Within the framework of IWRM, a major concern in the humid tropics is the effects of ‘global warming' on the storm rainfall-runoff hydrology of both forests and converted forest lands. Further how such effects need to be incorporated within adaptive, forest-water-land management. But since the mid- 20th century, dramatic changes in land- use (LU) and land cover (LC) have also occurred which have led to rapid rates of deforestation and an expansion of land--forest degradation.

This study aims to evaluate different methods of integrating optical and multipolarized radar data for land use and land cover (LULC) mapping in an agricultural frontier region in the Central Brazilian Amazon, which requires continuous monitoring due to the increasing human intervention. The evaluation is performed using different sets of fused and combined data.

A large number of mathematical models have been developed for supporting optimization of land-use allocation; however, few of them simultaneously consider land suitability (e.g., physical features and spatial information) and various uncertainties existing in many factors (e.g., land availabilities, land demands, land-use patterns, and ecological requirements). This paper incorporates geographic information system (GIS) technology into interval-probabilistic programming (IPP) for land-use planning management (IPP-LUPM).

Areal interpolation is used to transfer attribute data between geographically incongruous zonal systems. Remotely sensed land cover data are widely used in intelligent areal interpolation methods to solve this problem. This article examines the usefulness of different publicly available remotely sensed land cover data sets as ancillary data used in conjunction with different areal interpolation methods.

This paper contributes to the discussion on current issues in methodologies of mapping land cover in the agro-silvo-pastoral landscapes of the Mediterranean. These landscapes, characterized by intermixed land use and indefinite boundaries, require particular attention in applying the patch-corridor-matrix model when classifying patches and their delineation. In a case study area in southeast Portugal, mainly characterized by agro-silvo pastoral systems, the land cover for 1990 has been mapped.

Functional indicators are being increasingly used to assess waterway health but their responses to pressure in non-wadeable rivers have not been widely documented or applied in modern survey designs that provide unbiased estimates of extent. This study tests the response of river metabolism and loss in cotton strip tensile strength across a land use pressure gradient in non-wadeable rivers of northern New Zealand, and reports extent estimates for river metabolism and decomposition rates.

We used boosted regression trees (BRT) to model stream biological condition as measured by benthic macroinvertebrate taxonomic completeness, the ratio of observed to expected (O/E) taxa. Models were developed with and without exclusion of rare taxa at a site. BRT models are robust, requiring few assumptions compared with traditional modeling techniques such as multiple linear regression.

The economic value of pasture improvement in a farming system in a Mediterranean-type environment is assessed using farm modelling. Bioeconomic modelling combined with sensitivity analysis is used to explore how new annual legume species may impact on farm profit and land use. If introduced on all suitable soils the new pastures lead to a 26% increase in farm profit with an additional 12% of farm area being switched into pasture to support more livestock. However, stocking rates decline slightly, the enterprise mix becomes less diversified and several rotational changes are required.

This article presents a sufficient comparison of two types of advanced non-parametric classifiers implemented in remote sensing for land cover classification. A SPOT-5 HRG image of Yanqing County, Beijing, China, was used, in which agriculture and forest dominate land use. Artificial neural networks (ANNs), including the adaptive backpropagation (ABP) algorithm, Levenberg–Marquardt (LM) algorithm, Quasi-Newton (QN) algorithm and radial basis function (RBF) were carefully tested.

The Large-scale Biosphere-Atmosphere Experiment in Amazonia (LBA) is a multinational, interdisciplinary research program led by Brazil. Ecological studies in LBA focus on how tropical forest conversion, regrowth, and selective logging influence carbon storage, nutrient dynamics, trace gas fluxes, and the prospect for sustainable land use in the Amazon region. Early results from ecological studies within LBA emphasize the variability within the vast Amazon region and the profound effects that land-use and land-cover changes are having on that landscape.