基于多时相MODIS的NDVI分层数据在卢旺达玉米历法评估中的应用

摘要

作物历作为一种可以提供特定地区作物生长周期相关信息的重要工具,对有效的农业管理十分重要.由于农业生态系统的特点不同,不同地区的作物历也各不相同.本研究基于多时相MODIS 的NDVI分层数据,评估在卢旺达不同地区实践玉米作物日历的差异.有针对性地选取了4个以农业为主的NDVI层并随机采访433名当地农民.这些信息有助于了解当地玉米种植情况和收获日期,以便生成玉米生长历的各个 NDVI 层.对玉米生长历执行 k-均值非监督分类,将其分类生成4组不考虑NDVI层的玉米生长日历.ANOVA结果显示,在p=0.05时,无论NDVI层数如何,由NDVI层生成的玉米作物历与实际不考虑NDVI层的作物历之间均存在显著差异.在p=0.05时,卡方检验和t检验的结果显示,不仅玉米生长日历与作物生长季数之间有显著的相关关系,而且玉米生长日历与NDVI层数之间同样存在显著相关.最终来看,认为存在一个适用于卢旺达全国各地的通用玉米生长日历的现有认知与本研究的结果相左.然而,本研究结果与卢旺达农业生态系统从东到西主要在海拔和降雨模式变化方面存在差异的事实一致.%Crop calendar is an important tool providing relevant information on crops cycles in a specific area for effective agricultural management. Crop calendars vary in different areas given dissimilarities in agro-ecosystems' characteristics. This research used multi-temporal MODIS NDVI stratification to assess differences in practiced maize crop calendars in various areas of Rwanda. Four (4) sample NDVI strata dominated by agriculture were purposively chosen, and 433 local farmers were randomly selected from the strata for interviews. The collected in-formation helped to know about their maize planting as well as harvesting dates in order to generate maize calen-dars per NDVI strata. The generated crop calendars were later classified using k-means unsupervised classification, and produced 4 groupings of practiced maize calendars irrespective of NDVI strata. ANOVA results revealed sig-nificant differences between both the generated maize crop calendars by NDVI strata and the practiced crop cal-endars irrespective of NDVI strata, at p = 0.05. Moreover, chi-square tests and t-tests revealed not only a significant relationship between maize calendars and number of crop growing seasons, but also a significant relationship between maize calendars and NDVI strata, at p = 0.05. Finally, findings of this research contrasted the present conviction that there exist a single general maize calendar all over the country. Instead, the results were in accor-dance with the fact that Rwanda agro-ecosystems differ from East to West in terms of, mainly, altitude and rainfall patterns variations.