Columbia Climate School

This paper examines the relationship between environmental vacillation and the success of societal institutions in the Roman Empire, specifically the economy. A detailed investigation exploring the rise of the Roman economy is undertaken through the use of archaeological and climatological evidence. Climate, as defined in this paper, consists of four parts – environmental climate, social climate, economic climate, and political climate – thus establishing the Tetra-dimensional Climate Model. The results of this study indicate a strong relationship between the rise of agriculture and the rise of maritime trade during the Roman Republic and the Roman Empire. This increase in agricultural production was largely a result of climatological stability in Europe, as well as the temporary tranquillity of the political climate following the foundation of Rome. As political and social stability vacillated throughout Rome’s years in power, economic development– especially in the agricultural sector–also changed course.

Previously, cross-cultural studies have been limited by an immense, but largely inaccessible, volume of literature, most of which was not easily comparable to other studies. Recently, with the advent of D-PLACE (a global database of cultural, linguistic, and environmental data), cross-cultural analyses can be conducted between over 1,400 different cultural groups with relative ease. Utilising this new database, a re-examination of the revolutionary article by Roberts and collegues 1 is undertaken, specifically to address the hypothesis that games of chance are more prevalent in cultural groups with " benevolent " gods. Additionally, variables of political integration, mean community population size, geographic location, and the belief in trance states and possession are incorporated to explore correlations between game type presence and religious differences. A total of 377 different cultures are compared in this study, making this the largest sample size used to re-evaluate the Games in Culture hypotheses. Games are abundant in the archaeological record, and provide much detail regarding cultural, religious, and social characteristics of different groups. Thus, this study not only holds anthropological merit, but also benefits the archaeological community.

High-resolution multispectral imagery provides an effective means for measuring the archaeological record of Rapa Nui. Previous work has suggested that the island’s prehistoric cultivation features known as “lithic mulch gardens” can be identified using near infrared imagery (NIR). Lithic mulching was a laborious but critical strategy for prehistoric populations who relied on cultivating sweet potato and taro in nutrient poor soils for their subsistence. The new WorldView-3 satellite offers researchers access to short-wave infrared (SWIR) bands, imagery that provides additional information about moisture content and mineral composition. While these bands should provide a better means for identifying lithic mulch gardens, this new imagery is currently only available at a lower spatial resolution than NIR images (7.5 m vs. 1.24 m. . Here, I evaluate whether these lower-resolution SWIR images can be used for identifying “lithic mulch garden” features despite their resolution difference. Comparing the results of SWIR imagery with that of previous analyses reveals markedly similar classification accuracy despite having the lower spatial resolution. This result suggests that SWIR may provide a new tool for researchers interested in questions of prehistoric land-use that will become increasingly more powerful as greater spatial resolutions become available.

Artificial mounds and rings are morphologically heterogeneous. While they share many compositional traits, their outlines on landscapes widely vary across geographic regions. As such, automated identification of these deposits requires the use of algorithms that are capable of evaluating a range of criteria and assessing objects that fall within acceptable ranges for each analyzed attribute. Object-based image analysis (OBIA) is one solution to this problem. Here, I develop a combined OBIA method utilizing two different approaches – multiresolution segmentation and template matching – in order to locate these features semi-automatically. The results of this study indicate the success of this method for detecting previously undocumented archaeological features in South Carolina. Remote Sensing thus increases economic and time efficiency for archaeological survey.

Artificial mounds and rings are morphologically heterogeneous. While they share many compositional traits (i.e. many are composed of either soil or substantial shell material and have above-ground profiles), their outlines widely vary across geographic regions. As such, automated identification of such deposits requires the use of algorithms that are capable of evaluating a range of criteria and evaluating objects that fall within acceptable ranges for each analyzed attribute. Here, I evaluate three OBIA methods (multiresolution segmentation, inverse depression analysis, and template matching) in their ability to identify mound and ring features in Beaufort County, South Carolina. Then I develop a method that combines two OBIA methods – multiresolution segmentation and template matching. The two methods work together to find sites based on statistical similarity and morphometric characteristics. Ultimately, I show how a combined approach is more effective for archaeological prospection than any single method on its own. Materials • LiDAR data (collected by NOAA, USGS, and DNR) used to create DEM with spatial resolution of 1.2 meters • ArcGIS 5.1, eCognition 9.2, SAGA, Whitebox GAT • Land-use maps from USGS and transportation shapefiles from South Carolina DOT Methods (continued) Results Conclusion In reviewing the applicability of each of these methods, it is apparent that each offers specific benefits and drawbacks. Given the morphological diversity of mound-features, an all-encompassing algorithm is unlikely to be developed. Rather, researchers must create algorithms on a regional basis. When testing individual methods, it was clear that none of them were sufficient on their own. Using a pure segmentation approach yields too many results for archaeologists to manually process. IDA was useful for mound and midden detection, and when increasing the number of iterations, IDA was far more accurate and successfully identified ring features. TM was the most effective method tested for limiting false positives. The combination of TM with segmentation eliminates natural topographic changes from consideration via statistical analysis. Thus, a multifaceted approach should be taken when utilizing remote sensing technologies for archaeological purposes. Future work will sample additional features and expand the analysis to other regions of the American Southeast.

The study of pre-contact anthropogenic mounded features- earthen mounds, shell heaps, and shell rings - in the American Southeast is stymied by the spotty distribution of systematic surveys across the region. Many extant, yet unidentified, archaeological mound features continue to evade detection due to the heavily forested canopies that occupy large areas of the region, making pedestrian surveys difficult and preventing aerial observation. Object-based image analysis (OBIA) is a tool for analyzing light and radar (LiDAR) data and offers an inexpensive opportunity to address this challenge. Using publicly available LiDAR data from Beaufort County, South Carolina and an OBIA approach that incorporates morphometric classification and statistical template matching, we systematically identify over 160 previously undetected mound features. This result improves our overall knowledge of settlement patterns by providing systematic knowledge about past landscapes.

Object based image analysis (OBIA) is a method of assessing remote sensing data that uses morphometric and spectral parameters simultaneously to identify features in remote sensing imagery. Over the past 10-15 years, OBIA methods have been introduced to detect archaeological features. Improvements in accuracy have been attained by using a greater number of morphometric variables and multiple scales of analysis. This article highlights the developments that have occurred in the application of OBIA within archaeology and argues that OBIA is both a useful and necessary tool for archaeological research. Additionally, I discuss future research paths using this method. Some of the suggestions put forth here include: pushing for multifaceted research designs utilizing OBIA and manual interpretation, using OBIA methods for directly studying landscape settlement patterns, and increasing data sharing of methods between researchers.

One persistent archaeological challenge is the generation of systematic documentation for the extant archaeological record at the scale of landscapes. Often our information for landscapes is the result of haphazard and patchy surveys that stem from opportunistic and historic efforts. Consequently, overall knowledge of some regions is the product of ad hoc survey area delineation, degree of accessibility, effective ground visibility, and the fraction of areas that have survived destruction from development. These factors subsequently contribute unknown biases to our understanding of chronology, settlements patterns, interaction, and exchange. Aerial remote sensing offers one potential solution for improving our knowledge of landscapes. With sensors that include LiDAR, remote sensing can identify archaeological features that are otherwise obscured by vegetation. Object-based image analyses (OBIA) of remote sensing data hold particular promise to facilitate regional analyses thorough the automation of archaeological feature recognition. Here, we explore four OBIA algorithms for artificial mound feature detection using LiDAR from Beaufort County, South Carolina: multiresolution segmentation, inverse depression analysis, template matching, and a newly designed algorithm that combines elements of segmentation and template matching. While no single algorithm proved to be consistently superior to the others, a combination of methods is shown to be the most effective for detecting archaeological features.

Environmental archaeological enquiry has a long and vibrant history. Many of the same questions have persisted in archaeological dialogues over the past century. In particular, the effects of environmental change on demographic patterns, health, and societal stability are among the most pervasive questions being addressed by anthropological research. These studies have limitations, however. For example, evaluations of the complex relationships between environmental variables and human responses are only just beginning to emerge in anthropological literature. This goal requires high-resolution paleoclimate datasets and the use of quantitative modelling rooted in evolutionary and complex systems theory. This paper serves as a broad review of advances in environmental archaeological enquiry associated with environmental change and human response. I argue that the future of archaeological questions concerning human-environmental connection requires a re-evaluation of causality and the incorporation of complex systems approaches to address human responses to external pressures.

Despite decades of archaeological research, much of Madagascar remains archaeologically unexplored. The oldest sites on the island are difficult to locate, as they contain the ephemeral remains of mobile forager campsites. Thus, the known archaeological record is biased toward later sites, particularly in areas with histories of urbanization and connection to global trade networks.  Using theoretical models derived from human behavioral ecology in conjunction with remote sensing data, I illustrate how archaeological survey can be targeted towards subtle archaeological deposits on Madagascar, many of which may hold answers to long-held debates over the nature and timing of human occupation. Results of preliminary fieldwork suggests that early human populations maintained close social ties and actively engaged with environmental resources in similar ways as contemporary populations.

Africa represents a vast region where remote sensing technologies have been largely uneven in their archaeological applications. With impending climate related risks such as increased coastal erosion and rising sea levels, coupled with rapid urban development, gaps in our knowledge of the human history of this continent are in jeopardy of becoming permanent. Spaceborne and aerial remote sensing instruments are powerful tools for producing relatively complete records of archaeological settlement patterns and human behavior at landscape scales. These sensors allow for massive amounts of information to be recorded and analyzed in short spans of time and offer an effective means to increase survey areas and the discovery of new cultural deposits. In this paper, we review various case studies throughout Africa dealing with aerial and satellite remote sensing applications to landscape archaeology in order to highlight recent developments and future research avenues. Specifically, we argue that (semi)automated remote sensing methods stemming from machine learning developments will prove vital to expanding our knowledgebase of Africa’s archaeological record. This is especially important for coastal and island regions of the continent where climate change threatens the survival of much of the archaeological record.

A vast majority of the archaeological record, globally, is understudied and increasingly threatened by climate change, economic and political instability, and violent conflict. Archaeological data are crucial for understanding the past, and as such, documentation of this information is imperative. The development of machine intelligence approaches (including machine learning, artificial intelligence, and other automated processes) has resulted in massive gains in archaeological knowledge, as such computational methods have expedited the rate of archaeological survey and discovery via remote sensing instruments. Nevertheless, the progression of automated computational approaches is limited by distinct geographic imbalances in where these techniques are developed and applied. Here, I investigate the degree of this disparity and some potential reasons for this imbalance. Analyses from Web of Science and Microsoft Academic searches reveal that there is a substantial difference between the Global North and South in the output of machine intelligence remote sensing archaeology literature. There are also regional imbalances. I argue that one solution is to increase collaborations between research institutions in addition to data sharing efforts.