Mapping Gaps in Sugarcane by UAV RGB Imagery: The Lower and Earlier the Flight, the More Accurate

Imagery data prove useful for mapping gaps in sugarcane. However, if the quality of data is poor or the moment of flying an aerial platform is not compatible to phenology, prediction becomes rather inaccurate. Therefore, we analyzed how the combination of pixel size (3.5, 6.0 and 8.2 cm) and height of plant (0.5, 0.9, 1.0, 1.2 and 1.7 m) could impact the mapping of gaps on unmanned aerial vehicle (UAV) RGB imagery. Both factors significantly influenced mapping. The larger the pixel or plant, the less accurate the prediction. Error was more likely to occur for regions on the field where actively growing vegetation overlapped at gaps of 0.5 m. Hence, even 3.5 cm pixel did not capture them. Overall, pixels of 3.5 cm and plants of 0.5 m outstripped other combinations, making it the most accurate (absolute error ~0.015 m) solution for remote mapping on the field. Our insights are timely and provide forward knowledge that is particularly relevant to progress in the field’s prominence of flying a UAV to map gaps. They will enable producers to make decisions on replanting and fertilizing site-specific high-resolution imagery data.

Embodied Engagements: Body Mapping in a Sociology of Sexuality Classroom

Using a collaborative autoethnographic approach, we discuss body mapping as an embodied pedagogical practice for teaching sexuality. Body mapping centers stigmatized bodies through guided visual, oral, and textual self-representation. We begin by discussing embodied pedagogies and the bind of representation (ideas grounded in the work of feminists of color) in teaching and learning about sexuality. We then consider three body mapping experiences: in a sexuality education graduate seminar ( seminar mapping), as a remote synchronous practice ( remote mapping), and as a solo practice ( solo mapping). We explore challenges in representation, embodied difference, and the im/possibility of mapping the sexual. Finally, we consider the implications and applications of body-mapping exercises for sexualities classrooms.

Evaluation of Remote Mapping Techniques for Earthquake-Triggered Landslide Inventories in an Urban Subarctic Environment: A Case Study of the 2018 Anchorage, Alaska Earthquake

Earthquake-induced landslide inventories can be generated using field observations but doing so can be challenging if the affected landscape is large or inaccessible after an earthquake. Remote sensing data can be used to help overcome these limitations. The effectiveness of remotely sensed data to produce landslide inventories, however, is dependent on a variety of factors, such as the extent of coverage, timing, and data quality, as well as environmental factors such as atmospheric interference (e.g., clouds, water vapor) or snow and vegetation cover. With these challenges in mind, we use a combination of field observations and remote sensing data from multispectral, light detection and ranging (lidar), and synthetic aperture radar (SAR) sensors to produce a ground failure inventory for the urban areas affected by the 2018 magnitude (Mw) 7.1 Anchorage, Alaska earthquake. The earthquake occurred during late November at high latitude (∼61°N), and the lack of sunlight, persistent cloud cover, and snow cover that occurred after the earthquake made remote mapping challenging for this event. Despite these challenges, 43 landslides were manually mapped and classified using a combination of the datasets mentioned previously. Using this manually compiled inventory, we investigate the individual performance and reliability of three remote sensing techniques in this environment not typically hospitable to remotely sensed mapping. We found that differencing pre- and post-event normalized difference vegetation index maps and lidar worked best for identifying soil slumps and rapid soil flows, but not as well for small soil slides, soil block slides and rock falls. The SAR-based methods did not work well for identifying any landslide types because of high noise levels likely related to snow. Some landslides, especially those that resulted in minor surface displacement, were identifiable only from the field observations. This work highlights the importance of the rapid collection of field observations and provides guidance for future mappers on which techniques, or combination of techniques, will be most effective at remotely mapping landslides in a subarctic and urban environment.

Classical and Modern Remote Mapping Methods for Vegetation Cover

Plant classification is quite complex and multilevel. All living organisms are divided into domains, kingdoms, types, classes, ranks, families, tribes, and species. This classification complexity is also reflected in the classification of biogeographic maps, which is much simpler. Based on floristic dependence, vegetation is grouped by connecting it into spatial (territorial) complexes. This paper presents the interfaces of mapping methods with taxonomic vegetation types at different hierarchical levels. At the same time, examples of vegetation mapping techniques from national and thematic atlases of different countries are presented in this article. UAV aerial photographs are widely used for local mapping of vegetation areas. The authors of this article propose a new methodology that can be used to assess the ecological condition of young trees and the volume of mature forest wood. The methodology is based on the separation of tree crown areas in UAV aerial photographs and photo color analysis. For automated area calculation of young trees, a PixRGB software has been developed to determine the area of pixels of the same color in aerial photographs. The software is based on the comparison of young tree crown area calculations in AutoCAD software and area measurements of individual color spectrum pixels. In the initial stage, aerial photographs are transformed to the exact size of the photographed area. Transformations were performed with an error of less than 2–3 cm. The transformation of the spectrum of aerial photographs allowed to concentrate the color of the image of young trees in a relatively narrow color range. Studies performed in 2019–2020 to assess the ecological condition of trees and the amount of wood using UAV INSPIRE 1 and PixRGB color analysis software showed the effectiveness of the applied methodology.

Analysis of OpenStreetMap Data Quality at Different Stages of a Participatory Mapping Process: Evidence from Slums in Africa and Asia

This paper examines OpenStreetMap data quality at different stages of a participatory mapping process in seven slums in Africa and Asia. Data were drawn from an OpenStreetMap-based participatory mapping process developed as part of a research project focusing on understanding inequalities in healthcare access of slum residents in the Global South. Descriptive statistics and qualitative analysis were employed to examine the following research question: What is the spatial data quality of collaborative remote mapping achieved by volunteer mappers in morphologically complex urban areas? Findings show that the completeness achieved by remote mapping largely depends on the morphology and characteristics of slums such as building density and rooftop architecture, varying from 84% in the best case, to zero in the most difficult site. The major scientific contribution of this study is to provide evidence on the spatial data quality of remotely mapped data through volunteer mapping efforts in morphologically complex urban areas such as slums; the results could provide insights into how much fieldwork would be needed in what level of complexity and to what extent the involvement of local volunteers in these efforts is required.

The Impact of Community Happenings in OpenStreetMap—Establishing a Framework for Online Community Member Activity Analyses

The collaborative nature of activities in Web 2.0 projects leads to the formation of online communities. To reinforce this community, these projects often rely on happenings centred around data creation and curation activities. We suggest an integrated framework to directly assess online community member performance in a quantitative manner and applied it to the case study of OpenStreetMap. A set of mappers who participated in both field and remote mapping-related happenings was identified. To measure the effects of happenings, we computed attributes characterising the mappers’ contribution behaviour before and after the happenings and tested for significant impacts in relation to a control group. Results showed that newcomers to OpenStreetMap adopted a contribution behaviour similar to the contribution behaviour typical for the respective happening they attended: When contributing after the happening, newcomers who attended a remote mapping event tended to concentrate on creating new data with lower quality but high quantity in places foreign to their home region; newcomers who attended a field mapping event updated and enhanced existing local data with high accuracy. The behaviour of advanced mappers stayed largely unaffected by happenings. Unfortunately, our results did not reveal a positive effect on the community integration of newcomers through happenings.

Algorithmic justice and groundtruthing the remote mapping of informal settlements: The example of Ho Chi Minh City’s periphery

The significant advances made in interpreting satellite imagery to monitor urban expansion and informal settlements has made important contributions to urban studies and planning. This paper focuses on the under-examined dimensions of how improvements to classifications of urban areas are not only a technical challenge but lie at the society/technology nexus. We examine why three different research groups produced different urban land use classifications of Ho Chi Minh City, Vietnam from remote sensing images. We trace how a confluence of factors including how the technology intersects with field conditions, researcher assumptions and discretionary choices, and institutional norms and agendas shaped the differences in their results. The different spatial facts they produced raises the issue of adapting algorithms for not only technical accuracy but appropriate social use. In the case of detecting informal settlements, our study finds that groundtruthing through fieldwork or collaborative partnerships is needed to not systematically overlook vulnerable populations and misinform urban planning decisions.

An Open Data and Citizen Science Approach to Building Resilience to Natural Hazards in a Data-Scarce Remote Mountainous Part of Nepal

The citizen science approach has gained momentum in recent years. It can enable both experts and citizen scientists to co-create new knowledge. Better understanding of local environmental, social, and geographical contexts can help in designing appropriate plans for sustainable development. However, a lack of geospatial data, especially in the context of developing countries, often precludes context-specific development planning. This study therefore tests an innovative approach of volunteer citizen science and an open mapping platform to build resilience to natural hazards in the remote mountainous parts of western Nepal. In this study, citizen scientists and mapping experts jointly mapped two districts of Nepal (Bajhang and Bajura) using the OpenStreetMap (OSM) platform. Remote mapping based on satellite imagery, capacity building, and mobilization of citizen scientists was performed to collect the data. These data were then uploaded to OSM and later retrieved in ArcGIS to produce a usable map that could be exploited as a reference resource for evidence-based decision-making. The collected data are freely accessible to community members as well as government and humanitarian actors, and can be used for development planning and risk reduction. By piloting in two communities of western Nepal, we found that using open data platforms for collecting and analyzing location-based data has a mutual benefit for researchers and communities. Such data could be vital in understanding the local landscape, environmental risk, and distribution of resources. Furthermore, they enable both researchers and local people to transfer technical knowledge, collect location-specific data, and use them for better decision-making.