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

A high-resolution and harmonized model approach for reconstructing and analysing historic land changes in Europe

Human-induced land use changes are nowadays the second largest contributor to atmospheric carbon dioxide after fossil fuel combustion. Existing historic land change reconstructions on the European scale do not sufficiently meet the requirements of greenhouse gas (GHG) and cli- mate assessments, due to insufficient spatial and thematic detail and the consideration of various land change types. This paper investigates if the combination of different data sources, more detailed modelling techniques, and the inte- gration of land conversion types allow us to create accu- rate, high-resolution historic land change data for Europe suited for the needs of GHG and climate assessments. We validated our reconstruction with historic aerial photographs from 1950 and 1990 for 73 sample sites across Europe and compared it with other land reconstructions like Klein Gold- ewijk et al. (2010, 2011), Ramankutty and Foley (1999), Pon- gratz et al. (2008) and Hurtt et al. (2006). The results indicate that almost 700 000km2 (15.5%) of land cover in Europe has changed over the period 1950–2010, an area similar to France. In Southern Europe the relative amount was almost 3.5% higher than average (19 %). Based on the results the specific types of conversion, hot-spots of change and their relation to political decisions and socio-economic transitions were studied. The analysis indicates that the main drivers of land change over the studied period were urbanization, the reforestation program resulting from the timber shortage af- ter the Second World War, the fall of the Iron Curtain, the Common Agricultural Policy and accompanying afforesta- tion actions of the EU. Compared to existing land cover re- constructions, the new method considers the harmonization of different datasets by achieving a high spatial resolution and regional detail with a full coverage of different land cat- egories. These characteristics allow the data to be used to support and improve ongoing GHG inventories and climate research

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

Performance of the PROSPECT leaf radiative transfer model version 4 for Norway spruce needles

Leaf optical properties (LOPs) are a key input parameter for vegetation canopy radiative transfer models. The uncertainty introduced in the measurement and/or the simulation of this spectral information determines a final reliability of the modelled canopy reflectance. The broad-leaf radiative transfer model PROSPECT version 3.01 has been previously applied for some needle-leaf type species (e.g. pine trees) to estimate biochemical parameters through its inversion. Nevertheless, in a particular case of Norway spruce (Picea abies (L.) Karst.) PROSPECT 3.01 showed a poor performance in near infrared wavelengths and had to be recalibrated. Therefore, the applicability of PROSPECT version 4, which has been recently released, is verified for this type of leaves in this experiment. Forward simulations of an optimized version of the original PROSPECT 4 suggest that it is possible to reduce the average RMSE of reflectance and transmittance from 8% to 3.5- 4 % in the near infrared domain. For this achievement, the absorption coefficients for chlorophyll and dry matter together with the refractive index had to be simultaneously optimized via model inversion using measured LOPs of Norway spruce needles

Land cover classification using multi-temporal MERIS vegetation indices

The spectral, spatial, and temporal resolutions of Envisat's Medium Resolution Imaging Spectrometer (MERIS) data are attractive for regional- to global-scale land cover mapping. Moreover, two novel and operational vegetation indices derived from MERIS data have considerable potential as discriminating variables in land cover classification. Here, the potential of these two vegetation indices (the MERIS global vegetation index (MGVI), MERIS terrestrial chlorophyll index (MTCI)) was evaluated for mapping eleven broad land cover classes in Wisconsin. Data acquired in the high and low chlorophyll seasons were used to increase inter-class separability. The two vegetation indices provided a higher degree of inter-class separability than data acquired in many of the individual MERIS spectral wavebands. The most accurate landcover map (73.2%) was derived from a classification of vegetation index-derived data with a support vector machine (SVM), and was more accurate than the corresponding map derived from a classification using the data acquired in the original spectral wavebands

Detection of the tulip breaking virus (TBV) in tulips using optical sensors

The tulip breaking virus (TBV) causes severe economic losses for countries that export tulips such as the Netherlands. Infected plants have to be removed from the field as soon as possible. There is an urgent need for a rapid and objective method of screening. In this study, four proximal optical sensing techniques for the detection of TBV in tulip plants were evaluated and compared with a visual assessment by crop experts as well as with an ELISA (enzyme immunoassay) analysis of the same plants. The optical sensor techniques used were an RGB color camera, a spectrophotometer measuring from 350 to 2500 nm, a spectral imaging camera covering a spectral range from 400 to 900 nm and a chlorophyll fluorescence imaging system that measures the photosynthetic activity. Linear discriminant classification was used to compare the results of these optical techniques and the visual assessment with the ELISA score. The spectral imaging system was the best optical technique and its error was only slightly larger than the visual assessment error. The experimental results appear to be promising, and they have led to further research to develop an autonomous robot for the detection and removal of diseased tulip plants in the open field. The application of this robot system will reduce the amount of insecticides and the considerable pressure on labor for selecting diseased plants by the crop expert. © 2010 The Author(s

Added value of multiangular measurements for estimating forest variables from the top of the atmosphere using coupled radiative transfer models

Numerous studies have demonstrated the higher information content of multiangular reflectance data that can be used to improve the estimation of variables for surfaces having strong directional properties such as forests. Only a few studies, however, used physically- based radiative transfer (RT) models, and they were based on atmospherically-corrected data. The objective of this study was to investigate the potential of multi-angular top-of-atmosphere (TOA) radiance data for estimating surface variables using a coupled canopy-atmosphere model. The study area consisted of three Norway spruce stands located in the Czech Republic for which field data and a multi-angular set of four CHRIS/PROBA images were collected in September 2006. The coupled SLC (Soil-Leaf- Canopy) - MODTRAN model provided good simulations of the spectral and angular signatures measured by CHRIS. Local sensitivity analyses were performed to help with the model inversion. The singular values of the Jacobian matrix showed that the dimensionality of the estimation problem increased from 3 to 6 when increasing the number of angles from 1 to 4. One LUT was built for each stand, using the 7 most influential parameters: vertical crown cover, fraction of bark, tree shape factor, dissociation factor, and needle chlorophyll, dry matter, and brown pigments contents. All angular combinations were tested for estimating the variables. The best results were obtained when using two or three angles. The results show that although multi-angular TOA radiance data do have a higher potential than mono-angular data, it is still difficult to make full use of the information they contain for estimating forest variables

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)