Modelling patterned ground distribution in Finnish Lapland: an integration of topographical, ground and remote sensing information

New data technologies and modelling methods have gained more attention in the field of periglacial geomorphology during the last decade. In this paper we present a new modelling approach that integrates topographical, ground and remote sensing information in predictive geomorphological mapping using generalized additive modelling (GAM) . First, we explored the roles of different environmental variable groups in determining the occurrence of non‐sorted and sorted patterned ground in a fell region of 100 km2 at the resolution of 1 ha in northern Finland. Second, we compared the predictive accuracy of ground‐topography‐ and remote‐sensing‐based models. The results indicate that non‐sorted patterned ground is more common at lower altitudes where the ground moisture and vegetation abundance is relatively high, whereas sorted patterned ground is dominant at higher altitudes with relatively high slope angle and sparse vegetation cover. All modelling results were from good to excellent in model evaluation data using the area under the curve (AUC) values, derived from receiver operating characteristic (ROC) plots. Generally, models built with remotely sensed data were better than ground‐topography‐based models and combination of all environmental variables improved the predictive ability of the models. This paper confirms the potential utility of remote sensing information for modelling patterned ground distribution in subarctic landscapes.     

Scale matters–A multi-resolution study of the determinants of patterned ground activity in subarctic Finland

Numerical studies of earth surface processes in relation to their environment are one of the central topics in physical geography. However, collinearity between explanatory variables and spatial autocorrelation can hinder the detection of key environmental correlates underlying response–explanatory variables' relationships identified by traditional regression methods. Moreover, conclusions about the potential importance of environmental variables have generally made on analysis conducted only at one spatial scale (resolution). In this study, a variation partitioning method provided a framework to obtain new insights into the relative roles of different factors determining patterned ground activity at multiple spatial resolutions. The variation in the distribution of the sorted and non sorted patterned ground was decomposed into independent and joint effects of relief, soil and spatial variables (geographical location) based on a multi resolution system of 1 ha, 25 ha and 100 ha cells covering in total 100 km² of a subarctic landscape in northern Finland.The independent effects of relief and soil variables captured the largest fraction of the variation in the non-sorted patterned ground distribution, while relief had a major contribution for sorted patterned ground activity. The independent effect of spatial autocorrelation on sorted patterned ground was higher than that on non-sorted patterned ground. However, a considerable amount of variation in the distribution of both patterned ground types was accounted for by the joint effects of explanatory variables and may thus be causally related to two or all three groups of variables. Our analyses produced often contradicting results at different resolutions. Consequently, this has substantial implications for the study of geomorphological systems, since the choice of resolution can have a major effect on the inferences of analyses. Our results draw attention to the roles of resolution and spatial autocorrelation in the study of geomorphological systems.     

Effects of sample size on the accuracy of geomorphological models

Commonly, the most costly part of geomorphological distribution modelling studies is gathering the data. Thus, guidance for researchers concerning the quantity of field data needed would be extremely practical. This paper scrutinises the relationship between the sample size (the number of observations varied from 20 to 600) and the predictive ability of the generalized linear model (GLM), generalized additive model (GAM), generalized boosting method (GBM) and artificial neural network (ANN) in two data settings, i.e., independent and split-sample approaches. The study was performed using empirical data of periglacial processes from an area of 600 km² in northernmost Finland at grid resolutions of 1 ha (100 × 100 m) and 25 ha (500 × 500 m). A rather sharp increase in the predictive ability of the models was observed when the number of observations increased from 20 to 100, and the level of robust predictions was reached with 200 observations. The result indicates that no more than a few hundred observations are needed in geomorphological distribution modelling at a medium scale resolution (ca. 0.01–1 km²).     

SCHMIDT‐HAMMER EXPOSURE AGES FROM PERIGLACIAL PATTERNED GROUND (SORTED CIRCLES) IN JOTUNHEIMEN,NORWAY, AND THEIR INTERPRETATIVE PROBLEMS

Periglacial patterned ground (sorted circles and polygons) along an altitudinal profile at Juvflya in central Jotunheimen, southern Norway, is investigated using Schmidt‐hammer exposure‐age dating (SHD). The patterned ground surfaces exhibit R‐value distributions with platycurtic modes, broad plateaus, narrow tails, and a negative skew. Sample sites located between 1500 and 1925 m a.s.l. indicate a distinct altitudinal gradient of increasing mean R‐values towards higher altitudes interpreted as a chronological function. An established regional SHD calibration curve for Jotunheimen yielded mean boulder exposure ages in the range 6910 ± 510 to 8240 ± 495 years ago. These SHD ages are indicative of the timing of patterned ground formation, representing minimum ages for active boulder upfreezing and maximum ages for the stabilization of boulders in the encircling gutters. Despite uncertainties associated with the calibration curve and the age distribution of the boulders, the early‐Holocene age of the patterned ground surfaces, the apparent cessation of major activity during the Holocene Thermal Maximum (HTM) and continuing lack of late‐Holocene activity clarify existing understanding of the process dynamics and palaeoclimatic significance of large‐scale sorted patterned ground as an indicator of a permafrost environment. The interpretation of SHD ages from patterned ground surfaces remains challenging, however, owing to their diachronous nature, the potential for a complex history of formation, and the influence of local, non‐climatic factors.     

Earth hummocks in north-east Okstindan,northern Norway: Morphology,distribution and environmental constraints

Gurney, S.D. & Hayward, S. 2015. Earth hummocks in north-east Okstindan, northern Norway: Morphology, distribution and environmental constraints. Norsk Geografisk Tidsskrift–Norwegian Journal of Geography. ISSN 0029-1951.

Earth hummocks (also termed pounus or thúfur) are a common form of periglacial non-sorted patterned ground. The study objectives were to determine the morphology, distribution and development on slopes of earth hummocks in north-east Okstindan, Norway, an area with many hummocks but few documented accounts. The methodology involved detailed geomorphological mapping and precise measurement with a profileometer. The internal structure of the hummocks was investigated through excavations and sediment sample analyses. Fourteen sites with well-developed earth hummocks (accounting for over 650 individual hummock forms) were investigated. The sites have an average altitude of 750?m and occur on slopes with an average gradient of 7°. The hummock heights are in the range 0.11–0.52?m and their diameters 0.7–1.5?m, although coalescent forms are up to 5?m in length. The hummock morphology is characterised by a variable plan form, asymmetry with respect to upslope and downslope forms, downslope elongation, coalescence, and superimposed microtopography. The hummocks’ distribution appeared to have been controlled by the existence of a frost-susceptible ‘host’ sediment, but moisture availability and topographic position played a role. The authors conclude that differential frost heave and vegetation cover stability are critical for the hummocks’ longevity in the studied landscape.     

Mountain permafrost distribution modeling using Multivariate Adaptive Regression Spline (MARS) in the Wenquan area over the Qinghai-Tibet Plateau

In high mountainous areas, the development and distribution of alpine permafrost is greatly affected by macro- and micro-topographic factors. The effects of latitude, altitude, slope, and aspect on the distribution of permafrost were studied to understand the distribution patterns of permafrost in Wenquan on the Qinghai-Tibet Plateau. Cluster and correlation analysis were performed based on 30 m Global Digital Elevation Model (GDEM) data and field data obtained using geophysical exploration and borehole drilling methods. A Multivariate Adaptive Regression Spline model (MARS) was developed to simulate permafrost spatial distribution over the studied area. A validation was followed by comparing to 201 geophysical exploration sites, as well as by comparing to two other models, i.e., a binary logistic regression model and the Mean Annual Ground Temperature model (MAGT). The MARS model provides a better simulation than the other two models. Besides the control effect of elevation on permafrost distribution, the MARS model also takes into account the impact of direct solar radiation on permafrost distribution.     

Evaluating and comparing methods of sinkhole susceptibility mapping in the Ebro Valley evaporite karst (NE Spain)

Multiple sinkhole susceptibility models have been generated in three study areas of the Ebro Valley evaporite karst (NE Spain) applying different methods (nearest neighbour distance, sinkhole density, heuristic scoring system and probabilistic analysis) for each sinkhole type separately (cover collapse sinkholes, cover and bedrock collapse sinkholes and cover and bedrock sagging sinkholes). The quantitative and independent evaluation of the predictive capability of the models reveals that: (1) The most reliable susceptibility models are those derived from the nearest neighbour distance and sinkhole density. These models can be generated in a simple and rapid way from detailed geomorphological maps. (2) The reliability of the nearest neighbour distance and density models is conditioned by the degree of clustering of the sinkholes. Consequently, the karst areas in which sinkholes show a higher clustering are a priori more favourable for predicting new occurrences. (3) The predictive capability of the best models obtained in this research is significantly higher (12.5–82.5%) than that of the heuristic sinkhole susceptibility model incorporated into the General Urban Plan for the municipality of Zaragoza. Although the probabilistic approach provides lower quality results than the methods based on sinkhole proximity and density, it helps to identify the most significant factors and select the most effective mitigation strategies and may be applied to model susceptibility in different future scenarios.     

Plant functional traits and microbes vary with position on striped periglacial patterned ground at Glacier National Park,Montana

The retreating snowfields and glaciers of Glacier National Park, Montana, USA, present alpine plants with changes in habitat and hydrology. The adjacent and relic periglacial patterned ground consists of solifluction terraces of green, vegetation-rich stripes alternating with sparsely vegetated brown stripes. We established georeferenced transects on striped periglacial patterned ground for long-term monitoring and data collection on species distribution and plant functional traits at Siyeh Pass and at Piegan Pass at Glacier National Park. We documented species distribution and calculated the relative percent cover (RPC) of qualitative functional traits and used 16S rRNA from soil samples to characterize microbial distribution on green and brown stripes. Plant species distribution varied significantly and there were key differences in microbial distribution between the green and brown stripes. The rare arctic-alpine plants Draba macounii, Papaver pygmaeum, and Sagina nivalis were restricted to brown stripes, where the RPC of xeromorphic taprooted species was significantly higher at the leading edge of the Siyeh Pass snowfield. Brown stripes had a higher percentage of the thermophilic bacteria Thermacetogenium and Thermoflavimicrobium. Green stripes were co-dominated by the adventitiously-rooted dwarf shrubs Salix arctica and the possibly N-fixing Dryas octopetala. Green stripes were inhabited by Krummholz and seedlings of Abies lasiocarpa and Pinus albicaulus. Prosthecobacter, a hydrophilic bacterial genus, was more abundant on the green stripes, which had 6,524 bacterial sequences in comparison to the 1,183 sequences from the brown stripes. While further research can determine which functional traits are critical for these plants, knowledge of the current distribution of plant species and their functional traits can be used in predictive models of the responses of alpine plants to disappearing snowfields and glaciers. This research is important in conservation of rare arctic-alpine species on periglacial patterned ground.

     

Benefits and drawbacks of two modelling approaches for a generalist carnivore: can models predict where Wile E. Coyote will turn up next?

Wildlife ecologists frequently make use of limited information on locations of a species of interest in combination with readily available GIS data to build models to predict space use. In addition to a wide range of statistical data models that are more commonly used, machine learning approaches provide another means to develop predictive spatial models. However, comparison of output from these two families of models for the same data set is not often carried out. It is important that wildlife managers understand the pitfalls and limitations when a single set of models is used with limited GIS data to try to predict and understand species distribution. To illustrate this, we carried out two sets of models (generalized linear mixed models (GLMMs) and boosted regression trees (BRTs)) to predict geographic occupancy of the eastern coyote (Canis latrans) on the island of Newfoundland, Canada. This exercise is illustrative of common spatial questions in wildlife research and management. Our results show that models vary depending on the approach (GLMM vs. BRT) and that, overall, BRT had higher predictive ability. Although machine learning has been criticized because it is not explicitly hypothesis-driven, it has been used in other areas of spatial modelling with success. Here, we demonstrate that it may be a useful approach for predicting wildlife space use and to generate hypotheses when data are limited. The results of this comparison can help to improve other models for species distributions and also guide future sampling and modelling initiatives.     

A comparative approach to modelling multiple urban land use changes using tree-based methods and cellular automata: the case of Greater Tokyo Area

Urban multiple land use change (LUC) modelling enables the realistic simulation of LUC processes in complex urban systems; however, such modelling suffers from technical challenges posed by complicated transition rules and high spatial heterogeneity when predicting the LUC of a highly developed area. Tree-based methods are powerful tools for addressing this task, but their predictive capabilities need further examination. This study integrates tree-based methods and cellular automata to simulate multiple LUC processes in the Greater Tokyo Area. We examine the predictive capability of 4 tree-based models – bagged trees, random forests, extremely randomised trees (ERT) and bagged gradient boosting decision trees (bagged GBDT) – on transition probability prediction for 18 land use transitions derived from 8 land use types. We compare the predictive power of a tree-based model with multi-layer perceptron (MLP) and among themselves. The results show that tree-based models generally perform better than MLP, and ERT significantly outperforms the three other tree-based models. The outstanding predictive performance of ERT demonstrates the advantages of introducing bagging ensemble and a high degree of randomisation into transition probability modelling. In addition, through variable importance evaluation, we found the strongest explanatory powers of neighbourhood characteristics for all land use transitions; however, the size of the impacts depends on the neighbourhood land use type and the neighbourhood size. Furthermore, socio-economic and policy factors play important roles in transitions ending with high-rise buildings and transitions related to industrial areas.     

基于遥感与SRTM的青藏高原冰缘地貌信息提取方法——以1 ∶ 100万标准分幅拉萨幅(H46)为例

根据模型和分布函数,本文首先依据多年平均气温、地温和SRTM等数据对研究区域冰缘地貌的分布范围进行分别提取,并利用遥感数据和人工解译方式对其进行了修正。在此基础上,采用一定指标,利用SRTM数据对冰缘地貌次级类型(如起伏度、海拔高度和坡度等)进行了提取,从而完成研究区域冰缘地貌信息的提取。研究结果表明:①研究区域冰缘地貌总面积约5.15×10⁴km²,主要分布在研究区域的西北部和西南部,另外在东北部也有少量分布;通过提取,研究区域中最重要的冰缘地貌类型是冰缘作用的中起伏缓极高山,面积约0.82×10⁴km²,分布范围较广。②冰缘地貌的分布与海拔高度、气温和地温等有密切的关系,基于此提取的结果可为冰缘地貌的解译提供一定的参考;由于青藏高原气象站点较少,数据精度较低,自动提取精度受到很大限制,因此进行人工解译修正是非常重要和必不可少的。     

Comparative evaluation of geological disaster susceptibility using multi-regression methods and spatial accuracy validation

Geological disasters not only cause economic losses and ecological destruction, but also seriously threaten human survival. Selecting an appropriate method to evaluate susceptibility to geological disasters is an important part of geological disaster research. The aims of this study are to explore the accuracy and reliability of multi-regression methods for geological disaster susceptibility evaluation, including Logistic Regression (LR), Spatial Autoregression (SAR), Geographical Weighted Regression (GWR), and Support Vector Regression (SVR), all of which have been widely discussed in the literature. In this study, we selected Yunnan Province of China as the research site and collected data on typical geological disaster events and the associated hazards that occurred within the study area to construct a corresponding index system for geological disaster assessment. Four methods were used to model and evaluate geological disaster susceptibility. The predictive capabilities of the methods were verified using the receiver operating characteristic (ROC) curve and the success rate curve. Lastly, spatial accuracy validation was introduced to improve the results of the evaluation, which was demonstrated by the spatial receiver operating characteristic (SROC) curve and the spatial success rate (SSR) curve. The results suggest that: 1) these methods are all valid with respect to the SROC and SSR curves, and the spatial accuracy validation method improved their modelling results and accuracy, such that the area under the curve (AUC) values of the ROC curves increased by about 3%–13% and the AUC of the success rate curve values increased by 15%–20%; 2) the evaluation accuracies of LR, SAR, GWR, and SVR were 0.8325, 0.8393, 0.8370 and 0.8539, which proved the four statistical regression methods all have good evaluation capability for geological disaster susceptibility evaluation and the evaluation results of SVR are more reasonable than others; 3) according to the evaluation results of SVR, the central-southern Yunnan Province are the highest susceptibility areas and the lowest susceptibility is mainly located in the central and northern parts of the study area.     

Mapping transit‐based access: integrating GIS,routes and schedules

Seabed sediment textural parameters such as mud, sand and gravel content can be useful surrogates for predicting patterns of benthic biodiversity. Multibeam sonar mapping can provide near-complete spatial coverage of high-resolution bathymetry and backscatter data that are useful in predicting sediment parameters. Multibeam acoustic data collected across a ～1000 km² area of the Carnarvon Shelf, Western Australia, were used in a predictive modelling approach to map eight seabed sediment parameters. Four machine learning models were used for the predictive modelling: boosted decision tree, random forest decision tree, support vector machine and generalised regression neural network. The results indicate overall satisfactory statistical performance, especially for %Mud, %Sand, Sorting, Skewness and Mean Grain Size. The study also demonstrates that predictive modelling using the combination of machine learning models has provided the ability to generate prediction uncertainty maps. However, the single models were shown to have overall better prediction performance than the combined models. Another important finding was that choosing an appropriate set of explanatory variables, through a manual feature selection process, was a critical step for optimising model performance. In addition, machine learning models were able to identify important explanatory variables, which are useful in identifying underlying environmental processes and checking predictions against the existing knowledge of the study area. The sediment prediction maps obtained in this study provide reliable coverage of key physical variables that will be incorporated into the analysis of covariance of physical and biological data for this area.     

Multi-label class assignment in land-use modelling

During the last two decades, a variety of models have been applied to understand and predict changes in land use. These models assign a single-attribute label to each spatial unit at any particular time of the simulation. This is not realistic because mixed use of land is quite common. A more detailed classification allowing the modelling of mixed land use would be desirable for better understanding and interpreting the evolution of the use of land. A possible solution is the multi-label (ML) concept where each spatial unit can belong to multiple classes simultaneously. For example, a cluster of summer houses at a lake in a forested area should be classified as water, forest and residential (built-up). The ML concept was introduced recently, and it belongs to the machine learning field. In this article, the ML concept is introduced and applied in land-use modelling. As a novelty, we present a land-use change model that allows ML class assignment using the k nearest neighbour (kNN) method that derives a functional relationship between land use and a set of explanatory variables. A case study with a rich data-set from Luxembourg using biophysical data from aerial photography is described. The model achieves promising results based on the well-known ML evaluation criteria. The application described in this article highlights the value of the multi-label k nearest neighbour method (MLkNN) for land-use modelling.     

Soil-landscape modelling and spatial prediction of soil attributes

Abstract

Explicit and quantitative models for the spatial prediction of soil and landscape attributes are required for environmental modelling and management. In this study, advances in the spatial representation of hydrological and geomorphological processes using terrain analysis techniques are integrated with the development of a field sampling and soil-landscape model building strategy. Statistical models are developed using relationships between terrain attributes (plan curvature, compound topographic index, upslope mean plan curvature) and soil attributes (A horizon depth, Solum depth, E horizon presence/absence) in an area with uniform geology and geomorphic history. These techniques seem to provide appropriate methodologies for spatial prediction and understanding soil landscape processes.     

基于三种不同模型的区域滑坡灾害敏感性评价及结果检验研究

选取相对高差、坡度、坡向、水系、距断层距离、植被覆盖、地层岩性和道路等影响因子,采用信息量法、Logistic回归和人工神经网络3种模型进行滑坡灾害的敏感性评价,并对评价结果进行检验。结果表明：① 评价分类结果的准确性会关系到社会经济成本。经过采用Cohen’s Kappa系数法、Sridevi Jadi精度评估方法和ROC曲线3种方法对评价结果进行比较分析,结果显示人工神经网络模型具有更好的评价精度。② 宁强县滑坡地域分布上,呈现一带三区。其中高、中和低敏感区分别占全县总面积的39.96%,37.7%和22.33%。     

中国数字地形学研究进展（英文）

Geomorphometry,the science of digital terrain analysis(DTA),is an important focus of research in both geomorphology and geographical information science(GIS).Given that 70% of China is mountainous,geomorphological research is popular among Chinese scholars,and the development of GIS over the last 30 years has led to significant advances in geomorphometric research.In this paper,we review Chinese progress in geomorphometry based on the published literature.There are three major areas of progress:digital terrain modelling methods,DTA methods,and applications of digital terrain models(DTMs).First,traditional vector-and raster-based terrain modelling methods,including the assessment of uncertainty,have received widespread attention.New terrain modelling methods such as unified raster and vector,high-fidelity,and real-time dynamic geographical scene modelling have also attracted research attention and are now a major focus of digital terrain modelling research.Second,in addition to the popular DTA methods based on topographical derivatives,geomorphological features,and hydrological factors extracted from DTMs,DTA methods have been extended to include analyses of the structure of underlying strata,ocean surface features and even socioeconomic spatial structures.Third,DTMs have been applied to fields including global climate change,analysis of various typical regions,lunar surface and other related fields.Clearly,Chinese scholars have made significant progress in geomorphometry.Chinese scholars have had the greatest international impact in areas including high-fidelity digital terrain modelling and DTM-based regional geomorphological analysis,particularly in the Loess Plateau and the Tibetan Plateau regions.     

A comparison between diatom-based pH inference models using Artificial Neural Networks (ANN), Weighted Averaging (WA) and Weighted Averaging Partial Least Squares (WA-PLS) regressions

We explored the possibility of using artificial neural networks (ANN) to develop quantitative inference models in paleolimnology. ANNs are dynamic computer systems able to learn the relations between input and output data. We developed ANN models to infer pH from fossil diatom assemblages using a calibration data set of 76 lakes in Quebec. We evaluated the predictive power of these models in comparison with the two most commonly methods used in paleolimnology: Weighted Averaging (WA) and Weighted Averaging Partial Least Squares (WA-PLS). Results show that the relationship between species assemblages and environmental variables of interest can be modelled by a 3-layer back-propagation network, with apparent R² and RMSE of 0.9 and 0.24 pH units, respectively. Leave-one-out cross-validation was used to access the reliabilities of the WA, WA-PLS and ANN models. Validation results show that the ANN model (R² _jackknife = 0.63, RMSE_jackknife = 0.45, mean bias = 0.14, maximum bias = 1.13) gives a better predictive power than the WA model (R² _jackknife = 0.56, RMSE_jackknife = 0.5, mean bias = –0.09, maximum bias = –1.07) or WA-PLS model (R² _jackknife = 0.58, RMSE_jackknife = 0.48, mean bias = –0.15, maximum bias = –1.08). We also evaluated whether the removal of certain taxa according to their tolerance changed the performance of the models. Overall, we found that the removal of taxa with high tolerances for pH improved the predictive power of WA-PLS models whereas the removal of low tolerance taxa lowered its performance. However, ANN models were generally much less affected by the removal of taxa of either low or high pH tolerance. Moreover, the best model was obtained by averaging the predictions of WA-PLS and ANN models. This implies that the two modelling approaches capture and extract complementary information from diatom assemblages. We suggest that future modelling efforts might achieve better results using analogous multi-model strategies.