Remote sensing as the foundation for high-resolution United States landscape projections – The Land Change Monitoring, assessment, and projection (LCMAP) initiative

Sohl, Terry L.; Dornbierer, Jordan; Wika, Steve; Robison, C. Price

doi:10.1016/j.envsoft.2019.104495

Cited by 19 publications

(27 citation statements)

References 56 publications

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“…The most important distinction of our seeding algorithm from other contemporary seeding algorithms (e.g., [18,19]) is that we specifically designed it to generate multiple realizations, via a tunable stochastic process, to create a library of projected allocations, as opposed to a single or small number of outcomes (e.g., one per scenario). This distributional capability has immense utility.…”

Section: Plos Onementioning

confidence: 99%

“…Numerous avenues of future research stem from our results, many of which focus on adjustments to the algorithm. For example, all realizations in this study were based on the same transition probability models (one per grouping (Fig 3), from observed 2001 to 2011 transitions), but the stability of such empirically-based transition models over time is unknown at best [19]. With the recent release of the NLCD 2016 dataset [57], it is now possible to prepare additional land use data and compare models estimated from the decadal time steps of 2001-2011 (as used in this study) and 2006-2016, to get a better sense of transition model stability.…”

Section: Plos Onementioning

confidence: 99%

“…While there are a wide variety of land use change models available as the result of broad interest and research, which can be categorized along economic/non-economic, spatiallyexplicit/aggregated, and empirical/process-based lines [15,16]; demand-allocation methods [17][18][19][20][21] are particularly well-suited to situations where a projected land use change is available at a coarse scale and the intent is to downscale the projection. Demand-allocation models tend to input exogenously generated transition quantities (or quotas) for a given area (e.g., county), for example via socioeconomic models [10,18]; then allocate those quotas within the study area [22].…”

Section: Introductionmentioning

confidence: 99%

“…A patch of cells is then grown semi-contagiously from the seed (e.g., [18]), so that patch properties (such as patch sizes for the realized population of patches) correspond to empirical distributions of those properties. Seeding algorithms such as FORE-SCE [28] have been used to model land cover change at national scales, both historically [29] and into the future [19,30].…”

Section: Introductionmentioning

confidence: 99%

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Using a hybrid demand-allocation algorithm to enable distributional analysis of land use change patterns

et al. 2020

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Future land use projections are needed to inform long-term planning and policy. However, most projections require downscaling into spatially explicit projection rasters for ecosystem service analyses. Empirical demand-allocation algorithms input coarse-level transition quotas and convert cells across the raster, based on a modeled probability surface. Such algorithms typically employ contagious and/or random allocation approaches. We present a hybrid seeding approach designed to generate a stochastic collection of spatial realizations for distributional analysis, by 1) randomly selecting a seed cell from a sample of n cells, then 2) converting patches of neighboring cells based on transition probability and distance to the seed. We generated a collection of realizations from 2001-2011 for the conterminous USA at 90m resolution based on varying the value of n, then computed forest area by fragmentation class and compared the results with observed 2011 forest area by fragmentation class. We found that realizations based on values of n � 256 generally covered observed forest fragmentation at regional scales, for approximately 70% of assessed cases. We also demonstrate the potential of the seeding algorithm for distributional analysis by generating 20 trajectories of realizations from 2020-2070 from a single example scenario. Generating a library of such trajectories from across multiple scenarios will enable analysis of projected patterns and downstream ecosystem services, as well as their variation.

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Section: Plos Onementioning

confidence: 99%

Section: Plos Onementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Using a hybrid demand-allocation algorithm to enable distributional analysis of land use change patterns

et al. 2020

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“…Traditionally, studies on landscape changes usually focus on the change in spatial patterns based on multi-temporal satellite imagery with little consideration of the local community's perceptions [35][36][37]. This is despite the fact that humans actually respond to landscape changes, which acts, and impact on socio-ecological systems.…”

Section: Introductionmentioning

confidence: 99%

Survey of Community Livelihoods and Landscape Change along the Nzhelele and Levuvhu River Catchments in Limpopo Province, South Africa

Musakwa

Wang

Wei

et al. 2020

Land

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Landscape-change studies have attracted increasing interest because of their importance to land management and the sustainable livelihoods of rural communities. However, empirical studies on landscape change and its drivers are often poorly understood, particularly, in small rural communities in developing countries such as South Africa. The present study surveyed local community livelihoods and perceptions of landscape change in the Nzhelele and Levuvhu river catchments in Limpopo Province, South Africa. These areas have experienced land reform and are also characterized by environmental degradation, poverty, inequality and environmental justice concerns among other issues. Land-cover maps derived from Landsat satellite imagery were used for purposes of correlating and validating the survey data findings and results. The survey results showed that education levels, working status and marital status have statistically significant effects on community livelihoods (indicated by levels of income, p < 0.05). Maize, fruits and vegetables are the main cultivated crop varieties in the study area, and these crops are mainly used for subsistence to meet household self-consumption requirements. Moreover, local community members and stakeholders argue that the landscape has changed over the past 20 years mainly as a result of urban expansion, deforestation, agricultural diversification and forestry intensification. These landscape changes were largely confirmed by the land-cover change maps derived from satellite imagery. Soil erosion as a result of landscape changes was identified as a major threat and hazard in the study area. Political, natural, economic and cultural factors have been identified as the major underlying drivers for the observed landscape changes. These results have implications for understanding landscape change, coupled with human–nature relationships as well as informing government policy with respect to advancing land management and further promotion of the sustainable livelihoods of rural communities. Overall, the study proposes a multiple stakeholders’ approach and ecosystem-based approach to promote the sustainable management of landscapes in rural areas.

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Spatio-temporal analysis of land use/land cover change dynamics in Paraguai/Jauquara Basin, Brazil

Silva

Galvanin

Menezes

2022

Environ Monit Assess

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Although global climate change is receiving considerable attention, the loss of biodiversity worldwide continues. In this study, we investigated the dynamics of land use/land cover (LULC) change in the Paraguai/Jauquara Basin, Mato Grosso, Brazil. Two analyses were performed using R software. The first was a comparative study of LULC among the LULC classes at the polygon scale, and the second was a spatio-temporal analysis of moving polygons restricted to the agricultural regions in terms of topology, size, distance, and direction of change. The data consisted of Landsat images captured

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Remote sensing as the foundation for high-resolution United States landscape projections – The Land Change Monitoring, assessment, and projection (LCMAP) initiative

Cited by 19 publications

References 56 publications

Using a hybrid demand-allocation algorithm to enable distributional analysis of land use change patterns

Using a hybrid demand-allocation algorithm to enable distributional analysis of land use change patterns

Survey of Community Livelihoods and Landscape Change along the Nzhelele and Levuvhu River Catchments in Limpopo Province, South Africa

Spatio-temporal analysis of land use/land cover change dynamics in Paraguai/Jauquara Basin, Brazil

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