We can effectively monitor soil condition—and develop sound policies to offset the emissions of greenhouse gases—only with accurate data from which to define baselines. Currently, estimates of soil organic C for countries or continents are either unavailable or largely uncertain because they are derived from sparse data, with large gaps over many areas of the Earth. Here, we derive spatially explicit estimates, and their uncertainty, of the distribution and stock of organic C in the soil of Australia.

CONTEXT: The Natura 2000 network is the centerpiece of European nature conservation policy but its effectiveness is challenged by ongoing landscape change. OBJECTIVE: Our objective was to assess landscape connectivity between Natura 2000 sites in the biodiversity-rich western Mediterranean region. METHODS: We used the wood mouse as a focal species with short-range dispersal and obtained genetic data for 393 individuals uniformly distributed between two Natura 2000 sites in SW Portugal.

The iterative and convergent nature of ensemble learning algorithms provides potential for improving classification of complex landscapes. This study performs land-cover classification in a heterogeneous Massachusetts landscape by comparing three ensemble learning techniques (bagging, boosting, and random forests) and a non-ensemble learning algorithm (classification trees) using multiple criteria related to algorithm and training data characteristics.

Land cover of the Earth is changing dramatically because of human activities. Information about changes is useful for management of natural resources. Rapid land cover changes have taken place in many coastal areas of Turkey over the last two decades due to urbanization and land degradation. In this paper, land cover change dynamics were investigated by the combined use of satellite remote sensing and geographical information systems. The main objective of the study was to determine land-cover transition rates among land cover types in coastal areas of Turkey.

We tested how accurately image data from the Advanced Land Imager (ALI) sensor vs. the Landsat Thematic Mapper (TM) predict the land cover of Lonicera maackii in the forest understory, taking advantage of this invasive shrub's extended leaf retention in the fall when the canopy is leafless. Percent cover of L. maackii in 20 woodlots in southwestern Ohio was regressed on values for spectral vegetation indices (SVIs) derived for each image. The land cover of L. maackii was best explained by the Simple Ratio (SR) using TM data (R ² = 0.537). The regression results for SVIs from TM vs.

We present quantitative reconstructions of regional vegetation cover in north‐western Europe, western Europe north of the Alps, and eastern Europe for five time windows in the Holocene [around 6k, 3k, 0.5k, 0.2k, and 0.05k calendar years before present (bp)] at a 1° × 1° spatial scale with the objective of producing vegetation descriptions suitable for climate modelling.

We use the MEGAN (Model of emissions of Gases and Aerosols from Nature) module embedded within the global three-dimensional Goddard Earth Observing System chemical transport model (GEOS-Chem) to simulate the interannual variations in biogenic volatile organic compound (BVOC) emissions and concentrations of ozone and secondary organic aerosols (SOA) in China over years 2001–2006.

Full-waveform lidar data are increasingly being available. Morphological features can be retrieved from the echoes composing the waveforms, and are now extensively used for a large variety of land-cover mapping issues. However, the genuine contribution of these features with respect to those computed from standard discrete return lidar systems has been barely theoretically investigated. This paper therefore aims to study the potential of full-waveform data through the automatic classification of urban areas in building, ground, and vegetation points.

During the communist regime, Romania’s planned economy focused exclusively on production neglecting the environment protection. The lack of less polluting production technologies and of environmental protection measures led to excessive pollution in certain industrialized areas. This is the case of the town of Copsa Mica in Sibiu County, which in 1987 was considered one of the most polluted towns in Europe.

The assessment of landscape spatial patterns is a key issue in landscape management. Landscape pattern indices (LPIs) are tools appropriate for analyzing landscape spatial patterns. LPIs are often derived from raster land cover maps that are extracted from remotely sensed data through hard classification. However, pixel-based hard classification methods suffer from the mixed pixel problem (in which pixels contain more than one land cover class), making for inaccurate classification maps and LPIs.

Four upscaling methods for estimating daytime actual evapotranspiration (ET) from single time-of-day snapshots, as commonly retrieved using remote sensing, were compared. These methods assume self-preservation of the ratio between ET and a given reference variable over the daytime hours. The analysis was performed using eddy covariance data collected at 12 AmeriFlux towers, sampling a fairly wide range in climatic and land cover conditions. The choice of energy budget closure method significantly impacted performance using different scaling methodologies.

Land use and cover changes trigger multi-factorial responses with tangible and intangible effects. Driving forces governing changes are scale-, place- and time-dependent; because of their complexity, a multi-scalar analysis is required to determine conjoint causation. Studies using multi-scalar approach in semiarid scrubland ecosystems are scanty. This paper investigates land use changes on the largest semiarid scrubland ecosystem of Mexico, the Baja California Peninsula, using a multi-scalar analysis approach.