Creating spatially-explicit lawn maps without classifying remotely-sensed imagery: The case of suburban Boston, Massachusetts, USA

Residential lawns are a dominant and growing feature of US residential landscapes, and the resource-intensive management of this landscape feature presents major potential risks to both humans and the environment. In recent years, scientists and policymakers have been increasingly calling for large-extent measures of lawns and other similar landscape features. Unfortunately, the production of such datasets using traditional, remotely sensed measurement approaches can be prohibitively expensive and time consuming. This study uses two statistical prediction methods to extrapolate the quantity and spatial distribution of residential lawns from a sample of mapped lawns in a large study area in suburban Boston, Massachusetts. The goal is to find an inexpensive, broad-coverage dataset that will provide useable estimates of landscape features in places where we do not have direct measurements of those landscape features. The first estimation method uses OLS regression in conjunction with the sample of mapped lawns and freely available US Census data representing theoretically informed social driver variables. The second, simpler, and less computationally intensive estimation method allocates the mean of the sample of mapped lawns uniformly across the study area. Both estimation methods are performed 1000 times in a Monte Carlo framework where the sample is drawn randomly each realization, to assess the sensitivity of the prediction results to the selection of CBGs in each simple random sample. The outputs of each estimation method are then compared to a reference map where the quantity and spatial allocation of lawns is known for each spatial unit of analysis. Results indicate that the OLS prediction method specified with the independent social driver variables performs better than a uniform prediction method when both are compared to the full-study area reference map

The James Webb Space Telescope Mission

Twenty-six years ago a small committee report, building on earlier studies, expounded a compelling and poetic vision for the future of astronomy, calling for an infrared-optimized space telescope with an aperture of at least $4m$. With the support of their governments in the US, Europe, and Canada, 20,000 people realized that vision as the $6.5m$ James Webb Space Telescope. A generation of astronomers will celebrate their accomplishments for the life of the mission, potentially as long as 20 years, and beyond. This report and the scientific discoveries that follow are extended thank-you notes to the 20,000 team members. The telescope is working perfectly, with much better image quality than expected. In this and accompanying papers, we give a brief history, describe the observatory, outline its objectives and current observing program, and discuss the inventions and people who made it possible. We cite detailed reports on the design and the measured performance on orbit.Comment: Accepted by PASP for the special issue on The James Webb Space Telescope Overview, 29 pages, 4 figure

Spatial decision support systems

Spatial decision support systems (SDSS) combine storage, search, and retrieval capabilities of geographic information systems with decision models and optimizing algorithms to support decision making concerning spatial problems. These systems allow decision makers to use multiple spatial criteria to make locational choices by exploring alternatives, given spatial and attribute information. Characteristics of SDSS are becoming incorporated in social science theories and models that explain spatial decisions

Fundamentals for Using Geographic Information Science to Measure the Effectiveness of Land Conservation Projects

Some humans spend a tremendous amount of effort to change landscapes from a “natural” state to a “developed” state for a variety of desirable economic uses, such as urban, agriculture, transportation, and mining. Others spend a tremendous amount of effort to prevent such development in order to conserve the landscapes for a variety of important environmental uses, such as biodiversity maintenance, carbon storage, water filtration, and landslide prevention. It would be efficient in theory if a society were to focus its development efforts at locations that give the largest economic utility per area developed, and to focus its conservation efforts at locations that give the largest environmental utility per area conserved. However this is not necessarily the strategy of some important conservation policies. Some policy approaches, such as those proposed by the Clean Development Mechanism of the Kyoto Protocol on climate change and the subsequent Bali Roadmap, call for conservation on land that is under imminent threat of new development, not necessarily on land that gives the largest environmental utility (Sedjo et al. 1998, Clémençon 2008). The apparent motivation to focus policy strategies on land under immediate threat is to prevent development before it exerts its environmental impact. This strategy is nearly a perfect equation for escalation of conflict, because it motivates conservationists to prevent the actions that are highest priority for developers. If conservation is effective in preventing development, then conservationists win and developers lose. If conservation is not effective in preventing development, then developers win and conservationists lose. A third plausible outcome of this policy strategy is that a conservation project might inspire developers to shift their future development from their first priority locations to their second priority locations. The process whereby conservation at one location causes development to shift from that location to another location is known as leakage Leakage can undermine the overall effectiveness of a conservation project in terms of total environmental utility (Schwarze et al. 2002). This chapter presents a general conceptual framework to assess the effectiveness of land conservation projects by using Geographic Information Science (GIS) and land change modeling to analyze development and conservation in the presence of leakage. Reprinted from: Geoinformatics for Natural Resource Management, 2009, edited by P.K. Joshi et al. with permission from Nova Science Publishers, Inc

Detecting important categorical land changes while accounting for persistence

The cross-tabulation matrix is a fundamental starting point in the analysis of land change, but many scientists fail to analyze the matrix according to its various components and thus fail to gain as much insight as possible concerning the potential processes that determine a pattern of land change. This paper examines the cross-tabulation matrix to assess the total change of land categories according to two pairs of components: net change and swap, as well as gross gains and gross losses. Analysis of these components can distinguish between a clearly systematic landscape transition and a seemingly random landscape transition. Multiple resolution analysis provides additional information concerning the distances over which land change occurs. An example of change among four land categories in central Massachusetts illustrates the methods. These methods enable scientists to focus on the strongest signals of systematic landscape transitions, which is necessary ultimately to link pattern to process. © 2003 Elsevier B.V. All rights reserved

Historic forest change: New approaches to land use land cover

Using historic maps as significant data in environment and urban planning has provided key details of land practices and planning of the past. (Baily, 2007; Eremiasova and Skokanova, 2009; Gasperi, 2007; Grosso, 2009) While there has been growing research into the use and accuracy of historic maps, there has been little research into how to incorporate real data from historic maps into modern land cover land use change analysis. Using historic maps to create a time series of changes allows for research into the visualization and quantification landscapes through time. In this study, the land cover change of the surrounding Uxeau Commune of Burgundy France, a site rich in archaeology and history, is explored through a time series created from historic maps, aerial photos, and recent topographic maps. Covering 163 years, this time series is constructed to measure the change in forest pattern land change of forest to non-forest by using GIS methods of georeferencing, digitizing, as well as Intensity analysis, as a platform to investigate the land use/ land cover change found in the region from 1840-2003. Consideration into preprocessing of data layers to enable comparison must be followed. This in turn raises questions of methodology to best extract the data of a historic map. As such, this study investigates the conversion of historic attributes and features to modern counterparts, building on the existing research of georeferencing, digitalization and eventual extraction of historic maps for further analyze. Following these steps, attention is turned to how best analyze the changing landscape patterns. Intensity analysis, developed by S.Z. Aldwaik and Robert Pontius, is used to understand the interval change between forest loss, gain and persistence annually throughout the study site. The integration of historic and modern data to create the observed pattern of land cover change could aid in subsequent studies on extended temporal land studies. As it questions the validity of historic map use in current day studies, it also provides a methodology that integrates historic data and modern data through the combination of historic maps and land cover data using freely available software programs. With an extended time series available provided by historic maps we are able distinguish the change in land cover through the time span, leading to future research in assessing the drivers of anthropogenic and natural processes of land cover