Grass Biomass Inversion Based on Landsat 8 Spectral Derived Data Classification System
ZHANG Ai-wu1, 2, ZHANG Shuai1, 2, GUO Chao-fan1, 2*, LIU Lu-lu1, 2, HU Shao-xing3, CHAI Sha-tuo4
1. Key Laboratory of 3D Information Acquisition and Application, Ministry of Education, Capital Normal University, Beijing 100048, China
2. Engineering Research Center, Ministry of Education, Capital Information Technology, Capital Normal University, Beijing 100048, China
3. School of Mechanical Engineering and Automation, Beihang University, Beijing 100191, China
4. Qinghai University, College of Animal Husbandry and Veterinary Medicine (Qinghai Academy of Animal Science and Veterinary Medicine)，Xining 810016, China
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%, R2cv increased by 31%～53%, and RMSEcv decreased by more than 29%; Compared with the multivariate models, R2 increased by 29%～42%, RMSE decreased by more than 35%; and R2cv 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
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