Fanjinshan National Nature Reserve (FNNR) is a biodiversity hotspot in China that is part of a larger, multi-use landscape where farming, grazing, tourism, and other human activities occur. The steep terrain and persistent cloud cover pose challenges to robust vegetation and land use mapping. Our objective is to develop satellite image classification techniques that can reliably map forest cover and land use while minimizing the cloud and terrain issues, and provide the basis for long-term monitoring. Multi-seasonal Landsat image composites and elevation ancillary layers effectively minimize the persistent cloud cover and terrain issues. Spectral vegetation index (SVI) products and shade/illumination normalization approaches yield significantly higher mapping accuracies, compared to non-normalized spectral bands. Advanced machine learning image classification routines are implemented through the cloud-based Google Earth Engine platform. Optimal classifier parameters (e.g., number of trees and number of features for random forest classifiers) were achieved by using tuning techniques. Accuracy assessment results indicate consistent and effective overall classification (i.e., above 70% mapping accuracies) can be achieved using multi-temporal SVI composites with simple illumination normalization and elevation ancillary data, despite the fact limited training and reference data are available. This efficient and open-access image analysis workflow provides a reliable methodology to remotely monitor forest cover and land use in FNNR and other mountainous forested, cloud prevalent areas.

Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet

Agent based modeling (ABM) is a standard tool that is useful across many disciplines. Despite widespread and mounting interest in ABM, even broader adoption has been hindered by a set of methodological challenges that run from issues around basic tools to the need for a more complete conceptual foundation for the approach. A er several decades of progress, ABMs remain di icult to develop and use for many students, scholars, and policy makers. This di iculty holds especially true for models designed to represent spatial patterns and processes across a broad range of human, natural, and human-environment systems. In this paper, we describe the methodological challenges facing further development and use of spatial ABM (SABM) and suggest some potential solutions from multiple disciplines. We first define SABM to narrow our object of inquiry, and then explore how spatiality is a source of both advantages and challenges. We examine how time interacts with space in models and delve into issues of model development in general and modeling frameworks and tools specifically. We draw on lessons and insights from fields with a history of ABM contributions, including economics, ecology, geography, ecology, anthropology, and spatial science with the goal of identifying promising ways forward for this powerful means of modeling.

Global warming and climate variations are expected to alter hydrologic conditions and exacerbate flooding, primarily through increasingly frequent and intense storm events and sea-level rise. The interactions between coastlines and their inhabitants around the world are highly diverse, making them challenging to model due to the non-homogeneous, nonlinear, and complex nature of human decision-making. Agent-based modeling has proven valuable in various fields, enabling researchers to explore various social phenomena and emergent patterns under different institutional frameworks, including climate change scenarios and policy decisions, particularly at local scales. This approach is particularly useful in providing insights into possible outcomes and feedback resulting from the convergence of individual- and community-level adaptation decisions, and it has increasingly been used to model coastal systems. However, there are a limited number of studies that examine the effects of climate adaptation decisions on coastal tourism systems. This paper aims to address this gap by first providing an overview of the current state of agent-based modeling literature that explores coastal community adaptation responses to climate change. Subsequently, the paper argues for the application of these methods to simulate the effects of adaptation on coastal tourism dynamics. To better capture the interactions within subsystems and potential redistributed effects inherent in multi-scale and multi-stakeholder decision-making processes within these systems, we propose integrating agent-based modeling with a novel system of socio-environmental systems (SoSES) approach. This integration aims to assist city planners, policymakers, stakeholders, and attraction managers in effectively assessing adaptation options to safeguard their communities from the multifaceted impacts of climate change.