Land Library
Welcome to the Land Portal Library. Explore our vast collection of open-access resources (over 74,000) including reports, journal articles, research papers, peer-reviewed publications, legal documents, videos and much more.
/ library resources
Showing items 37 through 45 of 124.
Landscape provides many services for human wellbeing through its mosaic of ecosystems. Although different landscape spatial structures limit some access to these services for local residents, their demand for landscape benefits creates a crucial component in landscape planning.
This paper presents a new approach for rapidly assessing the extent of land use and land cover (LULC) areas in Mato Grosso state, Brazil. The novel idea is the use of an annual time series of fraction images derived from the linear spectral mixing model (LSMM) instead of original bands.
Landscape research involves a large number of scientific disciplines. Different disciplinary and scale approaches have led to the creation of numerous land use/land cover databases with different classification nomenclature.
Measuring, monitoring, and managing biodiversity across agricultural regions depends on methods that can combine high-resolution mapping of landscape patterns with local biodiversity observations.
The Prairie Pothole Region (PPR) is a biotically important region of grassland, wetland, and cropland that traverses the Canada-US border. Significant amounts of grasslands and wetlands within the PPR have been converted to croplands in recent years due to increasing demand for biofuels.
Understanding the trajectories and extents of land use/land cover change (LULCC) is important to generate and provide helpful information to policymakers and development practitioners about the magnitude and trends of LULCC.
Land use and land cover change (LULCC) is a critical factor for enhancing the soil erosion risk and land degradation process in the Wabi Shebelle Basin.
The wide availability of multispectral satellite imagery through projects such as Landsat and Sentinel, combined with the introduction of deep learning in general and Convolutional Neural Networks (CNNs) in particular, has allowed for the rapid and effective analysis of multiple classes of proble
In this study, we measured and characterized the relative dielectric constant of mineral soils over the 0.3–3.0 frequency range, and compared our measurements with values of three dielectric constant simulation models (the Wang, Dobson, and Mironov models).