Benjamin W. Heumann

Design of an Agent-Based Model to Examine Population-Environment Interactions in Nang Rong District, Thailand

The design of an Agent-Based Model (ABM) is described that integrates Social and Land Use Modules to examine population-environment interactions in a former agricultural frontier in Northeastern Thailand. The ABM is used to assess household income and wealth derived from agricultural production of lowland, rain-fed paddy rice and upland field crops in Nang Rong District as well as remittances returned to the household from family migrants who are engaged in off-farm employment in urban destinations. The ABM is supported by a longitudinal social survey of nearly 10,000 households, a deep satellite image time-series of land use change trajectories, multi-thematic social and ecological data organized within a GIS, and a suite of software modules that integrate data derived from an agricultural cropping system model (DSSAT - Decision Support for Agrotechnology Transfer) and a land suitability model (MAXENT - Maximum Entropy), in addition to multi-dimensional demographic survey data of individuals and households. The primary modules of the ABM are the Initialization Module, Migration Module, Assets Module, Land Suitability Module, Crop Yield Module, Fertilizer Module, and the Land Use Change Decision Module. The architecture of the ABM is described relative to module function and connectivity through uni-directional or bi-directional links. In general, the Social Modules simulate changes in human population and social networks, as well as changes in population migration and household assets, whereas the Land Use Modules simulate changes in land use types, land suitability, and crop yields. We emphasize the description of the Land Use Modules - the algorithms and interactions between the modules are described relative to the project goals of assessing household income and wealth relative to shifts in land use patterns, household demographics, population migration, social networks, and agricultural activities that collectively occur within a marginalized environment that is subjected to a suite of endogenous and exogenous dynamics.

Testing the spectral diversity hypothesis using spectroscopy data in a simulated wetland community

Abstract The spectral diversity hypothesis proposes that as the number of plant species increases for a given area, the diversity of spectra observed from that area should also increase. This approach could be very useful as an assessment and monitoring tool to help ecologists understand the spatial and temporal patterns of biodiversity without relying on consistently detecting individual species. While the spectral diversity hypothesis has been examined for a wide range of ecosystems using a variety of remote sensing data, it has not been tested using spectroscopy (i.e. hyperspectral) data for wetlands. Previous studies have not explicitly considered the impact that flowers may have on spectral diversity and how this may impact the spectral diversity hypothesis. To test the spectral diversity hypothesis and the potential impact on flowers, we used a simulation approach to combine leaf and flower spectra collected from a diverse prairie fen wetland ecosystem into datasets of virtual plots with varying levels of species diversity and different combination of species. To address the high dimensionality of the data, we compared spectral diversity and floristic diversity using partial least squares regression. Our results found that defining floristic diversity using the Shannons diversity index, which accounts for plant abundance in each plot, produced the best predictive models where the predicted values had a RMSE less than 40% of the mean observed value. We also found that the inclusion of flower spectra with leaf spectra did increase the RMSE of the best model, but across all models, correlation increased. Our results indicate that spectral diversity could be used as an initial biodiversity assessment tool for wetlands, especially with on-going advancements in unmanned aerial vehicle technology that can provide a low altitude platform for imaging spectroscopy.

The Multiple Comparison Problem in Empirical Remote Sensing

Abstract This paper seeks to draw attention to the multiple comparison problem ( mcp ) within the remote sensing community, and suggest some easily implemented solutions. The use of repeated statistical tests by remote sensing scientists to identify significant relationships, increases the chance identifying false positives (i.e., type-I errors) as the number of tests increases. This paper provides an introduction to the multiple comparison problem (i.e., the impact of the interpretation of p-values when repeated tests are made), outlines some simple solutions, and provides two case studies to demonstrate the potential impact of the problem in empirical remote sensing. The first case study looks at multiple potential texture metrics to predict leaf area index. The second case study examines pixel-wise temporal trend detection. The results show how applying solutions to the multiple comparison problem can greatly impact the interpretation of statistical results.

Can flowers provide better spectral discrimination between herbaceous wetland species than leaves

The identification of species is essential to ecosystem monitoring and assessment. Remote sensing can provide robust and repeatable spectral measurements that are spatially continuous across ecosystems. Typically, the remote sensing of vegetation has relied on the measurement of leaf spectra, or the measurement of mixed spectra that include leaves, flowers and other components such as stems and soils. This study compares the ability to discriminate between species using pure spectra of leaves and flowers using herbaceous vegetation from a prairie fen as a case study. Spectral data of leaves and flowers were collected from 22 species using a handheld spectroradiometer with a spectral resolution of 1 nm from 475 nm to 900 nm. The use of continuous wavelet transformation was explored to enhance detection of shape in the spectral signatures. Due to the inherent high dimensionality of spectroscopy data, a forward feature selection algorithm was used to identify the best sets of individual bands and continuous wavelet transformed features. The results show that flowers consistently provide better species discrimination in spectral space than leaves in terms of both overall discrimination and efficiency (i.e. consistently requiring fewer features to achieve maximum spectral discrimination). For both transformed leaf and flower spectra, most of the selected features were found to be between 500 nm and 600 nm, indicating the importance of this region to differentiate between species when measuring both leaves and flowers. These results indicate the potential to map vegetation using the spectra of flowers as the availability of ultra-high-resolution imagery from low-altitude unmanned aerial remote sensing platforms increases.

Habitat Suitability Modeling of the Federally Endangered Poweshiek Skipperling in Michigan

Abstract The Poweshiek skipperling Oarisma poweshiek (Lepidoptera: Hesperiidae) is a historically common prairie butterfly with a range extending throughout the mesic prairies and prairie fens of the upper Midwestern United States and southern Manitoba, Canada. Rapid, range-wide declines have reduced the number of verified Poweshiek skipperling locations to seven, four of which occur in Michigan. To assist with monitoring and, ultimately, conservation efforts, we developed a habitat model using the software Maxent with ecological and geographical factors. Using a lowest-presence threshold methodology, our habitat suitability model indicated potentially high suitability in 26 of 138 prairie fens with no documentation of Poweshiek skipperling occurrence. The strongest predictors of suitable habitat in our model were prairie fen area and surrounding natural land cover. Wildlife managers can use results from this analysis to expand monitoring to include sites with suitable habitat where Poweshiek skipperling ...