Landslide susceptibility mapping using GIS and the weight-of-evidence model

The weights-of-evidence model (a Bayesian probability model) was applied to the task of evaluating landslide susceptibility using GIS. Using landslide location and a spatial database containing information such as topography, soil, forest, geology, land cover and lineament, the weights-of-evidence model was applied to calculate each relevant factor's rating for the Boun area in Korea, which had suffered substantial landslide damage following heavy rain in 1998. In the topographic database, the factors were slope, aspect and curvature; in the soil database, they were soil texture, soil material, soil drainage, soil effective thickness and topographic type; in the forest map, they were forest type, timber diameter, timber age and forest density; lithology was derived from the geological database; land-use information came from Landsat TM satellite imagery; and lineament data from IRS satellite imagery. Tests of conditional independence were performed for the selection of factors, allowing 43 combinations of factors to be analysed. For the analysis of mapping landslide susceptibility, the contrast values, W ⁺ and W ^-, of each factor's rating were overlaid spatially. The results of the analysis were validated using the previous landslide locations. The combination of slope, curvature, topography, timber diameter, geology and lineament showed the best results. The results can be used for hazard prevention and land-use planning.     

Integrated Interpolation Methods for Geophysical Data: Applications to Mineral Exploration

Any mine planning requires careful prediction of both the head grade andtonnage ofmineralization. There are various methods of interpolation that attempt to provide reasonable estimatesat unsampled locations. All of these give realizations that are unduly smooth and extremevalues that occur in reality are not reflected in these estimates. Such methods, therefore,provide limited scope for accurate risk assessment. An alternative approach that is rapidlygaining popularity is the method of conditional simulation. This approach attempts to reproduceboth the grade distributions of the sample data as well as its spatial variability. In this paper,a case study is presented on a platinum mineralization to demonstrate and compare sequentialGaussian and sequential conditional simulation techniques and to quantify and discuss therelevant sensitivities.     

Minimum Acceptance Criteria for Geostatistical Realizations

Geostatistical simulation is being used increasingly for numerical modeling of natural phenomena. The development of simulation as an alternative to kriging is the result of improved characterization of heterogeneity and a model of joint uncertainty. The popularity of simulation has increased in both mining and petroleum industries. Simulation is widely available in commercial software. Many of these software packages, however, do not necessarily provide the tools for careful checking of the geostatistical realizations prior to their use in decision-making. Moreover, practitioners may not understand all that should be checked. There are some basic checks that should be performed on all geostatistical models. This paper identifies (1) the minimum criteria that should be met by all geostatistical simulation models, and (2) the checks required to verify that these minimum criteria are satisfied. All realizations should honor the input information including the geological interpretation, the data values at their locations, the data distribution, and the correlation structure, within acceptable statistical fluctuations. Moreover, the uncertainty measured by the differences between simulated realizations should be a reasonable measure of uncertainty. A number of different applications are shown to illustrate the various checks. These checks should be an integral part of any simulation modeling work flow.     

Implementation of the Iterative Proportion Fitting Algorithm for Geostatistical Facies Modeling

In geostatistics, most stochastic algorithm for simulation of categorical variables such as facies or rock types require a conditional probability distribution. The multivariate probability distribution of all the grouped locations including the unsampled location permits calculation of the conditional probability directly based on its definition. In this article, the iterative proportion fitting (IPF) algorithm is implemented to infer this multivariate probability. Using the IPF algorithm, the multivariate probability is obtained by iterative modification to an initial estimated multivariate probability using lower order bivariate probabilities as constraints. The imposed bivariate marginal probabilities are inferred from profiles along drill holes or wells. In the IPF process, a sparse matrix is used to calculate the marginal probabilities from the multivariate probability, which makes the iterative fitting more tractable and practical. This algorithm can be extended to higher order marginal probability constraints as used in multiple point statistics. The theoretical framework is developed and illustrated with estimation and simulation example.     

Uncertainty due to DEM error in landslide susceptibility mapping

Terrain attributes such as slope gradient and slope shape, computed from a gridded digital elevation model (DEM), are important input data for landslide susceptibility mapping. Errors in DEM can cause uncertainty in terrain attributes and thus influence landslide susceptibility mapping. Monte Carlo simulations have been used in this article to compare uncertainties due to DEM error in two representative landslide susceptibility mapping approaches: a recently developed expert knowledge and fuzzy logic-based approach to landslide susceptibility mapping (efLandslides), and a logistic regression approach that is representative of multivariate statistical approaches to landslide susceptibility mapping. The study area is located in the middle and upper reaches of the Yangtze River, China, and includes two adjacent areas with similar environmental conditions – one for efLandslides model development (approximately 250 km²) and the other for model extrapolation (approximately 4600 km²). Sequential Gaussian simulation was used to simulate DEM error fields at 25-m resolution with different magnitudes and spatial autocorrelation levels. Nine sets of simulations were generated. Each set included 100 realizations derived from a DEM error field specified by possible combinations of three standard deviation values (1, 7.5, and 15 m) for error magnitude and three range values (0, 60, and 120 m) for spatial autocorrelation. The overall uncertainties of both efLandslides and the logistic regression approach attributable to each model-simulated DEM error were evaluated based on a map of standard deviations of landslide susceptibility realizations. The uncertainty assessment showed that the overall uncertainty in efLandslides was less sensitive to DEM error than that in the logistic regression approach and that the overall uncertainties in both efLandslides and the logistic regression approach for the model-extrapolation area were generally lower than in the model-development area used in this study. Boxplots were produced by associating an independent validation set of 205 observed landslides in the model-extrapolation area with the resulting landslide susceptibility realizations. These boxplots showed that for all simulations, efLandslides produced more reasonable results than logistic regression.     

Nonparametric Geostatistical Simulation of Subsurface Facies: Tools for Validating the Reproduction of,and Uncertainty in,Facies Geometry

Delineation of facies in the subsurface and quantification of uncertainty in their boundaries are significant steps in mineral resource evaluation and reservoir modeling, which impact downstream analyses of a mining or petroleum project. This paper investigates the ability of nonparametric geostatistical simulation algorithms (sequential indicator, single normal equation and filter-based simulation) to construct realizations that reproduce some expected statistical and spatial features, namely facies proportions, boundary regularity, contact relationships and spatial correlation structure, as well as the expected fluctuations of these features across the realizations. The investigation is held through a synthetic case study and a real case study, in which a pluri-Gaussian model is considered as the reference for comparing the simulation results. Sequential indicator simulation and single normal equation simulation based on over-restricted neighborhood implementations yield the poorest results, followed by filter-based simulation, whereas single normal equation simulation with a large neighborhood implementation provides results that are closest to the reference pluri-Gaussian model. However, some biases and inaccurate fluctuations in the realization statistics (facies proportions, indicator direct and cross-variograms) still arise, which can be explained by the use of a single finite-size training image to construct the realizations.

     

SIMPLE MARKOV CHAIN MODEL OF SMOG PROBABILITY IN THE SOUTH COAST AIR BASIN OF CALIFORNIA

The simple Markov chain model approximates sequences of episode days for those air-monitoring stations with sufficient data for analysis. The conditional probability for episode days shows a distinct regional pattern, with highest values over the inland valleys and decreasing values toward the coast.     

Conditional Independence Test for Weights-of-Evidence Modeling

Weights-of-evidence modeling is a GIS-based technique for relating a point pattern for locations of discrete events with several map layers. In general, the map layers are binary or ternary. Weights for presence, absence or missing data are added to a prior logit. Updating with two or more map layers is allowed only if the map layers are approximately conditionally independent of the point pattern. The final product is a map of posterior probabilities of occurrence of the discrete event within a small unit cell. This paper contains formal proof that conditional independence of map layers implies that T, the sum of the posterior probabilities weighted according to unit cell area, is equal to n, being the total number of discrete events. This result is used in the overall or omnibus test for conditional independence. In practical applications, T generally exceeds n, indicating a possible lack of conditional independence. Estimation of the standard deviation of T allows performance of a one-tailed test to check whether or not T-n is significantly greater than zero. This new test is exact and simpler to use than other tests including the Kolmogorov-Smirnov test and various chi-squared tests adapted from discrete multivariate statistics.     

Characterizing the potential distribution of the invasive Asian longhorned beetle (Anoplophora glabripennis) in Worcester County,Massachusetts

The Asian longhorned beetle (ALB) (Anoplophora glabripennis) is an invasive insect pest that has established populations in Worcester County, Massachusetts as of 2008. ALB predominantly targets red maple (Acer rubrum), sugar maple (Acer saccharum), which are prominent in New England hardwood forests, and Norway maple (Acer platanoides), which was planted in built environments as street trees in response to severe weather and invasive insect and pathogen disturbances. Mahalanobis Typicality models related presence locations of ALB presence-only locations in Worcester County towns from 2008 to 2012 to biotic, abiotic, and anthropogenic variables to predict the potential distribution of ALB and to determine locations most characteristic of infestation. k-fold cross-validation and a continuous Boyce Index were employed to validate model performance and to identify threshold values at which continuous models of typicality could be reclassified into categorical maps. Distance-to-roads (r² = 0.19) and probability of maple presence (r² = 0.13) were the most important predictor variables in the ALB model. Locations that were most consistently susceptible to ALB infestation had significant high maple presence (p < 0.001) and significant low distance-to-roads (p < 0.001) compared to the whole study area, suggesting that ALB in Worcester County, MA, prefers maple-dominant edge habitats at the current stage of invasion. The localized ALB potential distribution model was consistently accurate (Boyce Continuous Index = 0.84) despite the lack of absence locations and incomplete knowledge of ALB niche breadth in both native and invaded ranges. The results from this study could be used as a baseline for effective adaptive management policies that could help prioritize the need for early detection/eradication measures and address the potential ecological and social ramifications of the current ALB outbreak in Worcester County. It is hoped that the model employed could be further tested for future outbreaks as they are discovered in the USA.     

Determining the susceptibility of Eucalyptus nitens forests to Coryphodema tristis (cossid moth) occurrence in Mpumalanga,South Africa

The cossid moth, Coryphodema tristis was first noted on Eucalyptus nitens trees in Mpumalanga province, South Africa during July 2004. Currently, the moth poses a major threat to commercial forestry in the country. In this study, selected climatic and topographical variables were used to model the susceptibility of E. nitens forests to cossid moth occurrence, thereby providing insight into the variables that may influence the occurrence and spread of the moth. A zigzag sampling technique was used to survey 5316 ha of E. nitens forests for the presence or absence of the moth. The random forest classification algorithm was then used to model the relationship between the climatic and topographical variables and the occurrence of the cossid moth. Results indicate that four variables that included elevation, maximum temperature for September, maximum temperature for April and the median rainfall for April best explained the presence or absence of C. tristis with an overall accuracy of 82% and a kappa value of 0.63. Partial dependence plots indicated that the areas that have a maximum temperature greater than 23°C in September and 22°C in April are likely to be infested by the cossid moth. The results from this study provide a robust and accurate spatial framework to assist forest managers in focussing their existing monitoring and control efforts to specific E. nitens forested areas that are highly susceptible to C. tristis infestations.     

Effects of Policy Measures and Moderating Factors on Climate Change Adaptation Among Private Forest Owners in Sweden

Threats associated with climate change may damage forests. To encourage adaptation in countries with a lot of privately owned forest, policy measures directed at private forest owners may be implemented. By means of policy scenarios, the present study examined the effects of two hypothetical policies: an advice and an economic incentive, on climate change adaptation among private forest owners in Sweden (n?=?753 and n?=?729). Both policies strengthened the intention to adapt although results also revealed that individual variables (i.e., forest values) and a contextual variable (i.e., being in an encouraging social risk management context) were important for the owners’ response. Production forest values and social risk management context furthermore moderated the effect of policy on intention to adapt, suggesting that the economic incentive was mainly more effective than the advice among owners with strong production values and among owners in less encouraging social risk management contexts.     

Prediction of Anopheles minimus habitat in India—a tool for malaria management

This paper reports research to predict the distribution of An. minimus, a malaria vector in forest fringe areas using GIS to support precision surveys for malaria control. Because An. minimus is a forest‐associated species, generalized thematic maps (1:6?000?000) of forest cover, soil type, altitude, rainfall and temperature were used. Digitization, overlaying, integration and analysis of thematic maps were done using Arc/Info 8.1 NT and Arc/View 3.2 (GIS, ESRI) software. GIS delineated favourable areas for An. minimus where the species is likely to be found, and precision surveys can be conducted. Precision field surveys in selected locations of favourable/non‐favourable areas were carried out. The species could be found in all locations designated as a favourable area and was absent in non‐favourable areas. In two districts, one where the species is reported to have disappeared in the early 1950s and the other where it was not reported in earlier surveys, GIS helped in precision surveys, and An. minimus was found. The technique can quickly cover vast and inaccessible areas and is easy to duplicate in other parts of the world to assist cost‐effective control of malaria. It can also delineate areas favourable for any species of flora and fauna to help precision surveys.     

New insights on measured and calculated vitrinite reflectance

Measurement of dispersed vitrinite reflectance in organic sediments is one of the few regional data sets used for placing bounds on the thermal history of a sedimentary basin. Reflectance data are important when access to complementary information such as high‐quality seismic data is unavailable to place bounds on subsidence history and in locations where uplift is an important part of the basin history. Attributes which make vitrinite reflectance measurements a useful data set are the relative ease of making the measurement, and the availability of archived well cores and cuttings in state, provincial, and federal facilities. In order to fully utilize vitrinite data for estimating the temperature history in a basin, physically based methods are required to calibrate an equivalent reflectance from a modelled temperature history with measured data. The most common method for calculating a numerical vitrinite reflectance from temperature history is the EASY%R_o method which we show systematically underestimates measured data. We present a new calculated reflectance model and an adjustment to EASY%R_o which makes the correlation between measured vitrinite values and calculated vitrinite values a physical relationship and more useful for constraining thermal models. We then show that calibrating the thermal history to vitrinite on a constant age date surface (e.g., top Cretaceous) instead of calibrating the thermal history in depth removes the heating rate component from the reflectance calculation and makes thermal history calibration easier to understand and more directly related to heat flow. Finally, we use bounds on the vitrinite–temperature relationships on a constant age date surface to show that significant uncertainty exists in the vitrinite data reported in most data sets.     

Changes in soil organic carbon and other physical soil properties along adjacent Mediterranean forest, grassland, and cropland ecosystems in Turkey

Cultivation, overgrazing, and overharvesting are seriously degrading forest and grassland ecosystems in the Taurus Mountains of the southern Mediterranean region of Turkey. This study investigated the effects of changes on soil organic carbon (SOC) content and other physical soil properties over a 12-year period in three adjacent ecosystems in a Mediterranean plateau. The ecosystems were cropland (converted from grasslands in 1990), open forest, and grassland. Soil samples from two depths, 0–10 and 10–20 cm, were collected for chemical and physical analyses at each of cropland, open forest, and grassland ecosystems. SOC pools at the 0–20 cm depth of cropland, forest, and grassland ecosystems were estimated at 32,636, 56,480, and 57,317 kg ha⁻¹, respectively. Conversion of grassland into cropland during the 12-year period increased the bulk density by 10.5% and soil erodibility by 46.2%; it decreased SOM by 48.8%, SOC content by 43%, available water capacity (AWC) by 30.5%, and total porosity by 9.1% for the 0–20 cm soil depth (p<0.001). The correlation matrix revealed that SOC content was positively correlated with AWC, total porosity, mean weight diameter (MWD), forest, and grassland, and negatively with bulk density, pH, soil erodibility factor, and cropland. The multiple regression (MLR) models indicated that any two of the three ecosystems and one of the two soil depths accounted for 86.5% of variation in mean SOC values ((p<0.001).     

中国东部南北样带森林生态系统WUE和NUE空间格局及驱动因子(英文)

From July 2008 to August 2008, 72 leaf samples from 22 species and 81 soil samples in the nine natural forest ecosystems were collected, from north to south along the North-South Transect of Eastern China (NSTEC). Based on these samples, we studied the geographical distribution patterns of vegetable water use efficiency (WUE) and nitrogen use efficiency (NUE), and analyzed their relationship with environmental factors. The vegetable WUE and NUE were calculated through the measurement of foliar δ 13C and C/N of predominant species, respectively. The results showed: (1) vegetable WUE, ranging from 2.13 to 28.67 mg C g-1 H2O, increased linearly from south to north in the representative forest ecosystems along the NSTEC, while vegetable NUE showed an opposite trend, increasing from north to south, ranging from 12.92 to 29.60 g C g-1 N. (2) Vegetable WUE and NUE were dominantly driven by climate and significantly affected by soil nutrient factors. Based on multiple stepwise regression analysis, mean annual temperature, soil phosphorus concentration, and soil nitrogen concentration were responding for 75.5% of the variations of WUE (p<0.001). While, mean annual precipitation and soil phosphorus concentration could explain 65.7% of the change in vegetable NUE (p<0.001). Moreover, vegetable WUE and NUE would also be seriously influenced by atmospheric nitrogen deposition in nitrogen saturated ecosystems. (3) There was a significant trade-off relationship between vegetable WUE and NUE in the typical forest ecosystems along the NSTEC (p<0.001), indicating a balanced strategy for vegetation in resource utilization in natural forest ecosystems along the NSTEC. This study suggests that global change would impact the resource use efficiency of forest ecosystems. However, vegetation could adapt to those changes by increasing the use efficiency of shortage resource while decreasing the relatively ample one. But extreme impacts, such as heavy nitrogen deposition, would break this trade-off mechanism and give a dramatic disturbance to the ecosystem biogeochemical cycle.     

A Hybrid Fuzzy Weights-of-Evidence Model for Mineral Potential Mapping

This paper describes a hybrid fuzzy weights-of-evidence (WofE) model for mineral potential mapping that generates fuzzy predictor patterns based on (a) knowledge-based fuzzy membership values and (b) data-based conditional probabilities. The fuzzy membership values are calculated using a knowledge-driven logistic membership function, which provides a framework for treating systemic uncertainty and also facilitates the use of multiclass predictor maps in the modeling procedure. The fuzzy predictor patterns are combined using Bayes’ rule in a log-linear form (under an assumption of conditional independence) to update the prior probability of target deposit-type occurrence in every unique combination of predictor patterns. The hybrid fuzzy WofE model is applied to a regional-scale mapping of base-metal deposit potential in the south-central part of the Aravalli metallogenic province (western India). The output map of fuzzy posterior probabilities of base-metal deposit occurrence is classified subsequently to delineate zones with high-favorability, moderate favorability, and low-favorability for occurrence of base-metal deposits. An analysis of the favorability map indicates (a) significant improvement of probability of base-metal deposit occurrence in the high-favorability and moderate-favorability zones and (b) significant deterioration of probability of base-metal deposit occurrence in the low-favorability zones. The results demonstrate usefulness of the hybrid fuzzy WofE model in representation and in integration of evidential features to map relative potential for mineral deposit occurrence.     

Assessment of biophysical properties of Royal Belum tropical forest,Malaysia

The Royal Belum forest reserve is one of the oldest tropical rainforests in the world and it is one of the largest virgin forest reserves in Malaysia. However, not many studies have been conducted to understand the ecology of this forest. In this study we estimated the aboveground biomass (AGB) of the forest using diameter at breast height (DBH) and height of trees (h ), tree species and hemispherical photographs of tree canopy. We estimated AGB using five allometric equations. Our results demonstrated that the AGB given by the one tree species specific allometric equation does not show any significant differences from the values given by the non‐tree species specific allometric equations at tree and plot levels. The AGB of Intsia bijuga species, Koompassia malaccensis species and Shorea genera were comparatively higher, owing to their greater wood density, DBH and h. This has added importance because some of these species are categorized as threatened species. Our results demonstrated that mean AGB values in this forest (293.16 t ha^‐1) are the highest compared to some studies of other areas in Malaysia, tropical Africa and tropical Bazilian Amazonia, implying that the Royal Belum forest reserve, is an important carbon reservoir.     

Application of inverse path distance weighting for high-density spatial mapping of coastal water quality patterns

One of the primary goals of coastal water quality monitoring is to characterize spatial variation. Generally, this monitoring takes place at a limited number of fixed sampling points. The alternative sampling methodology explored in this paper involves high-density sampling from an on-board flow-through water analysis system (Dataflow). Dataflow (West Palm Beach, FL, USA) has the potential to provide better spatial resolution of water quality features because it generates many closely spaced (<10 m) measurements. Regardless of the measurement technique, parameter values at unsampled locations must be interpolated from nearby measurement points in order to generate a comprehensive picture of spatial variations. Standard Euclidean interpolations in coastal settings tend to yield inaccurate results because they extend through barriers in the landscape such as peninsulas, islands, and submerged banks. We recently developed a method for non-Euclidean interpolation by inverse path distance weighting (IPDW) in order to account for these barriers. The algorithms were implemented as part of an R package and made available from R repositories. The combination of IPDW with Dataflow provided more accurate estimates of salinity patterning relative to Euclidean inverse distance weighting (IDW). IPDW was notably more accurate than IDW in the presence of intense spatial gradients.     

Sequential indicator conditional simulation and indicator kriging applied to discrimination of dolomitization in GER 63-channel imaging spectrometer data

Laboratory reflectance spectra of synthetic mixtures of the carbonate minerals calcite and dolomite were measured in the visible and near-infrared wavelength region (0.4–2.5 m) using a high-spectral resolution laboratory spectrometer. The instrument measured reflectivity with an accuracy of 0.001 m, allowing detailed resolution of the carbonate spectrum. The spectra of calcite and dolomite could be characterized by the presence of a strong absorption band centered at 2.3465 m for pure calcite and at 2.3039 m for pure dolomite. Nine mixtures of intermediate composition were analyzed demonstrating that the position of the carbonate absorption band is semilinearly related to the calcite content of the sample. Theoretically, this model allows mapping of dolomitization from high-spectral resolution remotely sensed imagery, GER 63-channel imaging spectrometer data from southern Spain were used to attempt such a mapping. First, pixels of vegetation were removed. For the remaining pixels, the wavelength center of the carbonate absorption band was detected and converted to a category of calcite fraction. The percentage of calcite for the remaining pixels was estimated using direct indicator kriging (IK) and sequential conditional indicator simulation, assuming that the calcite content could be represented as a category variable (SCIS category variable) and as a continuous variable (SCIS continuous variable). Four realizations of an SCIS (category variable) showed that on the average, 60 percent of the data was simulated in the same class and over 90 percent of the data within one class difference. A comparison with field samples showed that IK estimates of calcite content were within 20 percent accurate. The SCIS (continuous variable) does not perform as well with differences between –45% and +26% calcite; however, simulation reproduces the spatial variability better.     

Comparison analysis of sampling methods to estimate regional precipitation based on the Kriging interpolation methods: A case of northwestern China

The accuracy of spatial interpolation of precipitation data is determined by the actual spatial variability of the precipitation, the interpolation method, and the distribution of observatories whose selections are particularly important. In this paper, three spatial sampling programs, including spatial random sampling, spatial stratified sampling, and spatial sandwich sampling, are used to analyze the data from meteorological stations of northwestern China. We compared the accuracy of ordinary Kriging interpolation methods on the basis of the sampling results. The error values of the regional annual precipitation interpolation based on spatial sandwich sampling, including ME(0.1513), RMSE(95.91), ASE(101.84), MSE(-0.0036), and RMSSE(1.0397), were optimal under the premise of abundant prior knowledge. The result of spatial stratified sampling was poor, and spatial random sampling was even worse. Spatial sandwich sampling was the best sampling method, which minimized the error of regional precipitation estimation. It had a higher degree of accuracy compared with the other two methods and a wider scope of application.