In addition to its standard suite of mensuration variables, the Forest Inventory and Analysis (FIA) program of the U.S. Forest Service also collects data on forest health variables formerly measured by the Forest Health Monitoring program. FIA obtains forest health information on a subset of the base sample plots. Due to the sample size differences, the two sets of variables have traditionally ...

Classifying remote sensing imageries to obtain reliable and accurate LandUse/Cover (LUC) information still remains a challenge that depends on many factors suchas complexity of landscape especially in arid region. The aim of this paper is to extractreliable LUC information from Land sat imageries of the Pishkouh watershed of centralarid region, Iran. The classical Maximum Likelihood Classifier ...

Arctic sea ice has been on the decline over the past several decades, and multi-year sea ice has decreased significantly in its areal share of the overall sea ice cover. Changes in several key variables such as radiative balances, albedo, ice surface temperature, and ice thickness have driven much of the decline, but the motion of sea ice makes studying the effects of these variables on individ...

This paper investigates the contribution of Enhanced Vegetation Index (EVI) data to the improvement of object-based image analysis using multi-spectral Moderate Resolution Imaging Spectral-radiometer (MODIS) imagery. Object-based image analysis classifies objects instead of single pixels. The idea to classify objects stems from the fact that most often the important information to process an im...

Image labeling and parcellation are critical tasks for the assessment of volumetric and morphometric features in medical imaging data. The process of image labeling is inherently error prone as images are corrupted by noise and artifact. Even expert interpretations are subject to subjectivity and the precision of the individual raters. Hence, all labels must be considered imperfect with some de...

Accurate land cover maps based on remote sensing observations are required for a.o. the evaluation of vegetation change models. In this study, where we investigate the intensification and extensification of land use in an area in southern France, purely spectrally based classification accuracy proved not to be sufficient. Therefore, we present a method to classify Mediterranean vegetation commu...

The development of digital global databases containing data such as elevation and soil can greatly simplify and aid in the classification of remotely sensed data to create land-use classes. An efficient method that can simultaneously handle diverse input dimensions can be formed by merging fuzzy logic and neural networks. The so-called granular or fuzzy neural networks are able not only to achi...

2.1 Data Description Population distribution is often highly correlated with land cover types and we incorporate land cover information using the finest resolution contemporary ground-validated dataset available for each country. Land cover data are complemented by digital elevation data and its derived slope estimates, primarily from the SRTM-based HydroSheds data (Lehner et al., 2006). We als...