【作者】 王芳；

【导师】 安放舟；

【作者基本信息】 新疆大学 ， 自然地理学， 2008， 硕士

【摘要】 新疆是全国棉花的主产区,其播种面积和产量的变化直接影响着全国棉花的产量和库存情况;科学地监测新疆棉花长势、准确地预报其产量,可为各级政府提供及时、准确的棉花生产农情,对政府进行农业相关决策具有重大的意义。本研究以克拉玛依市为研究区,以遥感资料为基础,通过提取棉花归一化植被指数( NDVI),结合气温因子建立棉花单产的多元线性回归估产模型,以期提高估产精度,为新疆棉花大面积产量估测提供科学依据。经分析共有10个NDVI因子与棉花产量具有较好相关性,而且时段比较集中,主要在6月下旬至7月下旬以及9月上旬;其中,6月下旬至7月下旬的8个因子与棉花产量为正相关,9月上旬的2个因子均与棉花产量为负相关。共有5个气温因子与棉花单产的相关性较显著,且均为负相关。而且与NDVI因子同样,时段比较集中,主要在5月和8月中旬。研究采用强迫引入法建立基于7月NDVI累加值占主要生育期NDVI累加值百分比和8月中旬旬均温的多元线性回归方程:Y=-1624.061+11855.483XN10-40.679XT1; F值为13.297,显著性水平为0.017。将多元线性回归模型与单因子回归模型进行对比检验,经检验基于因子N10的单因子线性回归模型估产相对误差在-8.6﹪—13.3﹪之间,指数回归模型的相对误差在-7﹪—11.8﹪之间;基于因子T1的单因子线性回归模型估产相对误差在-13.4﹪—10.7﹪之间;基于因子N10和因子T1的多元线性回归模型估产的相对误差在-5.9﹪—9.9﹪之间。可以看出,多元线性回归模型的估产精度较高,且模型的稳定性较好,具有一定的可行性。更多还原

【Abstract】 Xinjiang is the main production area of cotton in china. The change of cotton’s sown area and output influences cotton’s output and stock situation of nation immediately. Monitors Xinjiang cotton growing trend scientifically and forecasts its output accurately, may provides prompt accurate cotton production state of agricultural production for all levels of the government, and it has the significant significance carries on the agricultural decision-making to the government.The research take Kalamy as an example, take the remote sensing material as the foundation, through extraction cotton normalization vegetation index, union temperature factor, establish multi-dimensional linear regression estimation model of cotton per unit area yield ,by time enhance the precision of field estimation,and provide the scientific basis for big area output estimation of cotton in Xinjiang.After analysis, altogether 10 NDVI factors has the good relevance with the cotton output, moreover the time interval is quite centralized ,mainly in late June to late July as well as early September. And there are 8 factors in late June to late July’s 8 factors, is being related with the cotton output; 2 factors in early September’s is inverse correlation with the cotton output. Altogether 5 temperature factors is remarkable relevance with the cotton per unit area yield’s, and all is inverse correlation, moreover the same as NDVI factors, the time interval is quite centralized, concentrates in May and mid-August.The study using force introduction law, establish Multi-dimensional linear regression equation based on July NDVI accumulation value accounts for the main period of duration NDVI accumulation value percentage and average temperature in mid-August ten-day period : Y=-1624.061+11855.483XN10-40.679XT1. The value of F is 13.297, and the significance level is 0.017.Carrying Multi-dimensional linear regression model and the single factor regression model on the contrast examination. After examination, single factor linear regression model based on factor N10 , has estimation relative error between -8.6﹪and 13.3﹪, index regression model based on factor N10 has relative error between -7﹪and 11.8﹪. Single factor linear regression model based on factor T1 has estimation relative error between -13.4﹪and 10.7﹪. The multi-dimensional linear regression model based on factor N10 and factor T1 has estimation relative error between - 5.9﹪and 9.9﹪.We may see the multi-dimensional linear regression model’s estimation precision is higher, and the model stability is better, it has certain feasibility.更多还原