The Northern Eurasian land mass encompasses a diverse array of land cover types including tundra, boreal forest, wetlands, semi-arid steppe, and agricultural land use. Despite the well-established importance of Northern Eurasia in the global carbon and climate system, the distribution and properties of land cover in this region are not well characterized. To address this knowledge and data gap, a hierarchical mapping approach was developed that encompasses the study area for the Northern Eurasia Earth System Partnership Initiative (NEESPI).

Coastal dunes and sand areas are reported to be among the habitats most invaded by alien species in Europe. Landscape pattern could be a significant driver in invasion processes in parallel with land-use legacy. Fragmentation of natural habitats combined with the availability of propagules from the surrounding matrix may enhance the invisibility of ecological communities.

Since mean nitrate concentration along single river channels increases significantly with percent arable land use upstream of sampling points and autumn/early winter flushes in nitrate concentration are widespread, it is generally concluded that farmers contribute most of the nitrate. For the River Derwent in North Yorkshire, the correlation between nitrate concentration and percent arable land use is much poorer when tributary data are included in the equation, because of greater variations in dilution by water draining upland areas and in other N input sources.

Water, forage and predation constrain ungulate distributions in savannas. To understand these constraints, we characterized distributions of 15 herbivore species from water, locations of peak density and degree of clustering around the peaks using zero-inflated count data models and mapping census data collected in the Mara reserve and the adjoining pastoral ranches in Kenya during a wet and dry year.

Feature extraction is highly important for classification of remote-sensing (RS) images. However, extraction of comprehensive spatial features from high-resolution imagery is still challenging, leading to many misclassifications in various applications. To address the problem, a shape-adaptive neighbourhood (SAN) technique is presented based on human visual perception. The SAN technique is an adaptive feature-extraction method that not only considers spectral feature information but also the spatial neighbourhood as well as the shape of features.

This paper describes the Gestop approach to calculate the profitability or production requirements of agricultural and forestry products.

A multi-stakeholder representative group was established to oversee a project examining the economic and environmental performance of a representative North Island hill country catchment farm at Whatawhata in the western Waikato region of New Zealand. The group included representation from landowners, government agencies and scientists. The group was facilitated through an action research approach incorporating three phases: (1) awareness; (2) forecasting; and (3) implementation.

We use satellite imagery to compare and contrast fire patterns across a repeating mosaic of vegetation types occurring within the tropical savanna of the Northern Territory, Australia. Our study area included different land management settings that encapsulate three contrasting styles of management that have developed following European settlement in northern Australia: Decentralized fire management carried out by small Aboriginal communities widely dispersed across a large landscape.

Kilca River basin is one of the major river systems before the mountain region, located in the center of the province of Neuquen. In this zone, humid climates, the dominant land use is "veranada", which consists of three annual steps of one type of livestock transhumance, based on the exploitation of natural grassland forage. Given the physiographic formation of these landscapes, there is in them a high propensity to develop geomorphological processes of erosion and landslides.

Increasing community dissimilarity across geographic distance has been described for a wide variety of organisms and understanding its underlying causes is key to understanding mechanisms driving patterns of biodiversity. Both niche‐based and neutral processes may produce a distance decay relationship; however, disentangling their relative influence requires simultaneous examination of multiple potential drivers.

The time-integrated normalized difference vegetation index (iNDVI) provides key remote-sensing-derived information on the interactions between vegetation growth, climatic and soil conditions, and land use. Using a time-series of Landsat imagery obtained for Queensland, Australia, it has been demonstrated how robust geostatistics can be used to predict iNDVI. This approach is novel because it explicitly quantifies the uncertainty of prediction and uses Winsorizing, a data-censoring method, to minimize the distorting effects of outliers.