Africa is home to some of the most vulnerable natural ecosystems and species on the planet. Around 7000 protected areas seek to safeguard the continent's rich biodiversity, but many of them face increasing management challenges. Human disturbances permeating into the parks directly and indirectly affect the ecological functioning and integrity of protected areas. With the envisaged expansion of the protected area network and further expected population and economic growth in the region, the competition between nature conservation and resources demands is likely to increase.

This study compared concentrations and export of total phosphorus (TP; including dissolved and particulate forms) and nitrogen (N) between quickflow and delayed flow at two adjacent agricultural catchments (Jennings and McLarens Creeks), one of which is undergoing increased urbanization. Annual runoff, frequency of quickflow events in the growing season (GS), and chloride (Cl⁻) concentrations and export were greater at the more urbanized Jennings Creek.

A novel approach to study vegetation dynamics is introduced, using the Empirical Mode Decomposition (EMD) to analyze NDVI time series. The NDVI time series which is nonlinear and nonstationary can be decomposed by EMD into components called intrinsic mode functions (IMFs), based on inherent temporal scales. The highest frequency component which has been found to represent noise is subtracted from the original NDVI series; thus smoothing the noisy signal. The different key features describing vegetation phenology have been extracted by analyzing the noise free signal.

Arctic soils contain three times as much carbon (C) as all aboveground biomass distributed globally, much of which is stored in permafrost soils. Here, we (1) determine the predictability of estimating soil organic carbon (SOC) using different satellite data, classifications, and methods; (2) estimate the quantity and distribution of SOC for the top 10 cm for the Ambolikha River watershed (~121 km ²) in northeast Siberia, a sub-watershed of the Kolyma River; and (3) produce a hybrid SOC map through data fusion, combining the strengths of each data type.

Urbanization is increasingly becoming a widespread phenomenon at all scales of development around the globe. Be it developing or developed nations, all are witnessing urbanization at very high pace. In order to study its impacts, various methodologies and techniques are being implemented to measure growth of urban extents over spatial and temporal domains. But urbanization being a very dynamic phenomenon has been facing ambiguities regarding methods to study its dynamism. This paper aims at quantifying urban expansion in Delhi, the capital city of India.

This study investigates the impact of using different combinations of Moderate Resolution Imaging Spectroradiometer (MODIS) and ancillary datasets on overall and per-class classification accuracies for nine land cover types modified from the classification system of the International Geosphere Biosphere Programme (IGBP).

In the present study, a semi‐distributed hydrological model soil and water assessment tool (SWAT) has been employed for the Ken basin of Central India to predict the water balance. The entire basin was divided into ten sub basins comprising 107 hydrological response units on the basis of unique slope, soil and land cover classes using SWAT model. Sensitivity analysis of SWAT model was performed to examine the critical input variables of the study area.

Comparability among population data enumerated within different time periods may be complicated by changing enumeration boundaries over time. Areal interpolation methods are commonly used to solve such zoning incompatibilities, but are frequently based on the questionable assumption of homogeneous population density within the different zones. To achieve more accurate estimates, land cover or other ancillary data may be used to better characterize the underlying source zone population density surface prior to areal interpolation.

This research aims to improve land-cover classification accuracy in a moist tropical region in Brazil by examining the use of different remote-sensing-derived variables and classification algorithms. Different scenarios based on Landsat Thematic Mapper (TM) spectral data and derived vegetation indices and textural images and different classification algorithms, maximum likelihood classification (MLC), artificial neural network (ANN), classification tree analysis (CTA) and object-based classification (OBC), were explored.

Improvement in remote sensing techniques in spatial/spectral resolution strengthens their applicability for urban environmental study. Unfortunately, high spatial resolution imagery also increases internal variability in land cover units and can cause a ‘salt-and-pepper’ effect, resulting in decreased accuracy using pixel-based classification results. Region-based classification techniques, using an image object (IO) rather than a pixel as a classification unit, appear to hold promise as a method for overcoming this problem.

Numerous studies have explored the relationship between environmental factors and White Stork Ciconia ciconia reproduction, mainly expressing breeding success as the number of fledglings. Nonetheless, one of the most critical life-history stages in birds falls between egg-laying and fledging, and identifying the factors causing offspring mortality during this period provides valuable knowledge. We quantified the number of laid White Stork eggs and the proportion of eggs that turned into fledglings in an agriculture-dominated region in Eastern Germany.

Strictly Protected Areas and riparian forests in Brazil are rarely large enough or connected enough to maintain viable populations of carnivores and animal movement over time, but these characteristics are fundamental for species conservation as they prevent the extinction of isolated animal populations. Therefore, the need to maintain connectivity for these species in human-dominated Atlantic landscapes is critical.