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Arid Land Geography >

2022 , Vol. 45 >Issue 2: 478 - 487

DOI: https://doi.org/10.12118/j.issn.1000–6060.2021.177

Earth Information Sciences

Estimation of aboveground biomass of sparse desert vegetation based on remote sensing techniques in hyper-arid area

  • Jingyun YE ,
  • Bo WU ,
  • Xiaohong JIA ,
  • Bingqiang FEI ,
  • Junliang GAO ,
  • Long CHENG ,
  • Yingjun PANG ,
  • Bin YAO ,
  • Deyong KONG
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  • 1. Institute of Desertification Studies, Chinese Academy of Forestry, Beijing 100091, China
    2. Key Laboratory of Desert Ecosystem and Global Change, State Administration of Forestry and Grassland, Beijing 100091, China
    3. Experimental Center of Desert Forestry, Chinese Academy of Forestry, Dengkou 015200, Inner Mongolia, China
    4. Shaoguan University, Shaoguan 512005, Guangdong, China

Received date: 2021-04-19

  Revised date: 2021-11-26

  Online published: 2022-04-02

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Abstract

The aboveground biomass (AGB) is an essential indicator of desert ecosystem health evaluation and desertification monitoring in arid areas. The estimation accuracy of the general remote sensing model is reduced because of the influence of small and sparse desert vegetation, low AGB per unit area, and background soil information. Improving biomass estimation accuracy of sparse desert vegetation can provide essential parameters for terrestrial ecosystem carbon storage estimation and carbon cycle research. It can also provide technical support for desertification monitoring. Herein, the Gobi area in front of the Altun Mountains in the south of Kumtag Desert, northwest China with an extremely arid climate is considered as the research area. The vegetation coverage area was accurately extracted using UAV image data. The estimation results based on the high-spatial resolution WorldView-3 remote sensing data were used to correct the estimation results based on the medium spatial resolution Landsat-OLI remote sensing data. Then, stepwise linear regression (SLR), LASSO regression, and ridge regression (RR) models were used to estimate the AGB of sparse desert vegetation. The results showed that: (1) High-spatial resolution satellite remote sensing data can accurately describe the spatial and temporal distribution characteristics of vegetation in arid areas. The RMSErel mean value of WorldView-3 remote sensing data was 44.68%. (2) The RR model had a better regression effect than SLR and LASSO models. The RMSErel of the WorldView-3 RR model was 44.68%, better than the SLR model (58.33%) and LASSO model (45.26%). Additionally, the RMSErel of the Landsat-OLI RR model was 114.34%, better than the SLR model (134.94%) and LASSO model (115.81%). (3) Using the WorldView-3 remote sensing data as an intermediate medium between ground and Landsat-OLI remote sensing data, the AGB estimation results of desert vegetation based on Landsat-OLI remote sensing data were corrected, and the RMSErel decreased by 31.18%. The spatial resolution of remote sensing data is the main factor affecting the estimation accuracy of the sparse desert vegetation AGB regression model. The main reason for the decrease in the estimation accuracy of AGB is the decrease in the sensitivity of Landsat-OLI remote sensing data to sparse desert vegetation information. The nonlinear multiple regression methods, SLR, LASSO, and RR, are used to build the regression model between remote sensing characteristic parameters and ground sample data, effectively improving model stability. When extracting sparse desert vegetation information, using high-spatial resolution remote sensing data as a medium between ground and medium spatial resolution remote sensing data can effectively improve the extraction accuracy of sparse desert vegetation information from medium spatial resolution remote sensing data.

Key words： remote sensing eigenvalue; sparse desert vegetation; aboveground biomass; regression model

Cite this article

Jingyun YE , Bo WU , Xiaohong JIA , Bingqiang FEI , Junliang GAO , Long CHENG , Yingjun PANG , Bin YAO , Deyong KONG . Estimation of aboveground biomass of sparse desert vegetation based on remote sensing techniques in hyper-arid area[J]. Arid Land Geography, 2022 , 45(2) : 478 -487 . DOI: 10.12118/j.issn.1000–6060.2021.177

References

[1] 陈曦. 中国干旱区自然地理[M]. 北京: 科学出版社, 2010: 89.
[1] [ Chen Xi. Physical geography of arid land in China[M]. Beijing: Science Press, 2001: 89. ]
[2] 陶冶, 张元明. 中亚干旱荒漠区植被碳储量估算[J]. 干旱区地理, 2013, 36(4):615-622.
[2] [ Tao Ye, Zhang Yuanming. Evaluation of vegetation biomass carbon storage in deserts of Central Asia[J]. Arid Land Geography, 2013, 36(4):615-622. ]
[3] Bao Y S, Cheng L L, Lu Q. Assessment of desert ecological assets and countermeasures for ecological compensation[J]. Journal of Resources and Ecology, 2019, 10(1):56-62.
[4] Mutanga O, Adam E, Cho M A. High density biomass estimation for wetland vegetation using WorldView-2 imagery and random forest regression algorithm[J]. International Journal of Applied Earth Observation and Geoinformation, 2012, 18:399-406.
[5] Cho M A, Skidmore A, Corsi F, et al. Estimation of green grass/herb biomass from airborne hyperspectral imagery using spectral indices and partial least squares regression[J]. International Journal of Applied Earth Observation and Geoinformation, 2007, 9(4):414-424.
[6] Chave J, Réjou-Méchain M, Búrquez A, et al. Improved allometric models to estimate the aboveground biomass of tropical trees[J]. Global Change Biology, 2014, 20(10):3177-3190.
[7] 贺金生, 方精云, 朴世龙, 等. 中国草地植被生物量及其空间分布格局[J]. 植物生态学报, 2004, 28(4):491-498.
[7] [ He Jinsheng, Fang Jingyun, Piao Shilong, et al. Spatial distribution of grassland biomass in China[J]. Chinese Journal of Plant Ecology, 2004, 28(4):491-498. ]
[8] Zhang X Y, Friedl M A, Schaaf C B, et al. Monitoring vegetation phenology using MODIS[J]. Remote Sensing of Environment, 2003, 84(3):471-475.
[9] White M A, Beurs K M D, Didan K, et al. Intercomparison, interpretation, and assessment of spring phenology in north America estimated from remote sensing for 1982—2006[J]. Global Change Biology, 2009, 15(10):2335-2359.
[10] 宋洁, 刘学录. 祁连山国家公园森林地上碳密度遥感估算[J]. 干旱区地理, 2021, 44(4):1045-1057.
[10] [ Song Jie, Liu Xuelu. Estimation of forest aboveground carbon density in Qilian Mountains National Park based on remote sensing[J]. Arid Land Geography, 2021, 44(4):1045-1057. ]
[11] Cunliffe A M, Brazier R E, Anderson K. Ultra-fine grain landscape-scale quantification of dryland vegetation structure with drone-acquired structure-from-motion photogrammetry[J]. Remote Sensing of Environment, 2016, 183:129-143.
[12] Yan F, Wu B, Wang Y. Estimating spatiotemporal patterns of aboveground biomass using Landsat TM and MODIS images in the Mu Us Sandy Land, China[J]. Agricultural and Forest Meteorology, 2015, 200:119-128.
[13] 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.
[13] [ Ye Jingyun, Wu Bo, Liu Minghu, et al. Estimation of aboveground biomass of vegetation in the desert-oasis ecotone on the northeastern edge of the Ulan Buh Desert[J]. Acta Ecologica Sinica, 2018, 38(4):1216-1225. ]
[14] 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.
[15] 吴娱, 张相锋, 董世魁, 等. 阿尔金山自然保护区东部典型植物群落的物种组成、多样性及生物量[J]. 生态学杂志, 2013, 32(9):2250-2256.
[15] [ Wu Yu, Zhang Xiangfeng, Dong Shikui, et al. Species composition, diversity, and biomass of representative plant communities in eastern Arjin Mountain Nature Reserve, northwest China[J]. Chinese Journal of Ecology, 2013, 32(9):2250-2256. ]
[16] 库姆塔格沙漠综合科学考察队. 库姆塔格沙漠研究[M]. 北京: 科学出版社, 2012: 1-5.
[16] [Scientific Expedition Team of the Kumtag Desert. A study of the Kumtag Desert[M]. Beijing: Science Press, 2012: 1-5. ]
[17] HJ 1170—2021. 中华人民共和国生态环境部: 全国生态状况调查评估技术规范——荒漠生态系统野外观测[S]. 北京: 生态环境部, 2021: 4.
[17] [HJ 1170—2021. Ministry of Ecology and Environment of the People’s Republic of China: National ecological status survey and evaluation technical specifications: Field observation of desert ecosystem[S]. Beijing: Ministry of Ecology and Environment, 2021: 4. ]
[18] 姚雪玲, 姜丽娜, 李龙, 等. 浑善达克沙地6种灌木生物量模拟[J]. 生态学报, 2019, 39(3):905-912.
[18] [ Yao Xueling, Jiang Lina, Li Long, et al. Biomass simulation of six shrub species in Otindag Sandy Land[J]. Acta Ecologica Sinica, 2019, 39(3):905-912. ]
[19] Gašparović M, Dobrinić D, Medak D. Geometric accuracy improvement of WorldView-2 imagery using freely available DEM data[J]. The Photogrammetric Record, 2019, 34(167):266-281.
[20] Pearson R L, Miller L D. Remote mapping of standing crop biomass for estimation of the productivity of the shortgrass prairie[J]. Remote Sensing of Environment, 1972, 45(2):7-12.
[21] Rouse J, Haas R H, Schell J A, et al. Monitoring vegetation systems in the Great Plains with ERTS[J]. NASA Special Publication, 1974, 351(1):309-317.
[22] Qi J, Chehbouni A, Huete A R, et al. A modified soil adjusted vegetation index[J]. Remote Sensing of Environment, 1994, 48(2):119-126.
[23] Addink E A, Jong A S M, Pebesma E J Object definition for aboveground biomass and leaf area index estimation[C]//Shimoni M, Van der Meer F D, Acheroy M. Proceedings of the 5th EARSeL Workshop on Imaging Spectroscopy. Bruges: EARSeL, 2007: 23-25.
[24] Zribi M, Le Hégarat-Mascle S, Taconet O, et al. Derivation of wild vegetation cover density in semi-arid regions: ERS2/SAR evaluation[J]. International Journal of Remote Sensing, 2003, 24(6):1335-1352.
[25] 黄鑫, 刘建红, 申克建, 等. 基于MODIS的青海草地产草量变化遥感分析[J]. 干旱区地理, 2020, 43(3):715-725.
[25] [ Huang Xin, Liu Jianhong, Shen Kejian, et al. Grassland yield change in Qinghai Province based on MODIS data[J]. Arid Land Geography, 2020, 43(3):715-725. ]
[26] Li M M, Wu B F, Yan C Z. Estimation of vegetation fraction in the upper basin of Miyun Reservoir by remote sensing[J]. Resources Science, 2004, 26(4):153-159.
[27] 陈晋, 陈云浩, 何春阳, 等. 基于土地覆盖分类的植被覆盖率估算亚像元模型与应用[J]. 遥感学报, 2001, 5(6):416-422.
[27] [ Chen Jin, Chen Yunhao, He Chunyang, et al. Sub-pixel model for vegetation fraction estimation based on land cover classification[J]. Journal of Remote Sensing, 2001, 5(6):416-422. ]
[28] Carlson T N, Ripley D A. On the relation between NDVI, fractional vegetation cover, and leaf area index[J]. Remote Sensing of Environment, 1997, 62(3):241-252.
[29] Rundquist B C. The influence of canopy green vegetation fraction on spectral measurements over native tallgrass prairie[J]. Remote Sensing of Environment, 2002, 81(1):129-135.
[30] Townshend J R G, Justice C O, Hardy J R, et al. Terrain analysis and remote sensing[M]. London: G. Allen & Unwin Press, 1981: 551.
[31] 岳健, 穆桂金, 唐自华, 等. 基于NDVI的新疆荒漠地区植被覆盖度遥感估算经验模型研究[J]. 干旱区地理, 2020, 43(1):153-160.
[31] [ Yue Jian, Mu Guijin, Tang Zihua, et al. Remote sensing estimation models for vegetation coverage in desert regions of Xinjiang based on NDVI[J]. Arid Land Geography, 2020, 43(1):153-160. ]
[32] Brenning A, Long S, Fieguth P. Detecting rock glacier flow structures using Gabor filters and IKONOS imagery[J]. Remote Sensing of Environment, 2012, 125:227-237.
[33] D’ Odorico P, Bhattachan A, Davis K F, et al. Global desertification: Drivers and feedbacks[J]. Advances in Water Resources, 2013, 51:326-344.
[34] Venables W N, Smith D M, The R Development Core Team. An introduction to R[M]. United Kingdom: Network Theory Limited, 1990: 275-286.
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