Scythian Burial Mounds Part I

These Scythian burial mounds are located in a valley in Siberia.  A mosaic DEM illustrates the variation in elevation in this region.  By examining the characteristics of the landscape, it may be possible to understand the reasons why the burial mounds were located here.

The inset map shows a georeferenced image of the mounds, overlaid on a DEM of the study area.

Predictive Modeling

Predictive modeling can be a useful tool for archaeologists trying to narrow down likely areas where cultural resources may be found.  Environmental factors such as proximity to water, soil and vegetation types, elevation, slope, and aspect (which direction a slope faces) are understood to have some value in predicting where settlement and activity occurred in the past.  This type of information is becoming increasingly easy to acquire, and the resulting models can be used to narrow down areas where field survey is more likely to result in finding cultural remains.  The map above shows a weighted overlay map indicating areas where there is a high, medium and low probability of finding archaeological material.  By choosing specific variables, and by weighting them according to their relative importance, archaeologists can guide field survey, saving money and time.  However, this technique has been criticized for being too focused on environmental variables, and not taking into account cultural factors that would likely impact choices made by those who settled in a particular area.  Predictive modeling is currently used in CRM work most often.  When used in an academic situation, it is critical that substantial ground-truthing and field survey are conducted in addition to the use of the predictive model.

Finding Angkor's Hidden Sites

While the use of satellite images for identifying potential archaeological sites is successful in several parts of the world, it is less so in others.  In Cambodia, where many monumental stone structures are hidden in the dense tropical vegetation, and where land mines and unexploded bombs pose a threat to those conducting ground survey, it is possible to use training samples to classify images for the identification of previously unknown sites.  However, it is problematic.  This map shows a supervised classification of the area surrounding the core of Angkor's monumental architecture.  The classification does identify several areas where potential sites may be located, and indeed one area, Phnom Kulen, has recently been identified as a previously undiscovered urban landscape associated with early Angkorian settlement (Evans et al, 2013).  The classification is not good at distinguishing the stone monuments from other classes, such as dense forest and water.  However, patterning that points to hydraulic features and geometric lines associated with Angkorian architectures is visible.

It appears that the use of lidar in this situation is far superior to the results that can be achieved using Landsat imagery, as seen here.  The following website and article provide additional information.

http://angkorlidar.org/publications/

Evans, D. H., R. J. Fletcher, C. Pottier, J.-B. Chevance, D. Soutif, B. S. Tan, S. Im, D. Ea, T. Tin, S. Kim, C. Cromarty, S. De Greef, K. Hanus, P. Bâty, R. Kuszinger, I. Shimoda and G. Boornazian. 2013. “Uncovering archaeological landscapes at Angkor using lidar,” Proceedings of the National Academy of Sciences of the United States of America 110: 12595-12600

Band Combinations, Training Samples, and Supervised Classification

Different band combinations can be used to bring out various characteristics in the environment.  The top two maps here use two different combinations of Landsat satellite imagery.  The NVDI map uses a False Color image composite, which joins bands 2,3, and 4.  When the NVDI process is added, the negative ouputs show up as red.  Bare rocks, sand, and snow have an output close to zero, and those with a higher measure of "greenness" have higher values.  This allows dense vegetation, like tropical rainforest, to show up clearly.  The combination of bands 4,5, and 1 in the second map is used to differentiate vegetation that is stressed and sparse with healthy vegetation.  

The map at the bottom shows a map resulting from a supervised classification,  First a training signature file was created by drawing polygons around samples of each class.  Known Mayan pyramid sites were used to create this class's sample.  The classification shows the locations of possible new sites, and creates a tool to be used in survey and ground-truthing.

Maya Pyramids Part I

This series of maps shows several different band combinations that can be useful in locating potential sites.  Landsat imagery from the USGS can be viewed in ways which reveal certain characteristics depending on the combination of bands.  ArcMap Image Analysis and Processing tools are used to create combinations suitable for different purposes.

For example, the Landsat Band 8 is a high resolution panchomatic view, used here by itself to show the location of the Mirador pyramid.  It can also be used to create a sharper composite image, using the Pan-sharpening option. 

The Natural Color map shows a band combination that displays a color image to visualize data like a color photo using Bands 1,2 and 3, which show visible light.  Band 1 distinguishes soil from vegetation, Band 2 is useful for showing which plants are stressed, and which are more healthy, and Band 3 is also used to highlight vegetation.

The False Color map uses Bands 2,3,and 4, adding the Near Infared which emphasizes biomass content.  This band combination is most useful for the dense jungle where the Mirador pyramid is located because the red band (#3) indicates areas where chlorophyll is being absorbed, and the NIR band (#4) indicates areas of high refelectivity of plant materials.  The NDVI(Normalied Difference Vegitation Index) tool is used to show relative biomass in the image.

Mapping the History of New Bedford

Printed historic maps have been used in the past to answer questions about social, environmental, and political dynamics within a regional landscape.  

This study uses GIS analysis to begin to identify and understand changes in political, social, and economic life that took place between the post-War of 1812 economic boom tied to the whaling industry, and the peak of manufacturing in New Bedford, MA.   Several families, including the Kemptons, Rotches, Rodmans, Hathaways, Russells, Allens, and Morgans, were prominent during the early development of the city.  Their influences in terms of land and business ownership shaped the political, social, and economic landscape.  Free African-Americans and immigrants from the Azores and Cape Verde Islands working on the waterfront and in the whaling industry shaped New Bedford as well.  As the industrial base shifted from whaling to manufacturing, and as the city expanded in terms of population, ethnic diversity, and physical size, one would expect to see the development social and spatial boundaries between those who owned property and were successful in the initial establishment and later growth of the city into a whaling and commercial capital, and those who settled later as the city expanded to the north and south.  The GIS analysis presented here shows where and how these boundaries developed. 

By 1815 a social, political and industrial center has been established, dominated by those who owned valuable waterfront property and businesses related to whaling and shipping.  The 1850 map shows many of the Registered Historic Sites clustered north of the city center, several blocks away from the waterfront.  These were built during the heyday of whaling and shipping and illustrate the prosperity and growth the city was experiencing.  Many of these are modest one-family homes, not businesses or mansions built by those involved in whaling and commerce.  Those were being built, in smaller numbers, to the south of the center and close to County Street at the top of the hill overlooking the busy harbor.  Two mills had been built by 1850, but housing was not yet being built specifically to accommodate mill workers.  The environmental context here relates to the developing social inequality in New Bedford, as those with lower incomes settle in areas away from the center. 

Central business district showing the establishment of several prominent families as dominant property holders.

The first mill is built, whaling is in decline.

 The housing boom that can be seen in both the 1871 and 1891 maps reflects the increase in population between 1850 and 1920 as immigrants came to work in the mills.

Urban growth to the north and south of the city's commercial center, and to the west of County St.

The 1891 map in particular shows clusters of identical rows of housing built near the Wamsutta and Howland Mills complexes.  As foreign-born workers arrived in greater numbers, social differences increased.  By 1911 it is possible to see this clearly when analyzing the surnames of property owners in various neighborhoods. Properties closer to industrial areas, in particular mills and other factories, show an abundance of surnames that reflect the immigrant make-up of these parts of the city.   By 1900, over 40% of New Bedford’s population was foreign born, the majority being Portuguese, Azorian, and Cape Verdean.  Other immigrant groups included French-Canadians, Irish, and English.  Property along County Street, and still to some extent in the downtown center, is still very much dominated by surnames from the past whaling and commercial era.  This is an example of the political landscape “constantly being defined and redefined to further accentuate the social boundaries that underlie ideologies of political order” (Kosiba 2013). 

Growth in mill housing construction.

1911: Near-peak manufacturing production

1911: Pattern of land ownership for elite families

1911: Housing in the Wamsutta Mills area, inset showing highway construction in 1960's and 1970's.

The city of New Bedford has a rich and colorful history which should continue to be preserved and celebrated.  Several organizations are committed to this mission, including the New Bedford Whaling Museum and the Waterfront Historic Area League (WHALE).  GIS analysis of historic maps can be extremely valuable in supporting it.  Research that focuses on the diverse aspects of urban development spreads the focus of preservation efforts to include the history of all social groups, not just those who are most often credited with the city’s establishment and success in whaling and commerce.  Ultimately this will help ensure that the entire picture of New Bedford’s development is represented in preservation efforts. 

The findings of this study reveal patterns of settlement in New Bedford as it transformed from a small village to a center of manufacturing.  By investigating the demographic makeup of the people living here at different points in history, and considering the ways in which the political landscape shapes social differences, a more complete understanding of the complexities of urban development here can occur. 

Classified Images

These two images of the same area reflect two types of classification - the top one is Unsupervised, where ArcGIS classifies the land cover based on a selected number of land types, in this case 8.  It creates its own signature first, then does the classification.  As you can see, there are errors and overlaps in the land types, and it is not very accurate.

The second image shows a Supervised classification, where I created signatures for 5 different land cover types based on 30 points spread out to capture the land types most accurately, especially in the area of the Cahokia Monk's Mound.  I also used only 5 classes.  Clearly this was much more accurate and effective.  The Unsupervised classification is a good starting point, but it is important to further refine that information for accuracy.