1,199 research outputs found

    Fernerkundung der Vegetationsphänologie über MODIS NDVI Daten - Herausforderungen bei der Datenverarbeitung und -validierung mittels Bodenbeobachtungen zahlreicher Arten und LiDAR

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    Phenology, the cyclic events in living organisms is triggered by climatic conditions and indicators of climate change. They are important factors influencing species interactions and ecosystem functioning. This thesis deals with the estimation of phenological metrics (Land Surface Phenology or LSP) from MODIS based time series NDVI data. Results of data analysis emphasises the role of ground observations, topography and LiDAR characteristics of forest stand in describing the variability in LSP.Phänologie, die zyklischen Stadien von lebenden Organismen werden über klimatische Verhältnisse gesteuert und dienen als Indikatoren des Klimawandels. Diese Faktoren beeinflussen maßgeblich die Interaktionen zwischen Arten und sind für das Funktionieren von Ökosystemen ausschlaggebend. Diese Arbeit behandelt die Bestimmung von phänologischen Metriken (Phänologie der Landoberfläche oder LSP) unter Verwendung von MODIS basierten NDVI Zeitreihen. Die Ergebnisse der Datenanalyse hebt die Wichtigkeit von Bodenbeobachtungen, Topographie und LiDAR Merkmalen von Waldbeständen bei der Beschreibung der LSP Variabilität hervor

    The application of time-series MODIS NDVI profiles for the acquisition of crop information across Afghanistan

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    We investigated and developed a prototype crop information system integrating 250 m Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) data with other available remotely sensed imagery, field data, and knowledge as part of a wider project monitoring opium and cereal crops. NDVI profiles exhibited large geographical variations in timing, height, shape, and number of peaks, with characteristics determined by underlying crop mixes, growth cycles, and agricultural practices. MODIS pixels were typically bigger than the field sizes, but profiles were indicators of crop phenology as the growth stages of the main first-cycle crops (opium poppy and cereals) were in phase. Profiles were used to investigate crop rotations, areas of newly exploited agriculture, localized variation in land management, and environmental factors such as water availability and disease. Near-real-time tracking of the current years’ profile provided forecasts of crop growth stages, early warning of drought, and mapping of affected areas. Derived data products and bulletins provided timely crop information to the UK Government and other international stakeholders to assist the development of counter-narcotic policy, plan activity, and measure progress. Results show the potential for transferring these techniques to other agricultural systems

    Development and analysis of spring plant phenology products: 36 years of 1-km grids over the conterminous US

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    Time series of phenological products provide information on the timings of recurrent biological events and on their temporal trends. This information is key to studying the impacts of climate change on our planet as well as for managing natural resources and agricultural production. Here we develop and analyze new long term phenological products: 1 km grids of the Extended Spring Indices (SI-x) over the conterminous United States from 1980 to 2015. These new products (based on Daymet daily temperature grids and created by using cloud computing) allow the analysis of two primary variables (first leaf and first bloom) and two derivative products (Damage Index and Last Freeze Day) at a much finer spatial resolution than previous gridded or interpolated products. Furthermore, our products provide enough temporal depth to reliably analyze trends and changes in the timing of spring arrival at continental scales. Validation results confirm that our products largely agree with lilac and honeysuckle leaf and flowering onset observations. The spatial analysis shows a significantly delayed spring onset in the northern US whereas in the western and the Great Lakes region, spring onset advances. The mean temporal variabilities of the indices were analyzed for the nine major climatic regions of the US and results showed a clear division into three main groups: early, average and late spring onset. Finally, the region belonging to each group was mapped. These examples show the potential of our four phenological products to improve understanding of the responses of ecosystems to a changing climat

    Validation and application of the MERIS Terrestrial Chlorophyll Index.

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    Climate is one of the key variables driving ecosystems at local to global scales. How and to what extent vegetation responds to climate variability is a challenging topic for global change analysis. Earth observation provides an opportunity to study temporal ecosystem dynamics, providing much needed information about the response of vegetation to environmental and climatic change at local to global scales. The European Space Agency (ESA) uses data recorded by the Medium Resolution Imaging Spectrometer (MERlS) in red I near infrared spectral bands to produce an operational product called the MERlS Terrestrial Chlorophyll Index (MTCI). The MTCI is related to the position of the red edge in vegetation spectra and can be used to estimate the chlorophyll content of vegetation. The MTCI therefore provides a powerful product to monitor phenology, stress and productivity. The MTCI needs full validation if it is to be embraced by the user community who require precise and consistent, spatial and temporal comparisons of vegetation condition. This research details experimental investigations into variables that may influence the relationship between the MTCI and vegetation chlorophyll content, namely soil background and sensor view angle, vegetation type and spatial scale. Validation campaigns in the New Forest and at Brooms Barn agricultural study site reinforced the strong correlation between chlorophyll content and MTCI that was evident from laboratory spectroscopy investigations, demonstrating the suitability of the MTCI as a surrogate for field chlorophyll content measurements independent of cover type. However, this relationship was significantly weakened where the leaf area index (LAI) was low, indicating that the MTCI is sensitive to the effects of soil background. In the light of such conclusions, this project then assessed the MTCI as a tool to monitor changes in ecosystem phenology as a function of climatic variability, and the suitability of the MTCI as a surrogate measure of photosynthetic light use efficiency, to model ecosystem gross primary productivity (GPP) at various sites in North America with contrasting vegetation types. Changes in MTCI throughout the growing season demonstrated the potential of the MTCI to estimate vegetation dynamics, characterising the temporal characteristics in both phenology and gross primary productivity

    Effects of climate on phenology, flowering, and berry production of boreal forest understory plants

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    Climate is changing faster than it was predicted before and consequently its impact is highly visible on many types of ecosystems globally. Effects of changing climate on forests and the environment are leading to threats for all sorts of ecosystems. Now the management of forests and decision making for environmental protection is following the trend of the impacts of climate change. Climate change is expected to affect plant and animal phenology. Phenological studies are a way to see how plants react to the changing climate. This study focused on this concept and tried to figure out the effects of climate on phenology, flowering, and berry production of four important boreal forest understory plants. Plant material was moved from colder locations (2-degree Celsius temperature difference) to three forest sites in southern, middle, and north Sweden. The potted plants were located in 10 different places at each site together with equally treated local plant material that served as a control. Sites used in the transplant experiment are from north to south: Tärnaby to Vindeln (northern Sweden), Idre to Siljan (middle of Sweden) and Tomtabacken to Vivarp (southern Sweden). For this experiment, the studied species were Solidago virgaurea L. (European golden rod), Vaccinium myrtillus L. (Billberry), Vaccinium vitis-idaea L. (lingonberry or cowberry) and Fragaria vesca L. (wild strawberry or woodland strawberry). Later, in the growing season, photos were taken of both provenances (cold provenance and warm provenance) in all three forest sites. This study aimed to find out translocation treatment effects for species-wise selected phenological traits. Finally, through image analysis considering selected phenological traits, this research has found strong treatment effects (at least at two sites out of three) for three phenological traits. DOY (day of the year) of first fruit development of F. vesca responded to the translocation treatment at Siljan and Vindeln. Again, DOY of seed setting and DOY of highest percentage of flowering of S. virgaurea responded to the translocation treatment at Vivarp and Vindeln. But, in most cases, no translocation treatment effects have been found for other phenological traits, and specifically for V. myrtillus and V. vitis-idaea, no treatment effects were found throughout this experiment which implies phenotypic plasticity. Absence of effects is an indication that these selected phenological traits would follow the effect of climate change through the adaptation process. This research work can be considered as a good reference for the phenology study of boreal forest understory plants, especially to know how studied phenological traits react to a translocation experiment with a temperature change, phenotypic plasticity and also to understand the change of phenology by climate change for above mentioned species

    Image time series processing for agriculture monitoring

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    AbstractGiven strong year-to-year variability, increasing competition for natural resources, and climate change impacts on agriculture, monitoring global crop and natural vegetation conditions is highly relevant, particularly in food insecure areas. Data from remote sensing image series at high temporal and low spatial resolution can help to assist in this monitoring as they provide key information in near-real time over large areas. The SPIRITS software, presented in this paper, is a stand-alone toolbox developed for environmental monitoring, particularly to produce clear and evidence-based information for crop production analysts and decision makers. It includes a large number of tools with the main aim of extracting vegetation indicators from image time series, estimating the potential impact of anomalies on crop production and sharing this information with different audiences. SPIRITS offers an integrated and flexible analysis environment with a user-friendly graphical interface, which allows sequential tasking and a high level of automation of processing chains. It is freely distributed for non-commercial use and extensively documented

    Development of a satellite-based dynamic regional vegetation model for the Drâa catchment

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    Analysing and modelling land cover dynamic of the vegetation under a changing hydrological cycle inside the semi-arid area resulting from the global climate change are a difficult task. It is important to be able to understand and predict the characteristics and availability of vegetation as result of the global climate. This study was carried out inside the upper and middle Drâa catchment in south Morocco, focusing on the natural vegetation outside rural and agricultural areas. Development of a dynamic regional land cover model is traditionally driven by site specific plant growing parameters or by spatial information from remote sensing (e.g. NDVI). By scaling both approaches to a regional level plant activity can be analysed with the MODIS sensor and interpreted by local measurements. By using signal processing techniques, a double regression approach was developed and tested under the conditions of temporal trends and performance parameters. Completed by a regional adopted vegetation model, important productivity parameters could be extracted. This semi-automatic approach is realized in the conceptual model MOVEG Drâa, bringing together remote sensing, meteorological and other data and techniques. An extensive phenological database was built up by integrating Terra MODIS NDVI time series (2000 until 2008), a vegetation monitoring network and 10 years of meteorological measurements. In order to validate the method a comprehensive field measurement along a North-South transect was established. The results show that a robust point conclusion on vegetation trends and parameters on a statistical significant level is possible. Based on these findings a spatial explicit output was realized by a spatial extrapolation technique considering the annual and intra-annual vegetation trends. Based on the IPCC Scenarios (A1B and B1) a forecast of vegetation activity and productivity was implemented until 2050. MOVEG DRAA is an improvement to the hitherto state of unknown atmospheric-vegetation-relationship for the semi-arid area of southern Morocco. The study reveals that the semi automatic modular model approach is capable of handling the highly variable vegetation signal and projecting further scenarios of environmental changes. The model output will help to refine all models using land cover information (e.g. pastoral modelling), hydrological modelling (e.g. SWAT) and meteorological parameterisations (e.g. FOOD3DK). The output of the MOVEG DRAA model can also built a valuable information source for all kind of land users
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