Staff Publications

Staff Publications

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    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

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    Assessment of workflow feature selection on forest LAI prediction with sentinel-2A MSI, landsat 7 ETM+ and Landsat 8 OLI
    Brede, Benjamin ; Verrelst, Jochem ; Gastellu-Etchegorry, Jean Philippe ; Clevers, Jan G.P.W. ; Goudzwaard, Leo ; Ouden, Jan den; Verbesselt, Jan ; Herold, Martin - \ 2020
    Remote Sensing 12 (2020)6. - ISSN 2072-4292
    Discrete anisotropic radiative transfer (DART) model - Forest - Leaf area index (LAI) - Machine learning - Sentinel-2 - Vegetation radiative transfer model

    The European Space Agency (ESA)'s Sentinel-2A (S2A) mission is providing time series that allow the characterisation of dynamic vegetation, especially when combined with the National Aeronautics and Space Administration (NASA)/United States Geological Survey (USGS) Landsat 7 (L7) and Landsat 8 (L8) missions. Hybrid retrieval workflows combining non-parametric Machine Learning Regression Algorithms (MLRAs) and vegetation Radiative Transfer Models (RTMs) were proposed as fast and accurate methods to infer biophysical parameters such as Leaf Area Index (LAI) from these data streams. However, the exact design of optimal retrieval workflows is rarely discussed. In this study, the impact of five retrieval workflow features on LAI prediction performance of MultiSpectral Instrument (MSI), Enhanced Thematic Mapper Plus (ETM+) and Operational Land Imager (OLI) observations was analysed over a Dutch beech forest site for a one-year period. The retrieval workflow features were the (1) addition of prior knowledge of leaf chemistry (two alternatives), (2) the choice of RTM (two alternatives), (3) the addition of Gaussian noise to RTM produced training data (four and five alternatives), (4) possibility of using Sun Zenith Angle (SZA) as an additional MLRA training feature (two alternatives), and (5) the choice of MLRA (six alternatives). The featureswere varied in a full grid resulting in 960 inversionmodels in order to find the overall impact on performance as well as possible interactions among the features. A combination of a Terrestrial Laser Scanning (TLS) time series with litter-trap derived LAI served as independent validation. The addition of absolute noise had the most significant impact on prediction performance. It improved the median prediction RootMean Square Error (RMSE) by 1.08m2m-2 when 5% noise was added compared to inversions with 0% absolute noise. The choice of the MLRA was second most important in terms of median prediction performance, which differed by 0.52m2m-2 between the best and worst model. The best inversion model achieved an RMSE of 0.91m2m-2 and explained 84.9% of the variance of the reference time series. The results underline the need to explicitly describe the used noise model in future studies. Similar studies should be conducted in other study areas, both forest and crop systems, in order to test the noise model as an integral part of hybrid retrieval workflows.

    Field data of "Monitoring forest phenology and leaf area index with the autonomous, low-cost transmittance sensor PASTiS-57"
    Brede, Benjamin ; Gastellu-Etchegorry, Jean Philippe ; Lauret, Nicolas ; Baret, Frederic ; Clevers, Jan ; Verbesselt, Jan ; Herold, Martin - \ 2020
    Wageningen University & Research
    forest - ground-based - Land Surface Phenology - Leaf Area Index - validation
    Land Surface Phenology (LSP) and Leaf Area Index (LAI) are important variables that describe the photosynthetically active phase and capacity of vegetation. Both are derived on the global scale from optical satellite sensors and require robust validation based on in situ sensors at high temporal resolution. This study assesses the PAI Autonomous System from Transmittance Sensors at 57? (PASTiS-57) instrument as a low-cost transmittance sensor for simultaneous monitoring of LSP and LAI in forest ecosystems. In a field experiment, spring leaf flush and autumn senescence in a Dutch beech forest were observed with PASTiS-57 and illumination independent, multi-temporal Terrestrial Laser Scanning (TLS) measurements in five plots. Both time series agreed to less than a day in Start Of Season (SOS) and End Of Season (EOS). LAI magnitude was strongly correlated with a Pearson correlation coefficient of 0.98. PASTiS-57 summer and winter LAI were on average 0.41m2m-2 and 1.43m2m-2 lower than TLS. This can be explained by previously reported overestimation of TLS. Additionally, PASTiS-57 was implemented in the Discrete Anisotropic Radiative Transfer (DART) Radiative Transfer Model (RTM) model for sensitivity analysis. This confirmed the robustness of the retrieval with respect to non-structural canopy properties and illumination conditions. Generally, PASTiS-57 fulfilled the CEOS LPV requirement of 20% accuracy in LAI for a wide range of biochemical and illumination conditions for turbid medium canopies. However, canopy non-randomness in discrete tree models led to strong biases. Overall, PASTiS-57 demonstrated the potential of autonomous devices for monitoring of phenology and LAI at daily temporal resolution as required for validation of satellite products that can be derived from ESA Copernicus’ optical missions, Sentinel-2 and -3.
    Non-destructive tree volume estimation through quantitative structure modelling: Comparing UAV laser scanning with terrestrial LIDAR
    Brede, Benjamin ; Calders, Kim ; Lau, Alvaro ; Raumonen, Pasi ; Bartholomeus, Harm M. ; Herold, Martin ; Kooistra, Lammert - \ 2019
    Remote Sensing of Environment 233 (2019). - ISSN 0034-4257
    Above-Ground Biomass (AGB) product calibration and validation require ground reference plots at hectometric scales to match space-borne missions' resolution. Traditional forest inventory methods that use allometric equations for single tree AGB estimation suffer from biases and low accuracy, especially when dealing with large trees. Terrestrial Laser Scanning (TLS) and explicit tree modelling show high potential for direct estimates of tree volume, but at the cost of time demanding fieldwork. This study aimed to assess if novel Unmanned Aerial Vehicle Laser Scanning (UAV-LS) could overcome this limitation, while delivering comparable results. For this purpose, the performance of UAV-LS in comparison with TLS for explicit tree modelling was tested in a Dutch temperate forest. In total, 200 trees with Diameter at Breast Height (DBH) ranging from 6 to 91 cm from 5 stands, including coniferous and deciduous species, have been scanned, segmented and subsequently modelled with TreeQSM. TreeQSM is a method that builds explicit tree models from laser scanner point clouds. Direct comparison with TLS derived models showed that UAV-LS reliably modelled the volume of trunks and branches with diameter ≥30 cm in the mature beech and oak stand with Concordance Correlation Coefficient (CCC) of 0.85 and RMSE of1.12 m3. Including smaller branch volume led to a considerable overestimation and decrease in correspondence to CCC of 0.51 and increase in RMSE to 6.59 m3. Denser stands prevented sensing of trunks and further decreased CCC to 0.36 in the Norway spruce stand. Also small, young trees posed problems by preventing a proper depiction of the trunk circumference and decreased CCC to 0.01. This dependence on stand indicated a strong impact of canopy structure on the UAV-LS volume modelling capacity. Improved flight paths, repeated acquisition flights or alternative modelling strategies could improve UAV-LS modelling performance under these conditions. This study contributes to the use of UAV-LS for fast tree volume and AGB estimation on scales relevant for satellite AGB product calibration and validation.

    Advancing forest structure product validation with ground, space and unmanned aerial vehicle sensors
    Brede, Benjamin - \ 2019
    Wageningen University. Promotor(en): M. Herold, co-promotor(en): J.G.P.W. Clevers; J.P. Verbesselt. - Wageningen : Wageningen University - ISBN 9789463439237 - 171

    Forests play a crucial role in the functioning of the Earth’s climate system, through their role in the carbon, energy and water cycles. The accurate description and quantification of their physical structure is essential to understand these roles, predict their behaviour under future climate change and adapt management practices accordingly. Remote sensing in particular from space-borne platforms is attractive for large area assessment of forest structure due to its cost-effectiveness, repeatability and objectiveness. However, the remote sensing signal is by nature ambiguous and needs to be interpreted with solid understanding of the underlying radiative mechanisms and uncertainties need to be rigorously quantified with independent ground data. The remote sensing community has produced a range of biophysical products describing vegetation and forest structure as well as best practice guidelines for their validation. However, the full implementation of anticipated products, including systematic repetition of validation across multiple sites (Committee on Earth Observing Satellites (CEOS) Land Product Validation (LPV) stage 4), is still to be concluded. A major challenge in this context is the provision of long-term validation data sets, which need to be cost-effective, repeatable and fast to acquire in the field.

    This thesis aims to investigate new ways of validation that meet the temporal and/or spatial scales of global forest structure products from space-borne missions with hectometric resolution. The particular focus is on Leaf Area Index (LAI) and Above-Ground Biomass (AGB) as metrics of physical forest structure. For the purpose of this thesis, the Speulderbos Reference site in the Veluwe forest area (The Netherlands) was established, where ground and Unmanned Aerial Vehicle (UAV)-borne sensors were tested.

    In Chapter 2, the automatic, passive optical sensor PAI Autonomous System from Transmittance Sensors at 57° (PASTiS-57) was tested for its suitability to monitor forest phenology and Plant Area Index (PAI), the total one-sided area of plant material per unit ground. For this, Radiative Transfer Model (RTM) experiments with turbid media and heterogeneous scenes were employed. PASTiS-57 generally meets the CEOS LPV requirement of 20% accuracy over a wide range of biochemical and illumination conditions for turbid medium canopies. However, canopy non-randomness in discrete tree models led to strong biases. In a field experiment, PASTiS-57 compared well in terms of phenological timing with Terrestrial Laser Scanning (TLS)-based PAI time series. PASTiS-57 represents a cost-effective way to continuously monitor PAI in forests.

    In Chapter 3, decametric resolution Sentinel-2 and Landsat 7/8 observations were analysed with hybrid LAI retrieval algorithms, which combine RTMs with Machine Learning Regression Algorithms (MLRAs). Several combinations of RTMs, MLRAs, and modifications to the processing chain were tested in order to assess their performance to predict a ground-based LAI time series, created from combined TLS and litter trap data. Most important for the success of the processing chain was the addition of a certain level of Gaussian noise to the RTM-produced database prior to MLRA training. With this processing chain, decametric resolution optical missions can produce reference LAI products for inter-comparison with hectometric products. Alternatively, the higher resolution can help to scale up small plot-based ground validation data.

    In Chapter 4, a novel Unmanned Aerial Vehicle Laser Scanning (UAV-LS), the RiCOPTER with VUX-1UAV laser scanner, was used to estimate canopy height and Diameter at Breast Height (DBH). TLS was used to derive reference datasets for both variables. Canopy height was comparable between both sensors with a slight underestimation for TLS, which was expected due to occlusion of the upper canopy when seen from below and hence lower TLS canopy heights. DBH was derived for the first time from UAV-LS data and compared well with TLS derived DBH. However, a part of the UAV-LS samples could not produce a meaningful estimate of DBH based on the extracted point cloud segment due to low point density. Repeated overpasses could counteract this to some degree. In this context, UAV-LS can support fast, plot-scale assessment of these two variables.

    In Chapter 5, the capabilities of UAV-LS are further explored in terms of explicit 3D modelling in order to estimate tree volume, which is the first step to retrieve tree AGB. For this purpose, 3D cylinder models were fitted to the segmented single trees with the TreeQSM routine. The resulting models were compared with TLS-based models and analysed separately for five different stands with varying architectures, including deciduous and coniferous species. UAV-LS was generally very successful in modelling large, deciduous trees, while coniferous trees with low branches and foliage as well as small trees proved more difficult. If successful, UAV-LS can provide the means to produce plot-scale assessment of woody volume and subsequently AGB at a fraction of time needed for TLS surveys.

    This thesis investigates new ways of forest structure product validation with techniques and sensors that meet the temporal and/or spatial resolution of hectometric space-borne missions.

    Linking Terrestrial LiDAR Scanner and Conventional Forest Structure Measurements with Multi-Modal Satellite Data
    Mulatu, Kalkidan ; Decuyper, Mathieu ; Brede, Benjamin ; Kooistra, Lammert ; Reiche, Johannes ; Mora, Brice ; Herold, Martin - \ 2019
    Forests 10 (2019)3. - ISSN 1999-4907 - 19 p.
    Obtaining information on vertical forest structure requires detailed data acquisition and analysis which is often performed at a plot level. With the growing availability of multi-modal satellite remote sensing (SRS) datasets, their usability towards forest structure estimation is increasing. We assessed the relationship of PlanetScope-, Sentinel-2-, and Landsat-7-derived vegetation indices (VIs), as well as ALOS-2 PALSAR-2- and Sentinel-1-derived backscatter intensities with a terrestrial laser scanner (TLS) and conventionally measured forest structure parameters acquired from 25 field plots in a tropical montane cloud forest in Kafa, Ethiopia. Results showed that canopy gap-related forest structure parameters had their highest correlation (|r| = 0.4 − 0.48) with optical sensor-derived VIs, while vegetation volume-related parameters were mainly correlated with red-edge- and short-wave infrared band-derived VIs (i.e., inverted red-edge chlorophyll index (IRECI), normalized difference moisture index), and synthetic aperture radar (SAR) backscatters (|r| = −0.57 − 0.49). Using stepwise multi-linear regression with the Akaike information criterion as evaluation parameter, we found that the fusion of different SRS-derived variables can improve the estimation of field-measured structural parameters. The combination of Sentinel-2 VIs and SAR backscatters was dominant in most of the predictive models, while IRECI was found to be the most common predictor for field-measured variables. The statistically significant regression models were able to estimate cumulative plant area volume density with an R2 of 0.58 and with the lowest relative root mean square error (RRMSE) value (0.23). Mean gap and number of gaps were also significantly estimated, but with higher RRMSE (R2 = 0.52, RRMSE = 1.4, R2 = 0.68, and RRMSE = 0.58, respectively). The models showed poor performance in predicting tree density and number of tree species (R2 = 0.28, RRMSE = 0.41, and R2 = 0.21, RRMSE = 0.39, respectively). This exploratory study demonstrated that SRS variables are sensitive to retrieve structural differences of tropical forests and have the potential to be used to upscale biodiversity relevant field-based forest structure estimates.
    Memory effects of climate and vegetation affecting net ecosystem CO2 fluxes in global forests
    Besnard, Simon ; Carvalhais, Nuno ; Arain, M.A. ; Black, Andrew ; Brede, Benjamin ; Buchmann, Nina ; Chen, Jiquan ; Clevers, Jan G.P.W. ; Dutrieux, Loïc P. ; Gans, Fabian ; Herold, Martin ; Jung, Martin ; Kosugi, Yoshiko ; Knohl, Alexander ; Law, Beverly E. ; Paul-Limoges, Eugénie ; Lohila, Annalea ; Merbold, Lutz ; Roupsard, Olivier ; Valentini, Riccardo ; Wolf, Sebastian ; Zhang, Xudong ; Reichstein, Markus - \ 2019
    PLoS ONE 14 (2019)2. - ISSN 1932-6203 - 22 p.
    Forests play a crucial role in the global carbon (C) cycle by storing and sequestering a substantial amount of C in the terrestrial biosphere. Due to temporal dynamics in climate and vegetation activity, there are significant regional variations in carbon dioxide (CO2) fluxes between the biosphere and atmosphere in forests that are affecting the global C cycle. Current forest CO2 flux dynamics are controlled by instantaneous climate, soil, and vegetation conditions, which carry legacy effects from disturbances and extreme climate events. Our level of understanding from the legacies of these processes on net CO2 fluxes is still limited due to their complexities and their long-term effects. Here, we combined remote sensing, climate, and eddy-covariance flux data to study net ecosystem CO2 exchange (NEE) at 185 forest sites globally. Instead of commonly used non-dynamic statistical methods, we employed a type of recurrent neural network (RNN), called Long Short-Term Memory network (LSTM) that captures information from the vegetation and climate’s temporal dynamics. The resulting data-driven model integrates interannual and seasonal variations of climate and vegetation by using Landsat and climate data at each site. The presented LSTM algorithm was able to effectively describe the overall seasonal variability (Nash-Sutcliffe efficiency, NSE = 0.66) and across-site (NSE = 0.42) variations in NEE, while it had less success in predicting specific seasonal and interannual anomalies (NSE = 0.07). This analysis demonstrated that an LSTM approach with embedded climate and vegetation memory effects outperformed a non-dynamic statistical model (i.e. Random Forest) for estimating NEE. Additionally, it is shown that the vegetation mean seasonal cycle embeds most of the information content to realistically explain the spatial and seasonal variations in NEE. These findings show the relevance of capturing memory effects from both climate and vegetation in quantifying spatio-temporal variations in forest NEE.
    Opportunities of UAV based Sensing for Vegetation Land Product Validation
    Brede, B. ; Suomalainen, J.M. ; Roosjen, P.P.J. ; Aasen, H. ; Kooistra, L. ; Bartholomeus, H.M. ; Clevers, J.G.P.W. ; Herold, M. - \ 2018
    Geometric Tree Modelling with UAV-based Lidar
    Brede, B. ; Raumonen, Pasi ; Calders, Kim ; Lau Sarmiento, A.I. ; Bartholomeus, H.M. ; Herold, M. ; Kooistra, L. - \ 2018
    Capturing crop height over the growing season from UAV based LiDAR
    Kooistra, L. ; Bartholomeus, H.M. ; Mücher, C.A. ; Kramer, H. ; Franke, G.J. ; Ivushkin, K. ; Brede, B. - \ 2018
    Assessing the structural differences between tropical forest types using Terrestrial Laser Scanning
    Decuyper, Mathieu ; Mulatu, Kalkidan Ayele ; Brede, Benjamin ; Calders, Kim ; Armston, John ; Rozendaal, Danaë M.A. ; Mora, Brice ; Clevers, Jan G.P.W. ; Kooistra, Lammert ; Herold, Martin ; Bongers, Frans - \ 2018
    Forest Ecology and Management 429 (2018). - ISSN 0378-1127 - p. 327 - 335.
    Increasing anthropogenic pressure leads to loss of habitat through deforestation and degradation in tropical forests. While deforestation can be monitored relatively easily, forest management practices are often subtle processes, that are difficult to capture with for example satellite monitoring. Conventional measurements are well established and can be useful for management decisions, but it is believed that Terrestrial Laser Scanning (TLS) has a role in quantitative monitoring and continuous improvement of methods. In this study we used a combination of TLS and conventional forest inventory measures to estimate forest structural parameters in four different forest types in a tropical montane cloud forest in Kafa, Ethiopia. Here, the four forest types (intact forest, coffee forest, silvopasture, and plantations) are a result of specific management practices (e.g. clearance of understory in coffee forest), and not different forest communities or tree types. Both conventional and TLS derived parameters confirmed our assumptions that intact forest had the highest biomass, silvopasture had the largest canopy gaps, and plantations had the lowest canopy openness. Contrary to our expectations, coffee forest had higher canopy openness and similar biomass as silvopasture, indicating a significant loss of forest structure. The 3D vegetation structure (PAVD – Plant area vegetation density) was different between the forest types with the highest PAVD in intact forest and plantation canopy. Silvopasture was characterised by a low canopy but high understorey PAVD, indicating regeneration of the vegetation and infrequent fuelwood collection and/or non-intensive grazing. Coffee forest canopy had low PAVD, indicating that many trees had been removed, despite coffee needing canopy shade. These findings may advocate for more tangible criteria such as canopy openness thresholds in sustainable coffee certification schemes. TLS as tool for monitoring forest structure in plots with different forest types shows potential as it can capture the 3D position of the vegetation volume and open spaces at all heights in the forest. To quantify changes in different forest types, consistent monitoring of 3D structure is needed and here TLS is an add-on or an alternative to conventional forest structure monitoring. However, for the tropics, TLS-based automated segmentation of trees to derive DBH and biomass is not widely operational yet, nor is species richness determination in forest monitoring. Integration of data sources is needed to fully understand forest structural diversity and implications of forest management practices on different forest types.
    Sentinel-2 for Forest Phenology and Forest Degradation Monitoring
    Brede, B. ; Hamunyela, E. ; Verbesselt, J. ; Herold, M. - \ 2018
    Opportunities of UAV based Sensing for Vegetation Land Product Validation
    Brede, Benjamin - \ 2018
    Monitoring Forest Phenology and Leaf Area Index with the Autonomous, Low-Cost Transmittance Sensor PASTiS-57
    Brede, Benjamin ; Gastellu-Etchegorry, Jean-Philippe ; Lauret, Nicolas ; Baret, Frederic ; Clevers, Jan ; Verbesselt, Jan ; Herold, Martin - \ 2018
    Remote Sensing 10 (2018)7. - ISSN 2072-4292 - 19 p.
    Land Surface Phenology (LSP) and Leaf Area Index (LAI) are important variables that describe the photosynthetically active phase and capacity of vegetation. Both are derived on the global scale from optical satellite sensors and require robust validation based on in situ sensors at high temporal resolution. This study assesses the PAI Autonomous System from Transmittance Sensors at 57° (PASTiS-57) instrument as a low-cost transmittance sensor for simultaneous monitoring of LSP and LAI in forest ecosystems. In a field experiment, spring leaf flush and autumn senescence in a Dutch beech forest were observed with PASTiS-57 and illumination independent, multi-temporal Terrestrial Laser Scanning (TLS) measurements in five plots. Both time series agreed to less than a day in Start Of Season (SOS) and End Of Season (EOS). LAI magnitude was strongly correlated with a Pearson correlation coefficient of 0.98. PASTiS-57 summer and winter LAI were on average 0.41 m2m−2 and 1.43 m2m−2 lower than TLS. This can be explained by previously reported overestimation of TLS. Additionally, PASTiS-57 was implemented in the Discrete Anisotropic Radiative Transfer (DART) Radiative Transfer Model (RTM) model for sensitivity analysis. This confirmed the robustness of the retrieval with respect to non-structural canopy properties and illumination conditions. Generally, PASTiS-57 fulfilled the CEOS LPV requirement of 20% accuracy in LAI for a wide range of biochemical and illumination conditions for turbid medium canopies. However, canopy non-randomness in discrete tree models led to strong biases. Overall, PASTiS-57 demonstrated the potential of autonomous devices for monitoring of phenology and LAI at daily temporal resolution as required for validation of satellite products that can be derived from ESA Copernicus’ optical missions, Sentinel-2 and -3.
    Improved estimation of leaf area index and leaf chlorophyll content of a potato crop using multi-angle spectral data – potential of unmanned aerial vehicle imagery
    Roosjen, Peter P.J. ; Brede, Benjamin ; Suomalainen, Juha M. ; Bartholomeus, Harm M. ; Kooistra, Lammert ; Clevers, Jan G.P.W. - \ 2018
    International Journal of applied Earth Observation and Geoinformation 66 (2018). - ISSN 0303-2434 - p. 14 - 26.
    In addition to single-angle reflectance data, multi-angular observations can be used as an additional information source for the retrieval of properties of an observed target surface. In this paper, we studied the potential of multi-angular reflectance data for the improvement of leaf area index (LAI) and leaf chlorophyll content (LCC) estimation by numerical inversion of the PROSAIL model. The potential for improvement of LAI and LCC was evaluated for both measured data and simulated data. The measured data was collected on 19 July 2016 by a frame-camera mounted on an unmanned aerial vehicle (UAV) over a potato field, where eight experimental plots of 30 × 30 m were designed with different fertilization levels. Dozens of viewing angles, covering the hemisphere up to around 30° from nadir, were obtained by a large forward and sideways overlap of collected images. Simultaneously to the UAV flight, in situ measurements of LAI and LCC were performed. Inversion of the PROSAIL model was done based on nadir data and based on multi-angular data collected by the UAV. Inversion based on the multi-angular data performed slightly better than inversion based on nadir data, indicated by the decrease in RMSE from 0.70 to 0.65 m2/m2 for the estimation of LAI, and from 17.35 to 17.29 μg/cm2 for the estimation of LCC, when nadir data were used and when multi-angular data were used, respectively. In addition to inversions based on measured data, we simulated several datasets at different multi-angular configurations and compared the accuracy of the inversions of these datasets with the inversion based on data simulated at nadir position. In general, the results based on simulated (synthetic) data indicated that when more viewing angles, more well distributed viewing angles, and viewing angles up to larger zenith angles were available for inversion, the most accurate estimations were obtained. Interestingly, when using spectra simulated at multi-angular sampling configurations as were captured by the UAV platform (view zenith angles up to 30°), already a huge improvement could be obtained when compared to solely using spectra simulated at nadir position. The results of this study show that the estimation of LAI and LCC by numerical inversion of the PROSAIL model can be improved when multi-angular observations are introduced. However, for the potato crop, PROSAIL inversion for measured data only showed moderate accuracy and slight improvements.
    Multi-sensor LAI and Cab retrieval for a Dutch Beech forest site
    Brede, B. ; Clevers, J.G.P.W. ; Verbesselt, J. ; Herold, M. ; Verrelst, Jochem ; Gascon, Ferran - \ 2017
    Benchmarking Radiative Transfer Model inversion schemes to estimate Leaf Area Index of a Dutch Beech Forest Site
    Brede, B. ; Verrelst, Jochem ; Gastellu-Etchegorry, J.P. ; Clevers, J.G.P.W. ; Verbesselt, J. ; Herold, M. - \ 2017
    Capturing forest structure and change – 5 years of laser scanning and future perspectives using UAV based LiDAR
    Bartholomeus, H.M. ; Lau Sarmiento, A.I. ; Gonzalez de Tanago Meñaca, J. ; Herold, M. ; Brede, B. ; Kooistra, L. ; Calders, Kim - \ 2017
    In: SilviLaser 2017 Program. - Blacksburg : Virginia Tech - p. 61 - 62.
    Capturing forest structure using UAV based LiDAR
    Bartholomeus, H.M. ; Brede, B. ; Lau Sarmiento, A.I. ; Kooistra, L. - \ 2017
    - 2 p.
    Comparing RIEGL RiCOPTER UAV LiDAR Derived Canopy Height and DBH with Terrestrial LiDAR
    Brede, Benjamin ; Lau Sarmiento, A.I. ; Bartholomeus, Harm ; Kooistra, Lammert - \ 2017
    Sensors 17 (2017)10. - ISSN 1424-8220 - 16 p.
    In recent years, LIght Detection And Ranging (LiDAR) and especially Terrestrial Laser Scanning (TLS) systems have shown the potential to revolutionise forest structural characterisation by providing unprecedented 3D data. However, manned Airborne Laser Scanning (ALS) requires costly campaigns and produces relatively low point density, while TLS is labour intense and time demanding. Unmanned Aerial Vehicle (UAV)-borne laser scanning can be the way in between. In this study, we present first results and experiences with the RIEGL RiCOPTER with VUX ®
    ®
    -1UAV ALS system and compare it with the well tested RIEGL VZ-400 TLS system. We scanned the same forest plots with both systems over the course of two days. We derived Digital Terrain Model (DTMs), Digital Surface Model (DSMs) and finally Canopy Height Model (CHMs) from the resulting point clouds. ALS CHMs were on average 11.5 cm
    cm
    higher in five plots with different canopy conditions. This showed that TLS could not always detect the top of canopy. Moreover, we extracted trunk segments of 58 trees for ALS and TLS simultaneously, of which 39 could be used to model Diameter at Breast Height (DBH). ALS DBH showed a high agreement with TLS DBH with a correlation coefficient of 0.98 and root mean square error of 4.24 cm
    cm
    . We conclude that RiCOPTER has the potential to perform comparable to TLS for estimating forest canopy height and DBH under the studied forest conditions. Further research should be directed to testing UAV-borne LiDAR for explicit 3D modelling of whole trees to estimate tree volume and subsequently Above-Ground Biomass (AGB).
    Spatiotemporal High-Resolution Cloud Mapping with a Ground-Based IR Scanner
    Brede, Benjamin ; Thies, Boris ; Bendix, Jörg ; Feister, Uwe - \ 2017
    Advances in Meteorology 2017 (2017). - ISSN 1687-9309 - 11 p.
    The high spatiotemporal variability of clouds requires automated monitoring systems. This study presents a retrieval algorithm that evaluates observations of a hemispherically scanning thermal infrared radiometer, the NubiScope, to produce georeferenced, spatially explicit cloud maps. The algorithm uses atmospheric temperature and moisture profiles and an atmospheric radiative transfer code to differentiate between cloudy and cloudless measurements. In case of a cloud, it estimates its position by using the temperature profile and viewing geometry. The proposed algorithm was tested with 25 cloud maps generated by the Fmask algorithm from Landsat 7 images. The overall cloud detection rate was ranging from 0.607 for zenith angles of 0 to 10° to 0.298 for 50–60° on a pixel basis. The overall detection of cloudless pixels was 0.987 for zenith angles of 30–40° and much more stable over the whole range of zenith angles compared to cloud detection. This proves the algorithm’s capability in detecting clouds, but even better cloudless areas. Cloud-base height was best estimated up to a height of 4000 m compared to ceilometer base heights but showed large deviation above that level. This study shows the potential of the NubiScope system to produce high spatial and temporal resolution cloud maps. Future development is needed for a more accurate determination of cloud height with thermal infrared measurements.
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