Plastics in the Indian Ocean – sources, transport, distribution, and impacts

Plastic debris is the most common and exponentially increasing human pollutant in the world's ocean. The distribution and impact of plastic in the Pacific and Atlantic oceans have been the subject of many publications but not so the Indian Ocean (IO). Some of the IO rim countries have the highest population densities globally and mismanagement of plastic waste is of concern in many of these rim states. Some of the most plastic-polluted rivers empty into the IO, with all this suggesting that the IO receives a tremendous amount of plastic debris each year. However, the concentration, distribution, and impacts of plastics in the IO are poorly understood as the region is under-sampled compared to other oceans. In this review, we discuss sources and sinks, which are specific to the IO. We also discuss unique atmospheric, oceanographic, and topographic features of the IO that control plastic distribution, such as reversing wind directions due to the monsoon, fronts, and upwelling regions. We identify hotspots of possible plastic accumulation in the IO, which differ between the two hemispheres. In the southern IO, plastics accumulate in a garbage patch in the subtropical gyre. However, this garbage patch is not well defined, and plastics may leak into the southern Atlantic or the Pacific Ocean. There is no subtropical gyre and associated garbage in the northern IO due to the presence of landmasses. Instead, the majority of buoyant plastics most likely end up on coastlines. Finally, we identify the vast knowledge gaps concerning plastics in the IO and point to the most pressing topics for future investigation.

Using Remote Sensing and Machine Learning to Locate Groundwater Discharge to Salmon-Bearing Streams

We hypothesized topographic features alone could be used to locate groundwater discharge, but only where diagnostic topographic signatures could first be identified through the use of limited field observations and geologic data. We built a geodatabase from geologic and topographic data, with the geologic data only covering ~40% of the study area and topographic data derived from airborne LiDAR covering the entire study area. We identified two types of groundwater discharge: shallow hillslope groundwater discharge, commonly manifested as diffuse seeps, and aquifer-outcrop groundwater discharge, commonly manifested as springs. We developed multistep manual procedures that allowed us to accurately predict the locations of both types of groundwater discharge in 93% of cases, though only where geologic data were available. However, field verification suggested that both types of groundwater discharge could be identified by specific combinations of topographic variables alone. We then applied maximum entropy modeling, a machine learning technique, to predict the prevalence of both types of groundwater discharge using six topographic variables: profile curvature range, with a permutation importance of 43.2%, followed by distance to flowlines, elevation, topographic roughness index, flow-weighted slope, and planform curvature, with permutation importance of 20.8%, 18.5%, 15.2%, 1.8%, and 0.5%, respectively. The AUC values for the model were 0.95 for training data and 0.91 for testing data, indicating outstanding model performance.

Sediment Transport and Associated Long-term Bathymetric Changes due to Tidal Currents in The Seto Inland Sea, Japan

The topography of the seafloor is essential for determining physical phenomena such as ocean currents, favorable habitats for marine organisms, optimal vessel navigation, and so on. Prevailing currents and waves, as well as associated shear stresses acting on the ocean floor, are responsible for the formation of typical topographic features including sea caldrons and sandbanks through erosion of bedrock and sediments and their deposition processes. In the Seto Inland Sea (SIS), the most extensive semi-enclosed estuary in Japan, tidal currents affect pronouncedly the formation of seafloor topographic features; however, they have not been fully studied, particularly from a hydrodynamic viewpoint. This study aims to understand bathymetric formation under the predominance of tidal currents in the SIS. A 3-D high-resolution SIS circulation model based on the JCOPE2-ROMS system in a triple-nested configuration was utilized to examine the detailed hydrodynamic processes for the topography formations. A high correlation between the bottom shear stress and the scour depth of the erosive areas was observed, demonstrating that local tidal forcing has continuously been exerted on the seafloor to erode. A diagnostic sediment budget analysis was then conducted for sediments typical of the SIS, that is, gravel, sand, and clay, using the modeled circulation field. The horizontal divergence of the residual flows indicates consistency between divergence (convergence) and erosion (deposition). The sediment budget model also shows that these sediments are generally transported from deep to shallow areas in eroded terrains to form deposited terrains fringing the eroded terrains, whereas sedimentation tendency differs largely from location to location.

Extraction of Kenyan Grassland Information Using PROBA-V Based on RFE-RF Algorithm

Africa has the largest grassland area among all grassland ecosystems in the world. As a typical agricultural and animal husbandry country in Africa, animal husbandry plays an important role in this region. The investigation of grassland resources and timely grasping the quantity and spatial distribution of grassland resources are of great significance to the stable development of local animal husbandry economy. Therefore, this paper uses Kenya as the study area to investigate the effective and fast approach for grassland mapping with 100-m resolution using the open resources in the Google Earth Engine cloud platform. The main conclusions are as follows. (1) In the feature combination optimization part of this paper, the machine learning algorithm is used to compare the scores and standard deviations of several common algorithms combined with RFE. It is concluded that the combination of RFE and random forest algorithm has the highest stability in modeling and the best feature optimization effect. (2) After feature optimization by the RFE-RF algorithm, the number of features is reduced from 12 to 8, which compressed the original feature space and reduced the redundancy of features. The optimal combination features are applied to random forest classification, and the overall accuracy and Kappa coefficient of classification are 0.87 and 0.85, respectively. The eight features are: elevation, NDVI, EVI, SWIR, RVI, BLUE, RED, and LSWI. (3) There are great differences in topographic features among the local land types in the study area, and the addition of topographic features is more conducive to the recognition and classification of various land types. There exists “salt-and-pepper phenomenon” in pixel-oriented classification. Later research focus will combine the RFE-RF algorithm and the segmentation algorithm to achieve object-oriented land cover classification.

Assessing the importance of feature selection in Landslide Susceptibility for Belluno province (Veneto Region, NE Italy)

In the domain of landslide risk science, landslide susceptibility mapping (LSM) is very important as it helps spatially identify potential landslide-prone regions. This study used a statistical ensemble model (Frequency Ratio and Evidence Belief Function) and two machine learning (ML) models (Random Forest and XG-Boost) for LSM in the Belluno province (Veneto Region, NE Italy). The study investigated the importance of the conditioning factors in predicting landslide occurrences using the mentioned models. In this paper, we evaluated the importance of the conditioning factors (features) in the overall prediction capabilities of the statistical and ML algorithms. By the trial-and-error method, we eliminated the least "important" features by using a common threshold. Conclusively, we found that removing the least "important" features does not impact the overall accuracy of the LSM for all three models. Based on the results of our study, the most commonly available features, for example, the topographic features, contributes to comparable results after removing the least "important" ones. This confirms that the requirement for the important factor maps can be assessed based on the physiography of the region. Based on the analysis of the three models, it was observed that most commonly available feature data can be useful for carrying out LSM at regional scale, eliminating the least available ones in most of the use cases due to data scarcity. Identifying LSMs at regional scale has implications for understanding landslide phenomena in the region and post-event relief measures, planning disaster risk reduction, mitigation, and evaluating potentially affected areas.

Urban perimeters issues in low-density areas

In the domain of spatial planning, there is a concern to preserve the territory whilst ensuring a proper expansion of urban perimeters. Cities, towns and villages should be contained within urban perimeters for building and urbanization purposes. The concept of urban perimeter is defined as the closed polygonal line demarcating the continuous territory of urban land-use. The spatial planning instruments should define the urban land-use referring to areas totally or partially urbanized or built. Regarding low-density territories with a greater propensity to urban sprawl and population ageing, the previously mentioned rules are not always met. Thus, this study focuses on the case of the Interior region of Portugal classified as low-density territory. Here the number of inhabitants is decreasing with low demand for urban spaces. However, the urban perimeters were mainly defined without being based on territorial features, topographic mapping or ecological sensitive areas. Thus, for a diversity of aspects, there are inappropriate areas to build that were wrongly included as part of urban areas, creating as a result urban void. One main reason for this problem are the topographic conditions that don’t fit with the urbanization and building requirements in urban perimeters. In this sense, this research aims to describe the articulation between the urbanization and building processes, under the rules of the Municipal Master Plans, regarding the topographic features of urban perimeters. The conclusion shows that the most sloped areas are those that were less sought after for urbanization, however these zones comprise a significant part of the urban perimeters. Finally, there is the need to stress that in low-density territories, many urban voids will never be urbanized.

Humans as geomorphic agents: Lidar detection of the past, present and future of the Teotihuacan Valley, Mexico

As humans are the primary geomorphic agents on the landscape, it is essential to assess the magnitude, chronological span, and future effects of artificial ground that is expanding under modern urbanization at an alarming rate. We argue humans have been primary geomorphic agents of landscapes since the rise of early urbanism that continue to structure our everyday lives. Past and present anthropogenic actions mold a dynamic “taskscape” (not just a landscape) onto the physical environment. For example, one of the largest Pre-Columbian metropolitan centers of the New World, the UNESCO world heritage site of Teotihuacan, demonstrates how past anthropogenic actions continue to inform the modern taskscape, including modern street and land alignments. This paper applies a multi-scalar, long durée approach to urban landscapes utilizing the first lidar map of the Teotihuacan Valley to create a geospatial database that links modern and topographic features visible on the lidar map with ground survey, historic survey, and excavation data. Already, we have recorded not only new features previously unrecognized by historic surveys, but also the complete erasure of archaeological features due to modern (post-2015) mining operations. The lidar map database will continue to evolve with the dynamic landscape, able to assess continuity and changes on the Teotihuacan Valley, which can benefit decision makers contemplating the stewardship, transformation, or destruction of this heritage landscape.