The Importance of Land-Cover Change in Simulating Future Climates

Feddema, Johannes J.; Oleson, Keith W.; Bonan, Gordon B.; Mearns, Linda O.; Buja, Lawrence; Meehl, Gerald A.; Washington, Warren M.

doi:10.1126/science.1118160

Cited by 984 publications

(711 citation statements)

References 23 publications

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“…We do not include many biogeophysical effects of LULCC, such as changes to surface latent and sensible heat fluxes and to the hydrological cycle, that impact climate (Defries et al, 2002;Feddema et al, 2005;Brovkin et al, 2006;Pitman et al, 2009;Lawrence and Chase, 2010). In general, while important for local or regional climate especially in the tropics (Strengers et al, 2010), these effects are considered minor on a global scale (Lawrence and Chase, 2010) and are difficult to quantify using the RF concept (Pielke et al, 2002).…”

Section: Uncertaintymentioning

confidence: 99%

“…Effective strategies for mitigation of human impacts on global climate require an understanding of the major sources of those impacts (Unger et al, 2010). Anthropogenic land use and changes to land cover have long been recognized as important contributors to global climate forcing (Feddema et al, 2005), and yet most studies on this topic focus on either land use (e.g., Unger et al, 2010) or land cover change (e.g., Davin et al, 2007;Pongratz et al, 2009), but not both. In this study we compute the fraction of anthropogenic RF that is attributable to LULCC activities including a more comprehensive range of forcing agents.…”

Section: Enhancement Of Land Use Co 2 Radiative Forcingmentioning

confidence: 99%

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Potential climate forcing of land use and land cover change

2014

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Abstract. Pressure on land resources is expected to increase as global population continues to climb and the world becomes more affluent, swelling the demand for food. Changing climate may exert additional pressures on natural lands as present-day productive regions may shift, or soil quality may degrade, and the recent rise in demand for biofuels increases competition with edible crops for arable land. Given these projected trends there is a need to understand the global climate impacts of land use and land cover change (LULCC). Here we quantify the climate impacts of global LULCC in terms of modifications to the balance between incoming and outgoing radiation at the top of the atmosphere (radiative forcing, RF) that are caused by changes in long-lived and short-lived greenhouse gas concentrations, aerosol effects, and land surface albedo. We attribute historical changes in terrestrial carbon storage, global fire emissions, secondary organic aerosol emissions, and surface albedo to LULCC using simulations with the Community Land Model version 3.5. These LULCC emissions are combined with estimates of agricultural emissions of important trace gases and mineral dust in two sets of Community Atmosphere Model simulations to calculate the RF of changes in atmospheric chemistry and aerosol concentrations attributed to LULCC. With all forcing agents considered together, we show that 40 % (±16 %) of the present-day anthropogenic RF can be attributed to LULCC. Changes in the emission of non-CO 2 greenhouse gases and aerosols from LULCC enhance the total LULCC RF by a factor of 2 to 3 with respect to the LULCC RF from CO 2 alone. This enhancement factor also applies to projected LULCC RF, which we compute for four future scenarios associated with the Representative Concentration Pathways. We attribute total RFs between 0.9 and 1.9 W m −2 to LULCC for the year 2100 (relative to a preindustrial state). To place an upper bound on the potential of LULCC to alter the global radiation budget, we include a fifth scenario in which all arable land is cultivated by 2100. This theoretical extreme case leads to a LULCC RF of 3.9 W m −2 (±0.9 W m −2 ), suggesting that not only energy policy but also land policy is necessary to minimize future increases in RF and associated climate changes.

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Section: Uncertaintymentioning

confidence: 99%

Section: Enhancement Of Land Use Co 2 Radiative Forcingmentioning

confidence: 99%

Potential climate forcing of land use and land cover change

2014

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“…Determining the accuracy of land cover classified from remotely sensed data is important. Land cover is an input into environmental models incorporating land-atmosphere interactions (GLP, 2005) and land cover change is a major variable in climate change analyses (Feddema et al, 2005). In this context, accuracy descriptions can help the user to assess the uncertainties associated with incorporating land cover data into their model or to decide between land cover datasets, especially where there is a choice between data with different thematic or spatial characteristics (See & Fritz, 2006).…”

Section: Introductionmentioning

confidence: 99%

Spatial analysis of remote sensing image classification accuracy

Comber

Fisher

Brunsdon

et al. 2012

Remote Sensing of Environment

195

119

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The error matrix is the most common way of expressing the accuracy of remote sensing image classifications, such as land cover. However, it and the measures that can be calculated from it have been criticised for not providing any indication of the spatial distribution of errors. Other research has identified the need for methods to analyse the spatial non-stationarity of error and to visualise the spatial variation in classification uncertainty. This research uses geographically weighted approaches to model the spatial variations in the accuracy of both (crisp) Boolean and (soft) fuzzy land cover classes. Remotely sensed data were classified using a maximum likelihood classifier and a fuzzy classifier to predict Boolean and fuzzy land cover classes respectively. Field data were collected at sub-pixel locations and used to generate soft and crisp validation data. A Geographically Weighted Regression was used to analyse spatial variations in the relationships between observations of Boolean land cover in the field and land cover classified from remote sensing imagery. A geographically weighted difference measure was used to analyse spatial variations in fuzzy land cover accuracy. Maps of the spatial distribution of accuracy were created for fuzzy and Boolean classes. This research demonstrates that data collected as part of a standard remote sensing validation exercise can be used to estimate mapped, spatial distributions of accuracy that would augment standard accuracy measures reported in the error matrix. It suggests that geographically weighted approaches, and the spatially explicit representations of accuracy they support, offer the opportunity to report land cover accuracy in a more informative way.

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“…Land cover is an essential climate variable [1] as it strongly influences current and future climate, particularly with rapid changes in the landscape due to human activities [2]. Baseline information on land use and land cover (LULC) is also a key input to many different types of models the areas to be mapped.…”

Section: Introductionmentioning

confidence: 99%

Generating Up-to-Date and Detailed Land Use and Land Cover Maps Using OpenStreetMap and GlobeLand30

Fonte

Minghini

Patriarca

et al. 2017

IJGI

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Abstract:With the opening up of the Landsat archive, global high resolution land cover maps have begun to appear. However, they often have only a small number of high level land cover classes and they are static products, corresponding to a particular period of time, e.g., the GlobeLand30 (GL30) map for 2010. The OpenStreetMap (OSM), in contrast, consists of a very detailed, dynamically updated, spatial database of mapped features from around the world, but it suffers from incomplete coverage, and layers of overlapping features that are tagged in a variety of ways. However, it clearly has potential for land use and land cover (LULC) mapping. Thus the aim of this paper is to demonstrate how the OSM can be converted into a LULC map and how this OSM-derived LULC map can then be used to first update the GL30 with more recent information and secondly, enhance the information content of the classes. The technique is demonstrated on two study areas where there is availability of OSM data but in locations where authoritative data are lacking, i.e., Kathmandu, Nepal and Dar es Salaam, Tanzania. The GL30 and its updated and enhanced versions are independently validated using a stratified random sample so that the three maps can be compared. The results show that the updated version of GL30 improves in terms of overall accuracy since certain classes were not captured well in the original GL30 (e.g., water in Kathmandu and water/wetlands in Dar es Salaam). In contrast, the enhanced GL30, which contains more detailed urban classes, results in a drop in the overall accuracy, possibly due to the increased number of classes, but the advantages include the appearance of more detailed features, such as the road network, that becomes clearly visible.

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The Importance of Land-Cover Change in Simulating Future Climates

Cited by 984 publications

References 23 publications

Potential climate forcing of land use and land cover change

Potential climate forcing of land use and land cover change

Spatial analysis of remote sensing image classification accuracy

Generating Up-to-Date and Detailed Land Use and Land Cover Maps Using OpenStreetMap and GlobeLand30

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