Analysis of dynamic remote sensing monitoring changes in Kulusitai grassland in Xinjiang from 2010 to 2021

doi:10.6048/j.issn.1001-4330.2024.01.025

Xinjiang Agricultural Sciences ›› 2024, Vol. 61 ›› Issue (1): 230-240.DOI: 10.6048/j.issn.1001-4330.2024.01.025

• Agricultural Equipment Engineering and Mechanization·Animal Husbandry Veterinarian • Previous Articles Next Articles

Analysis of dynamic remote sensing monitoring changes in Kulusitai grassland in Xinjiang from 2010 to 2021

LIU Liang¹(), PENG Jian², LI Gangyong², HAN Wanqiang¹, LIU Yujia¹, GUAN Jingyun¹, LIU Chengcai³, ZHENG Jianghua¹^,⁴()

1. College of Geography and Remote Sensing Sciences,Xinjiang University,Urumqi 830046,China
2. Xinjiang Uygur Autonomous Region Grassland Station,Urumqi 830000,China
3. Tacheng Forestry and Grassland Bureau,Tacheng Xinjiang 834700,China
4. Xinjiang Key Laboratory of Oasis Ecology,Urumqi 830046,China

Received:2023-05-05 Online:2024-01-20 Published:2024-02-21
Correspondence author: ZHENG Jianghua(1973-),man,born in Jiangshan,Zhejiang Province,professor,research interest is geographic information system and remote sensing technology application,(E-mail) zheng_jianghua@xju.edu.cn
Supported by:
Remote Sensing Monitoring of Kulusitai Grassland Ecological Protection and Restoration Project(202005140017);Xinjiang Tianshan Cedar Project(2020XS04);Xinjiang Land Spatial Ecological Restoration Planning Special Project(Section 2) Special Study on Strategies and Spatial Layout of Land Ecosystem Restoration in Xinjiang(202105140022);Xinjiang Important Ecosystem Protection and Restoration Program Project(202005140014)

2010~2021年新疆库鲁斯台草原动态遥感监测变化分析

刘亮¹(), 彭建², 李刚勇², 韩万强¹, 刘玉佳¹, 关靖云¹, 刘程才³, 郑江华¹^,⁴()

1.新疆大学地理与遥感科学学院,乌鲁木齐 830046
2.新疆维吾尔自治区草原总站,乌鲁木齐 830000
3.塔城地区林业和草原局,新疆塔城 834700
4.新疆绿洲生态重点实验室,乌鲁木齐 830046

通讯作者: 郑江华(1973-),男,浙江江山人,教授,博士,硕士生/博士生导师,研究方向为地理信息系统与遥感技术应用,(E-mail)zheng_jianghua@xju.edu.cn
作者简介:刘亮(1996-),男,新疆昌吉人,硕士研究生,研究方向为植被与环境遥感,(E-mail)liuliang19960423@163.com
基金资助:
库鲁斯台草原生态保护恢复工程遥感监测(202005140017);新疆天山雪松计划项目(2020XS04);新疆国土空间生态修复规划专题研究(二标段)新疆陆地生态系统修复策略和空间布局专题研究(202105140022);新疆重要生态系统保护和修复重大工程规划(202005140014)

Abstract

Abstract:

【Objective】 Grassland is an important part of terrestrial ecosystem in the world. Dynamic monitoring of grassland will help to understand grassland status and provide important theoretical support for sustainable development and management of ecosystem. 【Methods】 Using the 2010-2021 Landsat images of the Kulusitai grassland in Xinjiang,seven vegetation indices were calculated,and the grassland degradation index(GDI) was constructed based on field measurements of grassland height,cover and biomass. An inversion model of degradation in Kulusitai grassland was established based on optimal vegetation index and GDI. The grassland degradation status in the study area was graded and graded,and finally the change in Kulusitai grassland was comprehensively analyzed from three aspects of grassland degradation grade,change direction and change intensity. 【Results】 (1) The weight of aboveground biomass,coverage and height in grassland degradation monitoring was determined by factor analysis in descending order:coverage(37.6%) > aboveground biomass(34.3%) > height(28.1%).(2) NDVI had the highest correlation with GDI among the seven vegetation indices,with R² of 0.854,and the model of grassland degradation monitoring based on NDVI was GDI=0.860×NDVI+0.038.(3) From 2010 to 2021,the severely degraded area of Kulusitai grassland decreased,and the change direction was mainly in the degraded recovery type and the unchanged type,while the degraded enhanced type showed a decreasing trend,and the change intensity was mainly in the unchanged intensity and the slow intensity. 【Conclusion】 As a whole,the degradation process of the Xinjiang Kulusitai grassland has been weakening in the past 12 years.

Key words: grassland degradation monitoring; vegetation index; monitoring model; Kulusitai grassland

摘要：

【目的】监测新疆库鲁斯台草原近12年动态变化状况,为生态系统可持续发展和管理提供理论支撑。【方法】利用新疆库鲁斯台草原2010~2021年Landsat影像,计算7种植被指数,基于野外实测草地高度、盖度及生物量数据构建草地退化指数(Grassland degradation index,GDI),建立基于最优植被指数和GDI的库鲁斯台草原草地退化反演模型;并对库鲁斯台草原草地退化状况进行分等定级,从草地退化等级、变化方向及变化强度综合分析库鲁斯台草原动态变化。【结果】(1)地上生物量、覆盖度及高度3个指标在草地退化监测中的权重由大到小为覆盖度(37.6%)>地上生物量(34.3%)>高度(28.1%)。(2)7种植被指数中NDVI与GDI相关性最高,R²为0.854,基于NDVI的草地退化监测模型为GDI=0.860×NDVI+0.038。(3)2010~2021年库鲁斯台草原重度退化面积有所减少,变化方向主要以退化恢复型和未变化型为主,退化增强型面积为减少趋势,变化强度主要为无变化强度及慢速变化强度。【结论】近12年新疆库鲁斯台草原退化过程减弱。

关键词: 草地退化监测, 植被指数, 监测模型, 库鲁斯台草原

CLC Number:

S812

LIU Liang, PENG Jian, LI Gangyong, HAN Wanqiang, LIU Yujia, GUAN Jingyun, LIU Chengcai, ZHENG Jianghua. Analysis of dynamic remote sensing monitoring changes in Kulusitai grassland in Xinjiang from 2010 to 2021[J]. Xinjiang Agricultural Sciences, 2024, 61(1): 230-240.

刘亮, 彭建, 李刚勇, 韩万强, 刘玉佳, 关靖云, 刘程才, 郑江华. 2010~2021年新疆库鲁斯台草原动态遥感监测变化分析[J]. 新疆农业科学, 2024, 61(1): 230-240.

Figures/Tables 12

References 35

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指标 Index	综合得分模型系数 Comprehensive score model coefficient	指标权重 Index weight
高度Height	0.335	0.281
生物量Biomass	0.408	0.343
覆盖度Coverage	0.447	0.376

指标 Index	综合得分模型系数 Comprehensive score model coefficient	指标权重 Index weight
高度Height	0.335	0.281
生物量Biomass	0.408	0.343
覆盖度Coverage	0.447	0.376

方程 Equation	模型摘要 Model summary					参数估算值 Parameter estimation
方程 Equation	R²	F	df1	df2	显著性 Significance	常量 Constant	b₁	b₂	b₃
线性Linear	0.854 1	146.315	1	25	0.000	0.038	0.860
对数Logarithm	0.633 0	43.126	1	25	0.000	0.433	0.113
逆Contrary	0.297 0	10.553	1	25	0.003	0.285	-0.004
二次Secondary	0.854 0	70.319	2	24	0.000	0.043	0.821	0.073
三次Three times	0.854 0	45.000	3	23	0.000	0.039	0.911	-0.363	0.565
复合Composite	0.665 0	49.573	1	25	0.000	0.074	76.540
幂Cloth cover	0.853 0	157.420	1	25	0.000	0.727	0.755
S	0.755 0	76.991	1	25	0.000	-1.230	-0.033
增长Increase	0.665 0	49.573	1	25	0.000	-2.599	4.338
指数Index	0.665 0	49.573	1	25	0.000	0.074	4.338
Logistic	0.665 0	49.573	1	25	0.000	13.446	0.013

方程 Equation	模型摘要 Model summary					参数估算值 Parameter estimation
方程 Equation	R²	F	df1	df2	显著性 Significance	常量 Constant	b₁	b₂	b₃
线性Linear	0.854 1	146.315	1	25	0.000	0.038	0.860
对数Logarithm	0.633 0	43.126	1	25	0.000	0.433	0.113
逆Contrary	0.297 0	10.553	1	25	0.003	0.285	-0.004
二次Secondary	0.854 0	70.319	2	24	0.000	0.043	0.821	0.073
三次Three times	0.854 0	45.000	3	23	0.000	0.039	0.911	-0.363	0.565
复合Composite	0.665 0	49.573	1	25	0.000	0.074	76.540
幂Cloth cover	0.853 0	157.420	1	25	0.000	0.727	0.755
S	0.755 0	76.991	1	25	0.000	-1.230	-0.033
增长Increase	0.665 0	49.573	1	25	0.000	-2.599	4.338
指数Index	0.665 0	49.573	1	25	0.000	0.074	4.338
Logistic	0.665 0	49.573	1	25	0.000	13.446	0.013

等级 Grade	程度 Degree	GDI Grassland degradation index	NDVI Normalized difference vegetation index
1	无明显退化	GDI>0.404	NDVI>0.425
2	轻度退化	0.404>GDI>0.254	0.425>NDVI>0.251
3	中度退化	0.254>GDI>0.201	0.251>NDVI>0.189
4	重度退化	GDI<0.201	NDVI<0.189

Analysis of dynamic remote sensing monitoring changes in Kulusitai grassland in Xinjiang from 2010 to 2021

2010~2021年新疆库鲁斯台草原动态遥感监测变化分析

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Figures/Tables 12

References 35

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Metrics

年份 Year	无明显退化面积 No obvious degradation area (km²)	轻度退化面积 Slightly degraded area (km²)	中度退化面积 Moderately degraded area (km²)	重度退化面积 Severely degraded area (km²)
2010	209.756 9	631.38	968.87	35.89
2011	60.76	701.82	940.79	20.49
2012	1.4	657.85	1 032.49	5
2013	231.35	674.01	825.53	89.19
2014	75.01	700.86	818.21	57.98
2015	173.61	806.78	590.39	104.02
2016	410.97	496.91	473.62	123.94
2017	391.39	588.07	645.1	167.73
2018	301.73	610.33	690.78	134.75
2019	357.26	654.14	738.87	125.16
2020	271.68	767.75	689.66	63.86
2021	481.51	599.58	590.49	22.46

变化趋势 Change trend	退化等级 Degradation level
退化增强型 Degenerate enhanced type	1→2、1→3、1→4、2→3、2→4、3→4
退化恢复型 Degenerative recovery type	4→3、4→2、4→1、3→2、3→1、2→1
未变化型 Unchanged type	1→1、2→2、3→3、4→4

年份 Year	退化恢复型 Degenerate restorative (km²)	退化增强型 Degenerate enhanced (km²)	未变化型 Unchanged type (km²)
2010~2011	181.63	316.23	1 078.28
2011~2012	133.38	242.80	1 169.80
2012~2013	511.33	117.35	905.21
2013~2014	193.74	206.63	1 073.96
2014~2015	761.14	90.33	554.21
2015~2016	377.53	151.34	765.39
2016~2017	218.55	285.62	767.75
2017~2018	301.75	290.19	965.55
2018~2019	220.03	265.48	1 084.23
2019~2020	241.70	230.29	1 157.90
2020~2021	387.08	146.96	1 042.54
2010~2021	668.45	199.67	642.97

年份 Year	无变化 Unchanged (km²)	慢速 Slow (km²)	中速 Medium (km²)	快速 Fast (km²)
2010~2011	1 078.28	458.13	39.61	0.12
2011~2012	1 169.80	371.65	4.53	0.10
2012~2013	905.21	600.31	28.33	0.04
2013~2014	1 073.96	393.13	7.06	0.16
2014~2015	554.21	847.69	3.75	0.03
2015~2016	765.39	512.90	15.76	0.21
2016~2017	767.75	465.66	38.22	0.29
2017~2018	965.55	546.14	45.22	0.58
2018~2019	1 084.23	470.36	15.01	0.13
2019~2020	1 157.90	461.66	10.28	0.05
2020~2021	1 042.54	520.42	13.55	0.06
2010~2021	642.97	727.10	139.92	1.09

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[2]	HOU Zhengqing, YAN An, XIE Kaiyun, YUAN Yilin, XIA Wenqiu, XIAO Shuting, ZHANG Zhenfei, SUN Zhe. Estimation of aboveground biomass of Diarthron tianschanicum based on vegetation index fusion [J]. Xinjiang Agricultural Sciences, 2024, 61(11): 2787-2796.
[3]	CHEN Rong, LAI Ning, GENG Qinglong, LI Yongfu, XIN Huinan, LYU Caixia, LI Na, CHEN Shuhuang. Study on variable nitrogen fertilization model of winter wheat during booting period based on remote sensing data [J]. Xinjiang Agricultural Sciences, 2024, 61(11): 2705-2712.
[4]	SHI Zhiyu, PEI Yakun, ZHU Yutao, JIA Yujiao, HU Xiaoqian, HOU Shicong, HOU Yuxia. Study on Remote Monitoring of Cotton Verticillium wilt Diagnosis and Control Management Based on Digital Image [J]. Xinjiang Agricultural Sciences, 2020, 57(6): 1166-1174.
[5]	HAN Wanqiang, JIN Guili, YUE Yonghuan, WANG Huining, GONG Ke, WU Xueer, Wulupa Ardererkali. Spectral Characteristics and Modified Vegetation Index of ihree Main Plants of Seriphidium transiliense Desert Grassland [J]. Xinjiang Agricultural Sciences, 2020, 57(5): 950-957.
[6]	WU Wenli, YUAN Ye, LI Yuanyuan, CHU Guangming, ZHANG Hao. Comparison of Hyperspectral and Optical Image Vegetation Index (VI) in Desert Forest [J]. Xinjiang Agricultural Sciences, 2020, 57(1): 149-156.
[7]	LI Na, WANG Xin-jun, CHANG Meng-di, YAN Li-nan, XU Xiao-long. Response of Sparse Vegetation Patch Pattern to Precipitation Based on Landsat Image [J]. Xinjiang Agricultural Sciences, 2019, 56(10): 1895-1903.
[8]	LI Tian-sheng, CUI Jing, WANG Hai-jiang, YANG Jin. Study on Water Content Estimation Model of Winter Wheat Based on Hyperspectral Characteristic Wavelength [J]. Xinjiang Agricultural Sciences, 2019, 56(10): 1772-1782.
[9]	TIAN Chun-yan;HUANG Chun-yan;GUO Xiao-fei;LIU Xin-yue;WANG Deng-wei. Estimation of Cotton Canopy Fractional Interception of Absorbed Photosynthetic Active Radiation and Leaf Net Photosynthetic Rate Based on Hyperspectral Vegetation Index [J]. , 2017, 54(6): 981-987.
[10]	GUO Xiao-fei;HUANG Chun-yan;TIAN Chun-yan;LIU Xin-yue;WANG Deng-wei. Correlation Analysis between Hyperspectral Vegetation Indices and Photosynthetic Parameters of Cotton under Different Water Treatments [J]. , 2017, 54(1): 20-26.