Key Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, 73 East Beijing Road, Nanjing 210008, China.
Key Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, 73 East Beijing Road, Nanjing 210008, China.
Sci Total Environ. 2017 Dec 1;601-602:998-1007. doi: 10.1016/j.scitotenv.2017.05.163. Epub 2017 Jun 9.
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 R, 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 R, 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.
卫星遥感在大空间尺度上进行水生植被生物量的制图和监测具有优势。我们提出了一种将野外采样点生物量从方格转换为像素尺度的尺度变换（CT）方法，以及一种基于遥感图像的新归一化水调节植被指数（NWAVI），用于估算水生植被（不包括挺水植被）的生物量。我们使用了一种基于所提出的 CT 方法和 NWAVI 的建模方法，以及包括线性、二次、对数、立方、指数、倒数和幂回归在内的统计分析方法来估算水生植被生物量，并评估了生物量估算的性能。我们使用地理信息系统在不同月份的测试湖中绘制了水生植被生物量的空间分布和时间变化图。基于 CT 和 NWAVI 的指数回归模型在 2013 年 5 月和 8 月的调整 R、F 和 Sig 值均为最佳。观测与预测生物量的散点图显示，大多数验证的野外地点都接近 1:1 线。RMSE、ARE 和 RE 值都很小。研究区域的水生植被生物量的空间分布和变化表现出明显的可变性。在基于 NWAVI 的和其他植被指数的模型中，在两个测试中，基于 CT 和 NWAVI 的模型具有最大的调整 R、F 和最小的 ARE 值。所提出的建模方案可有效地估算湖泊中水生植被的生物量。这表明，所提出的方法可以为湖泊生态管理提供最准确的水生植被生物量空间分布图。更准确的水生植被生物量图对于实施保护政策和减少我们对湖泊碳循环理解的不确定性至关重要。
