Over the past few decades, energy and water fluxes have been directly measured by a global flux network, which was established by regional and continental network sites based on an eddy covariance (EC) method. Although, the EC method possesses many advantages, its typical data coverage could not exceed 65 % due to various environmental factors including micrometeorological conditions and systematic malfunctions. In this study, four different methodologies were used to fill the gap in latent heat flux (LE) data.

Understanding the intimate dynamics of urban–wildland interfaces in Mediterranean landscapes is particularly challenging because of multiple biophysical factors (dry or arid climate, low-quality soils, poor vegetation cover) determining an increased environmental sensitivity to human pressure. Although dense and compact cities were sprawling rapidly in the most recent decades, many suburban areas in southern Europe still preserve biodiversity-rich habitats, traditional crop mosaics and high-quality relict forest stands.

Land cover classifications of coarse-resolution data can aid the identification and quantification of natural variability and anthropogenic change at regional scales, but true landscape change can be distorted by misrepresentation of map classes. The Lerma–Chapala–Santiago (LCS) is biophysically diverse and heavily modified by urbanization and agricultural expansion.

Mapping urban expansion and impervious surfaces (IS) has become a useful tool for supporting watershed assessments. The lack of large-area time-series maps created the need to develop an approach and products that can easily be scaled. In this research application, 81 Landsat 1, 2, and 5 scenes for the epochs of 1975, 1985, and 1996 were used to map urban land use/land cover across New England, USA.

One of the prerequisites of the REDD+ mechanism is to effectively predict business-as-usual (BAU) scenarios for change in forest cover. This would enable estimation of how much carbon emission a project could potentially prevent and thus how much carbon credit should be rewarded. However, different factors like forest degradation and the lack of linearity in forest cover transitions challenge the accuracy of such scenarios. Here we predict and validate such BAU scenarios retrospectively based on forest cover changes at village and district level in North Central Vietnam.

This study developed three scenarios of future land use/land cover on a local level for the Kyung-An River Basin and its vicinity in South Korea at a 30-m resolution based on the two scenario families of the Intergovernmental Panel on Climate Change (IPCC) Special Report Emissions Scenarios (SRES): A2 and B1, as well as a business-as-usual scenario. The IPCC SRES A2 and B1 were used to define future local development patterns and associated land use change.

The present study was aimed to investigate how and to what extent urban land surface temperature (ULST) is affected by spatial pattern of green cover patch in an urban ambient in Isfahan, Iran. To materialize the effects of spatial pattern of green cover on ULST, Landsat ETM + image data on May 5, 2002 was acquired to be processed for ULST estimation and to generate Land Use/Land Cover (LULC) classes.

It is generally assumed that, when natural habitat is converted to human‐dominated land cover, such habitat is lost to its native species. Most literature assumes that species richness should vary as a function of remaining natural area, following the well‐known species–area relationship (i.e., classic SAR). However, classic SARs have consistently overestimated species losses resulting from conversion of natural forested land cover to human‐dominated landscapes. Moreover, richness is sometimes a peaked function of remaining natural habitat.

Landscapes intensively farmed over a long time period represent a threat for natural habitats and high levels of biodiversity. Information on the historical land use and spatial changes of natural habitats can help to explain the causes of a number of contemporary phenomena, which are important for the development of effective conservation and ecosystem management.

Vulnerability to climate change impacts is defined by three dimensions of human–environmental systems, such as exposure, sensitivity, and adaptive capacity. Climate change affects various aspects of human–environmental interactions, such as water stress, food security, human health, and well-being at multiple spatial and temporal scales. However, the existing protocols of vulnerability assessment fail to incorporate the multitude of scales associated with climate change processes.

Understanding agricultural ecosystems and their complex interactions with the environment is important for improving agricultural sustainability and environmental protection. Developing the necessary understanding requires approaches that integrate multi-source geospatial data and interdisciplinary relationships at different spatial scales. In order to identify and delineate landscape units representing relatively homogenous biophysical properties and eco-environmental functions at different spatial scales, a hierarchical system of uniform management zones (UMZ) is proposed.

Artificial neural networks (ANN) are nonlinear models widely investigated in hydrology due to their properties of universal approximation and parsimony. Their performance during the training phase is very good, and their ability to generalize can be improved by using regularization methods such as early stopping and cross-validation. In our research, two kinds of generic models are implemented: the feed-forward model and the recurrent model.