Proposal for a study of computer mapping of terrain using multispectral data from ERTS-A for the Yellowstone National Park test site

The author has identified the following significant results. A terrain map of Yellowstone National Park showed plant community types and other classes of ground cover in what is basically a wild land. The map comprised 12 classes, six of which were mapped with accuracies of 70 to 95%. The remaining six classes had spectral reflectances that overlapped appreciably, and hence, those were mapped less accurately. Techniques were devised for quantitatively comparing the recognition map of the park with control data acquired from ground inspection and from analysis of sidelooking radar images, a thermal IR mosaic, and IR aerial photos of several scales. Quantitative analyses were made in ten 40 sq km test areas. Comparison mechanics were performed by computer with the final results displayed on line printer output. Forested areas were mapped by computer using ERTS data for less than 1/4 the cost of the conventional forest mapping technique for topographic base maps

Arctic and subarctic environmental analyses utilizing ERTS-1 imagery

The author has identified the following significant results. ERTS-1 imagery provides a means of distinguishing and monitoring estuarine surface water circulation patterns and changes in the relative sediment load of discharging rivers on a regional basis. Physical boundaries mapped from ERTS-1 imagery in combination with ground truth obtained from existing small scale maps and other sources resulted in improved and more detailed maps of permafrost terrain and vegetation for the same area. Snowpack cover within a research watershed has been analyzed and compared to ground data. Large river icings along the proposed Alaska pipeline route from Prudhoe Bay to the Brooks Range have been monitored. Sea ice deformation and drift northeast of Point Barrow, Alaska have been measured during a four day period in March and shore-fast ice accumulation and ablation along the west coast of Alaska have been mapped for the spring and early summer seasons

Mapping surface features of an Alpine glacier through multispectral and thermal drone surveys

Glacier surfaces are highly heterogeneous mixtures of ice, snow, light-absorbing impurities and debris material. The spatial and temporal variability of these components affects ice surface characteristics and strongly influences glacier energy and mass balance. Remote sensing offers a unique opportunity to characterize glacier optical and thermal properties, enabling a better understanding of different processes occurring at the glacial surface. In this study, we evaluate the potential of optical and thermal data collected from field and drone platforms to map the abundances of predominant glacier surfaces (i.e., snow, clean ice, melting ice, dark ice, cryoconite, dusty snow and debris cover) on the Zebrù glacier in the Italian Alps. The drone surveys were conducted on the ablation zone of the glacier on 29 and 30 July 2020, corresponding to the middle of the ablation season. We identified very high heterogeneity of surface types dominated by melting ice (30% of the investigated area), dark ice (24%), clean ice (19%) and debris cover (17%). The surface temperature of debris cover was inversely related to debris-cover thickness. This relation is influenced by the petrology of debris cover, suggesting the importance of lithology when considering the role of debris over glaciers. Multispectral and thermal drone surveys can thus provide accurate high-resolution maps of different snow and ice types and their temperature, which are critical elements to better understand the glacier’s energy budget and melt rates

Monitoring forest canopy alteration around the world with digital analysis of LANDSAT imagery

There are no author-identified significant results in this report

Measures of Reliance and Compliance in Aided Visual Scanning

Dieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG geförderten) Allianz- bzw. Nationallizenz frei zugänglich.This publication is with permission of the rights owner freely accessible due to an Alliance licence and a national licence (funded by the DFG, German Research Foundation) respectively.Objective: We study the dependence or independence of reliance and compliance as two responses to alarms to understand the mechanisms behind these responses. Background: Alarms, alerts, and other binary cues affect user behavior in complex ways. The suggestion has been made that there are two different responses to alerts—compliance (the tendency to perform an action cued by the alert) and reliance (the tendency to refrain from actions as long as no alert is issued). The study tests the degree to which these two responses are indeed independent. Method: An experiment tested the effects of the positive and negative predictive values of the alerts (PPV and NPV) on measures of compliance and reliance based on cutoff settings, response times, and subjective confidence. Results: For cutoff settings and response times, compliance was unaffected by the irrelevant NPV, whereas reliance depended on the irrelevant PPV. For subjective estimates, there were no significant effects of the irrelevant variables. Conclusion: Results suggest that compliance is relatively stable and unaffected by irrelevant information (the NPV), whereas reliance is also affected by the PPV. The results support the notion that reliance and compliance are separate, but related, forms of trust. Application: False alarm rates, which affect PPV, determine both the response to alerts (compliance) and the tendency to limit precautions when no alert is issued (reliance)

Applications des techniques d'apprentissage profond aux images satellites THR pour l'aide au recensement de population en Afrique

La connaissance de la donnée démographique est une information précieuse pour les initiatives de planification. Généralement, ce sont les opérations de recensement, d’enquête et de projection démographique qui permettent de disposer de cette information. Ces opérations, dans certains pays en développement, posent divers défis d’ordre économique et logistique, privant ainsi les autorités d'informations précises et à jour sur leur population. Dans le souci d’apporter des approches de solution à cette situation, notre étude évalue une méthode d’estimation de population basée sur la détection de géo-objets résidentiels (maisons) sur des images satellites THR à l’aide de réseau de neurones convolutifs (CNN). L’approche s’appliquerait à des pays où un recensement complet est difficile à réaliser pour des raisons de moyens ou d’instabilité politique. Ainsi, une image satellite THR de 2008 du Soudan est annotée selon 07 classes de bâtis pour construire un ensemble de données qui a servi à entrainer un modèle de détection d’objets : Faster R-CNN, par Transfert Learning. Le modèle a obtenu une précision moyenne (mAP) de 79 % en entrainement et 99 % en validation. Ce modèle a été ensuite utilisé par fine tuning pour détecter les mêmes classes de bâtis sur des images de 2021. Un lien entre les géo-objets résidentiels et la taille de population a été établi en utilisant les données de population de 2008 et les données terrain disponibles. Ceci a permis une caractérisation de la population actuelle et devra aider à la préparation du recensement de 2023. Des limites de cette approche ont été soulevées. En autre, il pourra être utilisé pour améliorer le cadre de la collecte de données de population dans les pays en développement

For multidisciplinary research on the application of remote sensing to water resources problems

Research on the application of remote sensing to problems of water resources was concentrated on sediments and associated nonpoint source pollutants in lakes. Further transfer of the technology of remote sensing and the refinement of equipment and programs for thermal scanning and the digital analysis of images were also addressed