Landsat8光谱衍生数据分类体系下的牧草生物量反演

doi:10.3964/j.issn.1000-0593(2020)01-0239-08

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Abstract Estimation of forage biomass is of great significance for the rational use of grassland resources and monitoring of livestock load balance, and it is a key indicator for evaluating the sustainable development of grassland ecosystems and grassland resources. The rapid and non-destructive study of large-area vegetation biomass estimation based on Landsat remote sensing technology has been widely used. Most of the current researches are based on single variable or several commonly used vegetation indices to construct inversion models. These indices often cannot reflect the physical and chemical characteristics of vegetation inmany aspects. In this paper, the classification systems of different Landsat8-derived data were constructed by their corresponding physicochemical characteristics of vegetation andintersectional pattern with plants. A multivariable nonlinear biomass estimation model based on stochastic gradient boosting algorithm was proposed and the model estimation results were discussed with different combinations of derived data categories. The program feasibility study was carried out with Haiyan County in Qinghai Province as the study area. The results showed that the Landsat8-derived data reflected the physical and chemical characteristics of vegetation mainly from the aspects of vegetation greenness, yellowness, coverage, moisture content, texture characteristics and elimination of atmospheric disturbance and soil background interference(7 subcategories). On the other hand, these data can also be summarized into three categories: direct factors (greenness, yellowness, coverage, moisture content), indirect factors (eliminating atmospheric interference and eliminating soil background interference), and spatial factors (texture characteristics). The derived data categories have obvious complementarity. The direct factor (GNDVI，TCW，NDTI，NDSVI，TCD)-indirect factor (SAVI，VARI)-space factor(Mean_B3，Mean_B6，Hom_Ⅱ，Dis_B5) model had the best estimation accuracy, and R² reached 0.88; the RMSE was 141.00 g·m^-2, however the single direct factor model estimates result was the worst. Compared with the results of six typical biomass estimation models, the proposed method had obvious advantages. Compared with the univariate models, R² increased by 42%～60%, RMSE decreased by more than 47%, R²_cv increased by 31%～53%, and RMSE_cv decreased by more than 29%; Compared with the multivariate models, R² increased by 29%～42%, RMSE decreased by more than 35%; and R²_cv increased by 2%～18%, RMSEcv decreased by more than 2%. In addition, the proposed model also had some effect in eliminatingoversaturation problem. In summary, this paper uses Landsat8 data to construct an inversion model from the perspective of reflecting different physical and chemical characteristics of vegetation to achieve accurate estimation of forage biomass, which has important guiding significance for the real-time monitoring of pastage growth and the sustainable use and management of grassland resources. The research results can also provide reference and reference for future large-area regional grassland dynamic monitoring and other agricultural research.

Key words：Biomass; Stochastic gradient boosting algorithm; Landsat-derived data

Received: 2018-11-14 Accepted: 2019-03-19

ZTFLH:

TP79

Corresponding Authors: GUO Chao-fan E-mail: guochao881016@163.com

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https://www.gpxygpfx.com/EN/10.3964/j.issn.1000-0593(2020)01-0239-08 OR https://www.gpxygpfx.com/EN/Y2020/V40/I01/239

[1] ZHAO Ming-wei，YUE Tian-xiang，SUN Xiao-fang，et al（赵明伟，岳天祥，孙晓芳，等）. Acta Ecologica Sinica（生态学报），2014，34(17)：4891.
[2] YAN Rui-rui，TANG Huan，DING Lei，et al（闫瑞瑞，唐欢，丁蕾，等）. Transactions of the Chinese Society of Agricultural Engineering（农业工程学报），2017，33(15)：210.
[3] Gouveia C M，Trigo R M，Beguería S，et al. Global and Planetary Change，2017，151：15.
[4] Wilson N R，Norman L M. International Journal of Remote Sensing，2018，39(10)：3243.
[5] Candiago S，Remondino F，De Giglio M，et al. Remote Sensing，2015，7(4)：4026.
[6] QIAN Yu-rong，YANG Feng，LI Jian-long，et al（钱育蓉，杨峰，李建龙，等）. Pratacultural Science（草业科学），2013，30(9)：1330.
[7] ZHANG Yan-nan，NIU Jian-ming，ZHANG Qing，et al（张艳楠，牛建明，张庆，等）. Acta Prataculturae Sinica（草业学报），2012，21(1)：229.
[8] GAO Ming-liang，ZHAO Wen-ji，GONG Zhao-ning，et al（高明亮，赵文吉，宫兆宁，等）. Acta Ecologica Sinica（生态学报），2013，33(2)：542.
[9] Lawrence R，Bunn A，Powell S，et al. Remote Sensing of Environment，2004，90(3)：331.
[10] LI Wei，MU Meng，CHEN Guan-bin，et al（李微，牟蒙，陈官滨，等）. Spectroscopy and Spectral Analysis（光谱学与光谱分析），2016，36(5)：1418.
[11] Xue J，Su B. Journal of Sensors，2017，2017: doi.org/10.1155/2017/1353691.
[12] Van Deventer A P，Ward A D，Gowda P H，et al. Photogrammetric Engineering and Remote Sensing，1997，63：87.
[13] Qi J，Marsett R，Heilman P，et al. Transactions American Geophysical Union，2002，83(51)：601.
[14] Sriwongsitanon N，Gao H，Savenije H H G，et al. Hydrology and Earth System Sciences，2016，20(8)：8419.
[15] Duane M V，Cohen W B，Campbell J L，et al. Forest Science，2010，56(4)：405.
[16] Powell S L，Cohen W B，Healey S P，et al. Remote Sensing of Environment，2010，114(5)：1053.
[17] Haralick R M，Shanmugam K. IEEE Transactions on Systems，Man，and Cybernetics，1973，(6)：610.
[18] Friedman J H. Annals of Statistics，2001, 29: 1189.
[19] Friedman J H. Computational Statistics & Data Analysis，2002，38(4)：367.
[20] Dube T，Mutanga O，Elhadi A，et al. Sensors，2014，14(8)：15348.
[21] MENG Shi-li，PANG Yong，ZHANG Zhong-jun，et al（蒙诗栎，庞勇，张钟军，等）. Journal of Remote Sensing（遥感学报），2017，21(5)：812.