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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.


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Taylor & Francis Online contains many publications related to land issues, though mostly at the charge of a fee.

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Displaying 501 - 505 of 661

comparative analysis of spatial indices and wavelet-based classification

Journal Articles & Books
декабря, 2012

Spatial indices measure the geometric arrangement of land use and land cover classes at various scales and are computationally adaptive with wavelet transform coefficients. Decision rules built on permutations of three spatial indices – energy, log energy and Shannon's diversity – are used to improve the accuracy of multi-resolution hierarchical wavelet-based classifications. Comparisons are made with classification results derived from other texture measures, as well as with classification results calculated from more conventional per-pixel techniques.

Farmers' perception of coconut mite damage and crop diversification alternatives in the coastal belt of Tanzania

Journal Articles & Books
декабря, 2012
Tanzania

This article analysed farmers' perceptions of the effects of coconut mite in their livelihood and assessed crop diversification as a copping strategy for reduced coconut production. A socio-economic model of farmers' decisions on intercropping as an indicator for overall crop diversity was developed. The study was conducted between November 2009 and March 2010 in five districts in Tanzania, which were selected on the basis of the coconut's economic importance, using structured questionnaires which were administered to 200 household heads.

Multi-scale object-based image analysis and feature selection of multi-sensor earth observation imagery using random forests

Journal Articles & Books
декабря, 2012

The random forest (RF) classifier is a relatively new machine learning algorithm that can handle data sets with large numbers and types of variables. Multi-scale object-based image analysis (MOBIA) can generate dozens, and sometimes hundreds, of variables used to classify earth observation (EO) imagery. In this study, a MOBIA approach is used to classify the land cover in an area undergoing intensive agricultural development. The information derived from the elevation data and imagery from two EO satellites are classified using the RF algorithm.

Regional spatial pattern of deep soil water content and its influencing factors

Journal Articles & Books
декабря, 2012
China

Plant root systems can utilize soil water to depths of 10 m or more. Spatial pattern data of deep soil water content (SWC) at the regional scale are scarce due to the labour and time constraints of field measurements. We measured gravimetric deep SWC (DSWC) at depths of 200, 300, 400, 500, 600, 800 and 1000 cm at 382 sites across the Loess Plateau, China. The coefficient of variation was high for soil water content (SWC) in the horizontal direction (48%), but was relatively small for SWC in the vertical direction (9%).

Employment of Indigenous Australians in the forestry sector: a case study from northern Queensland

Journal Articles & Books
декабря, 2012

Summary There are compelling reasons to encourage the employment of Indigenous Australians in the forestry sector. The benefits of, and constraints to, Indigenous employment in the sector were examined using a case study approach focused on Indigenous participation in ‘Operation Farm Clear’, an emergency response following Cyclone Larry in northern Queensland in 2006. The findings suggested that, given a supportive environment, there are opportunities for Indigenous people to benefit from employment in the forestry sector.