Predictive Ecosystem Mapping (PEM) for 8.2 Million ha of Forestland, British Columbia, Canada

MacMillan, R.A.; Moon, David E.; Coupé, R.A.; Phillips, Nancy J.

doi:10.1007/978-90-481-8863-5_27

Cited by 11 publications

(9 citation statements)

References 12 publications

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“…This is higher than the 65% commonly accepted as the map accuracy of conventional soil maps (Marsman and De Gruijter, 1986). It also compares well with other studies using comparable methodology, such as the 69% of MacMillan et al (2010), 69% of Van Zijl et al (2012), and 76% of Zhu et al (2008).…”

Section: Resultssupporting

confidence: 86%

Creating a conceptual hydrological soil response map for the Stevenson Hamilton Research Supersite, Kruger National Park, South Africa

Zijl¹,

Roux²

2014

WSA

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The soil water regime is a defining ecosystem service, directly influencing vegetation and animal distribution. Therefore the understanding of hydrological processes is a vital building block in managing natural ecosystems. Soils contain morphological indicators of the water flow paths and rates in the soil profile, which are expressed as 'conceptual hydrological soil responses' (CHSR's). CHSR's can greatly aid in the understanding of hydrology within a landscape and catchment. Therefore a soil map could improve hydrological assessments by providing both the position and area of CHSR's. Conventional soil mapping is a tedious process, which limits the application of soil maps in hydrological studies. The use of a digital soil mapping (DSM) approach to soil mapping can speed up the mapping process and thereby extend soil map use in the field of hydrology. This research uses an expert-knowledge DSM approach to create a soil map for Stevenson Hamilton Research Supersite within the Kruger National Park, South Africa. One hundred and thirteen soil observations were made in the 4 001 ha area. Fifty-four of these observations were pre-determined by smart sampling and conditioned Latin hypercube sampling. These observations were used to determine soil distribution rules, from which the soil map was created in SoLIM. The map was validated by the remaining 59 observations. The soil map achieved an overall accuracy of 73%. The soil map units were converted to conceptual hydrological soil response units (CHSRUs), providing the size and position of the CHSRUs. Such input could potentially be used in hydrological modelling of the site.

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Section: Resultssupporting

confidence: 86%

Creating a conceptual hydrological soil response map for the Stevenson Hamilton Research Supersite, Kruger National Park, South Africa

Zijl¹,

Roux²

2014

WSA

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“…The amount of importance in assessing the uncertainty is almost as great as that in making a prediction map [58], uncertainty map helped researchers to identify the source of the uncertainty and propose solutions [59,60]. To obtain a very reliable prediction, we can control the uncertainty of soil TN map from two aspects.…”

Section: Uncertainty In Soil Tn Predictionmentioning

confidence: 99%

Fine-Resolution Mapping of Soil Total Nitrogen across China Based on Weighted Model Averaging

et al. 2019

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Accurate estimates of the spatial distribution of total nitrogen (TN) in soil are fundamental for soil quality assessment, decision making in land management, and global nitrogen cycle modeling. In China, current maps are limited to individual regions or are of coarse resolution. In this study, we compiled a new 90-m resolution map of soil TN in China by the weighted summation of random forest and extreme gradient boosting. After harmonizing soil data from 4022 soil profiles into a fixed soil depth (0–20 cm) by equal area spline, 18 environmental covariates were employed to characterize the spatial pattern of soil TN in topsoil across China. The accuracy assessments from independent validation data showed that the weighted model averaging gave the best predictions with an acceptable R2 (0.41). The prediction map showed that high-value areas of soil TN were mainly distributed in the eastern Tibetan Plateau, central Qilian Mountains and the north of the Greater Khingan Range. Climate factors had a considerable influence on the variation of the soil TN, and land-use types played a pivotal part in each climate zone. This high-resolution and high-quality soil TN data set in China can be very useful for future inventories of soil nitrogen, assessments of soil nutrient status, and management of arable land.

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“…Furthermore, they also found that as the cell size of the DEM increased, the accuracy of terrain attributes at particular locations tends to decrease. Other studies such as Kempen et al (2012), McKenzie et al (2000), McSweeney et al (1994), Moore et al(1993), MacMillan et al (2010, Arrouays et al (2014), Zhu (1997) have also used GIS-based soil mapping applications. They found that terrain attributes derived from DEM such as slope, elevation and topographic wetness index were related to soil properties and that these attributes were useful in digital soil mapping.…”

Section: Introductionmentioning

confidence: 97%

GIS-fuzzy logic based approach in modeling soil texture: Using parts of the Clay Belt and Hornepayne region in Ontario Canada as a case study

et al. 2015

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Predictive Ecosystem Mapping (PEM) for 8.2 Million ha of Forestland, British Columbia, Canada

Cited by 11 publications

References 12 publications

Creating a conceptual hydrological soil response map for the Stevenson Hamilton Research Supersite, Kruger National Park, South Africa

Creating a conceptual hydrological soil response map for the Stevenson Hamilton Research Supersite, Kruger National Park, South Africa

Fine-Resolution Mapping of Soil Total Nitrogen across China Based on Weighted Model Averaging

GIS-fuzzy logic based approach in modeling soil texture: Using parts of the Clay Belt and Hornepayne region in Ontario Canada as a case study

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