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Gully poses a great threat to agricultural production and ecological environment. Mapping accurate gully affected areas plays an important role in regional environmental monitoring and management. In this paper, an object-based image analysis method based on Google Earth Engine (OBIA-GEE) for mapping gully affected areas was proposed by using Sentinel 2 imagery and AW3D 30 DEM data. The method utilized the simple non-iterative clustering (SNIC) segmentation and the random forest (RF) algorithm to segment imagery and map gully affected areas. And terrain skeleton lines were further used to optimize the mapping results. The proposed method was applied to five study areas with different landform types on the Chinese Loess Plateau, and the results showed that the method achieved good performance with the overall accuracy of 86.44%, user’s accuracy of 84.97%, and producer’s accuracy of 83.90%. The OBIA-GEE method provides the possibility of large-scale gullies mapping, which is beneficial to monitor gullies and manage soil erosion.

Bacteria are widespread on Earth. They play important roles in shaping the global ecosystems as well as influencing human health. In nature, different bacterial species often coexist in a common environment. The interactions of them are diverse and often lead to intricate spatial-temporal dynamics. Quantitative experimental measurements as well as mathematical modeling could help us better understand the mechanisms behind such dynamics. In this dissertation, I discuss several spatial-temporal structures for multi-strain bacterial systems and different modeling strategies are used to help explore the mechanisms of different spatial-temporal patterns. A key question regarding a system of different bacterial species is how coexistence is maintained, considering that different bacterial species often have different growth rates and they can even kill each other sometimes. In Chapter Two, I discuss a novel theoretical framework for robust coexistence and pattern formation in bacterial mixtures with contact-dependent killing. In Chapter Three, I present a beautiful spatial flower-like pattern in two-species bacterial systems from experiments and build two different models, a discrete interface model and a phase-field model, to elucidate how differential motility and mechanical interactions between bacterial species can create complex spatial structures. In Chapter Four, I introduce the agent-based modeling, which simulates each individual bacterial cell in large populations. Two examples of applications of such agent-based modeling in microfluidic environments are described. All these chapters combine to highlight the effort towards understanding the spatial-temporal dynamics of multi-strain microbial systems quantitatively.

Infectious disease spread is a spatiotemporal process with significant regional differences that can be affected by multiple factors, such as human mobility and manner of contact. From a geographical perspective, the simulation and analysis of an epidemic can promote an understanding of the contagion mechanism and lead to an accurate prediction of its future trends. The existing methods fail to consider the mutual feedback mechanism of heterogeneities between the interregional population interaction and the regional transmission conditions (e.g., contact probability and the effective reproduction number). This disadvantage oversimplifies the transmission process and reduces the accuracy of the simulation results. To fill this gap, a general model considering the spatiotemporal characteristics is proposed, which includes compartment modeling of population categories, flow interaction modeling of population movements, and spatial spread modeling of an infectious disease. Furthermore, the correctness of a theoretical hypothesis for modeling and prediction accuracy of this model was tested with a synthetic data set and a real-world COVID-19 data set in China, respectively. The theoretical contribution of this article was to verify that the interplay of multiple types of geospatial heterogeneities dramatically influences the spatial spread of infectious disease. This model provides an effective method for solving infectious disease simulation problems involving dynamic, complex spatiotemporal processes of geographical elements, such as optimization of lockdown strategies, analyses of the medical resource carrying capacity, and risk assessment of herd immunity from the perspective of geography. Key Words: geospatial heterogeneities, health geography, interregional population interaction, spatiotemporal analysis, transmission modeling.

Terrain models are widely used to depict the shape of the Earth's surface. With the development of photogrammetric methods, point cloud data have become one of the most popular data sources for terrain modelling. However, the obtained point clouds are of high density, which often increases redundancy rather than improving accuracy. Therefore, point cloud simplification should be a core component of terrain modelling. This paper proposes a point cloud simplification method by integrating topographic knowledge into terrain modelling (TKPCS). The method contains two steps: (1) topographic knowledge recognition and construction and (2) point cloud simplification using this topographic knowledge for terrain modelling. The proposed approach is benchmarked against improved versions of existing methods to validate its capability and accuracy in digital elevation model construction and terrain derivative extraction. The results show that the simplified points of the TKPCS method can generate finer resolution terrain models with higher accuracy and greater information entropy. The good performance of the TKPCS method is also stable at different scales. This work endeavours to transform perceptive topographic knowledge into a process of point cloud simplification and can benefit future research related to terrain modelling.