资源科学 ›› 2021, Vol. 43 ›› Issue (12): 2393-2402.doi: 10.18402/resci.2021.12.03
• “澜沧江—湄公河流域农业资源与环境”专栏 • 上一篇 下一篇
收稿日期:
2021-04-23
修回日期:
2021-07-24
出版日期:
2021-12-25
发布日期:
2022-02-16
作者简介:
黄翀,男,安徽六安人,博士,副研究员,主要从事生态遥感研究。E-mail: huangch@lreis.ac.cn
基金资助:
Received:
2021-04-23
Revised:
2021-07-24
Online:
2021-12-25
Published:
2022-02-16
摘要:
柬埔寨可耕地资源丰富,温度适宜,水稻生产极具潜力。及时监测水稻种植时空格局对于区域水稻生产管理、灾害风险评估和粮食政策制定具有重要意义。传统的水稻遥感监测研究大多只提供年际尺度的水稻空间分布,缺乏年内尺度水稻种植与收获信息。本文首先利用一年内所有可获取的MODIS影像,构建基于像元的MODIS NDVI年时间序列曲线;然后,选取最大值、最小值、均值和标准差逐像元计算时序统计参数特征,采用FastDTW算法计算像元时序曲线与水稻参考时序曲线的相似性特征,将时序统计特征与时序曲线相似度特征相结合,利用随机森林分类器,通过机器学习进行监督分类,提取水稻熟制信息;最后,结合时序曲线提取水稻物候特征,生成水稻收获时间信息,并对水稻耕作类型进行识别。研究表明:①柬埔寨水稻种植主要集中在洞里萨湖周围的低地平原和南部的湄公河下游。尽管柬埔寨全年热量条件适宜,但水资源获取限制对柬埔寨水稻种植时空格局具有显著影响。②水稻熟制以单季稻为主,约占全年水稻种植面积的80%,且分布区域稳定;双季稻面积约占20%,年际种植空间分布变化较大。雨季稻是柬埔寨水稻的主要种植类型,种植面积约占全年水稻面积的70%左右,年际变化不大;旱季稻和前雨季稻面积约占30%,年际空间分布差异显著。③对2011年和2016年水稻种植模式分析可知,灌溉条件和洪水对柬埔寨水稻种植时空具有重要影响。本文通过对柬埔寨年内水稻种植时空格局的高精度监测,识别其主要影响因素,为制定因地制宜和有弹性的水稻种植制度、保障柬埔寨粮食安全提供借鉴。
黄翀. 基于时序遥感的柬埔寨水稻种植时空格局监测[J]. 资源科学, 2021, 43(12): 2393-2402.
HUANG Chong. Monitoring rice cropping system in Cambodia and its influencing factors using time series MODIS images[J]. Resources Science, 2021, 43(12): 2393-2402.
[1] | Elert E. Rice by the numbers: A good grain[J]. Nature, 2014, 514(7524):50-51. |
[2] | FAO. FAOSTAT[DB/OL]. (2019-12) [2021-07-29]. http://www.fao.org/faostat/en/#data. |
[3] |
Guan X D, Liu G H, Huang C, et al. An open-boundary locally weighted dynamic time warping method for cropland mapping[J]. ISPRS International Journal of Geo-Information, 2018, DOI: 10.3390/ijgi7020075.
doi: 10.3390/ijgi7020075 |
[4] |
Huang C, Zhang C C, He Y, et al. Land cover mapping in cloud-prone tropical areas using Sentinel-2 data: Integrating spectral features with NDVI temporal dynamics[J]. Remote Sensing, 2020, DOI: 10.3390/rs12071163.
doi: 10.3390/rs12071163 |
[5] | 管续栋, 黄翀, 刘高焕, 等. 基于DTW距离的时序相似性方法提取水稻遥感信息: 以泰国为例[J]. 资源科学, 2014, 36(2):267-272. |
[ Guan X D, Huang C, Liu G H, et al. Extraction of paddy rice area using a DTW distance based similarity measure[J]. Resources Science, 2014, 36(2):267-272.] | |
[6] |
Kontgis C, Schneider A, Ozdogan M. Mapping rice paddy extent and intensification in the Vietnamese Mekong River Delta with dense time stacks of Landsat data[J]. Remote Sensing of Environment, 2015, 169:255-269.
doi: 10.1016/j.rse.2015.08.004 |
[7] |
Clauss K, Yan H M, Kuenzer C. Mapping paddy rice in China in 2002, 2005, 2010 and 2014 with MODIS time series[J]. Remote Sensing, 2016, DOI: 10.3390/rs8050434.
doi: 10.3390/rs8050434 |
[8] | 黄翀, 许照鑫, 张晨晨, 等. 基于Sentinel-1数据时序特征的热带地区水稻种植结构提取方法[J]. 农业工程学报, 2020, 36(9):177-184. |
[ Huang C, Xu Z X, Zhang C C, et al. Extraction of rice planting structure in tropical region based on Sentinel-1 temporal features integration[J]. Transactions of the Chinese Society of Agricultural Engineering, 2020, 36(9):177-184.] | |
[9] |
Lasko K, Vadrevu K P, Tran V T, et al. Mapping double and single crop paddy rice with Sentinel-1A at varying spatial scales and polarizations in Hanoi, Vietnam[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2018, 11(2):498-512.
doi: 10.1109/JSTARS.4609443 |
[10] |
Arjasakusuma S, Swahyu Kusuma S, Rafif R, et al. Combination of Landsat 8 OLI and Sentinel-1 SAR time-series data for mapping paddy fields in parts of west and central Java provinces, Indonesia[J]. International Journal of Geo-information, 2020, DOI: 10.3390/ijgi9110663.
doi: 10.3390/ijgi9110663 |
[11] |
Kamoto M, Juntopas M. Lower Mekong Basin: Existing environment and development needs[J]. Geographical Review of Japan, 2007, 80(12):704-715.
doi: 10.4157/grj.80.704 |
[12] | Ministry of Agriculture, Forestry and Fisheries of Cambodia. Annual Report for Agriculture Forestry and Fisheries 2016-2017 and Direction 2017-2018[R]. Phnom Penh: MAFF Conference, 2017. |
[13] |
Tucker C J. Red and photographic infrared linear combinations for monitoring vegetation[J]. Remote Sensing of Environment, 1979, 8(2):127-150.
doi: 10.1016/0034-4257(79)90013-0 |
[14] |
Müller M. Dynamic time warping[J]. Information Retrieval for Music and Motion, 2007, DOI: 10.1007/978-3-540-74048-3_4.
doi: 10.1007/978-3-540-74048-3_4 |
[15] |
Salvador S, Chan P. Toward accurate dynamic time warping in linear time and space[J]. Intelligent Data Analysis, 2007, 11(5):561-580.
doi: 10.3233/IDA-2007-11508 |
[16] |
Breiman L. Random forests[J]. Machine Learning, 2001, 45:5-32.
doi: 10.1023/A:1010933404324 |
[17] |
Wurm M, Taubenböck H, Weigand M, et al. Slum mapping in polarimetric SAR data using spatial features[J]. Remote Sensing of Environment, 2017, 194:190-204.
doi: 10.1016/j.rse.2017.03.030 |
[18] |
Li L, Friedl M A, Xin Q C, et al. Mapping crop cycles in China using MODIS-EVI time series[J]. Remote Sensing, 2014, 6(3):2473-2493.
doi: 10.3390/rs6032473 |
[19] |
Galford G L, Mustard J F, Melillo J, et al. Wavelet analysis of MODIS time series to detect expansion and intensification of row-crop agriculture in Brazil[J]. Remote Sensing of Environment, 2008, 112(2):576-587.
doi: 10.1016/j.rse.2007.05.017 |
[20] |
Wang J, Xiao X M, Liu L, et al. Mapping sugarcane plantation dynamics in Guangxi, China, by time series Sentinel-1, Sentinel-2 and Landsat images[J]. Remote Sensing of Environment, 2020, DOI: 10.1016/j.rse.2020.111951.
doi: 10.1016/j.rse.2020.111951 |
[21] |
Chen Y L, Lu D S, Moran E, et al. Mapping croplands, cropping patterns, and crop types using MODIS time-series data[J]. International Journal of Applied Earth Observation and Geoinformation, 2018, 69:133-147.
doi: 10.1016/j.jag.2018.03.005 |
[22] |
You N S, Dong J W. Examining earliest identifiable timing of crops using all available Sentinel 1/2 imagery and Google Earth Engine[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2020, 161:109-123.
doi: 10.1016/j.isprsjprs.2020.01.001 |
[23] | 刘开强, 李丹婷, 吕荣华, 等. 柬埔寨水稻生产概况与发展战略[J]. 广西农业科学, 2010, 41(6):619-622. |
[ Liu K Q, Li D T, Lv R H, et al. Current status of rice production in Cambodia and its development strategies[J]. Guangxi Agricultural Science, 2010, 41(6):619-622.] | |
[24] | Bansok R, Chhun C, Phirun N. Agricultural Development and Climate Change: The Case of Cambodia[M]. Phnom Penh: CDRI, 2011. |
[25] |
Chung S, Takeuchi J, Fujihara M, et al. Flood damage assessment on rice crop in the Stung Sen River Basin of Cambodia[J]. Paddy and Water Environment, 2019, 17(2):255-263.
doi: 10.1007/s10333-019-00718-1 |
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