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%

Training of Crisis Mappers and Map Production from Multi-sensor Data: Vernazza Case Study (Cinque Terre National Park, Italy)

This aim of paper is to presents the development of a multidisciplinary project carried out by the cooperation between Politecnico di Torino and ITHACA (Information Technology for Humanitarian Assistance, Cooperation and Action). The goal of the project was the training in geospatial data acquiring and processing for students attending Architecture and Engineering Courses, in order to start up a team of "volunteer mappers". Indeed, the project is aimed to document the environmental and built heritage subject to disaster; the purpose is to improve the capabilities of the actors involved in the activities connected in geospatial data collection, integration and sharing. The proposed area for testing the training activities is the Cinque Terre National Park, registered in the World Heritage List since 1997. The area was affected by flood on the 25th of October 2011. According to other international experiences, the group is expected to be active after emergencies in order to upgrade maps, using data acquired by typical geomatic methods and techniques such as terrestrial and aerial Lidar, close-range and aerial photogrammetry, topographic and GNSS instruments etc.; or by non conventional systems and instruments such us UAV, mobile mapping etc. The ultimate goal is to implement a WebGIS platform to share all the data collected with local authorities and the Civil Protectio

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

Improving models for environmental applications of LiDAR: Novel approaches based on soft computing

This work proposes novel methodologies to improve the use of Light Detection And Ranging (LiDAR) for environ mental purposes, especially for thematic mapping (LiDAR only or fused with other remote sensors) and the estimation of for est variables. The methodologies make use of well-known techniques from soft computing (machine learning and evolutionary computation) and their adaptation to develop LiDAR-derived products.Ministerio de Educación y Ciencia TIN2007-68084-C-02-01Ministerio de Ciencia e Innovación TIN2011-28956-C02-02Junta de Andalucía TIC-752

A SVM and k-NN Restricted Stacking to Improve Land Use and Land Cover Classification

Land use and land cover (LULC) maps are remote sensing products that are used to classify areas into different landscapes. The newest techniques have been applied to improve the final LULC classification and most of them are based on SVM classifiers. In this paper, a new method based on a multiple classifiers ensemble to improve LULC map accuracy is shown. The method builds a statistical raster from LIDAR and image fusion data following a pixel-oriented strategy. Then, the pixels from a training area are used to build a SVM and k-NN restricted stacking taking into account the special characteristics of spatial data. A comparison between a SVM and the restricted stacking is carried out. The results of the tests show that our approach improves the results in the context of the real data from a riparian area of Huelva (Spain)

Automatic Methodology for Multi-modal Trip Generation with Roadside LiDAR

Transportation planning based on historical data and methods has major limitations. Trip data canbe useful to increase the transportation safety of the specific sites and the process and programming purposes. One of the challenges in this regard is data collecting to gain an accurate analysis of land use development. The previous methods of data gathering such as human observational data counting and automatic methods like pneumatic tubes and video camera suffers some limitations that affect the accuracy of trip analysis which cause over mitigating or set some wrong rules and regulations. Light Detection and Ranging (LiDAR) sensing is a powerful tool that has been vastly used for mapping, safety, and medical applications. [1] Also, its application in transportation has drawn attention in recent years. However, LiDAR sense is yet to be further explored in trip generation. This study is an initial attempt to: 1) perform a LiDAR-based trip generation data gathering for a local area in midtown, Reno, and 2) analyze the resulting data based on the GIS software to develop a systematic plan for the case study and beyond

Precision Agriculture Technology for Crop Farming

This book provides a review of precision agriculture technology development, followed by a presentation of the state-of-the-art and future requirements of precision agriculture technology. It presents different styles of precision agriculture technologies suitable for large scale mechanized farming; highly automated community-based mechanized production; and fully mechanized farming practices commonly seen in emerging economic regions. The book emphasizes the introduction of core technical features of sensing, data processing and interpretation technologies, crop modeling and production control theory, intelligent machinery and field robots for precision agriculture production

GEOSPATIAL-BASED ENVIRONMENTAL MODELLING FOR COASTAL DUNE ZONE MANAGEMENT

Tomaintain biodiversity and ecological functionof coastal dune areas, itis important that practical and effective environmentalmanagemental strategies are developed. Advances in geospatial technologies offer a potentially very useful source of data for studies in this environment. This research project aimto developgeospatialdata-basedenvironmentalmodellingforcoastaldunecomplexestocontributetoeffectiveconservationstrategieswithparticularreferencetotheBuckroneydunecomplexinCo.Wicklow,Ireland.Theprojectconducteda general comparison ofdifferent geospatial data collection methodsfor topographic modelling of the Buckroney dune complex. These data collection methodsincludedsmall-scale survey data from aerial photogrammetry, optical satellite imagery, radar and LiDAR data, and ground-based, large-scale survey data from Total Station(TS), Real Time Kinematic (RTK) Global Positioning System(GPS), terrestrial laser scanners (TLS) and Unmanned Aircraft Systems (UAS).The results identifiedthe advantages and disadvantages of the respective technologies and demonstrated thatspatial data from high-end methods based on LiDAR, TLS and UAS technologiesenabled high-resolution and high-accuracy 3D datasetto be gathered quickly and relatively easily for the Buckroney dune complex. Analysis of the 3D topographic modelling based on LiDAR, TLS and UAS technologieshighlighted the efficacy of UAS technology, in particular,for 3D topographicmodellingof the study site.Theproject then exploredthe application of a UAS-mounted multispectral sensor for 3D vegetation mappingof the site. The Sequoia multispectral sensorused in this researchhas green, red, red-edge and near-infrared(NIR)wavebands, and a normal RGB sensor. The outcomesincludedan orthomosiac model, a 3D surface model and multispectral imageryof the study site. Nineclassification strategies were usedto examine the efficacyof UAS-IVmounted multispectral data for vegetation mapping. These strategies involved different band combinations based on the three multispectral bands from the RGB sensor, the four multispectral bands from the multispectral sensor and sixwidely used vegetation indices. There were 235 sample areas (1 m × 1 m) used for anaccuracy assessment of the classification of thevegetation mapping. The results showed vegetation type classification accuracies ranging from 52% to 75%. The resultdemonstrated that the addition of UAS-mounted multispectral data improvedthe classification accuracy of coastal vegetation mapping of the Buckroney dune complex

Autonomous Vehicle and Smart Traffic

Long-term forecasting of technology has become extremely difficult due to the rapid realization of any suggested idea. Communication and software technologies can compensate for the problems that may arise during the transition period between idea generation and realization. However, this rapid process can cause problems for the automotive industry and transportation systems.Autonomous vehicles are currently a hot topic within the transportation sector. This development is related to the compatibility of vehicles of the near future with the development of the infrastructure on which these vehicles will be based. There are certain problems regarding the solutions that are currently being worked on, such as how autonomous should vehicles be, their control mechanisms, driving safety, energy requirements, and environmental use. The problem is not just about the design of autonomous vehicles. The user transportation systems of these vehicles also need problem-free solutions. The problem should not only be seen as financial because sociological effects are an important part of this feature.In this book, valuable research on the modeling, systems, transportation, technological necessity, and logistics of autonomous vehicles is presented. The content of the book will help researchers to create ideas for their future studies and to open up the discussion of autonomous vehicles