Object-Oriented Classification of Wetland Vegetation Community in Jilin-1 Remote Sensing Image

doi:10.13466/j.cnki.lyzygl.2023.01.017

Abstract

Abstract:

The use of remote sensing technology to extract the composition and distribution of wetland vegetation communities is of great significance to the construction of wetlands.Taking Qinghai Ulandulan Lake National Wetland Park as the research area,using the number of Jilin No.1 remote sensing images,KNN and RF classification models were selected through image segmentation and feature optimization,the vegetation community of Dulan Lake wetland was divided,and the classification accuracy was verified.The results showed that according to the segmentation scale provided by ESP 2 tool,the optimal segmentation scale for object-oriented classification of vegetation communities was 18,and the segmentation scales of vegetation and non-vegetation areas were 32 and 85,respectively.For character type division,only using the threshold of image band information and related index could not accurately extract the feature category,it was necessary to combine the image geometric features and texture features to improve the classification accuracy,use the feature space optimization tool to optimize 61 image features,and finally screen out 40 image features,and use them for classification.According to the confusion matrix classification accuracy evaluation results,the classification results of KNN algorithm were better than RF,among which the overall classification accuracy of KNN was 81.80%,the Kappa coefficient was 0.79,the overall classification accuracy of RF was 72.59%,and the Kappa coefficient was 0.68.According to the classification results,the vegetation coverage rate of Dulan Lake wetland was 44.41%,and the composition and distribution characteristics of vegetation communities in the results could provide a basis for wetland ecological construction and management.

Key words: Lake Dulan, vegetation community classification, object-oriented, image features, classification model

CLC Number:

S771.8

XIE Wenchun, LI Qiangfeng, LI Yanchun, WU Zhenshan, YANG Zhengfan. Object-Oriented Classification of Wetland Vegetation Community in Jilin-1 Remote Sensing Image[J]. FOREST RESOURCES WANAGEMENT, 2023, (1): 141-152.

Figures/Tables 11

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植被类型	优势种	主要伴生种	采样点
沼泽草甸	芦苇(Phragmites australis)	盐角草(Salicornia europaea)	40
高寒灌丛	小果白刺(Nitraria sibirica)	大白刺(Nitraria roborowskii)	40
高寒灌丛	细枝盐爪爪(Kalidium gracile)+ 芨芨草(Achnatherum splendens)	猪毛菜Kali collinum)、驼绒藜(Krascheninnikovia ceratoides)、盐地碱蓬(Suaeda salsa)、红砂(Reaumuria songarica)	36
沼泽草甸	盐角草(Salicornia europaea)	盐爪爪(Kalidium foliatum)	30
高寒灌丛	枸杞(Lycium chinense)	小果白刺(Nitraria sibirica)	28
高寒草甸	早熟禾(Poa annua)+ 盐地风毛菊(Saussurea salsa)	海乳草(Lysimachia maritima)、海韭菜(Triglochin maritimum)、马蔺(Iris lactea)	28

特征类别	特征描述		数量
光谱特征	归一化植被指数(NDVI)	NDVI=(NIR-R)/(NIR+R)	16
	土壤修正植被指数(SAVI)	SAVI=(NIR-R)×(1+0.5)/(NIR+R+0.5)
	比值植被指数(RVI)	RVI=NIR/R
	归一化水体指数(NDWI)	NDWI=(G-NIR)/(G+NIR)
	土壤调节植被指数(OSAVI)	OSAVI=(NIR-R)/(NIR+R+0.16)
	增强型植被指数(EVI)	EVI=2.5×(NIR-R)/(NIR+6×R-7.5×B+1)
	亮度、波段均值、波段标准差、最大差异度量
纹理特征	均值、同质性、对比度、墒、相关性、差异性、标准差、角二阶矩		40
几何特征	面积、紧致度、长宽比、矩形匹配度、形状指数		5

序号	特征名称	序号	特征名称	序号	特征名称	序号	特征名称
1	Area	11	Mean G	21	GLCM Correlation(90°)	31	GLCM Homogeneity(45°)
2	GLCM Mean(0°)	12	Standard deviation R	22	GLCM Homogeneity(135°)	32	EVI
3	Brightness	13	Standard deviation NIR	23	Rectandular fit	33	OSAVI
4	GLCM Mean(45°)	14	Standard deviation G	24	Max.diff.	34	NDWI
5	Compactness	15	Standard deviation B	25	GLCM Entropy(90°)	35	RVI
6	GLCM Entropy(all dir.)	16	GLCM Homogeneity(90°)	26	GLCM Mean(90°)	36	SAVI
7	GLCM Mean(all dir.)	17	GLCM Entropy(45°)	27	GLCM Mean(135°)	37	NDVI
8	Shape index	18	GLCM Homogeneity(0°)	28	GLCM StdDev(135°)	38	GLCM Correlation(45°)
9	Mean NIR	19	GLCM Entropy(135°)	29	GLCM Correlation(135°)	39	GLCM Correlation(all dir.)
10	Mean B	20	Length/Width	30	GLCM Correlation(0°)	40	GLCM Entropy(0°)

类别	类别
类别	PA	BG	BW	NS	KG+AS	LC	IS	PA+SS	SE
PA	0	16.84	18.04	8.20	12.46	14.11	16.53	11.71	7.32
BG	16.84	0	13.18	7.59	7.26	7.56	3.31	4.05	5.33
BW	18.04	13.18	0	17.75	17.34	22.16	13.55	14.57	16.63
NS	8.20	7.59	17.75	0	2.34	2.13	6.55	2.44	2.29
KG+AS	12.46	7.26	17.34	2.34	0	3.03	4.40	3.05	4.06
LC	14.11	7.56	22.16	2.13	3.04	0	5.94	3.07	2.52
IS	16.53	3.31	13.55	6.55	4.40	5.94	0	4.30	5.83
PA+SS	11.71	4.05	14.57	2.44	3.05	3.07	4.30	0	1.74
SE	7.32	5.33	16.63	2.29	4.06	2.52	5.83	1.74	0

植被群落及精度				BG		BW		IS		KG+AS		LC		NS		PA		PA+SS		SE		UA/%
KNN		BG		32		2		1		0		0		3		0		4		0		76.19
		BW		3		51		0		0		0		0		1		0		0		92.73
		IS		3		0		7		0		0		0		0		0		0		70.00
		KG+AS		1		0		0		59		4		9		2		0		1		77.63
		LC		0		0		0		0		31		2		0		0		0		93.94
		NS		0		0		0		2		2		56		1		2		0		88.89
		PA		0		0		0		1		0		4		43		0		3		84.31
		PA+SS		0		0		0		5		0		3		2		64		9		77.11
		SE		0		0		0		2		0		1		1		9		30		69.77
		PA/%		82.05		96.23		87.50		85.51		83.78		71.79		86.00		81.01		69.77
				OA%=81.80%,Kappa系数=0.79
RF	BG		21		2		1		5		0		3		0		4		0		58.33
	BW		0		51		0		0		0		0		0		0		0		100.00
	IS		5		0		5		0		0		0		0		0		0		50.00
	KG+AS		8		0		0		44		0		13		2		6		5		56.41
	LC		0		0		0		1		34		1		0		0		0		94.44
	NS		3		0		1		9		1		48		5		8		0		64.00
	PA		0		0		0		3		0		4		42		0		0		85.71
	PA+SS		2		0		1		3		2		8		0		59		11		68.60
	SE		0		0		0		4		0		1		1		2		27		77.14
	PA/%		53.85		96.23		62.50		63.77		91.89		61.54		84.00		74.68		62.79
			OA%=72.59%,Kappa系数=0.68