Welcome to Drew University’s Spatial Data Center, a part of the Environmental Studies and Sustainability Program, sponsored by generous grants from the Andrew W. Mellon Foundation and NASA.  This website is designed to help you get started with Geographic Information Systems (GIS) and Remote Sensing (RS).  Everything from identifying spatial data, performing spatial analysis, and reviewing GIS forestry applications is covered.

Exploring Spatial Data

Learning about spatial data is a bit like learning a new language.  Awareness of what GIS and Remote Sensing (RS) are, as well as the types of spatial data that are collected, opens a window into a new world of scientific observation, measurement, and analysis that can become a valuable toolkit for your growing skill set.  To explore spatial data, we will look at a brief overview of GIS, run through essential definitions related to GIS/RS, survey the agencies that collect and distribute spatial information, and review the websites where you can find and download spatial data.

1.  Overview

2.  Definitions

3.  Spatial Data Providers

Spatial Analysis Techniques

Once you’ve collected or obtained spatial data on your study area, you may choose from a wide variety of techniques to conduct a spatial analysis.  Common types of analyses relevant to forest ecology include processing and training of raw data, to correct for errors in data collection, data organization of many ’tiles’ of images mosaic-ed into larger data sets, generation of new surface data sets from sample data points, assessment of changes across time, comparison of variables collected from different locations, and interpretation of large data sets across many variables.  Common RS/GIS software packages to manage these tasks include IDRISI, ILWIS, ESRI ArcGIS, GeoDa, and a variety of specific, stand-alone packages like FUSION, for working with LiDAR data.

Three, brief in-class activities will demonstrate some of the spatial analysis techniques used with RS and GIS.

1.  Comparing Changes in Vegetation over Time

Check out the ESRI Change Matters Website to create comparisons over time.  Type in a place to study, e.g. Aral Sea, or Drew University, and look at the how vegetation has changed over time.  Change the years studied, and the data sets displayed to view different results.  Also, you can check out the details and metadata of the data sets to uncover how the mapped surfaces were generated.

2.  Assessing the Relationship among Multiple Variables Over Space

NASA’s Earth’s Observation website really breaks the mold for GIS web delivery, by actually allowing you to analyze multiple data sets. Choose up to three gridded data sets to begin comparing and contrasting, transects, scatterplots, and histograms over space and time, even for varying resolutions.


3.  Using LiDAR to Construct Canopy Data

Download the following data to your desktop or downloads folder.  Right-click on the file to extract the file contents, then open the DrewCanopy10.mxd file.  This will automatically open ArcMap on your computer.  This map file contains three different measures of elevation, based on LiDAR returns.  Can you see how LiDAR data generate information about canopies?

This concludes the section on spatial analysis techniques.  Hopefully you see that this short lab only covers the tip of the iceberg, and that a deep, rich field of data and methods enable ecologists to survey forest health and change in many new and evolving ways.  The last section allows you to begin surveying some of the intersections between forest ecology and GIS/RS research.

Introducing GIS/RS Applications

Ecological research that combines the power of RS/GIS with the theoretical foundations of the discipline have contributed significantly to a broad range of topics, including: IPCC climate change assessments, disaster prediction and planning (e.g. droughts, wildfires, and famine), and forest conservation.

With your group, browse for key contributions of RS/GIS and forest ecology, and list some of the findings that you discover from the abstracts of scientifically reviewed articles in the web of science.