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Estimating the leaf area index in Indian tropical forests using Landsat-8 OLI data

机译:使用Landsat-8 OLI数据估算印度热带森林的叶面积指数

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摘要

Leaf area index (LAI) is a key vegetation biophysical parameter and is extensively used in modelling of phenology, primary production, light interception, evapotranspiration, carbon, and nitrogen dynamics. In the present study, we attempt to spatially characterize LAI for natural forests of Western Ghats India, using ground based and Landsat-8 Operational Land Imager (OLI) sensor satellite data. For this, 41 ground-based LAI measurements were carried out across a gradient of tropical forest types, viz. dry, moist, and evergreen forests using LAI-2200 plant canopy analyser, during the month of March 2015. Initially, measured LAI values were regressed with 15 spectral variables, including nine spectral vegetation indices (SVIs) and six Landsat-8 surface reflectance (rho) variables using univariate correlation analysis. Results showed that the red (rho(red)), near-infrared (rho(NIR)), shortwave infrared (rho(SWIR1), rho(SWIR2)) reflectance bands (R-2 > 0.6), and all SVIs (R-2 > 0.7) except simple ratio (SR) have the highest and second highest coefficient of determination with ground-measured LAI. In the second step, to select significant (high R-2, low root mean square error (RMSE), and p-level < 0.05) SVIs to determine the best representative model, stepwise multiple linear regression (SMLR) was implemented. The results indicate that the SMLR model predicted LAI with better coefficient of determination (R-2 = 0.83, RMSE = 0.78) using normalized difference vegetation index, enhanced vegetation index, and soil-adjusted vegetation index variables compared to the univariate approach. The predicted SMLR model was used to estimate a spatial map of LAI. It is desirable to evaluate the stability and potentiality of regional LAI models in natural forest ecosystems against the operationally accepted Moderate Resolution Imaging Spectroradiometer (MODIS) global LAI product. To do this, the Landsat-8 pixel-based LAI map was resampled to 1 km resolution and compared with the MODIS derived LAI map. Results suggested that Landsat-8 OLI-based VIs provide significant LAI maps at moderate resolution (30 m) as well as coarse resolution (1 km) for regional climate models.
机译:叶面积指数(LAI)是关键的植被生物物理参数,已广泛用于物候,一次生,光截留,蒸散,碳和氮动力学的建模。在本研究中,我们尝试使用地面和Landsat-8作战陆地成像仪(OLI)传感器卫星数据对印度西高止山脉的天然林进行LAI的空间表征。为此,在热带森林类型的梯度(即梯度)上进行了41次基于地面的LAI测量。 2015年3月,使用LAI-2200植物冠层分析仪在干燥,潮湿和常绿的森林中进行测量。最初,对LAI的测量值与15个光谱变量进行回归,包括9个光谱植被指数(SVI)和6个Landsat-8表面反射率( rho)变量使用单变量相关分析。结果显示红色(rho(red)),近红外(rho(NIR)),短波红外(rho(SWIR1),rho(SWIR2))反射带(R-2> 0.6)和所有SVI(R -2> 0.7),除了简单比率(SR)在地面测量的LAI中具有最高和第二最高的确定系数。在第二步中,要选择显着(高R-2,低均方根误差(RMSE)和p水平<0.05)的SVI来确定最佳代表性模型,请实施逐步多元线性回归(SMLR)。结果表明,与单变量方法相比,SMLR模型使用归一化植被指数,增强植被指数和土壤调整植被指数变量,以更高的确定系数预测RAI(R-2 = 0.83,RMSE = 0.78)。预测的SMLR模型用于估计LAI的空间图。理想的是,根据可操作接受的中等分辨率成像光谱仪(MODIS)全球LAI产品评估天然森林生态系统中区域LAI模型的稳定性和潜力。为此,将基于Landsat-8像素的LAI地图重新采样为1 km分辨率,并与MODIS导出的LAI地图进行比较。结果表明,基于Landsat-8 OLI的VI可提供中等分辨率(30 m)和粗分辨率(1 km)的区域气候模型的重要LAI图。

著录项

  • 来源
    《International journal of remote sensing》 |2017年第23期|6769-6789|共21页
  • 作者单位

    Natl Remote Sensing Ctr, Forestry & Ecol Grp, Hyderabad 500037, Andhra Pradesh, India;

    Natl Remote Sensing Ctr, Forestry & Ecol Grp, Hyderabad 500037, Andhra Pradesh, India;

    Natl Remote Sensing Ctr, Forestry & Ecol Grp, Hyderabad 500037, Andhra Pradesh, India;

    Natl Remote Sensing Ctr, Forestry & Ecol Grp, Hyderabad 500037, Andhra Pradesh, India;

    Andhra Univ, Dept Environm Sci, Visakhapatnam, Andhra Pradesh, India;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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