田罗,
周文佐^,,
何万华,
赵晓,
刘东红,
章金城
西南大学地理科学学院 重庆 400715
基金项目: 科技基础资源调查专项课题2017FY100901-4

详细信息

作者简介:田罗, 主要从事GIS与RS在农业领域的应用研究。E-mail:awei32@outlook.com

通讯作者:周文佐, 主要从事遥感与GIS在资源与环境中的应用研究。E-mail:zhouwz@swu.edu.cn

中图分类号:S127

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出版历程

收稿日期:2017-11-24
录用日期:2018-01-18
刊出日期:2018-08-01

Spatio-temporal evolution and potential analysis of cropping index in Sichuan Province during 2000-2016

TIAN Luo,
ZHOU Wenzuo^,,
HE Wanhua,
ZHAO Xiao,
LIU Donghong,
ZHANG Jincheng
School of Geographic Science, Southwest University, Chongqing 400715, China
Funds: the Project of National Science & Technology Basic Resources Investigation of China2017FY100901-4

More Information

Corresponding author:ZHOU Wenzuo, E-mail:zhouwz@swu.edu.cn

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摘要
摘要:种植指数是反映耕地利用强度的基础性指标，明确其空间格局、时间演变是合理制定农业决策的基础。本文以四川省2000-2016年MODIS-NDVI长时间序列数据为数据源，通过提取耕地NDVI时序曲线峰值频数，进而提取四川省2000-2016年耕地种植指数，分析其时空格局及演变，并结合积温-降水模型对区域耕地潜力种植指数分布进行了分析，为明确四川省耕地种植指数时空特征及演变和引导耕地合理集约利用提供科学依据。结果显示：研究时段内，一年1熟一直是四川省主要的熟制制度，其面积占耕地面积比达78.05%，主要分布在川西北高原区、川东北和川南的低山丘陵区以及攀西河谷区；一年2熟/3熟制主要分布在成都市及其周边地区，空间分布上呈现由成都市及其周边地区向四周下降的态势。可提升潜力种植指数（PMCI_p）方面，省内89.7%的耕地仍具有较高的可提升潜力；成都市及其周边地区可提升潜力较低（PMCI_p < 50%），且实际种植指数年际变化剧烈（标准差>60%）；川东北和川南地区可提升潜力种植指数在50%以上，面积占耕地面积比达65.6%，是未来耕地种植潜力挖掘的理想区域；川西北高原区、攀西河谷区和川东北大巴山中山区为过度耕作的主要分布区（PMCI_p < 0），面积占耕地面积10.3%，是未来农业生态退耕的重点区域。本文结合农作物节律性，利用长时间序列的遥感数据反演，实现对区域耕地种植指数的动态监测，并分析区域耕地种植指数的潜力及可提升潜力的空间格局，为区域农业政策制定提供有益参考。
关键词:四川省/
耕地复种/
种植指数/
潜力种植指数/
MODIS-NDVI/
积温-降水模型
Abstract:Cropping index is one of the basic indexes used to measure use intensity, space-time distribution and changes of farmlands at regional scale, which is essential for basis agricultural decision-making. To investigate cropping indexes, the related changes and potential cropping indexes of farmlands in Sichuan Province, a long-term (2000-2016) series of MODIS-NDVI remote sensing data products were used to reconstruct NDVI time-series curve using Asymmetric Gauss Fitting Function (AGFF). The peak frequencies of the NDVI time-series curves were used for determining the spatial distributions and temporal changes of annual cropping indexes by using the second order difference method. Then the spatial pattern of potential and promotable potential cropping indexes of farmland in Sichuan Province were simulated using the temperature-rainfall model, meteorological data and land-use maps to refine farmland dynamics in the region. The aim of the study was to provide scientifically-drive recommendations to local governments on the regulation of intensive development of farmlands in the study area. The results showed that single cropping pattern, which made up 78.05% of cultivated land, was the main cropping system in Sichuan Province for the period 2000-2016. This mainly distributed in the Northwest Sichuan Plateau region, Northeast Sichuan, Southwest Sichuan Hilly area and Panxi Valley region. The double cropping system mainly distributed in Chengdu region and the surroundings. Overall, the spatial distribution of cropping index (MCI) in Sichuan Province decreased in trend from Chengdu and the surroundings to other regions. According to the promotable and potential cropping indexes, Sichuan Province had 89.7% of cultivated land with higher promotable potential in multiple cropping. Chengdu and the surroundings had lower promotable potential cropping index (PMCI_p < 50%) and higher inter-annual fluctuation (standard deviation of cropping index > 60%) than other regions in Sichuan Province. Cultivated land in the northeastern and southern areas of Sichuan (which accounted for 65.6% of the total area of Sichuan), was higher in promotable potential cropping index (PMCI_p ≥ 50%) and less inter-annual fluctuation compared with Chengdu and the surroundings. There was the likelihood for this to become the best suitable land with the highest cropping index in Sichuan Province. Accounting for 10.3% of cultivated land area of Sichuan in the northwest and northeast mountainous areas (with negative promotable cropping index) formed the main region of agricultural over-development in the study area. There was therefore the need to classify these farmlands into ecological restoration areas. The cropping indexes extracted by integrating the phenology of crops and long-term series of remote sensing data were significantly correlated with the statistics values in Sichuan, indicating the method was applicable in evaluation of cropping index in provincial scale. The result was beneficial reference to policy decision of regional planting structure.
Key words:Sichuan Province/
Multiple cropping of farmland/
Cropping index/
Potential cropping index/
MODIS-NDVI/
Temperature-rainfall model

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图1四川省农业功能分区
Figure1.Agricultural zoning in Sichuan Province


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图2不同耕地熟制的NDVI时序曲线(a: 1熟制; b: 2熟制; c: 3熟制; d:撂荒地)
Figure2.Time series NDVI curves of farm land with different cropping systems (a: single cropping system; b: double cropping system; c: triple-cropping system; d: non-cultivated land)


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图3四川省主要作物物候节律
图中1、2、3分别表示各月的上、中、下旬; *表示该作物种的NDVI峰值物候期。
Figure3.Phenology stages of main crops in Sichuan Province
In the figure, 1, 2 and 3 respectively indicate the first, middle and last ten-day of each month; * indicate the phonological stages when the NDVI value reaches the theoretically maximum value.


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图42000—2016年四川省耕地熟制空间格局演变
Figure4.Spatio-temporal pattern of cropping index in Sichuan Province from 2000 to 2016


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图5四川省2000—2016年耕地平均种植指数(a)及其标准偏差(b)的空间分布
Figure5.Spatial distribution patterns of average annual cropping index (MCI, a) and its standard deviation of MCI (b) in Sichuan Province from 2000 to 2016


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图6四川省耕地潜力种植指数(a)和可提升潜力种植指数(b)空间分布
Figure6.Spatial distribution pattern of potential cropping index (PMCI, a) and promotable PMCI (b) in Sichuan Province


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图7四川省耕地种植指数遥感提取结果与统计数据的相关分析(2000年)
Figure7.Correlation analysis of cropping index (MCI) from RS and that from statistical data of Sichuan Province (2000)


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表1四川省各市(州)实际种植指数与潜力种植指数
Table1.Cropping index and potential cropping index of each city in Sichuan Province

市(州) City	实际种植指数 Mean cropping index (%)	潜力种植指数 Potential cropping index (%)		市(州) City	实际种植指数 Mean cropping index (%)	潜力种植指数 Potential cropping index (%)		市(州) City	实际种植指数 Mean cropping index (%)	潜力种植指数 Potential cropping index (%)
甘孜州 Ganzi	118.33	102.97		德阳市 Deyang	165.12	199.34		内江市 Neijiang	126.71	200.00
阿坝州 Aba	112.88	102.95		成都市 Chengdu	152.32	198.18		乐山市 Leshan	130.20	186.77
绵阳市 Mianyang	150.99	193.69		遂宁市 Suining	144.41	200.00		自贡市 Zigong	122.24	200.00
广元市 Guangyuan	136.01	185.15		雅安市 Ya’an	134.68	165.79		泸州市 Luzhou	114.78	196.62
巴中市 Bazhong	123.12	186.94		广安市 Guang’an	117.95	199.98		凉山州 Liangshan	130.90	144.05
达州市 Dazhou	117.20	190.48		资阳市 Ziyang	144.11	200.00		宜宾市 Yibin	122.78	199.20
南充市 Nanchong	128.98	200.00		眉山市 Meishan	142.12	197.85		攀枝花市 Panzhihua	128.91	200.90

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