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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    Spatial evaluation and trade-off analysis of soil functions through Bayesian networks
    Vrebos, Dirk ; Jones, Arwyn ; Lugato, Emanuele ; O'Sullivan, Lilian ; Schulte, Rogier ; Staes, Jan ; Meire, Patrick - \ 2020
    European Journal of Soil Science (2020). - ISSN 1351-0754
    Bayesian modelling - DayCent - European Union - mapping - maximization - soil function - trade-offs

    There is increasing recognition that soils fulfil many functions for society. Each soil can deliver a range of functions, but some soils are more effective at some functions than others due to their intrinsic properties. In this study we mapped four different soil functions on agricultural lands across the European Union. For each soil function, indicators were developed to evaluate their performance. To calculate the indicators and assess the interdependencies between the soil functions, data from continental long-term simulation with the DayCent model were used to build crop-specific Bayesian networks. These Bayesian Networks were then used to calculate the soil functions' performance and trade-offs between the soil functions under current conditions. For each soil function the maximum potential was estimated across the European Union and changes in trade-offs were assessed. By deriving current and potential soil function delivery from Bayesian networks a better understanding is gained of how different soil functions and their interdependencies can differ depending on soil, climate and management. Highlights: When increasing a soil function, how do trade-offs affect the other functions under different conditions? Bayesian networks evaluate trade-offs between soil functions and estimate their maximal delivery. Maximizing a soil function has varied effects on other functions depending on soil, climate and management. Differences in trade-offs make some locations more suitable for increasing a soil function then others.

    Transferability of a soil variogram for sampling design : A case study of three grasslands in Ireland
    Sun, Xiao Lin ; Brus, Dick J. - \ 2020
    European Journal of Soil Science (2020). - ISSN 1351-0754
    grid spacing - mapping - MCMC - precision agriculture - sample size

    It is commonly accepted that an estimated soil variogram can be transferred to another similar area for deriving the tolerable spacing of a sampling grid or, more generally, the sample size, given a requirement on the quality of the soil property map of the recipient area. The quality of the derived tolerable grid spacing depends on how similar the population variograms of the donor area and recipient area are. In practice we are uncertain about the variograms of both areas due to sampling errors. Ideally, the uncertainty about the variogram of the donor area is accounted for in deriving the tolerable grid spacing. To assess the transferability, we should also account for uncertainty in the estimated variogram of the recipient area. In this study the transferability of variograms of soil pH, P, Mg and K is analysed for three grassland fields in Ireland, which are similar in soil-forming factors. One field served as donor area, the other two as recipient area. For all three fields and for each soil property, 500 variograms were sampled from the posterior distribution of the variogram parameters. Results showed that the estimated variogram parameters of the recipient fields differed largely from those of the transferred variograms. The ranges of estimated mean kriging variance values for the various grid spacings, as obtained with the two sets of variograms (one set of the donor field, one set of the recipient field), did not overlap. Even after scaling the transferred variogram with an estimate of the variance of the recipient field, the transferred variogram was of no use for determining the tolerable grid spacing. The difference in the variograms can possibly be explained by the difference in historical land use. Highlights: Transferability of variograms to derive tolerable grid spacing for mapping grassland fields is assessed Transferability should be based on the uncertainty distributions of the tolerable grid spacings Due to difference in historical land use, local and transferred variograms differed largely Transferability of a variogram is very poor, even after scaling the transferred variogram.

    A note on knowledge discovery and machine learning in digital soil mapping
    Wadoux, Alexandre M.J.C. ; Samuel-Rosa, Alessandro ; Poggio, Laura ; Mulder, Vera Leatitia - \ 2020
    European Journal of Soil Science 71 (2020)2. - ISSN 1351-0754 - p. 133 - 136.
    mapping - pedometrics - random forest - soil science - variable selection

    In digital soil mapping, machine learning (ML) techniques are being used to infer a relationship between a soil property and the covariates. The information derived from this process is often translated into pedological knowledge. This mechanism is referred to as knowledge discovery. This study shows that knowledge discovery based on ML must be treated with caution. We show how pseudo-covariates can be used to accurately predict soil organic carbon in a hypothetical case study. We demonstrate that ML methods can find relevant patterns even when the covariates are meaningless and not related to soil-forming factors and processes. We argue that pattern recognition for prediction should not be equated with knowledge discovery. Knowledge discovery requires more than the recognition of patterns and successful prediction. It requires the pre-selection and preprocessing of pedologically relevant environmental covariates and the posterior interpretation and evaluation of the recognized patterns. We argue that important ML covariates could serve the purpose of providing elements to postulate hypotheses about soil processes that, once validated through experiments, could result in new pedological knowledge. Highlights: We discuss the rationale of knowledge discovery based on the most important machine learning covariates We use pseudo-covariates to predict topsoil organic carbon with random forest Soil organic carbon was accurately predicted in a hypothetical case study Pattern recognition by random forest should not be equated to knowledge discovery.

    A selection of sensing techniques for mapping soil hydraulic properties
    Knotters, M. ; Egmond, F.M. van; Bakker, G. ; Walvoort, D.J.J. ; Brouwer, F. - \ 2017
    Wageningen : Wageningen Environmental Research (Wageningen Environmental Research rapport 2853) - 65
    remote sensing - soil physical properties - mapping - remote sensing - fysische bodemeigenschappen - cartografie
    Data on soil hydraulic properties are needed as input for many models, such as models to predict unsaturated water movement and crop growth, and models to predict leaching of nutrients and pesticides to groundwater. The soil physics database of the Netherlands shows several lacunae, and a substantial part of the data were collected more than thirty years ago and thus might not represent actual soil hydraulic conditions.
    Multidimensional remote sensing based mapping of tropical forests and their dynamics
    Dutrieux, L.P. - \ 2016
    Wageningen University. Promotor(en): Martin Herold, co-promotor(en): Lammert Kooistra; Lourens Poorter. - Wageningen : Wageningen UR - ISBN 9789462578906 - 146
    tropical forests - remote sensing - mapping - biodiversity - forest structure - monitoring - land use - landsat - tropische bossen - remote sensing - cartografie - biodiversiteit - bosstructuur - monitoring - landgebruik - landsat

    Tropical forests concentrate a large part of the terrestrial biodiversity, provide important resources, and deliver many ecosystem services such as climate regulation, carbon sequestration, and hence climate change mitigation. While in the current context of anthropogenic pressure these forests are threatened by deforestation, forest degradation and climate change, they also have shown to be, in certain cases, highly resilient and able to recover from disturbances. Quantitative measures of forest resources and insights into their dynamics and functioning are therefore crucial in this context of climate and land use change. Sensors on-board satellites have been collecting a large variety of data about the surface of the earth in a systematic and objective way, making remote sensing a tool that holds tremendous potential for mapping and monitoring the earth. The main aim of this research is to explore the potential of remote sensing for mapping forest attributes and dynamics. Tropical South America, which contains the largest area of tropical forest on the planet, and is therefore of global significance, is the regional focus of the research. Different methods are developed and assessed to: (i) map forest attributes at national scale, (ii) detect forest cover loss, (iii) quantify land use intensity over shifting cultivation landscapes, and (iv) measure spectral recovery and resilience of regrowing forests.

    Remote sensing data are diverse and multidimensional; a constellation of satellite sensors collects data at various spatial, temporal and spectral resolutions, which can be used to inform on different components of forests and their dynamics. To better map and monitor ecological processes, which are inherently multidimensional, this thesis develops methods that combine multiple data sources, and integrate the spatial, temporal and spectral dimensions contained in remote sensing datasets. This is achieved for instance by assembling time-series to fully exploit the temporal signal contained in the data, or by working with multiple spectral channels as a way to better capture subtle ecological features and processes.

    After introducing the general objectives of the thesis in Chapter 1, Chapter 2 presents an approach for mapping forest attributes at national scale. In this chapter, 28 coarse resolution remote sensing predictors from diverse sources are used in combination with in-situ data from 220 forest inventory plots to predict nine forest attributes over lowland Bolivia. The attributes include traditional forest inventory variables such as forest structure, floristic properties, and abundance of life forms. Modelling is done using the random forest approach and reasonable prediction potential was found for variables related to floristic properties, while forest attributes relating to structure had a low prediction potential. This methodological development demonstrates the potential of coarse resolution remote sensing for scaling local in-situ ecological measurements to country-wide maps, thus providing information that is highly valuable for biodiversity conservation, resource use planning, and for understanding tropical forest functioning.

    Chapter 3 presents an approach to detect forest cover loss from remote sensing time-series. While change detection has been the object of many studies, the novel contribution of the present example concerns the capacity to detect change in environments with strong inter-annual variations, such as seasonally dry tropical forests. By combining Landsat with Moderate Resolution Imaging Spectroradiometer (MODIS) time-series in a change detection framework, the approach provides information at 30 m resolution on forest cover loss, while normalizing for the natural variability of the ecosystem that would otherwise be detected as change. The proposed approach of combining two data streams at different spatial resolutions provides the opportunity to distinguish anthropogenic disturbances from natural change in tropical forests.

    Chapter 4 introduces a new method to quantify land use intensity in swidden agriculture systems, using remote sensing time-series. Land use intensity — a parameter known for influencing forest resilience — is retrieved in this case by applying a temporal segmentation algorithm derived from the econometrics field and capable of identifying shifts in land dynamic regimes, to Landsat time-series. These shifts, or breakpoints, are then classified into the different events of the swidden agriculture cycle, which allows to quantify the number of cultivation cycles that has taken place for a given agricultural field. The method enables the production of objective and spatially continuous information on land use intensity for large areas, hence benefiting the study of spatio-temporal patterns of land use and the resulting forest resilience. The results were validated against an independent dataset of reported cultivation frequency and proved to be a reliable indicator of land use intensity.

    Chapter 5 further explores the concept of forest resilience. A framework to quantify spectral recovery time of forests that regrow after disturbance is developed, and applied to regrowing forests of the Amazon. Spatial patterns of spectral resilience as well as relations with environmental conditions are explored. Regrowing forests take on average 7.8 years to recover their spectral properties, and large variations in spectral recovery time occur at a local scale. This large local variability suggests that local factors, rather than climate, drive the spectral recovery of tropical forests. While spectral recovery times do not directly correspond to the time required for complete recovery of the biomass and species pool of tropical forests, they provide an indication on the kinetics of the early stages of forest regrowth.

    Chapter 6 summarizes the main findings of the thesis and provides additional reflections and prospects for future research. By predicting forest attributes country-wide or retrieving land use history over the 30 years time-span of the Landsat archive, the developed methods provide insights at spatial and temporal scales that are beyond the reach of ground based data collection methods. Remote sensing was therefore able to provide valuable information for better understanding, managing and conserving tropical forest ecosystems, and this was partly achieved by combining multiple sources of data and taking advantage of the available remote sensing dimensions. However, the work presented only explores a small part of the potential of remote sensing, so that future research should intensively focus on further exploiting the multiple dimensions and multi-scale nature of remote sensing data as a way to provide insights on complex multi-scale processes such as interactions between climate change, anthropogenic pressure, and ecological processes. Inspired by recent advances in operational forest monitoring, operationalization of scientific methods to retrieve ecological variables from remote sensing is also discussed. Such transfer of scientific advances to operational platforms that can automatically produce and update ecologically relevant variables globally would largely benefit ecological research, public awareness and the conservation and wise use of natural resources.

    Boomkronen afleiden uit het Actueel Hoogtebestand Nederland : kwaliteitsaspecten rondom het geautomatiseerd in kaart brengen van bomen op basis van het AHN2-bestand
    Meijer, M. ; Rip, Frans ; Benthem, R. van; Clement, J. ; Sande, C. van der - \ 2015
    Wageningen : Alterra, Wageningen-UR (Alterra-rapport 2671) - 85
    bomen - kroondak - kroon - gegevensanalyse - gegevens verzamelen - methodologie - remote sensing - hoogteligging - cartografie - nederland - trees - canopy - crown - data analysis - data collection - methodology - remote sensing - altitude - mapping - netherlands
    Alom wordt erkend dat bomen belangrijk zijn. Zowel voor de mens, de natuur als het klimaat. Recentelijk is een procedure ontwikkeld om op basis van het nationale Nederlandse hoogtebestand AHN2 een bestand te genereren met alle boomkronen in Nederland, genaamd ‘CP’. Een dergelijk bestand kan onder andere het groenbeheer van de gemeenten in Nederland vereenvoudigen en helpen bij het inventariseren van landschapselementen. De vraag is echter: hoe goed is dit bestand? In dit rapport wordt voor een drietal verschillende gebieden onderzocht wat de kwaliteit is van CP. Verder wordt mede op basis van de ervaringen die tijdens het kwaliteitsonderzoek zijn gedaan een standaard kwaliteitsraamwerk opgezet voor het controleren van nieuwe versies van het boomkronenbestand. Daarnaast is dit document er ook op gericht om de potentiële gebruiker een beter beeld van de kwaliteit te geven.
    Actualisatie bodemkaart veengebieden : deelgebied en 2 in Noord Nederland
    Vries, F. de; Brus, D.J. ; Kempen, B. ; Brouwer, F. ; Heidema, A.H. - \ 2014
    Wageningen : Alterra, Wageningen-UR (Alterra-rapport 2556)
    veengronden - bodem - bodemkarteringen - cartografie - kaarten - noord-nederland - peat soils - soil - soil surveys - mapping - maps - north netherlands
    De bodemkaart onderscheidt allerlei bodemtypen met veenlagen ondiep in het profiel. Door oxidatie en klink neemt de veendikte geleidelijk af. Hierdoor treedt er een verschuiving op in bodemtypen; moerige gronden veranderen in minerale gronden en veengronden in moerige gronden. Vanwege deze dynamiek bij gronden met dunne veenlagen dient de bodemkundige informatie periodiek geactualiseerd te worden. Alle veengebieden in Friesland en een deel van de veengebieden in Drenthe, Groningen en Overijssel zijn opnieuw in kaart gebracht. Het project heeft geresulteerd in een veendiktekaart en een geactualiseerde bodemkaart.
    Mapping and modelling the effects of land use and land management change on ecosystem services from local ecosystems and landscapes to global biomes
    Petz, K. - \ 2014
    Wageningen University. Promotor(en): Rik Leemans, co-promotor(en): Rob Alkemade; Dolf de Groot. - Wageningen : Wageningen University - ISBN 9789461738509 - 212
    ecosysteemdiensten - ecosystemen - landgebruik - grondbeheer - cartografie - modelleren - extensieve weiden - begrazing - noord-brabant - zuid-afrika - ecosystem services - ecosystems - land use - land management - mapping - modeling - rangelands - grazing - noord-brabant - south africa
    Herstel en duurzaam beheer van biodiversiteit en ecosysteemdiensten worden steeds meer geïntegreerd in nationaal en internationaal beleid. In dit proefschrift wordt een methodologie ontwikkeld voor de kwantificering van effecten van landmanagement op de ruimtelijke verspreiding van ecosysteemdiensten, zodat de door landmanagement veroorzaakte trade-offs tussen ecosysteemdiensten bepaald kunnen worden voor zowel lokale ecosystemen en landschappen als regionale en mondiale biomen. Een groot aantal ecosysteemdiensten zijn bestudeerd. De karterings- en modelleringsmethoden zijn toegepast en gecombineerd met scenario-analyse in de Nederlandse en Zuid-Afrikaanse studies. Voor Nederland is het landschap van Het Groene Woud bestudeerd.
    Imaging spectroscopy for ecological analysis in forest and grassland ecosystems
    Homolova, L. - \ 2014
    Wageningen University. Promotor(en): Michael Schaepman, co-promotor(en): Jan Clevers. - Wageningen : Wageningen University - ISBN 9789461738240 - 177
    remote sensing - naaldbossen - alpenweiden - picea abies - bladoppervlakte - ecofysiologie - ecosysteemdiensten - vegetatie - chlorofyl - cartografie - beeldvormende spectroscopie - remote sensing - coniferous forests - alpine grasslands - picea abies - leaf area - ecophysiology - ecosystem services - vegetation - chlorophyll - mapping - imaging spectroscopy

    Terrestrial vegetation is an important component of the Earth’s biosphere and therefore playing an essential role in climate regulation, carbon sequestration, and it provides large variety of services to humans. For a sustainable management of terrestrial ecosystems it is essential to understand vegetation responses to various pressures, to monitor and to predict the spatial extent and the rate of ecosystem changes. Remote sensing (RS) therefore offers a unique opportunity for spatially continuous, and for some type of RS data, also frequent monitoring of terrestrial ecosystems.

    RS of vegetation is a broad research field, where a lot of progress has been made in the last three decades. However, the complexity of interactions between vegetation and solar radiation, constantly modulated by environmental factors, offers room for deeper investigation. Rather than solving one big research problem, this thesis built a few bridges on a way leading towards better understanding of using airborne imaging spectroscopy for ecological analysis in temperate coniferous forests and subalpine grasslands. The research was divided into a theoretical and an applied part. The theoretical part contributed to a critical evaluation of research achievements and challenges in optical RS of plant traits (Chapter 2). The applied part addressed three research topics: i) investigating variability of total to projected leaf area ratio in spruce canopies and its implications on RS of chlorophyll content (Chapter 3), ii) testing chlorophyll retrieval methods based on continuum removal in spruce canopies (Chapter 4), and iii) exploring potentials of imaging spectroscopy to map ecosystem properties and the capacity of subalpine grasslands in providing ecosystem services in comparison with a plant trait-based modelling approach (Chapter 5).

    In Chapter 2, we reviewed achievements and challenges in RS estimation of key plant traits and we concentrated our discussion on eight traits with the strongest potential to be mapped using RS (plant growth and life forms, flammability properties, photosynthetic pathways and photosynthesis activity, plant height, leaf lifespan and phenology, specific leaf area, leaf nitrogen and phosphorous). The review indicated that imaging spectroscopy facilitates better retrievals of plant traits related to leaf biochemistry, photosynthesis and phenology rather than traits related to vegetations structure. Estimation of the canopy structure related traits (e.g. plant height) can certainly benefit from increasing synergies between imaging spectroscopy and active RS (radar or laser scanning). One of major challenges in RS of plant traits is to effectively suppress the negative influences of water absorption and canopy structure, which would facilitate more accurate retrievals of biochemical and photosynthesis-related traits. Secondly, a successful integration of RS and plant ecology concepts would require careful matching of spatial scales of in-situ trait data with RS observations.

    In Chapter 3, measurement methods and variability of total to projected leaf area within spruce crowns were investigated. Comparison of six laboratory methods revealed that methods using an elliptic approximation of a needle shape underestimated total leaf area compared to methods using a parallelepiped approximation. The variability in total to projected leaf area was primarily driven by the vertical sampling position and less by needle age or forest stand age. We found that total leaf area estimation has an important implication on RS of leaf chlorophyll content. An error associated with biased estimates of total leaf area can reach up to 30% of the expected chlorophyll range commonly found in forest canopies and therefore negatively influences the validation of RS-based chlorophyll maps. In Chapter 4, potentials of the continuum removal transformation for mapping of chlorophyll content in spruce canopies were investigated. We tested two methods based on continuum removal: artificial neural networks and an optical index. The optical index was newly designed here and it was based on the spectral continuum between 650 and 720 nm. Both continuum removal based methods exhibited superior accuracy in chlorophyll retrieval compared to commonly used narrow-band vegetation indices (e.g. NDVI, TCARI/OSAVI). The newly designed index was equally accurate, but certainly provided a more operational approach as compared to the neural network.

    In Chapter 5, mapping of ecosystem properties that underline ecosystem services provided by subalpine grasslands using RS methods was tested and further compared with a statistical plant trait-based modelling approach. Imaging spectroscopy in combination with empirical retrieval methods was partly successful to map ecosystem properties. The prediction accuracy at the calibration phase was comparable to the trait-based modelling approach. Spatial comparison between the two approaches revealed rather small agreement. The average fuzzy similarity between the approaches was around 20% for ecosystem properties, but in case of the total ecosystem service supply it decreased below 10%. However, the RS approach detected more variability in ecosystem properties and thereby in services, which was driven by local topography and microclimatic conditions, which could not be detected by the plant trait-based approach. Especially Chapters 2 and 5 indicated that one of the future RS research directions may be in spatial ecology, i.e. spatially explicit mapping of plant traits, ecosystem properties and ecosystem services. High quality RS data are certainly essential building elements for spatial ecology. But in order to address the effects of climate and land use changes on biodiversity and ecosystems, their properties and services, the integration of in-situ and RS data will be ultimately required. Therefore, more coherent experiments, where in-situ and RS data are measured simultaneously at different spatial scales, are needed in the future.

    Runoff, discharge and flood occurrence in a poorly gauged tropical basin : the Mahakam River, Kalimantan
    Hidayat, H. - \ 2013
    Wageningen University. Promotor(en): Remko Uijlenhoet, co-promotor(en): Ton Hoitink. - S.l. : s.n. - ISBN 9789461737434 - 114
    oppervlakkige afvoer - afvoer - overstromingen - monitoring - tropen - modellen - rivieren - cartografie - voorspelling - kalimantan - indonesië - runoff - discharge - floods - monitoring - tropics - models - rivers - mapping - prediction - kalimantan - indonesia

    Tidal rivers and lowland wetlands present a transition region where the interests of hydrologists and physical oceanographers overlap. Physical oceanographers tend to simplify river hydrology, by often assuming a constant river discharge when studying estuarine dynamics. Hydrologists, in turn, generally ignore the direct or indirect effects of tides in water level and discharge records. This thesis aims to improve methods to monitor, model and predict discharge dynamics in tidal rivers and lowland wetlands, by focussing on the central and lower reaches of the River Mahakam (East Kalimantan, Indonesia), and the surrounding lakes area. The 980-km long river drains an area of about 77100 km2 between 2°N - 1°S and 113°E - 118°E. Due to its very mild bottom slope, a significant tidal influence occurs in this river. The middle reach of the river is located in a subsiding basin, parts of which are below mean sealevel, featuring peat swamps and about thirty lakes connected to the river via tie channels.

    Upstream of the lakes area, at about 300 km from the river mouth, an acoustic Doppler current profiler (H-ADCP) has been horizontally deployed at a station near the city of Melak (Chapter 2). The H-ADCP profiles of velocity are converted to discharge adopting a new calibration methodology. The obtained time-series of discharge show the tidal signal is clearly visible during low flow conditions. Besides tidal signatures, the discharge series show influences by variable backwater effects from the lakes, tributaries and floodplain ponds. The discharge rate at the station exceeds 3250 m3s-1 with a hysteretic behaviour. For a specific river stage, the discharge range can be as high as 2000 m3s-1. Analysis of alternative types of rating curves shows this is far beyond what can be explained from kinematic wave dynamics. Apart from backwater effects, the large variation of discharge for a specified river stage can be explained by river-tide interaction, impacting discharge variation especially in the fortnightly frequency band.

    A second H-ADCP station has been setup in the lower reach of the Mahakam, near the city of Samarinda, where the tidal discharge amplitude generally exceeds the discharge related to runoff (Chapter 3). Conventional rating curve techniques are inappropriate to model river discharge at this tidally influenced station. As an alternative, an artificial neural network (ANN) model is developed to investigate the degree to which tidal river discharge at Samarinda station can be predicted from an array of level gauge measurements along the tidal river, and from tidal level predictions at sea. The ANN-based model produces a good discharge estimation, as established from a consistent performance during both the training and the validation periods, showing the discharges can be predicted from water levels only, once that a trained model is available. The ANN models perform well in predicting discharges up to two days in advance.

    Chapter 4 addresses the role of backwater effects and tidal influences on discharge time-series used to calibrate a rainfall-runoff model. The HBV rainfall-runoff model is implemented for the Mahakam sub-catchment upstream of Melak (25700 km2). In a first approach, the model is calibrated using a discharge series derived from the H-ADCP measurements from Melak station. In a second approach, discharge estimates derived from a rating curve are used to calibrate the model. Adopting the first approach, a comparatively low model efficiency is obtained, which is attributed to the backwater and tidal effects that are not captured in the model. The second approach produces a relatively higher model efficiency, since the rating curve filters the backwater effects out of the discharge series. Seasonal variation of terms in the water balance is not affected by the choice for one of the two calibration strategies, which shows that backwaters do not have a systematic seasonal effect on the river discharge.

    To allow for investigation of the causes of backwater effects, satellite radar remote sensing is employed to monitor water levels in wetlands (Chapter 5). A series of Phased Array L-band Synthetic Aperture Radar (PALSAR) images is used to observe the dynamics of the Mahakam River floodplain. To analyze radar backscatter behavior for different land cover types, several regions of interest are selected, based on land cover classes. Medium shrub, high shrub, fern/grass, and degraded forest are found to be sensitive to flooding, whereas peat forest, riverine forest and tree plantation backscatter signatures only slightly change with flood inundation. An analysis of the relationship between radar backscatter and water levels is carried out. For lakes and shrub covered peatland, for which the range of water level variation is high, a good water level-backscatter correlation is obtained. In peat forest covered peatland, subject to a small range of water level variation, water level-backscatter correlations are poor, limiting the ability to obtain a floodplain-wide water surface topography from the radar images.

    Chapter 6 continues to investigate the degree in which satellite radar remote sensing can serve to distinguish between dry areas and wetlands, which is a difficult task in densely vegetated areas such as peat domes. Flood extent and flood occurrence information are successfully extracted from a series of radar images of the middle Mahakam lowland area. A fully inundated region is easily recognized from a dark signature on radar images. Open water flood occurrence is mapped using a threshold value taken from radar backscatter of the permanently inundated areas. Radar backscatter intensity analysis of the vegetated floodplain area reveals consistently higher backscatter values, indicating flood inundation under forest canopy. Those observations are used to establish thresholds for flood occurrence mapping in the vegetated area. An all-encompassing flood occurrence map is obtained by combining the flood occurrence maps for areas with and without vegetation.

    Chapter 7 synthesizes the findings from the previous chapters. It is concluded that the backwater effects and subtle tidal influences may prevent the option to predict river discharge using rating curves, which can best be interpreted as a stage-runoff relationship. H-ADCPs offer a promising alternative to monitor river discharge. For a tidal river, an ANN model can be used as a tool for data gap filling in an H-ADCP based discharge series, or even to derive discharge estimates solely from water levels and water level predictions. Discharge can be predicted several time-steps ahead, allowing water managers to take measures based on forecasts. The stage-runoff relationship derived from a continuous series of H-ADCP based discharge estimates may be expected to be much more accurate than a similar rating curve derived from a small number of boat surveys. The flood occurrence map derived from PALSAR images can offer a detailed insight into the hydroperiod, the period in which a soil area is waterlogged, and flood extent of the lowland area, illustrating the added value of radar remote sensing to wetland hydrological studies. In future work, radar-based floodplain observations may serve to calibrate hydrodynamic models simulating the processes of flooding and emptying of the lakes area.

    Spectroscopy-supported digital soil mapping
    Mulder, V.L. - \ 2013
    Wageningen University. Promotor(en): Michael Schaepman; Sytze de Bruin. - [S.l. : S.n. - ISBN 9789461736901 - 188
    bodemkarteringen - bodem - cartografie - spectroscopie - remote sensing - bodemsamenstelling - soil surveys - soil - mapping - spectroscopy - remote sensing - soil composition

    Global environmental changes have resulted in changes in key ecosystem services that soils provide. It is necessary to have up to date soil information on regional and global scales to ensure that these services continue to be provided. As a result, Digital Soil Mapping (DSM) research priorities are among others, advancing methods for data collection and analyses tailored towards large-scale mapping of soil properties. Scientifically, this thesis contributed to the development of methodologies, which aim to optimally use remote and proximal sensing (RS and PS) for DSM to facilitate regional soil mapping. The main contributions of this work with respect to the latter are (I) the critical evaluation of recent research achievements and identification of knowledge gaps for large-scale DSM using RS and PS data, (II) the development of a sparse RS-based sampling approach to represent major soil variability at regional scale, (III) the evaluation and development of different state-of-the-art methods to retrieve soil mineral information from PS, (IV) the improvement of spatially explicit soil prediction models and (V) the integration of RS and PS methods with geostatistical and DSM methods.

    A review on existing literature about the use of RS and PS for soil and terrain mapping was presented in Chapter 2. Recent work indicated the large potential of using RS and PS methods for DSM. However, for large-scale mapping, current methods will need to be extended beyond the plot. Improvements may be expected in the fields of developing more quantitative methods, enhanced geostatistical analysis and improved transferability to other areas. From these findings, three major research interests were selected: (I) soil sampling strategies, (II) retrieval of soil information from PS and (III) spatially continuous mapping of soil properties at larger scales using RS.

    Budgetary constraints, limited time and available soil legacy data restricted the soil data acquisition, presented in Chapter 3. A 15.000 km2 area located in Northern Morocco served as test case. Here, a sample was collected using constrained Latin Hypercube Sampling (cLHS) of RS and elevation data. The RS data served as proxy for soil variability, as alternative for the required soil legacy data supporting the sampling strategy. The sampling aim was to optimally sample the variability in the RS data while minimizing the acquisition efforts. This sample resulted in a dataset representing major soil variability. The cLHS sample failed to express spatial correlation; constraining the LHS by a distance criterion favoured large spatial variability over short distances. The absence of spatial correlation in the sampled soil variability precludes the use of additional geostatistical analyses to spatially predict soil properties. Predicting soil properties using the cLHS sample is thus restricted to a modelled statistical relation between the sample and exhaustive predictor variables. For this, the RS data provided the necessary spatial information because of the strong spatial correlation while the spectral information provided the variability of the environment (Chapter 3 and 6). Concluding, the RS-based cLHS approach is considered a time and cost efficient method for acquiring information on soil resources over extended areas.

    This sample was further used for developing methods to derive soil mineral information from PS, and to characterize regional soil mineralogy using RS. In Chapter 4, the influences of complex scattering within the mixture and overlapping absorption features were investigated. This was done by comparing the success of PRISM’s MICA in determining mineralogy of natural samples and modelled spectra. The modelled spectra were developed by a linearly forward model of reflectance spectra, using the fraction of known constituents within the sample. The modelled spectra accounted for the co-occurrence of absorption features but eluded the complex interaction between the components. It was found that more minerals could be determined with higher accuracy using modelled reflectance. The absorption features in the natural samples were less distinct or even absent, which hampered the classification routine. Nevertheless, grouping the individual minerals into mineral categories significantly improved the classification accuracy. These mineral categories are particularly useful for regional scale studies, as key soil property for parent material characterization and soil formation. Characterizing regional soil mineralogy by mineral categories was further described in Chapter 6. Retrieval of refined information from natural samples, such as mineral abundances, is more complex; estimating abundances requires a method that accounts for the interaction between minerals within the intimate mixture. This can be done by addressing the interaction with a non-linear model (Chapter 5).

    Chapter 5 showed that mineral abundances in complex mixtures could be estimated using absorption features in the 2.1–2.4 µm wavelength region. First, the absorption behaviour of mineral mixtures was parameterized by exponential Gaussian optimization (EGO). Next, mineral abundances were successfully predicted by regression tree analysis, using these parameters as inputs. Estimating mineral abundances using prepared mixes of calcite, kaolinite, montmorillonite and dioctahedral mica or field samples proved the validity of the proposed method. Estimating mineral abundances of field samples showed the necessity to deconvolve spectra by EGO. Due to the nature of the field samples, the simple representation of the complex scattering behaviour by a few Gaussian bands required the parameters asymmetry and saturation to accurately deconvolve the spectra. Also, asymmetry of the EGO profiles showed to be an important parameter for estimating the abundances of the field samples. The robustness of the method in handling the omission of minerals during the training phase was tested by replacing part of the quartz with chlorite. It was found that the accuracy of the predicted mineral content was hardly affected. Concluding, the proposed method allowed for estimating more than two minerals within a mixture. This approach advances existing PS methods and has the potential to quantify a wider set of soil properties. With this method the soil science community was provided an improved inference method to derive and quantify soil properties

    The final challenge of this thesis was to spatially explicit model regional soil mineralogy using the sparse sample from Chapter 3. Prediction models have especially difficulties relating predictor variables to sampled properties having high spatial correlation. Chapter 6 presented a methodology that improved prediction models by using scale-dependent spatial variability observed in RS data. Mineral predictions were made using the abundances from X-ray diffraction analysis and mineral categories determined by PRISM. The models indicated that using the original RS data resulted in lower model performance than those models using scaled RS data. Key to the improved predictions was representing the variability of the RS data at the same scale as the sampled soil variability. This was realized by considering the medium and long-range spatial variability in the RS data. Using Fixed Rank Kriging allowed smoothing the massive RS datasets to these ranges. The resulting images resembled more closely the regional spatial variability of soil and environmental properties. Further improvements resulted from using multi-scale soil-landscape relationships to predict mineralogy. The maps of predicted mineralogy showed agreement between the mineral categories and abundances. Using a geostatistical approach in combination with a small sample, substantially improves the feasibility to quantitatively map regional mineralogy. Moreover, the spectroscopic method appeared sufficiently detailed to map major mineral variability. Finally, this approach has the potential for modelling various natural resources and thereby enhances the perspective of a global system for inventorying and monitoring the earth’s soil resources.

    With this thesis it is demonstrated that RS and PS methods are an important but also an essential source for regional-scale DSM. Following the main findings from this thesis, it can be concluded that: Improvements in regional-scale DSM result from the integrated use of RS and PS with geostatistical methods. In every step of the soil mapping process, spectroscopy can play a key role and can deliver data in a time and cost efficient manner. Nevertheless, there are issues that need to be resolved in the near future. Research priorities involve the development of operational tools to quantify soil properties, sensor integration, spatiotemporal modelling and the use of geostatistical methods that allow working with massive RS datasets. This will allow us in the near future to deliver more accurate and comprehensive information about soils, soil resources and ecosystem services provided by soils at regional and, ultimately, global scale.

    The landscape at your service : spatial analysis of landscape services for sustainable development
    Gulickx, M.M.C. - \ 2013
    Wageningen University. Promotor(en): Peter de Ruiter, co-promotor(en): Jetse Stoorvogel; Kasper Kok. - S.l. : s.n. - ISBN 9789461736871 - 143
    ecosysteemdiensten - ruimtelijke verdeling - landschapselementen - gebiedsgericht beleid - cartografie - plattelandsplanning - scenario planning - multi-stakeholder processen - landschapsplanning - noord-brabant - intensieve veehouderij - grondwaterkwaliteit - zandgronden - de peel - ecosystem services - spatial distribution - landscape elements - integrated spatial planning policy - mapping - rural planning - scenario planning - multi-stakeholder processes - landscape planning - noord-brabant - intensive livestock farming - groundwater quality - sandy soils - de peel
    De mens beheert en verandert het landschap om diverse producten en diensten te verkrijgen en te versterken. Deze producten en diensten, landschapsdiensten genoemd, leveren een positieve bijdrage aan de maatschappij. Interacties tussen sociale (menselijke) en ecologische (natuurlijke) systemen maken de voorziening van Landschapsdiensten mogelijk. In het landschap zijn er synergiën en conflicten tussen diensten, waardoor keuzes gemaakt moeten worden, oftewel we hebben te maken met trade‐offs tussen diensten. De interactie tussen de systemen, samen met de ruimtelijke verwevenheid van de diensten zorgen voor hoge complexiteit. Kaarten van landschapsdiensten kunnen deze complexiteit, of een deel daarvan, helder weergeven en daardoor bijdragen aan een beter begrip van de interacties en bijkomende trade‐offs. Dit begrip helpt ons bij het voorkomen van conflicten tussen diensten en geeft ons betere mogelijkheden om gewenste diensten te versterken. Voornamelijk ruimtelijke planners en beleidsmakers hebben veel baat bij deze informatie. Geschikte kaarten van landschapsdiensten zijn daarom van groot belang. Meer onderzoek is nodig naar ruimtelijk relaties om zodoende landschappelijke elementen te identificeren die kunnen fungeren als indicatoren voor het karteren van landschapsdiensten. Voor de gemeenten Deurne en Asten zijn de landschapsdiensten moerashabitat, bosrecreatie, grondgebonden veehouderij en wandelrecreatie in kaart gebracht. Met behulp van veldwerk zijn voor 389 locaties de landschapsdiensten geïdentificeerd. Een ruimtelijke analyse gemaakt van de grondwaterkwaliteit in relatie tot landschappelijke elementen, landgebruik en landschapsdiensten. De studie is uitgevoerd voor het zuidelijk zandgebied in de regio de Peel in Nederland.
    Africa Soil Profiles Database, Version 1.1. A compilation of georeferenced and standardised legacy soil profile data for Sub-Saharan Africa (with dataset). Africa Soil Information Service (AfSIS) project.
    Leenaars, J.G.B. - \ 2013
    Wageningen : ISRIC - World Soil Information (ISRIC report 2013/03) - 160
    bodemprofielen - databanken - bodem - bodemkarteringen - cartografie - georeferentie - afrika ten zuiden van de sahara - soil profiles - databases - soil - soil surveys - mapping - georeference - africa south of sahara
    Habitat landscape pattern and connectivity indices : used at varying spatial scales for harmonized reporting in the EBONE project
    Estreguil, C. ; Caudullo, G. ; Whitmore, C. - \ 2012
    Wageningen : Alterra, Wageningen-UR (Alterra-rapport 2297) - 80 p.
    landschapsanalyse - patronen - habitats - grondbedekking - cartografie - landschapsecologie - biodiversiteit - west-europa - landscape analysis - patterns - habitats - ground cover - mapping - landscape ecology - biodiversity - western europe
    This study is motivated by biodiversity related policy information needs on ecosystem fragmentation and connectivity. The aim is to propose standardized and repeatable methods to characterize ecosystem landscape structure in a harmonized way at varying spatial scales and thematic resolutions (habitat in situ versus land cover satellite based observations). Habitat landscape pattern was assessed in terms of configuration, interface mosaic context and structural/functional connectivity on the basis of three available conceptual models (morphological analysis, landscape composition moving window, network graph theory) that were customized, automated and partly combined. Input data were from the EBONE General Habitat Categories maps available over sixty 1 km2 in-situ samples at fine scale (400 m2 Minimum Mapping Unit). Demonstration focused on the focal forest phanerophyte habitat. Forest spatial pattern, edge interfaces and connectivity related maps and indices were obtained for all samples, and then reported per European Environmental Zones. A prototype web-based mapping client (http://forest.jrc.ec.europa.eu/ebone) was also developed to view and query the map layers and indices. Finally, the same models and indices were applied to the satellite based European and regional land cover maps available at broad (25 ha MMU) and medium (1ha MMU) scales. Differences in patterns across the three scales were highlighted over the only common 1 km2 analysis unit. Further, the satellite based patterns were reported at the more suitable fixed area grid of 25 km x 25 km. The overlay with the 1 km2 in situ habitat pattern enabled to inform the macro-scale landscape structure context of the squares and compare with their micro-scale pattern. Such study should be repeated to study spatio-temporal patterns relationships across scales once multi-temporal and larger in situ dataset will be available.
    Analysis of vegetation-activity trends in a global land degradation framework
    Jong, R. de - \ 2012
    Wageningen University. Promotor(en): Michael Schaepman, co-promotor(en): Sytze de Bruin. - S.l. : s.n. - ISBN 9789461733122 - 147
    vegetatie - vegetatie-indexen - landdegradatie - cartografie - monitoring - observatie - exploratie - klimaat - seizoenvariatie - satellietbeelden - remote sensing - vegetation - vegetation indices - land degradation - mapping - monitoring - observation - exploration - climate - seasonal variation - satellite imagery - remote sensing

    Land degradation is a global issue on a par with climate change and loss of biodiversity, but its extent and severity are only roughly known and there is little detail on the immediate processes – let alone the drivers. Earth-observation methods enable monitoring of land resources in a consistent, physical way and on global scale by making use of vegetation activity and/or cover as proxies. A well-known spectral proxy is the normalized difference vegetation index (NDVI), which is available in high temporal resolution time series since the early 1980s. In this work, harmonic analyses and non-parametric trend tests were applied to the GIMMS NDVI dataset (1981–2008) in order to quantify positive changes (or greening) and negative changes (browning). Phenological shifts and variations in length of growing season were accounted for using analysis by vegetation development stage rather than by calendar day. This approach does not rely on temporal aggregation for elimination of seasonal variation. The latter might introduce artificial trends as demonstrated in the chapter on the modifiable temporal unit problem. Still, a major assumption underlying the analysis is that trends were invariant, i.e. linear or monotonic, over time. However, these monotonic trends in vegetation activity may consist of an alternating sequence of greening and/or browning periods. This effect and the contribution of short-term trends to longer-term change was analysed using a procedure for detection of trend breaks. Both abrupt and gradual changes were found in large parts of the world, especially in (semi-arid) shrubland and grassland. Many abrupt changes were found around large-scale natural influences like the Mt Pinatubo eruption in 1991 and the strong 1997/98 El Niño event. This marks the importance of accounting for trend changes in the analysis of long-term NDVI time series. These new change-detection techniques advance our understanding of vegetation variability at a multi-decadal scale, but do not provide links to driving processes. It is very complex to disentangle all natural and human drivers and their interactions. As a first step, the spatial relation between changes in climate parameters and changes in vegetation activity was addressed in this work. It appeared that a substantial proportion (54%) of the spatial variation in NDVI changes could be associated to climatic changes in temperature, precipitation and incident radiation, especially in forest biomes. In other regions, the lack of such associations might be interpreted as human-induced land degradation. With these steps we demonstrated the value of global satellite records for monitoring land resources, although many steps are still to be taken.

    Mapping maize yield gaps in Africa; Can a leopard change its spots?
    Dijk, M. van; Meijerink, G.W. ; Rau, M.L. ; Shutes, K. - \ 2012
    The Hague : LEI, part of Wageningen UR (Report / LEI Wageningen UR : Research area International policy ) - ISBN 9789086155750 - 80
    maïs - voedselproductie - voedselzekerheid - markten - kunstmeststoffen - productiviteit - cartografie - afrika - maize - food production - food security - markets - fertilizers - productivity - mapping - africa
    Validatie bodemkaart veengebieden provincie Utrecht, schaal 1 : 25 000
    Kempen, B. ; Brouwer, F. ; Brus, D.J. ; Pleijter, M. ; Vries, F. de - \ 2011
    Wageningen : Alterra (Alterra-rapport 2252) - 38
    cartografie - bodem - veengronden - kaarten - utrecht - mapping - soil - peat soils - maps - utrecht
    Door bodemgebruik en ontwatering oxideert er organische stof in de bodem. Ondiepe veenlagen worden hierdoor geleidelijk dunner, waardoor de bodemopbouw verandert. Door deze veranderingen is actualisatie van de Bodemkaart van Nederland, schaal 1 : 50 000 (BvN), noodzakelijk. In dit onderzoek is gekeken of een recente bodemkaart, de Bodemkaart Veengebieden provincie Utrecht, schaal 1 : 25 000 (BVU), kan worden gebruikt voor actualisatie van de veengebieden van de BvN voor de provincie Utrecht. De kwaliteit van de BVU is met een kanssteekproef vastgesteld, en vergeleken met die van de BvN. De validatieresultaten laten zien dat de BVU bruikbaar is voor actualisatie van de BvN. De meest snelle actualisatiemethode, het direct ‘inpluggen’ van de BVU in de BvN, leidt al tot een verbetering van de kaartkwaliteit van de BvN. De zuiverheden van de BVU en BvN kunnen lokaal echter (grote) verschillen vertonen, zoals bijvoorbeeld in de Eempolder door het ontbreken van waardveengronden op de BVU. Als hiermee rekening wordt gehouden kan bij de actualisatie van de BvN het ‘beste’ uit beide kaarten worden gecombineerd.
    Updating soil information with digital soil mapping
    Kempen, B. - \ 2011
    Wageningen University. Promotor(en): Tom Veldkamp, co-promotor(en): Gerard Heuvelink; Dick Brus. - [S.l.] : S.n. - ISBN 9789461730909 - 218
    bodem - cartografie - kaarten - informatiesystemen - bodemkarteringen - organisch bodemmateriaal - nederland - soil - mapping - maps - information systems - soil surveys - soil organic matter - netherlands
    De Bodemkaart van Nederland, schaal 1:50.000, is de belangrijkste bron van bodeminformatie in Nederland. Deze kaart raakt echter in gebieden met veengronden verouderd. Door intensief gebruik van deze gronden verdwijnt het veen. Actualisatie van de bodemkaart is daarom noodzakelijk. Bas Kempen promoveerde op zijn onderzoek hiernaar.
    Biodiversity hotspots on the Dutch Continental Shelf: a marine strategy framework directive perspective
    Bos, O.G. ; Witbaard, R. ; Lavaleye, M.S.S. ; Moorsel, G.W.N.M. ; Teal, L.R. ; Hal, R. van; Hammen, T. van der; Hofstede, R. ter; Bemmelen, R.S.A. van; Witte, R.H. ; Geelhoed, S.C.V. ; Dijkman, E.M. - \ 2011
    IJmuiden : IMARES (Report / IMARES Wageningen UR C071/11) - 145
    biodiversiteit - inventarisaties - bescherming - cartografie - biogeografie - benthos - vis - vogels - zeezoogdieren - habitats - nederland - biodiversity - inventories - protection - mapping - biogeography - benthos - fish - birds - marine mammals - habitats - netherlands
    This report presenst hotspots of biodiversity for benthos, fish, birds, marine mammals and habitats on the Dutch Continental Shelf. These hotspots are based on a spatial application of biodiversity metrics developed in this study for the GES(Good Environmental Status)-descriptor 1 ‘Biological diversity is maintained’ of the Marine Strategy Framework Directive (MSFD) (EU 2008). The choice of the biodiversity metrics is based on the proposed indicators of biodiversity in the Commission Decision (EU 2010). The purpose of this study is to provide insight in possibilities for spatial protection measures in the framework of the MSFD. This report feeds information and ideas into further work for the MSFD in the Netherlands. IMARES has compiled this report for the Dutch Ministry of Economic Affairs, Agriculture and Innovation (Ministry of EL&I) and the Ministry of Infrastructure and the Environment (I&M).
    Manual for habitat and vegetation surveillance and monitoring : temperate, mediterranean and desert biomes
    Bunce, R.G.H. ; Bogers, M.M.B. ; Roche, P. ; Walczak, M. ; Geijzendorffer, I.R. ; Jongman, R.H.G. - \ 2011
    Wageningen : Alterra (Alterra-report 2154) - 106
    biodiversiteit - habitats - vegetatie - cartografie - karteren - registreren - monitoring - biodiversity - habitats - vegetation - mapping - surveying - recording - monitoring
    The primary objective of this Manual is to describe the methodology appropriate for coordinating information on habitats and vegetation in order to obtain statistically robust estimates of their extent and associated changes in biodiversity. Such detailed rules are necessary if surveillance, i.e., recording information at a point in time, is to be repeated subsequently as monitoring, otherwise real changes cannot be separated reliably from background noise. The Manual has been produced as part of the EBONE (European Biodiversity Observation Network). There have been some modifications and additional from the previous Manual published in 2005. The basis of the General Habitat Categories is the classification of plant Life Forms produced by the Danish botanist Raunkiaer early in the 20th century. These Life Forms e.g. annuals or trees, transcend species. They are based on the scientific hypothesis that habitat structure is related to the environment. The General Habitat Categories and the Life Form Qualifiers, which are for defining habitats outside Europe, have 160 GHCs derived from 16 Life Forms (LF’s), 18 Non Life Forms (NLFs) and 24 Life Form Qualifiers. They have been field tested not only in all the environmental zones in Europe, but also in Mediterranean and desert biomes in Israel, Tunisia, South Africa and Australia. Variation within a General Habitat Category is then expressed by environmental and global qualifiers, which are combinations of soil humidity, nutrient status, acidity and other habitat characteristics. Important additional information is given by adding codes from predefined lists of site and management qualifiers. Also full lists of GHCs are added together with information on species. A procedure is described for recording vegetation plots in the GHCs. Every effort has been made to make the Manual consistent and robust, but inevitably a few errors may still be present, so please consult the authors if problems are encountered.
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