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首页> 外文期刊>Remote Sensing >Mapping Above-Ground Biomass by Integrating Optical and SAR Imagery: A Case Study of Xixi National Wetland Park, China
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Mapping Above-Ground Biomass by Integrating Optical and SAR Imagery: A Case Study of Xixi National Wetland Park, China

机译:结合光学和SAR影像绘制地上生物量:以中国西溪国家湿地公园为例

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Wetlands are important ecosystems as they are known as the “kidney of the earth”. Particularly, urban wetlands play an important role in providing both natural and social beneficial services. However, urban wetlands are suffering from various human impacts, such as excessive land use conversion, air and water pollution, especially those in developing countries undergoing rapid industrialization and urbanization. Therefore, it is of great necessity to derive timely biomass information for optimal design, management and protection of urban wetlands. In this paper, we develop a set of models for estimating above ground biomass (AGB) in Xixi National Wetland Park in Hangzhou, China by using optical images and Synthetic Aperture Radar (SAR) images. A series of vegetation indices (VIs) derived from optical data is introduced along with spectral data. The modeling methods consist of (1) curve estimation; (2) linear regression for multivariable model; (3) Back Propagation Neural Network (BPNN) modeling. Curve estimation is a combination of linear and nonlinear regressions. It is applied to generate AGB models from a single variable including Normalized Difference Vegetation Index (NDVI) and radar backscatter coefficient. The models are then compared via three accuracy metrics. According to the results, SAR models generally show better accuracy than optical models and BPNN models show the greatest accuracy among all the models. The BPNN model from the combination of Terra Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and European Remote-Sensing Satellite-2 (ERS-2) SAR (Synthetic Aperture Radar) image has the least root mean square (RMSE, 0.396 kg/m 2 ), least mean absolute error (MAE, 0.256 kg/m 2 ) and the greatest correlation coefficient (0.974). The results indicate that AGB can be estimated by integrating optical and SAR imagery. Four maps of AGB are derived to illustrate the distribution of AGB in the study area. The total AGB in the study area is estimated to be between 165,000 and 210,000 kg/m 2 .
机译:湿地被称为“地球的肾脏”,是重要的生态系统。特别是,城市湿地在提供自然和社会有益服务方面都起着重要作用。然而,城市湿地正遭受各种人类影响,例如过度的土地利用转换,空气和水污染,特别是那些正在经历快速工业化和城市化的发展中国家。因此,迫切需要及时获取生物量信息,以优化城市湿地的设计,管理和保护。在本文中,我们使用光学图像和合成孔径雷达(SAR)图像,开发了一套用于估算中国杭州西溪国家湿地公园地上生物量(AGB)的模型。引入了一系列从光学数据得出的植被指数(VI)以及光谱数据。建模方法包括:(1)曲线估计; (2)多变量模型的线性回归; (3)反向传播神经网络(BPNN)建模。曲线估计是线性和非线性回归的组合。它用于从包括归一化植被指数(NDVI)和雷达反向散射系数的单个变量生成AGB模型。然后通过三个准确性指标对模型进行比较。根据结果​​,SAR模型通常显示出比光学模型更好的准确性,而BPNN模型显示出所有模型中最高的准确性。结合Terra Advanced星载热发射和反射辐射计(ASTER)和欧洲遥感卫星2(ERS-2)SAR(合成孔径雷达)图像的BPNN模型具有最小均方根(RMSE,0.396 kg / m 2),最小平均绝对误差(MAE,0.256 kg / m 2)和最大相关系数(0.974)。结果表明,可以通过整合光学和SAR图像来估计AGB。推导了四张AGB图,以说明AGB在研究区域中的分布。研究区域的总AGB估计在165,000和210,000 kg / m 2之间。

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