The importance of monitoring land-cover changes in the catchment areas: case studies of Paphos area in Cyprus and Skiathos area in Greece

The importance of monitoring land-cover changes in the catchment areas: case studies of Paphos area in Cyprus and Skiathos area in Greece - ePrints Soton The University of Southampton Courses University life Research Business Global About Visit Alumni Departments News Events Contact × Search the Site Search Filter your search: All Courses Projects Staff University of Southampton Institutional Repository Search Advanced Search Policies & Help Latest Download Statistics Browse by Year Browse by Divisions LeftRight The importance of monitoring land-cover changes in the catchment areas: case studies of Paphos area in Cyprus and Skiathos area in Greece Hadjimitsis, DG, Shepherd, M., Toulios, L., Kyrou, A., Zlatoudis, AE and Clayton, CRI (2007) The importance of monitoring land-cover changes in the catchment areas: case studies of Paphos area in Cyprus and Skiathos area in Greece. SECOTOX

Support Vector Machine algorithm optimal parameterization for change detection mapping in Funil Hydroelectric Reservoir (Rio de Janeiro State, Brazil)

Change detection in Land Use and Land Cover (LULC) using Support Vector Machines (SVM) to mapping a geographic area is a way that has been studded and improved because of its advantages as low costs in terms of computing, field research and staff team. To use SVM, it is needed firstly to define the most efficient function to be used (linear, polynomial, and radial base function-RBF) and secondly to establish the most appropriate input parameters of them, based on the study area, which was the main challenge of using SVM algorithm. The main goal of this work was to test the performance of polynomial function and RBF, and to identify which input parameters combination are the best to use SVM algorithm for Funil Hydroelectric Reservoir (FHR) sub-watershed LULC mapping, using TM/Landsat-5 time-series images. After several tests and based on the obtained results, the RBF was identified as the best SVM's function, which was used to classify the time-series images. The referred SVM function has the following parameters to be defined: the error tolerance (n or C), the pyramid depths (P), the radial basis function parameter (gamma), and the threshold. The most effective combination of input parameters to RBF was C = 100; P = 2, gamma = 0.1, threshold = 0.05. LULC change detection analyses demonstrates that the obtained SVM parameterization made the algorithm able to differentiate large and continuous classes, lengthy and thin areas, as borders, and not continuous small areas located inside wide classes, through the usage of effective, but small, training sample. The parameterization proposed for this work to FHR sub-watershed area resulted in great statistics classification with the overall's accuracy among 86 and 98 % over the time-series, the producer's accuracy of 90 %, the user's accuracy higher than 86 %, and the Kappa statistics ranged from 86 to 91 %.Sao Paulo State Univ, Dept Cartog, Presidente Prudente, SP, BrazilIndiana Univ Purdue Univ, Dept Earth Sci, Indianapolis, IN 46202 USASao Paulo State Univ, Dept Cartog, Presidente Prudente, SP, Brazi

Protocol for the Definition of a Multi-Spectral Sensor for Specific Foliar Disease Detection: Case of “Flavescence Dorée”

International audienceFlavescence Dorée (FD) is a contagious and incurable grapevine disease that can be perceived on leaves. In order to contain its spread, the regulations obligate winegrowers to control each plant and to remove the suspected ones. Nevertheless, this monitoring is performed during the harvest and mobilizes many people during a strategic period for viticulture. To solve this problem, we aim to develop a Multi-Spectral (MS) imaging device ensuring an automated grapevine disease detection solution. If embedded on a UAV, the tool can provide disease outbreaks locations in a geographical information system allowing localized and direct treatment of infected vines. The high-resolution MS camera aims to allow the identification of potential FD occurrence, but the procedure can, more generally, be used to detect any type of foliar diseases on any type of vegetation