The applicability of empirical vegetation indices for determining leaf chlorophyll content over different leaf and canopy structures

Croft, Holly; Chen, J.M.; Zhang, Y.

doi:10.1016/j.ecocom.2013.11.005

Cited by 156 publications

(100 citation statements)

References 63 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…2) in BLs species compared to the Pine species [Heiskanen et al, 2013]. This is in agreement with observation by Croft et al [2014b] where broadleaf samples exhibit extremely strong relationships between spectral indices and leaf chlorophyll content and presented strong results at leaf and canopy scales. Amongst different VIs, GMI, NDVI, and VOG followed by REP techniques have shown the highest potential for discriminating tree species with different ages.…”

Section: Discussionsupporting

confidence: 79%

“…They can decrease the effects of irradiance and exposure while maintaining sensitivity to vegetation and increase contrast between different features on image. So far, there are several reports that are used VIs for discriminating tree species using airborne hyperspectral or spectrometer data, however, they are often developed and implemented on broadleaf species [Croft et al, 2014b]. Relatively, there are little study examined VIs for discriminating needle leaves tree species that represent a large component of boreal forests and significant global ecosystems [Croft et al, 2014b].…”

Section: Introductionmentioning

confidence: 99%

“…So far, there are several reports that are used VIs for discriminating tree species using airborne hyperspectral or spectrometer data, however, they are often developed and implemented on broadleaf species [Croft et al, 2014b]. Relatively, there are little study examined VIs for discriminating needle leaves tree species that represent a large component of boreal forests and significant global ecosystems [Croft et al, 2014b]. Pu [2009] used different spectral variables and VIs to discriminate 11 different broadleaf species with the aid of two supervised classification algorithms, i.e., artificial neural network (ANN) and linear discriminant analysis (LDA), with in situ hyperspectral data.…”

Section: Introductionmentioning

confidence: 99%

“…Heiskanen et al [2013] examined sensitivity of narrowband VIs for estimating LAI in southern boreal forest including coniferous and broadleaved, using three Hyperion images and compared with broadband VIs. Croft et al [2014b] employed different VIs for estimating foliar chlorophyll content from different leaf and canopy structures including broad-and needle-leaves using medium-spectral resolution imaging spectrometer data acquired by satellite. Further on tree species discrimination, there is very limited study reported on discriminating tree species with different ages using airborne hyperspectral data particularly employing VIs.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Influence of tree species complexity on discrimination performance of vegetation Indices

Ghiyamat

Shafri

Shariff

2016

European Journal of Remote Sensing

View full text Add to dashboard Cite

Performance of different vegetation indices (VIs) in combination with single-and multipleendmember (SEM and MEM) for discriminating Corsican and Scots pines with different ages and Broadleaves tree species is demonstrated by using an airborne hyperspectral data. The analysis is performed in three different complexity levels. The results show by increasing tree species complexity, overall accuracy significantly reduced. An overall accuracy up to 90% is obtained from the first category with the least complexity; however, it is reduced to 55% in the third category with the highest complexity. By employing MEM, performance of normalized difference vegetation index (NDVI) is increased by 10%.

show abstract

Section: Discussionsupporting

confidence: 79%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Influence of tree species complexity on discrimination performance of vegetation Indices

Ghiyamat

Shafri

Shariff

2016

European Journal of Remote Sensing

View full text Add to dashboard Cite

show abstract

“…Several studies have examined the relationship of VIs and CC leaf hyperspectral response at the leaf level [19,41,45]. Sensors on board different satellites have estimated vegetation CC using VIs at varying spatial resolutions, from a few to hundreds of meters [46][47][48]. While field spectroscopy and satellite-derived VIs have been used to estimate the chlorophyll content in leaves and canopies in different vegetation types, only few studies have focused on mangrove forests [21,22,30,32].…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal Variation in Mangrove Chlorophyll Concentration Using Landsat 8

et al. 2015

View full text Add to dashboard Cite

There is a need to develop indicators of mangrove condition using remotely sensed data. However, remote estimation of leaf and canopy biochemical properties and vegetation condition remains challenging. In this paper, we (i) tested the performance of selected hyperspectral and broad band indices to predict chlorophyll concentration (CC) on mangrove leaves and (ii) showed the potential of Landsat 8 for estimation of mangrove CC at the landscape level. Relative leaf CC and leaf spectral response were measured at 12 Elementary Sampling Units (ESU) distributed along the northwest coast of the Yucatan Peninsula, Mexico. Linear regression models and coefficients of determination were computed to measure the association between CC and spectral response. At leaf level, the narrow band indices with the largest correlation with CC were Vogelmann indices and the MTCI (R 2 > 0.5). Indices with spectral bands around the red edge (705-753 nm) were more OPEN ACCESSRemote Sens. 2015, 7 14531 sensitive to mangrove leaf CC. At the ESU level Landsat 8 NDVI green, which uses the green band in its formulation explained most of the variation in CC (R 2 > 0.8). Accuracy assessment between estimated CC and observed CC using the leave-one-out cross-validation (LOOCV) method yielded a root mean squared error (RMSE) = 15 μg·cm −2 , and R 2 = 0.703. CC maps showing the spatiotemporal variation of CC at landscape scale were created using the linear model. Our results indicate that Landsat 8 NDVI green can be employed to estimate CC in large mangrove areas where ground networks cannot be applied, and mapping techniques based on satellite data, are necessary. Furthermore, using upcoming technologies that will include two bands around the red edge such as Sentinel 2 will improve mangrove monitoring at higher spatial and temporal resolutions.

show abstract

Seasonal controls of canopy chlorophyll content on forest carbon uptake: Implications for GPP modeling

et al. 2015

Self Cite

View full text Add to dashboard Cite

Forested ecosystems represent an important part of the global carbon cycle, with accurate estimates of gross primary productivity (GPP) crucial for understanding ecosystem response to environmental controls and improving global carbon models. This research investigated the relationships between leaf area index (LAI) and leaf chlorophyll content (Chl Leaf ) with forest carbon uptake. Ground measurements of LAI and Chl Leaf were taken approximately every 9 days across the 2013 growing season from day of year (DOY) 130 to 290 at Borden Forest, Ontario. These biophysical measurements were supported by on-site eddy covariance flux measurements. Differences in the temporal development of LAI and Chl Leaf were considerable, with LAI reaching maximum values within approximately 10 days of bud burst at DOY 141. In contrast, Chl Leaf accumulation only reached maximum values at DOY 182. This divergence has important implications for GPP models which use LAI to represent the fraction of light absorbed by a canopy (fraction of absorbed photosynthetic active radiation (fAPAR)). Daily GPP values showed the strongest relationship with canopy chlorophyll content (Chl Canopy ; R 2 = 0.69, p < 0.001), with the LAI and GPP relationship displaying nonlinearity at the start and end of the growing season (R 2 = 0.55, p < 0.001). Modeled GPP derived from LAI × PAR and Chl Canopy × PAR was tested against measured GPP, giving R 2 = 0.63, p < 0.001 and R 2 = 0.82, p < 0.001, respectively. This work demonstrates the importance of considering canopy pigment status in deciduous forests, with models that use fAPAR LAI rather than fAPAR Chl neglecting to account for the importance of leaf photosynthetic potential.

show abstract

The applicability of empirical vegetation indices for determining leaf chlorophyll content over different leaf and canopy structures

Cited by 156 publications

References 63 publications

Influence of tree species complexity on discrimination performance of vegetation Indices

Influence of tree species complexity on discrimination performance of vegetation Indices

Spatiotemporal Variation in Mangrove Chlorophyll Concentration Using Landsat 8

Seasonal controls of canopy chlorophyll content on forest carbon uptake: Implications for GPP modeling

Contact Info

Product

Resources

About