Scientific Papers - Earth Sciences

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2007

Addink EA, De Jong SM, Pebesma EJ: The importance of scale in object-based mapping of vegetation parameters with hyperspectral imagery. In recent years, object-oriented image analysis has been widely adopted by the remote sensing community. Much attention has been given to its application, while the fundamental issue of scale, here characterized by spatial object-definition, seems largely neglected. In the case of vegetation parameters like aboveground biomass and leaf area index (LAI), fundamental objects are individual trees or shrubs, each of which has a specific value. Their spatial extent, however, does not match pixels in size and shape, nor does it fit the requirements of regional studies. Estimation of vegetation parameters consequently demands larger observation units, like vegetation patches, which are better represented by variably shaped objects than by square pixels. This study aims to investigate optimal object definition for biomass and LAI. We have data from 243 field plots in our test site in southern France. They cover a vegetation range from landes to garrigue to maquis, which is considered to be the climax vegetation in the area. A HyMap image covers the area. The image is subjected to a Minimum Noise Fraction (MNF) transformation, after which it is segmented with ten different heterogeneities. The result is ten object sets, each having a different mean object size. These object sets are combined with the original image with the mean band values serving as object attributes. Field observations are linked to the corresponding objects for each object set. Using Ridge regression, relations between field observations and spectral values are identified. The prediction error is determined for each object set by cross validation. The overall lowest prediction error indicates the optimal heterogeneity for segmentation. Results show that the scale of prediction affects prediction accuracy, that increasing the object size yields an optimum in prediction accuracy, and that aboveground biomass and LAI can be associated with different optimal object sizes. Furthermore, it is shown that the accuracy of parameter estimation is higher for object-oriented analysis than for per-pixel analysis. (In: Photogrammetric Engineering & Remote Sensing Vol. 73, No. 8, August 2007, pp. 905–912)

Elmqvist B, Khatir AR: The possibilities of bush fallows with changing roles of agriculture—An analysis combining remote sensing and interview data from Sudanese drylands The lengths of fallows have decreased in many parts of the Sahel due to agricultural expansion, which can have negative impacts on crop production when few other ways to improve soil fertility exist. However, the dynamics of agricultural expansion may change because of the changing role of agriculture in society due to increased livelihood diversification outside of agriculture. The results of this study, which combine very high resolution satellite images and interview data, show that the role of agriculture has changed in parts of central Sudan since the crop production per capita declined substantially during the past three decades. It is argued that this decline is linked to the increase in incomes from off-farm activities during the same period. The reduced role of agriculture implies that the majority of households have more than half of the land lying in fallow; however the amount of fallow land per household varies considerably. This has specific value for the debate about Acacia senegal bush fallows in Sudan since with respect to the availability of land, a potential for these fallows was shown. (In: Journal of Arid Environments Volume 70, Issue 2, July 2007, Pages 329-343)

Forghani A, Cechet B, Nadimpalli K: Object-based classification of multi-sensor optical imagery to generate terrain surface roughness information for input to wind risk simulation Poster presented by the Australian Centre for Remote Sensing (ACRES) et al. at the IEEE International Geoscience and Remote Sensing Symposium (IGARSS) 2007, July 23– 27, 2007, Barcelona, Spain

Forghani A, Cechet B, Nadimpalli K: Object-based classification of multi-sensor optical imagery to generate terrain surface roughness information for input to wind risk simulation Geoscience Australia is conducting a series of national risk assessments for a range of natural hazards such as severe winds. The impact of severe wind varies considerably between equivalent structures located at different sites due to local roughness of the upwind terrain, shielding provided by upwind structures and topographic factors. Terrain surface roughness information is a critical spatial input to generate wind multipliers. It is generally the first spatial field to be evaluated, as it is utilised in both the generation of the terrain and topographic wind multiplier. Landsat imagery was employed to generate a terrain surface roughness product for six major metropolitan areas across Australia. It was necessary to investigate the applicability of multi-sensor approaches to generate a regional/national terrain surface roughness map based on the Australian/New Zealand wind loading standard (AS/NZS 1170.2). This paper discusses the methodology that developed a procedure to derive terrain surface roughness from various multi-source satellite images. (Paper accepted for presentation at the IEEE International Geoscience and Remote Sensing Symposium (IGARSS) 2007, July 23 - 27, 2007, Barcelona, Spain)

Gitas I, Polychronaki A, Katagis T, Mallinis G, Minakou C: Fast mapping results provide deeper insights - wildfires & remote sensing In the beginning of the summer this year, a large fire in the area of the Mount Parnitha National Park near Athens in Greece resulted in the loss of approximately 5,000 ha of forest area, shrublands and agricultural land. Considering  the extent and the consequences of the damage caused by the large fire, the Aristotle University of Thessaloniki began to collaborate with national authorities to provide all the necessary information to develop forest protection and restoration plans. (In: GEOinformatics, Oct/Nov 2007, pp 16-19)

Green K, Lopez C: Using Object-Oriented Classification of ADS40 Data to Map the Benthic Habitats of the State of Texas This article reviews the innovative methods developed by the Fugro EarthData team to produce highly detailed maps of benthic habitats. The project is unique in that it successfully utilized multiple new technologies in a production environment including the use of digital airborne imagery rather than film aerial photographs. The project utilized the 2004 National Agriculture Imagery Program (NAIP) imagery collected over Texas for the U.S. Department of Agriculture with the Leica ADS40 airborne digital camera and processed by Fugro EarthData. The 1 meter pixel resolution, 12-bit NAIP imagery was re-sampled to 2 meter, 8-bit data and reprocessed to include the true color and color infrared bands. As the cover of this journal illustrates, although the imagery was collected for agriculture monitoring applications, it was unintentionally collected during a time period with great visibility into the water column and little or no wind or turbidity, which made it ideal for benthic mapping applications. (In: Photogrammetric Engineering & Remote Sensing Journal of the American Society for Photogrammetry Engineering and Remote Sensing, Volume 73, no.8, August 2007, pages 861-865) 

Groom G, Krag Petersen I, Fox AD: Sea bird distribution data with object-based mapping of high spatial resolution image data Wildlife research and management increasing demand information on the numbers and distribution of birds at high spatial  resolution. Duck and other sea birds present special possibilities for automated image-based mapping of bird numbers and  locations, data which have been gathered with poorer spatial resolution by airborne observers for many decades. In order to address environmental impact assessment demands for data collection at a fine geographical scale, (e.g. in connection with offshore windfarms), improved data collection methods must be considered. Aircraft mounted digital cameras provide very high spatial resolution (10 cm and less) image data and recent advances in object-based image information extraction tools represent significant developments. This paper assesses the representation of Common Eider and Common Scoter in very high spatial resolution image data and the success of both pixel-based and object-based extraction of information from these image data relevant to the counting and mapping of individual birds, using digital airborne images from Danish offshore areas. Results demonstrate the capacity of the 10 cm spatial resolution image data and the object-based methods for sea duck mapping. Certain pixel-based methods can also enable useful information extraction in some cases, but the more flexible and direct bird-individual mapping possibilities of the object-based methods are seen as significant. Initial Danish results from 2007 with sub-10 cm, 24-bit Vexcel UltraCam D image data are also noted. The potential benefits and problems of these technologies compared to standard sea bird census methods are discussed. (in: Challenges for earth observation - scientific, technical and commercial, Proceedings of the RSPsoc Annual Conference 2007, September 11-14, 2007, Newcastle University, Nottingham (UK), The Remote Sensing and Photogrammetry Society, paper 168)

Im J, Jensen JR, Tullis JA : Object-based change detection using correlation image analysis and image segmentation This study introduces change detection based on object/neighbourhood correlation image analysis and image segmentation techniques. The correlation image analysis is based on the fact that pairs of brightness values from the same geographic area (e.g. an object) between bi-temporal image datasets tend to be highly correlated when little change occurres, and uncorrelated when change occurs. Five different change detection methods were investigated to determine how new contextual features could improve change classification results, and if an object-based approach could improve change classification when compared with per-pixel analysis. The five methods examined include (1) object-based change classification incorporating object correlation images (OCIs), (2) object-based change classification incorporating neighbourhood correlation images (NCIs), (3) object-based change classification without contextual features, (4) per-pixel change classification incorporating NCIs, and (5) traditional per-pixel change classification using only bi-temporal image data. Two different classification algorithms (i.e. a machine-learning decision tree and nearest-neighbour) were also investigated. Comparison between the OCI and the NCI variables was evaluated. Object-based change classifications incorporating the OCIs or the NCIs produced more accurate change detection classes (Kappa approximated 90%) than other change detection results (Kappa ranged from 80 to 85%). ( In: International Journal of Remote Sensing,  June 09, 2007)

Lang S, Tiede, D: Definiens Developer In the remote sensing field, the call for image data of an increasingly higher resolution has been heard. Today, the problem revolves around the evaluation of this vast amount of available image information and gaining value from it. The EU/ESA GMES initiative (Global Monitoring for Environment and Security), for example, relies on the use of image data for the observation of system flows and process chains, from maritime ecosystems to social security, from ship detection to vulnerability mapping. In addition, all ranges of scales are covered, from examinations in medicine and pathology to earth observation. It’s no wonder that the Nobel Laureate in Physics Dr. Gerd Binnig, the inventor of the scanning tunneling microscope, has developed the basic concept of the Definiens product range. His development opened up a new approach to image interpretation and was a significant co-determinant in the image processing paradigm shift. In the context of this review and the geoinformatics viewpoint, we will concentrate on the company’s flagship product in the earth segment: Definiens Developer, currently available in version 7. (Original article publish in German in: GIS Business, 09/2007, pages 34-37)

Lucas R , Rowlands A, Brown A, Keyworth S, Bunting P: Rule-based classification of multi-temporal satellite imagery for habitat and agricultural land cover mapping Using Definiens eCognition, segmentation of the Landsat sensor data was undertaken for actively managed agricultural land based on Integrated Administration and Control System (IACS) land parcel boundaries, whilst a per-pixel level segmentation was undertaken for all remaining areas. Numerical decision rules based on fuzzy logic that coupled knowledge of ecology and the information content of single and multi-date remotely sensed data and derived products (e.g., vegetation indices) were developed to discriminate vegetation types based primarily on inferred differences in phenology, structure, wetness and productivity. The rule-based classification gave a good representation of the distribution of habitats and agricultural land. The more extensive, contiguous and homogeneous habitats could be mapped with accuracies exceeding 80%, although accuracies were lower for more complex environments (e.g., upland mosaics) or those with broad definition (e.g., semi-improved grasslands). (In: ISPRS Journal of Photogrammetry and Remote Sensing, Volume 62, Issue 3, August 2007, Pages 165-185)

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