Geographies, Volume 2, Issue 2 (June 2022) – 12 articles

Most of the methods for landslide susceptibility assessment are based on mathematical relationships established between factors responsible for the triggering of the phenomenon, named the conditioning factors. These are usually derived from geographic data commonly handled through Geographical Information System (GIS) technology. According to the adopted methodology, after an initial phase conducted on the GIS platform, data need to be transferred to specific software, e.g., MATLAB, for analysis and elaboration. GIS-based risk management platforms are thus sometimes hybrid, requiring relatively complex adaptive procedures before exchanging data among different environments. This paper describes how MATLAB can be used to derive the most common landslide conditioning factors, by managing the geographic data in their typical formats: raster, vector or point data. Specifically, it is discussed how to build matrices of parameters, needed to assess susceptibility, by using grid cell mapping units, and mapping them bypassing GIS. An application of these preliminary operations to a study area affected by shallow landslides in the past is shown; results show how geodata can be managed as easily as in GIS, as well as being displayed in a fashionable way too. Moreover, it is discussed how raster resolution affects the processing time. The paper sets the future development of MATLAB as a fully implemented platform for landslide susceptibility, based on any available methods. Full article

Unmanned Aerial Systems (UAS, UAV, or drones) have become an effective tool for applications in natural resources since the start of the 21st century. With their associated hardware and software technologies, UAS sensor data have provided high resolution and high accuracy results in a range of disciplines. Despite these achievements, only minimal progress has been made in (1) establishing standard operating practices and (2) communicating both the limitations and necessary next steps for future research. In this review of literature published between 2016 and 2022, UAS applications in forestry, freshwater ecosystems, grasslands and shrublands, and agriculture were synthesized to discuss the status and trends in UAS sensor data collection and processing. Two distinct conclusions were summarized from the over 120 UAS applications reviewed for this research. First, while each discipline exhibited similarities among their data collection and processing methods, best practices were not referenced in most instances. Second, there is still a considerable variability in the UAS sensor data methods described in UAS applications in natural resources, with fewer than half of the publications including an incomplete level of detail to replicate the study. If UAS are to increasingly provide data for important or complex challenges, they must be effectively utilized. Full article

Greenhouse gas (GHG) emissions from landcover conversions contribute to the total carbon (C) footprint (CF), which is the sum of GHG emissions from various sources and events expressed as carbon dioxide (CO₂) equivalent. Soil-based emissions from land conversions are often excluded from the total CF, which can lead to underreporting the CF. This study uses the state of Connecticut (CT) as a case study to demonstrate the importance of soil-based emissions from land cover conversions to the state’s CF. The state of CT Public Act 08-98 (2008): Global Warming Solutions Act (GWSA) set a statutory requirement to cut GHG emissions 10 percent below 1990 levels by 2020 and 80 percent below 2001 levels by 2050 without considering soil-based emissions from land conversions. This omission results in underestimates of past and current emissions related to CT’s CF. In addition, not accounting for soil-based emissions from land conversions may increase the future size of CT’s CF. Remote sensing and soil data analysis provide an opportunity for rapid, quantitative, and temporal assessment of the contribution of land cover conversions to CT’s CF by soil type, land cover type, and administrative units (counties). Results are reported for soil organic carbon (SOC), soil inorganic carbon (SIC), and total soil carbon (TSC) based on C contents and monetary values of social costs of carbon. The state of CT experienced soil-based emissions from land cover conversions from 2001 to 2016 with $388.1M (where $ = USD, M = million = 10⁶) worth of “realized” social costs of carbon dioxide (SC-CO₂) emissions which should be accounted for in CT’s total CF. The current methodology could be used to optimize future land conversions to minimize the amount of soil GHG emissions by considering the soil C resources in different development scenarios. With an extensive, densely populated coastal area, CT will be directly affected by rising sea levels and other climate change impacts. Future research can focus on owner-specific CF contributions to address the responsibility for costs of GHG emissions as well as limiting the CF impact of land conversions. Full article

Many books, textbooks and papers have been published in which the classification of map projections is based on auxiliary (developable) surfaces and projections are divided into conic, cylindrical and azimuthal projections. We argue that such a classification of map projections is unacceptable and give many reasons for that. Many authors wrote in more detail about the classification of map projections, and our intention is to give a new refined and rectified insight into the classification of map projections. Our approach can be included in map projection publications of general and thematic cartography. Doing this, misconceptions and unnecessary insistence on conceptuality instead of reality will be avoided. Full article

This article elaborates on map-quality evaluation and assessment as a result of the generalization of geospatial data through the development of a methodology, which incorporates a quality data model including constraints. These constraints are used to guide the generalization process and they operate as requirements in quality controls applied for the quality evaluation and assessment of the resulting cartographic data. The quality model stores the required map specifications compiled as constraints, and provides quality measures along with new techniques for the evaluation and assessment of cartographic data quality. This secures the map composition process in each and every step and for all features involved, at any map scale. The methodology developed results in the creation of a scale-dependent cartographic database that contains exclusively the features to be portrayed on the map, generalized properly according to the map scale. It will reduce cartographers’ need to review each transformation throughout the map-composition process with considerable savings in time and money and, on the other hand, it will secure the quality of the final map. The formulation of the proposed methodology amalgamates generalization theory with the authors’ research in computer-assisted cartography, taking into account the work conducted on the topic by other researchers. In this study, the quality requirements, the measures and the associated techniques together with the results of the application of the proposed methodology for area and line features are described in detail to allow others to replicate and build on the presented results. Full article

The digital age has brought about an explosion in the growth of data, of which data with a geographical component stands out. Proper use of geographical data comes with the need for correct coordinate reference systems (CRSs). They are considered the ultimate binder for interoperability between geospatial data actors and stakeholders. Moreover, CRSs are crucial for the visual and analytical integration of geospatial data from disparate data sources. However, CRSs might be—for numerous reasons—incorrectly assigned or even missing. The result is a time-consuming study of the map, literature, and available resources to ultimately find the alleged right CRS. This study provides a summary of prevailing resources from national mapping agencies of some European countries to address the above problem. Secondly, and most importantly, is the development of an open-source Python-based software package. This software package aims to accurately estimate the best candidate CRS, given a tuple of coordinates at a priori an approximately known location. It is controlled by geocoding the known location and intersecting the resulting coordinate with the bounding box of all CRSs in the EPSG-database. An in-depth review of CRS tools by mapping authorities reveals, in particular, limitations concerning the countries’ spatial areas, in combination with often required know-how of local CRSs. To address these shortcomings, our tool is developed to enable a more generic extraction of CRSs for any given location worldwide. Testing proved successful for 30 different maps, with a grid present on the map and the CRS of the map being included in the EPSG-database. Full article

Recent research has extended conventional hydrological algorithms into a hexagonal grid and noted that hydrological modeling on a hexagonal mesh grid outperformed that on a rectangular grid. Among the hydrological products, flow routing grids are the base of many other hydrological simulations, such as flow accumulation, watershed delineation, and stream networks. However, most of the previous research adopted the D6 algorithm, which is analogous to the D8 algorithm over a rectangular grid, to produce flow routing. This paper explored another four methods regarding generating flow directions in a hexagonal grid, based on four algorithms of slope aspect computation. We also developed and visualized hexagonal-grid-based hydrological operations, including flow accumulation, watershed delineation, and hydrological indices computation. Experiments were carried out across multiple grid resolutions with various terrain roughness. The results showed that flow direction can vary among different approaches, and the impact of such variation can propagate to flow accumulation, watershed delineation, and hydrological indices production, which was reflected by the cell-wise comparison and visualization. This research is practical for hydrological analysis in hexagonal, hierarchical grids, such as Discrete Global Grid Systems, and the developed operations can be used in flood modeling in the real world. Full article

Desertification has become one of the most pronounced ecological disasters, affecting arid and semi-arid areas of Nigeria. This phenomenon is more pronounced in the northern region, particularly the eleven frontline states of Nigeria, sharing borders with the Niger Republic. This has been attributed to a range of natural and anthropogenic factors. Rampant felling of trees for fuelwood, unsustainable agriculture, overgrazing, coupled with unfavourable climatic conditions are among the key factors that aggravate the desertification phenomenon. This study applied geospatial analysis to explore land use/land cover changes and detect major conversions from ecologically active land covers to sand dunes. Results indicate that areas covered by sand dunes (a major indicator of desertification) have doubled over the 25 years under consideration (1990 to 2015). Even though 0.71 km² of dunes was converted to vegetation, indicative of the success of various international, national, local and individual afforestation efforts, conversely about 10.1 km² of vegetation were converted to sand dunes, implying around 14 times more deforestation compared to afforestation. On average, our results revealed that the sand dune in the study area is progressing at a mean annual rate of 15.2 km² annually. The land cover conversion within the 25-year study period was from vegetated land to farmlands. Comparing the progression of a sand dune with climate records of the study area and examining the relationship between indicators of climate change and desertification suggested a mismatch between both processes, as increasing rainfall and lower temperatures observed in 1994, 2005, 2012, and 2014 did not translate into positive feedbacks for desertification in the study area. Likewise, the mean annual Normalized Difference Vegetation Index (NDVI) from 2000 to 2015 shows a deviation between vegetation peaks, mean temperatures and rainfall. On average, our results reveal that the sand dune is progressing at a mean annual rate of about 15.2 km² in the study area. Based on this study’s land cover change, trend and conversion assessment, visual reconciliation of climate records of land cover data, statistical analysis, observations from ground-truthing, as well as previous literature, it can be inferred that desertification in Nigeria is less a function of climate change, but more a product of human activities driven by poverty, population growth and failed government policies. Further projections by this study also reveal a high probability of more farmlands being converted to sand dunes by the years 2030 and 2045 if current practices prevail. Full article

The Conference of Parties (COP) 26 highlighted the need for global-level deep decarbonization and provided financial instruments to aid climate mitigation in the global south, as well as compensation avenues for loss and damage. This narrative reiterated the urgency of addressing climate change, as well as aiding advances in green products and green solutions whilst shifting a portion of responsibility upon the global south. While this is much needed, we argue that the science rhetoric driving this initiative continues to be advantageous to the global north due to their capacity to control consumption gaps and to access human knowledge and resource extraction. If not addressed, this will reinforce a continuing unjust north/south narrative, highlighting neo-climate colonialism precepts. Full article

The efficient modeling of population-density and urban-extent dynamics is a precondition for monitoring urban sprawl and managing the accompanying conflicts. Currently, one of the most promising approaches in this field is cellular automata—spatial models allowing one to anticipate the behavior of unit areas (e.g., evolution or degradation) in response to the influence of their neighborhood. In the present study, the possibility of modeling the population-density and urban-extent dynamics via a cellular automaton with density-specific parameters is tested. Using an adaptive genetic algorithm, three key model parameters (the evolution and degradation thresholds of a cell and its impact upon the neighbors) are optimized to ensure minimal deviation of the model predictions from actual population dynamics data for 24 Ukrainian provinces during three subsequent time windows from 2010–2019. The performance of the obtained optimized models is assessed in terms of the ability to (1) predict population-density classes and (2) discriminate between urban and rural areas. Generally, the obtained optimized models show high performance for both population-density and urban-extent dynamics (with the average Cohen’s Kappa reaching ~0.81 and ~0.91, respectively). Rare cases with poor prediction accuracy usually represent politically and economically unstable Eastern Ukrainian provinces involved in the military conflict since 2014. Statistical analysis of the obtained model parameters reveals significant differences (p < 0.001) in all of them among population-density classes, arguing for the plausibility of the selected density-specific model architecture. Upon exclusion of the above-mentioned Eastern Ukrainian provinces, all model coefficients appear rather stable (p > 0.135) through the three analyzed time windows, indicating the robustness of the model. The ability of the model to discriminate between urban and rural areas depends on the population density threshold. The best correspondence between actual and predicted urban areas emerges upon the 3000 persons/km² population-density threshold. Further improvement of the model seems possible via extending its input beyond the population density data alone, e.g., by accounting for the existing infrastructure and/or natural boundaries—known factors stimulating or inhibiting urban sprawl. Full article

This article explores a potential exploitation of fully polarimetric radar data for the management of rubber plantations, specifically for predicting tree circumference as a crucial information need for sustainable plantation management. Conventional backscatter coefficients along with Eigen-based and model-based decomposition features served as the predictors in models of tree girth using ten regression approaches. The findings suggest that backscatter coefficients and Eigen-based decomposition features yielded lower accuracy than model-based decomposition features. Model-based decompositions, especially the Singh decomposition, provided the best accuracies when they were coupled with guided regularized random forests regression. This research demonstrates that L-band SAR data can provide an accurate estimation of rubber plantation tree girth, with an RMSE of about 8 cm. Full article

Herring gulls (Larus argentatus) are declining globally, but there are populations who are taking advantage of the new foraging and nesting opportunities afforded to them by urban landscapes. Nest-site selection (NSS) in urban environs is understudied, despite its critical role in supporting planning policy, biodiversity conservation and the management of human–wildlife conflict. The aim of this study was to assess the contribution of anthropogenic habitat features to NSS in urban populations of L. argentatus at different hierarchical levels in Fingal County, Ireland. We used generalised linear models with a logit function to investigate the relationship among nest sites, building features, street furniture (i.e., streetlights and refuse bins), landscape features, and presence of conspecifics at three different hierarchical levels, including the county, town, and colony levels. L. argentatus preferentially chose buildings that were closer to streetlights and food sources at the colony level, while avoiding streetlights when considered in isolation. Conspecific attraction at the county and colony levels indicated that individuals avoided neighbouring nest sites, yet this relationship was inverted at the town level, suggesting preference. Moreover, 75% of nests were within 30 m of each other (the average road width in the study area) when measured at the county level. Various relationships with different food sources were identified, suggesting within-population variation among preferences for nest sites. There appears to be a substantial population variation among preferences for nest sites, which does appear to be driven by the cross-scale decisions involved in nest-site selection. Full article