基于遥感的广西甘蔗种植面积提取及长势监测

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谢鑫昌^1,,
杨云川^{1, 2, 3,,},
田忆¹,
廖丽萍^{1, 2, 3},
莫崇勋^{1, 2, 3},
韦钧培¹,
周津羽¹
1.广西大学土木建筑工程学院南宁 530004
2.广西大学工程防灾与结构安全教育部重点实验室南宁 530004
3.广西防灾减灾与工程安全重点实验室南宁 530004
基金项目: 国家自然科学基金项目41901132
国家自然科学基金项目51609041
广西自然科学基金项目2019GXNSFAA185015
广西自然科学基金项目2018GXNSFAA138187

详细信息

作者简介:谢鑫昌, 主要从事农业及水土资源遥感研究。E-mail: xiexinchanggxdx@163.com

通讯作者:杨云川, 主要从事农业与城市生态水文学研究。E-mail: yyc_sciences@163.com

中图分类号:TP79

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出版历程

收稿日期:2020-06-03
录用日期:2020-10-22
刊出日期:2021-02-01

Sugarcane planting area and growth monitoring based on remote sensing in Guangxi

XIE Xinchang^1,,
YANG Yunchuan^{1, 2, 3,,},
TIAN Yi¹,
LIAO Liping^{1, 2, 3},
MO Chongxun^{1, 2, 3},
WEI Junpei¹,
ZHOU Jinyu¹
1. College of Civil Engineering and Architecture, Guangxi University, Nanning 530004, China
2. Key Laboratory of Disaster Prevention and Structural Safety of Ministry of Education, Guangxi University, Nanning 530004, China
3. Key Laboratory of Disaster Prevention and Engineering Safety of Guangxi, Nanning 530004, China
Funds: the National Natural Science Foundation of China41901132
the National Natural Science Foundation of China51609041
the Natural Science Foundation Guangxi of China2019GXNSFAA185015
the Natural Science Foundation Guangxi of China2018GXNSFAA138187

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Corresponding author:YANG Yunchuan, E-mail: yyc_sciences@163.com

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摘要
摘要:广西是我国最大的甘蔗种植和蔗糖产业基地，但长期受自然灾害影响甘蔗单产产量不高，及时获取其多年种植面积与长势的时空动态信息，可为区域甘蔗种植优化、灾害风险管理及蔗糖产业结构调整等提供重要科学支撑。首先，基于LANDSAT8 OLI遥感影像的6-5-2优化波段组合，引入NDVI、DEM等辅助识别特征变量，采用随机森林分类法进行多时相连续解译，并借助Google Earth高清遥感影像比对修正，获得了高精度的2014-2018年广西甘蔗种植面积分布；其次，基于MODIS-NDVI数据，构建长势差值监测模型，实现了近5年广西甘蔗茎伸期长势动态监测。结果表明：1）本文解译方法效果良好，广西甘蔗种植面积的总体分类精度高于92%，Kappa系数均大于0.8，面积相对误差5年均值为-10.7%。2）2014-2018年，广西甘蔗种植面积呈“前期急减，后期缓增”的变化趋势；主要种植区以崇左、南宁及来宾市为主，全区种植面积呈局部成片集聚、总体破碎分散的分布格局，并与研究区地形地貌、土壤类型、河流水系分布等下垫面环境要素密切相关。3）NDVI差值模型能清晰反映广西甘蔗茎伸期长势的年际和年内的时空变化特征，各年度内的甘蔗长势在好、正常、差等状态间交替转变频繁。上述成果可为揭示广西甘蔗对区域气候变化、旱涝交替及下垫面水土墒情动态的响应机制，开展区域甘蔗种植结构优化及其资源利用效率评估等奠定科学基础。
Abstract:Sugarcane planting in Guangxi has been affected by natural disasters, resulting in decreased yields. The information on spatio-temporal dynamics of the sugarcane planting area and growth can provide a reference for planting structure optimization and facilitate disaster control. This study incorporated 652 optimized band combinations of the LANDSAT 8 Operational Land Imager (OLI), normalized difference vegetation index (NDVI), digital elevation model (DEM), and other auxiliary identification characteristic variables into the random forest classification method to interpret continuously in multi-temporal aspects. Google Earth and high-resolution remote sensing image comparison and correction were used to obtain a high-precision sugarcane planting area distribution in Guangxi from 2014 to 2018. The MODIS-NDVI data was used to build a monitoring model of the growth potential difference for dynamic monitoring of sugarcane stem elongation in Guangxi in the last five years. The results showed that: 1) the interpretation method was effective, the overall classification accuracy of sugarcane planting area in Guangxi was >92%, the Kappa coefficient was >0.8, and the five-year mean area relative error was -10.7%. 2) In 2014-2018, the planting area of sugarcane in Guangxi had rapidly decreased in the early stage and slowly increased in the late stage. The main planting areas were in Chongzuo, Nanning, and Laibin. The whole planting area showed a distribution pattern of local agglomeration and overall fragmentation and dispersion, which was closely related to the underlying environmental elements, such as topography, soil type, and river system distribution. 3) The NDVI difference model reflected the interannual and intra-annual spatio-temporal changes in the elongation trend of sugarcane stems in Guangxi, and the yearly growth trend of sugarcane changes frequently between good, normal, and poor. These results revealed the response mechanism of sugarcane in Guangxi to regional climate change, alternation of drought and flood, and the dynamics of soil and water conservation on the underlying surface. Furthermore, this study provides a scientific foundation for optimizing the regional sugarcane planting structure and evaluating water resource utilization efficiency.

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图1研究区概况图
Figure1.Overview of the research area

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图22018年广西各典型地物LANDSAT8波谱特征曲线图
Figure2.Spectral characteristics curves of LANDSAT8 of various typical surface features in Guangxi in 2018

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图3LANDSAT 8 OLI各波段间相关系数热力图
Figure3.LANDSAT 8 OLI correlation coefficient thermal map of each band

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图42014—2018年广西区甘蔗种植区空间分布图
Figure4.Spatial distribution maps of sugarcane planting areas in Guangxi from 2014 to 2018

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图52014—2018年广西(a)及其主要甘蔗种植区(b)的统计年鉴精度评价图
Figure5.Accuracy evaluation charts of a statistical yearbook of entire region (a) and major sugarcane planting areas (b) of Guangxi from 2014 to 2018

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图62016年广西甘蔗种植面积分布的下垫面影响因素特征
Figure6.Spatial distribution characteristics of influencing factors of sugarcane planting area distribution in Guangxi in 2016

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图72014—2018年广西区甘蔗茎伸期(8—9月)长势空间分布特征
Figure7.Spatial distribution of growth vigour of sugarcane stem extension period (August to September) in Guangxi from 2014 to 2018

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表1广西甘蔗长势的NDVI差值等级划分
Table1.NDVI difference classification of sugarcane growth in Guangxi

NDVI	(?∞, ?0.3]	(?0.3, ?0.1]	(?0.1, 0.1]	(0.1, 0.3]	(0.3, +∞)
长势等级 Growth grade	较常年差 Poor growth than usual	较常年稍差 Poor growth slightly than usual	与常年持平 The growth is the same as usual	较常年稍好 Good growth slightly than usual	较常年好 Good growth than usual
甘蔗长势等级 Sugarcane growth grade	长势差 Poor growth		长势正常 Normal growth	长势好 Good growth

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表2广西典型地物解译标志(包含云)
Table2.Interpretation signs of the typical features in Guangxi (including clouds)

典型地物类型 Typical feature type	谷歌地球影像 Google earth image	波段组合(4-3-2) Band combination (4-3-2)	波段组合(6-5-2) Band combination (6-5-2)	解译特征 Interpret characteristics
甘蔗地 Sugarcane field				在4-3-2波段组合中主要呈深绿色, 色调均匀; 在6-5-2波段组合中呈淡绿色, 色调均匀, 偏暗, 纹理比较规则, 大部分在空间上呈大片种植。 It is mainly dark green in band combination 4-3-2 with uniform tone. In band combination 6-5-2, it is light green with uniform tone and dark color. The texture is relatively regular. Most of them are planted in large areas in space.
其他耕地 Other cultivated land				在4-3-2波段组合中呈淡绿色, 较甘蔗亮; 在6-5-2波段组合中呈黄绿色夹杂着暗紫色, 色调较甘蔗暗, 纹理粗糙, 与甘蔗交叉镶嵌。 In band combination 4-3-2, it is light green and brighter than sugarcane. In band combination 6-5-2, it is yellow-green mixed with dark purple, darker than sugarcane, with rough texture and cross mosaic with sugarcane.
林地 Forest				在4-3-2波段组合中呈墨绿色; 6-5-2波段组合中呈淡青色; 纹理特征较粗糙, 呈不规则块状。 It is dark green in band combination 4-3-2. In band combination 6-5-2, the color is light blue. Texture is rough, irregular block.
城市及居民地 Urban and residential areas				在4-3-2波段组合中呈褐色、浅蓝色或白色; 在6-5-2波段组合中呈紫色或淡紫色; 纹理粗糙, 呈规则块状。 It is brown, light blue or white in band combination 4-3-2, and purple or mauve in band combination 6-5-2. The texture is rough and regular blocks.
裸地Bare land				在4-3-2波段组合中呈棕红色; 在6-5-2波段组合中呈淡紫色, 纹理粗糙, 呈不规则块状。 It is brownish red in band combination 4-3-2, and lavender in color in band combination 6-5-2. The texture is rough and irregular lumps.
裸地Bare land				在4-3-2波段组合中呈棕黄色; 在6-5-2波段组合中呈淡紫色偏黄; 纹理粗糙, 呈不规则块状。 It is brownish yellow in band combination 4-3-2, mauve and yellowish in band combination 6-5-2. The texture is rough and irregular.
水域 Water area				在4-3-2波段组合中呈淡蓝色; 在6-5-2波段组合中呈深蓝色; 主要呈不规则块状或带状, 纹理细腻。 It is light blue in band combination 4-3-2, dark blue in band combination 6-5-2. The texture is fine mainly with irregular block or ribbon.
云 Cloud	——			4-3-2波段组合和6-5-2波段组合中均呈纯白色, 其中少部分呈淡灰色, 云量密集纹理细腻, 稀少则相对粗糙, 多为块状。 It is pure white both in bond combination 4-3-2 and 6-5-2, with a small part of them being light gray. For the dense cloud cover, texture is fine; while for the rare ones, texture is relatively rough and mostly lumpy.

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表32014—2018年广西地物混淆矩阵分类精度表
Table3.Confusion matrix classification accuracy table of surface features in Guangxi from 2014 to 2018

分类年份 Classification year	总体精度 Overall accuracy (%)	Kappa系数Kappa coefficient	用户精度User accuracy (%)		生产者精度Producer accuracy (%)
分类年份 Classification year	总体精度 Overall accuracy (%)	Kappa系数Kappa coefficient	甘蔗地Sugarcane field	其他地物Other features	甘蔗地Sugarcane field	其他地物Other features
2014	96.88	0.9567	87.19	99.47	88.89	99.38
2015	98.91	0.9560	88.06	99.74	87.05	99.76
2016	98.58	0.9772	93.79	99.25	85.33	99.72
2017	92.44	0.8564	80.35	99.97	98.45	99.61
2018	95.57	0.9422	89.92	99.80	98.90	98.06

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