Integration of geostatistics and remote sensing for modeling human-environment
interactions and analyzing spatial uncertainty
Guangxing Wang
Dept. of Geography and Environmental Resources, Southern Illinois University at
Carbondale, 1000 Faner Dr., Carbondale IL 62901 USA
E-mail: gxwang@siu.edu
For modeling human-environment interactions, field data are collected and used
together with remotely sensed images to reproduce spatial distribution and variability
of an interest variable. The field data are often available only at sample plots, while
remotely sensed data exist everywhere within a given study area. Geostatistical
methods such as cokriging and conditional co-simulation can be used to combine the
sample plot and remotely sensed image data to estimate optimally local values. In
geostatistics, it is assumed that an interest variable is a random process that is
spatially auto-correlated. This feature is also called spatial variability of this variable
and can be characterized using variogram. Based the spatial autocorrelation,
geostatistical methods are developed and provide great potential to reproduce spatial
variability of interest variables and estimate their spatial uncertainty for remote
sensing mapping. Over the last two decades, many studies on applications of
geostatistical methods to modeling human-environment interactions have been
reported, including exploring spatial variation of both ground and remotely sensed
data, designing optimum sampling schemes, classifying land use and land cover types,
estimating continuous variables, and conducting spatial uncertainty analysis of the
estimated variables.
In this workshop, we will first give an introduction of spatial autocorrelation and
geostatistical methods. We will then demonstrate their applications to remote sensing
mapping. Through examples, especially, we will present several novel algorithms
developed by the presenter recent years based on spatial autocorrelation of variables,
including a local variability based sampling design, determination of optimal spatial
resolution for field data collection and mapping, a conditional co-simulation based
up-scaling algorithm, and a spatial uncertainty and error budget method for remote
sensing mapping.
Dr. Guangxing Wang (PI) is a tenured Associate Professor at the Department of
Geography and Environmental Resources, Southern Illinois University at Carbondale
(SIUC), USA, with a multidisciplinary background of forestry, biometrics, and
geospatial technologies (remote Sensing and GIS). He received his Ph.D. in Remote
Sensing of Forest Resources at the University of Helsinki (UH), Finland, MSc. in
forest biometrics and BSc. in forestry at the Central South University of Forestry and
Technology (CSUFT) of China. Before joining SIUC, he worked at CSUFT, UH, and
University of Illinois at Urbana-Champaign. He has been a major advisor for several
graduate students and currently he has two Ph.D. and five MS students. His research
focuses on geospatial technologies and their applications to modeling
human-environment interactions and forest carbon dynamics and uncertainty analysis.
His recent activities concentrate on 1) forest carbon modeling and spatial uncertainty
analysis and 2) spatial and temporal assessment of cumulative human activity impacts
on land condition. He has been awarded over 3 millions of research funding. He is an
author and co-author of more than 100 publications, including 4 books and more than
50 peer-reviewed journal articles.