Retrieval of seasonal dynamics of forest understory reflectance from semiarid to boreal forests using MODIS BRDF data

Písek, Jan; Chen, Jing M.; Kobayashi, Hideki; Rautiainen, Miina; Schaepman, Michael E.; Karnieli, Arnon; Sprinstin, Michael; Ryu, Youngryel; Nikopensius, Maris; Raabe, Kairi

doi:10.1002/2016jg003322

Cited by 21 publications

(17 citation statements)

References 64 publications

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“…Thus, the changes in the forest understory among years and within months, such as fire and other disturbances, are not considered in this paper. Recently, the background reflectivity has been retrieved from MODIS BRDF products with a temporal resolution of 8 days over selected sites (Pisek et al, , 2016. Such BRDF products have high temporal resolutions and should make it possible to account for the interannual and seasonal variations in the forest understory vegetation in the future.…”

Section: Discussionmentioning

confidence: 99%

“…Although the GLOBMAP LAI u in September (0.9) is very close to the estimated field LAI u in DOY250, its time series does not appear stable during the whole year. This suggests that large uncertainties may exist in the retrieved understory and overstory LAI for such dense and closed canopy which make it difficult to observe the signals from understory on the top of canopy, which has also been pointed out by Pisek et al (2016).…”

Section: Validation With Field Measurementsmentioning

confidence: 91%

“…The overstory is dominated by very big European beech and Norway spruce, with effective LAI up to 5.5, mean tree height of 30.6 m and maximum crown radius greater than 10 m (see details in Pisek et al, 2016). The un- derstory vegetation is sparse and consists mainly of Allium ursinum.…”

Section: Validation With Field Measurementsmentioning

confidence: 99%

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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%

Section: Validation With Field Measurementsmentioning

confidence: 91%

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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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“…Because larch density across this region is low [46], it is likely that NDVI trends are the result of combined understory and overstory responses to climate, or even dominated by understory responses. This highlights the need to separate overstory and understory spectral dynamics [47][48][49] in boreal larch forests. It is even possible that browning trends could result from increases in forest cover in these ecosystems.…”

Section: Implications For Interpreting Ndvi Trendsmentioning

confidence: 99%

Vegetation Indices Do Not Capture Forest Cover Variation in Upland Siberian Larch Forests

et al. 2018

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Boreal forests are changing in response to climate, with potentially important feedbacks to regional and global climate through altered carbon cycle and albedo dynamics. These feedback processes will be affected by vegetation changes, and feedback strengths will largely rely on the spatial extent and timing of vegetation change. Satellite remote sensing is widely used to monitor vegetation dynamics, and vegetation indices (VIs) are frequently used to characterize spatial and temporal trends in vegetation productivity. In this study we combine field observations of larch forest cover across a 25 km 2 upland landscape in northeastern Siberia with high-resolution satellite observations to determine how the Normalized Difference Vegetation Index (NDVI) and the Enhanced Vegetation Index (EVI) are related to forest cover. Across 46 forest stands ranging from 0% to 90% larch canopy cover, we find either no change, or declines in NDVI and EVI derived from PlanetScope CubeSat and Landsat data with increasing forest cover. In conjunction with field observations of NDVI, these results indicate that understory vegetation likely exerts a strong influence on vegetation indices in these ecosystems. This suggests that positive decadal trends in NDVI in Siberian larch forests may correspond primarily to increases in understory productivity, or even to declines in forest cover. Consequently, positive NDVI trends may be associated with declines in terrestrial carbon storage and increases in albedo, rather than increases in carbon storage and decreases in albedo that are commonly assumed. Moreover, it is also likely that important ecological changes such as large changes in forest density or variable forest regrowth after fire are not captured by long-term NDVI trends.

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“…Including its contribution in reflectance models for predicting forest variables, such as the leaf area index (LAI) from satellite imagery, is important for ensuring representativeness of the results (Eriksson et al 2006). Due to the laborious nature of collecting reference spectra by field measurements for large forest areas, promising results have been reported of algorithms that would enable direct retrieval of understory reflectance from multiangular airborne and satellite measurements (Pisek et al 2010(Pisek et al , 2016. However, in-situ remote sensing measurements remain the most reliable method if spectral characterization is desired for specific understory species or a composition of species.…”

Section: Introductionmentioning

confidence: 99%

Seasonal dynamics of lingonberry and blueberry spectra

Forsström¹,

Peltoniemi²,

Rautiainen³

2019

Silva Fenn.

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Accurate mapping of the spatial distribution of understory species from spectral images requires ground reference data which represent the prevailing phenological stage at the time of image acquisition. We measured the spectral bidirectional reflectance factors (BRFs, 350â2500 nm) at varying view angles for lingonberry ( L.) and blueberry ( L.) throughout the growing season of 2017 using Finnish Geospatial Research Instituteâs FIGIFIGO field goniometer. Additionally, we measured spectra of leaves and berries of both species, and flowers of lingonberry. Both lingonberry and blueberry showed seasonality in visible and near-infrared spectral regions which was linked to occurrences of leaf growth, flowering, berrying, and leaf senescence. The seasonality of spectra differed between species due to different phenologies (evergreen vs. deciduous). Vegetation indices, normalized difference vegetation index (NDVI), moisture stress index (MSI), plant senescence reflectance index (PSRI), and red-edge inflection point (REIP2), showed characteristic seasonal trends. NDVI and PSRI were sensitive to the presence of flowers and berries of lingonberry, while with blueberry the effects were less evident. Off-nadir observations supported differentiating the dwarf shrub species from each other but showed little improvement for detection of flowers and berries. Lingonberry and blueberry can be identified by their spectral signatures if ground reference data are available over the entire growing season. The spectral data measured in this study are reposited in the publicly open SPECCHIO Spectral Information System.Vaccinium vitis-idaeaVaccinium myrtillus

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Retrieval of seasonal dynamics of forest understory reflectance from semiarid to boreal forests using MODIS BRDF data

Cited by 21 publications

References 64 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

Vegetation Indices Do Not Capture Forest Cover Variation in Upland Siberian Larch Forests

Seasonal dynamics of lingonberry and blueberry spectra

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