Land-use allocation is an important way to promote the intensive and economic use of land resources and achieve the goal of sustainable development. It is a complex spatial optimization problem, and heuristic algorithms have been one of the most effective ways to solve it in past studies. However, heuristic algorithms lack the guidance of planning knowledge, which makes land-use patterns usually unreasonable in practice. This research proposes a spatially explicit genetic algorithm (SEGA) that integrates land-use planning knowledge with the genetic algorithm (GA).

The paper presents the IUTI program, a land use inventory of Italy, based on point sampling. It has been carried out to support the National Carbon Sink Accounting Register and it was realized within the framework of the Italian National Remote Sensing Plan managed by the Italian Ministry of Environment. IUTI has monitored the land use and land use change and forestry in the last two decades over the country at the years 1990, 2000, 2008, adopting a tessellated stratified sampling scheme with about 1.2 million sample points on aerial orthophotos.

Slash-and-burn shifting cultivation is the conventional farming practice in Sierra Leone, where communal land tenure is by far the most common form of land use. Alley farming, the alternative agricultural system, has been introduced as a technical option to increase yield, reduce or eliminate fallow and conserve soil water. This study uses a logistic regression model to determine the factors influencing the adoption of alley farming in Sierra Leone.

India has a maximum area (21.6%) under cashew nut and is the third largest producer (17.3%) of raw nuts in the world. The country is the second largest exporter, accounting for 34% of the world’s export of cashew kernels having a comparative advantage in production and processing on account of its cheap and skilled labour force. The yields in India are poor at 860 kg/ha as compared to 4,125 kg/ha in Vietnam and 2,000 kg/ha in Nigeria.

This paper provides a comparative analysis of land-use and land-cover (LULC) changes among three study areas with different biophysical environments in the Brazilian Amazon at multiple scales, from per-pixel, polygon, census sector, to study area. Landsat images acquired during the years of 1990/1991, 1999/2000, and 2008/2010 were used to examine LULC change trajectories with the post-classification comparison approach.

Stonefly samples were collected from disturbed and undisturbed regions of two river basins, tributaries of the Upper Váh River basin (the West Carpathians). Water temperature, oxygen content, coarse benthic matter and some stonefly metrics (abundance, biomass, richness, index of diversity, % predators, % shredders, stonefly total score and stonefly average score) were all negatively influenced by the extent of urban and field land cover.

This paper represents the first national effort of its kind to systematically investigate the impact of changes in climate and land use and land cover (LULC) on the carbon cycle with high-resolution dynamic LULC data at the decadal scale (1990s and 2000s).

Spatial heterogeneities of vegetation and soil can strongly affect ecological functions of ecosystems, particularly for arid and semi-arid ecosystems where vegetation has a patchy distribution and livestock grazing is one of the major land use types. However, little is known about the impact of grazing on spatial patterns of vegetation and soil, even though grazing has variously been shown to create, maintain or destroy those patterns.

The agricultural land classification (ALC) of England and Wales is a formal method of assessing the quality of agricultural land and guiding future land use. It assesses several soil, site and climate criteria and classifies land according to whichever is the most limiting. A common approach is required for calculating the necessary agroclimatic parameters over time in order to determine the effects of changes in the climate on land grading.