Compact High-Resolution Imaging Spectrometer/Project For On-Board Autonomy Research Articles

Functional Phenology of a Texas Post Oak Savanna from a CHRIS PROBA Time Series

Remnant midwestern oak savannas in the USA have been altered by fire suppression and the encroachment of woody evergreen trees and shrubs. The Gus Engeling Wildlife Management Area (GEWMA) near Palestine, Texas represents a relatively intact southern example of thickening and evergreen encroachment in oak savannas. In this study, 18 images from the CHRIS/PROBA (Compact High-Resolution Imaging Spectrometer/Project for On-Board Autonomy) sensor were acquired between June 2009 and October 2010 and used to explore variation in canopy dynamics among deciduous and evergreen trees and shrubs, and savanna grassland in seasonal leaf-on and leaf-off conditions. Nadir CHRIS images from the 11 useable dates were processed to surface reflectance and a selection of vegetation indices (VIs) sensitive to pigments, photosynthetic efficiency, and canopy water content were calculated. An analysis of temporal VI phenology was undertaken using a fishnet polygon at 90 m resolution incorporating tree densities from a classified aerial photo and soil type polygons. The results showed that the major differences in spectral phenology were associated with deciduous tree density, the density of evergreen trees and shrubs—especially during deciduous leaf-off periods—broad vegetation types, and soil type interactions with elevation. The VIs were sensitive to high densities of evergreens during the leaf-off period and indicative of a photosynthetic advantage over deciduous trees. The largest differences in VI profiles were associated with high and low tree density, and soil types with the lowest and highest available soil water. The study showed how time series of hyperspectral data could be used to monitor the relative abundance and vigor of desirable and less desirable species in conservation lands.

Estimation of forest LAI by inverting canopy reflectance models and multi-angle imagery

Computer simulation models have seldom been applied for estimating the structural and biophysical variables of forest canopy. In this study, an approach for the estimation of leaf area index (LAI) using the information contained in hyperspectral, multi-angle images and the inversion of a computer simulation model are explored. For this purpose, L-systems combined with forest growth model ZELIG were applied to render 3-D forest architectural scenarios. The Radiosity-graphics combined model (RGM) was used to estimate forest LAI from the Compact High-Resolution Imaging Spectrometer/Project for On-Board Autonomy (CHRIS/PROBA) data. LAI inversion was performed using the look-up table (LUT) method. The estimated LAI was evaluated against in situ LAI measurement and compared against the LAI predictions from CHRIS data obtained using the Li-Strahler geometric-optical canopy reflectance model (GOMS). The results indicated that the method used in this study can be efficient strategy to estimate LAI by RGM model inversion.

RLQ and fourth-corner analysis of plant species traits and spectral indices derived from HyMap and CHRIS-PROBA imagery

Databases on plant traits as well as the availability of global coverage of high spatial and spectral resolution remote-sensing data are constantly growing. However, little effort has been made to analyse the relationship between plant traits and remote-sensing data while simultaneously taking species identity and abundance into consideration. We correlated quantitative and qualitative plant traits from a dwarf shrub savanna in Namibia, with spectral indices derived from two hyperspectral sensors, HyMap and the Compact High Resolution Imaging Spectrometer Project for On-Board Autonomy (CHRIS-PROBA), which differ in their spatial and spectral resolution. We used RLQ analysis and the fourth-corner statistic, which are two three-table ordination approaches that circumvent the so-called fourth-corner problem. A higher spatial resolution helped to identify trait–index correlations linked to vegetation structure, while a lower spatial resolution pointed at traits linked to vegetation cover. A higher spectral resolution did not improve the relationships between spectral indices and plant traits. However, continuous hyperspectral signatures allowed for the calculation of spectral indices that make use of the detailed spectra allowing for more sophisticated spectral indices. We propose RLQ and the fourth-corner statistic as suitable tools for the remote sensing and Earth observation community that allow the direct correlation of trait databases with remotely sensed information.

Improving the Robustness of Cotton Status Characterisation by Radiative Transfer Model Inversion of Multi-Angular CHRIS/PROBA Data

The Compact High-Resolution Imaging Spectrometer/Project for On-Board Autonomy (CHRIS/PROBA) mission has provided an unprecedented opportunity to study the potential of hyperspectral multiangular data for improved vegetation characterisation at a fine spatial scale. This study exploits to what degree the spectral anisotropy contained in the data can contribute to a more robust retrieval of chlorophyll and leaf area index of cotton compared to retrievals based on single angle observations. To do so, a simplified automated radiative transfer model (RTM) inversion scheme was applied both to the single observation angles and to various combinations of multiple view angles. It is shown that significant increase in accuracy is obtained when directional information is included. However, the improvement obtained by assimilating a certain observation angle appears to be strongly related to the ability of the RTM in reconstructing canopy reflectance for this constellation. Hence, due to the deficiency of the 1-D RTM to reconstruct observed reflectance for the row crop in the extreme forward looking direction, the result strongly deteriorated when this view angle was included. Model inversion based on all but the extreme forward looking view angle provided a root-mean-square error of 18.5% (6.6 μg/cm2 ) and 32.7% (0.55 m2 /m2) for chlorophyll and leaf area index, respectively.

Land cover classification using CHRIS/PROBA images and multi-temporal texture

Most existing multi-temporal classification studies use spectral information alone and ignore the temporal correlation between two-date images. This article proposes a new method to characterize the local temporal correlation using multi-temporal texture measured with a geostatistical function called the pseudo cross variogram (PCV). The derived multi-temporal texture, as an additional band, was combined with the spectral information in multi-temporal classification. The performance of the multi-temporal texture was evaluated and compared with the use of multi-temporal spectral data alone and plus the traditional variogram texture in land cover classification using bitemporal hyperspectral Compact High Resolution Imaging Spectrometer/Project for On Board Autonomy (CHRIS/PROBA) images. The results show that although land cover classification using spectral information from bitemporal CHRIS/PROBA data alone had an acceptable overall accuracy of 85.66%, the inclusion of multi-temporal texture in land cover classification led to significant increases (at the 95% confidence level) in both overall accuracy (3.3–4.3% improvement) and the kappa coefficient (4.9–6.6% improvement), particularly for vegetation classes. The incorporation of multi-temporal texture into multi-temporal land cover classification also outperformed the incorporation of the traditional variogram texture. The proposed method provides a new way to exploit the temporal correlation between bitemporal images for improved land cover classification.

Accurate LAI retrieval method based on PROBA/CHRIS data

Abstract. Leaf area index (LAI) is one of the key structural variables in terrestrial vegetation ecosystems. Remote sensing offers an opportunity to accurately derive LAI at regional scales. The anisotropy of canopy reflectance, variations in background characteristics, and variability in atmospheric conditions constitute three factors that can strongly constrain the accuracy of retrieved LAI. Based on a hybrid canopy reflectance model, a new hyperspectral directional second derivative method (DSD) is proposed in this paper. This method can estimate LAI accurately through analyzing the canopy anisotropy. The effect of the background can also be effectively removed. With the aid of a widely-accepted atmospheric model, the influence of atmospheric conditions can be minimized as well. Thus the inversion precision and the dynamic range can be markedly improved, which has been proved by numerical simulations. As the derivative method is very sensitive to random noise, we put forward an innovative filtering approach, by which the data can be de-noised in spectral and spatial dimensions synchronously. It shows that the filtering method can remove random noise effectively; therefore, the method can be applied to hyperspectral images. The study region was situated in Zhangye, Gansu Province, China; hyperspectral and multi-angular images of the study region were acquired via the Compact High-Resolution Imaging Spectrometer/Project for On-Board Autonomy (CHRIS/PROBA), on 4 June 2008. After the pre-processing procedures, the DSD method was applied, and the retrieved LAI was validated by ground reference data at 11 sites. Results show that the new LAI inversion method is accurate and effective with the aid of the innovative filtering method.

Retrieval of Leaf Area Index from CHRIS/PROBA data: an analysis of the directional and spectral information content

The use of Earth Observation (EO) data to retrieve biophysical variables of vegetated surfaces has proved to be useful in many operative tools to gather information repetitively, at spatial and temporal resolution, for agricultural and water management applications. The launch of the European Space Agency (ESA) Compact High‐Resolution Imaging Spectrometer/Project for On‐Board Autonomy (CHRIS/PROBA) mission has provided an opportunity to study a multiangular and hyperspectral dataset of images with high spatial resolution. The objective of the study was to use the CHRIS/PROBA data, in both directional and spectral domains, to estimate the Leaf Area Index (LAI). For this purpose, inversion of a canopy reflectance model was performed against CHRIS data. LAI estimates were validated by using ground truth LAI measurements and compared, in terms of accuracy, to a semi‐empirical approach. It was shown that, for a given spectral configuration, the directional information always improved the LAI estimation. For the best case (corn), this was achieved with an LAI root mean square error (RMSE) of 0.41 by using five angles and 62 spectral bands compared to a value of 1.42 by using one angle and four bands, as in the Landsat Thematic Mapper (TM) configuration.

Assessment of canopy structure and heterogeneity from multi-angular CHRIS/PROBA data

The spaceborne ESA-mission CHRIS-PROBA (Compact High Resolution Imaging Spectrometer-Project for On-board Autonomy) provides hyperspectral and multi-angular data of selected terrestrial targets (Barnsley et al. 2004). For vegetated surfaces, the spectral information content of CHRIS data may yield the biochemical and biophysical properties of a vegetation canopy, while the directional component may deliever additional information on its canopy structure. Here, the multi-angular CHRIS observations are investigated and related to surface structure using the parametric Rahman-Pinty-Verstraete (RPV) model. RPV parameters, in particular the Minnaert function parameter k, are able to quantify the anisotropy of surface reflectance, which in turn is sensitive to the structure and heterogeneity of the observed surface. Thus to investigate the potential of multi-angular high spatial resolution data for delivering quantitative surface structure information the retrieved values of the k parameter are compared with proxies derived from detailed LIDAR observations describing the 3-D canopy structure of the target site.