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首页> 外文期刊>International journal of remote sensing >Upscaling of leaf area index in Terai forest plantations using fine- and moderate-resolution satellite data
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Upscaling of leaf area index in Terai forest plantations using fine- and moderate-resolution satellite data

机译:使用高分辨率和中分辨率的卫星数据来提高Terai人工林的叶面积指数

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摘要

Ecophysiological variables, such as leaf area index (LAI), play a key role in the functioning of ecosystem processes and are thus a useful determinant of primary production, evapotranspiration, and biogeochemical cycling. In the present study, upscaling of LAI was carried out by applying a transfer function from field LAI measurements to fine-resolution LISS III images and subsequently to coarse resolution Moderate Resolution Imaging Spectroradiometer (MODIS) data using a photosynthetically active radiation (PAR)/LAI ceptometer (AccuPAR model LP-80). Field data were collected from differently aged forest plantation types in the Terai Central Forest Division of Nainital district in Uttarakhand, India. The upscaling was done by establishing an empirical exponential relationship between normalized difference vegetation index (NDVI) and LAI. Results reveal a significant relationship (p < 0.01) between NDVI and LAI for each of the studied plantation types, i.e. teak, poplar, eucalyptus, and mixed plantation. The LAI was mapped at 23.5 m resolution by applying a plantation-specific LAI versus NDVI relationship derived from IRS Linear Imaging Self-Scanning Sensor images. The LAI maps were upscaled by using a simple linear averaging within nonoverlapping windows to match with Terra-MODIS 250 m resolution NDVI images. The upscaled time series of LAI was compared with representative field-measured LAI measurements and also with MODIS LAI at a resolution of 1000 m. Upscaled LAI was found to be significantly related to the field-measured LAI with a value of 0.69 for coefficient of determination and with a root mean square error of 0.77. On the other hand, upscaled LAI has less agreement with the MODIS LAI product (R-2 - 0.5 and root mean square error - 0.70).
机译:叶面积指数(LAI)等生态生理变量在生态系统过程的功能中起关键作用,因此是初级生产,蒸散量和生物地球化学循环的有用决定因素。在本研究中,通过应用从现场LAI测量到精细分辨率的LISS III图像的传递函数,然后将其应用到使用光合有效辐射(PAR)/ LAI的中等分辨率成像光谱仪(MODIS)的粗分辨率数据,来进行LAI的升级感知器(AccuPAR LP-80型)。实地数据是从印度北阿坎德邦奈尼塔尔地区的Terai中央森林局的不同年龄的人工林中收集的。通过建立归一化差异植被指数(NDVI)与LAI之间的经验指数关系来完成升级。结果表明,对于每种研究的人工林类型,即柚木,杨树,桉树和混合人工林,NDVI和LAI之间都存在显着的关系(p <0.01)。通过应用源自IRS线性成像自扫描传感器图像的特定于种植园的LAI与NDVI关系,将LAI映射为23.5 m分辨率。通过在不重叠的窗口内使用简单的线性平均来放大LAI地图,以与Terra-MODIS 250 m分辨率的NDVI图像匹配。将LAI的高级时间序列与代表性的现场测量LAI测量值以及MODIS LAI进行了比较,分辨率为1000 m。发现放大的LAI与现场测量的LAI显着相关,确定系数的值为0.69,均方根误差为0.77。另一方面,上乘的LAI与MODIS LAI乘积的一致性较小(R-2-0.5和均方根误差-0.70)。

著录项

  • 来源
    《International journal of remote sensing》 |2014年第22期|7749-7762|共14页
  • 作者单位

    Indian Inst Technol, CORAL, Kharagpur 721302, W Bengal, India;

    Indian Inst Remote Sensing, Agr & Soil Dept, Dehra Dun 248001, India;

    Indian Inst Remote Sensing, Agr & Soil Dept, Dehra Dun 248001, India;

    Indian Inst Remote Sensing, Agr & Soil Dept, Dehra Dun 248001, 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
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