Automatic environmental quality assessment for mixed-land zones using lidar and intelligent techniques

Human impact on the natural environment is an evident global fact. Natural, industrial and touristic areas coexist in a more than delicate balance. In Andalusia, in the south of Spain, the Regional Ministry for the Environment is responsible for the control and preservation of natural resources. This task bears a high cost in time and money. Remote sensing and the use of intelligent techniques are excellent tools to reduce such costs. This work explores the joint use of the lidar sensor, which provides a great quantity of information describing three dimensional space, and the application of intelligent techniques for rapid and efficient land use and land cover classification with the objective of differentiating urban land from natural ground close to protected areas of Huelva province. For this, seven types of land use and land cover have been studied for a riparian area next to the mouth of the rivers Tinto and Odiel, extracting 33 distinct features from the lidar point cloud. Subsequently, a supervised learning algorithm is applied to construct a model which, with a resolution of 4 m2, obtained relative precision between 71% and 100%and an average total precision of 85%

Decision Trees on LIDAR to Classify Land Uses and Covers

The area of Huelva, in the South of Spain, is a well-known case of human pressure on the natural environment. In Huelva, National Parks, like Donana, and industrial and tourist zones coexist in difficult balance. The Regional Ministry of Andalusia is commissioned ˜ to assure the preservation of the natural resources in this part of Spain although its cost can be high in time and money. Remote sensing is a very suitable tool to carry out this task and automatic land use and cover detection can be a key factor to reduce costs. In addition, Light Detection and Ranging (LIDAR) has the advantage of being able to create elevation surfaces that are in 3D, while also having information on LIDAR intensity values. Many measures based on its intensity, density and its capacity for describing third dimension have been used previously with other purposes and outstanding results. In this paper, a new approach to identify land cover at high resolution is proposed selecting the most interesting attributes from a set of LIDAR measures. Our approach is based on data mining principles to take advantage on intelligent techniques (attribute selection and C4.5 algorithm decision tree) to classify quickly and efficiently without the need for manipulating multiespectral images. Seven types of land cover have been classified in a very interesting zone at the mouth of the River Tinto and Odiel with results of accuracy between 71% and 100%. An overall accuracy of 85% has been reached for a resolution of 4 m2

Uncovering archaeological sites in airborne LiDAR data with data-centric artificial intelligence

Mapping potential archaeological sites using remote sensing and artificial intelligence can be an efficient tool to assist archaeologists during project planning and fieldwork. This paper explores the use of airborne LiDAR data and data-centric artificial intelligence for identifying potential burial mounds. The challenge of exploring the landscape and mapping new archaeological sites, coupled with the difficulty of identifying them through visual analysis of remote sensing data, results in the recurring issue of insufficient annotations. Additionally, the top-down nature of LiDAR data hinders artificial intelligence in its search, as the morphology of archaeological sites blends with the morphology of natural and artificial shapes, leading to a frequent occurrence of false positives. To address this problem, a novel data-centric artificial intelligence approach is proposed, exploring the available data and tools. The LiDAR data is pre-processed into a dataset of 2D digital elevation images, and the known burial mounds are annotated. This dataset is augmented with a copy-paste object embedding based on Location-Based Ranking. This technique uses the Land-Use and Occupation Charter to segment the regions of interest, where burial mounds can be pasted. YOLOv5 is trained on the resulting dataset to propose new burial mounds. These proposals go through a post-processing step, directly using the 3D data acquired by the LiDAR to verify if its 3D shape is similar to the annotated sites. This approach drastically reduced false positives, attaining a 72.53% positive rate, relevant for the ground-truthing phase where archaeologists visit the coordinates of proposed burial mounds to confirm their existence.This work was supported by the Project Odyssey: Platform for Automated Sensing in Archaeology Co-Financed by COMPETE 2020 and Regional Operational Program Lisboa 2020 through Portugal 2020 and FEDER under Grant ALG-01-0247-FEDER-070150.info:eu-repo/semantics/publishedVersio

Geographic Information Systems in Archaeology: A Systematic Review

GIS are an essential element in archaeology. Their use has become widespread for their potential to store, reference, analyse and visualise spatial information. Nonetheless, to the best of our knowledge, a systematic review of academic peer-reviewed publications related to the use of GIS, as a framework, in archaeology has never been presented before. Our goal in this work is to identify what has been published so far in relation to using GIS in archaeology within a small selected sample. We used the PRISMA guideline to perform a systematic review of 671 publications that we identified using the SCOPUS database and the keywords ‘GIS’ and ‘archaeology’. The collected publications were screened, analysed, and categorized into different relevant categories. Our analysis shows that GIS, in our selected sample, are mostly used for visualization and information management tasks. Moreover, spatial analysis studies were more common than other studies, and theoretical publications are scarce. The lack of a theoretical background in GIS may be the cause of some of the problems related to GIS applications in archaeology

Neutralising antibodies for West Nile virus in horses from Brazilian Pantanal

Despite evidence of West Nile virus (WNV) activity in Colombia, Venezuela and Argentina, this virus has not been reported in most South American countries. In February 2009, we commenced an investigation for WNV in mosquitoes, horses and caimans from the Pantanal, Central-West Brazil. The sera of 168 horses and 30 caimans were initially tested using a flaviviruses-specific epitope-blocking enzyme-linked immunosorbent assay (blocking ELISA) for the detection of flavivirus-reactive antibodies. The seropositive samples were further tested using a plaque-reduction neutralisation test (PRNT90) for WNV and its most closely-related flaviviruses that circulate in Brazil to confirm the detection of specific virus-neutralising antibodies. Of the 93 (55.4%) blocking ELISA-seropositive horse serum samples, five (3%) were seropositive for WNV, nine (5.4%) were seropositive for St. Louis encephalitis virus, 18 (10.7%) were seropositive for Ilheus virus, three (1.8%) were seropositive for Cacipacore virus and none were seropositive for Rocio virus using PRNT90, with a criteria of > four-fold antibody titre difference. All caimans were negative for flaviviruses-specific antibodies using the blocking ELISA. No virus genome was detected from caiman blood or mosquito samples. The present study is the first report of confirmed serological evidence of WNV activity in Brazil

Spatial structure analysis of a reptile community with airborne LiDAR data

The analysis of the spatial structure of animal communities requires spatial data to determine the distribution of individuals and their limiting factors. New technologies like very precise GPS as well as satellite imagery and aerial photographs of very high spatial resolution are now available. Data from airborne LiDAR (Light Detection and Ranging) sensors can provide digital models of ground and vegetation surfaces with pixel sizes of less than 1 m. We present the first study in terrestrial herpetology using LiDAR data. We aim to identify the spatial patterns of a community of four species of lizards (Lacerta schreiberi, Timon lepidus, Podarcis bocagei, and P. hispanica), and to determine how the habitat is influencing the distribution of the species spatially. The study area is located in Northern Portugal. The position of each lizard was recorded during 16 surveys of 1 h with a very precise GPS (error < 1 m). LiDAR data provided digital models of surface, terrain, and normalised height. From these data, we derived slope, ruggedness, orientation, and hill-shading variables. We applied spatial statistics to determine the spatial structure of the community. We computed Maxent ecological niche models to determine the importance of environmental variables. The community and its species presented a clustered distribution. We identified 14 clusters, composed of 1-3 species. Species records showed two distribution patterns, with clusters associated with steep and flat areas. Cluster outliers had the same patterns. Juveniles and subadults were associated with areas of low quality, while sexes used space in similar ways. Maxent models identified suitable habitats across the study area for two species and in the flat areas for the other two species. LiDAR allowed us to understand the local distributions of a lizard community. Remotely sensed data and LiDAR are giving new insights into the study of species ecology. Images of higher spatial resolutions are necessary to map important factors such as refuges. © 2014 © 2014 Taylor & Francis.This work was funded by the Fundação para a Ciência e a Tecnologia of Portugal with the HOUSE project [PTDC/BIA-BEC/102280/2008]. NS was partially supported by a post-doctoral grant [SFRH/BPD/26666/2006].Peer Reviewe

LiDAR data evaluation for archaeological purposes in Northwest Iberia

Comunicación presentada en la 40th Annual Conference on Computer Applications and Quatnitative Methods in Archaeology (CAA 2012), celebrada en Southampton del 26 al 29 de marzo de 2012.We seek to evaluate the potential for archaeological purposes of a set of LiDAR data, with a density of 0,5 points per square meter, made publically available by the Spanish National Geographic Institute (IGN) in the framework of the PNOA (Plan Nacional de Ortofotografía Aérea) project. This data will be compared with other available LiDAR datasets with a higher point density. Different case studies in Northwest Iberia will be presented.Peer Reviewe

Evaluation for GRASS (5.0.3) using the common GIS functionalities

Este informe presenta el resultado de una evaluación al programa SIG GRASS. La evaluación fue realizada a través de la implementación del proyecto ¿Implementación de información cartográfica dos Montes Vecinales de Mano Común de la comarca de Os Ancares (Galicia). Se analizó la herramienta en cuanto a capacidad, facilidad, funcionalidad y tiempos de ejecución. Los resultados han revelado que GRASS muestra una gran capacidad de análisis y manipulación de datos, cubre bastantes áreas de aplicación en proyectos SIG. Sin embargo, hemos verificado que tiene un entorno gráfico incompleto, que causa pérdidas de tiempo en el desempeño de algunas funciones tales como las salidas gráficas

LiDAR-derived Morphological Relief Models for the Detection of Archaeological Features using Mesh Decimation

Póster presentado en el Aerial Archaeology Research Group, AARG (The Past and Present from Aboye), celebrado en Amersfoor (Holanda) del 26 al 28 de septiembre de 2013.The interpretation of archaeological features in LiDAR-derived digital terrain models is very dependent on visualization techniques. Different methods have been proposed to highlight microtopographies, since the >simple> hillshade, which can be easily computed in any GIS software, to more complex ones like Local Relief Models (LRMs). LRMs are an interesting visualization technique that allow us to discriminate between positive and negative microtopographies at a local scale and that can be easily combined with other data sources, maintaining the original elevation units and representing real changes in elevation rather than calculations based on steepness and direction of slope or exposure to light. This study analyzes the potential of mathematical morphology to generate LRMs. In this sense, a morphological approach was developed to map on an automated way positive and negative microtopographies.Peer Reviewe