Generation of fuel type maps from Landsat TM images and ancillary data in Mediterranean ecosystems

2002 ◽  
Vol 32 (8) ◽  
pp. 1301-1315 ◽  
Author(s):  
David Riaño ◽  
Emilio Chuvieco ◽  
Javier Salas ◽  
Alicia Palacios-Orueta ◽  
Aitor Bastarrika
Keyword(s):  
Satellite Images ◽  
Structural Characteristics ◽  
Remote Sensing Data ◽  
Mediterranean Ecosystems ◽  
Landsat Tm ◽  
Fuel Type ◽  
Ancillary Data ◽  
Vegetation Height ◽  
Digital Elevation ◽  
Elevation Data

This paper presents methods to generate fuel type maps from remote sensing data at a spatial and temporal scale adequate for operational fire management applications. Fuel type maps account for structural characteristics of vegetation related to fire behaviour and fire propagation. A fuel type classification system adapted to the ecological characteristics of the European Mediterranean basin was adopted for this study. The Cabañeros National Park (in central Spain) area was selected for testing and validating the methods. Fuel type maps were derived from two Landsat TM satellite images and digital elevation data. Atmospheric and topographic corrections of the satellite images were performed to reduce spectral variability. A sensitivity analysis was carried out to determine the most appropriate bands for fuel type mapping. The final classification was checked by an intense field survey, the final classification accuracy being estimated at 83%. The main problem was discriminating among those fuel types that differ only in vegetation height or composition of the understory layer. The mean mapping accuracy was 15 m (0.6 pixels), and no areal discrepancy or boundary displacement with vegetation maps was apparent.

scholarly journals Environmental classification of administrative units of the Ukrainian Carpathians

2014 ◽  
pp. 245-253
Author(s):  
Ivan Kruhlov
Keyword(s):  
Cluster Analysis ◽  
Standard Deviation ◽  
Satellite Images ◽  
Environmental Classification ◽  
Digital Elevation ◽  
Elevation Data ◽  
Digital Elevation Data ◽  
Administrative Units ◽  
Elevation Model

Boundaries of 43 administrative units (raions and oblast towns) were digitized and manually rectified using official schemes and satellite images. SRTM digital elevation data were used to calculate mean relative elevation and its standard deviation for each unit, as well as to delineate altitudinal bioclimatic belts and their portions within the units. These parameters were used to classify the units via agglomerative cluster analysis into nine environmental classes. Key words: cluster analysis, digital elevation model, geoecosystem, geo-spatial analysis.

Vegetation mapping of Svalbard utilising Landsat TM/ETM+ data

Polar Record ◽  
2011 ◽  
Vol 48 (1) ◽  
pp. 47-63 ◽  
Author(s):  
Bernt E. Johansen ◽  
Stein Rune Karlsen ◽  
Hans Tømmervik
Keyword(s):  
Satellite Images ◽  
Satellite Image ◽  
Landsat Tm ◽  
Inland Waters ◽  
Vegetation Density ◽  
Ancillary Data ◽  
Vegetation Map ◽  
Digital Format ◽  
Different Types ◽  
Classified Image

ABSTRACTThe overall objective of this paper is to present and discuss the most recently developed vegetation map for Svalbard, Arctic Norway. The map is based on satellite images in which several Landsat TM/ETM+ images were processed through six operational stages involving: (1) automatic image classification, (2) spectral similarity analysis, (3) generation of classified image mosaics, (4) ancillary data analysis, (5) contextual correction, and (6) standardisation of the final map products. The developed map is differentiated into 18 map units interpreted from 37 spectral classes. Among the 18 units separated, six of the units comprise rivers, lakes and inland waters, glaciers, as well as non- to sparsely vegetated areas. The map unit 7 is a result of shadow effects and different types of distortions in the satellite image. The vegetation of the remaining eleven units varies from dense marshes and moss tundra communities to sparsely vegetated polar deserts and moist gravel snowbeds. The accuracy of the map is evaluated in areas were access to traditional maps have been available. The vegetation density and fertility is reflected in computed NDVI values. The map product is in digital format, which gives the opportunity to produce maps in different scales. A map sheet portraying the entire archipelago is one of the main products from this study, produced at a scale of 1:500,000.

scholarly journals REMOTE SENSING DATA FOR MAPPING THE VILLAGE HAZARD OF THE BAMA MILL SITE

2020 ◽  
Vol 1 (2) ◽  
pp. 28-40
Author(s):  
Vladimir P. Stupin
Keyword(s):  
Remote Sensing ◽  
Debris Flow ◽  
Satellite Images ◽  
Remote Sensing Data ◽  
Point Of View ◽  
Digital Elevation ◽  
Age Limits ◽  
The Village ◽  
Debris Flow Hazard ◽  
Mountain Country

The analysis of open access remote sensing materials (satellite images and digital elevation models) from the point of view of their use for the study and mapping of the debris flow hazard of the Baikal mountain country is carried out. Descriptive signs of mudflow phenomena are described, the age limits of their interpretation are substantiated. Maps of debris flow hazard of various territories of the studied region are given.

scholarly journals Quantitative Analysis of Different Environmental Factor Impacts on Land Cover in Nisos Elafonisos, Crete, Greece

2020 ◽  
Vol 17 (18) ◽  
pp. 6437
Author(s):  
Mohamed Elhag ◽  
Silvena Boteva
Keyword(s):  
Land Use ◽  
Environmental Factors ◽  
Land Cover ◽  
Remote Sensing Data ◽  
Mediterranean Ecosystems ◽  
Land Use Land Cover ◽  
Routine Work ◽  
Digital Elevation ◽  
Elevation Model ◽  
Sentinel 2

Land Cover monitoring is an essential task for a better understanding of the ecosystem’s dynamicity and complexity. The availability of Remote Sensing data improved the Land Use Land Cover mapping as it is routine work in ecosystem management. The complexity of the Mediterranean ecosystems involves a complexity of the surrounding environmental factors. An attempt to quantitatively investigate the interdependencies between land covers and affected environmental factors was conducted in Nisos Elafonisos to represent diverse and fragile coastal Mediterranean ecosystems. Sentinel-2 (MSI) sensor and ASTER Digital Elevation Model (DEM) data were used to classify the LULC as well as to draw different vegetation conditions over the designated study area. DEM derivatives were conducted and incorporated. The developed methodology is intended to assess the land use land cover for different practices under the present environmental condition of Nisos Elafonisos. Supervised classification resulted in six different land cover clusters and was tested against three different environmental clusters. The findings of the current research pointed out that the environmental variables are independent and there is a vertical distribution of the vegetation according to altitude.

Landslide Hazard Zoning for Highway in China Base on Remote Sensing and GIS

2011 ◽  
Vol 368-373 ◽  
pp. 1051-1057
Author(s):  
Fu Chun Wang ◽  
Ming Jian Dou ◽  
Wei Wei
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Landslide Hazard ◽  
Annual Rainfall ◽  
Expert Estimation ◽  
Hazard Zoning ◽  
Digital Elevation ◽  
Elevation Data ◽  
Calculation Results ◽  
Cover Ratio

Eight factors such as slope, incision density, incision depth, average annual rainfall, average annual >50mm rainfall days, soil types, river density, and vegetation cover ratio were used to quantify the Landslide Predict Index for highway in China. Using digital elevation data, rainfall data, thematic map of soil and river, remote sensing data of Spot/vegetation NVDI, these factors were calculated base on spatial analysis, hydrologic analysis, geostatistical analysis supported by ArcInfo9 software. The factors weights were confirmed by applying of expert estimation. The calculation results indicate that a highly spatial heterogeneity exists in the landslide Predict index for highway in China. Considering the maximum and minimum value of the index, the Landslide Predict Index for highway is divided into five levels. The landslide hazard zoning for highway is carried out base on the Landslide Predict Index mainly, then the landslide hazard zoning map for highway in China is formed.

Fuel type mapping with Landsat TM images and ancillary data in the Prealpine region of Italy

2006 ◽  
Vol 234 ◽  
pp. S259 ◽  
Author(s):  
Annalisa Francesetti ◽  
Andrea Camia ◽  
Giovanni Bovio
Keyword(s):  
Landsat Tm ◽  
Fuel Type ◽  
Ancillary Data ◽  
Type Mapping
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