Dirk Tiede

ESP: a tool to estimate scale parameter for multiresolution image segmentation of remotely sensed data

The spatial resolution of imaging sensors has increased dramatically in recent years, and so too have the challenges associated with extracting meaningful information from their data products. Object-based image analysis (OBIA) is gaining rapid popularity in remote sensing science as a means of bridging very high spatial resolution (VHSR) imagery and GIS. Multiscalar image segmentation is a fundamental step in OBIA, yet there is currently no tool available to objectively guide the selection of appropriate scales for segmentation. We present a technique for estimating the scale parameter in image segmentation of remotely sensed data with Definiens Developer®. The degree of heterogeneity within an image-object is controlled by a subjective measure called the ‘scale parameter’, as implemented in the mentioned software. We propose a tool, called estimation of scale parameter (ESP), that builds on the idea of local variance (LV) of object heterogeneity within a scene. The ESP tool iteratively generates image-objects at multiple scale levels in a bottom-up approach and calculates the LV for each scale. Variation in heterogeneity is explored by evaluating LV plotted against the corresponding scale. The thresholds in rates of change of LV (ROC-LV) indicate the scale levels at which the image can be segmented in the most appropriate manner, relative to the data properties at the scene level. Our tests on different types of imagery indicated fast processing times and accurate results. The simple yet robust ESP tool enables fast and objective parametrization when performing image segmentation and holds great potential for OBIA applications.

Application of hydrodynamic chromatography-ICP-MS to investigate the fate of silver nanoparticles in activated sludge

Detection and characterisation are two of the major challenges in understanding the fate, behaviour and occurrence of engineered nanoparticles (ENPs) in the natural environment. In a previous paper we described the development of hydrodynamic chromatography coupled to plasma mass spectrometry (HDC-ICP-MS) for detecting and characterising ENPs in aqueous matrices. This paper describes the applicability of the approach, to study the behaviour of silver nanoparticles in a much more complex and relevant environmental system i.e. sewage sludge supernatant. Batch sorption studies were performed at a range of nanosilver concentrations. Following completion, the sludge supernatant was characterised by ICP-MS, HDC-ICP-MS and transmission electron microscopy (TEM). It was found that, after a contact time of 6 h, most of the silver had partitioned to the sewage sludge (>90%). However, of the silver remaining in the supernatant, some of this was in the nanoparticle form, implying that closer consideration should be given to the longer-term impact of the release of silver ENPs into aquatic ecosystems. These preliminary data clearly show the utility of HDC-ICP-MS for studying the occurrence and behaviour of ENPs in complex natural environments.

A robust size-characterisation methodology for studying nanoparticle behaviour in ‘real’ environmental samples, using hydrodynamic chromatography coupled to ICP-MS

A hyphenated methodology has been developed and validated, which utilizes the extensive size separation range of hydrodynamic chromatography (here: 5–300 nm) combined with the multi-element selectivity of ICP-MS. This has been applied to the analysis of metal-based nanoparticles in environmental samples. The quality of the particle sizing data obtained from this exercise was enabled through the production of a range of gold nanoparticles (sterically stabilized to prevent aggregation in environmental matrices), which were validated for use as external size calibration standards as well as internal retention time markers, using TEM. The methodology was then successfully applied to a study where nanosilver was spiked into sewage sludge, preliminary data from which showed that a fraction of nanosilver survived as single nanoparticles in the sludge supernatant. The method was also tested on solutions containing other commonly used nanoparticles (TiO2, SiO2, Al2O3 and Fe2O3). Overall, the data showed that, by using ICP-MS with collision cell technology, the methodology would be helpful in investigating the fate of a significant range of nanoparticle types. Other characteristics of HDC-ICP-MS are: rapid analysis time ( 300 nm), and limited sample pre-treatment required.

Earth observation (EO)-based ex post assessment of internally displaced person (IDP) camp evolution and population dynamics in Zam Zam, Darfur

During humanitarian crises, when population figures are often urgently required but very difficult to obtain, remote sensing is able to provide evidence of both present and past population numbers. This research, conducted on QuickBird time-series imagery of the Zam Zam internally displaced person (IDP) camp in Northern Darfur, investigates automated analysis of the camps evolution between 2002 and 2008, including delineation of the camps outlines and inner structure, employment of rule-based extraction for two categories of dwelling units and derivation of population estimates for the time of image capture. Reference figures for dwelling occupancy were obtained from estimates made by aid agencies. Although validation of such ‘on-demand’ census techniques is still continuing, the benefits of a fast, efficient and objective information source are obvious. Spatial, as well thematic, accuracy was, in this instance, assessed against visual interpretation of eight 200 m × 200 m grid cells and accuracy statistics calculated. Total users and producers accuracy rates ranged from 71.6% up to 94.9%. While achieving promising results with respect to accuracy, transferability and usability, the remaining limitations of automated population estimation in dynamic crisis situations will provide a stimulus for future research.

A systematic comparison of different object-based classification techniques using high spatial resolution imagery in agricultural environments

Abstract Geographic Object-Based Image Analysis (GEOBIA) is becoming more prevalent in remote sensing classification, especially for high-resolution imagery. Many supervised classification approaches are applied to objects rather than pixels, and several studies have been conducted to evaluate the performance of such supervised classification techniques in GEOBIA. However, these studies did not systematically investigate all relevant factors affecting the classification (segmentation scale, training set size, feature selection and mixed objects). In this study, statistical methods and visual inspection were used to compare these factors systematically in two agricultural case studies in China. The results indicate that Random Forest (RF) and Support Vector Machines (SVM) are highly suitable for GEOBIA classifications in agricultural areas and confirm the expected general tendency, namely that the overall accuracies decline with increasing segmentation scale. All other investigated methods except for RF and SVM are more prone to obtain a lower accuracy due to the broken objects at fine scales. In contrast to some previous studies, the RF classifiers yielded the best results and the k-nearest neighbor classifier were the worst results, in most cases. Likewise, the RF and Decision Tree classifiers are the most robust with or without feature selection. The results of training sample analyses indicated that the RF and adaboost. M1 possess a superior generalization capability, except when dealing with small training sample sizes. Furthermore, the classification accuracies were directly related to the homogeneity/heterogeneity of the segmented objects for all classifiers. Finally, it was suggested that RF should be considered in most cases for agricultural mapping.

Object-based class modeling for cadastre-constrained delineation of geo-objects.

Remote sensing technology still faces challenges when it comes to monitoring tasks that must be able to stand up to validation from technical, scientific, and practical points of view, in other words, when entering into established, fully operational workflows. In this paper, we present an approach for delineating and monitoring aggregated spatial units relevant to regional planning tasks, which has been fully validated within a 3,654 km 2 area in the Stuttgart Region of southwestern Germany. This has been achieved by developing algorithms for semi-automated (geo-) object-based class modeling of biotope complexes, which are aggregated, functionally homogenous (but not necessarily spectrally homogeneous) units. High levels of complexity in the target classes and the need for integration of auxiliary geodata as a priori knowledge meant that different methods of information extraction were required to be combined in an operational workflow, and that new validation strategies were needed for quality assessment. A total of 31,698 biotope complexes were delineated for the entire Stuttgart Region, with an average size of 11.5 ha for each complex. Approximately 86 percent of the biotope complex boundaries were shown to have been correctly delineated.

Object validity for operational tasks in a policy context

(GE)OBIA (Geographic Object-Based Image Analysis) utilises intelligent tools and algorithms for automatically delineating and labelling geographical objects. OBIA aims at converting traditional and established photo-interpretation tasks into operational use. Moreover, it may help to support the realisation of novel tasks in addressing composite classes, modelling new geographical realities, or observing changes at the individual object level, etc. Ranging from object representations (real-world objects or bona fide objects), via complex composite classes (real-world modelled objects) to concept-related fiat objects, OBIA poses new challenges for evaluating object validity in an operational context. This paper reflects on the need for an object-based validation concept, illustrates policy-related application scenarios and discusses qualitative and quantitative pillars under thematic and geo-spatial aspects.

Virtual Globes: Serving Science and Society

Virtual Globes reached the mass market in 2005. They created multi-million dollar businesses in a very short time by providing novel ways to explore data geographically. We use the term “Virtual Globes” as the common denominator for technologies offering capabilities to annotate, edit and publish geographic information to a world-wide audience and to visualize information provided by the public and private sectors, as well as by citizens who volunteer new data. Unfortunately, but not surprising for a new trend or paradigm, overlapping terms such as “Virtual Globes”, “Digital Earth”, “Geospatial Web”, “Geoportal” or software specific terms are used heterogeneously. We analyze the terminologies and trends in scientific publications and ask whether these developments serve science and society. While usage can be answered quantitatively, the authors reason from the literature studied that these developments serve to educate the masses and may help to democratize geographic information by extending the producer base. We believe that we can contribute to a better distinction between software centered terms and the generic concept as such. The power of the visual, coupled with the potential of spatial analysis and modeling for public and private purposes raises new issues of reliability, standards, privacy and best practice. This is increasingly addressed in scientific literature but the required body of knowledge is still in its infancy.

Domain-specific class modelling for one-level representation of single trees

As a synthesis of a series of studies carried out by the authors this chapter discusses domain-specific class modelling which utilizes a priori knowledge on the specific scale domains of the target features addressed. Two near-natural forest settings served as testing environment for a combined use of airborne laser scanning (ALS) and optical image data to perform automated tree-crown delineation. The primary methodological aim was to represent the entire image data product in a single, spatially contiguous, set of scale-specific objects (one-level-representation, OLR). First, by high-level (broad-scale) segmentation an initial set of image regions was created. The regions, characterised by homogenous spectral behaviour and uniform ALS-based height information, represented different image object domains (in this case: areas of specific forest characteristics). The regions were then treated independently to perform domain-specific class modelling (i.e. the characteristics of each region controlled the generation of lower level objects). The class modelling was undertaken using Cognition Network Language (CNL), which allows for addressing single objects and enables supervising the object generation process through the provision of programming functions like branching and looping. Altogether, the single processes of segmentation and classification were coupled in a cyclic approach. Finally, representing the entire scene content in a scale finer than the initial regional level, has accomplished OLR. Building upon the preceding papers, we endeavoured to improve the algorithms for tree crown delineation and also extended the underlying workflow. The transferability of our approach was evaluated by (1) shifting the geographical setting from a hilly study area (National Park Bavarian Forest, South-Eastern Germany) to a mountainous site (Montafon area, Western Austria); and (2) by applying it to different data sets, wherein the latter differ from the initial ones in terms of spectral resolution (line scanner RGBI data vs. false colour infrared orthophotos) and spatial resolution (0.5 m vs. 0.25 m), as well as ALS point density, which was ten times higher in the original setting. Only minor adaptations had to be done. Additional steps, however, were necessary targeting the data sets of different resolution. In terms of accuracy, in both study areas 90% of the evaluated trees were correctly detected (concerning the location of trees). The following classification of tree types reached an accuracy of 75% in the first study area. It was not evaluated for the second study area which was nearly exclusively covered by coniferous trees.

Automatic Geographic Object Based Mapping of Streambed and Riparian Zone Extent from LiDAR Data in a Temperate Rural Urban Environment, Australia

This research presents a time-effective approach for mapping streambed and riparian zone extent from high spatial resolution LiDAR derived products, i.e., digital terrain model, terrain slope and plant projective cover. Geographic object based image analysis (GEOBIA) has proven useful for feature extraction from high spatial resolution image data because of the capacity to reduce effects of reflectance variations of pixels making up individual objects and to include contextual and shape information. This functionality increases the likelihood of developing transferable and automated mapping approaches. LiDAR data covered parts of the Werribee Catchment in Victoria, Australia, which is characterized by urban, agricultural, and forested land cover types. Field data of streamside vegetation structure and physical form properties were used for both calibration of the mapping routines and validation of the mapping results. To improve the transferability of the rule set, the GEOBIA approach was developed for an area representing different riparian zone environments, i.e., urbanized, agricultural and hilly forested areas. Results show that mapping streambed extent (R2 = 0.93, RMSE = 3.6 m, n = 35) and riparian zone extent (R2 = 0.74, RMSE = 3.9, n = 35) from LiDAR derived products can be automated using GEOBIA to enable derivation of spatial information in an accurate and time-effective manner suited for natural resource management agencies.