Shoot scattering phase function for Scots pine and its effect on canopy reflectance

Spectral and directional reflectance properties of coniferous forests are known to differ from those of broadleaf forests. Many reasons have been proposed for this, including differences in the optical properties of leaves and shoots, the latter being considered the basic unit in radiative transfer modeling of a coniferous canopy. Unfortunately, very little empirical data is available on the spectrodirectional scattering properties of shoots. Here, we present results of angular measurements (using an ASD FieldSpec 3 spectroradiometer mounted on LAGOS) of ten Scots pine shoots in the spectral range 400--2000 nm. The shoots were found to scatter anisotropically with most of the radiation reflected back into the hemisphere where the radiation source was positioned. To describe the measured directional scattering pattern, we propose a phase function consisting of isotropic and Lambertian scattering components. Next, we used the proposed scattering phase function in a Monte Carlo radiative transfer model. Angular reflectance of a modeled horizontally homogeneous shoot canopy has, due to shoot scattering anisotropy, an enhanced “dark spot” as compared with a canopy composed of isotropic scatterers and a quantitatively similar leaf canopy.Peer reviewe

Comparison of adaxial and abaxial spectral reflectance of Fagus orientalis Lipsky and Carpinus betulu using field spectroradiometer and spectral indices

The spectral reflectance of tree crown can be different from spectral reflectance of its leaves because of diverse leaf and branch angles as well as internal space of tree crown. For these reasons it is necessary to study spectral reflectance of both adaxial and abaxial surfaces of the leaves. Such information is necessity for modeling the reflectance of tree crown and forest stands. The main objective of this study was to obtain and study the spectral reflectance of both adaxial and abaxial leaves of beech and hornbeam in natural condition and to investigate their spectral differences using indices sensitive to chlorophyll, chlorophyll to carotenoid ratio and photosynthetic pigments. Field spectroradiometric measurements were performed using a portable spectroradiometer (ASD FieldSpec) in Kheyrud forest. A total of 52 trees were sampled and 312 spectra were recorded and analyzed. Spectral measurements cover the wavelength range between 350 – 2500 nm. The results of the spectral reflectance analysis of these two species showed that the abaxial spectral reflectance from 350 to 2500 nm was higher than the adaxial one for hornbeam species. However, for beech species in the visible region and far infrared region, the abaxial spectral reflectance was higher whereas in the near infrared it was lower than the adaxial one. For more detailed investigation of spectral reflectance difference for these two species, spectral indices sensitive to chlorophyll and carotenoid were calculated and statistically analyzed for both surfaces. The value of adaxial NDI index was found to be higher than abaxial for both species. In contrast, the values of adaxial SIPI and PRI indices were lower than abaxial. The differences significant (?= 0.01, p< 0.0001) for both species

Spectral reflectance of rice canopies and red edge position (REP) as indicator of High yield varieties

Rice is the staple food in Iran. More than 80 percent of rice area is distributed in the two northern provinces of Mazandaran and Gilan, so that investment in increasing the quantity and quality can impact an effective role in economic independence and sustainable agriculture. Increased efficiency in rice production is possible through varietal technology, advances in yield enhancement, and the successful development of hybrid technology. Nondestructive methods such as study the spectral reflectance of rice fields is a reliable way in remote sensing study. In this study we tested the possibility to predict highyielding rice varieties based on the spectral reflectance data in the red edge position (REP). Spectral reflectance of rice canopies from 350 to 2500 nm were acquired under clear sky in rice filed. The obtained results indicate that REP of Hybrid, Tarom, Neda and Khazar varieties are at longer wavelength, so they are predicted as more productive rice varieties

Merging the Minnaert-k parameter with spectral unmixing to map forest heterogeneity with CHRIS/PROBA data

The Compact High Resolution Imaging Spectrometer (CHRIS) mounted onboard the Project for Onboard Autonomy (PROBA) spacecraft is capable of sampling reflected radiation at five viewing angles over the visible and near-infrared regions of the solar spectrum with high spatial resolution. We combined the spectral domain with the angular domain of CHRIS data in order to map the surface heterogeneity of an Alpine coniferous forest during winter. In the spectral domain, linear spectral unmixing of the nadir image resulted in a canopy cover map. In the angular domain, pixelwise inversion of the Rahman-Pinty-Verstraete (RPV) model at a single wavelength at the red edge (722 nm) yielded a map of the Minnaert-k parameter that provided information on surface heterogeneity at a subpixel scale. However, the interpretation of the Minnaert-k parameter is not always straightforward because fully vegetated targets typically produce the same type of reflectance anisotropy as non-vegetated targets. Merging both maps resulted in a forest cover heterogeneity map, which contains more detailed information on canopy heterogeneity at the CHRIS subpixel scale than is possible to realize from a single-source optical data set

Using the Minnaert-k parameter derived from CHRIS/PROBA data for forest heterogeneity mapping

CHRIS/PROBA is capable of sampling reflected radiation at five viewing angles over the visible and near-infrared regions of the solar spectrum with a relatively high spatial resolution (~17m). We exploited both the spectral and angular domain of CHRIS data in order to map the surface heterogeneity of an Alpine coniferous forest during winter. In the spectral domain, linear spectral unmixing of the nadir image resulted in a canopy cover map. In the angular domain, pixelwise inversion of the Rahman–Pinty–Verstraete (RPV) model at a single wavelength at the red edge (722 nm) yielded a map of the Minnaert-k parameter that provided information on surface heterogeneity at subpixel scale. Merging both maps resulted in a forest cover heterogeneity map, which contains more detailed information on canopy heterogeneity at the CHRIS subpixel scale than can be obtained from a single-source data set

Using the right slope of the 970nm absorption feature for estimating canopy water content

Canopy water content (CWC) is important for understanding the functioning of terrestrial ecosystems. Biogeochemical processes like photosynthesis, transpiration and net primary production are related to foliar water. The first derivative of the reflectance spectrum at wavelengths corresponding to the left slope of the minor water absorption band at 970 nm was found to be highly correlated with CWC and PROSAIL model simulations showed that it was insensitive to differences in leaf and canopy structure, soil background and illumination and observation geometry. However, these wavelengths are also located close to the water vapour absorption band at about 940 nm. In order to avoid interference with absorption by atmospheric water vapour, the potential of estimating CWC using the first derivative at the right slope of the 970 nm absorption feature was studied. Measurements obtained with an ASD FieldSpec spectrometer for three test sites were related to CWC (calculated as the difference between fresh and dry weight). The first site was a homogeneous grassland parcel with a grass/clover mixture. The second site was a heterogeneous floodplain with natural vegetation like grasses and various shrubs. The third site was an extensively grazed fen meadow. Results for all three test sites showed that the first derivative of the reflectance spectrum at the right slope of the 970 nm absorption feature was linearly correlated with CWC. Correlations were a bit lower than those at the left slope (at 942.5 nm) as shown in previous studies, but better than those obtained with water band indices. FieldSpec measurements showed that one may use any derivative around the middle of the right slope within the interval between 1015 nm and 1050 nm. We calculated the average derivative at this interval. The first site with grassland yielded an R2 of 0.39 for the derivative at the previously mentioned interval with CWC (based on 20 samples). The second site at the heterogeneous floodplain yielded an R2 of 0.45 for this derivative with CWC (based on 14 samples). Finally, the third site with the fen meadow yielded an R2 of 0.68 for this derivative with CWC (based on 40 samples). Regression lines between the derivative at the right slope of the 970 nm absorption feature and CWC for all three test sites were similar although vegetation types were quite different. This indicates that results may be transferable to other vegetation types and other sites

An Empirical Bayesian Approach to Quantify Multi-Scale Spatial Structural Diversity in Remote Sensing Data

Landscape structure is as much a driver as a product of environmental and biological interactions and it manifests as scale-specific, but also as multi-scale patterns. Multi-scale structure affects processes on smaller and larger scales and its detection requires information from different scales to be combined. Herein, we propose a novel method to quantify multi-scale spatial structural diversity in continuous remote sensing data. We combined information from different extents with an empirical Bayesian model and we applied a new entropy metric and a value co-occurrence approach to capture heterogeneity. We tested this method on Normalized Difference Vegetation Index data in northern Eurasia and on simulated data and we also tested the effect of coarser pixel resolution. We find that multi-scale structural diversity can reveal itself as patches and linear landscape features, which persist or become apparent across spatial scales. Multi-scale line features reveal the transition zones between spatial regimes and multi-scale patches reveal those areas within transition zones where values are most different from each other. Additionally, spatial regimes themselves can be distinguished. We also find the choice of scale need not be informed by typical length-scales, which makes the method easy to implement. The proposed multi-scale approach can be applied to other contexts, following the roadmap we pave out in this study and using the tools available in the accompanying R package StrucDiv

Estimating forest parameters from top-of-atmosphere radiance measurements using coupled radiative transfer models

The canopy and atmosphere radiative transfer models SLC and MODTRAN were coupled to simulate top-of-atmosphere (TOA) radiance data for 3 Norway spruce stands in Eastern Czech Republic. The simulations fitted the near-nadir CHRIS radiance data well. A sensitivity analysis based on the singular value decomposition of the Jacobian matrix provided useful information for building the look up tables needed to estimate needle and canopy parameters. Canopy cover, fraction of bark in the plant area index, needle chlorophyll and dry matter content were estimated using the TOA CHRIS radiance. For comparison, the simulations, sensitivity analysis and parameter estimations were also conducted for the top of canopy (TOC) level, using atmospherically corrected CHRIS reflectance data. The results showed that the TOA approach performs as good as the TOC approach and allowed decreasing the ill-posedness for at least one stand

Characterization of forest understory using multi-temporal full-waveform airborne laser scanning

Der Unterwuchs als Teil der Waldstruktur hat eine wichtige Funktion im Hinblick auf die Dynamik der Waldentwicklung. Allerdings ist die Charakterisierung des Unterwuchses mittels Fernerkundungsmethoden problematisch, da die Vegetationsdichte eine Erfassung der vertikalen Struktur stark limitiert. Unter Verwendung von flugzeuggestütztem, multi-temporalen Laserscanning ist es möglich, den Unterwuchs in einem dichten Laubwald zu detektieren und zu charakterisieren. Basierend auf den geometrischen Informationen der Laser-Punktwolke und den zugehörigen full-waveform Charakteristiken wurden folgende Unterwuchsklassen abgeleitet: vegetationsfreie Flächen, Streu, Unterwuchs 3 m. Für die Validierung wurde sowohl terrestrisches Laserscanning als auch eine umfangreiche Feldmessung entsprechend dem VALERI Ansatzes verwendet. Die Detektion des Unterwuchses erfolgte mit einer Genauigkeit von 78%; die Klassifikation erreichte eine Genauigkeit von 64%