Explore the vast range of titles available.

People are constantly changing the land surface through construction, agriculture, energy production, and other activities. Changes both in how land is used by people (land use) and in the vegetation, rock, buildings, and other physical material that cover the Earth's surface (land cover) can be described and future land change can be projected using land-change models (LCMs). LCMs are a key means for understanding how humans are reshaping the Earth's surface in the past and present, for forecasting future landscape conditions, and for developing policies to manage our use of resources and the environment at scales ranging from an individual parcel of land in a city to vast expanses of forests around the world. Advancing Land Change Modeling: Opportunities and Research Requirements describes various LCM approaches, suggests guidance for their appropriate application, and makes recommendations to improve the integration of observation strategies into the models. This report provides a summary and evaluation of several modeling approaches, and their theoretical and empirical underpinnings, relative to complex land-change dynamics and processes, and identifies several opportunities for further advancing the science, data, and cyberinfrastructure involved in the LCM enterprise. Because of the numerous models available, the report focuses on describing the categories of approaches used along with selected examples, rather than providing a review of specific models. Additionally, because all modeling approaches have relative strengths and weaknesses, the report compares these relative to different purposes. Advancing Land Change Modeling's recommendations for assessment of future data and research needs will enable model outputs to better assist the science, policy, and decisionsupport communities.

The Amazon basin is a key component of the global carbon cycle. The old-growth rainforests in the basin represent storage of ~ 120 petagrams of carbon (Pg C) in their biomass. Annually, these tropical forests process approximately 18 Pg C through respiration and photosynthesis. This is more than twice the rate of global anthropogenic fossil fuel emissions. The basin is also the largest global repository of biodiversity and produces about 20 percent of the world's flow of fresh water into the oceans. Despite the large carbon dioxide (CO2) efflux from recent deforestation, the Amazon rainforest ecosystem is still considered to be a net carbon sinks of 0.8-1.1 Pg C per year because growth on average exceeds mortality (Phillips et al. 2008). However, current climate trends and human-induced deforestation may be transforming forest structure and behavior (Phillips et al. 2009). Increasing temperatures may accelerate respiration rates and thus carbon emissions from soils (Malhi and Grace 2000). High probabilities for modification in rainfall patterns (Malhi et al. 2008) and prolonged drought stress may lead to reductions in biomass density. Resulting changes in evapo-transpiration and therefore convective precipitation could further accelerate drought conditions and destabilize the tropical ecosystem as a whole, causing a reduction in its biomass carrying capacity or dieback. In turn, changes in the structure of the Amazon and its associated water cycle will have implications for the many endemic species it contains and result in changes at a continental scale. Clearly, with much at stake, if climate-induced damage alters the state of the Amazon ecosystem, there is a need to better understand its risk, process, and dynamics. The objective of this study is to assist in understanding the risk, process, and dynamics of potential Amazon dieback and its implications.

Changes in land use and land cover (LULC) have major effects on biodiversity and ecosystem services. Land change models can simulate future trends of ecosystem services under different scenarios to inform the actions of decision makers towards building a more sustainable society. LULC data are essential inputs for predicting future land changes. It is now possible to derive high-resolution LULC maps from satellite data using remote sensing techniques. However, the classification of land categories in these maps is too limited to sufficiently assess biodiversity and ecosystem services. This study aims to develop land-use scenarios, using an appropriate LULC map, to enable assessment of biodiversity and ecosystem services at the national scale. First, we developed an LULC dataset using vegetation inventories based on field records of vegetation collected throughout the country in the periods 1978–1987, 1988–1998 and 1999–2014. The vegetation maps consist of over 905 vegetation categories, from which we aggregated the most prevalent categories into 9 LULC categories. Second, we created a business-as-usual scenario and plausible future scenarios on the land use change maps using the Land Change Model tool. In the process of developing the model, we considered key drivers including biophysical and socio-economic factors. The results showed some key land changes as consequences of intensive/extensive land-use interventions. These derived scenario maps can be used to assess the impacts of future land change on biodiversity and ecosystem services.