Remote Sensing Commons^™

Accuracy Assessment Of Pictometry® Height Measurements Stratified By Cardinal Direction And Image Magnification Factor, Daniel Unger, David Kulhavy, I-Kuai Hung, Yanli Zhang

Faculty Publications

The aim of this project was to ascertain if Pictometry^® estimated height could be used in lieu of field-based height estimation. Height of a light pole measured with a telescopic height pole was compared to Pictometry^® hyperspatial 4-inch (10.2 centimeters) multispectral imagery estimated light pole height on the campus of Stephen F. Austin State University, Nacogdoches, Texas. Average percent agreement between light pole height and Pictometry^® estimated light pole height summarized by Pictometry^® image magnification factors at 100%, 125%, 150%, 200%, and 300% magnification were within 98% of light pole height with percent disagreement ranging from …

Incorporating Applied Undergraduate Research In Senior To Graduate Level Remote Sensing Courses, Richard Henley, Daniel Unger, David Kulhavy, I-Kuai Hung

Faculty Publications

An Arthur Temple College of Forestry and Agriculture (ATCOFA) senior spatial science undergraduate student engaged in a multi-course undergraduate research project to expand his expertise in remote sensing and assess the applied instruction methodology employed within ATCOFA. The project consisted of performing a change detection land-use/land-cover classification for Nacogdoches and Angelina counties in Texas using satellite imagery. The dates for the imagery were spaced approximately ten years apart and consisted of four different acquisitions between 1984 and 2013. The classification procedure followed and expanded upon a series of concrete theoretical remote sensing principles, transforming the four remotely sensed raster images …

Forest Baseline And Deforestation Map Of The Dominican Republic Through The Analysis Of Time Series Of Modis Data, Florencia Sangermano, Leslie Bol, Pedro Galvis, Raymond E. Gullison, Jared Hardner, Gail S. Ross

Geography

Deforestation is one of the major threats to habitats in the Dominican Republic. In this work we present a forest baseline for the year 2000 and a deforestation map for the year 2011. Maps were derived from Moderate Resolution Imaging Radiometer (MODIS) products at 250. m resolution. The vegetation continuous fields product (MOD44B) for the year 2000 was used to produce the forest baseline, while the vegetation indices product (MOD13Q1) was used to detect change between 2000 and 2011. Major findings based on the data presented here are reported in the manuscript "Habitat suitability and protection status of four species …

Lidar And Machine Learning Estimation Of Hardwood Forest Biomass In Mountainous And Bottomland Environments, Bowei Xue

Graduate Theses and Dissertations

Light detection and ranging (lidar) has been applied in various forest applications, such as to retrieve forest structural information, to build statistical models for identification of tree species, and to monitor forest growth. However, despite significant progress in these areas, the choice of regression approach and parameter tuning remains an ongoing critical question. This study focused on choosing the right spatial generalization level to transform lidar point clouds to 2D images which can be further processed by mature image processing and pattern recognition approaches. It also compared the prediction ability of popular machine learning algorithms applied to aboveground forest biomass …

St. Norbert College As Arboretum: Mapping The Trees On Campus, Jordan A. Mayer, Jason Mills, David Hunnicut

GIS Library

St. Norbert College as Arboretum: Mapping the Trees on Campus - Take a virtual tour of the trees on campus.

The tour is a multimedia ArcGIS Online story map and is available here.

Many of the trees on the St. Norbert Campus were planted by Fr. Anselm Keefe (1895- 1974) in the mid 20th century. It was Fr. Keefe’s vision to beautify the campus by creating gardens that were accessible to the public. This included planting a diverse variety of trees, including one of every tree species native to Wisconsin. It was Keefe’s mission to make St. Norbert College …

Application Of An Imputation Method For Geospatial Inventory Of Forest Structural Attributes Across Multiple Spatial Scales In The Lake States, U.S.A., Ram K. Deo

Dissertations, Master's Theses and Master's Reports - Open

Credible spatial information characterizing the structure and site quality of forests is critical to sustainable forest management and planning, especially given the increasing demands and threats to forest products and services. Forest managers and planners are required to evaluate forest conditions over a broad range of scales, contingent on operational or reporting requirements. Traditionally, forest inventory estimates are generated via a design-based approach that involves generalizing sample plot measurements to characterize an unknown population across a larger area of interest. However, field plot measurements are costly and as a consequence spatial coverage is limited. Remote sensing technologies have shown remarkable …

Evaluating Tree Height Using Pictometry® Hyperspatial Imagery, Daniel Unger, David Kulhavy, Matthew A. Wade

Faculty Publications

This study evaluated the use of Pictometry® hyperspatial 4-inch (10.2 centimeters) multispectral imagery to estimate height of baldcypress trees on the campus of Stephen F. Austin State University (SFASU), Nacogdoches, Texas. Actual tree heights of 60 baldcypress trees measured with a telescopic height pole were compared to Pictometry® estimated tree height. Linear correlation coefficients (r) and coefficient of determinations (R2) between actual tree height and Pictometry® estimated tree height for all 60 tress, and the shortest 30 and tallest 30 trees, were calculated. A paired t-test (alpha = 0.05) was calculated for all 60 tress, and the shortest 30 and …

Sub-Pixel Classification Of Forest Cover Types In East Texas, Joey Westbrook, I-Kuai Hung, Daniel Unger, Yanli Zhang

Faculty Publications

Sub-pixel classification is the extraction of information about the proportion of individual materials of interest within a pixel. Landcover classification at the sub-pixel scale provides more discrimination than traditional per-pixel multispectral classifiers for pixels where the material of interest is mixed with other materials. It allows for the un-mixing of pixels to show the proportion of each material of interest. The materials of interest for this study are pine, hardwood, mixed forest and non-forest. The goal of this project was to perform a sub-pixel classification, which allows a pixel to have multiple labels, and compare the result to a traditional …

Identifying Well Pads In The Haynesville Shale Region, Louisiana And Texas, With Digital Imagery, Darinda Dans, Daniel Unger, Kenneth W. Farrish, I-Kuai Hung

Faculty Publications

The Haynesville Shale is an underlying rock formation in northwest Louisiana and northeast Texas that contains vast quantities of natural gas. With new technology has come the ability to extract more natural gas from one of the largest gas deposits in the United States. With increased production, increased change in the local ecosystem will occur. It is necessary to examine oil and gas exploration effects on the local ecosystem due to changes in land cover, such as habitat loss and increased soil erosion. Remotely sensed imagery were utilized to ascertain the use of various digital image processing techniques to determine …

Radiative Forcing Over The Conterminous United States Due To Contemporary Land Cover Use Change And Sensitivity To Snow And Interannual Albedo Variability, Christoper A. Barnes, David P. Roy

GSCE Faculty Publications

Satellite‐derived land cover land use (LCLU), snow and albedo data, and incoming surface solar radiation reanalysis data were used to study the impact of LCLU change from 1973 to 2000 on surface albedo and radiative forcing for 58 ecoregions covering 69% of the conterminous United States. A net positive surface radiative forcing (i.e., warming) of 0.029 Wm−2 due to LCLU albedo change from 1973 to 2000 was estimated. The forcings for individual ecoregions were similar in magnitude to current global forcing estimates, with the most negative forcing (as low as −0.367 Wm−2) due to the transition to forest and the …

Assessing The Efficacy Of Modis Satellite-Derived Start Of Growing Season For Jurisdictional Determination Of East Texas Bottomland Hardwood Wetlands, Karen Malone, Hans Michael Williams, I-Kuai Hung, Daniel Unger

Faculty Publications

Introduction: Crucial to the determination of a jurisdictional wetland is the definition of “growing season”. Satellite imagery is being utilized in other ecological applications, but is lagging in wetland growing season determination. Both cost and temporal limitations historically have restrained use of satellite imagery in assessing the start up of the growing season. Multiple commercial satellites are available that provide high resolution imagery, but the cost are prohibitive for most studies. The National Aeronautics and Space Administration (NASA) and the U.S. Geological Survey (USGS) jointly manage the Landsat and the Moderate-resolution Imaging Spectroradiometer (MODIS) satellite programs. Landsat Enhanced Thematic Mapper …

Biomass Estimation And Classification Of Secondary Succession Using Radar And Optical Remote Sensing Data Based On Textural And Spectral Analysis In Amazonia, Ping Jiang

All-Inclusive List of Electronic Theses and Dissertations

Deforestation that happened during past decades in the Amazon Basin has been potentially affecting the global climate and carbon-cycle by contributing a vast amount of carbon into the atmosphere. Above-ground biomass (AGB) estimation of secondary successional and mature forests using remote sensing technology has been attracting scientists' attention. Optical sensor data are sensitive to forest canopies only, and therefore have limitations in AGB estimation, especially for tropical forests with complex structures and abundant species. Radar signals have the ability to penetrate canopies to detect the sub-layers of forests, and therefore potentially have a better performance in AGB estimation. Based on …

Accuracy Assessment Of Land Cover Maps Derived From Multiple Data Sources, Daniel Unger, Hillary Tribby, Hans Michael Williams, I-Kuai Hung

Faculty Publications

Maximum Likelihood (ML) and Artificial Neural Network (ANN) supervised classification methods were used to demarcate land cover types within IKONOS and Landsat ETM+ imagery. Three additional data sources were integrated into the classification process: Canopy Height Model (CHM), Digital Terrain Model (DTM) and Thermal data. Both the CHM and DTM were derived from multiple return small footprint LIDAR. Forty maps were created and assessed for overall map accuracy, user's accuracy, producer's accuracy, kappa statistic and Z statistic using classification schemes from U.S.G.S. 1976 levels 1 and 2 and T.G.l.C. 1999 levels 2 and 4. Results for overall accuracy of land …

Biomass Estimation Using Statistical And Neural Network Analysis Of Aster Data, Vijay O. Lulla

All-Inclusive List of Electronic Theses and Dissertations

This study assessed the performance of different biomass estimation methods using Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) lll data in a temperate forest. Multiple linear regression statistics of spectral band data and derived indices with biomass values were compared with advanced neural network models created with spectral band data and indices to model biomass values. Biomass for the study area was estimated using both of these methods and the results were discussed. The data were analyzed using multivariate statistical analysis. Correlation analysis and regression analysis were employed to understand the relationship of biomass to the spectral data and …

Remotely Sensed Data To Map Forest Age Class By Cover Type In East Texas, Daniel Unger, I-Kuai Hung, Jeffrey M. Williams, James Kroll, Dean W. Coble, Jason Grogan

Faculty Publications

• Remote sensing in conjunction with ground truthing, can accurately quantify forest composition and age distributions in East Texas.
• Method uses standardized and readily available data available to the general public.
• Method was shown to be effective in terms of time and cost.

The Effect Of Scaling-Up On Remotely Derived Leaf Area Index Estimators In The Brazilian Amazon, John I. Menzies

All-Inclusive List of Electronic Theses and Dissertations

This research compared a traditional statistical technique with artificial neural networks to determine their effectiveness for modeling LAI in the Santarem region of the Brazilian Amazon. In addition, different satellite sensors in the form of ETM+, ASTER, MO DIS, and IKONOS were used to assess if spatial and spectral resolutions play a role in the ability to estimate LAI. This study focused on the following: (1) Determining if neural networks perform more accurately than multiple regression for LAI modeling (2) the effectiveness of different resolution satellite data including ETM+, ASTER, MODIS, and IKONOS for LAI estimation; (3) determining if spatial …

Identification Of Spatial And Temporal Patterns Of Secondary Succession Changes In Altamira, Brazil: Integrating Remote Sensing And Gis Technology, Hui Li

All-Inclusive List of Electronic Theses and Dissertations

There has been extensive use of remote sensing to study secondary succession of moist tropical forests in the Amazon Basin. However, scientists have not totally understood the dynamic processes of secondary succession of Amazon forests. A lack of consensus remains about what is the best approach for biomass estimation in the Amazon Basin using remotely sensed data. It is not clear which spectral wavelength bands have the best relationship with biomass values and what are the specific relationships between biomass and TM satellite radiance. The strategies in this research, based on analysis of detailed ground-based data and satellite TM image …

Identification Of Spatial And Temporal Patterns Of Secondary Succession Changes In Altamira, Brazil: Integrating Remote Sensing And Gis Technology, Hui Li

All-Inclusive List of Electronic Theses and Dissertations

There has been extensive use of remote sensing to study secondary succession of moist tropical forests in the Amazon Basin. However, scientists have not totally understood the dynamic processes of secondary succession of Amazon forests. A lack of consensus remains about what is the best approach for biomass estimation in the Amazon Basin using remotely sensed data. It is not clear which spectral wavelength bands have the best relationship with biomass values and what are the specific relationships between biomass and TM satellite radiance. The strategies in this research, based on analysis of detailed ground-based data and satellite TM image …

Estimation Of Forest Stand Parameters And Application In Classification And Change Detection Of Forest Cover Types In The Brazilian Amazon Basin, Dengsheng Lu

All-Inclusive List of Electronic Theses and Dissertations

This research focuses on measurement of forest stand parameters using remote sensing which is applied to classification and change detection of tropical successional and mature forests in selected areas in the Brazilian Amazon Basin. Three study areas, Altamira, Bragantina, and Pedras, each with different biophysical environments, were selected for this research. Previous research has indicated that relationships between selected stand parameters and remotely sensed data are not clearly understood, especially in tropical areas. Rarely has remote sensing research been successfully conducted in quantitative estimation of forest stand parameters (e.g. biomass) and identification of vegetation growth stages. In this research, atmospherically …

Methodology For Integrating Aerial Photography And Landsat Tm Imagery For Inventory Of Forest Land Cover, Chris W. Bennett, Robert C. Weih Jr.

Journal of the Arkansas Academy of Science

Forest cover for 7.25 million acres (2.93 million hectares) in southeastern Georgia was characterized for the years 1988 and 1994 with the intent of assessing the efficacy of remote sensing procedures for broad scale forest inventory. Landsat-5 Thematic Mapper digital satellite scenes of seven spectral bands were obtained for winter and summer of each year and were analyzed two separate 14-band multi-temporal images. Images were geo-referenced to the universal transverse mercator (UTM) coordinate system prior to classification. Spectral classification with the 1SOCLUSTER algorithm produced 250 categories. Color infrared aerial photographs were mapped to the digital imagery and were used to …

Integrating Gis And Remote Sensing With Ecosystem Research, Suzanne Wiley, Robert C. Weih Jr.

Journal of the Arkansas Academy of Science

In the Phase II Ecosystem Management Research Program in the Ouachita and Ozark National Forests, an interdisciplinary group of scientists are evaluating the effects and trade-offs of partial cutting methods in a replicated stand level study. Information from approximately 2,000 plots is being collected by more than fifty researchers during this five-year project with plans to continue data collection long term. To evaluate the effects of different management strategies and their interactions with forest resources, data must be brought into a common format and made available to all researchers. To this end, a data support system was developed which utilizes …

Postfire Vegetation Change Detection Using Landsat Mss And Tm Data, Mark Edward Jakubauskas

All-Inclusive List of Electronic Theses and Dissertations

Three dates of Landsat digital data were classified and then analyzed using a geographic information system (GIS). The intent of this study was to determine if differences in burn severity relate to later vegetative cover within a northern pines forest. The May 5, 1980, Mack Lake Fire in the Huron National Forest, Michigan, was selected as the study site for this research. This wildfire burned over 9000 hectares of jack pine, red pine, and mixed deciduous forest within a six-hour period. Landsat MSS data from June 1973 and TM data from October 1982 were classified using an unsupervised approach to …

Application Of Satellite Data And Lars' Data Processing Techniques To Mapping Vegetation Of The Dismal Swamp, Jeffrey Allan Messmore

Biological Sciences Theses & Dissertations

This study concerned the feasibility of using digital satellite imagery and automatic data processing (ADP) techniques as a means of mapping swamp forest vegetation. Multispectral scanner data acquired by the Earth Resources Technology Satellite (ERTS-1; renamed LANDSAT-1) was analyzed using ADP techniques developed by Purdue University's Laboratory for Applications of Remote Sensing (LARS). The site for this investigation was the Dismal Swamp, a 210,000 acre swamp forest located south of Suffolk, Va. on the Virginia-North Carolina border. Two basic classification strategies were employed in determining the vegetation mapping capability of ERTS-1 data. The initial classification utilized unsupervised techniques which produced …