沙漠腹地绿洲植被覆盖度提取及植被指数优选

王宁; 周明通; 魏宣; 郭玉川

doi:10.13961/j.cnki.stbctb.2022.06.025

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沙漠腹地绿洲植被覆盖度提取及植被指数优选

试验研究

- 沙漠腹地绿洲植被覆盖度提取及植被指数优选
- Extraction of Vegetation Cover and Optimization of Vegetation Indices in a Desert Hinterland Oasis
- 水土保持通报 2022年42卷第6期页码：197-205
- 作者机构：
  
  1. 新疆大学地理与遥感科学学院,新疆,乌鲁木齐,830017
  2. 绿洲生态教育部重点实验室,新疆,乌鲁木齐,830017
- 作者简介：
- 基金信息：
  
  国家自然科学基金项目"干旱流域下游河道地表水文过程及地下水时空响应研究"(41961003);国家自然科学基金联合重点支持项目"地表水、浅层地下水对沙漠腹地天然绿洲植被格局影响机制研究"(U1703237)
- DOI：10.13961/j.cnki.stbctb.2022.06.025
  中图分类号： Q948;TP79
- 纸质出版：2022
- 稿件说明：
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王宁, 周明通, 魏宣, 等. 沙漠腹地绿洲植被覆盖度提取及植被指数优选[J]. 水土保持通报, 2022,42(6):197-205.

Wang Ning, Zhou Mingtong, Wei Xuan, et al. Extraction of Vegetation Cover and Optimization of Vegetation Indices in a Desert Hinterland Oasis[J]. Bulletin of Soiland Water Conservation, 2022, 42(6): 197-205.
王宁, 周明通, 魏宣, 等. 沙漠腹地绿洲植被覆盖度提取及植被指数优选[J]. 水土保持通报, 2022,42(6):197-205. DOI： 10.13961/j.cnki.stbctb.2022.06.025.

Wang Ning, Zhou Mingtong, Wei Xuan, et al. Extraction of Vegetation Cover and Optimization of Vegetation Indices in a Desert Hinterland Oasis[J]. Bulletin of Soiland Water Conservation, 2022, 42(6): 197-205. DOI： 10.13961/j.cnki.stbctb.2022.06.025.

摘要

[目的] 对沙漠腹地绿洲植被覆盖度提取及植被指数优选进行分析和研究，为该区选取最优植被指数反演极端干旱区绿洲植被覆盖状况提供科学依据。 [方法] 选取塔克拉玛干沙漠腹地达里雅布依绿洲天然植被作为研究对象，以无人机航拍样地影像获取的植被覆盖度为基准，采用Sentinel-2B卫星影像提取多种典型植被指数，运用回归统计方法建立植被指数-植被覆盖度统计模型，在卫星像元尺度上确定反演干旱绿洲覆盖度的最优植被指数。 [结果] ①利用Image J软件提取样方植被覆盖度精度较高，总体精度可达88.67%。 ②土壤调节型植被指数(SAVI，MSAVI)在标准回归系数、确定系数评价指标中表现良好，指示极端干旱区天然植被覆盖变化有较好的适用性。 [结论] 在极端干旱区，Image J提取稀疏植被效果较好，SAVI，MSAVI更适合绿洲植被覆盖变化研究。

Abstract

[Objective] The extraction of vegetation cover and optimization of vegetation indices in desert hinterland oasis were analyzed and studied in order to providing scientific basis for select the optimal vegetation indices to invert the vegetation cover status of extreme arid zone oasis. [Methods] Natural vegetation cover data from the Dariyabui Oasis in the hinterland of the Taklamakan Desert were obtained from UAV aerial photography sample images and used as the benchmark. A variety of typical vegetation indices were extracted from Sentinel-2B satellite images

and a vegetation index-vegetation cover statistical model was established using regression statistics to determine the optimal vegetation index for inversion to quantify arid oasis vegetation cover at the satellite image element scale. [Results] ① The accuracy of vegetation cover of the extracted samples using Image J software was high

and the overall accuracy reached 88.67%. ② The soil-regulated vegetation indices (SAVI

MSAVI) performed well as shown by standard regression coefficients and coefficients of determination

and had good applicability in reflecting natural vegetation cover changes in extreme arid zones. [Conclusion] In an extreme arid zone

Image J software did well in extracting sparse vegetation cover

and SAVI and MSAVI are the more suitable vegetation indices for oasis vegetation cover change studies.

关键词

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references

赵振勇,王让会,薛英,等.极端干旱地区绿洲边缘带天然植被的保育策略:以新疆鄯善绿洲为例[J].水土保持通报,2008, 28(1):130-134,183.

田庆久,闵祥军.植被指数研究进展[J].地球科学进展,1998,13(4):327-333.

叶静芸,吴波,刘明虎,等.乌兰布和沙漠东北缘荒漠-绿洲过渡带植被地上生物量估算[J].生态学报,2018,38(4):1216-1225.

崔万新,李锦荣,司前程,等.基于无人机可见光数据荒漠灌木覆盖度提取方法研究[J].水土保持研究,2021,28(6):175-182,189.

汪小钦,王苗苗,王绍强,等.基于可见光波段无人机遥感的植被信息提取[J].农业工程学报,2015,31(5):152-159.

郭玉川,何英,李霞.基于MODIS的干旱区植被覆盖度反演及植被指数优选[J].国土资源遥感,2011,23(2):115-118.

唐亮,何明珠,许华,等.基于无人机低空遥感的荒漠植被覆盖度与归一化植被指数验证及其对水热梯度的响应[J].应用生态学报,2020,31(1):35-44.

杨红艳,杜健民,阮培英,等.基于无人机遥感与随机森林的荒漠草原植被分类方法[J].农业机械学报,2021,52(6):186-194.

郝梦宇,秦龙君,毛鹏,等.基于无人机可见光影像的荒漠植被分布格局研究方法[J].中国沙漠,2020,40(6):169-179.

卞雪,马群宇,刘楚烨,等.基于低空可见光谱的植被覆盖率计算[J].水土保持通报,2017,37(5):270-275.

高永刚,林悦欢,温小乐,等.基于无人机影像的可见光波段植被信息识别[J].农业工程学报,2020,36(3):178-189.

姚镇海,吴丹娃,褚荣浩,等.安徽省植被覆盖度动态变化及其对地形的响应[J].水土保持通报,2021,41(3):283-290.

Kattenborn T, Lopatin J, Förster M, et al. UAV data as alternative to field sampling to map woody invasive species based on combined Sentinel-1 and Sentinel-2 data[J]. Remote Sensing of Environment, 2019,227:61-73.

Fawcett D, Panigada C, Tagliabue G, et al. Multi-scale evaluation of drone-based multispectral surface reflectance and vegetation indices in operational conditions[J]. Remote Sensing, 2020,12(3):514.

Younes N, Joyce K E, Northfield T D, et al. The effects of water depth on estimating fractional vegetation cover in mangrove forests[J]. International Journal of Applied Earth Observation and Geoinformation, 2019,83:101924.

Marcial-Pablo M J, Gonzalez-Sanchez A, Jimenez-Jimenez S I, et al. Estimation of vegetation fraction using RGB and multispectral images from UAV[J]. International Journal of Remote Sensing, 2019,40(2):420-438.

Nuijten R J G, Coops N C, Watson C, et al. Monitoring the structure of regenerating vegetation using drone-based digital aerial photogrammetry[J]. Remote Sensing, 2021,13(10):1942.

张和钰,管文轲,李志鹏,等.基于无人机影像的戈壁区植被空间分布特征及其主要影响因素研究[J].干旱区资源与环境,2020,34(2):161-167.

张文奇,周小龙,尼加提·卡斯木,等.沙漠腹地达理雅博依天然绿洲植物群落特征分析[J].新疆大学学报(自然科学版),2019,36(3):276-286.

万炜,肖生春,陈小红,等.无人机遥感在野外植被盖度调查中的应用:以阿拉善荒漠区灌木为例[J].干旱区资源与环境,2018,32(9):150-156.

高永平,康茂东,何明珠,等.基于无人机可见光波段对荒漠植被覆盖度提取的研究:以沙坡头地区为例[J].兰州大学学报(自然科学版),2018,54(6):770-775.

董弟文,阿布都热合曼·哈力克,王大伟,等.1994-2016年和田绿洲植被覆盖时空变化分析[J].生态学报,2019,39(10):3710-3719.

Haboudane D, Miller J R, Pattey E, et al. Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies:Modeling and validation in the context of precision agriculture[J]. Remote Sensing of Environment, 2004, 90(3):337-352.

Richardson A J, Wiegand C L. Distinguishing vegetation from soil background information[J]. Photogrammetry Enginerring & Remote Sensing, 1977,43(12):1541-1552.

Jordan C F. Derivation of leaf area index from quality of light on the forest floor[J]. Ecology, 1969, 50(4):663-666.

Huete A R. A soil adjusted vegetation index(SAVI)[J], Remote Sensing of Environment, 1988,25(3):295-309.

Qi J, Chehbouni A, Huete A R, et al. A modified soil adjusted vegetation index(MSAVI)[J]. Remote Sensing of Environment, 1994,48(2):119-126.

Pinty B, Verstraete M M. GEMI:A non-linear index to monitor global vegetation from satellites[J]. Vegetation, 1992,101(1):15-20.

Kaufman Y J, Tanre D. Atmospherically resistant vegetation index(ARVI)for EOS-MODIS[J]. IEEE Transaction on Geoscience and Remote Sensing, 1992,30(2):261-270.

Khatun N. Applications of normality test in statistical analysis[J]. Open Journal of Statistics, 2021,11(1):113-122.

马秀麟,姚自明,邬彤,等.数据分析方法及应用[M].北京:人民邮电出版社,2015.

刘婵,赵文智,刘冰,等.基于无人机和MODIS数据的巴丹吉林沙漠植被分布特征与动态变化研究[J].中国沙漠,2019,39(4):92-102.

姚雄,余坤勇,刘健.基于无人机多光谱遥感的马尾松林叶面积指数估测[J].农业机械学报,2021,52(7):213-221.

焦伟,陈亚宁,李稚,等.基于多种回归分析方法的西北干旱区植被NPP遥感反演研究[J].资源科学,2017,39(3):545-556.

Zandler H, Brenning A, Samimi C. Quantifying dwarf shrub biomass in an arid environment:comparing empirical methods in a high dimensional setting[J]. Remote Sensing of Environment, 2015,158:140-155.

王军,姜芸.基于无人机多光谱遥感的大豆叶面积指数反演[J].中国农学通报,2021,37(19):134-142.

王海燕,杨方廷,刘鲁.标准化系数与偏相关系数的比较与应用[J].数量经济技术经济研究,2006,23(9):150-155.

Johnson J W, LeBreton J M. History and Use of Relative Importance Indices in organizational research[J]. Organizational Research Methods, 2004,7(3):238-257.

孙红卫,王玖,罗文海.线性回归模型中自变量相对重要性的衡量[J].中国卫生统计,2012,29(6):900-902.

王杰,李卫朋.基于灵活的时空融合模型的植被覆盖度与植被指数关系[J].草业科学,2017,34(2):264-272.

赵晨光,程业森,李慧瑛,等.腾格里沙漠东北缘人工植被恢复区土地利用/覆被变化及其驱动因素分析[J].干旱区资源与环境,2021,35(6):131-138.

陈艳锋,杨美琳,陈军纪,等.基于植被指数极旱荒漠区生物量模型研究:以安西极旱荒漠保护区北片为例[J].干旱区资源与环境,2015,29(10):93-99.

Yan Feng, Wu Bo, Wang YanJiao. Estimating aboveground biomass in Mu Us sandy land using Landsat spectral derived vegetation indices over the past 30 years[J]. Journal of Arid Land, 2013,5(4):521-530.

杨峰,李建龙,钱育蓉,等.天山北坡典型退化草地植被覆盖度监测模型构建与评价[J].自然资源学报,2012,27(8):1340-1348.

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