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

Spatial resolution, spectral metrics and biomass are key aspects in estimating plant species richness from spectral diversity in species‐rich grasslands

Increasing evidence suggests that remotely sensed spectral diversity is linked to plant species richness. However, a conflicting spectral diversity–biodiversity relationship in grasslands has been found in previous studies. In particular, it remains unclear how well the spectral diversity–biodiversity relationship holds in naturally assembled species-rich grasslands. To address the linkage between spectral diversity and plant species richness in a species-rich alpine grassland ecosystem, we investigated (i) the trade-off between spectral and spatial resolution in remote sensing data; (ii) the suitability of three different spectral metrics to describe spectral diversity (coefficient of variation, convex hull volume and spectral species richness) and (iii) the importance of confounding effects of live plant biomass, dead plant biomass and plant life forms on the spectral diversity–biodiversity relationship. We addressed these questions using remote sensing data collected with consumer-grade cameras with four spectral bands and 10 cm spatial resolution on an unmanned aerial vehicle (UAV), airborne imaging spectrometer data (AVIRIS-NG) with 372 bands and 2.5 m spatial resolution, and a fused data product of both datasets. Our findings suggest that a fused dataset can cope with the requirement of both high spatial- and spectral resolution to remotely measure biodiversity. However, in contrast to several previous studies, we found a negative correlation between plant species richness and spectral metrics based on the spectral information content (i.e. spectral complexity). The spectral diversity calculated based on the spectral complexity was sensitive to live and dead plant biomass. Overall, our results suggest that remote sensing of plant species diversity requires a high spatial resolution, the use of classification-based spectral metrics, such as spectral species richness, and awareness of confounding factors (e.g. plant biomass), which may be ecosystem specific

Estimating grassland biomass using SVM band shaving of hyperspectral data

In this paper, the potential of a band shaving algorithm based on support vector machines (SVM) applied to hyperspectral data for estimating biomass within grasslands is studied. Field spectrometer data and biomass measurements were collected from a homogeneously managed grassland field. The SVM band shaving technique was compared with a partial least squares (PLS) and a stepwise forward selection analysis. Using their results, a range of vegetation indices was used as predictors for grassland biomass. Results from the band shaving showed that one band in the near-infrared region from 859 to 1,006 nm and one in the red-edge region from 668 to 776 nm used in the weighted difference vegetation index (WDVI) had the best predictive power, explaining 61 percent of grassland biomass variation. Indices based on short-wave infrared bands performed worse. Results could subsequently be applied to larger spatial extents using a high-resolution airborne digital camera (for example, Vexcel’s UltraCamTM)

rasterdiv ‐ an Information Theory tailored R package for measuring ecosystem heterogeneity from space: to the origin and back

Ecosystem heterogeneity has been widely recognized as a key ecological indicator of several ecological functions, diversity patterns and change, metapopulation dynamics, population connectivity or gene flow. In this paper, we present a new R package—rasterdiv—to calculate heterogeneity indices based on remotely sensed data. We also provide an ecological application at the landscape scale and demonstrate its power in revealing potentially hidden heterogeneity patterns. The rasterdiv package allows calculating multiple indices, robustly rooted in Information Theory, and based on reproducible open-source algorithms

Monitoring plant functional diversity from space

The world’s ecosystems are losing biodiversity fast. A satellite mission designed to track changes in plant functional diversity around the globe could deepen our understanding of the pace and consequences of this change and how to manage it

The Laegeren site: an augmented forest laboratory combining 3-D reconstruction and radiative transfer models for trait-based assessment of functional diversity

Given the increased pressure on forests and their diversity in the context of global change, new ways of monitoring diversity are needed. Remote sensing has the potential to inform essential biodiversity variables on the global scale, but validation of data and products, particularly in remote areas, is difficult. We show how radiative transfer (RT) models, parameterized with a detailed 3-D forest reconstruction based on laser scanning, can be used to upscale leaf-level information to canopy scale. The simulation approach is compared with actual remote sensing data, showing very good agreement in both the spectral and spatial domains. In addition, we compute a set of physiological and morphological traits from airborne imaging spectroscopy and laser scanning data and show how these traits can be used to estimate the functional richness of a forest at regional scale. The presented RT modeling framework has the potential to prototype and validate future spaceborne observation concepts aimed at informing variables of biodiversity, while the trait-based mapping of diversity could augment in situ networks of diversity, providing effective spatiotemporal gap filling for a comprehensive assessment of changes to diversity

Design and prototyping of the SPECTRA simulator architecture

SPECTRA (Surface Processes and Ecosystem Changes through Response Analysis) is a planned spaceborne multiangular hyperspectral and thermal imaging spectrometer in phase A early design led by ESA's earth observation group. Its mission is to describe, understand and model the role of terrestrial vegetation in the global carbon cycle and its response to climate variability. Even though the project has been terminated in November 2005, many results of the phase A studies are considered to be useful as input to future missions. The SPECTRA end-to-end simulator is intended to be used to test different aspects of the SPECTRA mission concept and for tuning the retrieval algorithms as well as assessing their performances. The intention of this ESA-commissioned study was not to build an actually working simulator, but to conceive an architecture for a simulator to be built during phase B of the SPECTRA design, as well as perform a limited validation of this architecture. The software architecture for the future SPECTRA end-to-end simulator has been designed to be modular, flexible and distributed. It consists of a central control unit with associated database, which is controlled and monitored via an internet-accessible web interface, and a flexible number of modules performing the actual calculations. The list of simulator modules currently includes but is not limited to state-of-the-art developments in radiative transfer (Onera), instrument modelling (ESA), atmospheric correction (Onera), and various level 2 algorithms (Alterra). Assimilation models and global carbon flux models are linked to the simulator via the SPECTRA field segment database (RSL and Princeton), for which a high level schema has been defined. The simulator structure has been validated using full end-to-end simulations from ground data to top-of-atmosphere, through the SPECTRA instrument simulator provided by industry, and back again. Test data from the Barrax field site are used for this purpose (University of Valencia)