A Simple Method for Retrieving Understory NDVI in Sparse Needleleaf Forests in Alaska Using MODIS BRDF Data

Abstract

Global products of leaf area index (LAI) usually show large uncertainties in sparsely vegetated areas because the understory contribution is not negligible in reflectance modeling for the case of low to intermediate canopy cover. Therefore, many efforts have been made to include understory properties in LAI estimation algorithms. Compared with the conventional data bank method, estimation of forest understory properties from satellite data is superior in studies at a global or continental scale over long periods. However, implementation of the current remote sensing method based on multi-angular observations is complicated. As an alternative, a simple method to retrieve understory NDVI (NDVIu) for sparse boreal forests was proposed in this study. The method is based on the fact that the bidirectional variation in NDVIu is smaller than that in canopy-level NDVI. To retrieve NDVIu for a certain pixel, linear extrapolation was applied using pixels within a 5 × 5 target-pixel-centered window. The NDVI values were reconstructed from the MODIS BRDF data corresponding to eight different solar-view angles. NDVIu was estimated as the average of the NDVI values corresponding to the position in which the stand NDVI had the smallest angular variation. Validation by a noise-free simulation data set yielded high agreement between estimated and true NDVIu, with R 2 and RMSE of 0.99 and 0.03, respectively. Using OPEN ACCESSRemote Sens. 2014, 6 11937 the MODIS BRDF data, we achieved an estimate of NDVIu close to the in situ measured value (0.61 vs. 0.66 for estimate and measurement, respectively) and reasonable seasonal patterns of NDVIu in 2010 to 2013. The results imply a potential application of the retrieved NDVIu to improve the estimation of overstory LAI for sparse boreal forests and ultimately to benefit studies on carbon cycle modeling over high-latitude areas.

Spectral Properties of Coniferous Forests: A Review of In Situ and Laboratory Measurements

“…Nevertheless, in situ measurements of understory layers or plants have been collected and reported in scientific publications by a few research teams in boreal and subarctic coniferous forests in Scandinavia [85,86] and North America [83,87,88], in European hemiboreal forests [89,90], and in larch forests in Siberia [91]. These sites do not cover the full variation in different understory types, nor do most of the datasets cover the SWIR region.…”

“…The forest floor can contribute significantly to the reflectance of coniferous forests measured by remote sensors [13,[75][76][77][78][79]. As the contribution of forest floor in sparse coniferous stands can easily reach up to 50% of forest total reflectance [12], several approaches for retrieving understory properties from optical satellite data have been developed [80][81][82][83]. All these methods need in situ data to evaluate their performance.…”

“…Nevertheless, in situ measurements of understory layers or plants have been collected and reported in scientific publications by a few research teams in boreal and subarctic coniferous forests in Scandinavia [85,86] and North America [83,87,88], in Figure 5. Reflectance spectra of woody components (tree stem or pieces of bark) measured in a laboratory for Picea, Larix, Abies, and Cryptomeria genera.…”

A Phenology-Based Method for Monitoring Woody and Herbaceous Vegetation in Mediterranean Forests from NDVI Time Series

“…They applied multi-angle observations of canopy reflectance to monitor changes in understory reflectance. Yang et al [18] proposed a more empirical-based method using the Moderate Resolution Imaging Spectroradiometer (MODIS) Bidirectional Reflectance Distribution Function (BRDF) product to retrieve ecosystem understory Normalized Difference Vegetation Index (NDVIu). Although both methods successfully retrieve understory reflectance in sparse forests [19] they are unable to separate the contribution of woody and herbaceous vegetation to the understory signal.…”

Exploiting Differential Vegetation Phenology for Satellite-Based Mapping of Semiarid Grass Vegetation in the Southwestern United States and Northern Mexico

“…Roderick et al [47] and Lu et al [48] used methods based on the seasonal-trend decomposition by LOESS (STL) procedure [49] to map fractional cover of evergreen (woody) vegetation by isolating its signal from the seasonally green (herbaceous) signal in time series NDVI data for the Australian continent acquired by AVHRR. Pisek et al [50] used a semi-empirical approach [51] and a physically-based approach [52] to retrieve the understory (herbaceous) and overstory (woody) vegetation components of the seasonal NDVI signal observed at high latitude forest sites in Northern Europe. Scanlon et al [53] used seasonally averaged NDVI and interannual variations in wet-season NDVI as state-space variables in a linear unmixing model to estimate fractional cover of trees, grass and bare soil along an aridity gradient in southern Africa.…”