韩春雷^{1, 2,},
沈彦俊^2,,,
武兰珍³,
郭英²,
陈晓璐^{2, 4}
1.河北师范大学资源与环境科学学院　石家庄　050024
2.中国科学院遗传与发育生物学研究所农业资源研究中心　石家庄　050022
3.甘肃农业大学水利水电工程学院　兰州　730070
4.青海师范大学地理科学学院　西宁　810008
基金项目:国家自然科学基金重大专项(42041007-02)和中国科学院国际伙伴计划“一带一路”专项项目(153E13KYSB20170010)资助

详细信息

作者简介:韩春雷, 主要从事农业遥感方面研究。E-mail: 952935651@qq.com

通讯作者:沈彦俊, 主要从事农业水文与水资源研究。E-mail: yj.shen@gmail.com

中图分类号:TP79

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出版历程

收稿日期:2021-03-05
录用日期:2021-08-09
网络出版日期:2021-09-08
刊出日期:2021-11-10

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

HAN Chunlei^{1, 2,},
SHEN Yanjun^2,,,
WU Lanzhen³,
GUO Ying²,
CHEN Xiaolu^{2, 4}
1. College of Resources and Environmental Sciences, Hebei Normal University, Shijiazhuang 050024, China
2. Center for Agricultural Resources Research, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Shijiazhuang 050022, China
3. College of Water Conservancy and Hydropower Engineering, Gansu Agricultural University, Lanzhou 730070, China
4. College of Geosciences, Qinghai Normal University, Xining 810008, China
Funds:This study was supported by the National Natural Science Foundation of China (42041007-02) and the One Belt One Road Special Project of the International Partnership Program of Chinese Academy of Sciences (153E13KYSB20170010)

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Corresponding author:E-mail: yj.shen@gmail.com

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摘要
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。
关键词:黄河上游/
耕地面积变化/
MODIS/
NDVI/
时序分析
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.
Key words:Upper Reaches of Yellow River/
Change of cultivated land/
MODIS/
NDVI/
Time series analysis

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图1黄河上游龙羊峡至河口镇地理位置及其4个区段的划分
Figure1.Location of Longyangxia to Hekou Town in the upper reaches of the Yellow River and the division of its four sections


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图2研究区耕地作物平滑前后NDVI曲线对比
Figure2.Comparison of cultivated land NDVI curves before and after smoothing in the study area


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图3耕地曲线特征决策树分类规则
NDVI为?32 768时, 为影像背景值; 将水体、裸地等NDVI值相对较低的地物归为“其他1”, 而林地等NDVI值相对较高的地物归为“其他2”。?32 768 NDVI is the background value of image. Classification of objects with relatively low NDVI values such as water and bare land are as ‘Other 1’. Classification of objects with relatively high NDVI values such as forest land are classified as ‘Other 2’.
Figure3.Classification rules of cultivated land curve feature decision tree


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图4研究区不同年份耕地提取空间分布
Figure4.Spatial distribution of cultivated land extraction in different years in the study area


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图52002—2018年黄河流域上游县级统计数据与遥感提取的耕地面积对比
Figure5.Comparison of cultivated land area based county-level statistical data and extracted by remote sensing in the upper reaches of the Yellow River Basin from 2002 to 2018


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图62002—2018年黄河上游及各地区耕地面积
Figure6.Cultivated land areas of the study area and its’ different regions from 2002 to 2018


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图72002—2018年研究区耕地面积变化情况
Figure7.Changes of cultivated land area in the study area from 2002 to 2018


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图82002—2018年研究区降水量分布情况
Figure8.Distribution of precipitation in the study area from 2002 to 2018


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图92002—2018年研究区NDVI变化趋势
Figure9.Trend of NDVI in the study area from 2002 to 2018


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图102002—2018年研究区平均降水量和NDVI平均值变化
Figure10.Changes of average precipitation and NDVI in the study area from 2002 to 2018


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图112002—2018年甘肃、宁夏南部地区年平均降水和NDVI平均值
Figure11.Annual average precipitation and NDVI in southern regions of Gansu and Ningxia from 2002 to 2018


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表1研究数据来源
Table1.Research data sources

数据类型 Data type	数据来源 Data sources
遥感数据 Remote sensing data	MOD13Q1数据集来源于美国地质调查局陆地过程分布式数据档案中心。 MOD13Q1 data set comes from Land Processes Distributed Active Archive Center, LPDAAC.
气象数据 Meteorological data	来源于中国气象数据网, 包括作物生育期、降水量等数据。 Meteorological data comes from China Meteorological Data Network, including crop growth period, precipitation and other data.
社会经济数据 Socio economic data	来源于研究区内各个省的统计年鉴、统计公报。 Statistical data comes from the statistical yearbook and statistical bulletin of each province in the study area.
实地考察数据 Field survey data	来自青海、甘肃、宁夏、内蒙古黄河流域部分进行的实地考察。 Field investigation data comes from the field investigation of the Yellow River Basin in Qinghai, Gansu, Ningxia and Inner Mongolia.

下载: 导出CSV
表2耕地提取混淆矩阵验证结果
Table2.Result of cultivated land confusion matrix verification

地区 Region	提取精度 Extraction accuracy (%)	Kappa系数 Kappa coefficient
内蒙段 Inner Mongolia section	80.58	0.698
宁夏段 Ningxia section	75.59	0.6124
甘肃段 Gansu section	83.81	0.8218
青海段 Qinghai section	76.72	0.7624

下载: 导出CSV

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