Taylor & Francis Group publishes books for all levels of academic study and professional development, across a wide range of subjects and disciplines.
Taylor & Francis Group publishes quality peer-reviewed journals under the Routledge and Taylor & Francis imprints. The newest part of the group, Cogent OA, offers a purely open access program.
Note from Land Portal:
Taylor & Francis Online contains many publications related to land issues, though mostly at the charge of a fee.
Members:
Resources
Displaying 291 - 295 of 661Requirements for labelling forest polygons in an object-based image analysis classification
The ability to spatially quantify changes in the landscape and create land-cover maps is one of the most powerful uses of remote sensing. Recent advances in object-based image analysis (OBIA) have also improved classification techniques for developing land-cover maps. However, when using an OBIA technique, collecting ground data to label reference units may not be straightforward, since these segments generally contain a variable number of pixels as well as a variety of pixel values, which may reflect variation in land-cover composition.
assessment of the effectiveness of a rotation forest ensemble for land-use and land-cover mapping
Increasing the accuracy of thematic maps produced through the process of image classification has been a hot topic in remote sensing. For this aim, various strategies, classifiers, improvements, and their combinations have been suggested in the literature. Ensembles that combine the prediction of individual classifiers with weights based on the estimated prediction accuracies are strategies aiming to improve the classifier performances.
GeoEye-1 and WorldView-2 pan-sharpened imagery for object-based classification in urban environments
The latest breed of very high resolution (VHR) commercial satellites opens new possibilities for cartographic and remote-sensing applications. In fact, one of the most common applications of remote-sensing images is the extraction of land-cover information for digital image base maps by means of classification techniques. The aim of the study was to compare the potential classification accuracy provided by pan-sharpened orthoimages from both GeoEye-1 and WorldView-2 (WV2) VHR satellites over urban environments.
Evaluation of MODIS gross primary productivity and land cover products for the humid tropics using oil palm trees in Peninsular Malaysia and Google Earth imagery
Conducting quantitative studies on the carbon balance or productivity of oil palm is important in understanding the role of this ecosystem in global climate change. In this study, we evaluated the accuracy of MODIS (Moderate Resolution Imaging Spectroradiometer) annual gross primary productivity (GPP) (the product termed MOD-17) and its upstream products, especially the MODIS land cover product (the product termed MOD-12). We used high-resolution Google Earth images to classify the land cover classes and their percentage cover within each 1 km spatial resolution MODIS pixel.
Identification of potential land cover changes on a continental scale using NDVI time-series from SPOT VEGETATION
The identification of land cover changes on a continental scale is a laborious and time-consuming process. A new methodology is proposed based exclusively on SPOT VGT data, illustrated for the African Continent using GLC2000 as reference to select 26 distinct land cover types (classes).