Burak K. Pekin

A big data urban growth simulation at a national scale: Configuring the GIS and neural network based Land Transformation Model to run in a High Performance Computing (HPC) environment

The Land Transformation Model (LTM) is a Land Use Land Cover Change (LUCC) model which was originally developed to simulate local scale LUCC patterns. The model uses a commercial windows-based GIS program to process and manage spatial data and an artificial neural network (ANN) program within a series of batch routines to learn about spatial patterns in data. In this paper, we provide an overview of a redesigned LTM capable of running at continental scales and at a fine (30m) resolution using a new architecture that employs a windows-based High Performance Computing (HPC) cluster. This paper provides an overview of the new architecture which we discuss within the context of modeling LUCC that requires: (1) using an HPC to run a modified version of our LTM; (2) managing large datasets in terms of size and quantity of files; (3) integration of tools that are executed using different scripting languages; and (4) a large number of steps necessitating several aspects of job management. Display Omitted Reconfigure the Land Transformation Model (LTM) using high performance computing.We present the design of software for managing big data simulations.We integrate software environments, such as Python, XML, ArcGIS, SNNS, and the .NET framework.We executed 285 instances of ArcGIS on an HPC.

Measurement of Crown Cover and Leaf Area Index Using Digital Cover Photography and Its Application to Remote Sensing

Digital cover photography (DCP) is a high resolution, vertical field-of-view method for ground-based estimation of forest metrics, and has advantages over fisheye sensors owing to its ease of application and high accuracy. We conducted the first thorough technical appraisal of DCP using both single-lens-reflex (DSLR) and point-and-shoot cameras and concluded that differences result primarily from the better quality optics available for the DSLR camera. File compression, image size and ISO equivalence had little or no effect on estimates of forest metrics. We discuss the application of DCP for ground truthing of remotely sensed canopy metrics, and highlight its strengths over fisheye photography for testing and calibration of vertical field-of-view remote sensing.

A primer of acoustic analysis for landscape ecologists

In this paper we present an introduction to the physical characteristics of sound, basic recording principles as well as several ways to analyze digital sound files using spectrogram analysis. This paper is designed to be a “primer” which we hope will encourage landscape ecologists to study soundscapes. This primer uses data from a long-term study that are analyzed using common software tools. The paper presents these analyses as exercises. Spectrogram analyses are presented here introducing indices familiar to ecologists (e.g., Shannon’s diversity, evenness, dominance) and GIS experts (patch analysis). A supplemental online tutorial provides detailed instructions with step by step directions for these exercises. We discuss specific terms when working with digital sound analysis, comment on the state of the art in acoustic analysis and present recommendations for future research.

Hierarchical modeling of urban growth across the conterminous USA: developing meso-scale quantity drivers for the Land Transformation Model

The Land Transformation Model (LTM) is hierarchically coupled with meso-scale drivers to project urban growth across the conterminous USA. Quantity of urban growth at county and place (i.e., city) scales is simulated using population, urban density and nearest neighbor dependent attributes. We compared three meso-scale LTMs to three null models that lack meso-scale drivers. Models were developed using circa 1990–2000 data and validated using change in the 2001 and 2006 National Land Cover Databases (NLCD). LTM and null models were assessed using the mean difference in quantity between simulated and actual growth measured at multiple spatial scales. We found that LTM models performed relatively well at spatial scales as small as 450 m, and that the mean difference between the NLCD and LTM with meso-scale drivers at 900 m was 2–3%, whereas null models produced a mean difference of ∼5%. Thus, introducing meso-scale modules into large-scale LTM simulations significantly increases model accuracy.

Mapping Open Space in an Old-Growth, Secondary-Growth, and Selectively-Logged Tropical Rainforest Using Discrete Return LIDAR

Light detection and ranging (LIDAR) is a valuable tool for mapping vegetation structure in dense forests. Although several LIDAR-derived metrics have been proposed for characterizing vertical forest structure in previous studies, none of these metrics explicitly measure open space, or vertical gaps, under a forest canopy. We develop new LIDAR metrics that characterize vertical gaps within a forest for use in forestry and forest management applications. The proposed metrics are extracted from discrete return LIDAR data acquired over the La Selva Biological Station, Costa Rica across three different forest management types (old-growth, secondary-growth, and selectively-logged). A comparison to common LIDAR metrics of vertical vegetation structure revealed that our new metrics provide unique information about the structure of the forest canopy. Maps showing the distribution of vertical gap and complex canopy patches identified from our LIDAR metrics demonstrate that the pattern of open space in tropical rain forests is linked to forest management strategies.