A.C. Seijmonsbergen

Improved Landsat-based forest mapping in steep mountainous terrain using object-based classification

The accuracy of forest stand type maps derived from a Landsat Thematic Mapper (Landsat TM) image of a heterogeneous forest covering rugged terrain is generally low. Therefore, the first objective of this study was to assess whether topographic correction of TM bands and adding the digital elevation model (DEM) as additional band improves the accuracy of Landsat TM-based forest stand type mapping in steep mountainous terrain. The second objective of this study was to compare object-based classification with per-pixel classification on the basis of the accuracy and the applicability of the derived forest stand type maps. To fulfil these objectives different classification schemes were applied to both topographically corrected and uncorrected Landsat TM images, both with and without the DEM as additional band. All the classification results were compared on the basis of confusion matrices and kappa statistics. It is found that both topographic correction and classification with the DEM as additional band increase the accuracy of Landsat TM-based forest stand type maps in steep mountainous terrain. Further it was found that the accuracies of per-pixel classifications were slightly higher, but object-based classification seemed to provide better overall results according to local foresters. It is concluded that Landsat TM images could provide basic information at regional scale for compiling forest stand type maps especially if they are classified with an object-based technique.

Comparing Landslide Hazard Maps

The objective of the method explained in this paper isto obtain a better insight in the decision rulesapplied by geomorphologists in the direct mapping oflandslide hazard. This can be obtained by forcinggeomorphologists to specify for each unit (polygon) intheir hazard map the criteria that they used toclassify the unit as high, medium or low hazard. Whenthis is done systemically for an entire area, it ispossible to analyze those criteria statistically, andto evaluate whether they can be grouped into generaldecision rules, or whether these criteria arecompletely site specific. The same area in the Alpagoregion in Italy was mapped at 1 : 5000 scale by threeteams of experts individually. The different methodsare presented and the results are compared.

Comparison of three GIS-based models for predicting rockfall runout zones at a regional scale

Site-specific information about the level of protection that mountain forests provide is often not available for large regions. Information regarding rockfalls is especially scarce. The most efficient way to obtain information about rockfall activity and the efficacy of protection forests at a regional scale is to use a simulation model. At present, it is still unknown which forest parameters could be incorporated best in such models. Therefore, the purpose of this study was to test and evaluate a model for rockfall assessment at a regional scale in which simple forest stand parameters, such as the number of trees per hectare and the diameter at breast height, are incorporated. Therefore, a newly developed Geographical Information System (GIS)-based distributed model is compared with two existing rockfall models. The developed model is the only model that calculates the rockfall velocity on the basis of energy loss due to collisions with trees and on the soil surface. The two existing models calculate energy loss over the distance between two cell centres, while the newly developed model is able to calculate multiple bounces within a pixel. The patterns of rockfall runout zones produced by the three models are compared with patterns of rockfall deposits derived from geomorphological field maps. Furthermore, the rockfall velocities modelled by the three models are compared. It is found that the models produced rockfall runout zone maps with rather similar accuracies. However, the developed model performs best on forested hillslopes and it also produces velocities that match best with field estimates on both forested and nonforested hillslopes irrespective of the slope gradient.

Engineering geomorphology of the widentobel catchment, appenzell and sankt gallen, switzerland. A geomorphologuical inventory system applied to geotechnical appraisal of slope stability

Abstract The paper presents a method of applied and detailed 1:2000 scale geomorphological and geotechnical surveying, developed in Alpine areas and tested to its engineering consulting value in the Widenbach valley, Appenzell and St. Gallen, Switzerland. The investigation was carried out by a pair of geomorphologists, trained in high relief terrain and well versed in mass movement characteristics. A programme of “Wildbachverbauung” — the controlling of fluviatile erosion by the erection of dams — was already undertaken in the twenties, but this first generation of wooden dams is now being replaced by concrete constructions. The Widenbach valley, situated in the marls and sandstones of the folded molasse zone (untere Susswasserzone) was subjected to glacial scouring and deposition by a branch of the Rhine glacier, transfluencing westwards across the Stosspass, separating the Widenbach valley at 940 m a.s.l. from the Appenzell depression. Postglacially, various earthflows have originated from the instable morainic deposits as well as from the weathered marls and sandstones underlying the glacially oversteepened valley flanks. They have built a two km long earthflow complex filling the Widenbach valley to considerable depth. The townships of Eichberg and Altstatten, situated at the outlet of the system, have since historic times experienced damage to forest and grazing land, houses and roads. In the midstream and upstream valley sections, the Widenbach is flowing on the earthflow morphology, while the downstream part, already constrained by new dams founded in exposed hard rock, is controlled by fluvial processes. The mass movement dynamics of the midstream and upstream sectors make a completely different engineering approach necesssary. When this was made clear by the geomorphological reconnaissance, the lack of data could only be filled by a detailed 1:2000 scale geomorphological site investigation, as time and funds for long-lasting monitoring of the earthflow system were not available. This geomorphological survey contained the following elements: pre-field airphoto-interpretation, mapping of micro-terrainforms, classification of materials, types and order of movement; it placed emphasis on the behaviour of the various discontinuity planes expressed in the terrain.

Geomorphological Change Detection Using Object-Based Feature Extraction From Multi-Temporal LiDAR Data

Multi-temporal LiDAR digital terrain models (DTMs) are used for the development and testing of a method for geomorphological change analysis in western Austria. Point data from two airborne LiDAR campaigns of 2003 and 2011 were filtered and interpolated into two 2m DTMs. Seven geomorphological features were mapped by using stratified object-based image analysis (OBIA) using terrain properties derived from the DTMs. Segmentation parameters and classification rules were set and applied to both data sets which allowed analysis of geomorphological change between 2003 and 2011. Volumetric change was calculated and summarized by their landform category. The multi-temporal landform classifications show where landforms changed into other landforms as the result of geomorphological process activity. However, differences in point densities and lack of data points below dense forest hindered accurate geomorphological change detection in these areas. When challenges related to interpolation techniques are tackled, stratified OBIA of multi-temporal LiDAR data sets is a promising tool for geomorphological change detection, and affiliated applications such as monitoring risk and natural hazards, rate of change analyses, and vulnerability assessments.

Using statistical learning algorithms in regional landslide susceptibility zonation with limited landslide field data

Regional Landslide Susceptibility Zonation (LSZ) is always challenged by the available amount of field data, especially in southwestern China where large mountainous areas and limited field information coincide. Statistical learning algorithms are believed to be superior to traditional statistical algorithms for their data adaptability. The aim of the paper is to evaluate how statistical learning algorithms perform on regional LSZ with limited field data. The focus is on three statistical learning algorithms, Logistic Regression (LR), Artificial Neural Networks (ANN) and Support Vector Machine (SVM). Hanzhong city, a landslide prone area in southwestern China is taken as a study case. Nine environmental factors are selected as inputs. The accuracies of the resulting LSZ maps are evaluated through landslide density analysis (LDA), receiver operating characteristic (ROC) curves and Kappa index statistics. The dependence of the algorithm on the size of field samples is examined by varying the sizes of the training set. The SVM has proven to be the most accurate and the most stable algorithm at small training set sizes and on all known landslide sizes. The accuracy of SVM shows a steadily increasing trend and reaches a high level at a small size of the training set, while accuracies of LR and ANN algorithms show distinct fluctuations. The geomorphological interpretations confirm the strength of SVM on all landslide sizes. Our results show that the strengths of SVM in generalization capability and model robustness make it an appropriate and efficient tool for regional LSZ with limited landslide field samples.

The Modern Geomorphological Map

Classical geomorphological maps are representations of the spatial distribution of landforms, materials and of the processes responsible for their formation, in a single paper map. They contain a wealth of information that is generally documented with the aid of symbol and color legends. Uniformity among geomorphological legends does not exist, mainly because the traditional mapping ‘schools’ independently developed systems for use at different scales in a variety of landscapes. Technological advances have changed the preparation, data collection, analysis, storage, and visualization of the modern geomorphological map. Modern geomorphological maps are digital collections of information layers consisting of georeferenced vector, raster and tabular data that are stored in a geodatabase. The data depicted in classical geomorphological maps can be scanned and converted into digital geomorphological information layers using conversion legends. Both the content and the precision of digital geomorphological information can be updated by using information from digital elevation models (DEMs). A further development is the (semi-)automated extraction and classification of geomorphological features from DEMs. Extraction and classification of geomorphological features are based on land-surface parameters derived from DEMs. The visualization and publication of digital geomorphological information layers have drastically changed. Birds-eye views, three-dimensional display and animations, virtual globe visualizations, geospatial portable document format maps or access to geomorphological maps via web-based services are possible through remote servers. The end-user interactively controls the information that is on-screen displayed, analyzed, and distributed. A case study from the Austrian Alps is presented to illustrate how: (1) geomorphological information from classical maps can be converted to digital information layers; (2) detailed DEMs are used in semi-automated object-based classification of landforms; and (3) a modern geomorphological map is used for application to geoconservation.

A potential geoconservation map of the Las Lagunas area, northern Peru

The Andean paramo ecosystems host geodiversity of global importance, but also have important societal functions, including agricultural production and delivery of water to people and industry. Paramo geo-ecosystems are highly susceptible to environmental degradation because of their alpine relief, extreme climate and fragile soils. In contrast to other parts of the world, geodiversity assessment studies in the Andes are scarce. A geodiversity assessment method was adapted and used to produce a potential geoconservation map of the Andean paramo. The Las Lagunas area (Cajamarca, Northern Peru) has a rich archive of climate proxy data and landscape reconstruction, and plays a key role in the functioning of regional geo-ecosystems. Undisturbed proxies for climate change are contained in four Late-Glacial recessional complexes of its former local ice cap and in pollen records preserved in the post-glacial peat cover. These new findings were used to develop a refined chronostratigraphy of the Late-Glacial warming period in the Andes of Northern Peru. Geo-ecosystem functions (for example water and carbon storage) depend on the environmental vulnerability and disturbance of the landforms and deposits. Therefore, potential geoconservation areas that combine a high scientific value and environmental vulnerability with a low disturbance and low frequency of occurrence should be prioritized for geoconservation. Such a strategy should also optimize sustainable use of resources and development of these areas. Mountainous countries like Peru require future management strategies that recognize and incorporate potential geoconservation information in regional planning, to prevent unnecessary loss of irreplaceable soils, climate proxies and geo-ecosystem functions upon future changes in land use and climate.

Assessing temporal couplings in social–ecological island systems: historical deforestation and soil loss on Mauritius (Indian Ocean)

Temporal couplings, such as historical interactions between deforestation and soil loss, are responsible for the current state of a wide range of ecosystem services of the social–ecological system on Mauritius. Islands are suitable study sites for understanding temporal couplings and telecouplings because of their: (1) clearly defined physical boundaries, (2) finite local resources, and (3) relatively short human history. Six well-documented historical deforestation maps, starting from the first colonization of Mauritius in 1638, were used as input parameters to model two scenarios of cumulative soil loss, with and without deforestation, using the revised universal soil loss equation in a geographic information system. The scenarios show that historical deforestation since 1638 has resulted in a cumulative soil loss that drastically exceeds soil loss under a natural baseline scenario without deforestation. The adopted method illustrates to what extent the current state of the soil of a social–ecological system is negatively affected by past human–environment interactions. We suggest that potential negative impacts on insular societies are mitigated by telecouplings such as food, fuel, and fertilizer imports.

LiDAR and Orthophoto Synergy to optimize Object-Based Landscape Change: Analysis of an Active Landslide

Active landslides have three major effects on landscapes: (1) land cover change, (2) topographical change, and (3) above ground biomass change. Data derived from multi-temporal Light Detection and Ranging technology (LiDAR) are used in combination with multi-temporal orthophotos to quantify these changes between 2006 and 2012, caused by an active deep-seated landslide near the village of Doren in Austria. Land-cover is classified by applying membership-based classification and contextual improvements based on the synergy of orthophotos and LiDAR-based elevation data. Topographical change is calculated by differencing of LiDAR derived digital terrain models. The above ground biomass is quantified by applying a local-maximum algorithm for tree top detection, in combination with allometric equations. The land cover classification accuracies were improved from 65% (using only LiDAR) and 76% (using only orthophotos) to 90% (using data synergy) for 2006. A similar increase from respectively 64% and 75% to 91% was established for 2012. The increased accuracies demonstrate the effectiveness of using data synergy of LiDAR and orthophotos using object-based image analysis to quantify landscape changes, caused by an active landslide. The method has great potential to be transferred to larger areas for use in landscape change analyses.