Resources
Displaying 1271 - 1275 of 2258Evaluating the Efficiency of Different Regression, Decision Tree, and Bayesian Machine Learning Algorithms in Spatial Piping Erosion Susceptibility Using ALOS/PALSAR Data
Piping erosion is one form of water erosion that leads to significant changes in the landscape and environmental degradation. In the present study, we evaluated piping erosion modeling in the Zarandieh watershed of Markazi province in Iran based on random forest (RF), support vector machine (SVM), and Bayesian generalized linear models (Bayesian GLM) machine learning algorithms.
Mapping Flood-Based Farming Systems with Bayesian Networks
Many actors in agricultural research, development, and policy arenas require accurate information on the spatial extents of cropping and farming practices. While remote sensing provides ways for obtaining such information, it is often difficult to distinguish between different types of agricultural practices or identify particular farming systems. Stochastic system behavior or similarity in the spectral signatures of different system components can lead to misclassification.
Potential Impacts of Soil Tillage System on Isoflavone Concentration of Soybean as Functional Food Ingredients
Soybean is an important natural source of isoflavones, but their concentration is likely to be influenced by external factors, such as climatic conditions and soil tillage systems. However, there is minimal information about the effects of such external factors on the isoflavone concentration in soybeans grown in Europe.
Four Decades of Land-Cover Change on the Kenai Peninsula, Alaska: Detecting Disturbance-Influenced Vegetation Shifts Using Landsat Legacy Data
Across Alaska’s Kenai Peninsula, disturbance events have removed large areas of forest over the last half century. Simultaneously, succession and landscape evolution have facilitated forest regrowth and expansion. Detecting forest loss within known pulse disturbance events is often straightforward given that reduction in tree cover is a readily detectable and measurable land-cover change. Land-cover change is more difficult to quantify when disturbance events are unknown, remote, or environmental response is slow in relation to human observation.
Aboveground Biomass Distribution in a Multi-Use Savannah Landscape in Southeastern Kenya: Impact of Land Use and Fences
Savannahs provide valuable ecosystem services and contribute to continental and global carbon budgets. In addition, savannahs exhibit multiple land uses, e.g., wildlife conservation, pastoralism, and crop farming. Despite their importance, the effect of land use on woody aboveground biomass (AGB) in savannahs is understudied. Furthermore, fences used to reduce human–wildlife conflicts may affect AGB patterns. We assessed AGB densities and patterns, and the effect of land use and fences on AGB in a multi-use savannah landscape in southeastern Kenya.