https://ssl.tamu.edu/media/11681/remotesensing-ramesh.pdf

Estimating regional forest cover in East Texas using Enhanced Thematic Mapper (ETM+) data

Forest or wildland vegetation classification is one of the most challenging applications of LANDSAT‐MSS data, because of the common heterogeneity of this cover type and the numerous factors affecting its spectral response. Vegetation classes are difficult to define on the basis of their spectral characteristics alone, they are better characterized by a spatially distributed pattern of spectral responses. Studies having indicated that spectral data analysis is a limited tool for classifying vegetation, in 1978 the Laurentian Forest Research Centre (LFRC), facing the problem of choosing an operational methodology to produce vegetation mapping over large areas, adopted a method that takes advantage of human interpretation and computer capabilities: the interpretation of digital enhancements based on principal component analysis. The main steps of the enhancement methodology are given, from the delineation of training areas for statistic generation to various histogram stretches of the output components mappe...

Visual interpretation of vegetation through digitally enhanced LANDSAT‐MSS images

The conservation of Jordan's Mediterranean forest requires the use of remote sensing. Among the most important parameters needed are the crown-cover percentage (C) and above-ground biomass (A). This study aims to: (1) identify the best predictor(s) of C using Landsat Enhanced Thematic Mapper (ETM) bands and the derived transformed normalized difference vegetation index (TNDVI); (2) determine if C is a good predictor of A, volume (V), Shannon diversity index (S) and basal area (B); and (3) generate maps of all these parameters. A Landsat ETM image, aerial photographs and ground surveys are used to model C using multiple regression. C is then modelled to A, V, S and B using linear regression. The relationship between C and Landsat ETM bands (1 and 7) plus the TNDVI is significantly high (coefficient of determination R2 = 0.8) and is used to produce the C map. The generated C map is used to predict A (R2 = 0.56), V (R2 = 0.58), S (R2 = 0.50) and B (R2 = 0.43). Cross validation for the predicted C map (cross-validation error = 5.3%) and for the predicted forest-parameter maps (cross-validation error = 13.7%-19.9%) shows acceptable error levels. Results indicate that Jordan's east Mediterranean forest parameters can be mapped and monitored for biomass accumulation and carbon dioxide (CO2) flux using Landsat ETM images.

Estimating east Mediterranean forest parameters using Landsat ETM

Since the introduction of geographic information systems (GIS) to natural resource management in the 1970s, there has been a logical increase in the use of GIS by natural resource management organizations. This article assesses the literature in applied North American forestry journals, which are read mainly by forest practitioners, and illustrates the trends of technological adoption by natural resource management organizations. We conclude that the diversity of GIS technology use in forestry is increasing and evolving to a high and complex level. While small-scale (local) and site-specific natural resource applications predominate the use of GIS in this literature, landscape applications have gained more attention and importance in recent years, mainly in the western and north-central United States. Although several of the journals we reviewed emphasize the practical nature and value of information, few papers were located that illustrate GIS implementation in natural resource organizations or advances ...

A Historical Perspective on the Use of GIS and Remote Sensing in Natural Resource Management, as Viewed through Papers Published in North American Forestry Journals from 1976 to 2005

Certaines plantes du sous-etage, qualifiees d'especes indicatrices, ont des possibilites limitees quant a la selection d'habitat. La teledetection de ces plantes pourrait fournir une information utile sur plusieurs attributs ecologiques pour les sites ou elles se presentent. Dans cette etude, on analyse la separabilite spectrale de diverses composantes du sous-etage incluant quatre plantes indicatrices communement rencontrees dans le comte de Kananaskis, en Alberta, et on determine l'influence de la fermeture du couvert sur la detectabilite de ces caracteristiques a l'aide d'images hyperspectrales CASI. Des spectres mixtes simules des composantes du sous-etage ont ete crees pour determiner leur separabilite. Des techniques de modelisation lineaire de spectres ont ete utilisees pour detecter les proportions au niveau du sous-pixel des composantes de l'etage dominant et du sous-etage pour 41 sites a partir d'images CASI a 60 cm et a 2 m. Les resultats demontrent qu'il est possible d'utiliser la modelisation des spectres mixtes avec succes pour separer les composantes forestieres du sous-etage et de l'etage dominant et que la fermeture du couvert joue un role important dans la detection des composantes du sous-etage.

The influence of canopy closure on the detection of understory indicator plants in Kananaskis country

Classification of forest resources with Landsat data.

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Classification of forest resources with Landsat data.