Development Of Environmental Studies Research Articles

Evaluating single and multi-date Landsat classifications of land-cover in a seasonally dry tropical forest

Accurate information on the land cover is crucial for efficient monitoring and development of environmental studies in the Brazilian Caatinga forest. It is one of the largest and most biodiverse dry forests on the planet. Distinguishing different patterns of land cover through medium spatial-resolution remote sensing, such as the Landsat image series, is challenging to Caatinga due to heterogeneous land cover, complex climate-soil - vegetation interactions, and anthropogenic disturbance. Two remote sensing approaches have a high potential for accurate and efficient land-cover mapping in Caatinga: single and multi-date imagery. The heterogeneity of the land cover of this environment can contribute to a better performance of multispectral approaches, although it is usually applied for single-date images. In a land-cover mapping effort in Caatinga, the temporal factor gains relevance, and the use of time series can bring advantages, but, in general, this approach uses vegetation index, losing multispectral information. This manuscript assesses the accuracies and advantages of single-date multispectral and multi-date Normalized Difference Vegetation Index (NDVI) approaches in land-cover classification. Both approaches use the Random Forest method, and the results are evaluated based on samples collected during field surveys. Results indicate that land-cover classification obtained from multi-date NDVI performs better (overall accuracy of 88.8% and kappa of 0.86) than single-date multispectral data (overall accuracy of 81.4% and kappa coefficient of 0.78). The Z-test indicated that the difference in performance between the two approaches was statistically significant. The lower performance observed for single-date multispectral classification is due to similarities in spectral responses for targets of deciduous vegetation that lose their foliage and can be misread as non-vegetated areas. Meanwhile, an accurate classification by time series of plant clusters in seasonal forests allows incorporating seasonal variability of land-cover classes during the rainy and dry seasons, as well as transitions between seasons. The most important variables that contributed to the accuracy were the red, Near Infrared (NIR) and Short-Wave Infrared (SWIR) bands in single-date multispectral classification and the months in the dry season were the most relevant in multi-date NDVI classification.

The prehistory of the Arabian peninsula: Deserts, dispersals, and demography

As a geographic connection between Africa and the rest of Eurasia, the Arabian Peninsula occupies a central position in elucidating hominin evolution and dispersals. Arabia has been characterized by extreme environmental fluctuation in the Quaternary, with profound evolutionary and demographic consequences. Despite the importance of the region, Arabia remains understudied. Recent years, however, have seen major developments in environmental studies and archeology, revealing that the region contains important records that should play a significant role in future paleoanthropological narratives.(1-3) The emerging picture of Arabia suggests that numerous dispersals of hominin populations into the region occurred. Populations subsequently followed autochthonous trajectories, creating a distinctive regional archeological record. Debates continue on the respective roles of regional hominin extinctions and population continuity, with the latter suggesting adaptation to arid conditions.

A Functional Approach to Diversity Profiles

Summary Diversity plays a central role in ecological theory and its conservation and management are important issues for the wellbeing and stability of ecosystems. The aim of this work is to provide a reliable theoretical framework for performing statistical analysis on ecological diversity by means of the joint use of diversity profiles and functional data analysis. We point out that ecological diversity is a multivariate concept as it is a function of the relative abundances of species in a biological community. For this, several researchers have suggested using parametric families of indices of diversity for obtaining more information from the data. Patil and Taillie introduced the concept of intrinsic diversity ordering which can be determined by using the diversity profile. It may be noted that the diversity profile is a non-negative and convex curve which consists of a sequence of measurements as a function of a given parameter. Thus, diversity profiles can be explained through a process that is described in a functional setting. Recent developments in environmental studies have focused on the opportunity to evaluate community diversity changes over space and/or correlation of diversity with environmental characteristics. For this, we develop an innovative analysis of diversity based on a functional data approach. Whereas conventional statistical methods process data as a sequence of individual observations, functional data analysis is designed to process a collection of functions or curves. Moreover, unconstrained models may lead to negative and/or non-convex estimates for the diversity profiles. To overcome this problem, a transformation is proposed which can be constrained to be non-negative and convex. We focus on some applications showing how functional data analysis provides an alternative way of understanding biological diversity and its interaction with natural and/or human factors.