Waterwijzers Landbouw en Natuur : Kwantificering effecten waterbeheer en klimaat

Landbouw en natuur stellen specifieke eisen aan de waterhuishouding, vooral aan beschikbaar water in de wortelzone. Bestaande beoordelingssystemen houden echter geen rekening met de gevolgen van klimaatverandering voor respectievelijk landbouwopbrengsten en natuurdoelen. Daarom zijn er nu twee systemen in ontwikkeling die dat, zo goed mogelijk, wel doen: de Waterwijzer Landbouw en de Waterwijzer Natuur. Beide kunnen worden gebruikt voor het vaststellen van schade aan landbouw en natuur, maar ook voor het optimaliseren van de waterhuishouding

A global spectral library to characterize the world's soil

Soil provides ecosystem services, supports human health and habitation, stores carbon and regulates emissions of greenhouse gases. Unprecedented pressures on soil from degradation and urbanization are threatening agro-ecological balances and food security. It is important that we learn more about soil to sustainably manage and preserve it for future generations. To this end, we developed and analyzed a global soil visible-near infrared (vis-NIR) spectral library. It is currently the largest and most diverse database of its kind. We show that the information encoded in the spectra can describe soil composition and be associated to land cover and its global geographic distribution, which acts as a surrogate for global climate variability. We also show the usefulness of the global spectra for predicting soil attributes such as soil organic and inorganic carbon, clay, silt, sand and iron contents, cation exchange capacity, and pH. Using wavelets to treat the spectra, which were recorded in different laboratories using different spectrometers and methods, helped to improve the spectroscopic modelling. We found that modelling a diverse set of spectra with a machine learning algorithm can find the local relationships in the data to produce accurate predictions of soil properties. The spectroscopic models that we derived are parsimonious and robust, and using them we derived a harmonized global soil attribute dataset, which might serve to facilitate research on soil at the global scale. This spectroscopic approach should help to deal with the shortage of data on soil to better understand it and to meet the growing demand for information to assess and monitor soil at scales ranging from regional to global. New contributions to the library are encouraged so that this work and our collaboration might progress to develop a dynamic and easily updatable database with better global coverage. We hope that this work will reinvigorate our community's discussion towards larger, more coordinated collaborations. We also hope that use of the database will deepen our understanding of soil so that we might sustainably manage it and extend the research outcomes of the soil, earth and environmental sciences towards applications that we have not yet dreamed of

Regioscan Zoetwatermaatregelen : beperken watervraag landbouw door kleinschalige maatregelen

Omdat zoetwatertekorten steeds talrijker worden, zoeken waterbeheerders met landbouwers naar manieren om de vraag te verminderen. Onbekend is in hoeverre kleinschalige maatregelen kunnen bijdragen aan de regionale zoetwateropgave en tegen welke kosten. De Regioscan Zoetwatermaatregelen geeft ruimtelijk inzicht in de rendabiliteit van maatregelen voor agrariërs, en effecten op gebiedsniveau. Het instrument ondersteunt hiermee de dialoog tussen waterbeheerder en boer. Vooralsnog lijken baten van kleinschalige zoetwatermaatregelen alleen in specifieke gebieden op te wegen tegen de kosten

A global spectral library to characterize the world's soil

Soil provides ecosystem services, supports human health and habitation, stores carbon and regulates emissions of greenhouse gases. Unprecedented pressures on soil from degradation and urbanization are threatening agro-ecological balances and food security. It is important that we learn more about soil to sustainably manage and preserve it for future generations. To this end, we developed and analyzed a global soil visible-near infrared (vis-NIR) spectral library. It is currently the largest and most diverse database of its kind. We show that the information encoded in the spectra can describe soil composition and be associated to land cover and its global geographic distribution, which acts as a surrogate for global climate variability. We also show the usefulness of the global spectra for predicting soil attributes such as soil organic and inorganic carbon, clay, silt, sand and iron contents, cation exchange capacity, and pH. Using wavelets to treat the spectra, which were recorded in different laboratories using different spectrometers and methods, helped to improve the spectroscopic modelling. We found that modelling a diverse set of spectra with a machine learning algorithm can find the local relationships in the data to produce accurate predictions of soil properties. The spectroscopic models that we derived are parsimonious and robust, and using them we derived a harmonized global soil attribute dataset, which might serve to facilitate research on soil at the global scale. This spectroscopic approach should help to deal with the shortage of data on soil to better understand it and to meet the growing demand for information to assess and monitor soil at scales ranging from regional to global. New contributions to the library are encouraged so that this work and our collaboration might progress to develop a dynamic and easily updatable database with better global coverage. We hope that this work will reinvigorate our community's discussion towards larger, more coordinated collaborations. We also hope that use of the database will deepen our understanding of soil so that we might sustainably manage it and extend the research outcomes of the soil, earth and environmental sciences towards applications that we have not yet dreamed of

Effects of soil moisture content on reflectance anisotropy - Laboratory goniometer measurements and RPV model inversions

Optical methods to study soil moisture content (SMC) are often based on empirically or physically based models that relate changes in reflectance intensity to SMC. The effects of SMC on the reflectance anisotropy, however, have not received much attention. In this paper the effects of SMC on the anisotropic reflectance behaviour of soils were studied. Biconical reflectance factors (BCRFs) of five different soil samples were acquired at 60 positions covering the full hemisphere in the optical domain at different SMC levels using Wageningen University's laboratory goniometer facility. In addition, we inverted the Rahman–Pinty–Verstraete (RPV) model against the measured BCRFs in the principal plane. The results show that the anisotropic reflectance behaviour of soils is strongly influenced by the SMC. Dry soils displayed strong backward scattering behaviour, with a maximum reflectance close to the hotspot position. An increase of the SMC level up to the soil's saturation point caused the soils to scatter more in the forward direction and induced a weakening of the hotspot effect. Oversaturated soils displayed a strong sun-glint-like reflectance peak in the anti-solar direction. The RPV model fitted the measured BCRFs in the principal plane up to saturated SMC levels in general with an R2 > 0.9. It was not possible to fit the model through observations of oversaturated soils, since the RPV model is not capable of simulating specular reflectance. The asymmetry parameter (T) of the RPV model, which controls the proportion of forward and backward scattering, showed a strong correlation to SMC for individual samples. This correlation remained significant when we considered all samples together with a maximum R2 of 0.797 at 2123 nm, indicating that reflectance anisotropy contained information on the water content of soils. The amplitude parameter (¿0), which is closely related to the reflectance intensity, only had a maximum R2 of 0.622 at 1921 nm. This indicated that reflectance anisotropy contained more information on SMC than the level of spectral reflectance as such

Influence of solar zenith angle on the enhanced vegetation index of a Guyanese rainforest

In this study, the effect of solar zenith angle () on enhanced vegetation index (EVI) of a Guyanese tropical rainforest was studied. For this sub-crown resolution, hyperspectral data have been collected with an unmanned aerial vehicle (UAV) at five different solar zenith angles in a 1-day period. The hyperspectral data were used to simulate Moderate Resolution Imaging Spectroradiometer (MODIS) spectral bands and generate EVI. The linear trend of EVI with solar zenith angle at nadir viewing conditions was found to be –0.00285 (). The direction of this trend was in agreement with earlier studies, but with a differing magnitude. Analysis of EVI images with sub-crown resolution pointed to strong influence of canopy shadows on EVI, which is supported by other studies. Additionally, the EVI–solar zenith angle trend was investigated in the semi-empirical RossThick-LiSparse-Reciprocal (RTLSR) model implemented in the MODIS MCD43 product suite. A database of model parameters has been created and the EVI–solar zenith angle trend was modelled with each set of parameters. The linear approximated trend was found to be –0.00219 on average, only slightly weaker compared to the trend derived from the UAV. Further analysis of the relationship between the single RTLSR model parameters and the EVI–solar zenith angle trend showed that the RTLSR produces the trend for the right reason, namely canopy shadowing expressed by the near-infrared geometric kernel. In total, this study delivers further evidence that EVI is dependent on solar zenith angle and this effect is mediated through EVI’s sensitivity to within-canopy shadows

A shortest path based tree isolation method for UAV LiDAR data

A description and test results for a new method for automatically isolating individual trees from UAV LiDAR point clouds is presented. The isolation method is based on shortest path computations with height from the ground working as a restriction. The method is tested applied to a 4 ha tropical forest plot, which is also scanned with TLS to provide comparison data for the isolation results. The comparison results show that the on average the intersection volume of the common voxels between TLS and UAV trees covers 56 % of the TLS tree and 44 % of the UAV trees