Showing papers by "Scott J. Goetz published in 2005"

Satellite-observed photosynthetic trends across boreal North America associated with climate and fire disturbance

Abstract: We analyzed trends in a time series of photosynthetic activity across boreal North America over 22 years (1981 through 2003). Nearly 15% of the region displayed significant trends, of which just over half involved temperature-related increases in growing season length and photosynthetic intensity, mostly in tundra. In contrast, forest areas unaffected by fire during the study period declined in photosynthetic activity and showed no systematic change in growing season length. Stochastic changes across the time series were predominantly associated with a frequent and increasing fire disturbance regime. These trends have implications for the direction of feedbacks to the climate system and emphasize the importance of longer term synoptic observations of arctic and boreal biomes.

Analysis of scale dependencies in an urban land-use-change model

Abstract: Different processes shaping land‐use patterns are observed at different scales. In land‐use modelling, scale can influence the measurement and quantitative description of land‐use patterns and can therefore significantly impact the behaviour of model parameters that describe land‐use change processes. We present results of a rigorous sensitivity analysis of a cellular urban land‐use‐change model, SLEUTH, testing its performance in response to varying cell resolutions. Specifically, we examine the behaviour of each type of urban growth rule across different cell sizes, and explore the model's ability to capture growth rates and patterns across scales. Our findings suggest that SLEUTH's sensitivity to scale extend beyond issues of calibration. While the model was able to capture the rate of growth reliably across all cell sizes, differences in its ability to simulate growth patterns across scales were substantial. We also observed significant differences in the sensitivity of the growth rules across cell si...

Urbanization and the loss of resource lands in the chesapeake bay watershed

Abstract: We made use of land cover maps, and land use change associated with urbanization, to provide estimates of the loss of natural resource lands (forest, agriculture, and wetland areas) across the 168,000 km2 Chesapeake Bay watershed. We conducted extensive accuracy assessments of the satellite-derived maps, most of which were produced by us using widely available multitemporal Landsat imagery. The change in urbanization was derived from impervious surface area maps (the built environment) for 1990 and 2000, from which we estimated the loss of resource lands that occurred during this decade. Within the watershed, we observed a 61% increase in developed land (from 5,177 to 8,363 km2). Most of this new development (64%) occurred on agricultural and grasslands, whereas 33% occurred on forested land. Some smaller municipalities lost as much as 17% of their forest lands and 36% of their agricultural lands to development, although in the outlying counties losses ranged from 0% to 1.4% for forests and 0% to 2.6% for agriculture. Fast-growing urban areas surrounded by forested land experienced the most loss of forest to impervious surfaces. These estimates could be used for the monitoring of the impacts of development across the Chesapeake Bay watershed, and the approach has utility for other regions nationwide. In turn, the results and the approach can help jurisdictions set goals for resource land protection and acquisition that are consistent with regional restoration goals.

Observed and predicted responses of plant growth to climate across Canada

Abstract: [1] Using satellite observations from 1981–2000, and data interpolated from surface weather stations, we examined the association between gross photosynthetic activity (Pg) and climate across the boreal forest and tundra of Canada. The response of annual and interannual Pg was tightly coupled to climate, and seasonal associations between Pg and climate varied with plant functional types. The most important variable for modeling summer growth of conifer forests was the previous spring minimum temperature, whereas tundra responded primarily to summer maximum temperature. Using general circulation model predictors to 2050, we project that tundra will continue to grow vigorously in the coming decades while conifer forests will not. Increased tundra productivity will likely be associated with changes in vegetation composition (e.g., woody proliferation). If these biotic responses are stationary and persist as predicted, terrestrial carbon budgets will need to be modified.

Stream health rankings predicted by satellite derived land cover metrics

Abstract: Land cover and land use change have long been known to influence the chemical, physical, and biological character- istics of streams. This study makes use of land cover maps derived from fine resolution satellite imagery and an extensive stream quality dataset to determine the relationship between small water- shed health rankings and land cover composition and configura- tion. Landscape metrics were derived from digital impervious surface area (ISA), tree cover (percent), and agricultural crop maps within Montgomery County, Maryland. Watershed rankings were developed by state and county collaborators (MD-DNR and MCDEP) using extensive biological and chemical measurements. In stepwise logistic regression models the factors accounting for the most variation in stream health ranking were the percent ISA, fol- lowed by the percent of tree cover. Riparian buffer zone tree cover was also a significant predictor. Of the metrics that considered the spatial configuration of the landscape, a contagion index and the percent of ISA in the flow path from the ISA to the stream were also found to be significant predictors of stream health. Despite limited ability to characterize landscape configuration or narrow riparian buffer zone vegetation with coarser resolution imagery (from Landsat), model results were not significantly different from those based on the use of fine-resolution ISA information, suggest- ing that broader area applications of the approach are possible. The results indicate that management practices designed to improve stream water quality should focus on the amount of ISA and tree cover in both the watershed and within the buffer zone. (KEY TERMS: land use planning; remote sensing; restoration; riparian buffers; stream health; urbanization; water quality; water- shed management.)

Resource land loss and forest vulnerability in the chesapeake bay watershed 1

Abstract: The contemporary pattern of urban development in industrialized countries is increasingly taking the form of low density, decentralized residential and commercial development. In the Chesapeake Bay watershed, which is located within the mid-Atlantic region of the United States, dispersed development patterns have been linked to habitat fragmentation and declining water quality. Our objectives were to document how this urbanization process has expanded throughout the watershed and to explore how lands comprising the natural resource base, particularly forests, have been replaced by a matrix of the built environment. We accomplished this by mapping impervious surface cover (houses, roads, etc.) across the ~168,000 km 2 area using a time series of satellite imagery. We calculated metrics of land use change and used these to estimate the loss of resource lands across the region. We conservatively estimate that 334 km 2 of forest, 888 km 2 of agriculture and 2 km 2 of wetlands have been converted to impervious surfaces between 1990 and 2000. We also used the time series to calibrate a spatial model of urban land use change, and forecasted future development patterns in Maryland out to 2030 under different policy scenarios. Using Maryland Department of Natural Resources’ (DNR) Strategic Forest Lands Assessment (SFLA), which evaluates forest resources in terms of their economic and ecologic value, and Maryland’s Green Infrastructure, which identifies ecologically valuable patches of contiguous forests and wetlands, we evaluated the vulnerability of natural resources in Maryland. Threats, associated with loss and fragmentation, were identified.

Spatial Predictive Modeling and Remote Sensing of Land Use Change in the Chesapeake Bay Watershed

Abstract: This project was focused on modeling the processes by which increasing demand for developed land uses, brought about by changes in the regional economy and the socio-demographics of the region, are translated into a changing spatial pattern of land use. Our study focused on a portion of the Chesapeake Bay Watershed where the spatial patterns of sprawl represent a set of conditions generally prevalent in much of the U.S. Working in the region permitted us access to (i) a time-series of multi-scale and multi-temporal (including historical) satellite imagery and (ii) an established network of collaborating partners and agencies willing to share resources and to utilize developed techniques and model results. In addition, a unique parcel-level tax assessment database and linked parcel boundary maps exists for two counties in the Maryland portion of this region that made it possible to establish a historical cross-section time-series database of parcel level development decisions. Scenario analyses of future land use dynamics provided critical quantitative insight into the impact of alternative land management and policy decisions. These also have been specifically aimed at addressing growth control policies aimed at curbing exurban (sprawl) development. Our initial technical approach included three components: (i) spatial econometric modeling of the development decision, (ii) remote sensing of suburban change and residential land use density, including comparisons of past change from Landsat analyses and more traditional sources, and (iii) linkages between the two through variable initialization and supplementation of parcel level data. To these we added a fourth component, (iv) cellular automata modeling of urbanization, which proved to be a valuable addition to the project. This project has generated both remote sensing and spatially explicit socio-economic data to estimate and calibrate the parameters for two different types of land use change models and has undertaken analyses of these models. One (the CA model) is driven largely by observations on past patterns of land use change, while the other (the EC model) is driven by mechanisms of the land use change decision at the parcel level. Our project may be the first serious attempt at developing both types of models for the same area, using as much common data as possible. We have identified the strengths and weaknesses of the two approaches and plan to continue to revise each model in the light of new data and new lessons learned through continued collaboration. Questions, approaches, findings, publication and presentation lists concerning the research are also presented.