Staff Publications

Staff Publications

  • external user (warningwarning)
  • Log in as
  • language uk
  • About

    '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.

    We have a manual that explains all the features 

    Records 1 - 17 / 17

    • help
    • print

      Print search results

    • export

      Export search results

    Check title to add to marked list
    Biodiversity recovery of Neotropical secondary forests
    Rozendaal, Danaë M.A. ; Bongers, Frans ; Aide, T.M. ; Alvarez-Dávila, Esteban ; Ascarrunz, Nataly ; Balvanera, Patricia ; Becknell, Justin M. ; Bentos, Tony V. ; Brancalion, Pedro H.S. ; Cabral, George A.L. ; Calvo-Rodriguez, Sofia ; Chave, Jerome ; César, Ricardo G. ; Chazdon, Robin L. ; Condit, Richard ; Dallinga, Jorn S. ; Almeida-Cortez, Jarcilene S. De; Jong, Ben de; Oliveira, Alexandre De; Denslow, Julie S. ; Dent, Daisy H. ; Dewalt, Saara J. ; Dupuy, Juan Manuel ; Durán, Sandra M. ; Dutrieux, Loïc P. ; Espírito-Santo, Mario M. ; Fandino, María C. ; Fernandes, G.W. ; Finegan, Bryan ; García, Hernando ; Gonzalez, Noel ; Moser, Vanessa Granda ; Hall, Jefferson S. ; Hernández-Stefanoni, José Luis ; Hubbell, Stephen ; Jakovac, Catarina C. ; Hernández, Alma Johanna ; Junqueira, André B. ; Kennard, Deborah ; Larpin, Denis ; Letcher, Susan G. ; Licona, Juan-Carlos ; Lebrija-trejos, Edwin ; Marín-Spiotta, Erika ; Martínez-Ramos, Miguel ; Massoca, Paulo E.S. ; Meave, Jorge A. ; Mesquita, Rita C.G. ; Mora, Francisco ; Müller, Sandra C. ; Muñoz, Rodrigo ; Oliveira Neto, Silvio Nolasco De; Norden, Natalia ; Nunes, Yule R.F. ; Ochoa-Gaona, Susana ; Ortiz-Malavassi, Edgar ; Ostertag, Rebecca ; Peña-Caros, Marielos ; Pérez-García, Eduardo A. ; Piotto, Daniel ; Powers, Jennifer S. ; Aguilar-Cano, José ; Rodriguez-Buritica, Susana ; Rodríguez-Velázquez, Jorge ; Romero-Romero, Marco Antonio ; Ruíz, Jorge ; Sanchez-Azofeifa, Arturo ; Almeida, Arlete Silva De; Silver, Whendee L. ; Schwartz, Naomi B. ; Thomas, William Wayt ; Toledo, Marisol ; Uriarte, Maria ; Sá Sampaio, Everardo Valadares De; Breugel, Michiel van; Wal, Hans van der; Martins, Sebastião Venâncio ; Veloso, Maria D.M. ; Vester, Hans F.M. ; Vicentini, Alberto ; Vieira, Ima C.G. ; Villa, Pedro ; Williamson, G.B. ; Zanini, Kátia J. ; Zimmerman, Jess ; Poorter, Lourens - \ 2019
    Science Advances 5 (2019)3. - ISSN 2375-2548 - 10 p.
    Old-growth tropical forests harbor an immense diversity of tree species but are rapidly being cleared, while secondary forests that regrow on abandoned agricultural lands increase in extent. We assess how tree species richness and composition recover during secondary succession across gradients in environmental conditions and anthropogenic disturbance in an unprecedented multisite analysis for the Neotropics. Secondary forests recover remarkably fast in species richness but slowly in species composition. Secondary forests take a median time of five decades to recover the species richness of old-growth forest (80% recovery after 20 years) based on rarefaction analysis. Full recovery of species composition takes centuries (only 34% recovery after 20 years). A dual strategy that maintains both old-growth forests and species-rich secondary forests is therefore crucial for biodiversity conservation in human-modified tropical landscapes.
    Spatial and temporal dynamics of shifting cultivation in the middle-Amazonas river : Expansion and intensification
    Jakovac, Catarina Conte ; Dutrieux, Loic ; Siti, Latifah ; Peña-Claros, Marielos ; Bongers, Frans - \ 2017
    PLoS ONE 12 (2017)7. - ISSN 1932-6203
    Shifting cultivation is the main land-use system transforming landscapes in riverine Amazonia. Increased concentration of the human population around villages and increasing market integration during the last decades may be causing agricultural intensification. Studies have shown that agricultural intensification, i.e. higher number of swidden-fallow cycles and shorter fallow periods, reduces crop productivity of swiddens and the regrowth capacity of fallows, undermining the resilience of the shifting cultivation system as a whole. We investigated the temporal and spatial dynamics of shifting cultivation in Brazilian Amazonia to test the hypotheses that (i) agriculture has become more intensive over time, and (ii) patterns of land-use intensity are related to land accessibility and human population density. We applied a breakpoint-detection algorithm to Landsat time-series spanning three decades (1984–2015) and retrieved the temporal dynamics of shifting cultivation fields, which go through alternating phases of crop production (swidden) and secondary forest regrowth (fallow). We found that fallow-period length has decreased from 6.4 to 5.1 years on average, and that expansion over old-growth forest has slowed down over time. Shorter fallow periods and higher frequency of slash and burn cycles are practiced closer to residences and around larger villages. Our results indicate that shifting cultivation in riverine Amazonia has gone through a process of agricultural intensification in the past three decades. The resulting landscape is predominantly covered by young secondary forests (≤ 12 yrs old), and 20% of it have gone through intensive use. Reversing this trend and avoiding the negative consequences of agricultural intensification requires land use planning that accounts for the constraints of land use in riverine areas.
    The integration of empirical, remote sensing and modelling approaches enhances insight in the role of biodiversity in climate change mitigation by tropical forests
    Sande, Masha T. van der; Poorter, Lourens ; Balvanera, Patricia ; Kooistra, Lammert ; Thonicke, Kirsten ; Boit, Alice ; Dutrieux, Loic ; Equihua, Julian ; Gerard, France ; Herold, Martin ; Kolb, Melanie ; Simões, Margareth ; Peña-Claros, Marielos - \ 2017
    Current Opinion in Environmental Sustainability 26-27 (2017). - ISSN 1877-3435 - p. 69 - 76.
    Tropical forests store and sequester high amounts of carbon and are the most diverse terrestrial ecosystem. A complete understanding of the relationship between biodiversity and carbon storage and sequestration across spatiotemporal scales relevant for climate change mitigation needs three approaches: empirical, remote sensing and ecosystem modelling. We review individual approaches and show that biodiversity has short-term and long-term benefits across spatial scales. We argue that enhanced understanding is obtained by combining approaches and, especially, integrating approaches through using ‘boundary objects’ that can be understood and measured by all approaches, such as diversity of leaf traits of the upper canopy. This will lead to better understanding of biodiversity effects on climate change mitigation, which is crucial for making sound policy decisions.
    Detecting, monitoring and charactering ecosystem change using multiple satellite sensor image time series
    Verbesselt, J. ; DeVries, B.R. ; Reiche, J. ; Dutrieux, L.P. ; Hamunyela, E. ; Herold, M. - \ 2016
    Multidimensional remote sensing based mapping of tropical forests and their dynamics
    Dutrieux, L.P. - \ 2016
    Wageningen University. Promotor(en): Martin Herold, co-promotor(en): Lammert Kooistra; Lourens Poorter. - Wageningen : Wageningen UR - ISBN 9789462578906 - 146
    tropical forests - remote sensing - mapping - biodiversity - forest structure - monitoring - land use - landsat - tropische bossen - remote sensing - cartografie - biodiversiteit - bosstructuur - monitoring - landgebruik - landsat

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

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

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

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

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

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

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

    Reconstructing land use history from Landsat time-series : Case study of a swidden agriculture system in Brazil
    Dutrieux, L.P. ; Jakovac, A.C. ; Latifah, Siti H. ; Kooistra, Lammert - \ 2016
    International Journal of applied Earth Observation and Geoinformation 47 (2016). - ISSN 0303-2434 - p. 112 - 124.
    We developed a method to reconstruct land use history from Landsat images time-series. The method uses a breakpoint detection framework derived from the econometrics field and applicable to time-series regression models. The Breaks For Additive Season and Trend (BFAST) framework is used for defining the time-series regression models which may contain trend and phenology, hence appropriately modelling vegetation intra and inter-annual dynamics. All available Landsat data are used for a selected study area, and the time-series are partitioned into segments delimited by breakpoints. Segments can be associated to land use regimes, while the breakpoints then correspond to shifts in land use regimes. In order to further characterize these shifts, we classified the unlabelled breakpoints returned by the algorithm into their corresponding processes. We used a Random Forest classifier, trained from a set of visually interpreted time-series profiles to infer the processes and assign labels to the breakpoints. The whole approach was applied to quantifying the number of cultivation cycles in a swidden agriculture system in Brazil (state of Amazonas). Number and frequency of cultivation cycles is of particular ecological relevance in these systems since they largely affect the capacity of the forest to regenerate after land abandonment. We applied the method to a Landsat time-series of Normalized Difference Moisture Index (NDMI) spanning the 1984–2015 period and derived from it the number of cultivation cycles during that period at the individual field scale level. Agricultural fields boundaries used to apply the method were derived using a multi-temporal segmentation approach. We validated the number of cultivation cycles predicted by the method against in-situ information collected from farmers interviews, resulting in a Normalized Residual Mean Squared Error (NRMSE) of 0.25. Overall the method performed well, producing maps with coherent spatial patterns. We identified various sources of error in the approach, including low data availability in the 90s and sub-object mixture of land uses. We conclude that the method holds great promise for land use history mapping in the tropics and beyond.
    Performance of the Enhanced Vegetation Index to Detect Inner-annual Dry Season and Drought Impacts on Amazon Forest Canopies
    Brede, B. ; Verbesselt, J. ; Dutrieux, L. ; Herold, M. - \ 2015
    In: International Archives of the Photogrammetry, Remote Sensing and Spatial Information Science. - - p. 337 - 344.
    The Amazon rainforests represent the largest connected forested area in the tropics and play an integral role in the global carbon cycle. In the last years the discussion about their phenology and response to drought has intensified. A recent study argued that seasonality in greenness expressed as Enhanced Vegetation Index (EVI) is an artifact of variations in sun-sensor geometry throughout the year. We aimed to reproduce these results with the Moderate-Resolution Imaging Spectroradiometer (MODIS) MCD43 product suite, which allows modeling the Bidirectional Reflectance Distribution Function (BRDF) and keeping sun-sensor geometry constant. The derived BRDF-adjusted EVI was spatially aggregated over large areas of central Amazon forests. The resulting time series of EVI spanning the 2000-2013 period contained distinct seasonal patterns with peak values at the onset of the dry season, but also followed the same pattern of sun geometry expressed as Solar Zenith Angle (SZA). Additionally, we assessed EVI's sensitivity to precipitation anomalies. For that we compared BRDF-adjusted EVI dry season anomalies to two drought indices (Maximum Cumulative Water Deficit, Standardized Precipitation Index). This analysis covered the whole of Amazonia and data from the years 2000 to 2013. The results showed no meaningful connection between EVI anomalies and drought. This is in contrast to other studies that investigate the drought impact on EVI and forest photosynthetic capacity. The results from both sub-analyses question the predictive power of EVI for large scale assessments of forest ecosystem functioning in Amazonia. Based on the presented results, we recommend a careful evaluation of the EVI for applications in tropical forests, including rigorous validation supported by ground plots.
    Reconstructing land use history from Landsat time-series: Case study of Swidden Agriculture Intensification in Brazil
    Dutrieux, L.P. ; Jakovac, A.C. ; Siti, L. ; Kooistra, L. - \ 2015
    We developed a method to reconstruct land use history from Landsat images time-series. The method uses a breakpoint detection framework derived from the econometrics field and applicable to time-series regression models. The BFAST framework is used for defining the time-series regression models which may contain trend and phenology, hence appropriately modelling vegetation intra and inter-annual dynamics. All available Landsat data are used, and the time-series are partitioned into segments delimited by breakpoints. Segments can be associated to land use regimes, while the breakpoints then correspond to shifts in regimes. To further characterize these shifts, we classified the unlabelled breakpoints returned by the algorithm into their corresponding processes. We used a Random Forest classifier, trained from a set of visually interpreted time-series profiles to infer the processes and assign labels to the breakpoints. The whole approach was applied to quantifying the number of cultivation cycles in a swidden agriculture system in Brazil. Number and frequency of cultivation cycles is of particular ecological relevance in these systems since they largely affect the capacity of the forest to regenerate after abandonment. We applied the method to a Landsat time-series of Normalized Difference Moisture Index (NDMI) spanning the 1984-2015 period and derived from it the number of cultivation cycles during that period at the individual field scale level. Agricultural fields boundaries used to apply the method were derived using a multi-temporal segmentation. We validated the number of cultivation cycles predicted against in-situ information collected from farmers interviews, resulting in a Normalized RMSE of 0.25. Overall the method performed well, producing maps with coherent patterns. We identified various sources of error in the approach, including low data availability in the 90s and sub-object mixture of land uses. We conclude that the method holds great promise for land use history mapping in the tropics and beyond. Spatial and temporal patterns were further analysed with an ecological perspective in a follow-up study. Results show that changes in land use patterns such as land use intensification and reduced agricultural expansion reflect the socio-economic transformations that occurred in the region
    Current contributions of biodiversity and ecosystems to climate change mitigation - an analysis using remote sensing datasets
    Kooistra, L. ; Dutrieux, L.P. ; Equihua, J. - \ 2015
    Wageningen UR (Report ROBIN project D113 )
    Public access to web-based project data products
    Kolb, M. ; Thonicke, K. ; Boit, A. ; Kooistra, L. ; Dutrieux, L.P. ; Herold, M. ; Cisowska, I. ; Blyth, E. - \ 2015
    Wageningen UR (Public report D4.3.2 from the EC ROBIN project )
    Diversity enhances carbon storage in tropical forests
    Poorter, L. ; Sande, M.T. van der; Thompson, J. ; Arets, E.J.M.M. ; Bongers, F. ; Steege, H. ter; Pena Claros, M. ; Hoosbeek, M.R. ; Dutrieux, L.P. ; Levis, C. ; Rozendaal, Danaë - \ 2015
    Global Ecology and Biogeography 24 (2015)11. - ISSN 1466-822X - p. 1314 - 1328.
    Aim Tropical forests store 25% of global carbon and harbour 96% of the world's tree species, but it is not clear whether this high biodiversity matters for carbon storage. Few studies have teased apart the relative importance of forest attributes and environmental drivers for ecosystem functioning, and no such study exists for the tropics. Location Neotropics. Methods We relate aboveground biomass (AGB) to forest attributes (diversity and structure) and environmental drivers (annual rainfall and soil fertility) using data from 144,000 trees, 2050 forest plots and 59 forest sites. The sites span the complete latitudinal and climatic gradients in the lowland Neotropics, with rainfall ranging from 750 to 4350¿mm¿year-1. Relationships were analysed within forest sites at scales of 0.1 and 1 ha and across forest sites along large-scale environmental gradients. We used a structural equation model to test the hypothesis that species richness, forest structural attributes and environmental drivers have independent, positive effects on AGB. Results Across sites, AGB was most strongly driven by rainfall, followed by average tree stem diameter and rarefied species richness, which all had positive effects on AGB. Our indicator of soil fertility (cation exchange capacity) had a negligible effect on AGB, perhaps because we used a global soil database. Taxonomic forest attributes (i.e. species richness, rarefied richness and Shannon diversity) had the strongest relationships with AGB at small spatial scales, where an additional species can still make a difference in terms of niche complementarity, while structural forest attributes (i.e. tree density and tree size) had strong relationships with AGB at all spatial scales. Main conclusions Biodiversity has an independent, positive effect on AGB and ecosystem functioning, not only in relatively simple temperate systems but also in structurally complex hyperdiverse tropical forests. Biodiversity conservation should therefore be a key component of the UN Reducing Emissions from Deforestation and Degradation strategy.
    Monitoring forest cover loss using multiple data streams, a case study of a tropical dry forest in Bolivia
    Dutrieux, L.P. ; Verbesselt, J. ; Kooistra, L. ; Herold, M. - \ 2015
    ISPRS Journal of Photogrammetry and Remote Sensing 107 (2015). - ISSN 0924-2716 - p. 112 - 125.
    landsat time-series - structural-change - vegetation indexes - rainfall products - detecting trends - east-africa - amazon - disturbance - validation - modis
    Automatically detecting forest disturbances as they occur can be extremely challenging for certain types of environments, particularly those presenting strong natural variations. Here, we use a generic structural break detection framework (BFAST) to improve the monitoring of forest cover loss by combining multiple data streams. Forest change monitoring is performed using Landsat data in combination with MODIS or rainfall data to further improve the modelling and monitoring. We tested the use of the Normalized Difference Vegetation Index (NDVI) from the Moderate Resolution Imaging Spectroradiometer (MODIS) with varying spatial aggregation window sizes as well as a rainfall derived index as external regressors. The method was evaluated on a dry tropical forest area in lowland Bolivia where forest cover loss is known to occur, and we validated the results against a set of ground truth samples manually interpreted using the TimeSync environment. We found that the addition of an external regressor allows to take advantage of the difference in spatial extent between human induced and naturally induced variations and only detect the processes of interest. Of all configurations, we found the 13 by 13 km MODIS NDVI window to be the most successful, with an overall accuracy of 87%. Compared with a single pixel approach, the proposed method produced better time-series model fits resulting in increases of overall accuracy (from 82% to 87%), and decrease in omission and commission errors (from 33% to 24% and from 3% to 0% respectively). The presented approach seems particularly relevant for areas with high inter-annual natural variability, such as forests regularly experiencing exceptional drought events.
    Tracking forest cover change using Landsat & Rapid Eye towards S2
    Hamunyela, E. ; Verbesselt, J. ; Schultz, M. ; Penndorf, A. ; Frotscher, K. ; Herold, M. ; Reiche, J. ; DeVries, B.R. ; Dutrieux, L.P. ; Calders, K. - \ 2014
    Spatio-temporal break detection for deforestation monitoring using Landsat and MODIS image time series
    Dutrieux, L.P. ; Hamunyela, E. ; Verbesselt, J. ; Kooistra, L. ; Herold, M. - \ 2014
    In: International Conference “Global Vegetation Monitoring and Modeling” (GV2M). - - p. 42 - 42.
    Combining medium and high-resolution data in multi-scale approach to detect breaks in satellite image time-series
    Dutrieux, L.P. ; Verbesselt, J. ; Kooistra, L. ; DeVries, B.R. ; Herold, M. - \ 2013
    The role of biodiversity In climate change mitigation (ROBIN): Remote Sensing contribution
    Dutrieux, L.P. ; Diaz, P. ; Equihua, J. ; Espinoza, D. ; Gerard, F. ; Herold, M. ; Kooistra, L. ; Mücher, C.A. ; Peña Claros, M. ; Roerink, G.J. ; Schmidt, M. ; Parr, T. - \ 2013
    Relationships between declining summer sea ice, increasing temperatures and changing vegetation in the Siberian Arctic tundra from MODIS time series (2000–11)
    Dutrieux, L.P. ; Bartholomeus, H. ; Herold, M. ; Verbesselt, J. - \ 2012
    Environmental Research Letters 7 (2012)4. - ISSN 1748-9326 - 12 p.
    climate-change - shrub expansion - high-latitudes - ndvi - responses - amplification - ecosystems - community - carbon - alaska
    The concern about Arctic greening has grown recently as the phenomenon is thought to have significant influence on global climate via atmospheric carbon emissions. Earlier work on Arctic vegetation highlighted the role of summer sea ice decline in the enhanced warming and greening phenomena observed in the region, but did not contain enough details for spatially characterizing the interactions between sea ice, temperature and vegetation photosynthetic absorption. By using 1 km resolution data from the Moderate Resolution Imaging Spectrometer (MODIS) as a primary data source, this study presents detailed maps of vegetation and temperature trends for the Siberian Arctic region, using the time integrated normalized difference vegetation index (TI-NDVI) and summer warmth index (SWI) calculated for the period 2000-11 to represent vegetation greenness and temperature respectively. Spatio-temporal relationships between the two indices and summer sea ice conditions were investigated with transects at eight locations using sea ice concentration data from the Special Sensor Microwave/Imager (SSM/I). In addition, the derived vegetation and temperature trends were compared among major Arctic vegetation types and bioclimate subzones. The fine resolution trend map produced confirms the overall greening (+1% yr(-1)) and warming (+0.27% yr(-1)) of the region, reported in previous studies, but also reveals browning areas. The causes of such local decreases in vegetation, while surrounding areas are experiencing the opposite reaction to changing conditions, are still unclear. Overall correlations between sea ice concentration and SWI as well as TI-NDVI decreased in strength with increasing distance from the coast, with a particularly pronounced pattern in the case of SWI. SWI appears to be driving TI-NDVI in many cases, but not systematically, highlighting the presence of limiting factors other than temperature for plant growth in the region. Further unravelling those limiting factors constitutes a priority in future research. This study demonstrates the use of medium resolution remotely sensed data for studying the complexity of spatio-temporal vegetation dynamics in the Arctic.
    Check title to add to marked list

    Show 20 50 100 records per page

     
    Please log in to use this service. Login as Wageningen University & Research user or guest user in upper right hand corner of this page.