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Displaying 1256 - 1260 of 1605Building on qualitative datasets and participatory processes to simulate land use change in a mountain watershed of Northwest Vietnam
In this article we investigate if qualitative soil fertility datasets derived during participatory processes can be combined with a corresponding land use change model (i) to improve the understanding of the social-ecological complexity of land use change and (ii) to allow testing of alternative scenarios even in data-poor environments. To test this hypothesis, a participatory assessment approach was combined with the spatially explicit, soil fertility driven FALLOW (Forest, Agroforest, Low-value Landscape Or Wasteland?) model.
CN-Idris: An Idrisi tool for generating curve number maps and estimating direct runoff
This article presents a raster-based Geographic Information Systems (GIS) tool called CN-Idris that outputs runoff estimates from land use/land cover and hydrologic soil group maps based on the Natural Resources Conservation Service Curve Number method. The tool enables the user to select among three antecedent moisture conditions and two values of the initial abstraction (Iₐ) parameter, and it has the option to include spatial variation in rainfall input.
Regional-scale boreal forest cover and change mapping using Landsat data composites for European Russia
Boreal forests are a critical component of the global carbon cycle, and timely monitoring allows for assessing forest cover change and its impacts on carbon dynamics. Earth observation data sets are an important source of information that allow for systematic monitoring of the entire biome. Landsat imagery, provided free of charge by the USGS Center for Earth Resources Observation and Science (EROS) enable consistent and timely forest cover updates.
Bandwidth determination for kernel density analysis of wildfire events at forest sub-district scale
Forest fire is regarded as one of the most significant factors leading to land degradation. While evaluating fire hazard or producing fire risk zone maps, quantitative analyses using historic fire data is often required, and during all these modeling and multi-criteria analysis processes, the fire event itself is taken as the dependent variable. However, there are two main problematic issues in analyzing historic fire data.
Retrieval of growing stock volume in boreal forest using hyper-temporal series of Envisat ASAR ScanSAR backscatter measurements
Methods for the estimation of forest growing stock volume (GSV) are a major topic of investigation in the remote sensing community. The boreal zone contains almost 30% of global forest by area but measurements of forest resources are often outdated. Although past and current spaceborne synthetic aperture radar (SAR) backscatter data are not optimal for forest-related studies, a multi-temporal combination of individual GSV estimates can improve the retrieval as compared to the single-image case.