Local Topographic Model using Position Index for Analyzing the Characteristics of Unserpentinized Lateritic Zones in Sorowako Nickeliferous Laterite Deposit, Indonesia

An investigation of the relationship between slope position classification and nickeliferous laterite zones over serpentinized ultramafic terrain in Sorowako, South Sulawesi has been conducted using topographic position index from Light Detection and Ranging data for digital elevation model as references with detail resolution of 5m. The index is calculated by comparing the elevation of each cell in the elevation model to the mean of a specified neighborhood around that cell. The classification has six classes, i.e., valleys, lower slopes, gentle slopes, middle slopes, upper slopes and the local ridges. Chemical properties data from 413 drill holes were used for analysis to confirm the laterite zones –limonite and saprolite. By topographic model, slope position class of local ridges, upper slopes and lower slopes with the slope average of 9.82°, 16.05°, and 14.61° respectively, generally indicated the distribution of thick limonite zones, while saprolite zones were in significantly different pattern due to less or no correlation. 2D semivariogram model for spatial thickness distribution also confirmed the corresponding factor between the significant direction of landform reliefs and limonite thickness. Based on the geochemistry profile of limonite zone, the rate of weathering process in laterite formation is longer than the physical process of removal top profile by erosion or accumulation by transported top materials

GIS-based landform classification of Bronze Age archaeological sites on Crete Island

Various physical attributes of the Earth's surface are factors that influence local topography and indirectly influence human behaviour in terms of habitation locations. The determination of geomorphological setting plays an important role in archaeological landscape research. Several landform types can be distinguished by characteristic geomorphic attributes that portray the landscape surrounding a settlement and influence its ability to sustain a population. Geomorphometric landform information, derived from digital elevation models (DEMs), such as the ASTER Global DEM, can provide useful insights into the processes shaping landscapes. This work examines the influence of landform classification on the settlement locations of Bronze Age (Minoan) Crete, focusing on the districts of Phaistos, Kavousi and Vrokastro. The landform classification was based on the topographic position index (TPI) and deviation from mean elevation (DEV) analysis to highlight slope steepness of various landform classes, characterizing the surrounding landscape environment of the settlements locations. The outcomes indicate no interrelationship between the settlement locations and topography during the Early Minoan period, but a significant interrelationship exists during the later Minoan periods with the presence of more organised societies. The landform classification can provide insights into factors favouring human habitation and can contribute to archaeological predictive modelling

Spatial Relation of Apparent Soil Electrical Conductivity with Crop Yields and Soil Properties at Different Topographic Positions in a Small Agricultural Watershed

Use of electromagnetic induction (EMI) sensors along with geospatial modeling provide a better opportunity for understanding spatial distribution of soil properties and crop yields on a landscape level and to map site-specific management zones. The first objective of this research was to evaluate the relationship of crop yields, soil properties and apparent electrical conductivity (ECa) at different topographic positions (shoulder, backslope, and deposition slope). The second objective was to examine whether the correlation of ECa with soil properties and crop yields on a watershed scale can be improved by considering topography in modeling ECa and soil properties compared to a whole field scale with no topographic separation. This study was conducted in two headwater agricultural watersheds in southern Illinois, USA. The experimental design consisted of three basins per watershed and each basin was divided into three topographic positions (shoulder, backslope and deposition) using the Slope Position Classification model in ESRI ArcMap. A combine harvester equipped with a GPS-based recording system was used for yield monitoring and mapping from 2012 to 2015. Soil samples were taken at depths from 0–15 cm and 15–30 cm from 54 locations in the two watersheds in fall 2015 and analyzed for physical and chemical properties. The ECa was measured using EMI device, EM38-MK2, which provides four dipole readings ECa-H-0.5, ECa-H-1, ECa-V-0.5, and ECa-V-1. Soybean and corn yields at depositional position were 38% and 62% lower than the shoulder position in 2014 and 2015, respectively. Soil pH, total carbon (TC), total nitrogen (TN), Mehlich-3 Phosphorus (P), Bray-1 P and ECa at depositional positions were significantly higher compared to shoulder positions. Corn and soybeans yields were weakly to moderately

Mapping the Landscape for Archaeological Detection, Preservation, and Interpretation: A Case Study in High Resolution Location Modeling from the Blue Mountains of Northeastern Oregon

Archaeological location modeling (ALM) is an important tool in most survey strategies, and has contributed substantially to economizing efforts to locate and characterize the archaeological record. The increasing availability of high resolution (\u3c3m) airborne light detection and ranging (lidar) data has the potential to refine the application and ultimately the role of ALM. This research tests the precision and accuracy gained by incorporating lidar derived data into an ALM. The site records and other environmental data used in this study were all generated over the last four decades by the resource specialists of the Malheur National Forest. The Weights-of-Evidence (WofE) probability method (Bonham-Carter 1994) was used to produce two ALMs; one based on a 10m digital elevation model (DEM) created from satellite imaging, and the second from a 3m resolution lidar derived DEM. Independent variables (e.g., slope, aspect, distance to water, etc.) commonly used in ALM were largely replaced by index variables (e.g., slope position classification, topographic wetness index, etc.). The final models were classified into areas of high, medium, and low archaeological potential, then cross-validated against a reserved random dataset. Models were then compared using the Kvamme gain statistic and site to area frequency ratio. The 3m model demonstrated a significant improvement over the results obtained from the 10m model and the current probability model used in the study area. A number of factors including model resolution, statistical methodology, and the character of the independent and dependent variables all contributed to the increase in precision and accuracy. The incremental improvement in modeling efficiency demonstrated here will create time and cost saving in the management and preservation of cultural resources, and ultimately contribute to a better understanding of patterns of past human land use

Factors Affecting Habitat Occupancy of Tiger in the Terai ARC Landscape, India

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Utilizing Remotely Piloted Air Systems in the Delineation of Functional Land Management Zones

Global food production must increase significantly before 2050 to ensure food security. This necessitates the intensification of agriculture to keep with land resource constraints. Meanwhile, climate change is occurring, and these two factors are exerting pressure on the medium through which food production occurs: the soil. Soil provides many of the ecosystem services provided by farmland and is essential for functions such as food production, water storage, carbon cycling and storage, functional and intrinsic biodiversity, as well as nutrient cycling. In order for agriculture to intensify sustainably, these soil functions must be maintained. However, we do not currently have a baseline measure of the overall functions soils are providing which is needed in order to track how climate change and agricultural intensification are impacting the soil. Precision agriculture provides an avenue to achieve this and management zones are essential to precision agriculture. Traditional methods of sampling to gather soil information are labor intensive and time consuming; it is necessary to find faster alternatives. Remote sensing and digital soil mapping (DSM) are two technologies with great potential for quickly gathering soil information at large spatial scales. The objectives of this study were to: 1) test remote sensing methods for the interpolation of surficial soil organic carbon using a remotely piloted air system (RPAS); and 2) develop a method of management zone delineation that accounts for multiple soil functions. Remote sensing was found to be the most effective at estimating soil organic carbon (SOC) through the use of DSM. SOC and topography were found to be key factors for multiple soil functions. These factors were used to develop a management zone delineation method that was indicative of multiple soil functions. An RPAS is not necessary for this method but remote sensing data is essential. This method assists land users to, within a familiar framework, quickly estimate and manage for multiple soil functions. It produces a measure of soil health that enables land productivity and value to be maximized while providing the opportunity to respond timely to the effects of climate change and agricultural intensification

SPATIAL AND TEMPORAL PATTERNS OF INVASIVE EXOTIC PLANT SPECIES IN RESPONSE TO TIMBER HARVESTING IN A MIXED MESOPHYTIC FOREST OF EASTERN KENTUCKY

Invasive exotic species (IES) responses to silvicultural treatments eight years after timber harvesting were examined and compared to one-year post-harvest IES survey in University of Kentucky’s Robinson Forest. The temporal effects of harvesting were further compared between harvested and non-harvested watersheds. Analyses were performed to identify IES spatial distribution and determine the relationships between IES presence and disturbance effects, biological, and environmental characteristics. IES prevalence was higher in the harvested watersheds and was influenced by canopy cover, shrub cover and disturbance proximity. Ailanthus altissima and Microstegium vimineum presence in the study area has decreased over time. Comparing to the 1-yr post-harvest study which only identified direct harvesting effects (e.g. canopy cover and disturbance proximities) as significant predictors, the 8-yr post-harvest survey results suggest that while harvesting effects and disturbance proximity still play an important role, environmental characteristics have also taken precedence in predicting IES presence. Overall IES prevalence has decreased but invasive plant species richness has increased over time. Results indicate that IES eradication may not need to be conducted immediately after harvesting, and when needed, can primarily target IES hotspots where low canopy cover, proximity to disturbance, and southwest facing slopes convene on the landscape

Comparison of Terrain Indices and Landform Classification Procedures in Low-Relief Agricultural Fields

Landforms control the spatial distribution of numerous factors associated with agronomy and water quality. Although curvature and slope are the fundamental surface derivatives used in landform classification procedures, methodologies for landform classifications have been performed with other terrain indices including the topographic position index (TPI) and the convergence index (CI). The objectives of this study are to compare plan curvature, the convergence index, profile curvature, and the topographic position index at various scales to determine which better identifies the spatial variability of soil phosphorus (P) within three low relief agricultural fields in central Illinois and to compare how two methods of landform classification, e.g. Pennock et al. (1987) and a modified approach to the TPI method (Weiss 2001, Jenness 2006), capture the variability of spatial soil P within an agricultural field. Soil sampling was performed on a 0.4 ha grid within three agricultural fields located near Decatur, IL and samples were analyzed for Mehlich-3 phosphorus. A 10-m DEM of the three fields was also generated from a survey performed with a real time kinematic global positioning system. The DEM was used to generate rasters of profile curvature, plan curvature, topographic position index, and convergence index in each of the three fields at scales ranging from 10 m to 150 m radii. In two of the three study sites, the TPI (r ≥ -0.42) was better correlated to soil P than profile curvature (r ≤ 0.41), while the CI (r ≥ -0.52) was better correlated to soil P than plan curvature (r ≥ -0.45) in all three sites. Although the Pennock method of landform classification failed to identify footslopes and shoulders, which are clearly part of these fields’ topographic framework, the Pennock method (R² = 0.29) and TPI method (R² = 0.30) classified landforms that captured similar amounts of soil P spatial variability in two of the three study sites. The TPI and CI should be further explored when performing terrain analysis at the agricultural field scale to create solutions for precision management objectives

Digital terrain analysis reveals new insights into the topographic context of Australian Aboriginal stone arrangements

Satellite-derived surface elevation models are an important resource for landscape archaeological studies. Digital elevation data is useful for classifying land features, characterizing terrain morphology, and discriminating the geomorphic context of archaeological phenomena. This paper shows how remotely sensed elevation data obtained from the Japan Aerospace Exploration Agency's Advanced Land Observing Satellite was integrated with local land system spatial data to digitally classify the topographic slope position of seven broad land classes. The motivation of our research was to employ an objective method that would allow researchers to geomorphometrically discriminate the topographic context of Aboriginal stone arrangements, an important archaeological site type in the Pilbara region of northwest Australia. The resulting digital terrain model demonstrates that stone arrangement sites are strongly correlated with upper topographic land features, a finding that contradicts previous site recordings and fundamentally changes our understanding of where stone arrangement sites are likely to have been constructed. The outcome of this research provides investigators with a stronger foundation for testing hypotheses and developing archaeological models. To some degree, our results also hint at the possible functions of stone arrangements, which have largely remained enigmatic to researchers

Predicting mire distribution using species distribution models (SDMs) and GISc : a case study of the Prince Edward Islands (PEIs)

Dissertation (MSc (Geoinformatics))--University of Pretoria, 2022.Peatlands are wetlands with peat-producing plants that account for one-third of wetlands worldwide and provide a variety of ecological functions and ecosystem services such as carbon storage, biomass production, biodiversity conservation, and climate regulation. As important ecological systems that are vulnerable to climate change, it is critical to assess what drives mire distribution in order to predict the potential impact of climate change on their distribution. As a Special Nature Reserve, the Prince Edward Islands (PEIs) are important conservation areas for South Africa. They support extensive peatlands, which are actively accumulating peat, and are, therefore, referred to as mires. The Islands have experienced severe reductions in precipitation and significant warming in the last decades; anecdotal evidence suggests that these have affected the occurrence and extent of mires on the PEIs. Factors that drive mire occurrence are unclear and must be identified in order to improve the ability to monitor them over time and this can be achieved using Species distribution models (SDMs). SDMs are a significant tool in studies on species distribution, the ecological consequences of climate change, and efforts to protect specific species or biodiversity as a whole. Predictive models have been used effectively to map and detect wetlands at both the local and regional levels. The aim of this study was to use species distribution modelling to understand the drivers and predict the island-wide distribution of mires on the PEIs. A total of 1415 mire presence-absence points from a vegetation field survey conducted on Marion Island from 2018 to 2020 were used. As there is no single best SDM algorithm, and it is difficult to accurately identify which environmental variables drive the distribution of mires on the PEIs, multiple regression- based and machine learning SDMs based on six different combinations of environmental factors were investigated. The environmental variables combinations included climate variables, topographic, geology and soils and satellite imagery variables, a combination thereof and wetland classification proxy variables from three wetland classification systems (Ramsar, Hydrogeomorphic (HGM), International Union for Conservation of Nature (IUCN) Global Ecosystem Typology 2.0 wetland classification systems). Random Forest model performed the best, only performing fairly in terms of the AUC (0.74) and TSS (0.42) metrics but managing a 99% correct classification rate (CCR) of all the mire presence-absence MM Sadiki: MSc Dissertation Page | iiobservations when trained and tested on Marion Island. The distribution of mires was largely influenced by surface wetness and slope. Low annual mean temperature, low temperature and precipitation seasonality, and increasing distance from coast (up to 7.2 km inland) also influenced the distribution of mires on the PEIs, though less strongly than surface wetness and slope. According to the model predictions, mires occupy 8.7 km2 (of ~290 km2; ~ 3%) of Marion Island and 2.63 km2 (of ~ 45 km2; ~6%) of Prince Edward Island respectively. The predictive performance and reliability of the models can be improved by making enhancements to the datasets of environmental variables in terms of resolution. This is especially true for the spatial, temporal, and spectral resolutions of satellite imagery used to model environmental variables, the spatial resolution of the WorldClim climate data (which is currently based on data from only one meteorological station on Marion Island), and the spatial resolution and accuracy of the geology dataset. The inclusion of other environmental variables may also improve the predictive ability of the models in this study.South African National Space Agency (SANSA)Geography, Geoinformatics and MeteorologyMSc (Geoinformatics)Unrestricte