Jonathan Van Beek

Energy potential for combustion and anaerobic digestion of biomass from low-input high-diversity systems in conservation areas.

In this study, we assessed the potential for bioenergy production of Low‐Input High‐Diversity (LIHD) systems in temperate West‐European conservation areas. A wide range of seminatural ecosystems (wet and dry grasslands, marshes, tall‐herb vegetation and heathlands) was sampled. Because LIHD biomass is often scattered and discontinuously available, we only considered the potential for anaerobic digestion and combustion. Both technologies are suitable for decentralized biomass utilization. The gross energy yield showed a promising range between 46–277 GJ per hectare per mowing cycle (MC). The energy efficiency of the anaerobic digestion process was rather low (10–30%) with a methane energy yield of 5.5–35.5 GJ ha−1 MC−1, experimentally determined by batch digestion tests. The water content, functional group composition and biochemical composition (hemicellulose, cellulose, lignin and Kjeldahl nitrogen) of the biomass were analyzed to assess the suitability of different valorization pathways. On the basis of the results, we were able to propose recommendations regarding the appropriate conversion techniques. Biomass from plant communities with ‘late’ harvest dates (August–October) or a high fraction of woody species like heathland and dune slacks, is best valorized through combustion, while herbaceous biomass of ‘early’ harvested grasslands (June–July) and tall‐herb vegetation can better be digested. The main advantages of the production of bioenergy from LIHD biomass originating from conservation management are the minimization of the competition with food production and its potential to reconcile renewable energy policies and biodiversity goals.

Stem water potential monitoring in pear orchards through Worldview 2 multispectral imagery

Remote sensing can provide good alternatives for traditional in situ water status measurements in orchard crops, such as stem water potential (Ψstem). However, the heterogeneity of these cropping systems causes significant differences with regards to remote sensing products within one orchard and between orchards. In this study, robust spectral indicators of Ψstem were sought after, independent of sensor viewing geometry, orchard architecture and management. To this end, Ψstem was monitored throughout three consecutive growing seasons in (deficit) irrigated and rainfed pear orchards and related to spectral observations of leaves, canopies and WorldView-2 imagery. On a leaf and canopy level, high correlations were observed between the shortwave infrared reflectance and in situ measured Ψstem. Additionally, for canopy measurements, visible and near-infrared wavelengths (R530/R600, R530/R700 and R720/R800) showed significant correlations. Therefore, the Red-edge Normalized Difference Vegetation Index (ReNDVI) was applied on fully sunlit satellite imagery and found strongly related with Ψstem (R 2 = 0.47; RMSE = 0.36 MPa), undoubtedly showing the potential of WorldView-2 to monitor water stress in pear orchards. The relationship between ReNDVI and Ψstem was independent of management, irrigation setup, phenology and environmental conditions. In addition, results showed that this relation was also independent of off-nadir viewing angle and almost independent of viewing geometry, as the correlation decreased after the inclusion of fully shaded scenes.

Temporal Dependency of Yield and Quality Estimation through Spectral Vegetation Indices in Pear Orchards

Abstract: Yield and quality estimations provide vital information to fruit growers, yet require accurate monitoring throughout the growing season. To this end, the temporal dependency of fruit yield and quality estimations through spectral vegetation indices was investigated in irrigated and rainfed pear orchards. Both orchards were monitored throughout three consecutive growing seasons, including spectral measurements ( i.e. , hyperspectral canopy reflectance measurements) as well as yield determination ( i.e. , total yield and number of fruits per tree) and quality assessment ( i.e ., fruit firmness, total soluble solids and fruit color). The results illustrated a clear association between spectral vegetation indices and both fruit yield and fruit quality (|r| > 0.75; p < 0.001). However, the correlations between vegetation indices and production variables varied throughout the growing season, depending on the phenological stage of fruit development. In the irrigated orchard, index values showed a strong association with production variables near time of

Viewing Geometry Sensitivity of Commonly Used Vegetation Indices towards the Estimation of Biophysical Variables in Orchards

Stress-related biophysical variables of capital intensive orchard crops can be estimated with proxies via spectral vegetation indices from off-nadir viewing satellite imagery. However, variable viewing compositions affect the relationship between spectral vegetation indices and stress-related variables (i.e., chlorophyll content, water content and Leaf Area Index (LAI)) and could obstruct change detection. A sensitivity analysis was performed on the estimation of biophysical variables via vegetation indices for a wide range of viewing geometries. Subsequently, off-nadir viewing satellite imagery of an experimental orchard was analyzed, while all influences of background admixture were minimized through vegetation index normalization. Results indicated significant differences between nadir and off-nadir viewing scenes (∆R2 > 0.4). The Photochemical Reflectance Index (PRI), Normalized Difference Infrared Index (NDII) and Simple Ratio Pigment Index (SRPI) showed increased R2 values for off-nadir scenes taken perpendicular compared to parallel to row orientation. Other indices, such as Normalized Difference Vegetation Index (NDVI), Gitelson and Merzlyak (GM) and Structure Insensitive Pigment Index (SIPI), showed a significant decrease in R2 values from nadir to off-nadir viewing scenes. These results show the necessity of vegetation index selection for variable viewing applications to obtain an optimal derivation of biophysical variables in all circumstances.

Vegetation index correction to reduce background effects in orchards with high spatial resolution imagery

High spatial resolution satellite imagery provides an alternative for time consuming and labor intensive in situ measurements of biophysical variables, such as chlorophyll and water content. However, despite the high spatial resolution of current satellite sensors, mixtures of canopies and backgrounds will be present, hampering the estimation of biophysical variables. Traditional correction methodologies use spectral differences between canopies and backgrounds, but fail with spectrally similar canopies and backgrounds. In this study, the lack of a generic solution to reduce background effects is tackled. Through synthetic imagery, the mixture problem was demonstrated with regards to the estimation of biophysical variables. A correction method was proposed, rescaling vegetation indices based on the canopy cover fraction. Furthermore, the proposed method was compared to traditional background correction methodologies (i.e. soil-adjusted vegetation indices and signal unmixing) for different background scenarios. The results of a soil background scenario showed the inability of soil-adjusted vegetation indices to reduce background admixture effects, while signal unmixing and the proposed method removed background influences for chlorophyll (ΔR2 = ~0.3; ΔRMSE = ~1.6 μg/cm2) and water (ΔR2 = ~0.3; ΔRMSE = ~0.5 mg/cm2) related vegetation indices. For the weed background scenario, signal unmixing was unable to remove the background influences for chlorophyll content (ΔR2 = -0.1; ΔRMSE = -0.6 μg/cm 2 ), while the proposed correction method reduced background effects (ΔR2= 0.1; ΔRMSE = 0.4 μg/cm2). Overall, the proposed vegetation index correction method reduced the background influence irrespective of background type, making useful comparison between management blocks possible.

Correction: Van Beek, J. et al. Stem Water Potential Monitoring in Pear Orchards through WorldView-2 Multispectral Imagery. Remote Sens. 2013, 5, 6647-6666

The suitability of high resolution satellite imagery to provide the water status in orchard crops, i.e. stem water potential (Ψstem) was evaluated in [1]. However, the contribution of a number of collaborators was not properly acknowledged. Pieter Janssens, Wendy Odeurs, Hilde Vandendriessche and Tom Deckers all provided a substantial contribution to the conception and the design of the work. They furthermore had a leading role in the acquisition, processing, analysis, and interpretation of the reference evapotranspiration (ETo) and Ψstem data. The article [1] would not have been possible without their valuable input, and the authors would like to correct the authors list as follows. [...]