Estimating the biomass of unevenly distributed aquatic vegetation in a lake using the normalized water-adjusted vegetation index and scale transformation method

• Published in: The Science of the total environment Vol. 601-602; pp. 998 - 1007
• Main Authors: Gao, Yongnian, Gao, Junfeng, Wang, Jing, Wang, Shuangshuang, Li, Qin, Zhai, Shuhua, Zhou, Ya
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 01.12.2017
• Subjects: Aquatic vegetation; Biomass; Lake; Remote sensing; Scale; Vegetation index; Scale; Biomass; Lake; Vegetation index; Aquatic vegetation; Remote sensing
• ISSN: 0048-9697; 1879-1026
• DOI: 10.1016/j.scitotenv.2017.05.163

[Display omitted] Satellite remote sensing is advantageous for the mapping and monitoring of aquatic vegetation biomass at large spatial scales. We proposed a scale transformation (CT) method of converting the field sampling-site biomass from the quadrat to pixel scale and a new normalized water-adjusted vegetation index (NWAVI) based on remotely sensed imagery for the biomass estimation of aquatic vegetation (excluding emergent vegetation). We used a modeling approach based on the proposed CT method and NWAVI as well as statistical analyses including linear, quadratic, logarithmic, cubic, exponential, inverse and power regression to estimate the aquatic vegetation biomass, and we evaluated the performance of the biomass estimation. We mapped the spatial distribution and temporal change of the aquatic vegetation biomass using a geographic information system in a test lake in different months. The exponential regression models based on CT and the NWAVI had optimal adjusted R2, F and Sig. values in both May and August 2013. The scatter plots of the observed versus the predicted biomass showed that most of the validated field sites were near the 1:1 line. The RMSE, ARE and RE values were small. The spatial distribution and change of the aquatic vegetation biomass in the study area showed clear variability. Among the NWAVI-based and other vegetation index-based models, the CT and NWAVI-based models had the largest adjusted R2, F and the smallest ARE values in both tests. The proposed modeling scheme is effective for the biomass estimation of aquatic vegetation in lakes. It indicated that the proposed method can provide a most accurate spatial distribution map of aquatic vegetation biomass for lake ecological management. More accurate biomass maps of aquatic vegetation are essential for implementing conservation policy and for reducing uncertainties in our understanding of the lake carbon cycle. •Remote sensing models for estimating biomass of aquatic vegetation were developed.•A new normalized water-adjusted vegetation index was established.•A new field biomass scale transformation method was proposed.•The proposed modeling scheme had a high accuracy in biomass estimation of aquatic vegetation.