Estimates of the Leaf Area Index Using Unmanned Aerial Vehicle Images of an Urban Mangrove in the Vitória Bay, Brazil

The urban mangrove of the Vitória Bay, Espírito Santo, Southern Brazil suffers from anthropogenic impacts, which interfere in the foliar spectral response of its species. Identifying the spectral behavior of these species and creating regression models to indirectly obtain structure data like the Leaf Area Index (LAI) are powerful environmental monitoring tools. In this study, LAI was obtained in 32 plots distributed in four stations. In situ LAI regression analysis with the SAVI resulted in significant positive relationships (r2 = 0.58). Forest variability regarding the degree of maturity and structural heterogeneity and LAI influenced the adjustment of vegetation indices (VIs). The highest regression values were obtained for the homogeneous field data, represented by R. mangle plots, which also had higher LAI values. The same field data were correlated with SAVI of a RapidEye image for comparison purposes. The results showed that, images obtained by a UAV have higher spatial resolution than the Rapideye image, and therefore had a greater influence of the background. Another point is that the statistical analysis of the field data with the IVs obtained from the RapidEye image did not present high regression coefficient (r2 = 0.7), suggesting that the use of VIs applied to the study of urban mangroves needs to be better evaluated, observing the factors that influence the leaf spectral response.

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Comparison of Vegetation Indices for Leaf Area Index Estimation in Vertical Shoot Positioned Vine Canopies with and without Grenbiule Hail-Protection Netting

Remote Sensing ◽

10.3390/rs11091073 ◽

2019 ◽

Vol 11 (9) ◽

pp. 1073 ◽

Cited By ~ 11

Author(s):

Pedro C. Towers ◽

Albert Strever ◽

Carlos Poblete-Echeverría

Keyword(s):

Leaf Area Index ◽

Leaf Area ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Linear Models ◽

Dynamic Range ◽

Spectral Response ◽

Incident Light ◽

Vegetation Indices ◽

Area Index

Leaf area per unit surface (LAI—leaf area index) is a valuable parameter to assess vine vigour in several applications, including direct mapping of vegetative–reproductive balance (VRB). Normalized difference vegetation index (NDVI) has been successfully used to assess the spatial variability of estimated LAI. However, sometimes NDVI is unsuitable due to its lack of sensitivity at high LAI values. Moreover, the presence of hail protection with Grenbiule netting also affects incident light and reflection, and consequently spectral response. This study analyses the effect of protective netting in the LAI–NDVI relationship and, using NDVI as a reference index, compares several indices in terms of accuracy and sensitivity using linear and logarithmic models. Among the indices compared, results show NDVI to be the most accurate, and ratio vegetation index (RVI) to be the most sensitive. The wide dynamic range vegetation index (WDRVI) presented a good balance between accuracy and sensitivity. Soil-adjusted vegetation index 2 (SAVI2) appears to be the best estimator of LAI with linear models. Logarithmic models provided higher determination coefficients, but this has little influence over the normal range of LAI values. A similar NDVI–LAI relationship holds for protected and unprotected canopies in initial vegetation stages, but different functions are preferable once the canopy is fully developed, in particular, if tipping is performed.

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