基于NDVI的2009—2018年黄河流域林草植被覆盖变化

doi:10.11707/j.1001-7488.20220302

摘要/Abstract

摘要：

目的: 研究黄河流域2009—2018年间林草植被覆盖的时空分布及变化特征，预测林草植被覆盖的未来发展趋势，为流域林草植被科学保护和精准修复、促进生态系统正向演替提供参考依据。方法: 以2009-2018年SPOT/VEGETATION的NDVI时间序列数据集为数据源，采用年均值法、Theil-Sen median趋势分析和Mann-Kendall检验方法，研究黄河流域林草植被覆盖的时空分布及变化特征，利用Hurst指数方法分析林草植被覆盖的可持续性特征和未来发展趋势。结果: 黄河流域林草地面积中，高植被覆盖度(NDVI≥0.7)面积占42.34%，呈聚集状分布于流域上游的祁连山、三江源东部、若尔盖草原，中游的秦岭北麓、黄土高原胡焕庸线以南地区，以及下游的济南市区周边区域；中植被覆盖度(0.3 < NDVI < 0.7)面积占44.92%，主要分布于流域上游的三江源西部和中游的宁夏、内蒙、陕西3省(区)交界处；低植被覆盖度(NDVI≤0.3)面积占12.74%，主要分布于流域上游的毛乌素沙地、陇中黄土高原、阴山南麓。2009—2018年间黄河流域林草地年均NDVI总体呈增加趋势，增速为0.007 4 a^-1；10年间流域林草植被覆盖变化趋势以改善为主，改善区域面积占81.62%，其中显著改善面积占33.36%，集中分布在林草高植被覆盖度区域；林草植被覆盖退化区域面积占13.86%，集中分布在林草低植被覆盖度区域。未来林草植被覆盖良性发展区域面积占林草地总面积的61.89%，恶性发展区域面积占33.60%，发展方向稳定不变和不确定区域面积占4.51%。结论: 黄河流域林草植被覆盖度整体呈现东南部和西北高山地区高、北部低的特征。2009—2018年间林草植被覆盖改善区域面积远大于退化区域面积，改善区域主要集中在上游的祁连山林区、若尔盖草原，中游的六盘山林区、子午岭林区、黄龙山林区、吕梁林区及秦岭北麓，退化区域主要分布在林草低植被覆盖度的毛乌素沙地、陇中黄土高原等，退化区域应作为重点治理修复区。黄河流域林草植被覆盖未来发展趋势整体较好，预测良性发展区域远大于恶性发展区域，恶性发展区域中三江源地区和毛乌素沙地的林草植被覆盖虽有轻微改善，但持续性较差，未来林草植被覆盖可能呈退化趋势，在流域生态保护治理中应重点关注。

关键词: 黄河流域, NDVI, 植被覆盖, 时空变化, 林地和草地

Abstract:

Objective: This research was aimed to investigate the spatiotemporal distribution and coverage changes of forest and grass vegetation in the Yellow River Basin during 2009 and 2018, and to forecast future development trend of the forest and grass coverage in order to provide a basis for scientific protection and precise restoration of forest and grass vegetation, and for the promotion of positive ecosystem succession in the Yellow River Basin. Method: Based on the SPOT/VEGETATION NDVI time series data from 2009 to 2018, the spatial distribution and temporal changes of the vegetation coverage of forest and grass in the Yellow River Basin were studied by using annual mean method, Theil-Sen median trend analysis and Mann-Kendall test. The sustainability characteristics and future development trend of vegetation coverage were analyzed using Hurst index method. Result: In the total area of forestland and grassland in the Yellow River Basin, the area with a high vegetation coverage (NDVI≥0.7) accounted for 42.34%, distributed in aggregation in the Qilian Mountains, the east of Three-Rivers' Origin and Zoige grassland in the upper reaches of the basin, the northern foot of Qinling Mountains in the middle reaches, the south of Huhuanyong line in the Loess Plateau, and the surrounding area of Jinan City in the lower reaches; The area with a medium vegetation coverage area (0.3 < NDVI < 0.7) accounted for 44.92%, mainly distributed in the west of Three-Rrivers' Origin in the upper reaches of the basin and the junction of Ningxia, Inner Mongolia, and Shaanxi in the middle reaches. The area with a low vegetation coverage (NDVI < 0.3) accounted for 12.74%, mainly distributed in the Mu Us sandy land, the Loess Plateau in central Gansu Province, and the southern foot of Yinshan Mountains in the upper reaches of the basin. From 2009 to 2018, the annual mean NDVI of vegetation in forestland and grassland of the Yellow River Basin showed an overall increasing trend, with a growth rate of 0.007 4 a^-1. In the past 10 years, the area with improved vegetation coverage accounted for 81.62% of the total area of forest and grass, of which the area of significant improvement accounted for 33.36%. The area with vegetation degradation accounted for 13.86%, which was basically the same as the area with a low vegetation coverage. In the future, 61.89% of the vegetation coverage will develop in a positive direction, 33.60% in a negative direction, and 4.51% in a stable and uncertain direction. Conclusion: The vegetation coverage in forestland and grassland in the Yellow River Basin is high in the southeast and northwest mountain areas, and low in the north. From 2009 to 2018, the improved area is much larger than the degraded area, mainly concentrated in the Qilian mountain forest area and Zoige grassland in the upper reaches, Liupanshan forest area, Ziwuling forest area, Huanglongshan forest area, Luliang Mountain forest area and the northern foot of Qinling Mountain in the middle reaches.The degraded areas are mainly distributed in the Mu Us sandy land and the Loess Plateau in central Gansu Province with low vegetation coverage, where is the priority area for harness and restoration. The future development of vegetation coverage in the Yellow River Basin tends to be good in general. It is predicted that the positive development area is much larger than the negative development area. In the negative development area, although the vegetation coverage in Three-Rivers' Origin and Mu Us sandy land has slightly improved, the sustainability is poorer, and there is a possibility of degradation in future, therefore it should be given great attention in the ecological conservation and restoration of the Yellow River Basin.

Key words: Yellow River Basin, NDVI, vegetation coverage, spatiotemporal variation, forestland and grassland

中图分类号:

S757
TP79

郝家田,胡云云,杜一尘,侯晓巍,向安民. 基于NDVI的2009—2018年黄河流域林草植被覆盖变化[J]. 林业科学, 2022, 58(3): 10-19.

Jiatian Hao,Yunyun Hu,Yichen Du,Xiaowei Hou,Anmin Xiang. NDVI-Based Coverage Changes of Forest and Grass Vegetation in Yellow River Basin during 2009 to 2018[J]. Scientia Silvae Sinicae, 2022, 58(3): 10-19.

图/表 8

图1

图2

表1

图3

图4

表2

图5

表3

参考文献 0

	蔡博峰, 于嵘. 基于遥感的植被长时序趋势特征研究进展及评价. 遥感学报, 2009, 13 (6): 1170- 1186.
	Cai B F , Yu R . Advance and evaluation in the long time series vegetation trends research based on remote sensing. Journal of Remote Sensing, 2009, 13 (6): 1170- 1186.
	国家林业局. 中国林业统计年鉴. 北京: 中国林业出版社, 2006—2015.
	State Forestry Administration . China forestry statistical yearbook. Beijing: China Forestry Publishing House, 2006—2015.
	贺可强, 孙林娜, 王思敬. 滑坡位移分形参数Hurst指数及其在堆积层滑坡预报中的应用. 岩石力学与工程学报, 2009, 28 (6): 1107- 1115. doi: 10.3321/j.issn:1000-6915.2009.06.004
	He K Q , Sun L N , Wang S J . Displacement fractal parameter Hurst index and its application to prediction of debris landslides. Chinese Journal of Rock Mechanics and Engineering, 2009, 28 (6): 1107- 1115. doi: 10.3321/j.issn:1000-6915.2009.06.004
	贺振, 贺俊平. 基于SPOT-VGT的黄河流域植被覆盖时空演变. 生态环境学报, 2012, 21 (10): 1655- 1659. doi: 10.3969/j.issn.1674-5906.2012.10.003
	He Z , He J P . Spatio-temporal variation of vegetation cover based on SPOT-VGT in Yellow River Basin. Ecology and Environmental Sciences, 2012, 21 (10): 1655- 1659. doi: 10.3969/j.issn.1674-5906.2012.10.003
	贺振, 贺俊平. 近32年黄河流域植被覆盖时空演化遥感监测. 农业机械学报, 2017, 48 (2): 179- 185.
	He Z , He J P . Remote sensing on spatio-temporal evolution of vegetation cover in the Yellow River Basin during 1982—2013. Transactions of the Chinese Society for Agricultural Machinery, 2017, 48 (2): 179- 185.
	胡必波, 王志慧, 武婕, 等. 2000—2014年黄河流域区域尺度植被结构时空分异特征分析. 科技信息, 2018, (4): 277- 282.
	Hu B B , Wang Z H , Wu J . Spatio-temporal change analysis of vegetation structural characteristics of Yellow River Basin during 2000—2014 based on MODIS Products. Science Technology Information, 2018, (4): 277- 282.
	李杰, 张军, 刘陈立, 等. 基于MODIS-NDVI的中老缅交界区近16年植被覆盖时空变化特征. 林业科学, 2019, 55 (8): 9- 18.
	Li J , Zhang J , Liu C L , et al. Spatiotemporal variation of vegetation coverage in recent 16 years in the border region of China, Laos, and Myanmar based on MODIS-NDVI. Scientia Silvae Sinicae, 2019, 55 (8): 9- 18.
	刘绿柳, 肖风劲. 黄河流域植被NDVI与温度、降水关系的时空变化. 生态学杂志, 2006, 25 (5): 477- 481. doi: 10.3321/j.issn:1000-4890.2006.05.002
	Liu L L , Xiao F J . Spatial-temporal correlations of NDVI with precipitation and temperature in Yellow River Basin. Chinese Journal of Ecology, 2006, 25 (5): 477- 481. doi: 10.3321/j.issn:1000-4890.2006.05.002
	彭丽娜, 杨湘豫. 基于Hurst指数的开放式基金风险的计量. 统计与决策, 2006, (12): 107. doi: 10.3969/j.issn.1002-6487.2006.15.057
	Peng L N , Yang X Y . Measurement of open-end fund risk based on Hurst index. Statistics & Decision, 2006, (15): 107. doi: 10.3969/j.issn.1002-6487.2006.15.057
	王情, 刘雪华, 吕宝磊. 基于SPOT-VGT数据的流域植被覆盖动态变化及空间格局特征——以淮河流域为例. 地理科学进展, 2013, 32 (2): 270- 277.
	Wang Q , Liu X H , Lü B L . Dynamic changes and spatial patterns of vegetation cover in a river basin based on SPOT-VGT data: a case study in the Huaihe River Basin. Progress in Geography, 2013, 32 (2): 270- 277.
	王孝礼, 胡宝清, 夏军. 水文时序趋势与变异点的R/S分析法. 武汉大学学报(工学版), 2002, 35 (2): 10- 12.
	Wang X L , Hu B Q , Xia J . R/S analysis method of trend and aberrance point on hydrological time series. Engineering Journal of Wuhan University, 2002, 35 (2): 10- 12.
	习近平. 在黄河流域生态保护和高质量发展座谈会上的讲话. 中国水利, 2019, (20): 1- 5. doi: 10.3969/j.issn.1000-1123.2019.20.006
	Xi J P . Speech at the symposium on ecological protection and high-quality development in the Yellow River Basin. China Water Resources, 2019, (20): 1- 5. doi: 10.3969/j.issn.1000-1123.2019.20.006
	许翔驰. 中国植被时空变化特征. 哈尔滨师范大学自然科学学报, 2019, 35 (2): 100- 104. doi: 10.3969/j.issn.1000-5617.2019.02.021
	Xu X C . Spatial and temporal change characteristics of vegetation in China. Natural Science Journal of Harbin Normal University, 2019, 35 (2): 100- 104. doi: 10.3969/j.issn.1000-5617.2019.02.021
	严恩萍, 林辉, 党永峰, 等. 2000—2012年京津风沙源治理区植被覆盖时空演变特征. 生态学报, 2014, 34 (17): 5007- 5020.
	Yan E P , Lin H , Dang Y F , et al. The spatiotemporal changes of vegetation cover in Beijing-Tianjin sandstorm source control region during 2000—2012. Acta Ecologica Sinica, 2014, 34 (17): 5007- 5020.
	颜明, 贺莉, 王随继, 等. 基于NDVI的1982—2012年黄河流域多时间尺度植被覆盖变化. 中国水土保持科学, 2018, 16 (3): 86- 94.
	Yan M , He L , Wang S J , et al. Changing trends of NDVI in the Yellow River Basin from 1982 to 2012 at different temporal scales. Science of Soil and Water Conservation, 2018, 16 (3): 86- 94.
	杨胜天, 刘昌明, 孙睿. 近20年来黄河流域植被覆盖变化分析. 地理学报, 2002, 57 (6): 679- 684. doi: 10.3321/j.issn:0375-5444.2002.06.007
	Yang S T , Liu C M , Sun R . The vegetation cover over last 20 years in Yellow River Basin. Acta Geographica Sinica, 2002, 57 (6): 679- 684. doi: 10.3321/j.issn:0375-5444.2002.06.007
	袁丽华, 蒋卫国, 申文明, 等. 2000—2010年黄河流域植被覆盖的时空变化. 生态学报, 2013, 33 (24): 7798- 7806.
	Yuan L H , Jiang W G , Shen W M , et al. The spatio-temporal variations of vegetation cover in the Yellow River Basin from 2000 to 2010. Acta Ecologica Sinica, 2013, 33 (24): 7798- 7806.
	郑子彦, 吕美霞, 马柱国. 黄河源区气候水文和植被覆盖变化及面临问题的对策建议. 中国科学院院刊, 2020, 35 (1): 61- 72.
	Zheng Z Y , Lü M X , Ma Z G . Climate, hydrology, and vegetation coverage changes in source region of Yellow River and countermeasures for challenges. Bulletin of Chinese Academy of Sciences, 2020, 35 (1): 61- 72.
	Baret F , Guyot G . Potentials and limits of vegetation indices for LAI and APAR assessment. Remote Sensing of Environment, 1991, 35 (2/3): 161- 173.
	Chen C , Park T , Wang X H , et al. China and India lead in greening of the world through land-use management. Nature Sustainability, 2019, 2 (2): 122- 129. doi: 10.1038/s41893-019-0220-7
	Chen H , Guo S L , Xu C Y , et al. Historical temporal trends of hydro-climatic variables and runoff response to climate variability and their relevance in water resource management in the Hanjiang Basin. Journal of Hydrology, 2007, 344 (3/4): 171- 184.
	Hurst H E . Long-term storage capacity of reservoirs. Transactions of the American Society of Civil Engineers, 1951, 116 (1): 770- 799. doi: 10.1061/TACEAT.0006518
	Mann H B . Nonparametric tests against trend. Econometrica, 1945, 13 (3): 245. doi: 10.2307/1907187
	Martinez B , Gilabert M A . Vegetation dynamics from NDVI time series analysis using the wavelet transform. Remote Sensing of Environment, 2009, 113 (9): 1823- 1842. doi: 10.1016/j.rse.2009.04.016
	Pouliot D , Latifovic R , Olthof I . Trends in vegetation NDVI from 1 km AVHRR data over Canada for the period 1985—2006. International Journal of Remote Sensing, 2009, 30 (1): 149- 168. doi: 10.1080/01431160802302090
	Sen P K . Estimates of the regression coefficient based on Kendall's tau. Journal of the American Statistical Association, 1968, 63 (324): 1379- 1389. doi: 10.1080/01621459.1968.10480934
	Sun Q H , Kong D X , Miao C Y , et al. Variations in global temperature and precipitation for the period of 1948 to 2010. Environmental Monitoring and Assessment, 2014, 186 (9): 5663- 5679. doi: 10.1007/s10661-014-3811-9
	Theil H . A rank-invariant method of linear and polynomial regression analysis. Ⅰ, Ⅱ and Ⅲ. Proceedings of Koninklijke Nederlandse Akademie van Wetenschappen, 1950, 53, 386- 1412.386-392, 521-525, 1397-1412
	Xu Z Y , Takeuchi K , Ishidaira H . Monotonic trend and step changes in Japanese precipitation. Journal of Hydrology, 2003, 279 (1): 144- 150.

NDVI	上游 Upper reaches		中游 Middle reaches		下游 Lower reaches		总体 Total
NDVI	面积 Area/km²	比例 Ratio(%)	面积 Area/km²	比例 Ratio(%)	面积 Area/km²	比例 Ratio(%)	面积 Area/km²	比例 Ratio(%)
0~0.3	60 769.37	20.31	3 863.97	1.91	29.05	0.48	64 662.39	12.74
0.3~0.4	47 452.47	15.85	10 820.73	5.36	40.89	0.67	58 314.09	11.50
0.4~0.5	27 233.71	9.10	17 774.47	8.81	75.33	1.23	45 083.51	8.89
0.5~0.6	23 147.54	7.73	29 381.52	14.56	171.10	2.79	52 700.16	10.39
0.6~0.7	30 534.16	10.20	40 176.01	19.91	1 008.30	16.46	71 718.47	14.14
0.7~1.0	110 163.93	36.81	99 763.73	49.45	4 800.46	78.37	214 728.12	42.34

Q	Z	林草植被覆盖变化趋势 Change trend of forestry and grass coverage	上游 Upper reaches		中游 Middle reaches		下游 Lower reaches		总体 Total
Q	Z	林草植被覆盖变化趋势 Change trend of forestry and grass coverage	面积 Area/km²	比例 Ratio (%)	面积 Area/km²	比例 Ratio (%)	面积 Area/km²	比例 Ratio (%)	面积 Area/km²	比例 Ratio (%)
＞0	＞1.96	显著改善 Obvious improvement	67 103.32	22.42	100 405.94	49.76	1 667.87	27.23	169 177.13	33.36
＞0	-1.96~1.96	轻微改善 Slight improvement	154 229.91	51. 53	87 673.60	43.45	2 892.29	47.22	244 795.80	48.26
0	-1.96~1.96	稳定 Keep stable	18 646.46	6.23	3 995.25	1.98	274.41	4.48	22 916.12	4.52
＜0	-1.96~1.96	轻微退化 Slight degradation	56 118.97	18.75	9 100.30	4.51	1 123.96	18.35	66 343.23	13.08
＜0	＜-1.96	显著退化 Obvious degradation	3 202.52	1.07	605.34	0.30	166.60	2.72	3 974.46	0.78

发展方向 Development direction	林草植被覆盖可持续性和变化趋势叠加结果 Superposition of forestry and grass coverage sustainability and change trend	上游 Upper reachesc		中游 Middle reaches		下游 Lower reaches		总体 Total
发展方向 Development direction		面积 Area/km²	比例 Ratio (%)	面积 Area/km²	比例 Ratio (%)	面积 Area/km²	比例 Ratio (%)	面积 Area/km²	比例 Ratio (%)
良性方向 Benign direction	持续性&轻微改善 Sustainability & slight improvement	109 098.23	36.44	66 495.03	32.95	1 922.68	31.39	177 515.94	35.01
	持续性&显著改善 Sustainability & obvious improvement	46 923.33	15.68	70 271.12	34.83	1 271.62	20.76	118 466.07	23.36
	反持续性&轻微退化 Reverse sustainability & slight degradation	14 181.39	4.74	2 223.15	1.10	402.83	6.58	16 807.37	3.31
	反持续性&显著退化 Reverse sustainability & obvious degradation	847.03	0.28	171.87	0.09	46.81	0.76	1 065.71	0.21
恶性方向 Malignant direction	持续性&轻微退化 Sustainability & slight degradation	41 920.28	14.01	6 906.39	3.42	718.55	11.73	49 545.22	9.77
	持续性&显著退化 Sustainability & obvious degradation	2 360.52	0.79	423.07	0.21	118.67	1.94	2 902.26	0.57
	反持续性&轻微改善 Reverse sustainability & slight improvement	45 309.41	15.14	21 284.63	10.55	972.23	15.87	67 566.27	13.32
	反持续性&显著改善 Reverse sustainability & obvious improvement	20 010.15	6.69	30 009.41	14.87	397.38	6.49	50 416.94	9.94
稳定 Unchanged	持续性&稳定 Sustainability & unchanged	13 102.05	4.38	3 076.40	1.52	177.46	2.90	16 355.92	3.22
不确定 Not sure	反持续性&稳定 Reverse sustainability & unchanged	5 548.79	1.85	919.36	0.46	96.90	1.58	6 565.05	1.29

[1]	金佳莉,王成,贾宝全. 我国4个典型城市近30年绿色空间时空演变规律[J]. 林业科学, 2020, 56(3): 61-72.
[2]	郝泽周,王成,裴男才,徐心慧,张昶,段文军,王子研. 深圳3处典型城市森林的春季生物声景多样性[J]. 林业科学, 2020, 56(2): 184-192.
[3]	温月仙, 甘红豪, 史胜青, 江泽平, 吴利禄, 褚建民. 基于叶绿体和核基因片段序列的甘蒙柽柳谱系地理研究[J]. 林业科学, 2020, 56(1): 55-64.
[4]	庄义琳, 周金星, 吴秀芹, 曹建华, 章维鑫. 2001—2016年喀斯特断陷盆地植被变化及其驱动因素[J]. 林业科学, 2019, 55(9): 177-184.
[5]	李杰, 张军, 刘陈立, 杨旭超. 基于MODIS-NDVI的中老缅交界区近16年植被覆盖时空变化特征[J]. 林业科学, 2019, 55(8): 9-18.
[6]	黎铭, 张会兰, 孟铖铖, 杨文涛, 田琰琰. 皇甫川流域2000—2015年植被NDVI时空变化特征[J]. 林业科学, 2019, 55(8): 36-44.
[7]	高文文, 曾源, 刘宇, 衣海燕, 吴炳方, 鞠洪波. 南水北调中线水源区2000—2015年森林动态变化遥感监测[J]. 林业科学, 2019, 55(4): 97-107.
[8]	张敏,曹春香,陈伟. 基于MODIS NDVI数据的广西植被覆盖时空变化遥感诊断[J]. 林业科学, 2019, 55(10): 27-37.
[9]	李晓红, 陈尔学, 李增元, 李世明. 综合应用多源遥感数据的面向对象土地覆盖分类方法[J]. 林业科学, 2018, 54(2): 68-80.
[10]	欧朝蓉, 朱清科, 孙永玉. 元谋干热河谷旱季植被覆盖度的时空异质性[J]. 林业科学, 2017, 53(11): 20-28.
[11]	曹宇佳, 陈尔学, 李世明. 基于多源数据的省级树种(组)成数空间分布信息估测方法[J]. 林业科学, 2016, 52(1): 18-29.
[12]	胡玉福, 邓良基, 刘宇, 蒋双龙, 李翔, 陈波, 王钰婷. 基于RS和GIS的大渡河上游植被覆盖时空变化[J]. 林业科学, 2015, 51(7): 49-59.
[13]	邵霜霜, 师庆东. 基于FVC的新疆植被覆盖度时空变化[J]. 林业科学, 2015, 51(10): 35-42.
[14]	马骏, 马朋, 李昌晓, 彭月, 魏虹. 基于土地利用的三峡库区(重庆段)生态系统服务价值时空变化[J]. 林业科学, 2014, 50(5): 17-26.
[15]	林杰;张金池;顾哲衍;吴玉敏. 基于叶面积指数的植被覆盖管理措施因子C的遥感定量估算[J]. , 2013, 49(2): 86-92.