Soil–landscape modeling across a physiographic region: Topographic patterns and model transportability

Thompson, James A.; Pena‐Yewtukhiw, E. M.; Grove, John H.

doi:10.1016/j.geoderma.2006.03.037

Cited by 104 publications

(52 citation statements)

References 34 publications

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“…However, some authors note that these models may have limited generality (Thompson et al, 2006) or that unmeasured variables such as local management practices may be more important (Page et al, 2005). b Hydrological variables.…”

Section: Physical Data Describing the Environmentmentioning

confidence: 99%

Potential contributions of remote sensing to ecosystem service assessments

Andrew

Wulder

Nelson

2014

Progress in Physical Geography: Earth and Environment

145

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Abstract. Ecological and conservation research has provided a strong scientific underpinning to the modeling of ecosystem services (ESs) over space and time, by identifying the ecological processes and components of biodiversity (ecosystem service providers, functional traits) that drive ES supply. Despite this knowledge, efforts to map the distribution of ESs often rely on simple spatial surrogates that provide incomplete and non-mechanistic representations of the biophysical variables they are intended to proxy. However, alternative datasets are available that allow for more direct, spatially nuanced inputs to ES mapping efforts. Many spatially explicit, quantitative estimates of biophysical parameters are currently supported by remote sensing, with great relevance to ES mapping. Additional parameters that are not amenable to direct detection by remote sensing may be indirectly modeled with spatial environmental data layers. We review the capabilities of modern remote sensing for describing biodiversity, plant traits, vegetation condition, ecological processes, soil properties, and hydrological variables and highlight how these products may contribute to ES assessments. Because these products often provide more direct estimates of the ecological properties controlling ESs than the spatial proxies currently in use, they can support greater mechanistic realism in models of ESs. By drawing on the increasing range of remote sensing instruments and measurements, datasets appropriate to the estimation of a given ES can be selected or developed. In so doing, we anticipate rapid progress to the spatial characterization of ecosystem services, in turn supporting ecological conservation, management, and integrated land use planning. Keywords I IntroductionNatural and managed ecosystems provide physical, emotional, and economic wellbeing to human societies via benefits known as ecosystem services (ESs). There are a great many ways by which ecosystems benefit humanity. Conceptually, this diversity of ecosystem services is often grouped into provisioning (natural resources provided by ecological systems such as food, forage, and timber), cultural (spiritual and heritage values derived from natural and managed systems, as well as natural areas tourism and recreation), and regulating and supporting services (life support services such as air or water purification, climate regulation, 2 and ecological processes that maintain functioning ecosystems, contributing to all services) (MEA, 2005).Historically, ESs have been given little formal attention, especially those services that are not traditionally traded in a market (Costanza et al., 1997), leading to unsustainable land use practices with unintended consequences (Bennett et al., 2009;MEA, 2005). There is growing recognition that conservation and land use planning should strive to maintain the multifunctionality of natural and managed systems through balanced portfolios of ESs. Knowledge about the environmental and anthropogenic controls of ESs and the spatial distribution o...

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Section: Physical Data Describing the Environmentmentioning

confidence: 99%

Potential contributions of remote sensing to ecosystem service assessments

Andrew

Wulder

Nelson

2014

Progress in Physical Geography: Earth and Environment

145

View full text Add to dashboard Cite

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“…We derived several primary and secondary topographic attributes to evaluate their use in predicting AL thickness. These topographic attributes included elevation, slope, aspect, curvature (plan, profile, and total), upslope contributing area, flow length, soil wetness index, sediment transport index, stream power index, terrain characterization index, and slope aspect index (Thompson et al, 2006). Land cover data of 60-m spatial resolution were extracted for Alaska from the National Land Cover Database (NLCD; Multi-Resolution Land Characteristics Consortium, 2011).…”

Section: Environmental Datasetsmentioning

confidence: 99%

Active-Layer Thickness across Alaska: Comparing Observation-Based Estimates with CMIP5 Earth System Model Predictions

Mishra

Riley

2014

Soil Science Society of America Journal

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“…Soil has long been considered as the result of the interaction of its formative environment, including climate, parent material, terrain, and vegetation conditions (Winklerprins, 1999;Mcbratney et al, 2003;Yang et al, 2008;Stoorvogel et al, 2009). Therefore, the relationships between soil and its environmental covariates can be used to map soil variations over space (Thompson et al, 2006;Sumfleth and Duttmann, 2008;Zhu et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

Mapping soil organic matter concentration at different scales using a mixed geographically weighted regression method

et al. 2016

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The present regression models in digital soil mapping usually assume that relationships between soil properties and environmental variables are always fixed (as in MLR) or varying (as in GWR) in geographical space. In reality, some of the environmental variables may be fixed in affecting soil property variation and some are local varying. In this study, a mixed geographically weighted regression (MGWR) method which can deal with fixed and varying spatial relationships between a target variable and its environmental variables were proposed and used to predict topsoil soil organic matter (SOM) concentration in two study areas (Heshan, Heilongjiang province and Xuancheng, Anhui province, China) at two scales. Three groups of sample sets were created based on the total samples in the study areas to evaluate the robustness and stability of the model. Multiple linear regression (MLR), geographically weighted regression (GWR), GWR-kriging (GWRK), local regression-kriging (LRK), kriging with an external drift (KED), and ordinary kriging (OK) were used for comparison with MGWR. The validation results showed that the use of MGWR reduced the RMSE of GWR by 10.5% and 7.6% on average, reduced the RMSE of MLR by 12.8% and 9.9% on average for Heshan and Xuancheng study areas respectively. MGWR also showed a good competitiveness when compared with GWRK, LRK, KED and OK. In Heshan study area, the influence of flow length, relative position index, foot slope and distance to the nearest drainage were constant, whereas the elevation, topographic wetness index and valley index showed different influence in different regions. In Xuancheng study area, the fixed environmental variables were profile curvature, topographic wetness index and slope, whereas the varying environmental variables were precipitation, temperature, elevation, and limestone. The results indicate that the accuracy of predictions can be improved by adaptive coefficient according to the variation of environmental variables as implemented in MGWR compared with others considering only the local or global relationships. It was concluded that mixed geographically weighted regression model could be a potential method for digital soil mapping.

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Soil–landscape modeling across a physiographic region: Topographic patterns and model transportability

Cited by 104 publications

References 34 publications

Potential contributions of remote sensing to ecosystem service assessments

Potential contributions of remote sensing to ecosystem service assessments

Active-Layer Thickness across Alaska: Comparing Observation-Based Estimates with CMIP5 Earth System Model Predictions

Mapping soil organic matter concentration at different scales using a mixed geographically weighted regression method

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