Impact of understorey on overstorey leaf area index estimation from optical remote sensing in five forest types in northeastern China

Qi, Yujiao; Li, Fengri; Liu, Zhili; Jin, Guangze

doi:10.1016/j.agrformet.2014.08.001

Cited by 25 publications

(16 citation statements)

References 63 publications

(73 reference statements)

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“…In addition, it is not entirely independent from the tree canopy since changes in canopy closure or tree layer LAI will lead to a change in the species composition and green LAI of ground vegetation (Rautiainen and Heiskanen, 2013). Generally, although the composition of understory is complex and site dependent, the typical species are shrubs, grasses and other herbaceous plants, mosses and lichens (e.g., Deering et al, 1999;Maeno and Hiura, 2000;Peltoniemi et al, 2005;Liang et al, 2012;Ryu et al, 2014;Qi et al, 2014;. In this paper, the understory LAI is estimated by averaging the retrievals based on the GLOBCARBON LAI algorithm for shrubs and grasses/crop/other non-forest vegetation.…”

Section: Discussionmentioning

confidence: 99%

Separating overstory and understory leaf area indices for global needleleaf and deciduous broadleaf forests by fusion of MODIS and MISR data

2017

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Abstract. Forest overstory and understory layers differ in carbon and water cycle regimes and phenology, as well as ecosystem functions. Separate retrievals of leaf area index (LAI) for these two layers would help to improve modeling forest biogeochemical cycles, evaluating forest ecosystem functions and also remote sensing of forest canopies by inversion of canopy reflectance models. In this paper, overstory and understory LAI values were estimated separately for global needleleaf and deciduous broadleaf forests by fusing MISR and MODIS observations. Monthly forest understory LAI was retrieved from the forest understory reflectivity estimated using MISR data. After correcting for the background contribution using monthly mean forest understory reflectivities, the forest overstory LAI was estimated from MODIS observations. The results demonstrate that the largest extent of forest understory vegetation is present in the boreal forest zones at northern latitudes. Significant seasonal variations occur for understory vegetation in these zones with LAI values up to 2-3 from June to August. The mean proportion of understory LAI to total LAI is greater than 30 %. Higher understory LAI values are found in needleleaf forests (with a mean value of 1.06 for evergreen needleleaf forests and 1.04 for deciduous needleleaf forests) than in deciduous broadleaf forests (0.96) due to the more clumped foliage and easier penetration of light to the forest floor in needleleaf forests. Spatially and seasonally variable forest understory reflectivity helps to account for the effects of the forest background on LAI retrieval while compared with constant forest background. The retrieved forest overstory and understory LAI values were compared with an existing dataset for larch forests in eastern Siberia (40-75 • N, 45-180 • E). The retrieved overstory and understory LAI is close to that of the existing dataset, with an absolute error of 0.02 (0.06), relative error of 1.3 % (14.3 %) and RMSE of 0.93 (0.29) for overstory (understory). The comparisons between our results and field measurements in eight forest sites show that the R 2 values are 0.52 and 0.62, and the RMSEs are 1.36 and 0.62 for overstory and understory LAI, respectively.

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Section: Discussionmentioning

confidence: 99%

Separating overstory and understory leaf area indices for global needleleaf and deciduous broadleaf forests by fusion of MODIS and MISR data

2017

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“…In b, the black line represents the mean relative difference for data from 2000 to 2015 quickly after a short constant period, which is a trend that also has been found in the MODIS LAI of deciduous broadleaf forest (Wang et al 2005). The MODIS LAI product is used as a key variable for the MODIS gross primary productivity/ net primary productivity (GPP/NPP) product, and therefore, errors in LAI will lead to uncertainty in the MODIS GPP/NPP product (Heinsch et al 2006;De Kauwe et al 2011;Qi et al 2014). An underestimation of the MODIS GPP product for Moso bamboo forest compared with observations from the flux tower has been confirmed (Xu et al 2013), and it might be strongly related to the underestimation of the MODIS LAI product.…”

Section: Comparison Of the Lai Estimates With The Modis Lai Productmentioning

confidence: 83%

“…The reason for this is that reflectance at 550 nm (central wavelength of the green band) might be highly sensitive to CC in some types of leaves, e.g., uniformly young leaves (Datt 1999). The retrieval of LAI data from remote sensing imagery can be hindered by the effect of the understory component on the relationships between the LAI and remote sensing reflectance data (De Kauwe et al 2011;Qi et al 2014). Leaf change in off-year will increase the effect of the understory component on the relationships between biophysical parameters and vegetation indices and bring about greater uncertainties in the models for estimating the LAI and CC in off-years compared to on-years (Table 2).…”

Section: Effect Of Various Factors On the Accuracy Of Lai And CC Estimentioning

confidence: 99%

Remote estimation of canopy leaf area index and chlorophyll content in Moso bamboo (Phyllostachys edulis (Carrière) J. Houz.) forest using MODIS reflectance data

Zhou

et al. 2018

Annals of Forest Science

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& Key message We estimated the leaf area index (LAI) and canopy chlorophyll content (CC) of Moso bamboo forest by using statistical models based on MODIS data and field measurements. Results showed that the statistical model driven by MODIS data has the potential to accurately estimate LAI and CC, while the structure of the calibration models varied between on-and off-years because of the different leaf change and bamboo shoot production characteristics between these types of years. & Context LAI and CC (gram per square meter of ground area) are important parameters for determining carbon exchange between Moso bamboo forest (Phyllostachys edulis (Carrière) J. Houz.) and the atmosphere. & Aims This study evaluated the ability of a statistical model driven by MODIS data to accurately estimate the LAI and CC in Moso bamboo forest, and differences in the LAI and CC between on-years (years with great shoot production) and off-years (years with less shoot production) were analyzed. & Methods The LAI and CC measurements were collected in Anji County, Zhejiang Province, China. Indicators of LAI and CC were calculated from MODIS data. Then, a regression analysis was used to build relationships between the LAI and CC and various indicators on the basis of leaf change and bamboo shoot production characteristics of Moso bamboo forest. & Results LAI and CC were accurately estimated by using the regression analysis driven by MODIS-derived indicators with a relative root mean squared error (RMSEr) of 9.04 and 13.1%, respectively. The structure of the calibration models varied between on-and offyears. Long-term time series analysis from 2000 to 2015 showed that LAI and CC differed largely between on-and off-years. & Conclusion This study demonstrates that LAI and CC of Moso bamboo forest can be estimated accurately by using a statistical model driven by MODIS-derived indicators, but attention should be paid to differences in the calibration models between on-and off-years.

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“…Besides, it is not entirely independent from the tree canopy since changes in canopy closure or tree layer LAI will lead to a change in the species composition and green LAI of ground vegetation (Rautianinen and Heiskanen, 2013). Generally, although the composition of understory is complex and site-dependent, the typical species are shrubs, grasses and other herbaceous plants, mosses and lichens (e.g., Deering et al, 1999;Maeno and Hiura, 2000;Peltoniemi et al, 2005;Liang et al, 2012;Ryu et al, 2014;Qi et al, 2014;Nikopensius et al, 2015). In this paper, the understory LAI is estimated by averaging the retrievals based on GLOBCARBON LAI algorithm for shrubs and grasses/crop/other non-forest vegetation.…”

Section: Discussionmentioning

confidence: 99%

Separating of Overstory and Understory Leaf Area Indices for Global Needleleaf and Deciduous Broadleaf Forests by Fusion of MODIS and MISR Data

Liu¹,

Písek²,

Chen³

2016

Preprint

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Abstract. Forest overstory and understory layers differ in carbon and water cycle regimes, phenology, as well as ecosystem functions. Separate retrievals of Leaf Area Index (LAI) for these two layers would help to improve modeling forest biogeochemical cycles and evaluating forest ecosystem functions. In this paper, overstory and understory LAI values were estimated separately for global needleleaf and deciduous broadleaf forests by fusing MISR and MODIS observations. Monthly forest understory LAI was retrieved from the forest understory reflectivity estimated using MISR data. After correcting for the background contribution using monthly mean forest understory reflectivities, the forest overstory LAI was estimated from MODIS observations. The results demonstrate that forest understory vegetation is mainly distributed in the boreal forest zones at northern latitudes. Significant seasonal variations are presented for understory vegetation in these zones with LAI values up to 2&#8211;3 from June to August. Higher understory LAI values are found in needleleaf forests (with mean value of 1.06 for evergreen needleleaf forests and 1.04 for deciduous needleleaf forests) than in deciduous broadleaf forests (0.96) due to the more clumped foliage and easier penetration of light to the forest floor in needleleaf forests. The magnitude of seasonal variations of overstory LAI is larger for deciduous needleleaf forests than those of evergreen and deciduous needleleaf forests. In contrast, for forest understory, needleleaf forests show larger seasonal variations than broadleaf forests. Spatially and seasonally variable forest understory reflectivity helps to account for the effects of the forest background on LAI retrieval while compared with constant forest background. The retrieved forest overstory and understory LAI values were compared with an existing dataset for larch forests in Northern Eurasia. The retrieved overstory and understory LAI is close to that of the existing dataset, with an absolute error of 0.34 (0.06), relative error of 21.1&#8201;% (14.3&#8201;%) and RMSE of 0.93 (0.29) for overstory (understory). The comparisons between our results and field measurements in eight forest sites show that the R2 are 0.62 and 0.52, and the RMSE are 0.62 and 1.36 for understory and overstory LAI, respectively.

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Impact of understorey on overstorey leaf area index estimation from optical remote sensing in five forest types in northeastern China

Cited by 25 publications

References 63 publications

Separating overstory and understory leaf area indices for global needleleaf and deciduous broadleaf forests by fusion of MODIS and MISR data

Separating overstory and understory leaf area indices for global needleleaf and deciduous broadleaf forests by fusion of MODIS and MISR data

Remote estimation of canopy leaf area index and chlorophyll content in Moso bamboo (Phyllostachys edulis (Carrière) J. Houz.) forest using MODIS reflectance data

Separating of Overstory and Understory Leaf Area Indices for Global Needleleaf and Deciduous Broadleaf Forests by Fusion of MODIS and MISR Data

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