Site environment characterization of downed woody fuels in the Rincon Mountains, Arizona: regression tree approach

2004 ◽  
Vol 13 (4) ◽  
pp. 467 ◽  
Author(s):  
Erick Sánchez-Flores ◽  
Stephen R. Yool
Keyword(s):  
Remote Sensing ◽  
Fire History ◽  
Forest Canopy ◽  
Vegetation Type ◽  
Regression Tree ◽  
Successful Implementation ◽  
Forest Fuels ◽  
Sensing Technology ◽  
Rincon Mountains

Characterization of forest fuels is key to successful implementation of any fire management system. Great strides have been made in the characterization of forest canopy fuels by the use of remote sensing technology. Remote sensing of surface fuels is, however, limited by the physical intervention of the overlaying canopy. This limitation underscores the importance of exploring alternative approaches that relate site environment characteristics to the production and accumulation of understory fuels. This study predicts downed woody fuel loadings based on variables such as topography, fire history, and vegetation type in the forested area of the Rincon Mountains in southern Arizona. We used classification and regression trees (CART) to make these predictions. Results show that fine woody fuel loadings are predicted best by vegetation type and slope. Coarse woody fuels are predicted best by differences in elevation.

scholarly journals GEOMETRIC AND REFLECTANCE SIGNATURE CHARACTERIZATION OF COMPLEX CANOPIES USING HYPERSPECTRAL STEREOSCOPIC IMAGES FROM UAV AND TERRESTRIAL PLATFORMS

2016 ◽  
Vol XLI-B7 ◽  
pp. 77-82
Author(s):  
E. Honkavaara ◽  
T. Hakala ◽  
O. Nevalainen ◽  
N. Viljanen ◽  
T. Rosnell ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Precision Agriculture ◽  
Data Sets ◽  
Hyperspectral Reflectance ◽  
Model Inversion ◽  
Sensing Applications ◽  
Sensing Technology ◽  
Remote Sensing Applications ◽  
Airborne Vehicle

Light-weight hyperspectral frame cameras represent novel developments in remote sensing technology. With frame camera technology, when capturing images with stereoscopic overlaps, it is possible to derive 3D hyperspectral reflectance information and 3D geometric data of targets of interest, which enables detailed geometric and radiometric characterization of the object. These technologies are expected to provide efficient tools in various environmental remote sensing applications, such as canopy classification, canopy stress analysis, precision agriculture, and urban material classification. Furthermore, these data sets enable advanced quantitative, physical based retrieval of biophysical and biochemical parameters by model inversion technologies. Objective of this investigation was to study the aspects of capturing hyperspectral reflectance data from unmanned airborne vehicle (UAV) and terrestrial platform with novel hyperspectral frame cameras in complex, forested environment.

scholarly journals Remote Sensing-Based Geostatistical Modeling For Conifererous Forest Inventory and Characterization

2000 ◽  
Vol 24 ◽  
pp. 153-155
Author(s):  
Mark Jakubauskas ◽  
Edward Martinko ◽  
Kevin Price
Keyword(s):  
Remote Sensing ◽  
Satellite Imagery ◽  
Habitat Management ◽  
Fire History ◽  
Forest Canopy ◽  
Information Source ◽  
The United States ◽  
Remotely Sensed ◽  
Forest Condition ◽  
Resource Managers

Resource managers of private and public forests are often faced with a host of questions on forest extent, condition, and change in the course of land management. With more than 700 million acres of land covered by forest in the United States, the task of mapping and inventorying forested lands is a challenging one. Detailed and accurate maps of forest condition and structure are a necessity for rigorous ecosystem management. Forest maps are a fundamental information source for fire behavior modeling, animal habitat management, prediction and mapping of forest insect infestations, and plant and animal biodiversity assessment. Digital images acquired by earth imaging satellites are being used to help forest managers provide this information. Satellite images, when analyzed using advanced geostatistical techniques, can produce information on forest condition and structure, information that can be used to help answer questions such as those posed above. Satellite imagery has been used for many years to map land cover in forested regions, but natural resource managers are also starting to use remotely sensed satellite imagery to calculate the age, density, species, and successional state of forests under their care. In May 1999, the Kansas Applied Remote Sensing (KARS) Program at the University of Kansas was selected by NASA Earth Science Enterprise Applications Division to develop methods that use remote-sensing data and advanced geostatistical methods to create maps of forest age and successional state, or "cover types," and of forest biophysical factors, including density, biomass, leaf area, basal area, and height. By calibrating remotely sensed multispectral data with a small number of ground measurements, characteristics of the forest measured at sample points can be extrapolated across a large geographic region. This has significant advantages for forest management, especially when forests are in remote or inaccessible locations. The goal of this research is to develop new methods for the analysis of forest canopy structure, secondary forest regrowth, and forest fire history that take advantage of both the spectral and spatial correlation of ground phenomena and remotely sensed information.

scholarly journals GEOMETRIC AND REFLECTANCE SIGNATURE CHARACTERIZATION OF COMPLEX CANOPIES USING HYPERSPECTRAL STEREOSCOPIC IMAGES FROM UAV AND TERRESTRIAL PLATFORMS

2016 ◽  
Vol XLI-B7 ◽  
pp. 77-82 ◽  
Author(s):  
E. Honkavaara ◽  
T. Hakala ◽  
O. Nevalainen ◽  
N. Viljanen ◽  
T. Rosnell ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Precision Agriculture ◽  
Data Sets ◽  
Hyperspectral Reflectance ◽  
Model Inversion ◽  
Sensing Applications ◽  
Sensing Technology ◽  
Remote Sensing Applications ◽  
Airborne Vehicle

Light-weight hyperspectral frame cameras represent novel developments in remote sensing technology. With frame camera technology, when capturing images with stereoscopic overlaps, it is possible to derive 3D hyperspectral reflectance information and 3D geometric data of targets of interest, which enables detailed geometric and radiometric characterization of the object. These technologies are expected to provide efficient tools in various environmental remote sensing applications, such as canopy classification, canopy stress analysis, precision agriculture, and urban material classification. Furthermore, these data sets enable advanced quantitative, physical based retrieval of biophysical and biochemical parameters by model inversion technologies. Objective of this investigation was to study the aspects of capturing hyperspectral reflectance data from unmanned airborne vehicle (UAV) and terrestrial platform with novel hyperspectral frame cameras in complex, forested environment.

scholarly journals Pemanfaatan Citra Landsat 8 Multitemporal dan Model Forest Canopy Density (FCD) untuk Analisis Perubahan Kerapatan Kanopi Hutan di Kawasan Fakultas Geografi Universitas Gadjah Mada Gunung Kelud, Jawa Timur

2017 ◽  
Vol 31 (1) ◽  
pp. 65
Author(s):  
Shafira Himayah ◽  
Hartono Hartono ◽  
Projo Danoedoro
Keyword(s):  
Remote Sensing ◽  
Forest Canopy ◽  
Landsat 8 ◽  
Canopy Density ◽  
Protection Forest ◽  
Image Recording ◽  
Remote Sensing Technology ◽  
Sensing Technology ◽  
Before And After ◽  
Forest Canopy Density

Penginderaan jauh memiliki keunggulan dalam hal resolusi temporal yang dapat dimanfaatkan untuk meneliti perubahan suatu obyek dalam waktu yang berbeda. Hutan Gunung Kelud mengalami perubahan setelah erupsi tahun 2014. Perubahan tersebut dapat dianalisis dengan memanfaatkan teknologi penginderaan jauh melalui citra multitemporal. Penelitian ini bertujuan untuk mengkaji kemampuan citra Landsat 8 multitemporal dan Forest Canopy Density (FCD) untuk perubahan kerapatan kanopi di Hutan Lindung Gunung Kelud sebelum dan sesudah erupsi tahun 2014.Citra penginderaan jauh yang digunakan adalah citra Landsat 8 perekaman 26 Juni 2013 dan 4 September 2015. Metode yang digunakan adalah pemodelan FCD yang menghasilkan kerapatan kanopi per piksel. Hasil pemodelan FCD kemudian digunakan untuk menganalisis perubahan kerapatan kanopi setelah erupsi. Berdasarkan penelitan ini didapatkan hasil bahwa citra Landsat 8 dapat dipergunakan untuk mengetahui kerapatan kanopi Hutan Lindung Gunung Kelud sebelum dan setelah erupsi dengan masing-masing akurasi sebesar 83,73% dan 81,14%. Terjadi perubahan luas kerapatan kanopi setelah erupsi, dimana terdapat 8833,95 Ha hutan yang mengalami penurunan kerapatan kanopi, sedangkan hutan dengan kerapatan kanopi yang tetap adalah seluas 2149,38 Ha, dan hutan yang mengalami peningkatan kerapatan kanopi adalah seluas 1643,31 Ha. Remote sensing has an advantage in terms of temporal resolution that can be exploited to examine the changes of an object in different times. Gunung Kelud Forest is changing after the eruption in 2014. The changes can be analyzed by utilizing remote sensing technology through multitemporal imagery. This study aims to examine the capabilities of Landsat 8 multitemporal and Forest Canopy Density (FCD) images for changes in canopy density in Kelud Protection Forest before and after the eruption in 2014. Remote sensing imagery used is Landsat 8 image recording June 26, 2013, and September 4, 2015, The method used is FCD modeling that produces a density of the canopy per pixel. FCD modeling results are then used to analyze changes in density of the canopy after the eruption. Based on this research, it can be concluded that Landsat 8 image can be used to determine the density of canopy of Kelud Protection Forest before and after eruption with 83.73% and 81.14% accuracy respectively. There was a change in the area of the canopy density after the eruption, where there was 8833.95 ha of forest that experienced a decrease in canopy density, whereas forests with fixed canopy densities were 2149.38 Ha, and forests with an increase in canopy density were 1643.31 Ha.

Some applications of electron beam microanalysis techniques in VLSI process evaluation

1983 ◽  
Vol 41 ◽  
pp. 160-161
Author(s):  
Simon Thomas
Keyword(s):  
Integrated Circuits ◽  
Process Evaluation ◽  
Large Scale ◽  
Technology Development ◽  
Analytical Techniques ◽  
Successful Implementation ◽  
Analysis Of Failures ◽  
Characterization Of Materials ◽  
Circuits Vlsi

Trends in the technology development of very large scale integrated circuits (VLSI) have been in the direction of higher density of components with smaller dimensions. The scaling down of device dimensions has been not only laterally but also in depth. Such efforts in miniaturization bring with them new developments in materials and processing. Successful implementation of these efforts is, to a large extent, dependent on the proper understanding of the material properties, process technologies and reliability issues, through adequate analytical studies. The analytical instrumentation technology has, fortunately, kept pace with the basic requirements of devices with lateral dimensions in the micron/ submicron range and depths of the order of nonometers. Often, newer analytical techniques have emerged or the more conventional techniques have been adapted to meet the more stringent requirements. As such, a variety of analytical techniques are available today to aid an analyst in the efforts of VLSI process evaluation. Generally such analytical efforts are divided into the characterization of materials, evaluation of processing steps and the analysis of failures.

Hyperspectral Remote Sensing Technology (HRST) program

1997 ◽  
Author(s):  
Tom Wilson ◽  
Rebecca Baugh ◽  
Ron Contillo ◽  
Tom Wilson ◽  
Rebecca Baugh ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Hyperspectral Remote Sensing ◽  
Remote Sensing Technology ◽  
Sensing Technology
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