Modelling changes in cation composition of a soil after clearfelling a eucalypt forest in East Gippsland, Australia

Ludwig, Bernard; Khanna, P.; Raison, R. J.; Jacobsen, K.L.

doi:10.1016/s0016-7061(97)00052-9

Cited by 9 publications

(3 citation statements)

References 26 publications

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“…Areas affected by severe soil disturbance and high fire intensity comprised approximately 16% and 24%, respectively of the total area (10 ha) logged at Cabbage Tree Creek. Impacts of soil disturbance on carbon and nitrogen mineralisation (Bauhus et al, 1993), availability of phosphorus (Bauhus and Khanna, 1994;Romanya et al, 1994), and exchangeable cations (Ludwig et al, 1997(Ludwig et al, , 1998 were evaluated during the first few years after harvesting.…”

Section: Cabbage Tree Creekmentioning

confidence: 99%

Carbon and nitrogen in forest soils: Potential indicators for sustainable management of eucalypt forests in south-eastern Australia

Hopmans¹,

Bauhus

Khanna

et al. 2005

Forest Ecology and Management

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Section: Cabbage Tree Creekmentioning

confidence: 99%

Carbon and nitrogen in forest soils: Potential indicators for sustainable management of eucalypt forests in south-eastern Australia

Hopmans¹,

Bauhus

Khanna

et al. 2005

Forest Ecology and Management

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“…To ensure the sustainability of forests, we need to characterize their structure [10]. This implies the understanding of the different variables associated with the structure of forests [11]. Forest structural variables, such as volume or biomass, average diameter and height, are important data needed to assess forest resources [12,13].…”

Section: Introductionmentioning

confidence: 99%

Spatial Analysis of Temperate Forest Structure: A Geostatistical Approach to Natural Forest Potential

Prieto‐Amparán

Santellano‐Estrada

Villarreal‐Guerrero

et al. 2019

Forests

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Forest ecosystems represent an important means of ecosystem services; they are key as carbon sinks, water collectors, soil stabilizers, suppliers of great biological diversity, among other benefits. In addition, regionalization based on forest conditions provides a valuable approach to understanding and analyzing spatial patterns, which is useful as a tool for the implementation of forest ecosystem protection and conservation programs. In this research, the structure of a temperate forest in the western Sierra Madre region of Mexico was analyzed and characterized. The study unit was the watershed and the analysis used a geospatial approach combined with multivariate techniques such as: Principal Component Analysis, Cluster Analysis (CA), Discriminant Analysis (DA) and Multivariate Analysis of Variance. We evaluated the relationships among spectral satellite data, thematic maps and structural forest variables. A total of 345 watersheds were grouped based on these variables. The grouping of watersheds under low, medium and high production conditions was carried out with CA, defining 3 groups. The validation of the grouping was performed through DA, estimating errors with the restitution method, as well as with the cross-validation method. Significant differences were found among the groups. The grouping of watersheds provides observable evidence of the variability of the forest condition throughout the area. This study allows identifying forest areas with different levels of productivity and can help to detect levels of vulnerability and ecological fragility in natural forests in temperate zones.

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“…Even though various models (analytic, deterministic, or stochastic) have been developed to simulate soil chemical [16] and hydraulics [3,8,26] processes in the vadose zone, these models have some weaknesses including: (1) unsatisfactory description of the cause-effect relationships among the processes for a given domain, (2) the determination of a large number of parameter values for these models to be applied for different environmental conditions. Soil data are numerous, nonlinear, complex, showing noise or outliers.…”

Section: Introductionmentioning

confidence: 99%

Self-organizing map artificial neural network application in multidimensional soil data analysis

Merdun

2010

Neural Comput & Applic

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Because of the complex nonlinear relationships between soil variables and their multivariable aspects, classical analytic, deterministic, or linear statistical methods are unreliable and cause difficulty to present or visualize the results. Using intelligent techniques, which have ability to analyze the multidimensional soil data with an intricate visualization technique, is crucial for nutrient and water management in soil, consequently, for sustainable agriculture and groundwater management. In this study, first, the Kohonen self-organizing feature maps (KSOFM) neural network was applied to analyze the effects of soil physical properties on soil chemical/hydraulic processes, and to diagnose the inter-relationships of the multivariable soil data in vadose zone. The inter-relationships among the soil variables were extracted and interpreted using the pattern analysis visualized in component planes. Then K-means clustering algorithm was used to determine the optimal number of clusters by using the Silhouette clustering validity index, resulting in six clusters or groups for soil variables. In conclusion, the KSOFM technique is an effective tool for analyzing and diagnosing the dynamics in soil and extracting information from the multidimensional soil data. These results suggest that this technique has a potential to monitor and diagnose not only soil physical/ chemical/hydraulic processes, but also soil morphological and microbiological processes.

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Modelling changes in cation composition of a soil after clearfelling a eucalypt forest in East Gippsland, Australia

Cited by 9 publications

References 26 publications

Carbon and nitrogen in forest soils: Potential indicators for sustainable management of eucalypt forests in south-eastern Australia

Carbon and nitrogen in forest soils: Potential indicators for sustainable management of eucalypt forests in south-eastern Australia

Spatial Analysis of Temperate Forest Structure: A Geostatistical Approach to Natural Forest Potential

Self-organizing map artificial neural network application in multidimensional soil data analysis

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