Records 1 - 20 / 511
Critical Soil Moisture Derived From Satellite Observations Over Europe
Denissen, Jasper M.C. ; Teuling, Adriaan J. ; Reichstein, Markus ; Orth, René - \ 2020
Journal of Geophysical Research: Atmospheres 125 (2020)6. - ISSN 2169-897X
evapotranspiration - land-atmosphere interactions - remote sensing - soil moisture
Evapotranspiration (ET) is a crucial quantity through which land surface conditions can impact near-surface weather and vice versa. ET can be limited by energy or water availability. The transition between water- and energy-limited regimes is marked by the critical soil moisture (CSM), which is traditionally derived from small-sample laboratory analyses. Here, we aim to determine the CSM at a larger spatial scale relevant for climate modeling, using state-of-the-art gridded data sets. For this purpose, we introduce a new correlation-difference metric with which the CSM can be accurately inferred using multiple data streams. We perform such an analysis at the continental scale and determine a large-scale CSM as an emergent property. In addition, we determine small-scale CSMs at the grid cell scale and find substantial spatial variability. Consistently from both analyses we find that soil texture, climate conditions, and vegetation characteristics are influencing the CSM, with similar respective importance. In contrast, comparable CSMs are found when applying alternative large-scale energy and vegetation data sets, highlighting the robustness of our results. Based on our findings, the state of the vegetation and corresponding land-atmosphere coupling can be inferred, to first order, from easily accessible satellite observations of surface soil moisture.
Saturated areas through the lens: 1. Spatio-temporal variability of surface saturation documented through thermal infrared imagery
Antonelli, Marta ; Glaser, Barbara ; Teuling, Adriaan J. ; Klaus, Julian ; Pfister, Laurent - \ 2020
Hydrological Processes 34 (2020)6. - ISSN 0885-6087 - p. 1310 - 1332.
catchment hydrology - ground-based thermal infrared imagery - intracatchment variability - remote sensing - riparian processes - surface saturation dynamics - surface saturation mapping
Surface saturated areas are key features in generating run-off. A detailed characterization of the expansion and contraction of surface saturation in riparian zones and its connectivity to the stream is fundamental to improve our understanding of the spatial and temporal variability of streamflow generation processes. In this first contribution of a series of two papers, we used ground-based thermal infrared imagery for characterizing riparian surface saturation seasonal dynamics of seven different riparian areas in the Weierbach catchment (0.42 km2), a small forested catchment in Luxembourg. We collected biweekly panoramic images of the seven areas over a period of 2 years. We identified the extension of saturation in each collected panoramic image (i.e., percentage of pixels corresponding to saturated surfaces in each riparian area) to generate time series of surface saturation. Riparian surface saturation in all areas was seasonally variable, and its dynamics were in accordance with lower hillslope groundwater level fluctuations. Surface saturation in the different areas related to catchment outlet discharge through power law relationships. Differences in these relationships for different areas could be associated with the location of the areas along the stream network and to a possible influence of local riparian morphology on the development of surface saturation, suggesting a certain degree of intracatchment heterogeneity. This study paves the way for a subsequent investigation of the spatio-temporal variability of streamflow generation in the catchment, presented in our second contribution.
Identifying tree health using sentinel-2 images: a case study on Tortrix viridana L. infected oak trees in Western Iran
Haghighian, Farshad ; Yousefi, Saleh ; Keesstra, Saskia - \ 2020
Geocarto International (2020). - ISSN 1010-6049
Chaharmahal and Bakhtiari - IPVI - IRECI - NDVI - remote sensing - SAVI
Forest land has a vital role in our planet ecosystem health. Forest areas are under natural and human pressure worldwide. Pests may have irreparable damages to vegetation cover; Tortrix viridana is one of the most important pests in the western forests of Iran and is mainly hosted by oak trees. In this study the performance of Sentinel-2 images to detect infected oaks by T. viridana in the Zagros forest habitat was considered. Vegetation indices (VIs) were extracted from affected and non-affected areas by T. viridana. The indices indices included normalized difference vegetation index (NDVI), soil-adjusted vegetation index (SAVI), infrared percentage vegetation index (IPVI) and inverted red-edge chlorophyll index (IRECI) which were extracted from Sentinel-2 satellite images. The results of the present study show that VIs in affected and non-affected areas of the study site have significant differences at 99% of confidence level. In addition, the Spearman’s correlation coefficients between the VIs values in the affected and non-affected were 0.213, 0.213, 0.168 and 0.121 for IPVI, NDVI, IRECI and SAVI, respectively. This shows that Sentinel-2 images can be used to detect pests in forest areas.
Commercial microwave link data for rainfall monitoring
Overeem, Aart - \ 2019
Wageningen University & Research
weather - observations - rain - microwave links - remote sensing - rainfall measurement - microwave link
Dataset of commercial microwave link (CML) data, which can be used to estimate path-average rainfall between telephone towers. Contains microwave frequency, end date & time of reading, minimum & maximum received power, path length, coordinates, and link identifier. For a network of on average ~2500 links covering the Netherlands having a temporal resolution of 15 min. The dataset, which consists of two files, can be used with the open-source R package RAINLINK (https://github.com/overeem11/RAINLINK) to estimate path-averaged rainfall and to make rainfall maps. Data have been used in the mentioned papers under "link to publication", but these use either a subset of the dataset or also other data. A manuscript which will describe the exact characteristics of this dataset is in preparation
Deep learning models to count buildings in high-resolution overhead images
Lobry, Sylvain ; Tuia, Devis - \ 2019
In: 2019 Joint Urban Remote Sensing Event, JURSE 2019. - Institute of Electrical and Electronics Engineers Inc. (Joint Urban Remote Sensing Event (JURSE) ) - ISBN 9781728100104
counting - Deep learning - equivariance - loss functions - regression - remote sensing
This paper addresses the problem of counting buildings in very high-resolution overhead true color imagery. We study and discuss the relevance of deep-learning based methods to this task. Two architectures and two loss functions are proposed and compared. We show that a model enforcing equivariance to rotations is beneficial for the task of counting in remotely sensed images. We also highlight the importance of robustness to outliers of the loss function when considering remote sensing applications.
Ecosystem service change caused by climatological and non-climatological drivers: a Swiss case study
Braun, Daniela ; Jong, Rogier de; Schaepman, Michael E. ; Furrer, Reinhard ; Hein, Lars ; Kienast, Felix ; Damm, Alexander - \ 2019
Ecological Applications 29 (2019)4. - ISSN 1051-0761
climate change - land use change - regulating services - remote sensing - time series - trends
Understanding the drivers of ecosystem change and their effects on ecosystem services are essential for management decisions and verification of progress towards national and international sustainability policies (e.g., Aichi Biodiversity Targets, Sustainable Development Goals). We aim to disentangle spatially the effect of climatological and non-climatological drivers on ecosystem service supply and trends. Therefore, we explored time series of three ecosystem services in Switzerland between 2004 and 2014: carbon dioxide regulation, soil erosion prevention, and air quality regulation. We applied additive models to describe the spatial variation attributed to climatological (i.e., temperature, precipitation and relative sunshine duration) and non-climatological drivers (i.e., random effects representing other spatially structured processes) that may affect ecosystem service change. Obtained results indicated strong influences of climatological drivers on ecosystem service trends in Switzerland. We identified equal contributions of all three climatological drivers on trends of carbon dioxide regulation and soil erosion prevention, while air quality regulation was more strongly influenced by temperature. Additionally, our results showed that climatological and non-climatological drivers affected ecosystem services both negatively and positively, depending on the regions (in particular lower and higher altitudinal areas), drivers, and services assessed. Our findings highlight stronger effects of climatological compared to non-climatological drivers on ecosystem service change in Switzerland. Furthermore, drivers of ecosystem change display a spatial heterogeneity in their influence on ecosystem service trends. We propose an approach building on an additive model to disentangle the effect of climatological and non-climatological drivers on ecosystem service trends. Such analyses should be extended in the future to ecosystem service flow and demand to complete ecosystem service assessments and to demonstrate and communicate more clearly the benefits of ecosystem services for human well-being.
Spatial early warning signals for impending regime shifts : A practical framework for application in real-world landscapes
Nijp, Jelmer J. ; Temme, Arnaud J.A.M. ; Voorn, George A.K. van; Kooistra, Lammert ; Hengeveld, Geerten M. ; Soons, Merel B. ; Teuling, Adriaan J. ; Wallinga, Jakob - \ 2019
Global Change Biology 25 (2019)6. - ISSN 1354-1013 - p. 1905 - 1921.
alternative stable states - critical slowing down - early warning signals - ecosystem resilience - environmental change - landscapes - regime shifts - remote sensing - spatial patterns - tipping points
Prediction of ecosystem response to global environmental change is a pressing scientific challenge of major societal relevance. Many ecosystems display nonlinear responses to environmental change, and may even undergo practically irreversible ‘regime shifts’ that initiate ecosystem collapse. Recently, early warning signals based on spatiotemporal metrics have been proposed for the identification of impending regime shifts. The rapidly increasing availability of remotely sensed data provides excellent opportunities to apply such model-based spatial early warning signals in the real world, to assess ecosystem resilience and identify impending regime shifts induced by global change. Such information would allow land-managers and policy makers to interfere and avoid catastrophic shifts, but also to induce regime shifts that move ecosystems to a desired state. Here, we show that the application of spatial early warning signals in real-world landscapes presents unique and unexpected challenges, and may result in misleading conclusions when employed without careful consideration of the spatial data and processes at hand. We identify key practical and theoretical issues and provide guidelines for applying spatial early warning signals in heterogeneous, real-world landscapes based on literature review and examples from real-world data. Major identified issues include (1) spatial heterogeneity in real-world landscapes may enhance reversibility of regime shifts and boost landscape-level resilience to environmental change (2) ecosystem states are often difficult to define, while these definitions have great impact on spatial early warning signals and (3) spatial environmental variability and socio-economic factors may affect spatial patterns, spatial early warning signals and associated regime shift predictions. We propose a novel framework, shifting from an ecosystem perspective towards a landscape approach. The framework can be used to identify conditions under which resilience assessment with spatial remotely sensed data may be successful, to support well-informed application of spatial early warning signals, and to improve predictions of ecosystem responses to global environmental change.
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.
CST, a freeware for predicting crop yield from remote sensing or crop model indicators: Illustration with RSA and Ethiopia
Kerdiles, H. ; Rembold, F. ; Leo, O. ; Boogaard, H. ; Hoek, S. - \ 2017
In: 2017 6th International Conference on Agro-Geoinformatics, Agro-Geoinformatics 2017. - Institute of Electrical and Electronics Engineers Inc. - ISBN 9781538638842
crop yield prediction - food security - remote sensing
CST (Crop Statistics Tool) is a standalone freeware for predicting crop yield statistics using indicators derived from crop models, weather or remote sensing data. The principle of CST is that years similar to the target year (e.g. the current year) should have similar yields, or similar yield deviations from a technological time trend. In practice, CST guides the crop analyst through standard steps: After data screening to identify possible outliers and analysis of time trend, the crop analyst has the choice between the following two approaches to forecast yield: (1) multiple regression analysis in which a linear relationship is calibrated between historical yield data and yield indicators, while accounting for a time trend if present; (2) scenario analysis, whereby CST looks for the years most similar (according to the indicators) to the current year to estimate a yield deviation from the time trend or the average yield. CST allows to assess models with standard statistics and tests as well as warnings, which is especially useful when many indicators are available. Moreover, thanks to batch processing, the crop analyst can test a given model for various dekads, regions or crops. This paper illustrates the interest of CST with two case studies made over Africa and based on the regression approach between crop yields and NDVI or cumulated rainfall at a given dekad. In the first one, South African maize yields at province level over 1987-2015 were found to be well correlated with Vegetation/ProbaV NDVI or CHIRPS rainfall for two of the three main maize producing provinces; for each province, we tested indicators from the 15 dekads between January and May. In the second study, we regressed the 1999-2014 maize yields from the main 26 crop production zones against also NDVI and cumulated rainfall with two different start dates (April and June); we tested 3000 models (26 zones, 15 dekads, 3 single indicators without and with time trend, all indicators together, and finally trend alone) and obtained mixed results: A strong dominance of the time trend and for the indicators, unstable relationships and sometimes wrong slope signs. Beyond these contrasting results that could be partly due to the quality of yield statistics or the relevance of the selected indicators, CST combined with the SPIRITS tool for extracting indicators at region level from raster time series, should help crop analysts predict crop yield, in particular where many indicators derived from remote sensing data or crop models are to be tested.
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.
UAV-based multi-angular measurements for improved crop parameter retrieval
Roosjen, Peter P.J. - \ 2017
Wageningen University. Promotor(en): M. Herold, co-promotor(en): J.G.P.W. Clevers; H.M. Bartholomeus. - Wageningen : Wageningen University - ISBN 9789463436717 - 133
reflectance - anisotropy - crops - soil water content - drones - remote sensing - reflectiefactor - anisotropie - gewassen - bodemwatergehalte - drones - remote sensing
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 - remote sensing - vegetation monitoring - dunes - netherlands
Citizen science and remote sensing for crop yield gap analysis
Beza, Eskender Andualem - \ 2017
Wageningen University. Promotor(en): M. Herold, co-promotor(en): L. Kooistra; P. Reidsma. - Wageningen : Wageningen University - ISBN 9789463436410 - 196
crop yield - maximum yield - yield forecasting - remote sensing - models - small farms - data collection - gewasopbrengst - maximum opbrengst - oogstvoorspelling - remote sensing - 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
Wageningen University. Promotor(en): R. Uijlenhoet, co-promotor(en): A. Overeem; H. Leijnse. - Wageningen : Wageningen University - ISBN 9789463432009 - 124
rain - remote sensing - satellites - estimation - netherlands - brazil - regen - remote sensing - 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 - remote sensing - shellfish fisheries - fishery management - surveying instruments - discards - 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.