Welcome to visit Scientia Silvae Sinicae，Today is

Scientia Silvae Sinicae ›› 2015, Vol. 51 ›› Issue (9): 24-34.doi: 10.11707/j.1001-7488.20150904

Previous Articles     Next Articles

A Comparison of Different Quickbird Image Information for Estimating the Effective Leaf Area Index of Robinia pseudoacacia Plantations

Zhou Jingjing1,2, Zhao Zhong1, Liu Jinliang1, Zhao Jun1, Zhao Qingxia1, Liu Jun3   

  1. 1. Key Laboratory of Environment and Ecology in Western China, Ministry of Education College of Forestry, Northwest A&F University Yangling 712100;
    2. College of Horticulture & Forestry Sciences, Huazhong Agricultural University Wuhan 430070;
    3. East China Forest Inventory and Planning Institute, State Forestry Administration Hangzhou 310019
  • Received:2014-07-31 Revised:2015-02-09 Online:2015-09-25 Published:2015-10-16

PDF (PC)

79444

Abstract:

[Objective] The spatial information of high resolution remote sensing image can improve the estimation accuracy of forestry parameters. This study precisely explored the combinational rule of spectral and spatial information with high resolution remote sensing in order to improve the effective leaf area index (LAIe) based on the existing research. Obtained results can be provide evidence and data for estimation of forestry parameters and assessments of forestry health. [Method] The black locust (Robinia pseudoacacia) plantations located in Weibei area of Loess Plateau were chosen as research objects. The LAIe values of 76 plots were measured. We also extracted seven textural parameters of panchromatic data including ASM, HOM, COR, CON, DIS, VAR, ENT and seven spectral parameters of multi-spectral image including b4, SAVI, MSAVI, NLI, EVI, DVI, NDVI from Quickbird imagey with high resolution. The combined spectral-textural indices of Quickbird imagery were obtained using method of raster operation. Four different techniques, including simple linear regression model, quadratic regression model, power model and exponential model, were developed to describe the relationship between image parameters and field measurements of LAIe. The predicted accuracy of combined spectral-textural index and sole texture parameter was compared to reveal the role of combined spectral index and texture parameters used for LAIe retrieval. [Result] The LAIe estimation accuracy was improved when ASM, COR and HOM were combined with SVIs. To a certain extent, the accuracy of SVIs to estimate LAIe was improved with the combination of CON, DIS, VAR and SVIs. The combination of HOM, ASM and COR with SVIs gained the higher r2 than those achieved using HOM, ASM or COR alone. The performances of CON, DIS and VAR were improved when combining with partly SVIs. The combination of Entropy data with SVIs invariably yielded adjusted r2 values that were lower than those achieved using ENT alone. Quadratic regression model and exponential model exhibited higher r2 values than power model and simple linear regression model slightly.[Conclusion] The combination of spectral and special information can improve the accuracy of LAIe estimation effectively when the high-resolution image was used to invert LAIe of black locust plantations. However, not all combined spectral and textural information can obtained higher accuracy comparing to the solely textural information. The model types influenced the accuracy of LAIe estimation slightly. Our results showed that comprehensive use of spatial and spectral information and appropriate selection of model was beneficial to accurate estimation and inversion of forestry parameters.

Key words: effective leaf area index(LAIe), spectral-textural information, texture, high resolution imagery, black locust plantation

CLC Number: