The implications of alternative developer decision-making strategies on land-use and land-cover in an agent-based land market model

Land developers play a key role in land-use and land cover change, as\ud they directly make land development decisions and bridge the land and housing\ud markets. Developers choose and purchase land from rural land owners, develop\ud and subdivide land into parcel lots, build structures on lots, and sell houses to residential households. Developers determine the initial landscaping states of developed parcels, affecting the state and future trajectories of residential land cover, as well as land market activity. Despite their importance, developers are underrepresented in land use change models due to paucity of data and knowledge regarding their decision-making. Drawing on economic theories and empirical literature, we have developed a generalized model of land development decision-making within a broader agent-based model of land-use change via land markets. Developer’s strategies combine their specialty in developing of particular subdivision types, their perception of and attitude towards market uncertainty, and their learning and adaptation strategies based on the dynamics of the simulated land and housing markets. We present a new agent-based land market model that includes these elements. The model will be used to experiment with these different development decision-making methods and compare their impacts on model outputs, particularly on the quantity and spatial pattern of resultant land use changes. Coupling between the land market and a carbon sequestration model, developed for the larger SLUCE2 project, will allow us, in future work, to examine how different developer’s strategies will affect the carbon balance in residential\ud landscape

Exploring Students\u27 Perceptions of Academically Based Living-Learning Communities

This qualitative study employed focus group interviews to explore students\u27 perceptions of three well established academically based living-learning communities at a large, land-grant university in the Midwest. Three themes emerged that illustrated students\u27 perceptions of a culture that promoted seamless learning, a scholarly environment, and an ethos of relatedness among faculty, staff, and peers. Implications for practice and future research are discussed

Learning-based superresolution land cover mapping

Super-resolution mapping (SRM) is a technique for generating a fine spatial resolution land cover map from coarse spatial resolution fraction images estimated by soft classification. The prior model used to describe the fine spatial resolution land cover pattern is a key issue in SRM. Here, a novel learning based SRM algorithm, whose prior model is learned from other available fine spatial resolution land cover maps, is proposed. The approach is based on the assumption that the spatial arrangement of the land cover components for mixed pixel patches with similar fractions is often similar. The proposed SRM algorithm produces a learning database that includes a large number of patch pairs for which there is a fine and coarse spatial resolution representation for the same area. From the learning database, patch pairs that have similar coarse spatial resolution patches as those in input fraction images are selected. Fine spatial resolution patches in these selected patch pairs are then used to estimate the latent fine spatial resolution land cover map, by solving an optimization problem. The approach is illustrated by comparison against state-of-the-art SRM methods using land cover map subsets generated from the USA’s National Land Cover Database. Results show that the proposed SRM algorithm better maintains the spatial pattern of land covers for a range of different landscapes. The proposed SRM algorithm has the highest overall accuracy and Kappa values in all these SRM algorithms, by using the entire maps in the accuracy assessment

Comparison of Five Spatio-Temporal Satellite Image Fusion Models over Landscapes with Various Spatial Heterogeneity and Temporal Variation

In recent years, many spatial and temporal satellite image fusion (STIF) methods have been developed to solve the problems of trade-off between spatial and temporal resolution of satellite sensors. This study, for the first time, conducted both scene-level and local-level comparison of five state-of-art STIF methods from four categories over landscapes with various spatial heterogeneity and temporal variation. The five STIF methods include the spatial and temporal adaptive reflectance fusion model (STARFM) and Fit-FC model from the weight function-based category, an unmixing-based data fusion (UBDF) method from the unmixing-based category, the one-pair learning method from the learning-based category, and the Flexible Spatiotemporal DAta Fusion (FSDAF) method from hybrid category. The relationship between the performances of the STIF methods and scene-level and local-level landscape heterogeneity index (LHI) and temporal variation index (TVI) were analyzed. Our results showed that (1) the FSDAF model was most robust regardless of variations in LHI and TVI at both scene level and local level, while it was less computationally efficient than the other models except for one-pair learning; (2) Fit-FC had the highest computing efficiency. It was accurate in predicting reflectance but less accurate than FSDAF and one-pair learning in capturing image structures; (3) One-pair learning had advantages in prediction of large-area land cover change with the capability of preserving image structures. However, it was the least computational efficient model; (4) STARFM was good at predicting phenological change, while it was not suitable for applications of land cover type change; (5) UBDF is not recommended for cases with strong temporal changes or abrupt changes. These findings could provide guidelines for users to select appropriate STIF method for their own applications

MASTER\u27S PROJECT: DIVERSE EXPERIENCES, STORIES, AND IMPACTS OF LAND-BASED EDUCATIONAL PROGRAMMING AT MAPLEHILL SCHOOL AND MY PERSONAL LEARNING JOURNEY IN RELATION TO POWER, PRIVILEGE, AND IDENTITY.

Throughout the past seven years I have been inspired and transformed by my relationship with Maplehill School and Community Farm. This research project seeks to explore a community’s connection with land through the lens of serving youth with social, emotional, cognitive, and developmental disabilities and traumas. This project explores my personal learning journey in relationship to power, privilege, and identity. What are the diverse experiences, stories, and impacts resulting from land-based educational programming at Maplehill School? How do I understand my personal learning journey in relation to power, privilege, and identity? The following research explores interwoven stories from youth at Maplehill School as well as the land it is situated on, and myself

Take It To The Bank: How Land Banks Are Strengthening America's Neighborhoods

This report scans the land banking field nationally and reports on the scope and state of this movement. It also includes insights and recommendations for land bank practitioners, based on Community Progress staff members' many collective years of experience working with land banks across the country. There is no land bank model kit. There are, however, common attributes of effective and successful land banks that current and future land bank staff, practitioners, governments, and partner organizations can adopt. This report is intended to help shorten the learning curve