Assessment of multiresolution segmentation for delimiting drumlins in digital elevation models

Mapping or "delimiting" landforms is one of geomorphology's primary tools. Computer-based techniques such as land-surface segmentation allow the emulation of the process of manual landform delineation. Land-surface segmentation exhaustively subdivides a digital elevation model (DEM) into morphometrically-homogeneous irregularly-shaped regions, called terrain segments. Terrain segments can be created from various land-surface parameters (LSP) at multiple scales, and may therefore potentially correspond to the spatial extents of landforms such as drumlins. However, this depends on the segmentation algorithm, the parameterization, and the LSPs. In the present study we assess the widely used multiresolution segmentation (MRS) algorithm for its potential in providing terrain segments which delimit drumlins. Supervised testing was based on five 5-m DEMs that represented a set of 173 synthetic drumlins at random but representative positions in the same landscape. Five LSPs were tested, and four variants were computed for each LSP to assess the impact of median filtering of DEMs, and logarithmic transformation of LSPs. The testing scheme (1) employs MRS to partition each LSP exhaustively into 200 coarser scales of terrain segments by increasing the scale parameter (SP), (2) identifies the spatially best matching terrain segment for each reference drumlin, and (3) computes four segmentation accuracy metrics for quantifying the overall spatial match between drumlin segments and reference drumlins. Results of 100 tests showed that MRS tends to perform best on LSPs that are regionally derived from filtered DEMs, and then log-transformed. MRS delineated 97% of the detected drumlins at SP values between 1 and 50. Drumlin delimitation rates with values up to 50% are in line with the success of manual interpretations. Synthetic DEMs are well-suited for assessing landform quantification methods such as MRS, since subjectivity in the reference data is avoided which increases the reliability, validity and applicability of results. © 2014 The Authors

Supervised methods of image segmentation accuracy assessment in land cover mapping

Land cover mapping via image classification is sometimes realized through object-based image analysis. Objects are typically constructed by partitioning imagery into spatially contiguous groups of pixels through image segmentation and used as the basic spatial unit of analysis. As it is typically desirable to know the accuracy with which the objects have been delimited prior to undertaking the classification, numerous methods have been used for accuracy assessment. This paper reviews the state-of-the-art of image segmentation accuracy assessment in land cover mapping applications. First the literature published in three major remote sensing journals during 2014–2015 is reviewed to provide an overview of the field. This revealed that qualitative assessment based on visual interpretation was a widely-used method, but a range of quantitative approaches is available. In particular, the empirical discrepancy or supervised methods that use reference data for assessment are thoroughly reviewed as they were the most frequently used approach in the literature surveyed. Supervised methods are grouped into two main categories, geometric and non-geometric, and are translated here to a common notation which enables them to be coherently and unambiguously described. Some key considerations on method selection for land cover mapping applications are provided, and some research needs are discussed

Investigations on the permafrost distribution in the Schrankkar (Stubai Alps)

Die exakte Verbreitung von Permafrost ist f\ufcr kleinr\ue4umige Gebiete nicht einfach nachzuweisen. Einige der daf\ufcr g\ue4ngigsten Methoden kommen gegenw\ue4rtig im Untersuchungsgebiet Schrankar (Stubaier Alpen) zum Einsatz. Seit 2008 sind Mini-Datenlogger im Gebiet installiert, welche die Oberfl\ue4chentemperatur des Bodens st\ufcndlich aufzeichnen. Auch Messungen der Basistemperatur der winterlichen Schneedecke (BTS) werden seit 2009 j\ue4hrlich im Sp\ue4twinter durchgef\ufchrt. Mit Hilfe einer zus\ue4tzlich erfolgten Blockgletscherkartierung wird im Zuge der Arbeit versucht die m\uf6gliche Verbreitung von Permafrost (PF) im Schrankar zu rekonstruieren. Diese Ergebnisse werden mit einer aus drei professionell erstellten Modellen zusammengefassten Simulation (?3 in 1 Modell?) verglichen. Im Zuge der Arbeit konnte gezeigt werden, dass die allgemeine Verbreitung von PF in der Simulation sehr gut mit den Ergebnissen der Gel\ue4ndemethoden \ufcbereinstimmt, betrachtet man jedoch die Fl\ue4chenverteilung der einzelnen PF-Wahrscheinlichkeitsklassen, so werden einige Unterschiede deutlich. Der Hauptgrund daf\ufcr liegt in der Betrachtung auf verschiedenen Skalenniveaus (Mesorelief: Modell, Nano- Mikrorelief: Messungen). Zus\ue4tzlich werden in der Modellierung die thermischen Eigenschaften des Untergrundes nur wenig bis gar nicht in die Berechnungen mit einbezogen. Generell kann behauptet werden, dass eine Modellierung basierend auf empirischen und statistischen Daten ein guter Ansatz ist, die allgemeine Verbreitung von PF darzustellen. Durch die Kombination mit den direkt vor Ort durchgef\ufchrten Messungen kann die PF-Verbreitung auch f\ufcr kleinr\ue4umige Gebiete sehr genau abgesch\ue4tzt werden. Das Ergebnis der Arbeit zeigt, dass 4 km der Schrankarfl\ue4che (Gesamtfl\ue4che: 6,36 km) in die Kategorie ?PF wahrscheinlich? und 1,1 km in die Klasse ?Permafrost m\uf6glich? geh\uf6ren sowie auf 1 km kein Permafrost im Untergrund angenommen wird. Die \ufcbrigen 0,26 km entfallen auf die Fl\ue4che des Schrankarferners.The exact distribution of permafrost in small scales is not easy to prove. Some of the most established methods have recently been applied in the Schrankar (Stubaier Alps). In 2008, Miniature Data Loggers have been installed to measure the ground surface temperature hourly. Since 2009, BTS-measurements have been made in late winter. With the aid of an additional rockglacier-mapping of the area, this paper attempted to reconstruct the distribution of permafrost in the Schrankar. The results of the methods were compared to a summarized simulation (?3 to 1 model?). In the paper it could be displayed, that the general simulated distribution of permafrost is pretty similar to the results of the field-methods. But if you take into account the areas of different permafrost probability, some differences in permafrost distribution were found. The main reason could be the view in different scales (mesorelief: model, nano- to microrelief: field-methods). In addition to that, thermal properties of the ground have not been taken into account enough in the model. In general it could be said that it is a fine approach to illustrate the general permafrost distribution by an empirical, statistical model. In combination with the results of the field measurements, the distribution of permafrost could be reconstructed precisely even for small-scale areas. The result of the thesis shows, that 4 km of the Schrankar (total area: 6.36 km) belong to the category ?PF probable? and 1.1 km to the category ?PF possible? as well as on 1 km of the cirque no permafrost is expected. The remaining 0.26 km are allotted to the area of the Schrankarferner.vorgelegt von Johanna EisankAbweichender Titel laut cbersetzung der Verfasserin/des VerfassersZsfassung in dt. und engl. SpracheGraz, Univ., Masterarb., 2012(VLID)22497

Investigations on the permafrost distribution in the Schrankkar (Stubai Alps)

Die exakte Verbreitung von Permafrost ist für kleinräumige Gebiete nicht einfach nachzuweisen. Einige der dafür gängigsten Methoden kommen gegenwärtig im Untersuchungsgebiet Schrankar (Stubaier Alpen) zum Einsatz. Seit 2008 sind Mini-Datenlogger im Gebiet installiert, welche die Oberflächentemperatur des Bodens stündlich aufzeichnen. Auch Messungen der Basistemperatur der winterlichen Schneedecke (BTS) werden seit 2009 jährlich im Spätwinter durchgeführt. Mit Hilfe einer zusätzlich erfolgten Blockgletscherkartierung wird im Zuge der Arbeit versucht die mögliche Verbreitung von Permafrost (PF) im Schrankar zu rekonstruieren. Diese Ergebnisse werden mit einer aus drei professionell erstellten Modellen zusammengefassten Simulation (?3 in 1 Modell?) verglichen. Im Zuge der Arbeit konnte gezeigt werden, dass die allgemeine Verbreitung von PF in der Simulation sehr gut mit den Ergebnissen der Geländemethoden übereinstimmt, betrachtet man jedoch die Flächenverteilung der einzelnen PF-Wahrscheinlichkeitsklassen, so werden einige Unterschiede deutlich. Der Hauptgrund dafür liegt in der Betrachtung auf verschiedenen Skalenniveaus (Mesorelief: Modell, Nano- Mikrorelief: Messungen). Zusätzlich werden in der Modellierung die thermischen Eigenschaften des Untergrundes nur wenig bis gar nicht in die Berechnungen mit einbezogen. Generell kann behauptet werden, dass eine Modellierung basierend auf empirischen und statistischen Daten ein guter Ansatz ist, die allgemeine Verbreitung von PF darzustellen. Durch die Kombination mit den direkt vor Ort durchgeführten Messungen kann die PF-Verbreitung auch für kleinräumige Gebiete sehr genau abgeschätzt werden. Das Ergebnis der Arbeit zeigt, dass 4 km² der Schrankarfläche (Gesamtfläche: 6,36 km²) in die Kategorie ?PF wahrscheinlich? und 1,1 km² in die Klasse ?Permafrost möglich? gehören sowie auf 1 km² kein Permafrost im Untergrund angenommen wird. Die übrigen 0,26 km² entfallen auf die Fläche des Schrankarferners.The exact distribution of permafrost in small scales is not easy to prove. Some of the most established methods have recently been applied in the Schrankar (Stubaier Alps). In 2008, Miniature Data Loggers have been installed to measure the ground surface temperature hourly. Since 2009, BTS-measurements have been made in late winter. With the aid of an additional rockglacier-mapping of the area, this paper attempted to reconstruct the distribution of permafrost in the Schrankar. The results of the methods were compared to a summarized simulation (?3 to 1 model?). In the paper it could be displayed, that the general simulated distribution of permafrost is pretty similar to the results of the field-methods. But if you take into account the areas of different permafrost probability, some differences in permafrost distribution were found. The main reason could be the view in different scales (mesorelief: model, nano- to microrelief: field-methods). In addition to that, thermal properties of the ground have not been taken into account enough in the model. In general it could be said that it is a fine approach to illustrate the general permafrost distribution by an empirical, statistical model. In combination with the results of the field measurements, the distribution of permafrost could be reconstructed precisely even for small-scale areas. The result of the thesis shows, that 4 km² of the Schrankar (total area: 6.36 km²) belong to the category ?PF probable? and 1.1 km² to the category ?PF possible? as well as on 1 km² of the cirque no permafrost is expected. The remaining 0.26 km² are allotted to the area of the Schrankarferner.vorgelegt von Johanna EisankAbweichender Titel laut Übersetzung der Verfasserin/des VerfassersGraz, Univ., Masterarb., 2012Zsfassung in dt. und engl. Sprach

Semi-automated Extraction of Landslides in Taiwan Based on SPOT Imagery and DEMs

[[abstract]]The vast availability and improved quality of optical satellite data and digital elevation models (DEMs), as well as the need for complete and up-to-date landslide inventories at various spatial scales have fostered the development of semi-automated landslide recognition systems. Among the tested approaches for designing such systems, object-based image analysis (OBIA) stepped out to be a highly promising methodology. OBIA offers a flexible, spatially enabled framework for effective landslide mapping. Most object-based landslide mapping systems, however, have been tailored to specific, mainly small-scale study areas or even to single landslides only. Even though reported mapping accuracies tend to be higher than for pixelbased approaches, accuracy values are still relatively low and depend on the particular study. There is still room to improve the applicability and objectivity of object-based landslide mapping systems. The presented study aims at developing a knowledge-based landslide mapping system implemented in an OBIA environment, i.e. Trimble eCognition. In comparison to previous knowledge-based approaches, the classification of segmentation-derived multi-scale image objects relies on digital landslide signatures. These signatures hold the common operational knowledge on digital landslide mapping, as reported by 25 Taiwanese landslide experts during personal semi-structured interviews. Specifically, the signatures include information on commonly used data layers, spectral and spatial features, and feature thresholds. The signatures guide the selection and implementation of mapping rules that were finally encoded in Cognition Network Language (CNL). Multi-scale image segmentation is optimized by using the improved Estimation of Scale Parameter (ESP) tool. The approach described above is developed and tested for mapping landslides in a sub-region of the Baichi catchment in Northern Taiwan based on SPOT imagery and a high-resolution DEM. An object-based accuracy assessment is conducted by quantitatively comparing extracted landslide objects with landslide polygons that were visually interpreted by local experts. The applicability and transferability of the mapping system are evaluated by comparing initial accuracies with those achieved for the following two tests: first, usage of a SPOT image from the same year, but for a different area within the Baichi catchment; second, usage of SPOT images from multiple years for the same region. The integration of the common knowledge via digital landslide signatures is new in object-based landslide studies. In combination with strategies to optimize image segmentation this may lead to a more objective, transferable and stable knowledge-based system for the mapping of landslides from optical satellite data and DEMs

Expert Knowledge for Object-Based Landslide Mapping in Taiwan

[[abstract]]With object‐based image analysis (OBIA) landslides can be mapped more accurately than with pixel‐based methods. While many authors have recognized the value of segmentation optimization for increasing the objectivity and transferability of landslide mapping, the optimization of the classification step is lagging behind. This study introduces a landslide mapping system that is based on expert knowledge models and implemented in OBIA. These models hold the operational knowledge about landslides and digital landslide mapping such as data, classification features, and feature thresholds. The knowledge was gathered during personal semi‐structured interviews of 20 Taiwanese landslide experts. The system was tested for mapping landslides in a sub‐region of the Baichi catchment in Northern Taiwan. The potential landslide areas were accurately extracted. The refinement of the potential landslide area into landslide types was mainly based on slope values. Additional expert‐based rules will be implemented to increase the accuracy and objectivity of the final landslide classification

Object representations at multiple scales from digital elevation models

AbstractIn the last decade landform classification and mapping has developed as one of the most active areas of geomorphometry. However, translation from continuous models of elevation and its derivatives (slope, aspect, and curvatures) to landform divisions (landforms and landform elements) is filtered by two important concepts: scale and object ontology. Although acknowledged as being important, these two issues have received surprisingly little attention.This contribution provides an overview and prospects of object representation from DEMs as a function of scale. Relationships between object delineation and classification or regionalization are explored, in the context of differences between general and specific geomorphometry. A review of scales issues in geomorphometry—ranging from scale effects to scale optimization techniques—is followed by an analysis of pros and cons of using cells and objects in DEM analysis. Prospects for coupling multi-scale analysis and object delineation are then discussed. Within this context, we propose discrete geomorphometry as a possible approach between general and specific geomorphometry. Discrete geomorphometry would apply to and describe land-surface divisions defined solely by the criteria of homogeneity in respect to a given land-surface parameter or a combination of several parameters. Homogeneity, in its turn, should always be relative to scale

Automated object-based classification of topography from SRTM data

AbstractWe introduce an object-based method to automatically classify topography from SRTM data. The new method relies on the concept of decomposing land-surface complexity into more homogeneous domains. An elevation layer is automatically segmented and classified at three scale levels that represent domains of complexity by using self-adaptive, data-driven techniques. For each domain, scales in the data are detected with the help of local variance and segmentation is performed at these appropriate scales. Objects resulting from segmentation are partitioned into sub-domains based on thresholds given by the mean values of elevation and standard deviation of elevation respectively. Results resemble reasonably patterns of existing global and regional classifications, displaying a level of detail close to manually drawn maps. Statistical evaluation indicates that most of classes satisfy the regionalization requirements of maximizing internal homogeneity while minimizing external homogeneity. Most objects have boundaries matching natural discontinuities at regional level. The method is simple and fully automated. The input data consist of only one layer, which does not need any pre-processing. Both segmentation and classification rely on only two parameters: elevation and standard deviation of elevation. The methodology is implemented as a customized process for the eCognition® software, available as online download. The results are embedded in a web application with functionalities of visualization and download

Local variance for multi-scale analysis in geomorphometry

AbstractIncreasing availability of high resolution Digital Elevation Models (DEMs) is leading to a paradigm shift regarding scale issues in geomorphometry, prompting new solutions to cope with multi-scale analysis and detection of characteristic scales. We tested the suitability of the local variance (LV) method, originally developed for image analysis, for multi-scale analysis in geomorphometry. The method consists of: 1) up-scaling land-surface parameters derived from a DEM; 2) calculating LV as the average standard deviation (SD) within a 3×3 moving window for each scale level; 3) calculating the rate of change of LV (ROC-LV) from one level to another, and 4) plotting values so obtained against scale levels. We interpreted peaks in the ROC-LV graphs as markers of scale levels where cells or segments match types of pattern elements characterized by (relatively) equal degrees of homogeneity. The proposed method has been applied to LiDAR DEMs in two test areas different in terms of roughness: low relief and mountainous, respectively. For each test area, scale levels for slope gradient, plan, and profile curvatures were produced at constant increments with either resampling (cell-based) or image segmentation (object-based). Visual assessment revealed homogeneous areas that convincingly associate into patterns of land-surface parameters well differentiated across scales. We found that the LV method performed better on scale levels generated through segmentation as compared to up-scaling through resampling. The results indicate that coupling multi-scale pattern analysis with delineation of morphometric primitives is possible. This approach could be further used for developing hierarchical classifications of landform elements