A comparison of three methods for estimating the LAI of black locust (Robinia pseudoacacia L.) plantations on the Loess Plateau, China

Mapping forest leaf area index using reflectance and textural information derived from WorldView-2 imagery in a mixed natural forest area in Florida, US

“…Therefore, such textural information extracted from the high spatial resolution image along with spectral information from the image should benefit characterization of forest structural parameters, including LAI compared to using spectral information only. For example, with spectral and textural features extracted from IKONOS, Colombo et al (2003), Song and Dickinson (2008) and Zhou et al (2014) all demonstrated that combining both spectral and spatial information provides some improvement in estimating LAI of vegetation canopies compared with spectral information only. For estimating other vegetation structural parameters such as vegetation communities and vegetation fractional coverage, Murray et al (2010) and Gu et al (2013) also proved that the combination of spectral and textural information could lead to increasing estimation accuracy.…”

“…In this study, per TM data, the 12 VIs consist of nine 2-band VIs and three 3-band VIs ( Table 2). The reason we chose the 12 VIs extracted from TM data is that they have been effectively and successfully used to estimate and map forest canopy LAI from moderate and high resolution multispectral remote sensing data (e.g., Colombo et al, 2003;Soudani et al, 2006;Song and Dickinson, 2008;Kraus et al, 2009;Gray and Song, 2012;Gu et al, 2012;Zhou et al, 2014). For the WV2 data, the 13 VIs consist of nine VIs that are the same as those constructed from TM data and four NDVI-like VIs (Table 2).…”

“…The selection of the 13 texture-based features was based on literature review and their potential for estimating and mapping forest structural parameters including LAI from high resolution MS data, which has been demonstrated in many existing studies (e.g., Kraus et al, 2009;Murray et al, 2010;Gebreslasie et al, 2011;Ozdemir and Karnieli, 2011;Gómez et al, 2011Gómez et al, , 2012Gu et al, 2012Gu et al, , 2013Shamsoddini et al, 2013;Zhou et al, 2014). The 1st-order statistical texture measures are derived from the pixel values in a moving window with different window sizes, but do not consider the spatial relationships among pixels within a window.…”

“…During the last two decades, many studies have demonstrated the potential of high resolution satellite remote sensing sensors (such as IKONOS and QuickBird) for estimating and mapping forest LAI spatially and temporally (e.g., Colombo et al, 2003;Soudani et al, 2006;Song and Dickinson, 2008;Gray and Song, 2012;Gu et al, 2012;Zhou et al, 2014). Frequently used spatial information derived from these high resolution images, called texture-based features (measures), could be used to effectively predict and map plant LAI.…”

“…both spatial and spectral information provided much improvement in estimating LAI compared with using spectral information only. With spatial information derived from QuickBird imagery, Zhou et al (2014) evaluated three different image processing techniques: processing based on SVIs, texture measures, and combinations of SVIs with textural analyses. They found that SVI-based approaches did not yield reliable LAI estimates, accounting for at best 68% of the observed variation in LAI; texture-based methods were somewhat better, explaining up to 72% of the observed variation; however, a combination of the two approaches yielded an even better adjusted R 2 value of 0.84.…”