Christopher Bone

Integrating high resolution remote sensing, GIS and fuzzy set theory for identifying susceptibility areas of forest insect infestations

The use of fuzzy set theory has become common in remote sensing and geographical information system (GIS) applications to deal with issues surrounding the uncertainty of geospatial datasets. The objective of this study is to develop a model that integrates the concept of fuzzy set theory with remote sensing and GIS in order to produce susceptibility maps of insect infestations in forest landscapes. Fuzzy set theory was applied to information extracted from multiple‐year high resolution remote sensing data and integrated in a raster‐based GIS to create a map indicating the spatial variation of insect susceptibility in a landscape. Variable‐specific fuzzy membership functions were developed based on expert knowledge and existing data, and integrated through a semantic import model. The results from a case study on mountain pine beetle (Dendroctonus ponderosae Hopkins) illustrate that the model provides a method to successfully estimate areas of varying susceptibility to insect infestation from high resolution remote sensing images. It was concluded that fuzzy sets are an adequate method for dealing with uncertainty in defining susceptibility variables. The susceptibility maps can be utilized for guiding management decisions based on the spatial aspects of insect–host relationships.

Assessing the Impacts of Local Knowledge and Technology on Climate Change Vulnerability in Remote Communities

The introduction of new technologies into small remote communities can alter how individuals acquire knowledge about their surrounding environment. This is especially true when technologies that satisfy basic needs, such as freshwater use, create a distance (i.e., diminishing exposure) between individuals and their environment. However, such distancing can potentially be countered by the transfer of local knowledge between community members and from one generation to the next. The objective of this study is to simulate by way of agent-based modeling the tensions between technology-induced distancing and local knowledge that are exerted on community vulnerability to climate change. A model is developed that simulates how a collection of individual perceptions about changes to climatic-related variables manifest into community perceptions, how perceptions are influenced by the movement away from traditional resource use, and how the transmission of knowledge mitigates the potentially adverse effects of technology-induced distancing. The model is implemented utilizing climate and social data for two remote communities located on the Seward Peninsula in western Alaska. The agent-based model simulates a set of scenarios that depict different ways in which these communities may potentially engage with their natural resources, utilize knowledge transfer, and develop perceptions of how the local climate is different from previous years. A loosely-coupled pan-arctic climate model simulates changes monthly changes to climatic variables. The discrepancy between the perceptions derived from the agent-based model and the projections simulated by the climate model represent community vulnerability. The results demonstrate how demographics, the communication of knowledge and the types of ‘knowledge-providers’ influence community perception about changes to their local climate.

Modeling-in-the-middle: bridging the gap between agent-based modeling and multi-objective decision-making for land use change

A spectrum of methods exists for investigating and providing solutions for land use change. These methods can be broadly categorized as either ‘top-down’ or ‘bottom-up’ approaches according to how land use change is modeled and analyzed. Although there has been much research in recent years advancing the use of these techniques for both theoretical and practical applications, integrating top-down and bottom-up approaches for enhancing land use change modeling has received minimal attention. The objective of this study is to address this gap in the literature by bridging the bottom-up simulation of agent-based modeling and the top-down analytical capabilities of multi-objective decision-making by means of a heuristic modeling approach called reinforcement learning (RL). A model is developed in which computer agents representing households and commercial enterprises select locations to inhabit based on population densities and attractivity preferences. The land use change resulting from these dynamics is evaluated by a set of agents representing different stakeholders who are embedded with RL algorithms that allow them to influence the land use change process so that their objectives are addressed. The results demonstrate that bridging bottom-up and top-down models leads to negotiated land use patterns in which the desires and objectives of all individuals are constrained by behaviors of others. This study suggests that a movement toward a ‘modeling-in-the-middle’ approach is desirable to incorporate the real yet conflicting forces that shape land use change and that are rarely considered in unison.

Simulation and validation of a reinforcement learning agent-based model for multi-stakeholder forest management

Spatial optimization and agent-based modeling present two distinct approaches that have been implemented in forest management research for incorporating the objectives of multiple stakeholders. However, challenges arise in their implementation as optimization procedures do not consider the interactions amongst stakeholders, and agent-based models generate results from which it is difficult to determine if objectives have been successfully achieved. The purpose of this research is to overcome these limitations by improving the ability of an agent-based model to achieve optimal forest harvesting strategies through the integration of reinforcement learning (RL). A simulation model is developed in which forest company agents harvest trees in order to maximize their profits while considering the potential to cooperate with a conservationist agent whose objectives are based on protecting species habitat. RL algorithms are implemented to allow the forest company agents and the conservationist agent to learn where harvesting should occur in order to achieve their objectives. The model is validated by determining if generated solutions can be considered optimal given system constraints, and by comparing observed agent behavior against learning functions as defined by the RL algorithms. The obtained results demonstrate a non-linear relationship between different levels of cooperation and the ability of agents to achieve their objectives. The model also provides outputs that depict the relative quality of forest areas and the tradeoffs between objectives for different optimal solutions.

Applying content analysis for investigating the reporting of water issues

Abstract This article presents a content analysis approach for contextualizing the reporting of water and water-related issues. The intent of our approach is to enable an understanding of how important environmental topics such as water-related issues are presented to the public, and thus potentially influencing public perceptions on the issues. Multiple statistical and analytical methods are integrated in order to analyze online newspapers articles to evaluate the context, regionalism and relevance of the reporting of water issues. Using 10 online newspapers from Nebraska, USA, the content analysis approach revealed that water is most often reported in the state in the context of agriculture, while other topics such as water quality and habitat are less frequently discussed. Second, there is a lack of spatial dependency in the reporting of water across Nebraska as newspapers in close proximity to one another do not demonstrate similar reporting. Finally, the reporting of water in some newspapers is noticeably linked to local daily water quantity observations. These results suggest that, although the topic of water as an environmental issue may be vitally important across a region, the context of how water issues are reported is driven by local issues and, in some cases, relevant physical processes. Results show that there is a relative lack of coverage on major water and environmental issues except when issues are of immediate public concern. We discuss how these results could be used by resource managers to interpret media content and the public’s understanding of important environmental topics.

A geospatial search engine for discovering multi-format geospatial data across the web

The volume of publically available geospatial data on the web is rapidly increasing due to advances in server-based technologies and the ease at which data can now be created. However, challenges remain with connecting individuals searching for geospatial data with servers and websites where such data exist. The objective of this paper is to present a publically available Geospatial Search Engine (GSE) that utilizes a web crawler built on top of the Google search engine in order to search the web for geospatial data. The crawler seeding mechanism combines search terms entered by users with predefined keywords that identify geospatial data services. A procedure runs daily to update map server layers and metadata, and to eliminate servers that go offline. The GSE supports Web Map Services, ArcGIS services, and websites that have geospatial data for download. We applied the GSE to search for all available geospatial services under these formats and provide search results including the spatial distribution of all obtained services. While enhancements to our GSE and to web crawler technology in general lie ahead, our work represents an important step toward realizing the potential of a publically accessible tool for discovering the global availability of geospatial data.

Influence of statistical methods and reference dates on describing temperature change in Alaska

Quantifying temperature trends across multiple decades in Alaska is an essential component for informing policy on climate change in the region. However, Alaskas climate is governed by a complex set of drivers operating at various spatial and temporal scales, which we posit should result in a sensitivity of trend estimates to the selection of reference start and end dates as well as the choice of statistical methods employed for quantifying temperature change. As such, this study attempts to address three questions: (1) How sensitive are temperature trend estimates in Alaska to reference start dates? (2) To what degree do methods vary with respect to estimating temperature change in Alaska? and (3) How do different reference start dates and statistical methods respond to climatic events that impact Alaskas temperature? To answer these questions, we examine the use of five methods for quantifying temperature trends at 10 weather stations in Alaska and compare multiple reference start dates from 1958 to 1993 while using a single reference end date of 2003. The results from this analysis demonstrate that, with some methods, the discrepancy in temperature trend estimates between consecutive start dates can be larger than the overall temperature change reported for the second half of the 20th century. Second, different methods capture different climatic patterns, thus influencing temperature trend estimates. Third, temperature trend estimation varies more significantly when a reference start date is defined by an extreme temperature. These findings emphasize that sensitivity analyses should be an essential component in estimating multidecadal temperature trends and that comparing estimates derived from different methods should be performed with caution. Furthermore, the ability to describe temperature change using current methods may be compromised given the increase in temperature extremes in contemporary climate change.

Adaptation to a landscape-scale mountain pine beetle epidemic in the era of networked governance: the enduring importance of bureaucratic institutions

The authors sincerely thank our interviewees for sharing their time and perspectives with us. Thank you to Tony Cheng for supplying data on CBBC membership. Thank you to Kelly Jacobson for assistance with Figure 4. This research is based on work supported by the National Science Foundation under grant No. 1414041.

A network modeling approach to policy implementation in natural resource management agencies

Abstract Natural resource agencies are responsible for managing specific aspects of the environment through the development and implementation of policies. Computational advances have emerged in recent years that provide opportunities for simulating the influence that agency structure has on policy outcomes, particularly those stemming from the area of network theory and analysis. However, there remains a need for methods that can measure and visualize the confounding effects that multiple agency characteristics may impose on policy implementation. The complex interactions among these factors require an approach that can evaluate these factors in relation to one another and provide a way to abstract meaningful findings that can be useful for both scientists and agencies to consider for future policy development. In this study, we present a network simulation modeling approach that (1) builds upon existing conceptualizations of bureaucrat decision-making within agency networks, (2) uses network theory to construct idealized natural resource agency networks that can be used to evaluate how agency structure influences policy implementation, and (3) visualizes simulation results to better understand how bureaucrat behaviors and relationships in concert with agency structure influence policy outcomes. Using this approach, we demonstrate how different aspects of decision-making by bureaucrats interact with the spatial constraints of institutional networks to influence policy outcomes. The network modeling and visualization methods presented here offer an alternative approach in the policy science toolbox that can help generate new assumptions and questions about the ways in which natural resources are governed.

Defining Transition Rules with Reinforcement Learning for Modeling Land Cover Change

Spatio-temporal modeling provides the opportunity to simulate geographic processes of land use and land cover change (LUCC) by integrating geographic information systems (GIS) with various machine learning approaches to computing. Contemporary models are often developed using a training dataset to define a set of probabilistic transition rules that govern how a landscape changes over time. However, the use of training datasets can be problematic for spatio-temporal modeling, as they can limit the ability to incorporate system complexity and hinder the transferability of the model to different datasets. The purpose of this study is to evaluate a machine learning approach called reinforcement learning (RL) for defining transition rules for GIS-based models of land cover change due to natural resource extraction. Specifically, RL is evaluated based on its potential for constructing models independent of training datasets that can handle different levels of complexity and be transferred across different spatial extents. An RL model for Land Cover Change (RL-LCC) is developed for considering economic and ecological goals involved in natural resource management, and implemented using a hypothetical forest management scenario. Simulation results reveal that agents in the RL-LCC model are able to develop transition rules from their experience in their landscape in a variety of simulation scenarios that allow them to achieve their goals. This study demonstrates the benefits of integrating RL and GIS in order to address important issues of space, time and complexity.