UNDERSTANDING ENVIRONMENTAL FACTORS DRIVING WILDLAND FIRE IGNITIONS IN ALASKAN TUNDRA

Wildland fire is a dominant disturbance agent that drives ecosystem change, climate forcing, and carbon cycle in the boreal forest and tundra ecosystems of the High Northern Latitudes (HNL). Tundra fires can exert a considerable influence on the local ecosystem functioning and contribute to climate change through biogeochemical and biogeophysical effects. However, the drivers and mechanisms of tundra fires are still poorly understood. Research on modeling contemporary fire occurrence in the tundra is also lacking. This dissertation addresses the overarching scientific question of “What environmental factors and mechanisms drive wildfire ignition in Alaskan tundra?” Environmental factors from multiple aspects are considered including fuel type and state, fire weather, topography, and ignition source. First, to understand the spatial distribution of fuel types in the tundra, multi- year satellite observations and field data were used to develop the first fractional coverage product of major fuel type components across the entire Alaskan tundra at 30 m resolution. Second, to account for the primary ignition source of fires in the HNL, an empirical-dynamical modeling framework was developed to predict the probability of cloud-to-ground (CG) lightning across Alaskan tundra, through the integration of Weather Research and Forecast (WRF) model and machine learning algorithm. Finally, environmental factors including fuel type distribution, fuel moisture state, WRF simulated ignition source and fire weather, and topographical features, were combined with empirical modeling methods to understand their roles in driving wildland fire ignitions across Alaskan tundra from 2001 to 2019. This work demonstrates the strong capability for accurate prediction of CG lightning and wildland fire probabilities, by incorporating dynamic weather models, empirical methods, and satellite observations in data-scarce regions like the HNL. The developed models present a novel component of fire danger modeling that can considerably strengthen the current capability to forecast fire occurrence and support operational fire management agencies in the HNL. In addition, the insights gained from this research will allow for more accurate representation of wildfire ignition probabilities in studies focused on assessing the impact of the projected climate change in HNL tundra which has largely absent in previous modeling efforts

Biophysical Effects of Afforestation on Land Surface Temperature in Guangdong Province, Southern China

Developing effective climate mitigation strategies under global warming requires a comprehensive understanding of the biophysical mechanism of how afforestation affects the climate and environment. The planted forests in southern China are an essential carbon sink. However, the impacts of radiative and non-radiative processes on land surface temperature caused by converting open land (i.e., grassland and cropland) and natural forests to planted forests remain unclear. We used satellite observations and intrinsic biophysical mechanism theory-based energy balance models to estimate the biophysical impacts of potential afforestation of open land and natural forests on surface temperature from 2000 to 2010 in Guangdong Province, southern China. Results showed that afforestation of open land had a consistent net cooling effect. Due to the afforestation of natural forests, the modeled results revealed that afforestation among all conversion types had a net warming effect of 0.15 ± 0.5 K, which caused by the change in energy redistribution factor although uncertainty remains. While the most significant warming caused by converting natural forest to planted forests was also slightly affected by albedo. The afforestation's non-radiative and radiative processes led to a slight warming of 0.143 ± 0.43 K and a cooling of −0.096 ± 0.19 K, respectively. The non-radiative process dominates the effect of afforestation on the surface temperature, with the overall non-radiative forcing index greater than 73% ± 0.59%. Our study highlights the need of protecting natural forests and provides a practical method for assessing the impacts of afforestation on the local climate and the effectiveness of climate mitigation efforts.Biophysical Effects of Afforestation on Land Surface Temperature in Guangdong Province, Southern ChinaacceptedVersio

Issues in Chinese Requirements Specifications: Insights from Survey Data and Static Analysis

Requirements engineering is crucial for software project success. Issues like requirements ambiguity, inconsistency, and unverifiability contribute to unclear, conflicting, or untestable specifications, which can undermine the effectiveness and success of a software project. These issues have been identified as factors contributing to software project failure. However, there’s limited research on the current state of these issues in China. The research objectives of this study are to address the most commonly used methods for expressing Chinese software requirements and uncover issues related to ambiguity, inconsistency, and unverifiability, which can be solved by using artificial intelligence techniques to investigate possible solutions to these problems. An online survey of 422 software professionals in China identifies key issues in Chinese software requirement expressions that AI techniques can address. The study examines various expression methods, tools for enhancing clarity, and challenges specific to Chinese requirements. Findings reveal that ambiguity, inconsistency, and unverifiability significantly impact project success. While natural language specification and prototyping improve clarity, they may increase the time required for requirements engineering. Effective communication is typically achieved through natural language, prototyping, storyboarding, and pseudo-coding, whereas decision tables and block diagrams are less commonly used and linked to problematic requirements. Using tables, prototype diagrams, and natural language descriptions helps mitigate these issues, though it may extend engineering time. The study suggests strategies to improve the efficiency and quality of requirements expression and highlights the need to develop Chinese boilerplates and refining tools to enhance clarity in the future

Impacts of wildfire and landscape factors on organic soil properties in Arctic tussock tundra

Tundra ecosystems contain some of the largest stores of soil organic carbon among all biomes worldwide. Wildfire, the primary disturbance agent in Arctic tundra, is likely to impact soil properties in ways that enable carbon release and modify ecosystem functioning more broadly through impacts on organic soils, based on evidence from a recent extreme Anaktuvuk River Fire (ARF). However, comparatively little is known about the long-term impacts of typical tundra fires that are short-lived and transient. Here we quantitatively investigated how these transient tundra fires and other landscape factors affected organic soil properties, including soil organic layer (SOL) thickness, soil temperature, and soil moisture, in the tussock tundra. We examined extensive field observations collected from nearly 200 plots across a wide range of fire-impacted tundra regions in AK within the scope of NASA\u27s Arctic Boreal Vulnerability Experiment. We found an overall shallower SOL in our field regions (∼15 cm on average) compared to areas with no known fire record or the ARF (∼20 cm or thicker), suggesting that estimations based on evidence from the extreme ARF event could result in gross overestimation of soil organic carbon (SOC) stock and fire impacts across the tundra. Typical tundra fires could be too short-lived to result in substantial SOL consumption and yield less robust results of SOL and carbon storage. Yet, repeated fires may amount to a larger amount of SOC loss than one single severe burning. As expected, our study showed that wildfire could affect soil moisture and temperature in the tussock tundra over decades after the fire, with drier and warmer soils found to be associated with more frequent and severe burnings. Soil temperature was also associated with vegetation cover and air temperature

Uncovering the spatially distant feedback loops of global trade: A network and input-output approach

Land-use change is increasingly driven by global trade. The term “telecoupling” has been gaining ground as a means to describe how human actions in one part of the world can have spatially distant impacts on land and land-use in another. These interactions can, over time, create both direct and spatially distant feedback loops, in which human activity and land use mutually impact one another over great expanses. In this paper, we develop an analytical framework to clarify spatially distant feedbacks in the case of land use and global trade. We use an innovative mix of multi-regional input-output (MRIO) analysis and stochastic actor-oriented models (SAOMs) for analyzing the co-evolution of changes in trade network patterns with those of land use, as embodied in trade. Our results indicate that the formation of trade ties and changes in embodied land use mutually impact one another, and further, that these changes are linked to disparities in countries' wealth. Through identifying this feedback loop, our results support ongoing discussions about the unequal trade patterns between rich and poor countries that result in uneven distributions of negative environmental impacts. Finally, evidence for this feedback loop is present even when controlling for a number of underlying mechanisms, such as countries' land endowments, their geographical distance from one another, and a number of endogenous network tendencies

Design of an efficient multi-objective recognition approach for 8-ball billiards vision system

In this paper, some key technologies based on colour image processing for 8-ball billiards robot vision system are discussed and an efficient approach for multi-objective recognition is proposed. This approach is divided into two parts, i.e. multi-objective detection and ball pattern recognition. In image pre-processing, the normalized RGB colour space and histogram statistics are adopted for segmentation of background (table cover) and foregrounds. In order to accurately locate and isolate the single ball in a local region, the improved Hough Transform (HT) algorithm and the Least Squares (LS) method are adopted in combination. The improved HT algorithm is used for the purpose of eliminating the noise concentrated at edge points, and the LS method is used for fitting the circle center accurately with the least mean square error. Based on single ball detection in a local region, the multi-ball detection approach has been worked out to locate the position of each ball on the table. In the experiment, the proposed approach has been proved to complete the detection with an accuracy of 99.4% in 0.65s in average, and the performance is better than the traditional Circular Hough Transform (CHT) algorithm and the K-means cluster method. In addition, the Convolution Neural Network (CNN) method is adopted for pattern recognition of each target ball being segmented, i.e. identification of a solid ball or a striped ball. In order to improve the quality of CNN training: the colour segmentation and morphologic operation are applied for the segmented ball image pre-processing; the training set images are rotated for augmentation; pre-training stage is introduced in for optimizing the initial weight matrices. The calibrated image blocks are imported to the network for training. In the verification test, the trained CNN model shows a recognition rate of over 98.5%, and outperforms the other three classic methods. The introduction of CNN method has been proved to be correct and effective, and is an innovative and significant step for the design process of the 8-ball billiards robot vision system.

Issues in Chinese requirements specifications: insights from survey data and static analysis

Requirements engineering is crucial for software project success. Issues like requirements ambiguity, inconsistency and unverifiability contribute to unclear, conflicting, or untestable specifications, which can undermine the effectiveness and success of a software project. These issues have been identified as factors that contribute to the failure of software projects. However, there’s limited research on the current state of these issues in China. The research objectives of this study are to address the most commonly used methods for expressing Chinese software requirements and uncover issues related to ambiguity, inconsistency, and unverifiability which can be solved by using artificial intelligence techniques, in order to investigate possible solutions to these problems. An online survey of 422 software professionals in China identifies key issues in Chinese software requirement expressions that AI techniques can address. The study examines various expression methods, tools for enhancing clarity, and challenges specific to Chinese requirements. Findings reveal that ambiguity, inconsistency, and unverifiability significantly impact project success. While methods like natural language specification and prototyping improve clarity, they may increase the time required for requirements engineering. Effective communication is typically achieved through natural language, prototyping, storyboarding, and pseudo-coding, whereas decision tables and block diagrams are less commonly used and linked to problematic requirements. Using tables, prototype diagrams, and natural language descriptions helps mitigate these issues, though it may extend engineering time. The study suggests strategies to improve the efficiency and quality of requirements expression and highlights the need for developing Chinese boilerplates and refining tools to enhance clarity in the future