BRDF measurement of understory vegetation in pine forests: dwarf shrubs, lichen, and moss

Peltoniemi, Jouni; Kaasalainen, Sanna; Näränen, Jyri; Rautiainen, Miina; Stenberg, Pauline; Smolander, Heikki; Smolander, Sampo; Voipio, Pekka

doi:10.1016/j.rse.2004.10.009

Cited by 113 publications

(69 citation statements)

References 20 publications

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“…Improving the retrieval rate of NDVIu will be carried out in future works. However, the present approach can be regarded as an alternative to the data bank method for dealing with the background properties (e.g., [12,14]), and it provides a much larger number of samples than conventional in situ methods do by covering wide areas and long periods. It is also worth noting that the performance of the proposed method would be influenced by the accuracy of land-cover products.…”

Section: Toward Practical Applications Of the Retrieved Ndviumentioning

confidence: 99%

“…In conventional LAI retrieval algorithms, the understory reflectance is either given as fixed value for each wavelength [8] or modeled by simple bidirectional reflectance distribution function (BRDF) models based on field measurements [9,10]. In fact, large spatial and temporal variations in understory layers have been observed, even among the same species [11][12][13].…”

Section: Introductionmentioning

confidence: 99%

“…To account for the understory effects on stand reflectance, several efforts to collect spectral data banks for forest understory have been undertaken in specific study regions, for example, Kuusk et al [14] in Estonian and Swedish sub-boreal forest and Peltoneimi et al [12] in the pine forests of Finland. However, such a data bank method is very time consuming and costly when we consider study areas at a global or continental scale, even considering only common understory types.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2014

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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.

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Section: Toward Practical Applications Of the Retrieved Ndviumentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

et al. 2014

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“…measured spectral libraries of common background components such as shrub, grass, lichen, and moss [e.g., Lang et al, 2002;Miller et al, 1997;Peltoniemi et al, 2005;Rautiainen et al, 2007;Rees et al, 2004] as input in forest canopy models. However, the limited spectral libraries would not represent well the optical properties of real forest background which often has highly spatial and temporal variations [Peltoniemi et al, 2005;Rautiainen et al, 2011]. The misrepresentation of forest background would finally result in great uncertainty in the retrieved canopy LAI.…”

Section: Introductionmentioning

confidence: 99%

Mapping global seasonal forest background reflectivity with Multi‐angle Imaging Spectroradiometer data

Jiao

Liu

et al. 2014

JGR Biogeosciences

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Forest background reflectivities with seasonal and spatial variations are critically important in the estimation of canopy biophysical parameters of the forest canopy. In this paper, seasonal background reflectivity for global forested areas was mapped at 1.1 km resolution using four-scale model and Multi-angle Imaging Spectroradiometer data of the nadir and 45°forward directions. The largest seasonal variation of forest background reflectivities was observed in middle and high latitudes of Northern Hemisphere. The background reflectivity differs between deciduous broadleaf forest and coniferous forest in the near-infrared band and varies with increasing canopy leaf area index. The partial validation of forest background reflectivity with adjacent grassland in the Northern Hemisphere and the comparison of understory leaf area index on leaf appearance day for larch forest in North Asia both indicate the relative reliability of results. The nearly 70% spatial coverage of retrieval with high-quality flags makes it eligible for applications over global coniferous and deciduous broadleaf forest areas.

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“…Information of understory trees is of relevance for forest management. Remote sensing was applied to understory vegetation (Eriksson et al 2006;Peltoniemi et al 2005;Korpela 2008). In passive optical data, the signals from the over and understory are mixed, which hampers the interpretation.…”

Section: Introductionmentioning

confidence: 99%

Mapping Understory Trees Using Airborne Discrete-Return Lidar Data

Korpela

Hovi

Morsdorf

2012

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Understory trees in multi-layer stands are often ignored in forest inventories. Information about them would benefit silviculture, wood procurement and biodiversity management. Cost-efficient inventory methods for the assessment of the presence, density, species-and size-distributions are called for. LiDAR remote sensing is a promising addition to field work. Unlike in passive image data, in which the signals from multiple layers mix, the 3D position of each hot-spot reflection is known in LiDAR data. The overstory however prevents from obtaining a wall-to-wall sample of understory, and measurements are subject to transmission losses. Discriminating between the crowns of dominant and suppressed trees can also be challenging. We examined the potential of LiDAR for the mapping of the understory trees in Scots pine stands (62°N, 24°E), using carefully georeferenced reference data and several LiDAR data sets. We present results that highlight differences in echo-triggering between sensors that affect the near-ground height data. A conceptual model for the transmission losses in the overstory was created and formulated into simple compensation models that reduced the intensity variation in second-and third return data. The task is highly ill-posed in discrete-return LiDAR data, and our models employed the geometry of the overstory as well as the intensity of previous returns. We showed that even first-return data in the understory is subject to losses in the overstory that did not trigger an echo. Even with compensation of the losses, the intensity data was deemed of low value in species discrimination. Area-based LiDAR height metrics that were derived from the data belonging to the crown volume of the understory showed reasonable correlation with the density and mean height of the understory trees. Assessment of the species seems out of reach in discrete-return LiDAR data, which is a drastic drawback.

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BRDF measurement of understory vegetation in pine forests: dwarf shrubs, lichen, and moss

Cited by 113 publications

References 20 publications

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

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

Mapping global seasonal forest background reflectivity with Multi‐angle Imaging Spectroradiometer data

Mapping Understory Trees Using Airborne Discrete-Return Lidar Data

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