Scholarly Works, Center for Environmental Applications of Remote Sensing (CEARS)

Permanent URI for this collection

https://hdl.handle.net/10919/73612
Research articles, presentations, and other scholarship

Browse

Browsing Scholarly Works, Center for Environmental Applications of Remote Sensing (CEARS) by Issue Date

Filter results by year or month
Now showing 1 - 10 of 10
  • Thumbnail Image
  • Thumbnail Image
    Landscape Dynamics on the Island of La Gonave, Haiti, 1990-2010
    White, Justin H.; Shao, Yang; Kennedy, Lisa M.; Campbell, James B. Jr. (MDPI, 2013-09-16)
    The island of La Gonave lies northwest of Port-au-Prince and is representative of the subsistence Haitian lifestyle. Little is known about the land cover changes and conversion rates on La Gonave. Using Landsat images from 1990 to 2010, this research investigates landscape dynamics through image classification, change detection, and landscape pattern analysis. Five land cover classes were considered: Agriculture, Forest/Dense Vegetation (DV), Shrub, Barren/Eroded, and Nonforested Wetlands. Overall image classification accuracy was 87%. Results of land cover change analysis show that all major land cover types experienced substantial changes from 1990 to 2010. The area percent change was _39.7, _22.7, 87.4, and _7.0 for Agriculture, Forest/Dense Vegetation, Shrub, and Barren/Eroded. Landscape pattern analysis illustrated the encroachment of Shrub cover in core Forest/DV patches and the decline of Agricultural patch integrity. Agricultural abandonment, deforestation, and forest regrowth combined to generate a dynamic island landscape, resulting in higher levels of land cover fragmentation.
  • Thumbnail Image
    Analysis of Crop Phenology Using Time-Series MODIS Data and Climate Data
    Ren, Jie; Campbell, James B. Jr.; Shao, Yang; Thomas, R. Quinn (2014)
    Understanding crop phenology is fundamental to agricultural production, management, planning and decision-making. In the continental United States, key phenological stages are strongly influenced by meteorological and climatological conditions. This study used remote sensing satellite data and climate data to determine key phenological states of corn and soybean and evaluated estimates of these phenological parameters. A time series of Moderate Resolution Imaging Spectrometer (MODIS) Normalized Difference Vegetation Index (NDVI) 16-day composites from 2001 to 2013 was analyzed with the TIMESAT program to automatically retrieve key phenological stages such as the start of season (emergence), peak (heading) and end of season (maturity). These stages were simulated with 6 hourly temperature data from 1980 to 2013 on the basis of crop model under the Community Land Model (CLM) (version 4.5). With these two methods, planting date, heading date, harvesting date, and length of growing season from 2001 to 2013 were determined and compared. There should be a good correlation between estimates derived from satellites and estimates produced with the climate data based on the crop model.
  • Thumbnail Image
    Investigating Forest Conversion Across Several Scales of Urbanization in the Eastern United States
    Wu, Yi-Jei; Thomas, Valerie A.; Oliver, Robert D. (2014)
    Urbanization in the United States has clearly impacted land cover, and land use and land cover change (LULCC) patterns. A great body of literature has addressed the negative results of increased sprawl and a supporting literature has catalogued the story of forest loss—to grassland/ shrub, to agricultural land, to developed land and other land use categories. The Micropolitan Statistical Area (μSA) is a new geographic entity created in 2003 by the Office of Management and Budget (OMB) to depict the transitional area between Metropolitan Statistics Areas (MSAs) and non-designated areas (i.e., rural regions). Our prior work has demonstrated that μSAs are unique with regards to the dynamics of land conversion for development, and that there is a clear need to investigate the regional drivers of specific types of land-cover change at this scale. This research seeks to (1) tabulate the amount difference of forest conversion among MSAs, μSAs, and non-designated areas in select megaregions; and (2) highlight/ depict the change in key μSAs (computed as a percentage of forest cover change) across east coast. By combining μSA boundaries with the National Land Cover Database (NLCD) change product and change detection results from Google Earth Engine platform to examine forest cover change patterns across four East-Coast megaregions (as defined by America 2050 [Florida, Piedmont Atlantic, Great Lakes and the North East megaregion]). We have an opportunity to illustrate that in rare circumstances there are μSAs that have witnessed an irregularities in forest conversion between 2001 and 2006.
  • Thumbnail Image
    Accuracy Assessment of the NLCD 2006 Impervious Surface for Roanoke and Blacksburg
    Zhao, Suwen; Feng, Leyang; Shao, Yang; Dymond, Randel L. (2014)
    Impervious surface map products are important for the study of urbanization, urban heat island effects, watershed hydrology, water pollution, and ecosystem services in general. At the conterminous US scale, impervious surfaces are mapped for 2001 and 2006. The accuracy of the 2006 NLCD impervious surface, however, has not been thoroughly examined, especially for small and intermediate size cities (e.g., regional city). In this study, we selected two transects in two cities and visually interpreted aerial photo to develop impervious surface reference maps. We then compared percent impervious surface of the NLCD and aerial photo-interpreted reference maps. The comparison was conducted at 90m resolution to minimize the errors in image registration. Overall, we found that the 2006 NLCD impervious surface matched well with our reference data, although slight skewness at two extremes is present. The R² and RMSE statistics improved when the two datasets are compared at coarse aggregation levels (e.g. 180m).
  • Thumbnail Image
    Mapping Stable Nitrogen Isotopes Using Hyperspectral Imagery
    Correll, Katie; Strahm, Brian D.; Thomas, Valerie A. (2014)
    As nitrogen deposition increases globally, ecosystem changes will occur. It is important to understand the growth response of different ecosystems and where nitrogen retention will occur. Stable isotopes of foliar nitrogen can provide insight into how this process is occurring in the soil. Previous studies have found links between foliar nitrogen and optical properties.This study focuses on the Southern Piedmont Forests. A study at the Duke Forest's Blackwood Division in Chapel Hill, North Carolina, allowed for foliar sampling across various soil types, elevations, and species. Concurrent hyperspectral imagery was taken, allowing for the relationship between environmental drivers, optical properties, and nitrogen content to be identified. These relationships will be used to map nitrogen content at the canopy level. Foliar sampling was performed in species identified as major contributors to the canopy. Major canopy contributors were oak, hickory, poplar, sweetgum, and pine. Foliar samples were analyzed for chlorophyll, macronutrients, carbon, nitrogen, and stable isotope N15. The relationship of these characteristics, as well as elevation, soil type, species, and optical properties, were input to predict the spectral signature associated with the N15 content.Ancillary data on elevation, soil type, and species, coupled with hyperspectral imagery, will use the relationships to predict canopy level nitrogen at the image scale.
  • Thumbnail Image
    Producing a Canopy Height Map Over a Large Region Using Heterogeneous LIDAR Datasets
    Gopalakrishnan, Ranjith; Thomas, Valerie A.; Coulston, John W.; Wynne, Randolph H. (2014)
    Accurate and unbiased wall-to-wall canopy height maps for large regions are useful to forest scientists and managers for several reasons such as carbon accounting and wildfire fuel-load monitoring. Airborne lidar is establishing itself as the most promising technology for this. However, mapping large areas often involves using lidar data from different projects executed by different agencies, involving varying acquisition dates, sensors, pulse densities, etc. In this work, we address the important question of how accurately one can predict and model canopy heights over large areas of the Southeastern US using a heterogeneous lidar datasets (with more than 90 separate lidar projects). A unique aspect of this effort is the use of extensive and robust field data from the Forest Inventory and Analysis (FIA) program of the US Forest Service. We construct a simple linear model to predict canopy height at plots from distributional lidar metrics. Preliminary results are quite promising: over all lidar projects, we observe a correlation of 81.8% between the 95th percentile of lidar heights and field-measured height, with an RMSE of 3.66 meters (n=3078). We further estimated that ~1.21 m (33%) of this RMSE could be attributed to co-registration inaccuracies. The RMSE of 3.66 m compares quite well to previous efforts that used spaceborne lidar sensors to estimate canopy heights over large regions. We also identify and quantify the importance of several factors (like point density, the predominance of hardwoods or softwood) that also influence the efficacy of our prediction model.
  • Thumbnail Image
    Crowds for Clouds: Using an Internet Workforce to Interpret Satellite Images
    Yu, Ling; Ball, Sheryl B.; Blinn, Christine E.; Moeltner, Klaus; Peery, Seth; Thomas, Valerie A.; Wynne, Randolph H. (2014)
    A chronologically ordered sequence of satellite images can be used to learn how natural features of the landscape change over time. For example, we can learn how forests react to human interventions or climate change. Before these satellite images can be used for this purpose, they need to be examined for clouds and cloud shadow that may hide important features of the landscape and would lead to misinterpretation of forest conditions. Once clouds and their shadow have been identified, researchers can then look for other images that include the feature of interest, taken a bit earlier or later in time, to fill in the "missing information" for the original image. Therefore, the task of identifying clouds and their shadow is extremely important for the correct and efficient use of each image. Computer algorithms are only imperfectly suited for this task. The aim of this project is to outsource the cloud interpretation task to a global internet community of "turkers" -workers recruited via amazon.com's online job market known as "Mechanical Turk."
  • Thumbnail Image
    On-the-Fly Massively Multitemporal Change Detection Using Statistical Quality Control Charts and Landsat Data
    Brooks, Evan B.; Wynne, Randolph H.; Thomas, Valerie A.; Blinn, Christine E.; Coulston, John W. (Institute of Electrical and Electronics Engineers (IEEE), 2014-06)
    One challenge to implementing spectral change detection algorithms using multitemporal Landsat data is that key dates and periods are often missing from the record due to weather disturbances and lapses in continuous coverage. This paper presents a method that utilizes residuals from harmonic regression over years of Landsat data, in conjunction with statistical quality control charts, to signal subtle disturbances in vegetative cover. These charts are able to detect changes from both deforestation and subtler forest degradation and thinning. First, harmonic regression residuals are computed after fitting models to interannual training data. These residual time series are then subjected to Shewhart X-bar control charts and exponentially weighted moving average charts. The Shewhart X-bar charts are also utilized in the algorithm to generate a data-driven cloud filter, effectively removing clouds and cloud shadows on a location-specific basis. Disturbed pixels are indicated when the charts signal a deviation from data-driven control limits. The methods are applied to a collection of loblolly pine (Pinus taeda) stands in Alabama, USA. The results are compared with stands for which known thinning has occurred at known times. The method yielded an overall accuracy of 85%, with the particular result that it provided afforestation/deforestation maps on a per-image basis, producing new maps with each successive incorporated image. These maps matched very well with observed changes in aerial photography over the test period. Accordingly, the method is highly recommended for on-the-fly change detection, for changes in both land use and land management within a given land use.
  • Thumbnail Image
    A model using marginal efficiency of investment to analyze carbon and nitrogen interactions in terrestrial ecosystems
    Thomas, R. Quinn; Williams, Mat (European Geosciences Union, 2014-09-12)
    Carbon (C) and nitrogen (N) cycles are coupled in terrestrial ecosystems through multiple processes including photosynthesis, tissue allocation, respiration, N fixation, N uptake, and decomposition of litter and soil organic matter. Capturing the constraint of N on terrestrial C uptake and storage has been a focus of the Earth System Modeling community. However, there is little understanding of the trade-offs and sensitivities of allocating C and N to different tissues in order to optimize the productivity of plants. Here we describe a new, simple model of ecosystem C–N cycling and interactions (ACONITE), that builds on theory related to plant economics in order to predict key ecosystem properties (leaf area index, leaf C : N, N fixation, and plant C use efficiency) based on the outcome of assessments of the marginal change in net C or N uptake associated with a change in allocation of C or N to plant tissues. We simulated and evaluated steady-state ecosystem stocks and fluxes in three different forest ecosystems types (tropical evergreen, temperate deciduous, and temperate evergreen). Leaf C : N differed among the three ecosystem types (temperate deciduous < tropical evergreen < temperature evergreen), a result that compared well to observations from a global database describing plant traits. Gross primary productivity (GPP) and net primary productivity (NPP) estimates compared well to observed fluxes at the simulation sites. Simulated N fixation at steady-state, calculated based on relative demand for N and the marginal return on C investment to acquire N, was an order of magnitude higher in the tropical forest than in the temperate forest, consistent with observations. A sensitivity analysis revealed that parameterization of the relationship between leaf N and leaf respiration had the largest influence on leaf area index and leaf C : N. A parameter governing how photosynthesis scales with day length had the largest influence on total vegetation C, GPP, and NPP. Multiple parameters associated with photosynthesis, respiration, and N uptake influenced the rate of N fixation. Overall, our ability to constrain leaf area index and allow spatially and temporally variable leaf C : N can help address challenges simulating these properties in ecosystem and Earth System models. Furthermore, the simple approach with emergent properties based on coupled C–N dynamics has potential for use in research that uses data-assimilation methods to integrate data on both the C and N cycles to improve C flux forecasts.