1,764 research outputs found
Assessing the role of EO in biodiversity monitoring: options for integrating in-situ observations with EO within the context of the EBONE concept
The European Biodiversity Observation Network (EBONE) is a European contribution on terrestrial monitoring to GEO BON, the Group on Earth Observations Biodiversity Observation Network. EBONEâs aims are to develop a system of biodiversity observation at regional, national and European levels by assessing existing approaches in terms of their validity and applicability starting in Europe, then expanding to regions in Africa. The objective of EBONE is to deliver:
1. A sound scientific basis for the production of statistical estimates of stock and change of key indicators;
2. The development of a system for estimating past changes and forecasting and testing policy options and management strategies for threatened ecosystems and species;
3. A proposal for a cost-effective biodiversity monitoring system.
There is a consensus that Earth Observation (EO) has a role to play in monitoring biodiversity. With its capacity to observe detailed spatial patterns and variability across large areas at regular intervals, our instinct suggests that EO could deliver the type of spatial and temporal coverage that is beyond reach with in-situ efforts. Furthermore, when considering the emerging networks of in-situ observations, the prospect of enhancing the quality of the information whilst reducing cost through integration is compelling. This report gives a realistic assessment of the role of EO in biodiversity monitoring and the options for integrating in-situ observations with EO within the context of the EBONE concept (cfr. EBONE-ID1.4). The assessment is mainly based on a set of targeted pilot studies. Building on this assessment, the report then presents a series of recommendations on the best options for using EO in an effective, consistent and sustainable biodiversity monitoring scheme.
The issues that we faced were many:
1. Integration can be interpreted in different ways. One possible interpretation is: the combined use of independent data sets to deliver a different but improved data set; another is: the use of one data set to complement another dataset.
2. The targeted improvement will vary with stakeholder group: some will seek for more efficiency, others for more reliable estimates (accuracy and/or precision); others for more detail in space and/or time or more of everything.
3. Integration requires a link between the datasets (EO and in-situ). The strength of the link between reflected electromagnetic radiation and the habitats and their biodiversity observed in-situ is function of many variables, for example: the spatial scale of the observations; timing of the observations; the adopted nomenclature for classification; the complexity of the landscape in terms of composition, spatial structure and the physical environment; the habitat and land cover types under consideration.
4. The type of the EO data available varies (function of e.g. budget, size and location of region, cloudiness, national and/or international investment in airborne campaigns or space technology) which determines its capability to deliver the required output.
EO and in-situ could be combined in different ways, depending on the type of integration we wanted to achieve and the targeted improvement. We aimed for an improvement in accuracy (i.e. the reduction in error of our indicator estimate calculated for an environmental zone). Furthermore, EO would also provide the spatial patterns for correlated in-situ data.
EBONE in its initial development, focused on three main indicators covering:
(i) the extent and change of habitats of European interest in the context of a general habitat assessment;
(ii) abundance and distribution of selected species (birds, butterflies and plants); and
(iii) fragmentation of natural and semi-natural areas.
For habitat extent, we decided that it did not matter how in-situ was integrated with EO as long as we could demonstrate that acceptable accuracies could be achieved and the precision could consistently be improved. The nomenclature used to map habitats in-situ was the General Habitat Classification. We considered the following options where the EO and in-situ play different roles:
using in-situ samples to re-calibrate a habitat map independently derived from EO; improving the accuracy of in-situ sampled habitat statistics, by post-stratification with correlated EO data; and using in-situ samples to train the classification of EO data into habitat types where the EO data delivers full coverage or a larger number of samples.
For some of the above cases we also considered the impact that the sampling strategy employed to deliver the samples would have on the accuracy and precision achieved.
Restricted access to European wide species data prevented work on the indicator âabundance and distribution of speciesâ.
With respect to the indicator âfragmentationâ, we investigated ways of delivering EO derived measures of habitat patterns that are meaningful to sampled in-situ observations
An Alternate Approach to Ecosystem Mapping: Fusing Orthophotography with LANDSAT ETM+ Data for a Object-Based Classification, South Eastern Arkansas.
Maintaining representative sampling of biologically rich and rare ecosystems has become an important means to preventing biodiversity loss. A limitation in indentifying and quantifying ecosystems is the cost of obtaining high resolution imagery necessary for a high resolution land cover assessment. This research shows how free, different resolution imagery (orthoimages and LANDSAT ETM+) could be combined to produce a hybrid dataset with enhanced spectral, spectral and temporal properties, and processed to obtain a object-based classification of land cover of bottomland and pine hardwood forest in south eastern Arkansas. Three classification techniques were evaluated: 1) a human derived, rule based method, 2) A nearest neighbor classification using only the infrared orthoimage (SRGB), and 3) A nearest neighbor classification using the infrared orthoimage and LANDSAT ETM+ derived multitemporal NDVI values (SNDVI). Overall accuracy of the rule based method and SNDVI were comparable, and significantly higher (~10-20%) than the SRGB. Further, when compared to existing land cover maps, both the rule based method and SNDVI had far greater visual appeal and accuracy
Multi-feature combined cloud and cloud shadow detection in GaoFen-1 wide field of view imagery
The wide field of view (WFV) imaging system onboard the Chinese GaoFen-1
(GF-1) optical satellite has a 16-m resolution and four-day revisit cycle for
large-scale Earth observation. The advantages of the high temporal-spatial
resolution and the wide field of view make the GF-1 WFV imagery very popular.
However, cloud cover is an inevitable problem in GF-1 WFV imagery, which
influences its precise application. Accurate cloud and cloud shadow detection
in GF-1 WFV imagery is quite difficult due to the fact that there are only
three visible bands and one near-infrared band. In this paper, an automatic
multi-feature combined (MFC) method is proposed for cloud and cloud shadow
detection in GF-1 WFV imagery. The MFC algorithm first implements threshold
segmentation based on the spectral features and mask refinement based on guided
filtering to generate a preliminary cloud mask. The geometric features are then
used in combination with the texture features to improve the cloud detection
results and produce the final cloud mask. Finally, the cloud shadow mask can be
acquired by means of the cloud and shadow matching and follow-up correction
process. The method was validated using 108 globally distributed scenes. The
results indicate that MFC performs well under most conditions, and the average
overall accuracy of MFC cloud detection is as high as 96.8%. In the contrastive
analysis with the official provided cloud fractions, MFC shows a significant
improvement in cloud fraction estimation, and achieves a high accuracy for the
cloud and cloud shadow detection in the GF-1 WFV imagery with fewer spectral
bands. The proposed method could be used as a preprocessing step in the future
to monitor land-cover change, and it could also be easily extended to other
optical satellite imagery which has a similar spectral setting.Comment: This manuscript has been accepted for publication in Remote Sensing
of Environment, vol. 191, pp.342-358, 2017.
(http://www.sciencedirect.com/science/article/pii/S003442571730038X
Towards a multi-scale approach for an Earth observation-based assessment of natural resource exploitation in conflict regions
The exploitation of resources, if not properly managed, can lead to spoiling natural habitats as well as to threatening peopleâs health, livelihoods and security. The paper discusses a multi-scale Earth observation-based approach to provide independent information related to exploitation activities of natural resources for countries which are experiencing armed conflict. The analyses are based on medium to very high spatial resolution optical satellite data. Object-based image analysis is used for information extraction at these different scales. On a subnational level, conflict-related land cover changes as an indication of potential hot spots for exploitation activities are classified. The regional assessment provides information about potential activity areas of resource exploitation, whereas on a local scale, a site-specific assessment of exploitation areas is performed. The study demonstrates the potential of remote sensing for supporting the monitoring and documentation of natural resource exploitation in conflict regions
Forest Cover Classification by Optimal Segmentation of High Resolution Satellite Imagery
This study investigated whether high-resolution satellite imagery is suitable for preparing a detailed digital forest cover map that discriminates forest cover at the tree species level. First, we tried to find an optimal process for segmenting the high-resolution images using a region-growing method with the scale, color and shape factors in DefiniensÂź Professional 5.0. The image was classified by a traditional, pixel-based, maximum likelihood classification approach using the spectral information of the pixels. The pixels in each segment were reclassified using a segment-based classification (SBC) with a majority rule. Segmentation with strongly weighted color was less sensitive to the scale parameter and led to optimal forest cover segmentation and classification. The pixel-based classification (PBC) suffered from the âsalt-and-pepper effectâ and performed poorly in the classification of forest cover types, whereas the SBC helped to attenuate the effect and notably improved the classification accuracy. As a whole, SBC proved to be more suitable for classifying and delineating forest cover using high-resolution satellite images
The application of remote sensing to identify and measure sealed soil and vegetated surfaces in urban environments
Soil is an important non-renewable source. Its protection and allocation is critical to
sustainable development goals. Urban development presents an important drive of soil
loss due to sealing over by buildings, pavements and transport infrastructure.
Monitoring sealed soil surfaces in urban environments is gaining increasing interest
not only for scientific research studies but also for local planning and national
authorities.
The aim of this research was to investigate the extent to which automated classification
methods can detect soil sealing in UK urban environments, by remote sensing. The
objectives include development of object-based classification methods, using two
types of earth observation data, and evaluation by comparison with manual aerial
photo interpretation techniques.
Four sample areas within the city of Cambridge were used for the development of an
object-based classification model. The acquired data was a true-colour aerial
photography (0.125 m resolution) and a QuickBird satellite imagery (2.8 multi-spectral
resolution). The classification scheme included the following land cover classes: sealed
surfaces, vegetated surfaces, trees, bare soil and rail tracks. Shadowed areas were also
identified as an initial class and attempts were made to reclassify them into the actual
land cover type. The accuracy of the thematic maps was determined by comparison
with polygons derived from manual air-photo interpretation; the average overall
accuracy was 84%. The creation of simple binary maps of sealed vs. vegetated surfaces
resulted in a statistically significant accuracy increase to 92%. The integration of
ancillary data (OS MasterMap) into the object-based model did not improve the
performance of the model (overall accuracy of 91%). The use of satellite data in the
object-based model gave an overall accuracy of 80%, a 7% decrease compared to the
aerial photography.
Future investigation will explore whether the integration of elevation data will aid to
discriminate features such as trees from other vegetation types. The use of colour
infrared aerial photography should also be tested. Finally, the application of the object-
based classification model into a different study area would test its transferability
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