A land-use and land-cover modeling strategy to support a national assessment of carbon stocks and fluxes

Sohl, Terry L.; Sleeter, Benjamin M.; Zhu, Zhiliang; Sayler, Kristi L.; Bennett, Stacie L.; Bouchard, Michelle; Reker, Ryan R.; Hawbaker, Todd J.; Wein, Anne; Liu, Shuguang; Kanengieter, Ronald L.; Acevedo, William

doi:10.1016/j.apgeog.2011.10.019

Cited by 77 publications

(60 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Scenario-based ''demand,'' development of probability surfaces, designation of forest cutting cycles, and parameterization of all other model components were done independently for each Level III ecoregion. Complete details on scenario development and methodology are described in Sleeter et al (2012a) and Sohl et al (2007Sohl et al ( , 2012b. Validation of results is discussed in Sleeter et al (2012a) and model limitations and uncertainty are discussed in Sohl et al (2014).…”

Section: Spatially Explicit Modeling Of Scenariosmentioning

confidence: 99%

Potential future land use threats to California’s protected areas

2014

View full text Add to dashboard Cite

Increasing pressures from land use coupled with future changes in climate will present unique challenges for California's protected areas. We assessed the potential for future land use conversion on land surrounding existing protected areas in California's twelve ecoregions, utilizing annual, spatially explicit (250 m) scenario projections of land use for 2006-2100 based on the Intergovernmental Panel on Climate Change Special Report on Emission Scenarios to examine future changes in development, agriculture, and logging. We calculated a conversion threat index (CTI) for each unprotected pixel, combining land use conversion potential with proximity to protected area boundaries, in order to identify ecoregions and protected areas at greatest potential risk of proximal land conversion. Our results indicate that California's Coast Range ecoregion had the highest CTI with competition for extractive logging placing the greatest demand on land in close proximity to existing protected areas. For more permanent land use conversions into agriculture and developed uses, our CTI results indicate that protected areas in the Central California Valley and Oak Woodlands are most vulnerable. Overall, the Eastern Cascades, Central California Valley, and Oak Woodlands ecoregions had the lowest areal percent of protected lands and highest conversion threat values. With limited resources and time, rapid, landscape-level analysis of potential land use threats can help quickly identify areas with higher conversion probability of future land use and potential changes to both habitat and potential ecosystem reserves. Given the broad range of future uncertainties, LULC projections are a useful tool allowing land managers to visualize alternative landscape futures, improve planning, and optimize management practices.

show abstract

Section: Spatially Explicit Modeling Of Scenariosmentioning

confidence: 99%

Potential future land use threats to California’s protected areas

2014

View full text Add to dashboard Cite

show abstract

“…Although the demographic and economic data were introduced as inputs of the model already, much literature [50][51][52][53] also suggests that it will improve the good performance of the model to incorporate the population and economic growth rate for future predictions. This study considers that reason to integrate both important data to transition rate calculation through the statistical extrapolation and SD.…”

Section: Model Applicationmentioning

confidence: 99%

Modeling Urban Expansion in Bangkok Metropolitan Region Using Demographic–Economic Data through Cellular Automata-Markov Chain and Multi-Layer Perceptron-Markov Chain Models

et al. 2016

View full text Add to dashboard Cite

Urban expansion is considered as one of the most important problems in several developing countries. Bangkok Metropolitan Region (BMR) is the urbanized and agglomerated area of Bangkok Metropolis (BM) and its vicinity, which confronts the expansion problem from the center of the city. Landsat images of 1988Landsat images of , 1993Landsat images of , 1998Landsat images of , 2003Landsat images of , 2008, and 2011 were used to detect the land use and land cover (LULC) changes. The demographic and economic data together with corresponding maps were used to determine the driving factors for land conversions. This study applied Cellular Automata-Markov Chain (CA-MC) and Multi-Layer Perceptron-Markov Chain (MLP-MC) to model LULC and urban expansions. The performance of the CA-MC and MLP-MC yielded more than 90% overall accuracy to predict the LULC, especially the MLP-MC method. Further, the annual population and economic growth rates were considered to produce the land demand for the LULC in 2014 and 2035 using the statistical extrapolation and system dynamics (SD). It was evident that the simulated map in 2014 resulting from the SD yielded the highest accuracy. Therefore, this study applied the SD method to generate the land demand for simulating LULC in 2035. The outcome showed that urban occupied the land around a half of the BMR.

show abstract

“…The biomass-derived C cycle, unlike the petrochemical C cycle, is nearly balanced [171]. However, crop and soil management practices have implications for C cycling in soils [127].…”

Section: Carbon and Nitrogen Cyclesmentioning

confidence: 99%

“…Some bioenergy crops (e.g., switchgrass, transgenic short-rotation coppice) have been bred or genetically modified to produce cultivars with increased biomass and feedstock quality [89]. Lignin, a major component of the cell wall of vascular plants, has long been recognized for its negative impact on cellulosic biofuel production [289]; it accounts for about 50% of the over 1.4 Â 10 12 kg C sequestered each year [171]. Plant species or genotypes that allocate large amounts of C to structural components, like lignin, generally have low-quality biomass for ethanol production using current conversion technologies [90].…”

Section: Carbon Quality In Bioenergy Cropsmentioning

confidence: 99%

Can Carbon in Bioenergy Crops Mitigate Global Climate Change?

Jaradat

2013

Climate Change and Plant Abiotic Stress Tolerance

View full text Add to dashboard Cite

Different forms of carbon (C) cycle continuously through several pools in natural and managed ecosystems and spheres. Carbon's recent "commodification," as a negative environmental externality, has rendered it a "scarce" and "tradable" element. Although C supply in nature is not limited, energy is required to make it available as a plant nutrient, assimilate it in plant tissues, and sequester it as temporary or recalcitrant C in soils. Human demand for C-based energy and plantfixed C has accelerated, and altered its global cycle, raised its atmospheric content, contributed to climate change through global warming, and impacted several provisioning, regulating, and supporting ecosystem services. Agroecosystems are both sources and sinks of C. In a carbon dioxide-constrained world, plant-fixed and sequestered C in natural and managed ecosystems has a potential role in mitigating climate change, providing C-neutral and renewable bioenergy, and positively affecting ecosystem services. Due to the intricacies of the complex, interconnected biogeochemical cycles involving C, nitrogen, and water in which soils play an important role, bioenergy crops do not provide an easy solution to climate change mitigation, they may contribute to it. This chapter presents a critical review assessing the state of knowledge, and exploring opportunities and challenges of the role of C in bioenergy crops in mitigating global climate change, while sustainably providing other ecosystem services.

show abstract

A land-use and land-cover modeling strategy to support a national assessment of carbon stocks and fluxes

Cited by 77 publications

References 30 publications

Potential future land use threats to California’s protected areas

Potential future land use threats to California’s protected areas

Modeling Urban Expansion in Bangkok Metropolitan Region Using Demographic–Economic Data through Cellular Automata-Markov Chain and Multi-Layer Perceptron-Markov Chain Models

Can Carbon in Bioenergy Crops Mitigate Global Climate Change?

Contact Info

Product

Resources

About