision than their results. Using 2010 daily mean temperature data of 296 meteorological stations of 13 DCR provinces and cities in South China (Hunan, Hubei, Jiangsu, Zhejiang, Jiangxi, Guangdong, Guangxi, Guizhou, Chongqing, Hainan, Fujian, Anhui and Shanghai), through correlation analysis with geographical factors, the correlation coefficient are obtained within DCR growth period (Figs. 10-14). We can see from these figures that the correlation between daily mean temperature and latitude and altitude is strong. The correlation with longitude is weak. There are no correlation between daily mean temperature and slope and aspect. Through scenario analysis of latitude longitude and altitude change (Table 1), it can help us understand correlation coefficient change between geographical factors and of stations in spatial analysis model of cold damage risk index (Table 2). While average annual cold damage risk index was affected by altitude and longitude, it decreased relatively in September, therefore, to the north and west, cold damage risk level was declined compared with May, especially in Changsha and sounth of Yueyang, risk significantly decreased. This law is embodied in the risk assessment results (Figs. 4-7). In this paper, the experiment result is affected by three factors (latitude longi-
cal time sequence length. When the other items are equal to each other in the equation of average annual cold damage risk index, to compare two stations, the more times of cold damage on the station has happened, the lower risk of cold damage on this station. This is related to the characteristics of cold damage, namely, harmfulness caused by successive several times with lower-intensity cold damage was not as large as that caused by one times with higher-intensity cold damage. Because there is temperature compensation between several times with lower-intensity cold damage, the dam-
Fig. 10 Correlation of latitude and daily mean temperature.
Fig. 8 Risk level change map in the range of physiological adaptation of DCR.
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Fig. 11 Correlation of longitude and daily mean temperature.
Fig. 13 Correlation of slope and daily mean temperature.
Fig. 12 Correlation of altitude and daily mean temperature.
Fig. 14 Correlation of aspect and daily mean temperature.
tude and altitude). The accuracy of altitude map is lower than latitude and longitude map compared to DSER, which directly lead to decline in accuracy of risk assessment of DSLR. This is embodied in accuracy verification. Namely, the companion probability of DSER is higher than that of DSLR. It has contributed to further improve evaluation accuracy of cold damage risk to use high-precision altitude data in the future research. Compared to traditional cold damage risk assessment based on meteorological station or administrative area, this paper make clear hazard-affected body, and effectively expand station risk index to each grid through index spatial model. This improved spatial precision of cold damage risk assessment and expanded information of single station to whole study area. The risk assessment of this paper is only from meteorology perspective to discuss that somewhere may happen what kind of meteorological grade of cold damage. This paper belongs to intensity risk assessment, does not involve losses risk assessment and disaster resistance assessment (Li et al. 2004). Therefore, the risk as-
Table 2 Correlations between geographical factors and IY of stations j used to establish fitting model Item IYj of DSER IYj of DSLR
Latitude
Longitude
0.70 ** 0.68 **
-0.59 ** -0.55 **
Altitude 0.63 ** 0.65 **
Slope 0.19 5.30×10 -2
Aspect -0.12 -0.70×10 -2
, significance at the 0.01 level (2-tailed).
**
sessment is not belong to a comprehensive cold damage risk assessment, it still needs to study how to integrate them into the comprehensive cold damage risk assessment of small grid level in the future.
CONCLUSION In the 10-year stacking map, DCR planting in low risk region accounted for 11.92% of total extraction area, in moderate risk region accounted for 69.62%, in high risk region accounted for 18.46%. Cold damage risk of DSER is higher than risk of DSLR in Hunan Province. The area where cold damage risk level of DSER is high level should be as far as possible to re-
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duce DCR cultivation and to increase single rice cultivation, especially for this area where both risk is high. The area where cold damage risk level of DSER and DSLR are all moderately strong level, if there is cold damage of DSER, should not continue to plant DSLR. The area where cold damage risk level of DSER is moderately strong and that of DSLR is lower than this level, if there is cold damage of DSER, usually could continue to plant DSLR. The area where cold damage risk level of DSER and DSLR are all moderately weak or lower than this level, will be helpful for planting DCR. Annual average intensity of cold damage is exponential function of cold damage annual frequency. Average annual cold damage risk index is directly proportional to cold damage cumulant and cold damage annual frequency, and is inversely proportional to happen times of cold damage and the square of statistical time sequence length. Precision influence of altitude to risk assessment of DSLR is higher than that of it to risk assessment of DSER, so it is necessary for cold damage risk assessment of DSLR to use more accurate altitude data.
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Data collecting and preprocessing Four types of data were used in this study. Remote sensing data, 2001-2010 MOD09A1 8-d composite land surface reflectance product with 500 m spatial resolution, are available freely from the Land Processes Distributed Active Archive Center (http://lpdaac.usgs.gov). DEM at spatial resolution of 30 m was obtained through the website (https://wist.echo.nasa.gov/~wist/api/imswelcome/). Administrative map (at the scale of 1/4 000 000) of Hunan Province was downloaded from the National Fundamental Geographic Information Systems (http://nfgis.nsdi.gov.cn/). Statistical data, including 1951-2010 daily mean temperature of Hunan and neighboring meteorological stations and rice growth period data, was collected from the China Meteorological Data Sharing Network (http://cdc.cma. gov.cn/satellite/). To cover the whole extent of Hunan Province for a year, 3 tiles of MOD09A1 images with 46 dates are needed. The tile numbers are H27V05, H27V06 and H28V06. The 3 individual tiles were respectively mosaicked into five large images according to different bands (band 1-3, band 6, QA) by MRT (MODIS Re-projection tools). Each band mosaic image was stacked according to time series by ENVI/ IDL. The enhanced vegetation index (EVI, eq. (1)) and land surface water index (LSWI, eq. (2)) were calculated by use of time-series image that had been stacked. (1)
MATERIALS AND METHODS Study area Hunan Province is located at the middle reach of Yangtze River, south of Dongting Lake (Lat. 24°39´-30°08´N, Long. 108°47´-114°15´E). The climate of the region is continental subtropical monsoon humid climate. The annual average temperature is around 17°C. Due to impact of monsoon and topography, the general trend of temperature distribution is the south higher than the north and the east higher than the west. Average annual precipitation is 1 300-1 600 mm. The province’s topography is the south higher than the north. Hunan is surrounded by mountains on the east, south and west. The north is plains of Dongting Lake. The central is mostly hills and basins. The total land area is about 211 800 km2, 51.25% of which is mountainous, 29.30% is hills and basins, plains account for 13.10%, and water area accounts for 6.40%. More than 18% of the total land area is arable (Fig. 15). DCR is a major grain crop in Hunan. It is mostly planted in this region where altitude is less than 500 m and slope less than 6° (Xie 1984; Shuai et al. 2005).
(2) Although the 8-d composite surface reflectance products of MODIS have been strictly preprocessed to reduce the effects of clouds, shadows and aerosols, obviously residual noises still exist due to atmospheric effects and lasting heavy clouds in Hunan Province. Therefore, it is essential to further remove the noises. There are some methods to remove noise in time-series analysis, such as moving average, best index slope extraction algorithm (BISE), Fourier smoothing algorithm, wavelet smoothing algorithm and conditional temporal interpolation filtering (CTIF). In order to maintain the cloud-free original pixel values and effectively repair the pixel values that are interfered by the clouds and cloud shadows, we used the CTIF methods same as previous research (Huang et al. 2009). The specific process is as follows: First, data quality and cloud state based on QA information were implemented to examine whether a pixel is clear and is produced at ideal quality in all bands. If it is true, keep its EVI value. Otherwise, replace the pixel EVI value with average value of the previous and the following images when both are cloudless. If only one of the EVI values of the previous and the following images is clear, then use the clear one as a substitute. If pixel EVI
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Cold Damage Risk Assessment of Double Cropping Rice in Hunan, China
values are all contaminated in the three adjacent images, then it is regarded as invalid pixel. However, all the smoothing methods are unsuitable for LSWI, because soil moisture normally changes suddenly due to irrigation or precipitation, and soil moisture may be reduced with the evaporation in a period of time, so this phenomenon is normal. LSWI is very sensitive to smoothing. A lot of useful information may be also removed after smoothing. Therefore, we did not perform smoothing on LSWI but kept the original data.
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The slope and aspect maps of Hunan Province were extracted with DEM data. Altitude and slop masks were built in accordance with condition, altitude less than 500 m and slope less than 6°. All of the data above has been projected and re-sampled according to MODIS data. Each growth period scope of DCR in Hunan Province was obtained by analyzing long-term statistical data (Fig. 16). Planting and seedling time of DSER is usually between March 21 and April 15, transplanting between April 22 and May 8, booting and heading between May 27 and July 4,
Fig. 15 Meteorological station location and altitude distribution in Hunan and neighboring provinces. The total number of modeling and validation stations are 41 and 43, respectively.
Fig. 16 Phenological calendar of DCR in Hunan Province.
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and ripening and harvesting between July 6 and August 3. Planting and seedling time of DSLR is between June 13 and July 9, transplanting between July 16 and August 8, booting and heading between August 26 and September 24, and ripening and harvesting between October 1 and October 29. All the preprocessing results above were used to subsequent information extraction of DCR or cold damage risk assessment.
Methodology Information extraction of DCR DCR production has special rhythm: LSWI ascended when DSER was transplanted in the spring, at this time EVI remained at low
CHENG Yong-xiang et al.
level. Along with the growth of DSER, EVI ascended gradually; LSWI ascended again when DSLR was transplanted, at this time EVI remained at relatively low level because of harvesting of DSER. Along with the growth of DSLR, EVI again ascended gradually. With reference to preprocessing result, we know that transplanting period of DSER in Hunan Province probably corresponds to the 14-16 dates of MOD09A1 8-d composites, and booting and heading periods probably correspond to 19-23 dates. Transplanting period of DSLR probably corresponds to 25-27 dates of MOD09A1 8-d composites, and booting and heading periods probably correspond to 30-34 dates. Finally, combined with altitude and slop masks, information extraction of DCR was performed by ENVI/IDL of eq. (3).
(3)
In the equation, x=(14, 15, 16) are dates of MOD09A1 8-d composites, when DSER was transplanted, y=(25, 26, 27) are dates of MOD09A1 8-d composites, when DSLR was transplanted. Through 9 different combinations of x and y, all kinds of farming variance caused by sowing periods and rice varieties can be mostly covered for each year. 10 years DCR maps were merged into one map. The result as hazardaffected body was used to cold damage risk assessment. Spatial analysis model construction of average annual cold damage risk index Cold damage of DCR in Hunan Province mainly includes late spring cold, low temperature in May, and cold dew wind (Ye 1986). This article chooses low temperature in May and cold dew wing as the research objects. According to national standard GB/ T 27959-2011 (http://standard.cma.gov.cn/) of low temperature disaster of southern rice, low temperature in May is that daily mean temperature less than or equal to 20°C and 5 consecutive days or more when DSER is at the period of tiller and booting. This standard was selected as cold damage identification index for DSER in Hunan. According to national standard GB/T 27959-2011 or Hunan local standard DB43/T 234-2004 (http://standard.cma.gov. cn/), cold dew wind is that daily mean temperature less than or equal to 20°C and 3 consecutive days when DSLR is at the period of heading. Considering the difference of cold dew wind index between japonica and indica rice (Xu 2004), for japonica rice, it is that daily mean temperature less than or equal to 18-20°C and 3 consecutive days or more, for indica rice, it is that daily mean temperature less than or equal to 20-22°C and 3 consecutive days or more, the paper finally chose 20°C and 3 consecutive days, which applies to both japonica and indica rice, as the critical threshold of cold dew wind for DSLR. We can see from Fig. 16 that heading period in Hunan generally is from September 6 to September 24. But according to
Hunan local standard, time range of cold dew wind is the whole month of September. And scholars have usually chosen September as study period of cold dew wind of Hunan, in order to facilitate the comparison with previous results, this article also chosen September as study period for cold dew wind. Happen times of cold damage and the times of years with cold damage at each meteorological station was calculated according to cold damage identification index above and the number of years recorded was counted for each station. On this basis, average annual cold damage risk index was calculated for each station. fj=
(4)
(5) (6) (7) (8)
(9) Where, tj is happen times of cold damage at j station for all years recorded. Yj is all years recorded at j station. fj is average times of cold damage in every year. is cold damage cumulant at j station
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Cold Damage Risk Assessment of Double Cropping Rice in Hunan, China
for all years recorded, in this item, Cp is cold damage idenis daily mean tification value of DCR, equal to 20°C, temperature of k d at i times cold damage for j station. dji is days for i times cold damage.
is average cold damage
is average cold damage in intensity for every times. tensity for every year. P j is frequency of cold damage year, in this item nj is times of year with cold damage. is average annual cold damage risk index at j station. Average annual cold damage risk index is directly related to the amount of solar radiation that meteorological station received. The amount of incoming solar radiation varies with latitude, longitude and altitude of the station. Therefore, the relational model of average annual cold damage risk index and geographical factors can be expressed as eq. (10): (10) In the equation,
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Average annual intensity of cold damage, corresponding to cold damage annual frequency (0.2, 0.4, 0.5, 0.6, 0.8, and 1), was calculated by relation equations of frequency and intensity (Table 4). Then the corresponding average annual cold damage risk index was calculated by eq. (9). The results were taken as threshold to rank risk index maps of DSER and DSLR (Table 5). Cold damage risk assessment was finally completed for DCR (Fig. 19). Table 3 Spatial analysis model of IYj Equations
R2 value
F value
DSER
Item
IYj=31.74+1.69j-0.67l+1.70×10-2h
0.87
81.20
DSLR
IYj=5.45+0.536j-0.17l+0.60×10-2h
0.87
78.52
Table 4 Relation equations of Pj and SYj Item
Equations
R2 value
is average annual cold damage risk
DSER
SYj=-1.22×10-9×exp(-Pj /-3.98×10-2)-3.46+30.74×Pj
0.98
index, j, l, h, represent latitude, longitude and altitude.
DSLR
SYj=0.16×exp(-Pj /-0.22)-0.08+5.38×Pj
0.97
Function F(j, l, h) is called climate expression of risk index,
e is the integrated geographic residuals eq. (11). (11) Using the method of part modeling and part validation, taking geographical factors (latitude, longitude, altitude) of some stations as independent variables, and taking average annual cold damage risk index of these stations as the dependent variables, spatial analysis model of of DSER and DSLR were established respectively with multiple linear regression analysis (Table 3). Tow model all passed the 0.01 significance test and can be used to calculate grid average annual cold damage index. By use of the 500 m×500 m grid maps of latitude, longitude and altitude generated in the preprocessing step, of DSER and DSLR were calculated according to the equations above. The residuals of risk index for each modeling station were calculated using eq. (11), and then the residuals were interpolated with GIS geostatistical analysis tools according to the Inverse Distance Weighting Method (IDW). Finally, fitting results and interpolation results were summing and obtained maps of DSER and DSLR respectively. The accuracy of the results was validated using the validation stations. Threshold division of
Fig. 17 Relation of cold damage annual frequency and average annual intensity of cold damage for DSER.
The higher annual frequency of
cold damage the station has, the higher average annual intensity of cold damage it has. Relation equations between frequency and intensity of cold damage are built by nonlinear regression analysis for DSER and DSLR respectively (Talbe 4), the two equations both complies with exponential form (Figs. 17 and 18).
Fig. 18 Relation of cold damage annual frequency and average annual intensity of cold damage for DSLR.
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Table 5 Risk grade table of cold damage of DCR Cold damage risk level
DSER
DSLR
Slight risk 1 Low risk 2 Moderate risk Moderately weak 3 Moderately strong 4 High risk 5
(Pj 0.2) (0.2
IYj 0.54 0.54
IYj 0.28 0.28
(0.4
3.53
1.23
Extra-high risk 6
(0.8
17.42
8.32
Fig. 19 Flowchart of cold damage risk assessment of DCR.
Acknowledgements This research was funded by the Key Technologies R&D Program of China during the 12th Five-Year Plan period (2011BAD32B01), the National Natural Science Foundation of China (40875070), the Research Fund for Doctoral Program of Higher Education, China (20100101110035). In addition, we thank Prof. Zhao Yanxia from Chinese Academy of Meteorological Science for her help with meteorological station data.
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