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Climate change and increasing human activities are impacting ecosystems and their biodiversity. Quantitative measurements of essential biodiversity variables (EBV) and essential climate variables are used to monitor biodiversity and carbon dynamics and evaluate policy and management interventions. Ecosystem structure is at the core of EBVs and carbon stock estimation and can help to inform assessments of species and species diversity. Ecosystem structure is also used as an indirect indicator of habitat quality and expected species richness or species community composition. Spaceborne measurements can provide large-scale insight into monitoring the structural dynamics of ecosystems, but they generally lack consistent, robust, timely and detailed information regarding their full three-dimensional vegetation structure at local scales. Here we demonstrate the potential of high-frequency ground-based laser scanning to systematically monitor structural changes in vegetation. We present a proof-of-concept high-temporal ecosystem structure time series of 5 years in a temperate forest using terrestrial laser scanning (TLS). We also present data from automated high-temporal laser scanning that can allow upscaling of vegetation structure scanning, overcoming the limitations of a typically opportunistic TLS measurement approach. Automated monitoring will be a critical component to build a network of field monitoring sites that can provide the required calibration data for satellite missions to effectively monitor the structural dynamics of vegetation over large areas. Within this perspective, we reflect on how this network could be designed and discuss implementation pathways.

The increase in food demand associated to the projected population growth in the next decades comes with different challenges and one of them is how to increase productivity while reducing or limiting environmental impacts of agricultural activities. For that, two crucial aspects for more efficient production systems comprise crop monitoring, targeting stress factors that can undermine productivity, and the selection of new cultivars under field conditions, which depends on phenotyping protocols. In both cases, remote or proximal sensing can potentially provide an alternative to conventional methods of crop traits evaluation. In particular, optical imaging systems on-board of Unmanned Aerial Vehicles (UAVs) have proved to be one of the most accessible and flexible sensing options for crop characterization. A range of UAV-compatible cameras are currently available for data acquisition in the optical domain, from high-resolution RGB to multispectral and hyperspectral systems, with images in tens to hundreds of spectral bands. Despite the successful use of such imaging systems in different cases, their inclusion in specific applications targeting the assessment of disease incidence and severity is still lacking in multiple scenarios. In this context, an attempt is made in this thesis to tackle key components of crop characterization and disease detection based on UAV optical imaging systems, with focus on hyperspectral sub-decimeter imagery, which still have unexplored potential for application in these topics.In Chapter 2, the use of narrow-band hyperspectral imagery coupled with straightforward modelling methods (Vegetation Indices – VIs and univariate linear regression) is evaluated in the context of crop traits retrieval in organic potato production systems. In addition, results obtained with the hyperspectral camera are compared with those of ground-based spectral measurements (hand-held sensor) and the possible interchangeable use of both approaches is assessed.

Over the last two decades, terrestrial light detection and ranging (LiDAR), also known as terrestrial laser scanning (TLS) has become a valuable tool in assessing the woody structure of trees, in a method that is accurate, non-destructive, and replicable. This technique provides the ability to scan an area, and utilizes specialized software to create highly detailed 3D point cloud representations of its surroundings. Although the original usage of LiDAR was for precision survey applications, researchers have begun to apply LiDAR to forest research. Tree metrics can be extracted from TLS tree point clouds, and in combination with structure modelling, can be used to extract tree volume, aboveground biomass (AGB), growth, species, and to understand ecological questions such as tree mechanics, branching architecture, and surface area. TLS can provide a robust and rapid assessment of tree characteristics. These characteristics will improve current global efforts to measure forest carbon emissions, understand their uncertainties, and provide new insight into tropical forest ecology. Thus, the main objective of this PhD is to explore the use of 3D models from terrestrial laser scanning point clouds to estimate biomass and architecture of tropical trees. TLS-derived biomass and TLS-derived architecture can potentially be used to generate significant quality data for a better understanding of ecological challenges in tropical forests. In this thesis, a dataset of forest inventory with TLS point clouds and destructive tree harvesting were created from three tropical regions: Indonesia, Guyana, and Peru. A total of 1858 trees were traditionally inventoried, 135 trees were TLS scanned, and 55 trees were destructively harvested. In this thesis, procedures to estimate tree metrics such as tree height (H), diameter at breast height (D), crown diameter (CD), and the length and diameter of individual branches were developed using 3D point clouds and 3D modelling. From these tree met

AimBiodiversity is rapidly disappearing at local and global scales also affecting the functional diversity of ecosystems. We aimed to assess whether functional diversity was correlated with species diversity and whether both were affected by similar land use and vegetation structure drivers. Better understanding of these relationships will allow us to improve our predictions regarding the effects of future changes in land use on ecosystem functions and services.LocationThe Netherlands.MethodsWe compiled a dataset of c. 3 million observations of 66 out of 106 known Dutch butterfly species collected across 6,075 sampling locations during a period of 7 years, together with very high-resolution maps of land use and countrywide vegetation structure data. Using a mixed-effects modelling framework, we investigated the relationship between functional and species diversity and their main land use and vegetation structure drivers.ResultsWe found that high species diversity does not translate into high functional diversity, as shown by their different spatial distribution patterns in the landscape. Functional and species diversity are mainly driven by different sets of structural and land use parameters (especially average vegetation height, amount of vegetation between 0.5 and 2 m, natural grassland, sandy soils vegetation, marsh vegetation and urban areas). We showed that it is a combination of both vegetation structural characteristics and land use variables that defines functional and species diversity.Main conclusionsFunctional diversity and species diversity of butterflies are not consistently correlated and must therefore be treated separately. High functional diversity levels occurred even in areas with low species diversity. Thus, conservation actions may differ depending on whether the focus is on conservation of high functional diversity or high species diversity. A more integrative analysis of biodiversity at both species and trait levels is neede

Productivity of cropping systems can be constrained simultaneously by different limiting factors and approaches allowing to indicate and identify plants under stress in field conditions can be valuable for farmers and breeders. In organic production systems, sensing solutions are not frequently studied, despite their potential for crop traits retrieval and stress assessment. In this study, spectral data in the optical domain acquired using a pushbroom spectrometer on board of a unmanned aerial vehicle is used to evaluate the potential of this information for assessment of late blight (Phytophthora infestans) incidence on potato (Solanum tuberosum) under organic cultivation. Vegetation indices formulations with two and three spectral bands were tested for the complete range of the spectral information acquired (i.e., from 450 to 900 nm, with 10 nm of spectral resolution). This evaluation concerned the discrimination between plots cultivated with only one resistant potato variety in contrast with plots with a variety mixture, with resistant and susceptible cultivars. Results indicated that indices based on three spectral bands performed better and optimal wavelengths (i.e., near 490, 530 and 670 nm) are not only related to chlorophyll content but also to other leaf pigments like carotenoids.

Variatie in het gewas kan pas verder onderzocht worden als men zicht heeft op de variatie in de bodem. Vanuit die gedachte zet precisielandbouwbedrijf Van den Borne Aardappelen sterk in op bodeminventarisatie met gebruik van de meest recente technologie. Welke technieken hiervoor gebruikt worden en wat de praktische toepassingen van deze technieken zijn leest u in dit artikel.

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 c

During 2014–2015 we have developed a new method to measure reflectance factor anisotropy using a pushbroom spectrometer mounted on a multicopter UAV. In this paper/presentation we describe the acquisition method and show the preliminary results of the experiment. To validate the measurements the same targets have also been measured with a laboratory goniometer system. The first experiments over sugar beet fields in 2014 show similar trends in both UAV and laboratory anisotropy data, but also some differences caused by differences in sampling and diffuse illumination. In 2015 a more extensive study on wheat, barley and potato fields were performed. The measurements were repeated on three days over the growth of the crops allowing linking the development of the crops to the anisotropy signals. On each day the anisotropy measurement was repeated 4–5 times with different solar zenith angles ranging from 60° to 40° allowing analysis how the solar angle affects the anisotropy. The first results of these experiments will be presented in this conference.

During the last years commercial hyperspectral imaging sensors have been miniaturized and their performance has been demonstrated on Unmanned Aerial Vehicles (UAV). However currently the commercial hyperspectral systems still require minimum payload capacity of approximately 3 kg, forcing usage of rather large UAVs. In this article we present a lightweight hyperspectral mapping system (HYMSY) for rotor-based UAVs, the novel processing chain for the system, and its potential for agricultural mapping and monitoring applications. The HYMSY consists of a custom-made pushbroom spectrometer (400–950 nm, 9 nm FWHM, 25 lines/s, 328 px/line), a photogrammetric camera, and a miniature GPS-Inertial Navigation System. The weight of HYMSY in ready-to-fly configuration is only 2.0 kg and it has been constructed mostly from off-the-shelf components. The processing chain uses a photogrammetric algorithm to produce a Digital Surface Model (DSM) and provides high accuracy orientation of the system over the DSM. The pushbroom data is georectified by projecting it onto the DSM with the support of photogrammetric orientations and the GPS-INS data. Since an up-to-date DSM is produced internally, no external data are required and the processing chain is capable to georectify pushbroom data fully automatically. The system has been adopted for several experimental flights related to agricultural and habitat monitoring applications. For a typical flight, an area of 2–10 ha was mapped, producing a RGB orthomosaic at 1–5 cm resolution, a DSM at 5–10 cm resolution, and a hyperspectral datacube at 10–50 cm resolution.

Surface moisture is an important supply limiting factor for aeolian sand transport, which is the primary driver of coastal dune development. As such, it is critical to account for the control of surface moisture on available sand for dune building. Optical remote sensing has the potential to measure surface moisture at a high spatio-temporal resolution. It is based on the principle that wet sand appears darker than dry sand: it is less reflective. The goals of this study are (1) to measure and model reflectance under controlled laboratory conditions as function of wavelength () and surface moisture () over the optical domain of 350–2500 nm, and (2) to explore the implications of our laboratory findings for accurately mapping the distribution of surface moisture under natural conditions. A laboratory spectroscopy experiment was conducted to measure spectral reflectance (1 nm interval) under different surface moisture conditions using beach sand. A non-linear increase of reflectance upon drying was observed over the full range of wavelengths. Two models were developed and tested. The first model is grounded in optics and describes the proportional contribution of scattering and absorption of light by pore water in an unsaturated sand matrix. The second model is grounded in soil physics and links the hydraulic behaviour of pore water in an unsaturated sand matrix to its optical properties. The optical model performed well for volumetric moisture content 24% ( 0.97), but underestimated reflectance for between 24–30% ( 0.92), most notable around the 1940 nm water absorption peak. The soil-physical model performed very well ( 0.99) but is limited to 4% 24%. Results from a field experiment show that a short-wave infrared terrestrial laser scanner ( = 1550 nm) can accurately relate surface moisture to reflectance (standard error 2.6%), demonstrating its potential to derive spatially extensive surface moisture maps of a natural coastal beach.