基于时序MODIS的黄河上游2002—2018年耕地时空变化特征分析

Spatial and temporal variation characteristics of cultivated land in the upper Yellow River from 2002 to 2018 based on time series MODIS

  • 摘要: 黄河上游贡献全流域水资源的56.77%, 而上游农业用水占全流域40%以上, 监测黄河上游耕地的分布对于定量评估农业用水变化至关重要。本文以黄河上游龙羊峡至河口镇流域范围为对象, 基于MOD13Q1数据集, 使用时间序列谐波分析法(Harmonic Analysis of Time Series, HANTS)对NDVI时间序列曲线进行平滑, 再结合作物生育期等物候信息与决策树分类方法提取了研究区2002—2018年的耕地分布与变化情况, 为研究上游耕地变化对流域水资源消耗的影响提供基础数据。用野外实地考察获取的地面真实样点数据与研究区内县级统计数据对提取结果进行验证, 耕地提取精度在75%以上, R2达0.85。本研究主要结果显示: 2002—2018年耕地面积总体呈增加状态, 共增加88.21万hm2; 宁蒙灌区的耕地增长最为迅速, 宁夏段耕地总面积增加64%, 2018年达76.61万hm2; 内蒙古段的耕地面积也大幅增加, 达44.74万hm2, 占总面积的44%; 甘肃段面积同样增加18.89万hm2; 青海段则显示出明显的耕地减少趋势, 共减少5.36万hm2。黄河上游整体上耕地面积增加速率为5.18万hm2·a−1, 其中青海段以0.20万hm2·a−1的速率减少, 甘肃段增加速率为1.05万hm2·a−1, 宁夏段增加速率为1.87万hm2·a−1, 内蒙段增加速率为2.46万hm2·a−1。通过定性与定量方法对耕地变化因素进行分析, 水资源政策和地方社会经济环境变化是研究区耕地面积变化的主要驱动因素。

     

    Abstract: The upper reaches of the Yellow River contribute 56.77% of the water resources in the whole basin, while agricultural water in the upper reaches accounts for more than 40% of the whole basin. Using MODIS data with medium-resolution remote sensing and crop phenology data, the cultivated land area in the upper reaches of the Yellow River from 2002 to 2018 was extracted, which provided basic data for studying the impact of cultivated land change in the upper reaches on water resource consumption in the basin. Using Harmonic Analysis of Time Series (HANTS) to smooth the cut and spliced MOD13Q1 data, the NDVI time series curve was easier to identify. Combined with the decision tree classification method and crop growth period and other phenological information, the decision tree rules were compiled, and then the smooth MOD13Q1 data were classified by using the classification rules. The distribution and change of cultivated land in the study area from 2002 to 2018 are obtained. Then, the confusion matrix verification of the extraction results was carried out by using the real ground sample data obtained from field investigation and the county-level statistical data in the study area. The extraction accuracy of cultivated land was above 75%, and R2 reached 0.85. The main results showed that the cultivated land in the study area were increased from 2002 to 2018, with a total increase of 88.21×104 hm2. The most rapidly increased was Ningmeng Irrigation District, the total area of cultivated land in Ningxia increased by 64%, reaching 76.61×104 hm2 in 2018; and Inner Mongolia increased reaching 44.74×104 hm2, accounting for 44% of the total area; and Gansu section increased reaching 18.89×104 hm2; Qinghai section showed an obvious trend of decrease in cultivated land, a total of 5.36×104 hm2. On the whole, the increase rate of cultivated land area is 5.18×104 hm2·a−1, in which the Qinghai section decreases at a rate of 0.2×104 hm2·a−1, the Gansu section increases at a rate of 1.05×104 hm2·a−1, the Ningxia section increases at a rate of 1.87×104 hm2·a−1, and the Inner Mongolia section increases at a rate of 2.46×104 hm2·a−1. Mann-Kendall analysis method was used to analyze the trend of NDVI change in the study area, and it was found that NDVI showed an increasing trend. In addition, compared with the change of precipitation, it also showed an increasing trend. Then using stepwise regression analysis to analyze the selected main indicators, the residents’ disposable income was the main factor affecting the change of cultivated land. Finally, using the collected data for qualitative analysis, it was concluded that water resources policy and engineering facilities construction are the factors affecting the change of cultivated land. The main conclusions of this paper were as follows. The cultivated land in the basin from Longyangxia to Hekou Town in the upper reaches of the Yellow River shows an increasing trend. The medium-resolution MODIS data can be used to extract the cultivated land in the upper reaches of the Yellow River. Water resources policy and local socio-economic environment change were the main driving factors for the change of cultivated land area in the study area.

     

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