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  • 1
    Language: German
    Keywords: Cover ; Zwei ; D-E-Cover ; Engineering
    Source: DataCite
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  • 2
    Language: English
    In: Journal for Nature Conservation, June 2012, Vol.20(3), pp.125-134
    Description: Grassland is a land cover in the area of conflict between agriculture and conservation, where intensification of land use is a major threat to grassland biodiversity. Grassland use intensity is a key factor for the conservation value of grassland, and detailed spatial data on grassland use intensity is needed to improve strategies for biodiversity conservation. A new remote sensing-based approach using multi-temporal high resolution RapidEye satellite data was developed in the present study that makes a large-scale assessment of grassland use intensity possible. RapidEye is a constellation of five satellites with 6.5 m spatial resolution, which allows frequent and timely image acquisition targeted at specific growing seasons. Semi-natural grassland, extensively used grassland, intensively used grassland and tilled grassland could be reliably differentiated at the management plot level in a study area in southern Germany. Various combinations of images from different observation dates have been tested as classification input and their overall classification accuracies were validated by field data. Best results were achieved using a combination of five multi-temporal scenes with an overall accuracy of 85.7%. A three-scene combination resulted in an overall accuracy of 82.2%. The analysis showed that seasonal aspects are very important when selecting adequate observation dates. Grassland use intensity was also assessed on peatlands using a peat soil map, since land use intensity significantly affects greenhouse gas emissions from peatlands. The results demonstrate the potential of targeted multi-spectral, high spatial resolution remote sensing for the large-scale monitoring of dynamic habitats, which is of vital importance to support various environmental conservation schemes through improved monitoring and reporting capabilities.
    Keywords: Earth Observation ; Rapideye ; Grassland Management ; Habitat Mapping ; Monitoring ; Natura 2000 ; Peat ; Ecology
    ISSN: 1617-1381
    E-ISSN: 1618-1093
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  • 3
    In: SPIE Newsroom, 08/29/2011
    ISSN: SPIE Newsroom
    E-ISSN: 18182259
    Source: CrossRef
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  • 4
    In: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 02/2012, Vol.5(1), pp.298-306
    Description: In the context of climate change mitigation mechanisms for avoiding deforestation, i.e., reducing emissions from deforestation and forest degradation (REDD+), comprehensive forest monitoring, especially in tropical regions, is of high relevance. A precise determination of forest carbon stocks or aboveground biomass (AGB) for large areas is of special importance.
    Keywords: Artificial Neural Networks ; Biomass ; Biological System Modeling ; Laser Radar ; Backscatter ; Support Vector Machines ; Estimation ; Alos Palsar ; Artificial Neural Network (Ann) ; Biomass ; Forest ; Indonesia ; Redd+ ; Regression ; Support Vector Regression (Svr) ; Geology;
    ISSN: 1939-1404
    E-ISSN: 2151-1535
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  • 5
    In: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 12/2012, Vol.5(6), pp.1811-1820
    Description: Climate change mitigation schemes, such as REDD and biodiversity conservation in tropical rainforests, necessitate remote sensing based forest monitoring capabilities with high spatial resolution and temporal coverage. Regular monitoring has to be capable of detecting rapid changes in forest extent, i.e. deforestation, and subtle changes to the forest cover caused by logging and/or fire, described as forest degradation. Particularly the early detection of illegal logging activities is important for the conservation of tropical forests. In the present study, a forest disturbance monitoring approach was developed and tested, which makes use of high resolution satellite imagery. A time series consisting of three images, acquired between May 2009 and June 2010, was analyzed covering a remote area of tropical peat swamp forest in Central Kalimantan, Indonesia. The forest area was assessed by an object-oriented classification. Logging activities and the impact of fire were detected by a pixel-based spectral mixture analysis. Forest, non-forest and logging trails could be differentiated with an overall classification accuracy of 91.5% (Kappa of 0.87). A high forest disturbance rate of 8.7% was found in the study area. Low impact logging could be detected reliably and the progress was tracked over time. The results show that the timely detection of forest disturbances is necessary because of the fast regrowth of vegetation. The study emphasises the importance of high resolution satellite imagery for tropical forest monitoring and for timely updating forest status assessments, which is important for the implementation of REDD.
    Keywords: Monitoring ; Satellites ; Forestry ; Vegetation Mapping ; Degradation ; Spatial Resolution ; Matched Filters ; Activity Data ; Deforestation ; Degradation ; Forest Monitoring ; Indonesia ; Mixture Tuned Matched Filtering ; Rapideye ; Redd ; Spectral Mixture Analysis ; Forestry ; Geology;
    ISSN: 1939-1404
    E-ISSN: 2151-1535
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  • 6
    Language: English
    In: Remote Sensing, 01 June 2016, Vol.8(6), p.471
    Description: For mapping, quantifying and monitoring regional and global forest health, satellite remote sensing provides fundamental data for the observation of spatial and temporal forest patterns and processes. While new remote-sensing technologies are able to detect forest data in high quality and large...
    Keywords: Remote Sensing ; in Situ Sampling ; Sensor Networks ; Monitoring ; Standardization ; Forest Health ; Sentinel Satellites ; Copernicus ; Geography
    E-ISSN: 2072-4292
    Source: Directory of Open Access Journals (DOAJ)
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