Broad-scale distribution of the winter protozooplankton community in the North Sea
Bils, Franziska ; Moyano, Marta ; Aberle, Nicole ; Damme, Cindy J.G. Van; Nash, Richard D.M. ; Kloppmann, Matthias ; Loots, Christophe ; Peck, Myron A. - \ 2019
Journal of Sea Research 144 (2019). - ISSN 1385-1101 - p. 112 - 121.
microzooplankton - time-series - monitoring - International bottom trawl survey - ecological indicators - ecosystem-based management
Protozooplankton (PZP) (here size range: 12–200 μm) are rarely sampled over a broad scale, especially in ecosystem monitoring programs, despite their trophodynamic importance as grazers in the microbial loop and as prey for larger zooplankton and early life stages of fish. In this study we sampled PZP from Dutch, French, German and Norwegian research vessels taking part in the annual ICES coordinated International Bottom Trawl Survey (IBTS) which provides data on fish stock abundances and status for the entire North Sea. The abundance, biomass, composition and distribution of PZP were examined at 39 stations across the North Sea (from 3.2°W to 7.6°E and 50.5 to 59.8°N) in mid-winter (January–February 2014), a period of the year which is under-investigated so far. Twenty four taxa of dinoflagellates and ciliates were identified. Two groups comprised 89% of the total abundance of PZP: Gymnodinium spp. and other athecate dinoflagellates (68%) and Strombidium spp. and other naked ciliates (21%). The biomass of PZP at each station ranged between 0.08 and 2.4 μg C L−1, which is much lower than that reported for spring or summer (≥100 μg C L−1) in the North Sea. Relatively small-sized (< 40 μm) PZP contributed 46% of the total biomass. No significant spatial pattern in the composition of the PZP community was found, although the total abundance of tintinnids was highest in the southern North Sea, an important over-wintering area for marine fish larvae. Using this fish survey (IBTS) as a sampling platform allowed us to obtain a synoptic view of the PZP community over a large area. The present collaborative effort provides an example of how existing monitoring platforms can be augmented in the future to collect relevant data and potential ecological indicators needed to advance the ecosystem-based approach to managing marine systems.
Characterisation of hydroclimatological trends and variability in the Lake Naivasha basin, Kenya
Odongo, V.O. ; Tol, C. van der; Oel, P.R. van; Meins, F.M. ; Becht, R. ; Onyando, J.O. ; Su, Z. - \ 2015
Hydrological Processes 29 (2015)15. - ISSN 0885-6087 - p. 3276 - 3293.
long-term persistence - time-series - hurst phenomenon - climatic-change - impact - identification - hydrology - ethiopia - water - precipitation
Recent hydro-climatological trends and variability characteristics were investigated for the Lake Naivasha basin with the aim of understanding the changes in water balance components and their evolution over the past 50¿years. Using a Bayesian change point analysis and modified Mann–Kendall tests, time series of annual mean, maximum, minimum, and seasonal precipitation and flow, as well as annual mean lake volumes, were analysed for the period 1960–2010 to uncover possible abrupt shifts and gradual trends. Double cumulative curve analysis was used to investigate the changes in hydrological response attributable to either human influence or climatic variability. The results indicate a significant decline in lake volumes at a mean rate of 9.35¿×¿106¿m3¿year-1. Most of the river gauging stations showed no evidence of trends in the annual mean and maximum flows as well as seasonal flows. Annual minimum flows, however, showed abrupt shifts and significant (upward/downward) trends at the main outlet stations. Precipitation in the basin showed no evidence of abrupt shifts, but a few stations showed gradual decline. The observed changes in precipitation could not explain the decline in both minimum flows and lake volumes. The findings show no evidence of any impact of climate change for the Lake Naivasha basin over the past 50¿years. This implies that other factors, such as changes in land cover and infrastructure development, have been responsible for the observed changes in streamflow and lake volumes.
Do Arctic breeding geese track or overtake a green wave during spring migration?
Si, Y. ; Xin, Q. ; Boer, W.F. de; Gong, P. ; Ydenberg, R.C. ; Prins, H.H.T. - \ 2015
Scientific Reports 5 (2015). - ISSN 2045-2322
goose branta-leucopsis - russian barnacle geese - anser-brachyrhynchus - time-series - brent geese - large herbivores - forage quality - bird migration - decision - bernicla
Geese breeding in the Arctic have to do so in a short time-window while having sufficient body reserves. Hence, arrival time and body condition upon arrival largely influence breeding success. The green wave hypothesis posits that geese track a successively delayed spring flush of plant development on the way to their breeding sites. The green wave has been interpreted as representing either the onset of spring or the peak in nutrient biomass. However, geese tend to adopt a partial capital breeding strategy and might overtake the green wave to accomplish a timely arrival on the breeding site. To test the green wave hypothesis, we link the satellite-derived onset of spring and peak in nutrient biomass with the stopover schedule of individual Barnacle Geese. We find that geese track neither the onset of spring nor the peak in nutrient biomass. Rather, they arrive at the southernmost stopover site around the peak in nutrient biomass, and gradually overtake the green wave to match their arrival at the breeding site with the local onset of spring, thereby ensuring gosling benefit from the peak in nutrient biomass. Our approach for estimating plant development stages is critical in testing the migration strategies of migratory herbivores.
Modelling the influence of urbanization on the 20th century temperature record of weather station De Bilt (The Netherlands)
Koopmans, S. ; Theeuwes, N.E. ; Steeneveld, G.J. ; Holtslag, A.A.M. - \ 2015
International Journal of Climatology 35 (2015)8. - ISSN 0899-8418 - p. 1732 - 1748.
surface air-temperature - anthropogenic heat emissions - boundary-layer diffusion - tokyo metropolitan-area - urban canopy model - numerical experiment - time-series - data set - land-use - part i
Many cities have expanded during the 20th century, and consequently some weather stations are currently located closer to cities than before. Due to the urban heat island (UHI) effect, those weather stations may show a positive bias in their 2-m temperature record. In this study, we estimate the impact of urbanization on the temperature record of WMO station De Bilt (The Netherlands). This station has a long historical record, but the nearby city of Utrecht and its suburbs expanded during the 20th century. The temperature rise due to urbanization is estimated by conducting representative mesoscale model simulations for the land-use situation for the years 1900 and 2000. This is performed for 14 different episodes of a week, each representing a typical large-scale flow regime (Grosswettertypes) in both the winter and the summer season. Frequency distributions of these flow regimes are used to estimate an average temperature rise. We find that the model results with two rather different atmospheric boundary-layer schemes, robustly indicate that the urbanization during the 20th century has resulted in a temperature rise of 0.22¿±¿0.06¿K. This is more than a factor of 2 higher than a previously estimated temperature trend by using observed temperature records of stations close to De Bilt.
Strong contribution of autumn phenology to changes in satellite-derived growing season length estimates across Europe (1982–2011)
Garonna, I. ; Jong, R. de; Wit, A.J.W. de; Mücher, C.A. ; Schmid, B. ; Schaepman, M.E. - \ 2014
Global Change Biology 20 (2014)11. - ISSN 1354-1013 - p. 3457 - 3470.
land-surface phenology - high-resolution radiometer - vegetation index ndvi - spring phenology - climate-change - time-series - monitoring vegetation - trends - avhrr - models
Land Surface Phenology (LSP) is the most direct representation of intra-annual dynamics of vegetated land surfaces as observed from satellite imagery. LSP plays a key role in characterizing land-surface fluxes, and is central to accurately parameterizing terrestrial biosphere–atmosphere interactions, as well as climate models. In this article, we present an evaluation of Pan-European LSP and its changes over the past 30 years, using the longest continuous record of Normalized Difference Vegetation Index (NDVI) available to date in combination with a landscape-based aggregation scheme. We used indicators of Start-Of-Season, End-Of-Season and Growing Season Length (SOS, EOS and GSL, respectively) for the period 1982–2011 to test for temporal trends in activity of terrestrial vegetation and their spatial distribution. We aggregated pixels into ecologically representative spatial units using the European Landscape Classification (LANMAP) and assessed the relative contribution of spring and autumn phenology. GSL increased significantly by 18–24 days decade-1 over 18–30% of the land area of Europe, depending on methodology. This trend varied extensively within and between climatic zones and landscape classes. The areas of greatest growing-season lengthening were the Continental and Boreal zones, with hotspots concentrated in southern Fennoscandia, Western Russia and pockets of continental Europe. For the Atlantic and Steppic zones, we found an average shortening of the growing season with hotspots in Western France, the Po valley, and around the Caspian Sea. In many zones, changes in the NDVI-derived end-of-season contributed more to the GSL trend than changes in spring green-up, resulting in asymmetric trends. This underlines the importance of investigating senescence and its underlying processes more closely as a driver of LSP and global change.
Combining satellite data and community-based observations for forest monitoring
Pratihast, A.K. ; DeVries, B.R. ; Avitabile, V. ; Bruin, S. de; Kooistra, L. ; Tekle, M. ; Herold, M. - \ 2014
Forests 5 (2014)10. - ISSN 1999-4907 - p. 2464 - 2489.
cover change - global change - time-series - redd plus - deforestation - challenges - quality - participation - degradation - information
Within the Reducing Emissions from Deforestation and Degradation (REDD+) framework, the involvement of local communities in national forest monitoring activities has the potential to enhance monitoring efficiency at lower costs while simultaneously promoting transparency and better forest management. We assessed the consistency of forest monitoring data (mostly activity data related to forest change) collected by local experts in the UNESCO Kafa Biosphere Reserve, Ethiopia. Professional ground measurements and high resolution satellite images were used as validation data to assess over 700 forest change observations collected by the local experts. Furthermore, we examined the complementary use of local datasets and remote sensing by assessing spatial, temporal and thematic data quality factors. Based on this complementarity, we propose a framework to integrate local expert monitoring data with satellite-based monitoring data into a National Forest Monitoring System (NFMS) in support of REDD+ Measuring, Reporting and Verifying (MRV) and near real-time forest change monitoring.
Drivers of extinction risk in African mammals: the interplay of distribution state, human pressure, conservation response and species biology
Marco, M. Di; Buchanan, G.M. ; Szantoi, Z. ; Holmgren, M. ; Grottolo Marasini, G. ; Gross, D. ; Tranquili, S. ; Boitani, L. ; Rondini, C. - \ 2014
Philosophical Transactions of the Royal Society B. Biological sciences 369 (2014). - ISSN 0962-8436 - 12 p.
protected areas - population declines - tropical forest - human footprint - time-series - land-cover - strategy - deforestation - 21st-century - ecosystem
Although conservation intervention has reversed the decline of some species, our success is outweighed by a much larger number of species moving towards extinction. Extinction risk modelling can identify correlates of risk and species not yet recognized to be threatened. Here, we use machine learning models to identify correlates of extinction risk in African terrestrial mammals using a set of variables belonging to four classes: species distribution state, human pressures, conservation response and species biology. We derived information on distribution state and human pressure from satellite- borne imagery. Variables in all four classes were identified as important predictors of extinction risk, and interactions were observed among variables in different classes (e.g. level of protection, human threats, species distribution ranges). Species biology had a key role in mediating the effect of external variables. The model was 90% accurate in classifying extinction risk status of species, but in a few cases the observed and modelled extinction risk mismatched. Species in this condition might suffer from an incorrect classification of extinction risk (hence require reassessment). An increased availability of satellite imagery combined with improved resolution and classification accuracy of the resulting maps will play a progressively greater role in conservation monitoring.
Drought response of five conifer species under contrasting water availability suggests high vulnerability of Norway spruce and European larch
Lévesque, M. ; Saurer, M. ; Siegwolf, R. ; Eilmann, B. ; Brang, P. ; Bugmann, H. ; Rigling, A. - \ 2013
Global Change Biology 19 (2013)10. - ISSN 1354-1013 - p. 3184 - 3199.
oxygen-isotope signals - tree-ring width - scots pine - picea-abies - time-series - precipitation series - climate variability - extreme events - pubescent oak - stable carbon
The ability of tree species to cope with anticipated decrease in water availability is still poorly understood. We evaluated the potential of Norway spruce, Scots pine, European larch, black pine, and Douglas-fir to withstand drought in a drier future climate by analyzing their past growth and physiological responses at a xeric and a mesic site in Central Europe using dendroecological methods. Earlywood, latewood, and total ring width, as well as the d13C and d18O in early- and latewood were measured and statistically related to a multiscalar soil water deficit index from 1961 to 2009. At the xeric site, d13C values of all species were strongly linked to water deficits that lasted longer than 11 months, indicating a long-term cumulative effect on the carbon pool. Trees at the xeric site were particularly sensitive to soil water recharge in the preceding autumn and early spring. The native species European larch and Norway spruce, growing close to their dry distribution limit at the xeric site, were found to be the most vulnerable species to soil water deficits. At the mesic site, summer water availability was critical for all species, whereas water availability prior to the growing season was less important. Trees at the mesic were more vulnerable to water deficits of shorter duration than the xeric site. We conclude that if summers become drier, trees growing on mesic sites will undergo significant growth reductions, whereas at their dry distribution limit in the Alps, tree growth of the highly sensitive spruce and larch may collapse, likely inducing dieback and compromising the provision of ecosystem services. However, the magnitude of these changes will be mediated strongly by soil water recharge in winter and thus water availability at the beginning of the growing season.
Elephant response to spatial heterogeneity in a savanna landscape of northern Tanzania
Pittiglio, C. ; Skidmore, A.K. ; Gils, H.A.M.J. van; Prins, H.H.T. - \ 2013
Ecography 36 (2013)7. - ISSN 0906-7590 - p. 819 - 831.
habitat heterogeneity - wavelet analysis - time-series - vegetation - ecology - scale - conservation - stability - patterns - models
Landscape heterogeneity, namely the variation of a landscape property across space and time, can influence the distribution of a species and its abundance. Quantifying landscape heterogeneity is important for the management of semi-natural areas through predicting species response to landscape changes, such as habitat fragmentation. In this paper, we tested whether the change in spatial heterogeneity of the vegetation cover due to farming expansion affected the distribution of the African elephant in the Tarangire-Manyara ecosystem, northern Tanzania. Spatial heterogeneity (based on the normalized difference vegetation index) was characterized at multiple spatial scales using the wavelet transform and the intensity-dominant scale method. Elephant distribution was estimated from time-series aerial surveys using a kernel density function. The intensity, which relates to the contrast in vegetation cover, quantified the maximum variation in NDVI across multiple spatial scales, whereas the dominant scale, which represents the scale at which this maximum variation occurs, identified the dominant inter-patches distance, i.e. the size of dominant landscape features. We related the dominant scale of spatial heterogeneity to the probability of elephant occurrence in order to identify: 1) the scale that maximizes elephant occurrence, and 2) its change between 1988 and 2001. Neither the dominant scale and intensity of spatial heterogeneity, nor the probability of the elephant occurrence changed significantly between 1988 and 2001. The spatial scale maximizing elephant occurrence remained constant at 7000 to 8000 m during each wet season. Compared to the findings of a recent, similar study in Zimbabwe, our results suggest that the change in the dominant scale was relatively small in Tarangire-Manyara ecosystem and well within the critical threshold for elephant persistence. The method is a useful tool for monitoring ecosystems and their properties.
Phenology and gross primary production of two dominant savanna woodland ecosystems in Southern Africa
Cui, Jin ; Xiangming, Xiao ; Merbold, L. ; Arneth, A. ; Veenendaal, E.M. ; Kutsch, W.L. - \ 2013
Remote Sensing of Environment 135 (2013). - ISSN 0034-4257 - p. 189 - 201.
light use efficiency - eddy covariance data - evergreen needleleaf forest - net primary productivity - carbon-dioxide exchange - time-series - vegetation index - climate data - interannual variability - process model
Accurate estimation of gross primary production (GPP) of savanna woodlands is needed for evaluating the terrestrial carbon cycle at various spatial and temporal scales. The eddy covariance (EC) technique provides continuous measurements of net CO2 exchange (NEE) between terrestrial ecosystems and the atmosphere. Only a few flux tower sites were run in Africa and very limited observational data of savanna woodlands in Africa are available. Although several publications have reported on the seasonal dynamics and interannual variation of GPP of savanna vegetation through partitioning the measured NEE data, current knowledge about GPP and phenology of savanna ecosystems is still limited. This study focused on two savanna woodland flux tower sites in Botswana and Zambia, representing two dominant savanna woodlands (mopane and miombo) and climate patterns (semi-arid and semi-humid) in Southern Africa. Phenology of these savanna woodlands was delineated from three vegetation indices derived from Moderate Resolution Imaging Spectroradiometer (MODIS) and GPP estimated from eddy covariance measurements at flux tower sites (GPPEC). The Vegetation Photosynthesis Model (VPM), which is driven by satellite images and meteorological data, was also evaluated, and the results showed that the VPM-based GPP estimates (GPPVPM) were able to track the seasonal dynamics of GPPEC. The total GPPVPM and GPPEC within the plant growing season defined by a water-related vegetation index differed within the range of ±6%. This study suggests that the VPM is a valuable tool for estimating GPP of semi-arid and semi-humid savanna woodland ecosystems in Southern Africa.
Spatial relationship between climatologies and changes in global vegetation activity
Jong, R. de; Schaepman, M.E. ; Furrer, R. ; Bruin, S. de; Verburg, P.H. - \ 2013
Global Change Biology 19 (2013)6. - ISSN 1354-1013 - p. 1953 - 1964.
net primary production - drought-induced reduction - land-cover data - time-series - avhrr data - ndvi data - spot-vegetation - african sahel - term trends - index data
Vegetation forms a main component of the terrestrial biosphere and plays a crucial role in land-cover and climate-related studies. Activity of vegetation systems is commonly quantified using remotely sensed vegetation indices (VI). Extensive reports on temporal trends over the past decades in time series of such indices can be found in literature. However, little remains known about the processes underlying these changes at large spatial scales. In this study, we aimed at quantifying the spatial relationship between changes in potential climatic growth constraints (i.e. temperature, precipitation and incident solar radiation) and changes in vegetation activity (1982–2008). We demonstrate an additive spatial model with 0.5° resolution, consisting of a regression component representing climate-associated effects and a spatially correlated field representing the combined influence of other factors, including land-use change. Little over 50% of the spatial variance could be attributed to changes in climatologies; conspicuously, many greening trends and browning hotspots in Argentina and Australia. The nonassociated model component may contain large-scale human interventions, feedback mechanisms or natural effects, which were not captured by the climatologies. Browning hotspots in this component were especially found in subequatorial Africa. On the scale of land-cover types, strongest relationships between climatologies and vegetation activity were found in forests, including indications for browning under warming conditions (analogous to the divergence issue discussed in dendroclimatology).
Differentiation of plant age in grasses using remote sensing
Knox, N. ; Skidmore, A.K. ; Werff, H.M.A. van der; Groen, T.A. ; Boer, W.F. de; Prins, H.H.T. ; Kohi, E. ; Peel, M. - \ 2013
International Journal of applied Earth Observation and Geoinformation 24 (2013)10. - ISSN 0303-2434 - p. 54 - 62.
difference water index - monitoring vegetation - nitrogen concentration - imaging spectroscopy - hyperspectral data - boreal regions - time-series - green-up - phenology - reflectance
Phenological or plant age classification across a landscape allows for examination of micro-topographical effects on plant growth, improvement in the accuracy of species discrimination, and will improve our understanding of the spatial variation in plant growth. In this paper six vegetation indices used in phenological studies (including the newly proposed PhIX index) were analysed for their ability to statistically differentiate grasses of different ages in the sequence of their development. Spectra of grasses of different ages were collected from a greenhouse study. These were used to determine if NDVI, NDWI, CAI, EVI, EVI2 and the newly proposed PhIX index could sequentially discriminate grasses of different ages, and subsequently classify grasses into their respective age category. The PhIX index was defined as: (An VNIR+ log(An SWIR2))/(An VNIR- log(An SWIR2)), where An VNIRand An SWIR2are the respective nor- malised areas under the continuum removed reflectance curve within the VNIR (500-800 nm) and SWIR2 (2000-2210 nm) regions. The PhIX index was found to produce the highest phenological classification accuracy (Overall Accuracy: 79%, and Kappa Accuracy: 75%) and similar to the NDVI, EVI and EVI2 indices it statistically sequentially separates out the developmental age classes. Discrimination between seedling and dormant age classes and the adult and flowering classes was problematic for most of the tested indices. Combining information from the visible near infrared (VNIR) and shortwave infrared region (SWIR) region into a single phenological index captures the phenological changes associated with plant pigments and the ligno-cellulose absorption feature, providing a robust method to discriminate the age classes of grasses. This work provides a valuable contribution into mapping spatial variation and monitoring plant growth across savanna and grassland ecosystems.
Mapping land cover gradients through analysis of hyper-temporal NDVI imagery
Ali, A. ; Bie, C.A.J.M. de; Skidmore, A.K. ; Scarrott, R.G. ; Hamad, A. ; Venus, V. ; Lymberakis, P. - \ 2013
International Journal of applied Earth Observation and Geoinformation 23 (2013). - ISSN 0303-2434 - p. 301 - 312.
multitemporal modis images - remotely-sensed imagery - time-series - fuzzy-sets - species composition - accuracy assessment - neural-networks - classification - vegetation - boundaries
The green cover of the earth exhibits various spatial gradients that represent gradual changes in space of vegetation density and/or in species composition. To date, land cover mapping methods differentiate at best, mapping units with different cover densities and/or species compositions, but typically fail to express such differences as gradients. Present interpretation techniques still make insufficient use of freely available spatial-temporal Earth Observation (EO) data that allow detection of existing land cover gradients. This study explores the use of hyper-temporal NDVI imagery to detect and delineate land cover gradients analyzing the temporal behavior of NDVI values. MODIS-Terra MVC-images (250 m, 16-day) of Crete, Greece, from February 2000 to July 2009 are used. The analysis approach uses an ISODATA unsupervised classification in combination with a Hierarchical Clustering Analysis (HCA). Clustering of class-specific temporal NDVI profiles through HCA resulted in the identification of gradients in landcover vegetation growth patterns. The detected gradients were arranged in a relational diagram, and mapped. Three groups of NDVI-classes were evaluated by correlating their class-specific annual average NDVI values with the field data (tree, shrub, grass, bare soil, stone, litter fraction covers). Multiple regression analysis showed that within each NDVI group, the fraction cover data were linearly related with the NDVI data, while NDVI groups were significantly different with respect to tree cover (adj. R 2 = 0.96), shrub cover (adj. R 2 = 0.83), grass cover (adj. R 2 = 0.71), bare soil (adj. R 2 = 0.88), stone cover (adj. R 2 = 0.83) and litter cover (adj. R 2 = 0.69) fractions. Similarly, the mean Sorenson dissimilarity values were found high and significant at confidence interval of 95% in all pairs of three NDVI groups. The study demonstrates that hyper-temporal NDVI imagery can successfully detect and map land cover gradients. The results may improve land cover assessment and aid in agricultural and ecological studies.
Predicting ecosystem stability from community composition and biodiversity
Mazancourt, C. ; Isbell, F. ; Larocque, A. ; Berendse, F. ; Ruijven, J. van - \ 2013
Ecology Letters 16 (2013)5. - ISSN 1461-023X - p. 617 - 625.
competitive communities - species interactions - temporal stability - time-series - diversity - productivity - population - dynamics - consequences - variability
As biodiversity is declining at an unprecedented rate, an important current scientific challenge is to understand and predict the consequences of biodiversity loss. Here, we develop a theory that predicts the temporal variability of community biomass from the properties of individual component species in monoculture. Our theory shows that biodiversity stabilises ecosystems through three main mechanisms: (1) asynchrony in species’ responses to environmental fluctuations, (2) reduced demographic stochasticity due to overyielding in species mixtures and (3) reduced observation error (including spatial and sampling variability). Parameterised with empirical data from four long-term grassland biodiversity experiments, our prediction explained 22–75% of the observed variability, and captured much of the effect of species richness. Richness stabilised communities mainly by increasing community biomass and reducing the strength of demographic stochasticity. Our approach calls for a re-evaluation of the mechanisms explaining the effects of biodiversity on ecosystem stability.
Hyper-temporal remote sensing helps in relating epiphyllous liverworts and evergreen forests
Jiang, Y. ; Bie, C.A.J.M. de; Wang, T. ; Skidmore, A.K. ; Liu, X. ; Song, S. ; Shao, X. - \ 2013
Journal of Vegetation Science 24 (2013)2. - ISSN 1100-9233 - p. 214 - 226.
brazilian atlantic forest - vegetation cover - rain-forest - bryophyte communities - satellite imagery - time-series - sensor data - costa-rica - land-cover - china
Is there, at the macro-habitat scale, a relationship between the fraction of evergreen forests and the presence probability of epiphyllous liverworts? Can these two parameters be estimated and mapped using an NDVI-based indicator that is derived from time-series of SPOT-VGT imagery?
Designing the emerging EU pesticide policy: A literature review
Skevas, T. ; Oude Lansink, A.G.J.M. ; Stefanou, S.E. - \ 2013
NJAS Wageningen Journal of Life Sciences 64-65 (2013). - ISSN 1573-5214 - p. 95 - 103.
willingness-to-pay - damage control - economic incentives - moral hazard - chemical use - time-series - risk - econometrics - specification - agriculture
A European Union (EU) wide pesticide tax scheme is among the future plans of EUpolicy makers. This study examines the information needs for applying an optimal pesticidepolicy framework at the EU level. Damage control specification studies, empirical results from pesticide demand elasticity, issues on pesticide risk valuation and uncertainty, and knowledge on the indirect effects of pesticides in relation to current pesticidepolicies are analysed. Knowledge gaps based on reviewing this information are identified and an illustration is provided of the direction future pesticidepolicies should take.
Synergies of multiple remote sensing data sources for REDD+ monitoring
Sy, V. De; Herold, M. ; Achard, F. ; Asner, G.P. ; Held, A. ; Kellndorfer, J. ; Verbesselt, J. - \ 2012
Current Opinion in Environmental Sustainability 4 (2012)6. - ISSN 1877-3435 - p. 696 - 706.
synthetic-aperture radar - fire radiative power - forest carbon stocks - time-series - spatial information - central-america - alos palsar - emissions - deforestation - satellite
Remote sensing technologies can provide objective, practical and cost-effective solutions for developing and maintaining REDD+ monitoring systems. This paper reviews the potential and status of available remote sensing data sources with a focus on different forest information products and synergies among various approaches and evolving technologies. There is significant technical capability of remote sensing technologies but operational usefulness is constrained by lack of consistent and continuous coverage, data availability in developing countries, appropriate methodologies for national-scale use and available capacities in developing countries. Coordinated international efforts, regional cooperation and continued research efforts are essential to further develop national approaches and capacities to fully explore and use the potential remote sensing has to offer for REDD+ forest monitoring
Revisiting land cover observations to address the needs of the climate modeling community
Bontemps, S. ; Herold, M. ; Kooistra, L. ; Groenestijn, A. van; Hartley, A. ; Arino, O. ; Moreau, I. ; Defourny, P. - \ 2012
Biogeosciences 9 (2012). - ISSN 1726-4170 - p. 2145 - 2157.
time-series - satellite - validation - resolution - system
Improving systematic observations of land cover, as an Essential Climate Variable, should contribute to a better understanding of the global climate system and thus improve our ability to predict climatic change. The aim of this paper is to bring global land cover observations closer to meeting the needs of climate science. First, consultation mechanisms were established with the climate modeling community to identify its specific requirements in terms of satellite-based global land cover products. This assessment highlighted specific needs in terms of land cover characterization, accuracy of products, as well as stability and consistency needs that are currently not met or even addressed. The current land cover representation and mapping techniques were then called into question to specifically focus on the critical need of stable products expressed by climate users. Decoupling the stable and dynamic components of the land cover characterization and using a multi-year dataset were proposed as two key approaches to allow generating consistent suites of global land cover products over time.
Integration of multi-sensor data to assess grassland dynamics in a Yellow River sub-watershed
Ouyang, W. ; Hao, F. ; Skidmore, A.K. ; Groen, T.A. ; Toxopeus, A.G. ; Wang, T. - \ 2012
Ecological Indicators 18 (2012)1. - ISSN 1470-160X - p. 163 - 170.
qinghai-xizang plateau - time-series - land-cover - west-africa - vegetation - modis - variability - patterns - imagery - china
Grasslands form the dominant land cover in the upper reaches of the Yellow River and provide a reliable indicator by being strongly correlated with regional terrestrial ecological status. Remote sensing can provide information useful for vegetation quality assessments, but no single sensor can meet the needs for the high temporal-spatial resolution required for such assessments on a watershed scale. To observe long-term grassland dynamics in the Longliu Watershed located in the upper reaches of the Yellow River, Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat images were integrated to obtain Normalized Difference Vegetation Index (NDVI) data. The MODIS images were used to identify patterns of monthly variation. With the temporal dynamics of NDVI provided by the MODIS images, an exponential regression model was obtained that described the relationship between Julian day and NDVI. Four time-series data sets from multi-spectral sensors were constructed to obtain regional grassland NDVI information from 1977 to 2006 in the Longliu Watershed. Using the daily NDVI correlation coefficient, NDVI values for different days were obtained from Landsat series images, standardised to the same day and integrated into TM format by using NDVI coefficients between the four different sensors. Thus, the NDVI data obtained from multi-sensors on different days were integrated into a comparable format. A regression analysis correlating the NDVI data from two sensors with fresh grass biomass showed that the integration procedure was reliable.
Mapping the irrigated rice cropping patterns of the Mekong delta, Vietnam through hyper-temporal SPOT NDVI image analysis
Nguyen, Thi Thu Ha ; Bie, C.A.J.M. de; Ali, A. ; Smaling, E.M.A. ; Hoanh, C.T. - \ 2012
International Journal of Remote Sensing 33 (2012)2. - ISSN 0143-1161 - p. 415 - 434.
multitemporal modis images - time-series - agriculture - areas - china - south - classification - expansion - systems - fields
Successful identification and mapping of different cropping patterns under cloudy conditions of a specific crop through remote sensing provides important baseline information for planning and monitoring. In Vietnam, this information is either missing or unavailable; several ongoing projects studying options with radar to avoid earth observation problems caused by the prevailing cloudy conditions have to date produced only partial successes. In this research, optical hyper-temporal Satellite Pour l'Observation de la Terre (SPOT) VEGETATION (SPOT VGT) data (1998–2008) were used to describe and map variability in irrigated rice cropping patterns of the Mekong delta. Divergence statistics were used to evaluate signature separabilities of normalized difference vegetation index (NDVI) classes generated from the iterative self-organizing data analysis technique algorithm (ISODATA) classification of 10-day SPOT NDVI image series. Based on this evaluation, a map with 77 classes was selected. Out of these 77 mapped classes, 26 classes with prior knowledge that they represent rice were selected to design the sampling scheme for fieldwork and for crop calendar characterization. Using the collected information of 112 farmers’ fields belonging to the 26 selected classes, the map produced provides highly accurate information on rice cropping patterns (94% overall accuracy, 0.93 Kappa coefficient). We found that the spatial distributions of the triple and the double rice cropping systems are highly related to the flooding regime from the Hau and Tien rivers. Areas that are highly vulnerable to flooding in the upper part and those that are saline in the north-western part of the delta mostly have a double rice cropping system, whilst areas in the central and the south-eastern parts mostly have a triple rice cropping system. In turn, the duration of flooding is highly correlated with the decision by farmers to cultivate shorter or longer duration rice varieties. The overall spatial variability mostly coincides with administrative units, indicating that crop pattern choices and water control measures are locally synchronized. Water supply risks, soil acidity and salinity constraints and the anticipated highly fluctuating rice market prices all strongly influence specific farmers’ choices of rice varieties. These choices vary considerably annually, and therefore grown rice varieties are difficult to map. Our study demonstrates the high potential of optical hyper-temporal images, taken on a daily basis, to differentiate and map a high variety of irrigated rice cropping patterns and crop calendars at a high level of accuracy in spite of cloudy conditions