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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Analysis of drought and vulnerability in the North Darfur region of Sudan
Mohmmed, Alnail ; Zhang, Ke ; Kabenge, Martin ; Keesstra, Saskia ; Cerdà, Artemi ; Reuben, Makomere ; Elbashier, Mohammed M.A. ; Dalson, Twecan ; Ali, Albashir A.S. - \ 2018
Land Degradation and Development 29 (2018)12. - ISSN 1085-3278 - p. 4424 - 4438.
drought - meteorology - North Darfur region - remote sensing - vulnerability index

North Darfur of Sudan is located on the edge of the Sahara Desert and endures frequent droughts due to water shortages and high summer temperatures. Monitoring and understanding drought characteristics are essential for integrated drought risk mitigation and prevetion of land degradation. This study evaluates drought conditions in North Darfur by analyzing the spatiotemporal distribution of drought using three drought indices (Standardized Precipitation Index, Vegetation Condition Index, and Soil Moisture Content Index) and their combined drought index (CDI) from 2004 to 2013. Biophysical and socioeconomic indicators are further used to measure vulnerability to drought risk and its three components (exposure, sensitivity, and adaptive capacity) through a comprehensive risk assessment framework. The results show that most of North Darfur has experienced prolonged droughts during the study period, especially from 2007 to 2011. There is also a significant correlation between the monsoon season CDI and annual crop yield anomaly. The results confirm the validity of the CDI index, which provides a comprehensive description of the drought situation by combing four drought indices quantifying different drought aspects. The vulnerability results show that the majority of this region is highly exposed and sensitive to drought risks. In particular, the northern zone of the region is highly vulnerable, which is categorized by less-crop diversity, higher land degradation, frequent droughts, and high-poverty levels. This study provides valuable information for coping with climate change-induced drought risk in this region and demonstrates that there is still a large room for enhancing the adaptation capacity in this region.

Resilience of tropical tree cover : The roles of climate, fire, and herbivory
Staal, Arie ; Nes, Egbert H. van; Hantson, Stijn ; Holmgren, Milena ; Dekker, Stefan C. ; Pueyo, Salvador ; Xu, Chi ; Scheffer, Marten - \ 2018
Global Change Biology 24 (2018)11. - ISSN 1354-1013 - p. 5096 - 5109.
alternative stable states - bistability - forest - grasslands - livestock - model - regime shifts - remote sensing - tipping points - wildfire

Fires and herbivores shape tropical vegetation structure, but their effects on the stability of tree cover in different climates remain elusive. Here, we integrate empirical and theoretical approaches to determine the effects of climate on fire- and herbivore-driven forest-savanna shifts. We analyzed time series of remotely sensed tree cover and fire observations with estimates of herbivore pressure across the tropics to quantify the fire–tree cover and herbivore–tree cover feedbacks along climatic gradients. From these empirical results, we developed a spatially explicit, stochastic fire-vegetation model that accounts for herbivore pressure. We find emergent alternative stable states in tree cover with hysteresis across rainfall conditions. Whereas the herbivore–tree cover feedback can maintain low tree cover below 1,100 mm mean annual rainfall, the fire–tree cover feedback can maintain low tree cover at higher rainfall levels. Interestingly, the rainfall range where fire-driven alternative vegetation states can be found depends strongly on rainfall variability. Both higher seasonal and interannual variability in rainfall increase fire frequency, but only seasonality expands the distribution of fire-maintained savannas into wetter climates. The strength of the fire–tree cover feedback depends on the spatial configuration of tree cover: Landscapes with clustered low tree-cover areas are more susceptible to cross a tipping point of fire-driven forest loss than landscapes with scattered deforested patches. Our study shows how feedbacks involving fire, herbivores, and the spatial structure of tree cover explain the resilience of tree cover across climates.

A global climate niche for giant trees
Scheffer, Marten ; Xu, Chi ; Hantson, Stijn ; Holmgren, Milena ; Los, Sietse O. ; Nes, Egbert H. van - \ 2018
Global Change Biology 24 (2018)7. - ISSN 1354-1013 - p. 2875 - 2883.
alternative ecosystem state - canopy height - LiDAR - precipitation temperate rainforest - remote sensing - resilience - threshold - tropical rainforest

Rainforests are among the most charismatic as well as the most endangered ecosystems of the world. However, although the effects of climate change on tropical forests resilience is a focus of intense research, the conditions for their equally impressive temperate counterparts remain poorly understood, and it remains unclear whether tropical and temperate rainforests have fundamental similarities or not. Here we use new global data from high precision laser altimetry equipment on satellites to reveal for the first time that across climate zones ‘giant forests’ are a distinct and universal phenomenon, reflected in a separate mode of canopy height (~40 m) worldwide. Occurrence of these giant forests (cutoff height > 25 m) is negatively correlated with variability in rainfall and temperature. We also demonstrate that their distribution is sharply limited to situations with a mean annual precipitation above a threshold of 1,500 mm that is surprisingly universal across tropical and temperate climates. The total area with such precipitation levels is projected to increase by ~4 million km2 globally. Our results thus imply that strategic management could in principle facilitate the expansion of giant forests, securing critically endangered biodiversity as well as carbon storage in selected regions.

A dataset of spectral and biophysical measurements over Russian wheat fields
Wit, A.J.W. de; Roerink, G.J. ; Virchenko, Oleg ; Kleschenko, Alexander ; Bartalev, Sergey ; Savin, Igor ; Plotnikov, Dmitry ; Defourny, Pierre ; Andrimont, Raphael d' - \ 2018
wheat - Russia - remote sensing - experimental data
From 2011 to 2013 the MOCCCASIN project (MOnitoring Crops in Continental Climates through Asimillation of Satellite INformation) was carried out financed by the European Commission 7th Framework Programme. During the project, two field campaigns (2011 and 2012) were carried out at two sites (Odoyev and Plavsk) in the Tula region of Russia. During these two campaigns, observations were made at selected winter-wheat fields consisting of phenological stage, biomass samples, hemispherical photographs, spectral properties of the canopy and the soil as well as ancillary information about the field. Meteorological observations from synoptic and agrometeorological stations were collected from the stations in and surrounding the Tula region. Finally, a large trajectory throughout the whole Tula region was surveyed in order to collect fields with different crop types.
Downscaling AMSR-2 Soil Moisture Data With Geographically Weighted Area-to-Area Regression Kriging
Jin, Yan ; Ge, Yong ; Wang, Jianghao ; Chen, Yuehong ; Heuvelink, Gerard B.M. ; Atkinson, Peter M. - \ 2018
IEEE Transactions on Geoscience and Remote Sensing 56 (2018)4. - ISSN 0196-2892 - p. 2362 - 2376.
Covariance matrices - geospatial analysis - high-resolution imaging - Land surface - Market research - Microwave radiometry - Microwave theory and techniques - remote sensing - Sensors - Spatial resolution - spatial resolution.

Soil moisture (SM) plays an important role in the land surface energy balance and water cycle. Microwave remote sensing has been applied widely to estimate SM. However, the application of such data is generally restricted because of their coarse spatial resolution. Downscaling methods have been applied to predict fine-resolution SM from original data with coarse spatial resolution. Commonly, SM is highly spatially variable and, consequently, such local spatial heterogeneity should be considered in a downscaling process. Here, a hybrid geostatistical approach, which integrates geographically weighted regression and area-to-area kriging, is proposed for downscaling microwave SM products. The proposed geographically weighted area-to-area regression kriging (GWATARK) method combines fine-spatial-resolution optical remote sensing data and coarse-spatial-resolution passive microwave remote sensing data, because the combination of both information sources has great potential for mapping fine-spatial-resolution near-surface SM. The GWATARK method was evaluated by producing downscaled SM at 1-km resolution from the 25-km-resolution daily AMSR-2 SM product. Comparison of the downscaled predictions from the GWATARK method and two benchmark methods on three sets of covariates with in situ observations showed that the GWATARK method is more accurate than the two benchmarks. On average, the root-mean-square error value decreased by 20%. The use of additional covariates further increased the accuracy of the downscaled predictions, particularly when using topography-corrected land surface temperature and vegetation-temperature condition index covariates.

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 - 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.
UAV-based multi-angular measurements for improved crop parameter retrieval
Roosjen, Peter P.J. - \ 2017
University. Promotor(en): Martin Herold, co-promotor(en): Jan Clevers; Harm Bartholomeus. - Wageningen : Wageningen University - ISBN 9789463436717 - 133
reflectance - anisotropy - crops - soil water content - drones - remote sensing - reflectiefactor - anisotropie - gewassen - bodemwatergehalte

Optical remote sensing enables the estimation of crop parameters based on reflected light through empirical-statistical methods or inversion of radiative transfer models. Natural surfaces, however, reflect light anisotropically, which means that the intensity of reflected light depends on the viewing and illumination geometry. Therefore, reflectance anisotropy can be considered as an unwanted effect since it may lead to inaccuracies in parameter estimations. However, it can also be considered as information source due to its unique response to the optical and structural properties of the observed surface. In the past, reflectance anisotropy was studied by multi-angular reflectance measurements from space-borne or ground-based sensors. In this research, the opportunities of Unmanned Aerial Vehicles (UAVs) to collect multi-angular measurements were explored. The main results of this research show that multi-angular measurements can be done with UAVs and that the reflectance anisotropy signal can be used to improve the retrieval of crop parameters.

Ontwikkelen van een Remote Sensing monitoringssystematiek voor vegetatiestructuur : pilotstudie: detectie verruiging Grijze Duinen (H2130) voor het Natura 2000-gebied Meijendel-Berkheide
Mücher, Sander ; Kramer, Henk ; Wijngaart, Raymond van der; Huiskes, Rik - \ 2017
Wageningen : Wageningen Environmental Research (Wageningen Environmental Research rapport 2838) - 45
remote sensing - vegetatiemonitoring - duinen - nederland - vegetation monitoring - dunes - netherlands
Citizen science and remote sensing for crop yield gap analysis
Beza, Eskender Andualem - \ 2017
University. Promotor(en): Martin Herold, co-promotor(en): Lammert Kooistra; Pytrik Reidsma. - Wageningen : Wageningen University - ISBN 9789463436410 - 196
crop yield - maximum yield - yield forecasting - remote sensing - models - small farms - data collection - gewasopbrengst - maximum opbrengst - oogstvoorspelling - modellen - kleine landbouwbedrijven - gegevens verzamelen

The world population is anticipated to be around 9.1 billion in 2050 and the challenge is how to feed this huge number of people without affecting natural ecosystems. Different approaches have been proposed and closing the ‘yield gap’ on currently available agricultural lands is one of them. The concept of ‘yield gap’ is based on production ecological principles and can be estimated as the difference between a benchmark (e.g. climatic potential or water-limited yield) and the actual yield. Yield gap analysis can be performed at different scales: from field to global level. Of particular importance is estimating the yield gap and revealing the underlying explanatory factors contributing to it. As decisions are made by farmers, farm level yield gap analysis specifically contributes to better understanding, and provides entry points to increased production levels in specific farming systems. A major challenge for this type of analysis is the high data standards required which typically refer to (a) large sample size, (b) fine resolution and (c) great level of detail. Clearly, obtaining information about biophysical characteristics and crop and farm management for individual agricultural activities within a farm, as well as farm and farmer’s characteristics and socio-economic conditions for a large number of farms is costly and time-consuming. Nowadays, the proliferation of different types of mobile phones (e.g., smartphones) equipped with sensors (e.g., GPS, camera) makes it possible to implement effective and low-cost “bottom-up” data collection approaches such as citizen science. Using these innovative methodologies facilitate the collection of relatively large amounts of information directly from local communities. Moreover, other data collection methods such as remote sensing can provide data (e.g., on actual crop yield) for yield gap analysis.

The main objective of this thesis, therefore, was to investigate the applicability of innovative data collection approaches such as crowdsourcing and remote sensing to support the assessment and monitoring of crop yield gaps. To address the main objective, the following research questions were formulated: 1) What are the main factors causing the yield gaps at the global, regional and crop level? 2) How could data for yield gap explaining factors be collected with innovative “bottom-up” approaches? 3) What are motivations of farmers to participate in agricultural citizen science? 4) What determines smallholder farmers to use technologies (e.g., mobile SMS) for agricultural data collection? 5) How can synergy of crowdsourced data and remote sensing improve the estimation and explanation of yield variability?

Chapter 2 assesses data availability and data collection approaches for yield gap analysis and provides a summary of yield gap explaining factors at the global, regional and crop level, identified by previous studies. For this purpose, a review of yield gap studies (50 agronomic-based peer-reviewed articles) was performed to identify the most commonly considered and explaining factors of the yield gap. Using the review, we show that management and edaphic factors are more often considered to explain the yield gap compared to farm(er) characteristics and socio-economic factors. However, when considered, both farm(er) characteristics and socio-economic factors often explain the yield gap. Furthermore, within group comparison shows that fertilization and soil fertility factors are the most often considered management and edaphic groups. In the fertilization group, factors related to quantity (e.g., N fertilizer quantity) are more often considered compared to factors related to timing (e.g., N fertilizer timing). However, when considered, timing explained the yield gap more often. Finally, from the results at regional and crop level, it was evident that the relevance of factors depends on the location and crop, and that generalizations should not be made. Although the data included in yield gap analysis also depends on the objective, knowledge of explaining factors, and methods applied, data availability is a major limiting factor. Therefore, bottom-up data collection approaches (e.g., crowdsourcing) involving agricultural communities can provide alternatives to overcome this limitation and improve yield gap analysis.

Chapter 3 explores the motivations of farmers to participate in citizen science. Building on motivational factors identified from previous citizen science studies, a questionnaire based methodology was developed which allowed the analysis of motivational factors and their relation to farmers’ characteristics. Using the developed questionnaire, semi-structured interviews were conducted with smallholder farmers in three countries (Ethiopia, Honduras and India). The results show that for Indian farmers a collectivistic type of motivation (i.e., contribute to scientific research) was more important than egoistic and altruistic motivations. For Ethiopian and Honduran farmers an egoistic intrinsic type of motivation (i.e., interest in sharing information) was most important. Moreover, the majority of the farmers in the three countries indicated that they would like to receive agronomic advice, capacity building and seed innovation as the main returns from the citizen science process. Country and education level were the two most important farmers’ characteristics that explained around 20% of the variation in farmers’ motivations. The results also show that motivations to participate in citizen science are different for smallholders in agriculture compared to other sectors. For example fun has appeared to be an important egoistic intrinsic factor to participate in other citizen science projects, the smallholder farmers involved in this research valued ‘passing free time’ the lowest.

Chapter 4 investigates the factors that determine farmers to adopt mobile technology for agricultural data collection. To identify the factors, the unified theory of acceptance and use of technology (UTAUT2) model was employed and extended with additional constructs of trust, mastery-approach goals and personal innovativeness in information technology. As part of the research, we setup data collection platforms using open source applications (Frontline SMS and Ushahidi) and farmers provided their farm related information using SMS for two growing seasons. The sample for this research consisted of group of farmers involved in a mobile SMS experiment (n=110) and another group of farmers which was not involved in a mobile SMS experiment (n=110), in three regions of Ethiopia. The results from the structural equation modelling showed that performance expectancy, effort expectancy, price value and trust were the main factors that influence farmers to adopt mobile SMS technology for agricultural data collection. Among these factors, trust is the strongest predictor of farmer’s intention to adopt mobile SMS. This clearly indicates that in order to use the citizen science approach in the agricultural domain, establishing a trusted relationship with the smallholder farming community is crucial. Given that performance expectancy significantly predicted farmer’s behavioural intention to adopt mobile SMS, managers of agricultural citizen science projects need to ensure that using mobile SMS for agricultural data collection offers utilitarian benefits to the farmers. The importance of effort expectancy on farmer’s intention to adopt mobile SMS clearly indicates that mobile phone software developers need to develop easy to use mobile applications.

Chapter 5 demonstrates the results of synergetic use of remote sensing and crowdsourcing for estimating and explaining crop yields at the field level. Sesame production on medium and large farms in Ethiopia was used as a case study. To evaluate the added value of the crowdsourcing approach to improve the prediction of sesame yield using remote sensing, two independent models based on the relationship between vegetation indices (VIs) and farmers reported yield were developed and compared. The first model was based on VI values extracted from all available remote sensing imagery acquired during the optimum growing period (hereafter optimum growing period VI). The second model was based on VI values extracted from remote sensing imagery acquired after sowing and before harvest dates per field (hereafter phenologically adjusted VI). To select the images acquired between sowing and harvesting dates per field, farmers crowdsourced crop phenology information was used. Results showed that vegetation indices derived based on farmers crowdsourced crop phenology information had a stronger relationship with sesame yield compared to vegetation indices derived based on the optimum growing period. This implies that using crowdsourced information related to crop phenology per field used to adjust the VIs, improved the performance of the model to predict sesame yield. Crowdsourcing was further used to identify the factors causing the yield variability within a field. According to the perception of farmers, overall soil fertility was the most important factor explaining the yield variability within a field, followed by high presence of weeds.

Chapter 6 discusses the main findings of this thesis. It draws conclusions about the main research findings in each of the research questions addressed in the four main chapters. Finally, it discusses the necessary additional steps (e.g., data quality, sustainability) in a broader context that need to be considered to utilize the full potential of innovative data collection approaches for agricultural citizen science.

MODIS VCF should not be used to detect discontinuities in tree cover due to binning bias. A comment on Hanan et al. (2014) and Staver and Hansen (2015)
Gerard, France ; Hooftman, Danny ; Langevelde, Frank van; Veenendaal, Elmar ; White, Steven M. ; Lloyd, Jon - \ 2017
Global Ecology and Biogeography 26 (2017)7. - ISSN 1466-822X - p. 854 - 859.
alternative stable states - forest - frequency distribution - MODIS VCF - remote sensing - savanna - tree cover

In their recent paper, Staver and Hansen (Global Ecology and Biogeography, 2015, 24, 985–987) refute the case made by Hanan et al. (Global Ecology and Biogeography, 2014, 23, 259–263) that the use of classification and regression trees (CARTs) to predict tree cover from remotely sensed imagery (MODIS VCF) inherently introduces biases, thus making the resulting tree cover unsuitable for showing alternative stable states through tree cover frequency distribution analyses. Here we provide a new and equally fundamental argument for why the published frequency distributions should not be used for such purposes. We show that the practice of pre-average binning of tree cover values used to derive cover values to train the CART model will also introduce errors in the frequency distributions of the final product. We demonstrate that the frequency minima found at tree covers of 8–18%, 33–45% and 55–75% can be attributed to numerical biases introduced when training samples are derived from landscapes containing asymmetric tree cover distributions and/or a tree cover gradient. So it is highly likely that the CART, used to produce MODIS VCF, delivers tree cover frequency distributions that do not reflect the real world situation.

Rainfall over the Netherlands & beyond: a remote sensing perspective
Rí́os Gaona, Manual Felipe - \ 2017
University. Promotor(en): Remko Uijlenhoet, co-promotor(en): Aart Overeem; Hidde Leijnse. - Wageningen : Wageningen University - ISBN 9789463432009 - 124
rain - remote sensing - satellites - estimation - netherlands - brazil - regen - satellieten - schatting - nederland - brazilië

Earthlings like to measure everything (especially now that we are undergoing the era of big-data revolution) maybe because it is such a nice hobby... although a more serious school of thought believes that when measuring our environment we get to understand physics and ourselves.

This thesis explores the uncertainties in rainfall measurements from state-of-the-art technologies like commercial microwave links (CML) and meteorological satellites. Rainfall has been measured by rain gauges since quite some time ago; and by weather radars since the end of WWII. Here we evaluate the performance of gridded-rainfall products for the land surface of the Netherlands. These gridded-rainfall products are CML-rainfall maps produced by the Royal Netherlands Meteorology Institute (KNMI), and the IMERG product developed by Global Precipitation Measurement mission (GPM).

Overall, this thesis shows that CML-rainfall products are very reliable sources with regards to rainfall estimates for the land surface of the Netherlands... even better than the satellite products for rainfall estimation. We are also confident in the promising potential these technologies hold for places around the world where conventional technologies like gauges or radars are not scarce or not affordable.

KB WOT Fisheries 2017 : maintaining excellence and innovation in fisheries research
Damme, C.J.G. van; Verver, S.W. - \ 2017
IJmuiden : Stichting Wageningen Research, Centre for Fisheries Research (CVO) (CVO report / Centre for Fisheries Research 17.006) - 89
remote sensing - schaal- en schelpdierenvisserij - visserijbeheer - landmeetapparatuur - discards - visvangsten - zeevisserij - shellfish fisheries - fishery management - surveying instruments - fish catches - marine fisheries
The KB WOT Fisheries programme is developed to maintain and advance the expertise needed to carry out the statutory obligations in fisheries monitoring and advice of The Netherlands. The contents of the KB WOT Fisheries programme for 2017 reflects the scientific and management needs of the WOT fisheries programme. The strength of KB WOT Fisheries lies in the top-down development of the programme while allowing bottom-up input, with calls for proposals, to secure innovation. To avoid missing research priorities relevant to WOT and EZ needs, the programme is built from a closed call for proposals to WOT Fisheries project leaders. To keep the innovation WOT project leaders are requested to seek input from other Wageningen Marine Research scientists. The KB WOT Fisheries programme will fund 13 projects in 2017 which will focus on remote sensing of fish and shell fish in the ecosystem, new methods and tools for surveys, discard and catch sampling and investigating the effects of fisheries. International exchange of new expertise and developments, as well as continuous quality assurance, forms a major part of the programme.
Verdrogingsinformatie voor de Nederlandse natuur : een vergelijking tussen de actuele en gewenste grondwatersituatie
Delft, S.P.J. van; Hoogland, T. ; Meijninger, W.M.L. ; Roerink, G.J. - \ 2017
Wageningen : Wageningen Environmental Research (Wageningen Environmental Research rapport 2792) - 85
grondwater - natuur - verdroging (milieu) - monitoring - remote sensing - nederland - groundwater - nature - groundwater depletion - netherlands
Dit rapport beschrijft een studie die is uitgevoerd om de actuele verdrogingssituatie in kaart te brengen en een werkwijze te ontwikkelen om ook in de toekomst op een objectieve, reproduceerbare en gedragen wijze de verdrogingssituatie in kaart brengen.
Two and a half years of country-wide rainfall maps using radio links from commercial cellular telecommunication networks
Overeem, A. ; Leijnse, H. ; Uijlenhoet, R. - \ 2016
Water Resources Research 52 (2016)10. - ISSN 0043-1397 - p. 8039 - 8065.
measurement - microwave links - precipitation - remote sensing

Although rainfall estimation employing microwave links from cellular telecommunication networks is recognized as a new promising measurement technique, its potential for long-term large-scale operational rainfall monitoring remains to be demonstrated. This study contributes to this endeavor by deriving a continuous series of rainfall maps from a large 2.5 year microwave link data set of, on average, 3383 links (2044 link paths) covering Netherlands (∼3.5 × 104 km2), a midlatitude country (∼5°E, ∼52°N) with a temperate climate. Maps are extensively verified against an independent gauge-adjusted radar rainfall data set for different temporal (15 min, 1 h, 1 day, 1 month) and spatial (0.9, 74 km2) scales. The usefulness of different steps in the rainfall retrieval algorithm, i.e., a wet-dry classification method and a filter to remove outliers, is systematically assessed. A novel dew filter is developed to correct for dew-induced wet antenna attenuation, which, although a relative underestimation of 6% to 9% is found, generally yields good results. The microwave link rainfall estimation technique performs well for the summer months (June, July, August), even outperforming interpolation of automatic rain gauge data (with a density of ∼1 gauge per 1000 km2), but large deviations are found for the winter months (December, January, February). These deviations are generally expected to be related to frozen or melting precipitation. Hence, our results show the potential of commercial microwave links for long-term large-scale operational rainfall monitoring.

Verkenning sensing laanboomkwekerij : toepassing van de bodemscan in de laanboomkwekerij
Baltissen, A.H.M.C. ; Sluis, B.J. van der - \ 2016
Randwijk : Praktijkonderzoek Plant & Omgeving BBF - 17 p.
aftasten - remote sensing - sensors - bos- en haagplantsoen - bomen - sensing - woody nursery stock - trees
De doelstelling van dit project is het verkennen van de mogelijkheden van nieuwe sensing technieken in de laanboomkwekerij. Gekozen is voor het uitvoeren van een sensing van de bodem. De bodem is de basis van de teelt. Het verkrijgen van inzicht in de variatie van de bodem kan helpen om teeltmaatregelen af te stemmen op die variatie. Dit rapport beschrijft een eerste verkenning naar de mogelijkheden van Proximal Soil Sensing en heeft als doel het vaststellen van de variatie van de bodem met een specifieke bodemsensor (EM38-mk2) en onderzoeken wat deze variatie betekent voor de laanboomteelt.
Multidimensional remote sensing based mapping of tropical forests and their dynamics
Dutrieux, L.P. - \ 2016
University. Promotor(en): Martin Herold, co-promotor(en): Lammert Kooistra; Lourens Poorter. - Wageningen : Wageningen UR - ISBN 9789462578906 - 146 p.
tropical forests - remote sensing - mapping - biodiversity - forest structure - monitoring - land use - landsat - tropische bossen - cartografie - biodiversiteit - bosstructuur - landgebruik

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.

Physically-Based Modelling of the Post-Fire Runoff Response of a Forest Catchment in Central Portugal : Using Field versus Remote Sensing Based Estimates of Vegetation Recovery
Eck, Christel M. Van; Nunes, Joao P. ; Vieira, Diana C.S. ; Keesstra, Saskia ; Keizer, Jan Jacob - \ 2016
Land Degradation and Development 27 (2016)5. - ISSN 1085-3278 - p. 1535 - 1544.
LISEM - post-fire hydrology - remote sensing - runoff modelling - vegetation recovery

Forest fires are a recurrent phenomenon in Mediterranean forests, with impacts for human landscapes and communities, which must be understood before they can be managed. This study used the physically based Limburg Soil Erosion Model (LISEM) to simulate rainfall–runoff response, under soil water repellent (SWR) conditions and different stages of vegetation recovery. Five rainfall–runoff events were selected, representing wet and dry conditions, spread over two years after a wildfire which burned eucalypt and maritime pine plantations in the Colmeal experimental micro-catchment, central Portugal. Each event was simulated using three Leaf Area Index (LAI) estimates: indirect field-based measurements (TC–LAI), NDVI-based estimates derived from Landsat-5 TM and Landsat-7 ETM+ imagery (NDVI–LAI), and the LAI of a fully restored canopy to test model sensitivity to interception parameters. LISEM was able to simulate events in relative terms but underestimated peak runoff (r2 = 0·36, mean error = −31%, and NSE = −0·15) and total runoff (r2 = 0·52, mean error = −15% and NSE = 0·09), which could be related to the presence of SWR or saturated areas, according to pre-rainfall soil moisture conditions. The model performed better for individual hydrographs, especially under wet conditions. Modelling the full-cover scenario showed minor sensitivity of LISEM to the observed changes in LAI. NDVI–LAI data gave a close to equal model performance with TC–LAI and therefore can be considered a suitable substitute for ground-based measurements in post-fire runoff predictions. However, more attention should be given to representing pre-rainfall soil moisture conditions and especially the presence of SWR.

Nowcasten actuele vullingsgraad bodem (met behulp van een model en remote sensing data)
Toorn, Linda ; Klutman, W.A.J. ; Hanhart-van den Brink, M. ; Heijkers, J. ; Bakel, J. van; Bastiaanssen, M. ; Spijker, Maarten ; Veldhuizen, A.A. - \ 2016
Amersfoort : Stowa (Stowa rapport 2016-20) - 57 p.
waterbeheer - waterbergend vermogen - remote sensing - wateropslag - ondergrondse opslag - grondwaterstand - water management - water holding capacity - water storage - underground storage - groundwater level
Een belangrijke opgave van de waterschappen is het voorkomen van wateroverlast en droogte. In deze studie is onderzocht of het haalbaar is om een instrument te ontwikkelen waarmee de hoeveelheid water die in de bodem kan worden geborgen, inzichtelijk kan worden gemaakt. Het gaat hierbij om een combinatie van data van satellieten (over verdamping en bodemvocht), aangevuld met data uit het veld en data van hydrologische modellen. Het zo verkregen inzicht in de ‘vullingsgraad’ van de bodem biedt veel potentie voor het operationele waterbeheer, waaronder peilbeheer. De informatie kan als inhoudelijke basis worden gebruikt om projecten als Slim Watermanagement vorm te geven.
Bistability, Spatial Interaction, and the Distribution of Tropical Forests and Savannas
Staal, Arie ; Dekker, Stefan C. ; Xu, Chi ; Nes, Egbert H. van - \ 2016
Ecosystems 19 (2016)6. - ISSN 1432-9840 - p. 1080 - 1091.
catastrophe theory - climate change - critical transition - ecotone - Maxwell point - reaction–diffusion system - regime shift - remote sensing - tipping point - wildfire

Recent work has indicated that tropical forest and savanna can be alternative stable states under a range of climatic conditions. However, dynamical systems theory suggests that in case of strong spatial interactions between patches of forest and savanna, a boundary between both states is only possible at conditions in which forest and savanna are equally stable, called the ‘Maxwell point.’ Frequency distributions of MODIS tree-cover data at 250 m resolution were used to estimate such Maxwell points with respect to the amount and seasonality of rainfall in both South America and Africa. We tested on a 0.5° scale whether there is a larger probability of local coexistence of forests and savannas near the estimated Maxwell points. Maxwell points for South America and Africa were estimated at 1760 and 1580 mm mean annual precipitation and at Markham’s Seasonality Index values of 50 and 24 %. Although the probability of local coexistence was indeed highest around these Maxwell points, local coexistence was not limited to the Maxwell points. We conclude that critical transitions between forest and savanna may occur when climatic changes exceed a critical value. However, we also conclude that spatial interactions between patches of forest and savanna may reduce the hysteresis that can be observed in isolated patches, causing more predictable forest-savanna boundaries than continental-scale analyses of tree cover indicate. This effect could be less pronounced in Africa than in South America, where the forest-savanna boundary is substantially affected by rainfall seasonality.

Remote sensing of land use and carbon losses following tropical deforestation
Sy, V. de - \ 2016
University. Promotor(en): Martin Herold, co-promotor(en): Jan Clevers; L. Verchot. - Wageningen : Wageningen University - ISBN 9789462578036 - 142 p.
remote sensing - tropical forests - land use - carbon - losses - environmental degradation - forest monitoring - tropische bossen - landgebruik - koolstof - verliezen - milieuafbraak - bosmonitoring

The new Paris Agreement, approved by 195 countries under the auspice of the United Nations Framework Convention on Climate Change (UNFCCC), calls for limiting global warming to “well below" 2°Celsius. An important part of the climate agreement relates to reducing emissions from deforestation and forest degradation, and enhancing carbon stocks (REDD+) in non-Annex I (mostly developing) countries. Over the last decades the growing demand for food, fibre and fuel has accelerated the pace of forest loss. In consequence, tropical deforestation and forest degradation are responsible for a large portion of global carbon emissions to the atmosphere, and destroy an important global carbon sink that is critical in future climate change mitigation.

Within the REDD+ framework, participating countries are given incentives to develop national strategies and implementation plans that reduce emissions and enhance sinks from forests and to invest in low carbon development pathways. For REDD+ activities to be effective, accurate and robust methodologies to estimate emissions from deforestation and forest degradation are crucial. Remote sensing is an essential REDD+ observation tool, and in combination with ground measurements it provides an objective, practical and cost-effective solution for developing and maintaining REDD+ monitoring systems. The remote sensing monitoring objective for REDD+ is not only to map deforestation but also to support policy formulation and implementation. Identifying and addressing drivers and activities causing forest carbon change is crucial in this respect. Despite the importance of identifying and addressing drivers, quantitative information on these drivers, and the related carbon emissions, is scarce at the national level.

The main objective of this thesis is to explore the role of remote sensing for monitoring tropical forests for REDD+ in general, and for assessing land use and related carbon emissions linked to drivers of tropical deforestation in particular. To achieve this, this thesis investigates the following research questions:

What is the current role and potential of remote sensing technologies and methodologies for monitoring tropical forests for REDD+ and for assessing drivers of deforestation?

What is the current state of knowledge on drivers of deforestation and degradation in REDD+ countries?

What are land use patterns and related carbon emissions following deforestation, capitalising on available land use and biomass remote sensing data?

The research conducted in this PhD thesis contributes to the understanding of the role of remote sensing in forest monitoring for REDD+ and in the assessment of drivers of deforestation. In addition, this thesis contributes to the improvement of spatial and temporal quantification of land use and related carbon emissions linked to drivers of tropical deforestation. The results and insights described herein are valuable for ongoing REDD+ forest monitoring efforts and capacity development as REDD+ moves closer to becoming an operational mitigation mechanism.

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