Questions: Can the climatic adaptive capacity of natural ecosystems be estimated with using landscape indicators based on vegetation or land-cover data? Can species distribution model (SDM) outputs be enhanced using such indicators? What are the data requirements and optimal parameter values of potential indicators? Location: Indicator framework: unspecified. Case study: Kiskunság, Hungary. Methods: (1) We define a general framework for handling adaptation in ecological climate change impact assessments based on IPCC definitions.

A dam's construction always imposes some risks to the environment. In this article, the environmental risks of the Polrood dam, located in a northern province of Iran, during its construction phase, were identified, ranked, and evaluated. The risk factors were initially identified by Delphi questionnaire and then rated using the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). Subsequently, the Analytical Hierarchy Process (AHP) was applied to classify the risk factors into four major categories, and Expert Choice software was used to weight them.

Mount Kilimanjaro is considered the highest free-standing mountain in the world and a symbol of the African continent. Steep slopes and high altitudes are on the backdrop of unique biophysical characteristics, in which changes between savannas, tropical cloud forests, and subalpine vegetation can be observed in relatively small distances. In the context of this complex and heterogeneous landscape, describing the interactions between climatic variables and ecosystem functions is crucial for understanding the drivers of biodiversity.

China’s economic development over the past three decades has been remarkable due to the establishment of the “Reform and Opening-up” program. Meanwhile, urbanization, one of the most intensive human activities, has significantly changed the land cover across China. Here we used remote sensing data and landscape metrics to explore the spatiotemporal patterns of urbanization in two large Chinese cities, Chengdu (1978–2010) and Chongqing (1976–2010).

Quantifying spatial and temporal patterns of carbon sources and sinks and their uncertainties across agriculture-dominated areas remains challenging for understanding regional carbon cycles. Characteristics of local land cover inputs could impact the regional carbon estimates but the effect has not been fully evaluated in the past.

Species distribution modelling (SDM) can help conservation by providing information on the ecological requirements of species at risk. We developed habitat suitability models at multiple spatial scales for a threatened freshwater turtle, Emydoidea blandingii, in Ontario as a case study. We also explored the effect of background data selection and modelling algorithm selection on habitat suitability predictions. We used sighting records, high-resolution land cover data (25m), and two SDM techniques: boosted regression trees; and maximum entropy modelling.

The composition and configuration of landscape elements as well as their size and shape co-determine the character of the flows and processes in the landscape. Using remote sensing data and landscape metrics, this article sets out to analyse changes in the landscape structure at two different spatial scales, focusing on two study areas in the Czech Republic in the latter half of the twentieth century.

Improving the understanding of land use and land cover is a major research challenge for the human-environmental sciences and is essential for many aspects of global environmental research. Considering seasonal vegetation dynamics or phenological dynamics in multi-year series leads to a broader view of land use and land cover. This study is based on the hypothesis that a pixel representing a complex but consistent land use has a typical, distinct and repeated temporal pattern of vegetation index inter-annually, which can be used as characteristic signatures for land use classification.

Quantifying landscape‐scale methane (CH₄) fluxes from boreal and arctic regions, and determining how they are controlled, is critical for predicting the magnitude of any CH₄ emission feedback to climate change. Furthermore, there remains uncertainty regarding the relative importance of small areas of strong methanogenic activity, vs. larger areas with net CH₄ uptake, in controlling landscape‐level fluxes.

The fundamental land use/land cover change (LUCC) of Naoli River (NLR) Basin in Sanjiang Plain since 1954 is characterized with a very significant shrink of wetland area, primarily due to agricultural reclamation, especially farm construction. In this paper, LUCC data of six periods were generated to explore the process of LUCC in NLR; the driving factors were discovered and the regression analysis of these driving factors was realized using the Dyna-CLUE model; and the land use pattern of 2020 in NLR Basin was projected under six scenarios.

Land is one of the prime natural resources. A city grows not only by population but also by changes in spatial dimensions. Urban population growth and urban sprawl induced land use changes and land transformation. The land transformation is a natural process and cannot be stopped but it can be regulated. Many geographical changes at the urban periphery are associated with the transfer of land from rural to urban purpose. There is an urgent need for fast growing areas like Delhi, which can be easily done by high-resolution remote sensing data.

More than three-quarters of Mexico's coffee is grown on small plots shaded by the existing forest. Because they preserve forest cover, shade coffee farms provide vital ecological services including harboring biodiversity and preventing soil erosion. Unfortunately, tree cover in Mexico's shade coffee areas is increasingly being cleared to make way for subsistence agriculture, a direct result of the unprecedented decline of international coffee prices over the past decade.