Peer-Reviewed Articles — Focus Issue
167 Using Geospatial Technologies to Enhance and
Sustain Resource Planning on Native Lands
Ray A. Williamson and Jhon Goes In Center
Abstract
Download
Full Article
The quality of life of Native Peoples will be unavoidably altered as
a result of long-term climate change and increased interannual climate
variability, especially as it relates to air quality, water resources,
forests, agriculture, and wetlands. Native Peoples have had centuries
of experience on the land; they have responded to many changes and
have found ways to live sustainably. Nevertheless, in addition to
facing uncertain environmental changes as a result of climate change,
today Native Peoples face diverse internal and external challenges
to their ability to manage their natural and cultural resources.
These include logging, mining, tourism, and urban encroachment.
Sophisticated geographic information tools, including geographic information systems (GIS), the Global Positioning System (GPS), and remote sensing systems, can assist in meeting these challenges by empowering Native Peoples in the development and execution of their own resource strategies. Yet, because of cultural differences between Native communities and the dominant, European-influenced culture, these powerful geospatial technologies cannot be simply incorporated into a Native management framework without recognizing and bridging these cultural differences.
171 Mapping Blackfeet Indian Reservation Irrigation
Systems with GPS and GIS
Delmar E. Seagle and Larry V. Bagwell
Abstract
Download
Full Article
Efficient irrigation system management requires accurate location and
condition information for system components. Bureau of Indian Affairs
(BIA) irrigation managers currently work with outdated paper manuscripts
or minimally attributed digital datasets. This paper examines how
the Global Positioning System (GPS), a geographic information system
(GIS), and digital cameras were used to perform a detailed inventory
of three irrigation units managed by the BIA on the Blackfeet Indian
Reservation, Montana. More than 500 digital pictures were captured
on approximately 500 kilometers (315 miles) of linear structures.
Over 2,100 point structures were inventoried. The resultant dataset
provides vastly improved analysis and display capabilities. Managers
can identify and locate system components attributed as "repair immediately" and
simultaneously view digital pictures of the irrigation system components,
along with associated attributes and data components. This greatly
facilitates their ability to estimate repair cost and complexity,
as well as to determine materials needed to accomplish the repairs.
The digital and photographÍic data thus become a "living" dataset
into which updates can be easily incorporated.
179 Riparian Vegetation Mapping and Image Processing
Techniques, Hopi Indian Reservation, Arizona
Robert M. Weber and Glenn A. Dunno
Abstract
Download
Full Article
Color infrared photography and airborne ATLAS images were utilized
to develop a vegetation map and a supervised land-cover classification
for the Blue Canyon reach of Moenkopi Wash on the Hopi Reservation,
Arizona. An orthophoto mosaic was produced, enabling photointerpretation
of riparian vegetation within the study area. Polygons representing
homogeneous vegetation patches were delineated using stereo pairs
and ground verification techniques. Vegetation transects measured
percent cover and species composition. The Spence-Romme-Floyd-Rowlands
vegetation classification scheme served as a framework to map the
vegetation. Image processing techniques such as Tasseled Cap transformations
and image masking were used in an attempt to minimize soil noise
from the ATLAS images prior to classification. Land-cover classification
accuracy for the pilot study area was 40 percent. TNDVI and MSAVI2
vegetation indices were evaluated for their ability to indicate vegetation
extent and relative plant vigor.
187 NativeView: A Gateway to the Earth for Native
Americans
Kenneth D. Bailey and Robert C. Frohn
189 Remote Sensing Analysis of Wild Rice Production
Using Landsat 7 for the Leech Lake Band of Chippewa in Minnesota
Kenneth D. Bailey, Robert C. Frohn, Richard A. Beck, and Michael
W. Price
Abstract
Download
Full Article
The efforts of a project to address the needs of the Leech Lake Tribe
to estimate and monitor wild rice production are described. Native
American Remote Sensing Incorporated (NARSINC), in a joint effort
with the University of Cincinnati, is using Landsat 7 data to provide
the Leech Lake Tribe with remote sensing and GIS data products to
effectively manage wild rice production. NARSINC was able to estimate
wild rice crop areas for 1999 using multi-temporal Landsat 7 data.
A significant crop loss for 1999 and subsequent insurance claim was
verified in this study. Data products were delivered to the Leech
Lake Tribe and stored on an image map server as a tool for development
of future wild rice management strategies. A latter phase of this
project will also involve site-specific training of tribal members
for creating and managing these data products in the future. We hope
to continue these efforts on a more large scale basis and train Native
Americans on a National level to utilize these tools to manage their
own resources.
193 Native American Remote Sensing Distance Education
Prototype (NARSDEP)
Kenneth Bailey, Richard Beck, Robert Frohn, Dave Pleva, Dave Plumer,
Michael Price, Robert Krute, Calvin Ramos, and Robert South
Abstract
Download
Full Article
The purpose of the Native American Remote Sensing Distance Education
Prototype (NARSDEP) is to provide remote sensing classes to students
of the Leech Lake Tribal College (LLTC) and tribal government representatives
in cooperation with the University of Cincinnati (UC). The Leech
Lake Indian Reservation is located in a rural part of northern Minnesota.
High-speed Internet access is very limited in this community. The
distance-learning program to be conducted by LLTC and the UC is the
central component of the Gateway2Earth: OhioView Pilot in cooperation
with the NASA Glenn Research Center and the USGS EROS Data Center.
Gateway2Earth is a national consortium of universities, colleges,
schools, federal, tribal, state, and local governments and industry
designed to promote the development of the satellite remote sensing
industry in the United States through improved access to geospatial
data and technology for education and research. NARSDEP represents
the beginning of the Native American educational and research component
of Gateway2Earth.
Peer-Reviewed Articles — General
199 Evaluating the Accuracy of Digital Orthophoto
Quadrangles (DOQ) in the Context of Parcel-Based GIS
Joshua Greenfeld
Abstract
Download
Full Article
A crucial component in developing an effective GIS based on digital
parcel base maps is the acquisition of accurate digital land base
data. Accurate land base data make spatial analysis less troublesome
and enables better decision making. However, acquisition of accurate
spatial data from traditional data compilation techniques could come
with a hefty price tag. A popular solution for the accuracy versus
cost dilemma is the use of USGS Digital Orthophoto Quadrangles (DOQ).
DOQs provide very inexpensive continuous land coverage that could
be converted with relatively modest means and expense into a digital
parcel map.
Given that DOQs are becoming a common solution for establishing digital parcel coverages, it is prudent to evaluate their accuracy. DOQs are considered to comply with the National Mapping Accuracy Standards (NMAS), but that statement does not render a constructive measure for the accuracy of the data because of the way (or lack of) a dataset is certified as being in compliance with the NMAS. A better approach for evaluating the accuracy of DOQs is to follow the National Standard for Spatial Data Accuracy (NSSDA) guidelines.
In this paper a DOQ was evaluated with the NSSDA standards in order to establish the positional accuracy of the data. The accuracy was found to be within ;pm25 feet (;pm7.6 m) at the 95 percent confidence level. The DOQ was also evaluated for its geometric, radiometric, and mosaicking accuracies. This aspect of the DOQ was found to be satisfactory. Finally, the appropriateness of DOQs in the context of a parcel-based GIS was addressed.
207 Spring Wheat Classification in an AVHRR Image
by Signature Extension from a Landsat TM Classified Image
Alan J. Stern, Paul C. Doraiswamy, and Paul W. Cook
Abstract
Download
Full Article
Landsat TM imagery data have been used for the classification of crops
in small areas; however, NOAA AVHRR imagery is more appropriate
for regional and continental scales for few specific categories of
vegetation. The U.S. Department of Agriculture (USDA) is interested
in assessing crop acreage at the county and state levels. The objective
in this study was to determine the feasibility of using AVHRR data
to classify spring wheat in North Dakota. Two methods were developed
to categorize large areas using AVHRR data along with a minimal
amount of Landsat TM data. Areas of intense agriculture were used
for the performance of an unsupervised classification with AVHRR
data. Differences in precipitation and climatic conditions between
the eastern and western parts of the State created some difficulties
in proper classification; therefore, to improve classification accuracy,
additional ancillary data were needed. The number of Landsat TM spring
wheat pixels in the overlapping AVHRR pixels provided a means
for predicting the percentages of spring wheat for each AVHRR class.
The accuracy of the spring wheat acreage at the State level closely
matched the USDA State report for 1994.
213 Detecting the Nature of Change in an Urban
Environment: A Comparison of Machine Learning Algorithms
Jonathan Cheung-Wai Chan, Kwok-Ping Chan, and Anthony Gar-On
Yeh
Abstract
Download
Full Article
The performance of difference machine learning algorithms for detecting
nature of change was compared. To alleviate the problem of obtaining
enough training data, simulated training data were generated from
single-date images. A one-pass classification with four machine learning
algorithms, namely, Multi-Layer Perceptrons (MLP), Learning Vector
Quantization (LVQ), Decision Tree Classifiers (DTC), and the Maximum-Likelihood
Classifier (MLC), were tested. Recognition rates,
ease of use, and degree of automation of the four algorithms were assessed.
The results showed that the incorporation of cross-combined simulated
training data enhanced the detection of nature of change. Compared
to conventional post-classification comparison methods, LVQ and DTC
did better in terms of overall accuracy. In terms of average accuracy
of the change classes, LVQ was the best performer. DTC was the easiest
to use and the most robust in training. MLP procedures were the most
difficult to replicate.
227 An Evaluation of an Off-the-Shelf Digital Close-Range
Photogrammetric Software Package
Gang Deng and Wolfgang Faig
Abstract
Download
Full Article
A series of digital close-range photogrammetric measurement tests with
different non-metric images were carried out with the off-the-shelf
digital close-range photogrammetric software package PhotoModeler
Pro for two different test fields, to investigate the photogrammetric
performance of the software, and also the strategies for different
practical applications. The test results are generally promising.
Theoretical discussions about some related concerns regarding possible
improvements of this or other similar software are presented as well.
| Top | Home |