L’indice de surface foliaire (Leaf Area Index, LAI), défini comme la moitié de la surface foliaire par unité de surface de sol, est un paramètre clé du cycle écologique de la Terre, et sa précision d'acquisition a toujours la nécessité et la possibilité d'amélioration. La technologie du scanner laser actif offre une possibilité d'obtention cohérente du LAI à plusieurs échelles, car le scanner laser terrestre et le scanner laser aéroporté fonctionnent sur le même mécanisme physique. Cependant, les informations tridimensionnelles du scanner laser ne sont pas complètement explorées dans les méthodes actuelles et les théories traditionnelles ont besoin d'adaptation. Dans cette thèse, le modèle de distribution de longueur de trajet est introduit pour corriger l'effet d’agrégation, et il est appliqué aux données du scanner laser terrestre et du scanner laser aéroporté. La méthode d'obtention de la distribution de longueur de trajet de différentes plates-formes est étudiée et le modèle de récupération cohérent est établi. Cette méthode permet d’améliorer la mesure du LAI des arbres individuels dans les zones urbaines et la cartographie LAI dans les forêts naturelles, et ses résultats sont cohérents à différentes échelles. Le modèle devrait faciliter la détermination cohérente de l'indice de surface foliaire des forêts à l'aide de données au sol et aéroportées. / Leaf Area Index (LAI), defined as one half of the total leaf area per unit ground surface area, is a key parameter of vegetation structure for modeling Earth's ecological cycle and its acquisition accuracy always has the need and opportunity for improvement. Active laser scanning provides an opportunity for consistent LAI retrieval at multiple scales because terrestrial laser scanning (TLS) and airborne laser scanning (ALS) have the similar physical mechanism. However, the three-dimensional information of laser scanning is not fully explored in current methods and the traditional theories require adaptation. In this thesis, the path length distribution model is proposed to model the clumping effect, and it is applied to the TLS and ALS data. The method of obtaining the path length distribution of different platforms is studied, and the consistent retrieval model is established. This method is found to improve the individual tree measurement in urban areas and LAI mapping in natural forest, and its results at consistent at different scales. The model is expected to facilitate the consistent retrieval of the forest leaf area index using ground and airborne data.

Abstract This thesis focuses on topographic mapping of rock formations with the use of new technologies in a comprehensive manner, from airborne laser scanning (ALS) data acquisition and processing in rocky terrains, followed by their processing to the content of topographic databases and their cartographic processing in maps. The introduction discusses issues of importance for practice, and the relation between topographic mapping of rocks and other fields of human activity. The ALS section describes products for topographic mapping of rocks derived from ALS data, and discusses the specifics of ALS data acquisition and processing in wooded rugged terrain. Existing solutions of this problem are explained and their limitations are identified. Author's own approaches to solving this task are presented as case studies, including three made a further three designed experiments with ALS data processing and evaluation of their results. Recommendation regarding mapping of sandstone landscapes in Czechia have been also addressed. The topographic section describes the current representation of rocks and related objects in the ZABAGED database (Czech national digital topographic database), explains the historical context, analyzes this data and identifies their shortcomings in relation to the ALS. Research...

<p dir="ltr">This dissertation explores the integration of remote sensing and artificial intelligence (AI) in geospatial mapping, specifically through the development of knowledge-based mapping systems. Remote sensing has revolutionized Earth observation by providing data that far surpasses traditional in-situ measurements. Over the last decade, significant advancements in inferential capabilities have been achieved through the fusion of geospatial sciences and AI (GeoAI), particularly with the application of deep learning. Despite its benefits, the reliance on data-driven AI has introduced challenges, including unpredictable errors and biases due to imperfect labeling and the opaque nature of the processes involved.</p><p dir="ltr">The research highlights the limitations of solely using data-driven AI methods for geospatial mapping, which tend to produce spatially heterogeneous errors and lack transparency, thus compromising the trustworthiness of the outputs. In response, it proposes novel knowledge-based mapping systems that prioritize transparency and scalability. This research has developed comprehensive techniques to extract key Earth and urban features and has introduced a 3D urban land cover mapping system, including a 3D Landscape Clustering framework aimed at enhancing urban climate studies. The developed systems utilize universally applicable physical knowledge of targets, captured through remote sensing, to enhance mapping accuracy and reliability without the typical drawbacks of data-driven approaches.</p><p dir="ltr">The dissertation emphasizes the importance of moving beyond mere accuracy to consider the broader implications of error patterns in geospatial mappings. It demonstrates the value of integrating generalizable target knowledge, explicitly represented in remote sensing data, into geospatial mapping to address the trustworthiness challenges in AI mapping systems. By developing mapping systems that are open, transparent, and scalable, this work aims to mitigate the effects of spatially heterogeneous errors, thereby improving the trustworthiness of geospatial mapping and analysis across various fields. Additionally, the dissertation introduces methodologies to support urban pathway accessibility and flood management studies through dependable geospatial systems. These efforts aim to establish a robust foundation for informed urban planning, efficient resource allocation, and enriched environmental insights, contributing to the development of more sustainable, resilient, and smart cities.</p>