河南省冬小麦种植频率时空变化及影响因素分析

doi:10.3864/j.issn.0578-1752.2020.09.007

摘要/Abstract

摘要：

【目的】通过对河南省2001—2015年间不同时期（2001—2005、2006—2010及2011—2015年）冬小麦种植频率（winter wheat planting frequency,WWPF）时空变化及其主要影响因素定量分析,进一步明晰区域作物种植频率变化时空变化分布特征和主要影响因素顺序。【方法】以河南省为研究区,冬小麦为研究作物,在利用中低分辨率MODIS EVI时序遥感数据和CART决策树算法进行连续15年（2001—2015年）作物种植空间分布信息提取基础上,获取了研究区不同时期冬小麦种植频率空间信息。在此基础上,开展不同时期冬小麦种植频率时空变化分析,并利用相关分析、主成分分析和线性回归分析等数理统计方法对不同时期研究区种植频率变化的影响因素进行分析,最终确定主要影响因素的重要性排序。【结果】基于MODIS EVI时序遥感数据和CART决策树算法可获得河南省较高精度连续多年冬小麦种植空间分布信息,经验证,研究区冬小麦遥感提取平均总体精度为90.39%,Kappa系数在0.82—0.92之间,可满足区域冬小麦种植频率变化研究所需作物空间分布精度要求;通过分析河南省不同时期冬小麦种植频率时空变化信息,省域内冬小麦主产区大部分具有较高的冬小麦种植频率（WWPF>80%）,而豫西南和豫南等山区由于地形复杂、自然条件较差导致冬小麦种植频率普遍较低（WWPF≤40%）。此外,3个时段期间,河南省冬小麦主产区高频种植冬小麦面积呈逐步增加趋势,WWPF>80%的面积比例分别为42.68%、59.94%和63.07%,低频种植面积呈减小趋势,WWPF≤40%的面积比例分别为28.53%、17.99%和16.63%,这对我国冬小麦主产区稳定粮食种植面积具有重要意义;从冬小麦种植频率影响因素分析结果看,河南省冬小麦种植频率与有效灌溉面积比例、土壤质量综合指数、播期气候适宜度、坡度和高程等指标间均存在显著的相关性,且除与坡度、高程呈负相关外,与其余因素均为正相关关系。以上指标对河南省冬小麦种植频率变化影响程度的排序结果为土壤综合质量指数>播期气候适宜度>有效灌溉面积比例>坡度（高程）,即土壤质量>播期气候条件>灌溉条件>地形条件。【结论】通过对河南省冬小麦种植频率时空变化及其影响因素进行定量分析,明确了河南省冬小麦种植频率时空分布特征和变化规律,明晰了河南省区域冬小麦种植频率变化影响因素及其重要性排序,为开展作物种植面积变化分析提供了一定技术方法和思路借鉴,为区域农业土地利用决策模型构建提供一定基础理论支撑。

关键词: 河南省, 冬小麦, 作物面积提取, 作物种植频率, 时空变化, 影响因素

Abstract:

【Objective】Quantitative analysis was made on spatio-temporal variations of winter wheat planting frequency (WWPF) and its main influencing factors in different stages (2001-2005, 2006-2010 and 2011-2015) in Henan province, in order to further clarify the characteristics of crop planting frequency change and the sequence of main influencing factors. 【Method】In this study, winter wheat in Henan province was taken as the research crop. The spatial information of winter wheat planting frequency in different periods in the study area were obtained by extracting the spatial crop distribution information for 15 consecutive years (2001-2015) using mid-low resolution MODIS EVI time series remote sensing data and CART decision tree algorithm. On this basis, the spatio-temporal variations of winter wheat planting frequency in different stages were analyzed, and the influencing factors of the winter wheat planting frequency variations in different stages were investigated by correlation analysis, principal component analysis and linear regression analysis. Finally, the sequence of the main influence factors was determined. 【Result】It was verified that the average overall precision of winter wheat extraction was 90.39%, and the Kappa coefficient was between 0.82 and 0.92, which could meet the requirements of spatial distribution accuracy of crops required for regional winter wheat planting frequency change research. By analyzing the spatial and temporal variation information of the winter wheat planting frequency in different periods in Henan province, most of the main winter wheat producing regions in Henan province had higher winter wheat planting frequency (WWPF>80%), while the planting frequency of winter wheat was generally lower in southwestern Henan and in mountainous areas of southern Henan due to complex topography and poor natural conditions (WWPF≤40%). In addition, during the three periods, the area of winter wheat planted at high frequency increased gradually in the main winter wheat producing areas of Henan province, the area proportion of WWPF>80% was 42.68%, 59.94% and 63.07%, respectively. And the area planted at low frequency showed a decreasing trend, the area proportion of WWPF≤40% was 28.53%, 17.99% and 16.63%, respectively, which were of great significance to stabilize the planted area of grain in the main winter wheat producing areas of China. According to the analysis results of influencing factors of winter wheat planting frequency, there was a significant correlation between the WWPF in Henan province and the ratio of effective irrigation area, soil quality comprehensive index, climate suitability at sowing stage, slope and elevation, etc. Besides the negative correlation between slope/elevation and WWPF, other factors were all positively correlated with WWPF. The ranking results of the above indicators affecting on the WWPF were as follows: soil comprehensive quality index>climatic suitability at sowing stage>effective irrigated area ratio>slope (elevation), that was to say, the soil quality>sowing date climatic condition>irrigation condition>topographic condition. 【Conclusion】Based on quantitative analysis of spatio-temporal variations of winter wheat planting frequency and its influencing factors in Henan province, the spatio-temporal distribution characteristics and variation law of winter wheat planting frequency in Henan province were clarified, and the influencing factors and importance ranking of winter wheat planting frequency in Henan province were gotten, which could provide some technical methods and ideas for the analysis of crop planting area change, and provide some basic theoretical support for the construction of regional agricultural land use decision-making model.

Key words: Henan province, winter wheat, crop area extraction, crop planting frequency, spatio-temporal variations, influencing factors

李方杰,任建强,吴尚蓉,陈仲新,张宁丹. 河南省冬小麦种植频率时空变化及影响因素分析[J]. 中国农业科学, 2020, 53(9): 1773-1794.

FangJie LI,JianQiang REN,ShangRong WU,ZhongXin CHEN,NingDan ZHANG. Spatio-Temporal Variations of Winter Wheat Planting Frequency and Their Analysis of Influencing Factors in Henan Province[J]. Scientia Agricultura Sinica, 2020, 53(9): 1773-1794.

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年份 Year	地面样点 Ground samples			Google Earth样点 Google Earth samples			训练样点 Training samples
年份 Year	冬小麦 Winter wheat	非冬小麦 Non-winter wheat	总计 Total	冬小麦 Winter wheat	非冬小麦 Non-winter wheat	总计 Total	冬小麦 Winter wheat	非冬小麦 Non-winter wheat	总计 Total
2001	—	—	—	1145	1073	2218	295	423	718
2002	—	—	—	1165	1118	2283	315	468	783
2003	—	—	—	1155	1101	2256	305	451	756
2004	—	—	—	1095	1150	2245	245	500	745
2005	—	—	—	1100	1117	2217	250	467	717
2006	39	42	81	1129	1081	2210	318	473	791
2007	53	54	107	1027	1026	2053	230	430	660
2008	44	41	85	1027	1024	2051	221	415	636
2009	47	45	92	1043	1115	2158	240	510	750
2010	45	45	90	1015	1105	2120	210	500	710
2011	51	53	104	1049	1022	2071	250	425	675
2012	57	58	115	1073	1057	2130	280	465	745
2013	62	60	122	1023	999	2022	235	409	644
2014	69	71	140	998	1006	2004	217	427	644
2015	71	70	141	984	995	1979	205	415	620

指标名称 Indicator name	输入参数 Input parameter	计算公式 Computational formulas	备注 Remarks
旬温度适宜度 Ten days temperature suitability	温度适宜度S(T) Temperature suitability S(T)	$S(T)=[(T-T_1)(T_2-T)^B]/[(T_0-T_1)(T_2-T_0)^B]\\B= (T_2-T_0)/(T_0-T_1)$	T是旬平均温度,T₁、T₂、T₀分别是该时段生长发育下限温度、上限温度和最适温度,分别取值4、33、15^[42] T is the ten days average temperature, T₁, T₂, T₀ are the lower limit temperature, upper limit temperature and the most suitable temperature for growth and development in this period, taking 4, 33, 15 respectively ^[42]
旬降水适宜度 Ten days precipitation suitability	降水适宜度S(R) Precipitation suitability S(R)	$S(R)=\begin{cases} R/ R_{0} & R<R_{0} \\ R_{0}/R & R> R_{0} \end{cases}$	R为旬平均降水量（mm）,R₀为作物生理需水量,研究中,10月上旬和中旬R₀分别11mm和9.1mm^[42] R is tens day average precipitation (mm), R₀ is the physiological water demand of crops, in the first ten days and the middle ten days of October, R₀ is 11mm and 9.1mm respectively ^[42]
旬日照适宜度 Ten days sunshine suitability	日照适宜度S(L) Sunshine suitability S(L)	$S(L)=\begin{cases} e^{-[(L-L_{0})/b]^{2}} & L<L_{0} \\ l & L\geq L_{0} \end{cases}$	L表示实际日照时数（小时）,L₀表示日照百分率为70%的日照时数,b为常数,文中L₀=7.69,b=4.15^[42] L represents the actual sunshine hours (hours), L₀ is the sunshine hours with the sunshine percentage of 70%, and b is a constant. In this paper, L₀ = 7.69, b = 4.15 ^[42]