La verità del suolo: breve storia del Critical GIS (1983-2007)

International audienceI sistemi informativi geografici (GIS): ritorno del positivismo e della geografia quantitativa o chiave per il rilancio della disciplina? Questioni nodali nel dibattito fra i geografi anglosassoni

Using deep learning to count albatrosses from space: Assessing results in light of ground truth uncertainty

Many wildlife species inhabit inaccessible environments, limiting researchers ability to conduct essential population surveys. Recently, very high resolution (sub-metre) satellite imagery has enabled remote monitoring of certain species directly from space; however, manual analysis of the imagery is time-consuming, expensive and subjective. State-of-the-art deep learning approaches can automate this process; however, often image datasets are small, and uncertainty in ground truth labels can affect supervised training schemes and the interpretation of errors. In this paper, we investigate these challenges by conducting both manual and automated counts of nesting Wandering Albatrosses on four separate islands, captured by the 31 cm resolution WorldView-3 sensor. We collect counts from six observers, and train a convolutional neural network (U-Net) using leave-one-island-out cross-validation and different combinations of ground truth labels. We show that (1) interobserver variation in manual counts is significant and differs between the four islands, (2) the small dataset can limit the networks ability to generalise to unseen imagery and (3) the choice of ground truth labels can have a significant impact on our assessment of network performance. Our final results show the network detects albatrosses as accurately as human observers for two of the islands, while in the other two misclassifications are largely caused by the presence of noise, cloud cover and habitat, which was not present in the training dataset. While the results show promise, we stress the importance of considering these factors for any study where data is limited and observer confidence is variable

Developing a Monitoring Protocol for the Monkey River Watershed, Belize, Central America

The study of tropical aquatic systems has been limited. Research in developing countries can be challenging due to inadequate resources and cultural variety. Generally, efforts are concentrated on developing and maintaining economic stability rather than ecological sustainability. The aim of this project was to preliminarily develop and utilize a rapid bioassessment protocol (RBP) for the Monkey River watershed in Belize by determining which metrics best described overall stream health. Like biomonitoring protocols already established for temperate systems, a regional tropical aquatic watershed monitoring program should provide information including stream and watershed health. These protocols score systems on a variety of parameters including water chemistry, land use, stream physiognomy, and biological components. Since an understanding of tropical aquatic environments cannot be gained through studying temperate systems, this project was necessary. Human impacts are an important factor in aquatic systems. Changes in land use practices in a watershed can drastically alter stream processes. The RPB used measures of basic water chemistry and stream morphometrics. The protocol included categorical assessment of biological attributes of each reach. Land cover was determined using satellite imagery and ground truth data. Results from human impact assessment, land cover determination, and the RBP were compared to show trends in the aquatic ecosystem of the Monkey River basin. Few factors measured using the RBP showed significant trends with regard to human impact. Temperature, pH, fish, and algae all showed trends with increasing human impacts. PCA showed that pH, specific conductivity, depth, and riparian zone width were important in determining differences among sites. Future studies including continuous monitoring of land use and stream ecosystems may show evidence of how land affects streams in Belize

Public Geospatial Data in Wisconsin: Information Access, Data Sharing, and the University

This research explores public geospatial data sharing in Wisconsin. The research is informed by literature on GIS and Society, Participatory GIS, Spatial Data Infrastructure, Information Justice, The Digital Divide, and Library and Information Science. Original research consists of a survey and follow up interview to public land information professionals in Wisconsin gauging their interest in a UW System-wide geographic information portal for distributing public spatial data to UW System users. The research finds that social and institutional rather than technical factors are major drivers of data-sharing activities in Wisconsin. However, technical aspects of geographic information are changing quickly with a move to more hosted services in the cloud. This research explores how this shift influences data-sharing, academic library GIS services, and university level education. While social and institutional influences are critical, GIS professionals, students, and educators must be ready for the cloud

The Digital Humanities and Literary Studies

This book delivers an introduction and overview of developing intersections between digital methods and literary studies. The Digital Humanities and Literary Studies serves as a starting place for those who wish to learn more about the possibilities, and the limitations, of the oft-touted digital humanities in the literary space. The volume engages with the proponents of digital humanities and its detractors alike, aiming to offer a fair and balanced perspective on this controversial topic. The book combines a survey and background approach with original literary research and, therefore, straddles the divide between seasoned digital experts and interested newcomers

A Comparison Of The Effects Of Analysis Techniques And Computer Systems In Remote Sensing Technology And A Reference Data Collection Technique

Thesis (Ph.D.) University of Alaska Fairbanks, 1981A technique for collecting and recording reference data which considers the spectral and spatial characteristics of Landsat data, the computer system being used, and the gradient nature of wildland vegetation was developed and described. Different analysis techniques for four critical factors affecting the accuracy of computer-aided analysis products were evaluated. Comparisons were made on the basis of accuracy evaluations of two methods of data/analyst interface, three methods of deriving training statistics, three methods of spectral class descriptions, and two levels of map category detail. The primary data set used was digital Landsat multispectral data for a study area around Fairbanks, Alaska. Reference data were developed from field work and photo-interpretation. The training methods compared were supervised, unsupervised, and modified clustering. The three spectral class description methods were: (1) labels derived from the training data; (2) the color display screen; and (3) from ground plot data. Community level cover types were compared with generalized map categories. The effect of post-classification stratification was evaluated. The reference data technique provides geographically located stands and cover types identifications with a flexible coding system that can be aggregated to correspond to the spectral data categories. No difference in classification accuracy was found for an experienced analyst using a printout oriented system such as EDITOR or a screen oriented system such as IDIMS. The modified cluster method of developing training statistics was more effective and efficient than supervised or unsupervised training methods. The use of ground plot data and subsequent stratification improved the descriptions of spectral classes. Generalized mapping categories were more accurate than detailed mapping categories. Knowledge of the ecologic, floristic, and spectral characteristics of the cover types in the study area is necessary to develop spectral class descriptions and stratification criteria

An evaluation of Landsat MSS data for ecological land classification and mapping in the Northern Cape

This paper examines the issues that arise in the use of visual interpretation of Landsat data during the analysis, classification and mapping of the natural vegetation of the semi-arid Northern Cape. Initial research involved the classifying and mapping of the vegetation using conventional methods. A vegetation map, accompanying legend and descriptive key were produced. The problems encountered during this process, and the constraints of manpower, time and funds, stimulated the investigation of Landsat imagery as a means of improving the speed and accuracy of vegetation classification and mapping. A study area comprising one Landsat scene and which met certain requirements was selected: a) The area had already been surveyed and mapped at a scale of 1:250 000. b) As many vegetation units as possible were included. c) There was maximum diversity, complexity and variability in terms of soil, geology and terrain morphology. Initially a suitable mapping scale was selected, viz. 1:250 000, as it met the requirements of nature conservation authorities and agricultural planners. The scales of survey and remote sensing were based on this. The basic unit of survey was the 1:50 000 topographical map and satellite imagery at a scale of 1:250 000 was found to meet the requirements of reconnaissance level mapping. The usefulness of Landsat imagery was markedly affected by the quality of image production and enhancement. Optimum image production was vitally important and to this end, interaction between the user and the operations engineer at the Satellite Applications Centre, Hartebeeshoek was essential. All images used, were edge-enhanced and systematically corrected. While these procedures were costly, they proved to be fundamental to the success of the investigation. Precision geometric correction was not required for reconnaissance level investigation. The manual superimposition of the UTM grid, using ground control points from 1:250 000 topographical maps, proved to be accurate and convenient. Pattern recognition on single-band, panchromatic imagery was difficult. The scene lacked crispness and contrast, and it was evident that black and white imagery did not satisfy the objectives of the study. Three-band false colour composite imagery was superior to single-band imagery in terms of clarity and number of cover classes. The addition of colour undoubtedly facilitated visual interpretation. False colour composite imagery was investigated further to establish which year, season and possibly time of season would best suit the objectives of the investigation. It was found that the environmental parameters affecting reflectance are relatively stable over time and it was not necessary to acquire imagery of the same year as field surveys. However, the year of imagery should be chosen so that similar climatic conditions prevail. While, in certain instances, imagery captured during winter had advantages in separating complex mosaics, summer imagery was superior in most respects. Furthermore, given "normal" climatic conditions, the ideal period during which there was maximum contrast between and within ground classes, and thus spectral classes, was narrowed to mid-January to mid-April. Units which were acceptably heterogeneous (relatively homogeneous) in terms of reflectance levels were delineated manually on the image. This delineation was done at three levels of complexity and the units were compared with the vegetation map. A series of field trips aided the interpretation of the images, especially where discrepancies occurred between the map and the image. In general, there was a close degree of correspondence between the prepared vegetation map and the delineated image. Field investigation revealed the image units to be more accurate than those on the vegetation map, and the image served to highlight the inadequacies inherent in classifying and mapping vegetation of extensive areas with limited resources

The Digital Humanities and Literary Studies

This book delivers an introduction and overview of developing intersections between digital methods and literary studies. The Digital Humanities and Literary Studies serves as a starting place for those who wish to learn more about the possibilities, and the limitations, of the oft-touted digital humanities in the literary space. The volume engages with the proponents of digital humanities and its detractors alike, aiming to offer a fair and balanced perspective on this controversial topic. The book combines a survey and background approach with original literary research and, therefore, straddles the divide between seasoned digital experts and interested newcomers