Conventional Fieldwork Research Articles

Sub-alpine shrub classification using UAV images: Performance of human observers vs DL classifiers

In recent years, the automatic analysis of natural environment images acquired with unmanned aerial vehicles (UAV) has rapidly gained popularity. UAVs are specially important in mountainous forests where access is difficult and large areas need to be surveyed. In Zao mountains in northeastern Japan, regenerated fir saplings are competing with sub-alpine vegetation shrubs after a severe fir tree mortality caused by bark beetle infestation. A detailed survey of vegetation distribution is key to improve our understanding of species succession and the influence of climate change in that process. To that end, we evaluated the suitability of deep-learning-based automatic image classification of UAV images in order to map sub-alpine vegetation succession in large areas and the potential of fir regeneration.In order to assess the contribution of this technology in this research field, we first conducted an observer study to assess the difficulty for humans of the task of classifying vegetation from images. Afterwards, we compared the observers' accuracy to four state-of-the art deep learning networks for automatic image classification. The best observer accuracy of 55% demonstrates the limitations of species classification using only images. Furthermore, a detailed analysis of the sources of error showed that even though humans could differentiate between deciduous and evergreen species with an accuracy of 96%, identifying the correct species within each group proved much more challenging. In contrast, deep learning networks achieved accuracy values in the range of 70–80% for species classification, clearly demonstrating capabilities beyond human experts. Our experiments also indicated that the performance of these networks was significantly influenced by the similarity between the datasets used to fine-tune them and evaluate them. This fact highlights the importance of building publicly available images databases to further improve the results.Nevertheless, the results presented in this paper show that the analysis of UAV-acquired with deep learning networks can usher in a new type of large-scale study, spanning tenths or even hundreds of hectares with high spatial resolution (of a few cms per pixel), providing the ability to assess challenging vegetation dynamics problems that go beyond the ability of conventional fieldwork methodologies.

Multiscale (microscopic to remote sensing) preliminary exploration of auriferous-uraniferous marbles: A case study from the Egyptian Nubian Shield

Since their recent first record within the Egyptian Nubian Shield, auriferous and uraniferous marbles (Au = 0.98–2.76 g/t; U = 133–640 g/t) have rarely been addressed, despite not only their probable economic importance but also the fact that it is a new genetic style of gold and uranium mineralization in the Nubian Shield rocks. This is mainly attributed to the inadequate localization of these marbles within harsh terrains, as well as the cost and time spent with conventional fieldwork for their identification compared to the main lithological components of the Nubian Shield. On the contrary, remote sensing and machine learning techniques save time and effort while introducing reliable feature identification with reasonable accuracy. Consequently, the current research is an attempt to apply the well-known machine learning algorithm (Support vector Machine—SVM) over Sentinel 2 remote sensing data (with a spatial resolution of up to 10 m) to delineate the distribution of auriferous-uraniferous marbles in the Barramiya-Daghbagh district (Eastern Desert of Egypt), as a case study from the Nubian Shield. Towards better results, marbles were accurately distinguished utilizing ALOS PRISM (2.5 m) pan-sharpened Sentinel 2 data and well-known exposures during fieldwork. With an overall accuracy of more than 90%, a thematic map for auriferous-uraniferous marbles and the major rock units in the Barramiya-Daghbagh district was produced. Marbles are spatially related to ophiolitic serpentinite rocks, as consistent with their genesis within the Neoproterozoic oceanic lithosphere. Field and petrographic investigations have confirmed the newly detected Au and U-bearing zones (impure calcitic to impure dolomitic marbles in Wadi Al Barramiya and Wadi Daghbagh areas and impure calcitic marble in Gebel El-Rukham area). Additionally, X-ray diffraction (XRD), back-scattered electron images (BSEIs), and Energy Dispersive X-ray spectroscopy (EDX) results were integrated to verify our remote sensing results and petrographic investigations. Different times of mineralization are indicated, ranging from syn-metamorphism (gold in Wadi Al Barramiya and Gebel El-Rukham) to post-metamorphism (gold in Wadi Daghbagh and uranium in all locations). Based on the application of geological, mineralogical, machine learning and remote sensing results for the construction of a preliminary exploration model of the auriferous-uraniferous marble in the Egyptian Nubian Shield, we recommend a detailed exploration of Au and U-bearing zones in Barramiya-Dghbagh district and applying the adopted approach to other districts of similar geological environments.

Training the creation, visualization and interpretation of fault maps for the subsurface – using tectonic geomorphology

Abstract Fault mapping is essential for understanding subsurface geological structure. However, effective training of students in developing and understanding fault patterns in 3D seismic imagery is impeded by the time-investment needed to acquire competence using software and then creating depth-structure maps of stratal horizons. Here an exercise is laid out that can achieve the desired experience – using the young fault systems of Afar (Djibouti), where former land-surface-defining basalt flows are offset by arrays of normal faults. The top-basalt surface, displayed on GoogleEarth, is in effect a depth-structure map and the gaps (‘fault-loss’) in this surface approximate to exposed fault surfaces. The mapping exercise is described and illustrated here step-wise. The fault system is gradually mapped out to reveal examples of long-continuous faults, branching patterns, relaying faults and isolated fault segments. Alternative criteria for identifying faults can be examined, with analogies in seismic interpretation. This can inform discussion of the approaches and uncertainties inherent in mapping faults in the subsurface. The study may be extended to consider the pattern of early–syn-rift depositional systems. Collectively these tasks can be progressed within an hour or so, providing effective insights into the structure of normal fault systems that cannot be replicated by conventional fieldwork.

Groundwater flow modeling in the basaltic hard rock area of Maharashtra, India

The ecological sustainable development and planning of groundwater resources is an excessive challenge for many countries currently facing water insufficiency. The main focus of this work was to determine the direction of groundwater flow, head value, and water level using the steady-state finite difference model (MODFLOW software) in basaltic formations in Maharashtra, India. The MODFLOW model was integrated with ground data using Geographic Information System (GIS) for sustainable groundwater resource management in the hard rock terrain. The MODFLOW-2005 model simulated the interaction between heads and time in 2014–18 by steady-state conditions. In this present study, four observation wells were selected. During the field survey, four observation wells have been monitored regularly as per the Central Groundwater Board guidelines. MODFLOW software has been conceptualized as a double-layered rigid and fractured aquifer area feast over 18,312 m × 11,265 m area. This research demonstrates that the integration of GIS, conventional fieldwork, and mathematical model can support to understand groundwater demand and supply in a better way.

Rethinking the Landscape: Emerging Approaches to Archaeological Remote Sensing

An emerging arena of archaeological research is beginning to deploy remote sensing technologies—including aerial and satellite imagery, digital topographic data, and drone-acquired and terrestrial geophysical data—not only in support of conventional fieldwork but also as an independent means of exploring the archaeological landscape. This article provides a critical review of recent research that relies on an ever-growing arsenal of imagery and instruments to undertake innovative investigations: mapping regional-scale settlement histories, documenting ancient land use practices, revealing the complexity of settled spaces, building nuanced pictures of environmental contexts, and monitoring at-risk cultural heritage. At the same time, the disruptive nature of these technologies is generating complex new challenges and controversies surrounding data access and preservation, approaches to a deluge of information, and issues of ethical remote sensing. As we navigate these challenges, remote sensing technologies nonetheless offer revolutionary ways of interrogating the archaeological record and transformative insights into the human past.

Going Virtual: The Impact of COVID-19 on Internships in Tourism, Events, and Hospitality Education

ABSTRACT This study explored virtual internship practices and the adaptation to changes brought by the current COVID-19 pandemic crisis by interviewing a small group of interns and site supervisors to understand how virtual internships were practiced in the wake of COVID-19. The findings of the study provided several insights into virtual internships. Overall, just like the conventional fieldwork, well-planned and designed virtual internships could be an empowering learning experience that allows students to capitalize on the practical strengths while applying classroom learning experiences to actual management situations. The study also discussed the advantages and challenges of virtual internships perceived by the student interns. The implications of the study and suggestions for future research were addressed.

Analysis of Remote Sensing based Vegetation Indices (VIs) for Unmanned Aerial System (UAS): A Review

Unmanned Aerial System (UAS) is an efficient tool to bridge the gap between high expensive satellite remote sensing, manned aerial surveys, and labors time consuming conventional fieldwork techniques of data collection. UAS can provide spatial data at very fine (up to a few mm) and desirable temporal resolution. Several studies have used vegetation indices (VIs) calculated from UAS based on optical- and MSS-datasets to model the parameters of biophysical units of the Earth surface. They have used different techniques of estimations, predictions and classifications. However, these results vary according to used datasets and techniques and appear very site-specific. These existing approaches aren’t optimal and applicable for all cases and need to be tested according to sensor category and different geophysical environmental conditions for global applications. UAS remote sensing is a challenging and interesting area of research for sustainable land management.

Landslide susceptibility mapping and rock slope stability assessment using frequency ratio and kinematic analysis in the mountains of Mgulat area, Northern Ethiopia

Landslides, mainly triggered by rainfall, are one of the most common natural hazards in the northern and central highlands of Ethiopia, resulting in the loss of human life and property. As part of the Ethiopian highlands, Mgulat Mountain (88.6 km2) is a typical example for the occurrence of landslides and associated damages. The main aim of this research was to generate landslide susceptibility map (LSM) for the first time by considering various causative factors typical for the Mgulat Mountain area. The methods used include frequency ratio (FR) and kinematic analysis (KA) supported by GIS tools. The research involved conventional fieldwork (e.g., geological mapping, landslides inventory) and analysis and interpretation of images. From the geological mapping, major rock units were identified: quaternary deposit, basalt, red sandstone, tillite, and white sandstone. A total of 220 landslides (15.45 km2) were identified in the study area from field survey, Google Earth and aerial photo interpretation. Five causative factors (lithology, slope steepness, distance from drainage, land use-land cover (LULC), and slope aspect) were selected based on their contribution to landslide initiation as the landslide conditioning factors for generation of the LSM. From the five causative factors, lithology and slope steepness were found to be influential as landslide conditioning factors. Specifically, lithology (red sandstone and tillite), distance to stream (0 to 300 m), LULC (bare land and settlement), and slope steepness (> 25°) have FR > 1, indicating a high relationship with landslide occurrence. Based on the generated LSM, five susceptibility classes or zones were identified (very low, low, moderate, high, and very high susceptibility). The area of each class as percentage of total area was found to be 19.53, 24.15, 22.57, 18.74, and 15.01%, for the very low to the very high class, respectively. The results of KA show potentially and marginally planar, topple, and wedge failures. The generated LSM can serve as an important tool or guide for further land-use planning and to prevent and mitigate landslide-related risks. Consequently, to consider cost-effective and technically sound mitigation measures, detailed and site-specific multidisciplinary investigations are recommended.

Introduction of a New Index of Field Operations Efficiency

The evaluation and prediction of the agricultural machinery field efficiency is essential for agricultural operations management. Field efficiency is affected by unpredictable (e.g., machine breakdowns) and stochastic (e.g., yield) factors, and thus, it is generally provided by average norms. However, the average values and ranges of the field efficiency are of limited value when a decision has to be made on the selection of the appropriate machinery system for a specific operational set up. To this end, in this paper, a new index for field operability, the field traversing efficiency (FTE), a distance-based measure, is introduced and a dedicated tool for estimation of this measure is presented. In order to show the degree of the dependence of the FTE index on the operational features, a number of 864 scenarios derived from the consideration of six sample field shapes, three conventional fieldwork patterns, four driving directions, and twelve combinations of machine unit kinematics and implement width were evaluated by the developed tool. The test results showed that variation of FTE was up to 23% in the tested scenarios when using different operational setups.

Genetic and ecological conservation issues for oceanic island birds, revealed by a combination of the latest molecular techniques and conventional field work

AbstractOceanic island ecosystems are vulnerable to artificial disturbances, and many island endemics are at risk of extinction. Conservation of island endemics is a high priority for reducing biodiversity loss, but fundamental studies on many species remain lacking due to the small population size and limited accessibility to island habitats. In this review, I explored conservation issues of endangered bird species in oceanic island habitat, which have been revealed by the latest molecular techniques with a combination of conventional field work. Recent genetic studies revealed low genetic diversity of endangered island endemics. Analyses of genetic structure have revealed the gene flow of endemic birds among islands according to their flying abilities. Seasonal inter‐island movement of Columbiformes and some finches have been observed, which might be related to fluctuating food resource availability among island habitats. Dietary analyses based on DNA metabarcoding for Columba janthina nitens revealed frequent consumption of introduced plants by this bird. This review may provide a new perspective for conservation biology in oceanic island ecosystems. That is, (a) appropriate management of genetic diversity within and outside of habitat before serious population declines occur, (b) detection of meaningful evolutionarily significant units (ESUs) considering flying ability of birds among islands and management of total habitat range within each ESU and (c) introduced species eradication designed to not to cause a negative impact on endangered species that may depend on introduced species for their food.

The micro-politics of emotions in legal space: an autoethnography about sexual violence and displacement in Norway

This article is an autoethnography of fieldwork carried out as part of a research project about the legal processing of rape cases in Norway. The author observed 15 rape trials and interviewed ten defense lawyers. During this fieldwork, the author repeatedly experienced the shortcomings of conventional fieldwork methodology as she tried to build rapport with defense lawyers. By examining ‘out of place’ emotions like embarrassment, shame and isolation, the author seeks to map the social world that is ‘filtered’ through the relations between researcher and researched. Using a feminist interactionist approach to analyzing ‘educational moments’ in field encounters characterized by participant’s resistance or dominance, this article asks how reflection on the researcher’s feelings and experiences (emotional reflexivity) can contribute to an understanding of the micro-politics of gender and power in the field.

Feasibility of Google Tango and Kinect for Crowdsourcing Forestry Information

In this paper, we demonstrate the feasibility of using the Microsoft Kinect and Google Tango frame-based depth sensors for individual tree stem measurements and reconstruction for the purpose of forest inventory. Conventionally field reference data in forest inventory are collected at tree and sample plot level by means of manual measurements (e.g., a caliper), which are both labor-intensive and time-consuming. In this study, color (i.e., red, green and blue channels, RGB) and range images acquired by a Kinect and Tango systems were processed and used to extract tree diameter measurements for the individual tree stems. For this, 121 reference stem diameter measurements were made with tape and caliper. Kinect-derived tree diameters agreed with tape measurements to a 1.90 cm root-mean-square error (RMSE). The stem curve from the ground to the diameter at breast height agreed with a bias of 0.7 cm and random error of 0.8 cm with respect to the reference trunk. For Tango measurements, the obtained stem diameters matched those from tape measurement with an RMSE of 0.73 cm, having an average bias of 0.3 cm. As highly portable and inexpensive systems, both Kinect and Tango provide an easy way to collect tree stem diameter and stem curve information vital to forest inventory. These inexpensive instruments may in future compete with both terrestrial and mobile laser scanning or conventional fieldwork using calipers or tape. Accuracy is adequate for practical applications in forestry. Measurements made using Kinect and Tango type systems could also be applied in crowdsourcing context.

Objetos textuales y dispositivos colaborativos: de la etnografía como plataforma pública

This article traces the unexpected becoming-collaborative of an ethnographic project in Hackney Wick (London) which looked at the role of art in the critique to the urban transformations brought about by the 2012 Olympics. The derailment of what should have been a conventional fieldwork exercise led to a reformulation of the ethnographic practice as a collaborative device aimed at the production of textual objects which in turn acted as public forums or platforms. This process is here discussed as a recursive (or parasitic) gesture which appropriates native ways of doing.

Characterization of rock slopes through slope mass rating using 3D point clouds

Rock mass classification systems are widely used tools for assessing the stability of rock slopes. Their calculation requires the prior quantification of several parameters during conventional fieldwork campaigns, such as the orientation of the discontinuity sets, the main properties of the existing discontinuities and the geo-mechanical characterization of the intact rock mass, which can be time-consuming and an often risky task. Conversely, the use of relatively new remote sensing data for modelling the rock mass surface by means of 3D point clouds is changing the current investigation strategies in different rock slope engineering applications. In this paper, the main practical issues affecting the application of Slope Mass Rating (SMR) for the characterization of rock slopes from 3D point clouds are reviewed, using three case studies from an end-user point of view. To this end, the SMR adjustment factors, which were calculated from different sources of information and processes, using the different softwares, are compared with those calculated using conventional fieldwork data. In the presented analysis, special attention is paid to the differences between the SMR indexes derived from the 3D point cloud and conventional field work approaches, the main factors that determine the quality of the data and some recognized practical issues. Finally, the reliability of Slope Mass Rating for the characterization of rocky slopes is highlighted.

Living with floods and coping with vulnerability

Purpose – Using Anthropological methodology to achieve an understanding from a “local point of view” the purpose of this paper is to explore how safety is established in what clearly is, at least from the outside, a risky everyday. Floods are a recurring problem for people in Jakarta. However, for poor families living on river banks in the city center the floods also constitute a necessary condition to create a viable livelihood. The floods keep land grabbers and urban developers at bay and keep costs for living low. For the families living in these areas there is a constant “trade off” between safety and risk taking with the purpose to create a living. Design/methodology/approach – The methodology applied in the paper is conventional Anthropological field work. The material is collected through participant observation and formal interviews. The data produced are of an experience near quality which is analyzed in terms of how it addresses and relate to the infrastructural policies of Jakarta and the speci...

An integrated measurement and modeling methodology for estuarine water quality management

This paper describes research undertaken by the authors to develop an integrated measurement and modeling methodology for water quality management of estuaries. The approach developed utilizes modeling and measurement results in a synergistic manner. Modeling results were initially used to inform the field campaign of appropriate sampling locations and times, and field data were used to develop accurate models. Remote sensing techniques were used to capture data for both model development and model validation. Field surveys were undertaken to provide model initial conditions through data assimilation and determine nutrient fluxes into the model domain. From field data, salinity relationships were developed with various water quality parameters, and relationships between chlorophyll a concentrations, transparency, and light attenuation were also developed. These relationships proved to be invaluable in model development, particularly in modeling the growth and decay of chlorophyll a. Cork Harbour, an estuary that regularly experiences summer algal blooms due to anthropogenic sources of nutrients, was used as a case study to develop the methodology. The integration of remote sensing, conventional fieldwork, and modeling is one of the novel aspects of this research and the approach developed has widespread applicability.

New Approaches to Spatial Archaeometry: Applications from the Near East

This paper reviews key recent developments in geospatial technologies and their archaeological applications, an emerging area of research that can be termed “spatial archaeometry.” I focus on three areas: 1) regional-scale satellite remote sensing of archaeological landscapes, 2) kite, balloon, and drone-based low-altitude aerial imaging, and 3) innovative approaches to archaeological geophysics at complex Near Eastern sites. Far from being simply a support to traditional archaeological investigations, these and other spatial archaeometric technologies offer powerful new means to explore the past, producing rich, robust, and large-scale datasets that could not be replicated through conventional fieldwork.

Comparison of discrete and full-waveform ALS for dead wood detection

Abstract. The amount of dead wood is a significant parameter for the description and assessment of forest habitat quality under the terms of the Habitats directive and Natura 2000 guidelines. EU member states are obliged by the Natura 2000 regulations to report on habitat quality in a regular interval of six years. To fulfil this task, the areas should be surveyed in the field, which requires an enormous amount of workload if done only by conventional field work. In this study the applicability of airborne laser scanning data as the single data source for the detection of downed trees in forest habitats is investigated. A focus is laid on the comparison of point clouds with only discrete (XYZ) and full-waveform (including echo width) information as input data. In our paper we present an automatic workflow which is able to detect downed trees with high completeness for both data sets (77.8% for discrete and 75.6% for full-waveform data). Due to large amount of false positives, the correctness using discrete ALS data is poorer (63.1%) than for full-waveform data (89.9%). It was found that the quality of the result is also influenced by factors such as dimension, state of decay, vegetation density and penetration of the foliage by the laser.

Enhanced detection of 3D individual trees in forested areas using airborne full-waveform LiDAR data by combining normalized cuts with spatial density clustering

Abstract. A detailed understanding of the spatial distribution of forest understory is important but difficult. LiDAR remote sensing has been developing as a promising additional instrument to the conventional field work towards automated forest inventory. Unfortunately, understory (up to 50% of the top-tree height) in mixed and multilayered forests is often ignored due to a difficult observation scenario and limitation of the tree detection algorithm. Currently, the full-waveform (FWF) LiDAR with high penetration ability against overstory crowns can give us new hope to resolve the forest understory. Former approach based on 3D segmentation confirmed that the tree detection rates in both middle and lower forest layers are still low. Therefore, detecting sub-dominant and suppressed trees cannot be regarded as fully solved. In this work, we aim to improve the performance of the FWF laser scanner for the mapping of forest understory. The paper is to develop an enhanced methodology for detecting 3D individual trees by partitioning point clouds of airborne LiDAR. After extracting 3D coordinates of the laser beam echoes, the pulse intensity and width by waveform decomposition, the newly developed approach resolves 3D single trees are by an integrated approach, which delineates tree crowns by applying normalized cuts segmentation to the graph structure of local dense modes in point clouds constructed by mean shift clustering. In the context of our strategy, the mean shift clusters approximate primitives of (sub) single trees in LiDAR data and allow to define more significant features to reflect geometric and reflectional characteristics towards the single tree level. The developed methodology can be regarded as an object-based point cloud analysis approach for tree detection and is applied to datasets captured with the Riegl LMS-Q560 laser scanner at a point density of 25 points/m2 in the Bavarian Forest National Park, Germany, respectively under leaf-on and leaf-off conditions. The experiments lead to a detection rate of up to 67% for trees in the middle height layer and up to 53% for trees in the lower forest layer. It corresponds to an overall improvement in the detection rate of nearly 25% for forest understory compared to that obtained by the former method by extracting individual trees using normalized cuts segmentation solely.

Benefits from optimal route planning based on B-patterns

B-patterns are algorithmically-computed sequences of field-work tracks for agricultural vehicles completely covering an area that do not follow any pre-determined standard motif, as in the conventional field-work patterns, but are a result of an optimisation process under one or more selected criteria. Here, an initial approach for the assessment of the benefits derived from the implementation of B-patterns is presented. This approach is based on the comparison of B-patterns against conventional field work patters, by simulating the operational performance of the different patterns on template fields for different combinations of field and machinery characteristics. The assessment of the benefits of B-patterns showed reductions in non-working distance up to 58.65% and increases in the area capacity, i.e. the processed area per time unit, were up to 19.23% when compared with different types of conventional field-work patterns.