国家尺度上基于地形因子的光温及气候生产潜力修正算法

A Correcting Algorithm of Crop Productive Potentiality Based on the Terrain Factors in National Scale

【目的】光温/气候生产潜力作为农用地分等中的重要指标之一,直接影响分等结果的准确性。从理论上来说,不同地形地区的光温条件应各不相同,以目前这种一个县一种作物只具有一个生产潜力值的情况来看,当县内地形差异明显时,仅使用一个生产潜力值不能反应出光温条件在县内的异质性,从而使分等结果不能准确描述耕地质量的差异性。论文旨在解决这一问题。【方法】从地形对于光照、温度和降水等与生产潜力密切相关的因子具有严重关联性的角度入手,通过寻找地形因子与生产潜力的关系,利用地形因子对生产潜力进行修正。由于生产潜力是以国家级尺度的数据进行计算的,为了保证修正后生产潜力值的可比性,在国家级尺度上开展修正,以900 m×900 m的DEM数据为计算地形因子的数据来源,首先利用SPSS软件,分别对坡度、坡向、海拔与生产潜力做回归分析,筛选相关性最高的回归模型,确定不同地形因子与生产潜力的相关性;其次利用回归方程、县内平均地形因子值、平均生产潜力值和待修正区的地形因子值得出生产潜力修正公式;最后以不同地形因子与生产潜力的相关系数为权重,将单因子修正后的生产潜力值进行加权,得到最终的综合修正生产潜力值。【结果】以目前农用地分等中正在使用的生产潜力值和DEM数据生成的地形因子做回归分析,其中,参与修正光温生产潜力的样点共3 779个,参与修正气候生产潜力的样点共2 765个。回归分析结果表明,坡度和坡向与光温生产潜力的相关系数分别为0.0008和0.0002,说明在国家级尺度上,以900 m×900 m的DEM数据对坡度、坡向和生产潜力进行回归分析时,这两者与生产潜力的相关性过小,故暂不列为修正生产潜力的因子;海拔与光温生产潜力的相关系数达到0.835,与气候生产潜力的相关系数达到0.721,说明海拔与生产潜力具有高度相关性。根据海拔与生产潜力的回归方程得出,海拔对光温生产潜力的影响系数为1.479,对气候生产潜力的影响系数为1.095。论文以四川省长宁县为例进行了实例验证,结果表明,修正后的生产潜力值与海拔的趋势相同,体现出地势差异对光温条件的影响,并且海拔偏离县平均海拔越大的地区,生产潜力修正后偏离潜力的平均值越多。【结论】国家尺度范围内,海拔对于生产潜力具有重要的影响作用,并且海拔对于光温生产潜力的影响程度高于对气候生产潜力的影响程度,而坡度、坡向在该尺度内与生产潜力不具有明显的相关性。基于数据的限制,论文旨在侧重数学模型方法和修正思想的论述,与实际应用还有一定的距离,在未来研究中可探索利用国家级控制可比性、分区域利用更加精细的数据进行局部修正的方法进一步分析坡度、坡向对生产潜力的影响。

【Objective】 Light temperature/climate productive potentiality as one of the important index for farmland classification, which directly affects the accuracy of the classification results. In theory, light and temperature conditions should vary in different terrain regions, but existing productive potentiality value that one county, one crop just owns one value can＇t accurately reflect the differences of productive potentiality when the terrain differences apparent in the county, which leads to the classification results can＇t accurately describe the differences of the cultivated land quality. The objective of this study is to solve this problem.【Method】 Based on terrain had serious relationship with the light, temperature and precipitation which were closely related to productive potentiality, this paper proposes to find the relationship between terrain factor and productive potentiality using the relationship to correct the value of productive potentiality. As productive potentiality was calculated based on a national scale data, in order to ensure the comparability of revised productive potentiality value, this paper carried out correction in national scale and used 900 m × 900 m DEM data as data source of calculating terrain factors. Firstly, by SPSS software, regression analysis was done between altitude, gradient, aspect and productive potentiality respectively, then the highest correlation regression model was screened to reflect their relationships. Secondly, the regression equation, county average terrain values, average productive potentiality and the terrain values of correcting area were used to get correction formula for productive potentiality. Finally, the correlation coefficients of different terrain factors and productive potentiality were used as weights to weight the values of each corrected productive potentiality value by single factor to get the comprehensive correction productive potentiality value.【Result】 This paper did regression analysis using the data productive potentiality value at the current farmland classification and the DEM data. There were 3 779 samples participated in correcting light temperature productive potentiality and 2 765 samples participated in correcting climate productive potentiality. Regression analysis results showed that the correlation coefficient between light temperature productive potentiality and gradient was 0.0008 and the correlation coefficient between light temperature productive potentiality and aspect was 0.0002. This proved that when 900 m × 900 m DEM data were used as data source to calculate gradient and aspect in national scale, both gradient and aspect almost had no correlation with productive potentiality. On the other hand, correlation coefficient was 0.835 between altitude and light temperature productive potentiality, and the value of correlation coefficient between climate productive potentiality and altitude was 0.721, which meant there was high correlation between altitude and productive potentiality. According to the regression equation between altitude and productive potentiality, the influence coefficient of altitude to the light temperature productive potentiality was 1.479, to the climate productive potentiality was 1.095. Changning County in Sichuan Province was used as a case example verification. The results showed that the revised production potential value had the same trend as elevation of the trend, which reflected that terrain impacted light and temperature conditions, and the more elevation deviating from average elevation was, the greater the revised productive potentiality was different from average productive potentiality.【Conclusion】In national scale, altitude has an important effect for productive potentiality, and the impact for light temperature productive potentiality is greater than climate productive potentiality. On the contrary, both gradient and aspect have no obvious correlation with productive potentiality in national scale. Based on the data limitation, the purpose of this paper is to focus on discussion of mathematical model method and the fixed thought, there is still a certain distance with the practical application, in the future study, we can use more detailed data to analyze the impacts of gradient and aspect to productive potentiality in local area on the premise using the national control of comparability.