Development of a New Wetness Index Based on RADARSAT-1 ScanSAR Data

Teasing Apart Silvopasture System Components Using Machine Learning for Optimization

Soil Systems ◽

10.3390/soilsystems5030041 ◽

2021 ◽

Vol 5 (3) ◽

pp. 41

Author(s):

Tulsi P. Kharel ◽

Amanda J. Ashworth ◽

Phillip R. Owens ◽

Dirk Philipp ◽

Andrew L. Thomas ◽

...

Keyword(s):

Machine Learning ◽

Linear Regression ◽

Regression Tree ◽

Plant Production ◽

System Level ◽

Learning Approaches ◽

Nutrient Distribution ◽

Wetness Index ◽

Variable Approach ◽

Animal Grazing

Silvopasture systems combine tree and livestock production to minimize market risk and enhance ecological services. Our objective was to explore and develop a method for identifying driving factors linked to productivity in a silvopastoral system using machine learning. A multi-variable approach was used to detect factors that affect system-level output (i.e., plant production (tree and forage), soil factors, and animal response based on grazing preference). Variables from a three-year (2017–2019) grazing study, including forage, tree, soil, and terrain attribute parameters, were analyzed. Hierarchical variable clustering and random forest model selected 10 important variables for each of four major clusters. A stepwise multiple linear regression and regression tree approach was used to predict cattle grazing hours per animal unit (h ha−1 AU−1) using 40 variables (10 per cluster) selected from 130 total variables. Overall, the variable ranking method selected more weighted variables for systems-level analysis. The regression tree performed better than stepwise linear regression for interpreting factor-level effects on animal grazing preference. Cattle were more likely to graze forage on soils with Cd levels <0.04 mg kg−1 (126% greater grazing hours per AU), soil Cr <0.098 mg kg−1 (108%), and a SAGA wetness index of <2.7 (57%). Cattle also preferred grazing (88%) native grasses compared to orchardgrass (Dactylis glomerata L.). The result shows water flow within the landscape position (wetness index), and associated metals distribution may be used as an indicator of animal grazing preference. Overall, soil nutrient distribution patterns drove grazing response, although animal grazing preference was also influenced by aboveground (forage and tree), soil, and landscape attributes. Machine learning approaches helped explain pasture use and overall drivers of grazing preference in a multifunctional system.

Download Full-text

Dynamic estimation of free water surface coverage from a basin wetness index of the Mackenzie River basin using SSM/I measurements

Canadian Journal of Remote Sensing ◽

10.5589/m07-016 ◽

2007 ◽

Vol 33 (2) ◽

pp. 121-129 ◽

Cited By ~ 3

Author(s):

M. Temimi ◽

R. Leconte ◽

F. Brissette ◽

N. Chaouch

Keyword(s):

River Basin ◽

Water Surface ◽

Surface Coverage ◽

Free Water ◽

Wetness Index ◽

Free Water Surface ◽

Mackenzie River Basin ◽

Dynamic Estimation ◽

Mackenzie River

Download Full-text

The Analysis of Drainage Time and Resolution of the Quasi-dynamic and Dynamic Wetness Index

Journal of Korea Water Resources Association ◽

10.3741/jkwra.2003.36.6.949 ◽

2003 ◽

Vol 36 (6) ◽

pp. 949-960 ◽

Cited By ~ 1

Author(s):

Sang-Hyun Kim ◽

Ji-Young Han ◽

Ga-Young Lee ◽

Nam-Won Kim

Keyword(s):

Wetness Index ◽

Drainage Time

Download Full-text

Numerical Modelling of the Topographic Wetness Index: An Analysis at Different Scales

International Journal of Geosciences ◽

10.4236/ijg.2011.24050 ◽

2011 ◽

Vol 02 (04) ◽

pp. 476-483 ◽

Cited By ~ 8

Author(s):

Anderson Luis Ruhoff ◽

Nilza Maria Reis Castro ◽

Alfonso Risso

Keyword(s):

Numerical Modelling ◽

Topographic Wetness Index ◽

Wetness Index

Download Full-text

Identification of Surface Water Storing Sites Using Topographic Wetness Index (TWI) and Normalized Difference Vegetation Index (NDVI)

Journal of Natural Resources and Development ◽

10.5027/jnrd.v8i0.09 ◽

2018 ◽

Vol 8 ◽

pp. 91-100

Author(s):

Belete Berhanu ◽

Ethiopia Bisrat

Keyword(s):

Surface Water ◽

Water Resource ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Cost Effective ◽

Water Shortage ◽

Sample Volume ◽

Runoff Generation ◽

Topographic Wetness Index ◽

Wetness Index

Ethiopia is endowed with water and has a high runoff generation area compared to many countries, but the total stored water only goes up to approximately 36BCM. The problem of water shortage in Ethiopia emanates from the seasonality of rainfall and the lack of infrastructure for storage to capture excess runoff during flood seasons. Based on this premise, a method for a syndicate use of topography, land use and vegetation was applied to locate potential surface water storing sites. The steady-state Topographic Wetness Index (TWI) was used to represent the spatial distribution of water flow and water stagnating across the study area and the Normalized Difference Vegetation Index (NDVI) was used to detect surface water through multispectral analysis. With this approach, a number of water storing sites were identified in three categories: primary sources (water bodies based), secondary sources (Swampy/wetland based) and tertiary sources (the land based). A sample volume analysis for the 120354 water storing sites in category two, gives a 44.92BCM potential storing capacity with average depth of 4 m that improves the annual storage capacity of the country to 81BCM (8.6 % of annual renewable water sources). Finally, the research confirmed the TWI and NDVI based approach for water storing sites works without huge and complicated earth work; it is cost effective and has the potential of solving complex water resource challenges through spatial representation of water resource systems. Furthermore, the application of remote sensing captures temporal diversity and includes repetitive archives of data, enabling the monitoring of areas, even those that are inaccessible, at regular intervals.

Download Full-text