Geomatics, Volume 2, Issue 4 (December 2022) – 8 articles

In the world of seafloor mapping, the ability to explore and experiment with a dataset in its raw and processed forms is critical. Kluster is an open-source multibeam data processing software package written in Python that enables this exploration. Kluster provides a suite of multibeam processing features, including analysis, visualization, gridding, and data cleaning. We demonstrated these features using a recently acquired dataset from a Kongsberg EM712 multibeam echosounder aboard NOAA Ship Fairweather. This test dataset served to illustrate the fundamental analysis abilities of the software, as well as its utility as a troubleshooting tool both in the field and during post-processing. Kluster has the capability to perform the Sonar Acceptance Test in full, including common experiments like the patch test, extinction test, and accuracy test, which are generally performed on new systems. When questions arise regarding the integration or parameter settings of a system, this software allows the user to quickly and clearly visualize much of the raw data and its associated metadata, which is a vital step in any investigative effort. With its emphasis on accessibility and ease of use, Kluster is an excellent tool for users who are inexperienced with multibeam sonar data processing. Full article

Understanding the spatial spread pathways and connectivity of Land Use/Cover (LULC) change within basins is critical to natural resources management. However, existing studies approach LULC change as distinct patches but ignore the connectivity between them. It is crucial to investigate approaches that can detect the spread pathways of LULC change to aid natural resource management and decision-making. This study aims to evaluate the utility of the Circuit Theory to detect the spread and connectivity of LULC change within the Okavango basin. Patches of LULC change sites that were derived from change detection of LULC based on the Deep Neural Network (DNN) for the period between 2004 and 2020 were used. The changed sites were categorized based on the nature of the change of the classes, namely Category A (natural classes to artificial classes), Category B (artificial classes to natural classes), and Category C (natural classes to natural classes). In order to generate the resistance layer; an ensemble of machine learning algorithms was first calibrated with social-ecological drivers of LULC change and centroids of LULC change patches to determine the susceptibility of the landscape to LULC change. An inverse function was then applied to the susceptibility layer to derive the resistance layer. In order to analyze the connectivity and potential spread pathways of LULC change, the Circuit Theory (CT) model was built for each LULC change category. The CT model was calibrated using the resistance layer and patches of LULC change in Circuitscape 4.0. The corridor validation index was used to evaluate the performance of CT modeling. The use of the CT model calibrated with a resistance layer (derived from susceptibility modeling) successfully established the spread pathways and connectivity of LULC change for all the categories (validation index > 0.60). Novel maps of LULC change spread pathways in the Okavango basin were generated. The spread pathways were found to be concentrated in the northwestern, central, and southern parts of the basin for Category A transitions. As for category B transitions, the spread pathways were mainly concentrated in the northeastern and southern parts of the basin and along the major rivers. While for Category C transitions were found to be spreading from the central towards the southern parts, mainly in areas associated with semi-arid climatic conditions. A total of 186 pinch points (Category A: 57, Category B: 71, Category C: 58) were detected. The pinch points can guide targeted management LULC change through the setting up of conservation areas, forest restoration projects, drought monitoring, and invasive species control programs. This study provides a new decision-making method for targeted LULC change management in transboundary basins. The findings of this study provide insights into underlying processes driving the spread of LULC change and enhanced indicators for the evaluation of LULC spread in complex environments. Such information is crucial to inform land use planning, monitoring, and sustainable natural resource management, particularly water resources. Full article

Soil loss caused by erosion has destroyed landscapes, as well as depositing sterile material on fertile lands and rivers, clogged waterways and accelerated flash floods, declined the populations of fish and other species, and diminish soil fertility. In some places, erosion has also destroyed buildings, caused mudflow, create new landforms, displaced people, and slowed down the economy of the affected community by destroying roads and homes. Erosion is aggravated by climate change and anthropogenic factors such as deforestation, overgrazing, inappropriate methods of tillage, and unsustainable agricultural practices. In this study, remote sensing (RS) and geographic information (GIS) data and tools were used to model erosion and estimate soil loss in the catchment area of Koromi-Federe in Jos East, Plateau State Nigeria which is our study area. Soil loss estimation was performed using the revised universal soil loss equation (RUSLE) model and was computed by substituting the corresponding values of each factor inherent in the equation (rainfall erosivity, soil erodibility, slope steepness and slope length, cover management, and conservation practices) using RS and GIS tools. Soil data was obtained from the study area and analyzed in the laboratory, rainfall data, land cover, digital elevation model (DEM), as well as the management practice of the study area were the parameters computed in spatial analyst tool using map algebra based on RUSLE. The soil loss generated was classified into four classes and the results revealed 95.27% of the catchment with a tolerable loss of less than 10 t/h⁻¹/y⁻¹. At 3.6%, a low or minimal loss of 10–20 t/h⁻¹/y⁻¹, at 1.03% there exists a moderate loss of 20–50 t/h⁻¹/y⁻¹, while there was and critical or high loss of >50 t/h⁻¹/y⁻¹ at 0.12% of the catchment. The result showed that critical soil loss in the catchment area is exacerbated by the influence of the slope length and steepness, and the amount of rainfall received. This poses great concern with annual rainfall projected to increase up to 12% in West Africa. However, our sensitivity analysis revealed that it can be reduced with the effect of vegetated cover and management practices. This is an important finding as it can guide sustainability practices to control erosion and the loss of valuable lands in the region, especially now under climate change. Full article

Denmark’s Depth Model (DDM) is a Digital Bathymetric Model based on hundreds of bathymetric survey datasets and historical sources within the Danish Exclusive Economic Zone. The DDM represents the first publicly released model covering the Danish waters with a grid resolution of 50 m. When modern datasets are not available for a given area, historical sources are used, or, as the last resort, interpolation is applied. The model is generated by averaging depths values from validated sources, thus, not targeted for safety of navigation. The model is available by download from the Danish Geodata Agency website. DDM is also made available by means of Open Geospatial Consortium web services (i.e., Web Map Service). The original datasets—not distributed with the model—are described in the auxiliary layers to provide information about the bathymetric sources used during the compilation. Full article

Change detection is an important step for the characterization of object dynamics at the earth’s surface. In multi-temporal point clouds, the main challenge is to detect true changes at different granularities in a scene subject to significant noise and occlusion. To better understand new research perspectives in this field, a deep review of recent advances in 3D change detection methods is needed. To this end, we present a comprehensive review of the state of the art of 3D change detection approaches, mainly those using 3D point clouds. We review standard methods and recent advances in the use of machine and deep learning for change detection. In addition, the paper presents a summary of 3D point cloud benchmark datasets from different sensors (aerial, mobile, and static), together with associated information. We also investigate representative evaluation metrics for this task. To finish, we present open questions and research perspectives. By reviewing the relevant papers in the field, we highlight the potential of bi- and multi-temporal point clouds for better monitoring analysis for various applications. Full article

The satellite image time series are used for several applications such as predictive analysis. New techniques such as deep learning (DL) algorithms generally require long sequences of data to perform well; however, the complexity of satellite image preprocessing tasks leads to a lack of preprocessed datasets. Moreover, using conventional collection and preprocessing methods is time- and storage-consuming. In this paper, a workflow for collecting, preprocessing, and preparing Sentinel-1 images to use with DL algorithms is proposed. The process mainly consists of using scripts for collecting and preprocessing operations. The goal of this work is not only to provide the community with easily modifiable programs for image collection and batch preprocessing but also to publish a database with prepared images. The experimental results allowed the researchers to build three time series of Sentinel-1 images corresponding to three study areas, namely the Bouba Ndjida National Park, the Dja Biosphere Reserve, and the Wildlife Reserve of Togodo. A total of 628 images were processed using scripts based on the SNAP graph processing tool (GPT). In order to test the effectiveness of the proposed methodology, three DL models were trained with the Bouba Ndjida and Togodo images for the prediction of the next occurrence in a sequence. Full article

The effect of climate prevails on a diverse time scale from days to seasons and decades. Between 1993 and 2013, global warming appeared to have paused even though there was an increase in atmospheric greenhouse gases. The variations in oceanographic variables, like current speed and sea surface temperature (SST), under the influence of the global warming hiatus (1993–2013), have drawn the attention of the global research community. However, the magnitude of ocean current and SST characteristics oscillates and varies with their geographic locations. Consequently, investigating the spatio-temporal changing aspects of oceanographic parameters in the backdrop of climate change is essential, specifically in coastal regions along Kuroshio current (KC), where fisheries are predominant. This study analyzes the trend of ocean current and SST induced mainly during the global warming hiatus, before and till the recent time based on the daily ocean current data from 1993 to 2020 and SST between 1982 and 2020. The Kuroshio extent is delineated from its surrounding water masses using an aggregation of raster classification, stretching, equalization, and spatial filters such as edge detection, convolution, and Laplacian. Finally, on the extracted Kuroshio extent, analyses such as time series decomposition (additive) and statistical trend computation methods (Yue and Wang trend test and Theil–Sen’s slope estimator) were applied to dissect and investigate the situations. An interesting downward trend is observed in the KC between the East coast of Taiwan and Tokara Strait (Tau = −0.05, S = −2430, Sen’s slope = −5.19 × 10⁻⁵, and Z = −2.61), whereas an upward trend from Tokara Strait to Nagoya (Tau = 0.89, S = 4344, Sen’s slope = 8.4 × 10⁻⁵, and Z = 2.56). In contrast, a consistent increasing SST in trend is visualized in the southern and mid-KC sections but with varying magnitude. Full article

There are only two species of native vascular plants found on the Antarctic Peninsula and the surrounding islands, Deschampsia Antarctica, and Colobanthus quitensis. Poa annua, a successful invasive species, poses a threat to D. antarctica and C. quitensis. This region may experience extreme changes in biodiversity due to climate change over the next 100 years. This study explores the relationship between vascular vegetation and changing temperature on the Antarctic Peninsula and uses a systems modelling approach to account for three climate change scenarios over a 100-year period. The results of this study indicate that (1) D. antarctica, C. quitensis, and P. annua will likely be impacted by temperature increases, and greater temperature increases will facilitate more rapid species expansion, (2) in all scenarios D. antarctica species occurrences increase to higher values compared to C. quitensis and P. annua, suggesting that D. antarctica populations may be more successful at expanding into newly forming ice-free areas, (3) C. quitensis may be more vulnerable to the spread of P. annua than D. antarctica if less extreme warming occurs, and (4) C. quitensis relative growth rate is capable of reaching higher values than D. antarctica and P. annua, but only under extreme warming conditions. Full article