宁夏东部半干旱区典型植物群落遥感分类特征

doi:10.13866/j.azr.2022.06.23

Abstract

Abstract:

The vegetation information extraction technology by remote sensing has been widely used in environmental monitoring and ecological protection. It can be seen that this technology has more important significance and application value in desertification frontier areas, such as arid and semi-arid regions. Taking the Haba Lake National Nature Reserve as the research area, the remote sensing extraction of typical plant communities at the regional scale was studied, verifying the applicability of the extraction of plant communities in this area based on the multi-temporal Landsat8 data. Based on the optimal index factor, the optimal band combination was determined. Simultaneously, combined with the object-oriented classification method, eight groups of classification experiments using single-phase and two-phase images with different band combinations were compared and analyzed to explore the influence of multi-temporal data on classification accuracy. The research results show that: (1) Different segmentation parameter settings influence the classification accuracy. Among these, when the compactness factor and shape factor are 0.7 and 0.1, respectively, the optimal classification effect of the experiment can be achieved. (2) The vegetation planted artificially in a large area of the study area has a better classification effect. Natural mixed plant communities, such as Bletilla striata and Achnatherum splendens, easily cause misclassification and mixed classification. (3) According to the final classification accuracy, the classification of multi-temporal data can significantly improve the classification accuracy. Compared with the overall classification accuracy and Kappa coefficient of single-phase data, the maximum improvement is 8.24% and 0.10, which can effectively improve the extraction accuracy of vegetation information in the study area.

Key words: object oriented classification, mulit-temporal image, plant community, band combination, classification accuracy, semi-arid areas

PANG Haiwei,YU Dian,REN Chengbao,ZHANG Yu,ZHENG Caizhi,GUO Jiacheng,BIAN Zhen,SANG Guoqing. Remote sensing classification characteristics of typical plant communities in the semi-arid areas of eastern Ningxia[J].Arid Zone Research, 2022, 39(6): 1930-1941.

Figures/Tables 17

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References 28

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类别	数据层	对象属性名称	对象特征
光谱特征	Coastal	Mean、Max、Brightness、Standard	分割图层平均值、最大值、标准差及参与波段亮度加权平均值
	Blue
	Green
	Red
	Nir
	SWIR1
	SWIR2
植被指数	NDVI	NDVI	平均值和标准差

实验方案	波段组合
A	最优3波段（7月31日影像）
B	最优3波段（7月31日影像）+波段1（2月21日影像）
C	最优3波段（7月31日影像）+波段2（2月21日影像）
D	最优3波段（7月31日影像）+波段3（2月21日影像）
E	最优3波段（7月31日影像）+波段4（2月21日影像）
F	最优3波段（7月31日影像）+波段5（2月21日影像）
G	最优3波段（7月31日影像）+波段6（2月21日影像）
H	最优3波段（7月31日影像）+波段7（2月21日影像）

植被	群系组	群落型
植被	盐生灌丛	白刺群落（Nitraria tangutorum）
	丛生禾草盐生草甸	芨芨草群落（Achnatherum splendens）
	沙地落叶灌丛及半灌丛	柠条群落（Caragana microphylla）
	沙地落叶灌丛及半灌丛	黑沙蒿群落（Artemisia desertorum）
	沙地人工落叶灌木林	沙柳群落（Salix cheilophila.）
	盐碱地人工落叶灌木林	柽柳群落（Tamarix chinensis）
	人工寒温性针叶松林	樟子松群落（Pinus sylvestris var. mongolica）
	人工温性落叶阔叶林	杨树群落（Populus ）
	人工温性落叶阔叶林	榆树群落（Ulmus pumila）

Correlation	Band1	Band2	Band3	Band4	Band5	Band6	Band7
Band 1	1.00
Band 2	0.99	1.00
Band 3	0.98	0.99	1.00
Band 4	0.95	0.96	0.99	1.00
Band 5	0.85	0.83	0.85	0.81	1.00
Band 6	0.91	0.92	0.95	0.96	0.88	1.00
Band 7	0.90	0.91	0.95	0.97	0.82	0.99	1.00

OIF值排序	波段组合	标准差和	相关系数和	OIF
1	4、5、7	3363.75	2.60	1293.63
2	5、6、7	3389.62	2.69	1261.78
3	4、5、6	3196.66	2.65	1206.68
4	4、6、7	3440.88	2.92	1179.95
5	3、5、7	3023.22	2.62	1153.20
6	2、5、7	2774.02	2.56	1082.91
7	3、6、7	3100.36	2.89	1071.73
8	3、4、5	2830.26	2.66	1065.43
9	3、5、6	2856.13	2.68	1064.29
10	1、5、7	2721.39	2.57	1058.27

Remote sensing classification characteristics of typical plant communities in the semi-arid areas of eastern Ningxia

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波段组合	总体精度/%	Kappa系数
A：4、5、7	75.74	0.71
B:4、5、7+1	81.46	0.78
C:4、5、7+2	82.15	0.79
D:4、5、7+3	80.55	0.77
E:4、5、7+4	83.98	0.81
F:4、5、7+5	79.17	0.75
G:4、5、7+6	79.63	0.76
H:4、5、7+7	78.26	0.75

实际地类	参考地类											用户精度
实际地类	白刺	芨芨草	柽柳	盐爪爪	沙柳	柠条	榆树	樟子松	黑沙蒿	其他	水体	用户精度
白刺	43	0	0	0	0	0	0	0	0	1	0	91.67
芨芨草	0	50	0	0	0	0	0	3	0	0	0	100
柽柳	0	0	43	0	0	33	0	0	1	0	0	92.10
盐爪爪	0	0	0	34	0	0	0	0	0	3	0	91.90
沙柳	0	0	0	0	18	0	0	0	0	0	0	100
柠条	0	0	0	0	1	15	8	4	3	0	0	48.39
榆树	0	0	0	0	2	5	16	1	0	0	0	66.67
樟子松	0	0	0	0	0	0	0	15	0	0	0	100
黑沙蒿	0	0	3	0	0	0	0	3	41	0	0	87.23
其它	9	2	0	0	3	10	0	2	2	119	2	79.87
水体	0	0	0	0	0	0	0	0	0	1	24	96.00
生产精度	70.97	90.32	92.10	100	75.00	46.88	66.67	60.00	87.23	95.20	92.30
总体精度：83.98% Kappa系数：0.81

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