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Showing items 41878 through 41886 of 73379.Forest productivity is strongly affected by seasonal weather patterns and by natural or anthropogenic disturbances. However weather effects on forest productivity are not currently represented in inventory-based models such as CBM-CFS3 used in national forest C accounting programs.
Support vector machine (SVM) was applied for land-cover characterization using MODIS time-series data. Classification performance was examined with respect to training sample size, sample variability, and landscape homogeneity (purity).
The island of Sri Lanka is free from serious natural hazards such as volcanic activity and earthquakes resulting from climatic extremes, but there are impacts of many natural disasters, such as landslides, floods and droughts, the intensity and frequency of which are increasing due to human inter
The following paper assesses the impact of different policy options on the land use and associatedbiodiversity values of jointly organized low intensity grazing systems (‘Allmende’) inSouthern Bavaria.
The random forest (RF) classifier is a relatively new machine learning algorithm that can handle data sets with large numbers and types of variables.
This paper describes the physical nature of the Tongass National Forest, its salient natural resources, the social and economic importance of the resources, the complexity of the land management planning process, the chronology of the plan development, and the structure and function of the Pacifi
Data on humification is important to assessing the rate and magnitude of soil carbon (C) sequestration.
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