proper forest management needs quantitative and precise estimates of forest stands characteristics. remotely sensed imageries, due to accurate and broad spatial information, has become a cost-effective tool in forest management. classification of forest attributes and generation of thematic maps are among the common applications of remote sensing. the objective of this study was to optimize the...

Phenology-based multi-index with the random forest (RF) algorithm can be used to overcome the shortcomings of traditional deforestation mapping that involves pixel-based classification, such as ISODATA or decision trees, and single images. The purpose of this study was to investigate methods to identify specific types of deforestation in North Korea, and to increase the accuracy of classificati...

Random forests are a popular classification method based on an ensemble of a single type of decision trees from subspaces of data. In the literature, there are many different types of decision tree algorithms, including C4.5, CART, and CHAID. Each type of decision tree algorithm may capture different information and structure. This paper proposes a hybrid weighted random forest algorithm, simul...

specific features of the structure of the ground litter invertebrate community in forest belt ecosystems in the ukrainian steppe zone have been considered. for 14 years invertebrate fauna of the litter of 176 forest belt sites with different composition has been studied with the aid of soil traps. the main characteristics of litter invertebrate communities (total population, a number of species...

Climate change is a factor that largely contributes to the increase of forest areas affected by natural damages. Therefore, the development of methodologies for forest monitoring and rapid assessment of affected areas is required. Space-borne synthetic aperture radar (SAR) imagery with high resolution is now available for large-scale forest mapping and forest monitoring applications. However, a...

Improved technologies are needed to advance our knowledge of the biophysical and human factors influencing tropical dry forests, one of the world’s most threatened ecosystems. We evaluated the use of light detection and ranging (LiDAR) data to address two major needs in remote sensing of tropical dry forests, i.e., classification of forest types and delineation of forest successional status. We...

An efficient means to map tree plantations is needed to detect tropical land use change and evaluate reforestation projects. To analyze recent tree plantation expansion in northeastern Costa Rica, we examined the potential of combining moderate-resolution hyperspectral imagery (2005 HyMap mosaic) with multitemporal, multispectral data (Landsat) to accurately classify (1) general forest types an...

Accurate, current, and cost-effective fire fuel data are required by management and fire science communities for use in reducing wildland fire hazards over large areas. In this paper we present results of applying decision-tree techniques to mapping vegetation parameters (such as vegetation types and canopy structure classification) required for fire fuel characterization. Specifically, we pres...

This study investigated whether high-resolution satellite imagery is suitable for preparing a detailed digital forest cover map that discriminates forest cover at the tree species level. First, we tried to find an optimal process for segmenting the high-resolution images using a region-growing method with the scale, color and shape factors in Definiens(®) Professional 5.0. The image was classif...