Space, Settlement, and Environment: Detecting Undocumented Maya Archaeological Sites with Remotely Sensed Data

This study utilizes an integrated remote sensing approach to augment settlement pattern research in the Yalahau Region of northern Quintana Roo, Mexico. The region has a long history of human occupation and an environment ranging from coasts, freshwater wetlands, forests, to fields and towns all above a porous karst geology. By utilizing various sensors (LiDAR, GeoEye and Landsat) and collection methods (satellite, aerial) as well as post-processing (band combinations, component analyses and indices) and cross-referencing the data, it is possible to generate a signature, which strongly correlates with evidence of prehistoric occupation. Field verification of a selection of identified signatures was conducted to assess the presence of human cultural material. The results of this investigation are presented together with other regional settlement pattern data in order to assess the status of a number of methodological and archaeological questions and supplement other regional data already available

The Assessment of Land Degradation and Desertification in Mexico: Mapping Regional Trend Indicators with Satellite Data

Understanding the patterns of land degradation and desertification to develop mitigation strategies requires identification of methods for accurate and spatially explicit assessment and monitoring. Remote sensing data offer the possibility to develop strategies that outline degradation and desertification. The free access policy on satellite imagery enables a new pathway to measure, assess, and monitor land degradation using indicators derived from multispectral satellite data. This chapter seeks to explore a methodology for land degradation and desertification assessment and monitoring, based on freely available multispectral satellite data. The method identifies net primary productivity (NPP) and canopy cover (CC) as indicators of degradation. The trajectories of these indicators show patterns and trends over time. The methodological development presented here is intended to be a tool for regional landscape monitoring and assessment, enabling the formulation of corrective action plans. This methodology was tested in a semi-deciduous ecosystem in the southeast of Mexico

Using Landsat 5 TM Data to Identify and Map Areas of Mangrove in Tulum, Quintana Roo, Mexico

Mangroves are recognized worldwide as a major ecosystem that provides significant ecosystem services. They are threatened due to rising pressures from human overpopulation and economic development. The Caribbean Coast of Mexico\u27s Yucatan Peninsula contains mangrove habitat that have been negatively impacted by the development of the region\u27s tourist industry. However, little research has been done to map and quantify the extent of mangrove in the region. This study used remote sensing techniques to identify mangrove in the Municipality of Tulum located in Quintana Roo, and to produce an accurate vector based thematic map that inventories these areas. Anatomical differences were analyzed and related to high-resolution field spectral data for each mangrove species. A vector map of mangrove habitat, including areas of inland mangrove, was produced with an overall accuracy of 88%. The 19,262 ha. of mangrove identified by this study represents a 140% increase in area over previous studies

Análisis de imágenes orientadas a objetos para mapear y monitorear cambios en la cobertura terrestre de la Región Costa Maya, México: 1993-2010

Accurate, cost effective and timely multiple spatial-temporal information on the patterns of land cover change is crucial for environmental management and understanding. For this reason, segmentation and object-oriented classification was applied to Landsat TM/ ETM+ imagery to map and monitor land cover dynamics in the Región Costa Maya (RCM) in 1993, 2000 and 2010. Overall mapping accuracy for land-cover map in 2000 was 94.29% (ĸ= 0.9141). Post-classification approach, involving cross tabulation of three generated maps, was used to characterize spatial temporal rates and patterns of land cover change to infer major processes of changes over 17 years. Results revealed rapid urbanization, agricultural land abandonment (forest transition) and destruction of mangrove forests, mediated by socio-economic factors linked to tourism development as the leading drivers of land cover change, with grave implications on environmental sustainability in the Costa Maya area. The study has confirmed the value of segmentation and object-oriented classification for mapping and monitoring land cover change at regional scale.La información espacio-temporal múltiple, precisa, rentable y pertinente sobre los cambios en la cobertura del suelo es fundamental para el mejor manejo del medio ambiente. De esta forma, en este estudio se aplicó la segmentación y clasificación orientada a objetos a las imágenes de Landsat TM /ETM + para mapear la cobertura del suelo en la Región Costa Maya, de 1993, 2000 y 2010. Se obtuvo una precisión global de mapeo del mapa del año 2000 de 94,32% (ĸ= 0.9141). Para caracterizar las tasas y los patrones espacio-temporales de cambio en la cobertura terrestre, se aplicó la comparación en post-clasificación, utilizando tabulaciones cruzadas de tres mapas, posteriormente generados, para inferir los principales procesos de cambios durante 17 años. Los resultados revelaron una rápida urbanización, el abandono de tierras agrícolas (transición forestal) y la destrucción de los manglares, mediado por factores socio-económicos vinculados al desarrollo del turismo, como los principales impulsores del cambio, con graves implicaciones sobre la sostenibilidad del medio ambiente en la Costa Maya. El estudio ha confirmado el valor de la segmentación y clasificación orientadas a objetos en el mapeo y monitoreo del cambio de cobertura terrestre a escala regional

A novel approach to modelling mangrove phenology from satellite images: a case study from Northern Australia

Around the world, the effects of changing plant phenology are evident in many ways: from earlier and longer growing seasons to altering the relationships between plants and their natural pollinators. Plant phenology is often monitored using satellite images and parametric methods. Parametric methods assume that ecosystems have unimodal phenologies and that the phenology model is invariant through space and time. In evergreen ecosystems such as mangrove forests, these assumptions may not hold true. Here we present a novel, data-driven approach to extract plant phenology from Landsat imagery using Generalized Additive Models (GAMs). Using GAMs, we created models for six different mangrove forests across Australia. In contrast to parametric methods, GAMs let the data define the shape of the phenological curve, hence showing the unique characteristics of each study site. We found that the Enhanced Vegetation Index (EVI) model is related to leaf production rate (from in situ data), leaf gain and net leaf production (from the published literature). We also found that EVI does not respond immediately to leaf gain in most cases, but has a two- to three-month lag. We also identified the start of season and peak growing season dates at our field site. The former occurs between September and October and the latter May and July. The GAMs allowed us to identify dual phenology events in our study sites, indicated by two instances of high EVI and two instances of low EVI values throughout the year. We contribute to a better understanding of mangrove phenology by presenting a data-driven method that allows us to link physical changes of mangrove forests with satellite imagery. In the future, we will use GAMs to (1) relate phenology to environmental variables (e.g., temperature and rainfall) and (2) predict phenological changes

The annual cycle of the Gulf Loop Current Part I: Observations during a one-year time series

The Gulf Loop Current is that portion of the Gulf Stream System which connects the Yucatan Current and the Florida Current in the eastern Gulf of Mexico. An experiment to test the annual cycle proposed by Leipper (1970) was conducted from August, 1972, through September, 1973. Twelve pathlines of the 22°C isotherm at 100 meters depth were made from Yucatan to the Florida Keys at 36-day intervals in conjunction with a satellite oceanography project...

Monsoon Flooding Response: a Multi-scale Approach to Water-extent Change Detection

This paper has the aim of illustrating an automatic and speditive way for retrieving inundation extent from multispectral and multitemporal satellite data, together with land-cover changes caused by flooding events, which is a fundamental issue for managing a reconstruction plan after the event. A straightforward method to map inundated areas was applied in the North-Eastern region of Bangladesh, heavily struck by monsoonal rains in September 2000. This method in based on the Principal Components Transform (PCT) of multispectral satellite data, in its Spectral-Temporal implementation, followed by logical filtering and image segmentation, in order to reach the needed coherency of the results. The use of multiresolution data (28.5-meters ground resolution Landsat-7/ETM+ and 1,100-meters ground resolution NOAA-14/AVHRR) makes possible to evaluate hazard affected areas at different scales. Comparison to RADARSAT-derived water extension maps assessed an Overall Accuracy between 86.4% (for the flood map derived with NOAA-14/AVHRR data over the whole Bangladesh) and 90.6% (for the flood map derived with Landsat-7/ETM+ data over the North-East part of the country)