Classification and changes of vegetation in Sugan Lake wetland in the extreme arid region

doi:10.7522/j.issn.1000-694X.2024.00054

Journal of Desert Research ›› 2024, Vol. 44 ›› Issue (4): 81-90.DOI: 10.7522/j.issn.1000-694X.2024.00054

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Classification and changes of vegetation in Sugan Lake wetland in the extreme arid region

Teng Zhang¹^,²(), Yunfa Miao¹^,²(), Yaguo Zou¹^,², Ziyue Zhang¹^,², Guoping Feng³

^1.Key Laboratory of Ecological Safety and Sustainable Development in Arid Lands，Northwest Institute of Eco-Environment and Resources，Chinese Academy of Sciences，Lanzhou 730000，China
^2.University of Chinese Academy of Sciences，Beijing 100049，China
^3.Management Station of Dasugan Lake Migratory Bird Provincial Nature Reserve in Akse Kazakh Autonomous County，Akse Kazakh Autonomous County 736400，Gansu，China

Received:2024-03-02 Revised:2024-04-12 Online:2024-07-20 Published:2024-08-29
Contact: Yunfa Miao

Abstract

Abstract:

Wetland is the most biodiverse ecosystem on Earth， and wetland vegetation plays a crucial role in maintaining the stability and functionality of these ecosystems， so mastering wetland vegetation types and distribution characteristics is extremely important for biodiversity conservation. Due to factors such as lacking or unsystematic vegetation community information and remote sensing resolution， the research on vegetation distribution in arid wetlands is limited. Taking the Sugan Lake wetland in the northern part of the Qaidam Basin of the extremely arid region in northwest China as the study area， based on the field vegetation survey data of 116 points and 626 unmanned aerial vehicle image sample points data， Sentinel-1 Synthetic Aperture Radar （Synthetic Aperture Radar， SAR） data and Sentinel-2 Multispectral Imager imagery （MultiSpectral Instrument， MSI） data were used to construct a new remote sensing feature database. The vegetation in Sugan Lake wetland was classified and mapped using the Random Forest algorithm. The results show that：（1） The combination of SAR and MSI data can improve the accuracy of wetland vegetation classification， with overall accuracy of wetland vegetation classification exceeding 0.81 for the years 2019-2023， and Kappa coefficients of 0.82， 0.84， 0.86， 0.82， and 0.82 respectively. （2） From 2019 to 2023， the area of Sugan Lake wetland remained stable， with a vegetation distribution area of 783.90 km2. The distribution area ofreed （Phragmites australis） communities increased by 28.49 km2， and the area of leymus （Leymus secalinus） communities increased by 27.21 km2. In contrast， the coverage of triglochin palustre （Triglochin palustre） and eleocharis palustris （Eleocharis palustris） communities decreased by 64.49 km2. It is preliminarily considered that increased runoff and grazing prohibition policies are important reasons for the changes in wetland vegetation distribution. This study provides an effective method for surveying vegetation in arid area wetlands. High-quality dynamic monitoring of wetland vegetation offers theoretical references for the construction of ecological civilization and restoration measures.

Key words: remote sensing monitoring, arid region, Sugan Lake wetland, random forest algorithm, vegetation classification

CLC Number:

Q948

Teng Zhang, Yunfa Miao, Yaguo Zou, Ziyue Zhang, Guoping Feng. Classification and changes of vegetation in Sugan Lake wetland in the extreme arid region[J]. Journal of Desert Research, 2024, 44(4): 81-90.

Figures/Tables 10

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指标	植被群落类型
指标	芦苇	赖草	水麦冬	沼泽荸荠	乳苣	盐角草	心叶独行菜	菖蒲
样本标签数量	186	118	49	100	57	24	40	52

指标	植被群落类型
指标	芦苇	赖草	水麦冬	沼泽荸荠	乳苣	盐角草	心叶独行菜	菖蒲
样本标签数量	186	118	49	100	57	24	40	52

特征指数	特征说明	数量	数据来源
原始波段	B2、B3、B4、B8、B11、B12	6	MSI
植被水体指数	NDVI、NDWI、RVI…	24	MSI
缨帽指数	Brightness、Greenness、Wetness	3	MSI
红边指数	Ndvire1、Ndvire2、Ndvire3…	5	MSI
纹理特征指数	Asm、Contrast、Corr…	9	MSI
SAR时序特征指数	VH max、VH min、VH mean…	8	SAR

特征指数	特征说明	数量	数据来源
原始波段	B2、B3、B4、B8、B11、B12	6	MSI
植被水体指数	NDVI、NDWI、RVI…	24	MSI
缨帽指数	Brightness、Greenness、Wetness	3	MSI
红边指数	Ndvire1、Ndvire2、Ndvire3…	5	MSI
纹理特征指数	Asm、Contrast、Corr…	9	MSI
SAR时序特征指数	VH max、VH min、VH mean…	8	SAR

指标	植被群落类型
指标	芦苇	赖草	水麦冬	沼泽荸荠	乳苣	盐角草	心叶独行菜	菖蒲
DTW_VH	19.46	19.44	13.78	16.16	15.79	19.40	16.86	20.25
DTW_VV	12.88	12.45	8.15	7.32	6.67	5.33	4.96	10.97
ED	0.44	0.37	0.52	0.63	0.31	0.33	0.23	0.35
SAD	0.96	0.98	0.97	0.96	0.98	0.99	0.99	0.98

Classification and changes of vegetation in Sugan Lake wetland in the extreme arid region

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编号	特征指数组合	总体分类精度	Kappa系数
1	原始波段	0.79	0.75
2	原始波段+植被水体指数	0.82	0.79
3	原始波段+植被水体指数+纹理特征指数	0.83	0.80
4	原始波段+植被水体指数+红边指数+缨帽指数	0.83	0.81
5	原始波段+植被水体指数+红边指数+纹理特征指数+缨帽指数	0.84	0.82
6	原始波段+植被水体指数+红边指数+纹理特征指数+缨帽指数+SAR时序特征指数	0.85	0.82

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