Assessing the spatial probability of landslides using GIS and informative value model in the Bamenda highlands

Landslides are a major natural hazard in the Bamenda highlands of Cameroon, and their occurrence in this region has most often been studied using qualitative methods. The aim of this research is to quantitatively assess the spatial probability of landslides using GIS and the informative value model. Landslide inventory was done through literature review, aerial photo-interpretation, participatory GIS and field survey. Six geo-environmental factors including slope, curvature, aspect, land use, lithology and geomorphology were used as landslide conditioning (static) factors. The susceptibility of the area to future landslide events was assessed by making a correlation between past landslides and geo-environmental factors using the informative value model. The landslide inventory involving 110 landslides was divided into two equal groups using random division criterion and was used to train and validate the model. The analysis showed that slope and land use are the most important causal factors of landslides in the area. The susceptibility index map predicted most landslides to occur around the steep slopes of the Bamenda escarpment that is being used for multiple anthropic activities. The training model had a success rate of 87%, and the validation model had a prediction rate of 90%. The prediction rate curve shows that 44, 32, 18 and 6% of future landslides will occur on 3, 8, 21 and 68% of the study area. The model correctly classified 89% of unstable areas and 81% of the stable areas with an accuracy rate of 0.90. This quantitative result complement other qualitative assessment results that show the Bamenda escarpment zone as a high-risk area. However, the area susceptible to landslide in this study goes beyond what earlier studies had indicated as houses and other infrastructure were found on old landslide sites whose scars have been eroded by human activities. This new input thus improves the quality of information placed at the disposal of civil protection units and land use managers during decision making.     

Drivers of afforestation in Northern Vietnam: Assessing local variations using geographically weighted regression

This article identifies drivers of forest transition in a province of Northern Vietnam between 1993 and 2000 by applying geographically weighted regression (GWR) analysis to remotely sensed and statistical data. The regression model highlighted the spatial variation of the relationship between the percentage of land afforested and its proximate causes. Factors identified as having a major impact on afforestation are: the presence or proximity of a wood-processing industry, the distance to highways, and land allocation to households. Whereas the two former variables are in most areas of the province positively correlated with afforestation, an unexpected negative correlation was observed for the latter. The analysis of these results, supported by an in-depth knowledge of the area and of the political context, leads to the conclusion that, during the time period considered, afforestation was largely driven by state organisations on protected state-owned land, and forestry was not a significant component of household economic activities.     

Retrieval of chlorophyll and nitrogen in Norway spruce (Picea abies L. Karst.) using imaging spectroscopy

The research evaluated the information content of spectral reflectance (laboratory and airborne data) for the estimation of needle chlorophyll (C_AB) and nitrogen (C_N) concentration in Norway spruce (Picea abies L. Karst.) needles. To identify reliable predictive models different types of spectral transformations were systematically compared regarding the accuracy of prediction. The results of the cross-validated analysis showed that C_AB can be well estimated from laboratory and canopy reflectance data. The best predictive model to estimate C_AB was achieved from laboratory spectra using continuum-removal transformed data (R²cv = 0.83 and a relative RMSEcv of 8.1%, n = 78) and from hyperspectral HyMap data using band-depth normalised spectra (R²cv = 0.90, relative RMSEcv = 2.8%, n = 13). Concerning the nitrogen concentration, we observed somewhat weaker relations, with however still acceptable accuracies (at canopy level: R²cv = 0.57, relative RMSEcv = 4.6%). The wavebands selected in the regression models to estimate C_AB were typically located in the red edge region and near the green reflectance peak. For C_N, additional wavebands related to a known protein absorption feature at 2350 nm were selected. The portion of selected wavebands attributable to known absorption features strongly depends on the type of spectral transformation applied. A method called “water removal” (WR) produced for canopy spectra the largest percentage of wavebands directly or indirectly related to known absorption features. The derived chlorophyll and nitrogen maps may support the detection and the monitoring of environmental stressors and are also important inputs to many bio-geochemical process models.     

Analytical solutions for sequentially coupled one-dimensional reactive transport problems – Part I: Mathematical derivations

Multi-species reactive transport equations coupled through sorption and sequential first-order reactions are commonly used to model sites contaminated with radioactive wastes, chlorinated solvents and nitrogenous species. Although researchers have been attempting to solve various forms of these reactive transport equations for over 50 years, a general closed-form analytical solution to this problem is not available in the published literature. In Part I of this two-part article, we derive a closed-form analytical solution to this problem for spatially-varying initial conditions. The proposed solution procedure employs a combination of Laplace and linear transform methods to uncouple and solve the system of partial differential equations. Two distinct solutions are derived for Dirichlet and Cauchy boundary conditions each with Bateman-type source terms. We organize and present the final solutions in a common format that represents the solutions to both boundary conditions. In addition, we provide the mathematical concepts for deriving the solution within a generic framework that can be used for solving similar transport problems.     

Satellite-based analysis of the role of land use/land cover and vegetation density on surface temperature regime of Delhi,India

The knowledge of the surface temperature is important to a range of issues and themes in earth sciences central to urban climatology, global environmental change and human-environment interactions. The study analyses land surface temperature (LST) estimation using temporal ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) datasets (day time and night time) over National Capital Territory Delhi using the surface emissivity information at pixel level. The spatial variations of LST over different land use/land cover (LU/LC) at day time and night time were analysed and relationship between the spatial distribution of LU/LC and vegetation density with LST was developed. Minimum noise fraction (MNF) was used for LU/LC classification which gave better accuracy than classification with original bands. The satellite derived emissivity values were found to be in good agreement with literature and field measured values. It was observed that fallow land, waste land/bare soil, commercial/industrial and high dense built-up area have high surface temperature values during day time, compared to those over water bodies, agricultural cropland, and dense vegetation. During night time high surface temperature values are found over high dense built-up, water bodies, commercial/industrial and low dense built-up than over fallow land, dense vegetation and agricultural cropland. It was found that there is a strong negative correlation between surface temperature and NDVI over dense vegetation, sparse vegetation and low dense built-up area while with fraction vegetation cover, it indicates a moderate negative correlation. The results suggest that the methodology is feasible to estimate NDVI, surface emissivity and surface temperature with reasonable accuracy over heterogeneous urban area. The analysis also indicates that the relationship between the spatial distribution of LU/LC and vegetation density is closely related to the development of urban heat islands (UHI).     

A bio-available strontium isoscape for eastern Beringia: a tool for tracking landscape use of Pleistocene megafauna

Numerous paleoecological questions concern the mobility of ancient fauna in eastern Beringia. Strontium (Sr) isotope ratio (⁸⁷Sr/⁸⁶Sr) analysis has emerged as a powerful tracer for determining the provenance of ancient biological materials. However, it is important to characterize ⁸⁷Sr/⁸⁶Sr variation across a landscape. We measured the ⁸⁷Sr/⁸⁶Sr composition of teeth from present-day, herbivorous rodents (n = 162) sampled from across eastern Beringia to estimate bio-available ⁸⁷Sr/⁸⁶Sr values. We compiled these data with the very limited number of previously published ⁸⁷Sr/⁸⁶Sr values from the region. We then used this dataset and a machine learning, random-forest regression to predict bio-available ⁸⁷Sr/⁸⁶Sr variations across eastern Beringia. As a case study using our new ⁸⁷Sr/⁸⁶Sr map (isoscape), we measured the ⁸⁷Sr/⁸⁶Sr and oxygen stable isotope values (δ¹⁸O) of five radiocarbon-dated steppe bison from eastern Beringia and compared these to our ⁸⁷Sr/⁸⁶Sr isoscape and a δ¹⁸O isoscape to estimate the probable landscape use of these ancient fauna. Our model and isoscape provide important foundations for a wide range of additional applications, including studies of the paleo-mobility of other fauna, ancient people and present-day fauna in eastern Beringia.     

Rapid, oxygen-dependent microbial Mn(II) oxidation kinetics at sub-micromolar oxygen concentrations in the Black Sea suboxic zone

Microbial Mn(II) oxidation kinetics in response to oxygen concentration were assessed in suboxic zone water at six sites throughout the Black Sea. Mn(II) oxidation rates increased asymptotically with increasing oxygen concentration, consistent with Michaelis-Menten enzyme kinetics. The environmental half-saturation constant, K_E, of Mn(II) removal (oxidation) varied from 0.30 to 10.5 μM dissolved oxygen while the maximal environmental rate, V_E−max, ranged from 4 to 50 nM h⁻¹. These parameters varied spatially and temporally, consistent with a diverse population of enzymes catalyzing Mn oxide production in the Black Sea. Coastally-influenced sites produced lower K_E and higher V_E−max constants relative to the Western and Eastern Gyre sites. In the Bosporus Region, the Mn(II) residence time calculated using our K_E and V_E−max values with 0.1 μM oxygen was 4 days, 25-fold less than previous estimates. Our results (i) indicate that rapid Mn(II) oxidation to solid phase Mn oxides in the Black Sea’s suboxic zone is stimulated by oxygen concentrations well below the 3-5 μM concentration reliably detected by current oceanographic methods, (ii) suggest the existence of multiple, diverse Mn(II)-oxidizing enzymes, (iii) are consistent with shorter residence times than previously calculated for Mn(II) in the suboxic zone and (iv) cast further doubt on the existence of proposed reactions coupling solid phase Mn oxide production to electron acceptors other than oxygen.     

Domenico solution--is it valid?

The Domenico solution is widely used in several analytical models for simulating ground water contaminant transport scenarios. Unfortunately, many textbook as well as journal article treatments of this approximate solution are full of empirical statements that are developed without mathematical rigor. For this reason, a rigorous analysis of this solution is warranted. In this article, we present a mathematical method to derive the Domenico solution and explore its limits. Our analysis shows that the Domenico solution is a true analytical solution when the value of longitudinal dispersivity is zero. For nonzero longitudinal dispersivity values, the Domenico solution will introduce a finite amount of error. We use an example problem to quantify the nature of this error and suggest some general guidelines for the appropriate use of this solution.     

A three-dimensional analytical tool for modeling reactive transport

In this note, we present a public domain analytical reactive transport modeling tool (ART3D, version 2.0). The tool is developed in FORTRAN and can be used for solving a system of a set of partial differential equations coupled with a first-order reaction network. ART3D uses a novel analytic solution technique proposed by Clement. The new software includes options for performing Monte Carlo simulations and automated parameter estimation.