基于多特征融合的面向对象冰川边界提取

doi:10.13866/j.azr.2025.06.07

Abstract

Abstract:

Pixel-based classification struggles with the accurate identification of glacier changes in areas with similar spectral characteristics, particularly in debris-covered areas where spectral features closely resemble the surrounding mountains and rocks, thereby resulting in low extraction accuracy. This study investigates the Yinsugaiti and Yalong Glaciers using Google Earth Engine to integrate spectral indices, microwave texture features, and topographic data. An object-based (OB) machine learning algorithm is applied for automated glacier extraction and compared to pixel-based (PB) classification methods. The results show the following. (1) The OB classification approach, integrating multi-feature fusion, significantly improved the glacier extraction accuracy. The OB_RF classifier achieved an overall accuracy of 98.1%, a Kappa coefficient of 0.97, and an F1-score of 98.67%, outperforming the OB_CART and OB_GTB classifiers. When compared to PB_RF, the overall accuracy, Kappa coefficient, and F1-score increased by 1.7%, 0.024, and 5.57%, respectively. (2) Between 2001-2022, the Yinsugaiti and Yalong Glaciers retreated at average annual rates of 0.08% and 0.13%, respectively. (3) Supraglacial debris was primarily distributed below 5000 and 4800 m on the Yinsugaiti and Yalong Glacier, respectively. Over the same period, debris-covered areas on both glaciers expanded upward.

Key words: glacier boundary extraction, object-based, pixel-based, machine learning, multi-feature fusion

LIN Zhouyan, WANG Xiaying, XIA Yuanping. Object-based glacier boundary extraction utilizing multi-feature fusion[J].Arid Zone Research, 2025, 42(6): 1032-1042.

Figures/Tables 12

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影像源	获取时间	分辨率/m	GEE来源
Landsat 5	2010-08-23	30	LANDSAT/LT05/C02/T1_L2/LT05_148035_20100823
Landsat 7	2001-07-03	30	LANDSAT/LE07/C02/T1_L2/LE07_134040_20010703
Landsat 7	2001-07-21	30	LANDSAT/LE07/C02/T1_L2/LE07_148035_20010721
Landsat 7	2009-09-27	30	LANDSAT/LE07/C02/T1_L2/LE07_134040_20090927
Landsat 8	2022-07-23	30	LANDSAT/LC08/C02/T1_L2/LC08_148035_20220723
Landsat 8	2022-07-05	30	LANDSAT/LC08/C02/T1_L2/LC08_134040_20200705
PALSAR/PALSAR-2	2009-2010年	25	JAXA/ALOS/PALSAR/YEARLY/SAR
Sentinel-1	2022年	25	COPERNICUS/S1_GRD
NASADEM	2000年	30	NASA/NASADEM_HGT/001

超像素种子	OB_RF		OB_GTB		OB_CART
超像素种子	OA/%	Kappa系数	OA/%	Kappa系数	OA/%	Kappa系数
6	95.95	0.94	94.80	0.92	88.44	0.82
13	98.84	0.98	96.53	0.94	93.06	0.89
21	93.64	0.90	91.33	0.86	91.91	0.87
30	95.35	0.93	93.60	0.90	87.21	0.80
40	87.35	0.80	89.16	0.83	87.95	0.81

方法	准确率/%	召回率/%	F1分数/%
OB_RF	98.37	98.97	98.67
OB_GTB	96.27	97.60	96.87
OB_CART	93.53	93.93	93.73
PB_RF	91.47	94.80	93.10
PB_GTB	90.87	94.83	92.77
PB_CART	85.93	89.43	87.53

	音苏盖提冰川面积/km²			雅弄冰川面积/km²			总面积/km²
	裸冰区	表碛区	冰川区	裸冰区	表碛区	冰川区	裸冰区	表碛区	冰川区
2001年	318.88	37.38	356.26	170.25	9.76	180.01	489.14	47.14	536.27
2010年	313.79	39.47	353.25	166.35	12.20	178.55	480.14	51.67	531.81
2022年	308.70	41.84	350.54	161.43	13.51	174.94	470.12	55.36	525.48
相对变化/km²	-10.19	4.47	-5.72	-8.82	3.75	-5.07	-19.01	8.22	-10.79
绝对变化/%	-3.19	11.95	-1.61	-5.18	38.44	-2.82	-3.89	17.44	-2.01
年变化量/km²	-0.49	0.21	-0.27	-0.42	0.18	-0.24	-0.91	0.39	-0.51
年变化率/%	-0.15	0.57	-0.08	-0.25	1.83	-0.13	-0.19	0.83	-0.10

Object-based glacier boundary extraction utilizing multi-feature fusion

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