Socioeconomic growth and urban change have been an increasing concern for decision makers in recent decades. The monitoring, mapping, and analysis of agricultural land use change, especially in areas where urban change has been high, is crucial. The collision between traditional economic activities related to agriculture in tourist areas such as the Algarve and current demand for tourism infrastructures in urban regions is also leading to loss of economic activity.

Mapping key areas for ecosystem service (ES) supply is essential for the development of strategies that will ensure their future supply. Given the rapid development in this area of research, we performed a review of different approaches used to map ES, with a special focus on those that use social–ecological data. We used an analytical framework based on five criteria for analyzing and comparing the methodological approaches: the types of ES, availability of data sources, types of data sources, spatial scale, and methods used to model ES.

We conducted nocturnal surveys in the insular and coastal areas of Coiba National Park (CNP) and its mainland buffer zone in Panama (Chiriquí conservation site) from 2009–2012 to determine the conservation status of Crocodylus acutus. In 99 nights, we surveyed 147.2 km and captured 185 animals during nocturnal transects inspection with headlamps. Overall, sex ratio was 1.00:1.01 female/male with significant differences by size/age class and year. Females were slightly larger in total length than males (115.1 ± 56.9 cm-females, 105.4 ± 71.8 cm-males).

The growth of human populations around protected areas accelerates land conversion and isolation, negatively impacting biodiversity and ecosystem function, and can be exacerbated by immigration. It is often assumed that immigration around protected areas is driven by attraction in the form of economic benefits, but in many cases, people may be pushed from their areas of origin toward protected areas. Mitigating the effects of immigration around protected areas necessitates understanding the actual mechanisms causing it, which can be aided by analysis of patterns of land-cover change.

To assess future interactions between the environment and human well-being, spatially explicit ecosystem service models are needed. Currently available models mainly focus on provisioning services and do not distinguish changes in the functioning of the ecosystem (Ecosystem Functions – ESFs) and human use of such functions (Ecosystem Services – ESSs). This limits the insight on the impact of global change on human well-being. We present a set of models for assessing ESFs and ESSs.

With limited resources for habitat conservation, the accurate identification of high-value avian habitat is crucial. Habitat structure affects avian biodiversity but is difficult to quantify over broad extents. Our goal was to identify which measures of vertical and horizontal habitat structure are most strongly related to patterns of avian biodiversity across the conterminous United States and to determine whether new measures of vertical structure are complementary to existing, primarily horizontal, measures.

Recent studies using low-resolution satellite time series show that the Sahelian belt of West Africa is witnessing an increase in vegetation cover/biomass, called re-greening. However, detailed information on local processing and changes is rare or lacking. A multi-temporal set of Landsat images was used to produce land-cover maps for the years 2000 and 2007 in a semi-arid region of Niger, where an anomalous vegetation trend was previously detected.

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.

Detailed and accurate land cover data are widely used for various purposes, such as global land use change detection. This study aimed to investigate Australian land use and cover change by using 774 Landsat scenes in 2000 and 2010. The reference data included pictures or high-resolution images from Google Earth, global land cover data, Shuttle Radar Topography Mission data, and other literature.

Substitution of the Intergovernmental Panel on Climate Change (IPCC) default methodology by country-specific activity data is recommended for improved estimation of baseline soil organic carbon (SOC) stocks and their changes. In the Republic of Ireland (ROI), previous studies focused either predominantly on grassland or on all land cover types but were depth-limited. To improve the accuracy, Tier 2 approaches are proposed by the IPCC.

Time series of vegetation index (VI) information derived from remote sensing is important for land-cover change detection. Although traditional change vector analysis (TCVA) is an effective method for extracting land-cover change information from a time series of VI data, it has the disadvantage of being too sensitive to temporal fluctuations in VI values. The method tends to overestimate the changes and confuse the actual land-cover conversion with the land covers that have not been converted but experience significant VI changes.

Intensive forest management is one of the main land cover changes over the last century in Central Europe, resulting in forest monoculture. It has been proposed that these monoculture stands impact hydrological processes, water yield, water quality and ecosystem services.