The primeval vegetation of Singapore was largely lowland dipterocarp forest, with mangrove forest lining much of the coast and freshwater swamp forest found further inland adjacent to the streams and rivers. After colonization by the British in 1819, almost all the primeval vegetation was cleared for agriculture and other land uses. The most comprehensive vegetation map of Singapore was made in the 1970s and has not been updated since. Here we present an updated vegetation map of Singapore using information from satellite images, published works, and extensive ground-truthing.

The Brazilian tropical savanna (Cerrado), encompassing more than 204 million hectares in the central part of the country, is the second richest biome in Brazil in terms of biodiversity and presents high land use pressure. The objective of this study was to map the land cover of the Cerrado biome based on the segmentation and visual interpretation of 170 Landsat Enhanced Thematic Mapper Plus satellite scenes acquired in 2002. The following land cover classes were discriminated: grasslands, shrublands, forestlands, croplands, pasturelands, reforestations, urban areas, and mining areas.

In this study, land cover maps of Bozcaada district were developed using Landsat satellite images obtained in 2006, 2007 and 2008. In addition to original images (6 band Landsat TM), the new images constituted with image processing techniques were also used. A total of ten images were formed by supervised classification method using principal component analysis (PCA), normalized difference vegetation index (NDVI), and tasseled cap (TC) transformation methods.

The study aimed to analyze the performance indicators of self-sustainability, taking into account their potential for production in irrigated areas located in the basin Baixo Acaraú and Curu. The survey is a survey of information available on the perimeters studied by merging the existing bibliographies and materials with statistical data available. We evaluated its performance through the analysis of indicator values for the year 2008, starting at data provided by DNOCS, and the association of irrigation districts of the perimeters.

From the advent of the application of satellite imagery to land cover mapping, one of the growing areas of research interest has been in the area of image classification. Image classifiers are algorithms used to extract land cover information from satellite imagery. Most of the initial research has focussed on the development and application of algorithms to better existing and emerging classifiers. In this paper, a paradigm shift is proposed whereby a committee of classifiers is used to determine the final classification output.

This analysis and recommendations stem from USAID/Kenya’s request for an assessment of Kenya’s draft National Land Policy (dNLP).4 It was conducted under the global task order: Property Rights and Resource Governance Program, a mechanism designed and supervised by USAID-EGAT’s Land Resources Management Team under the Office of Natural Resources Management.

Ensemble classification is an emerging approach to land cover mapping whereby the final classification output is a result of a consensus of classifiers. Intuitively, an ensemble system should consist of base classifiers which are diverse i.e. classifiers whose decision boundaries err differently. In this paper ensemble feature selection is used to impose diversity in ensembles. The features of the constituent base classifiers for each ensemble were created through an exhaustive search algorithm using different separability indices.

This is a techincal paper by Department of Land Resources, GoI. At present land records databases which are being maintained in various states are following state specific set of codes for different attributes like crop, soil, land use, irrigation, season, area unit and location. The Land Records Information Systems Division of National Informatics Centre, New Delhi has initiated an exercise to standardize the coding scheme for different entities in the land records database exclusively for Data warehousing and Mining purposes for central/state datacentres. 

The need for accurate carbon budgeting, climate change modelling, and sustainable resource management has lead to an increase in the number of large area forest monitoring programs. Large area forest monitoring programs often utilize field and remotely sensed data sources. Sampling, via field or photo plots, enables the collection of data with the desired level of categorical detail in a timely and efficient manner. When sampling, the aim is to collect representative detailed data enabling the statistical reporting upon the characteristics of larger areas.

The figures of public resources estimated to have been channeled into private pockets are so high one hopes, obviously against hope, that they would turn out to be typographical errors. The figures of public resources estimated to have been channeled into private pockets are so high one hopes, obviously against hope, that they would turn out to be typographical errors.

The Role Of Wetlands In Poverty Reduction- Extreme poverty among rural poor people living around wetlands remains a daily reality for more than 56% of Kenya’s population, who subsist on less than one dollar a day. Seventy percent of extremely poor households, a majority of who live in rural areas where hunger and poverty prevails, are now being caught up in a new web of lack of access to wetlands as safety-net during hard times due to appropriation of wetlands by private developers.

The Thailand Land Titling Project is an outstanding success story of inter-agency cooperation and received the World Bank Award for Excellence in 1997. It was designed as a four-phase project over 20 years and will finish in 2004. The project partners the Royal Thai Government, the Bank, and the government of Australia provided funds and personnel, with the Australian Agency for International Development (AusAID) supplying technical assistance and training programs to the Department of Lands (Thailand).