Application of remote sensing, GIS, and GPS in precision forestry practices
Başlık çevirisi mevcut değil.
- Tez No: 540374
- Danışmanlar: Prof. PETE BETTINGER
- Tez Türü: Doktora
- Konular: Ormancılık ve Orman Mühendisliği, Coğrafya, Şehircilik ve Bölge Planlama, Forestry and Forest Engineering, Geography, Urban and Regional Planning
- Anahtar Kelimeler: Belirtilmemiş.
- Yıl: 2017
- Dil: İngilizce
- Üniversite: The Unıversıty Of Georgıa
- Enstitü: Yurtdışı Enstitü
- Ana Bilim Dalı: Belirtilmemiş.
- Bilim Dalı: Belirtilmemiş.
- Sayfa Sayısı: Belirtilmemiş.
Özet
Özet yok.
Özet (Çeviri)
This dissertation is composed of three studies that evaluated the use of precision technologies; remote sensing, geographic information systems (GIS), and global positioning systems (GPS) for the capture and development of forestry and natural resources information. We explored the use of new protocols and procedures to collect data with GPS, estimated urban canopy cover with different remotely sensed sources, and assessed the accuracy of the processes. In the first study, a statistical design was developed to collect data using a type of recreation-grade GPS receiver in the forest, during two different seasons. The dynamically-collected data were used in conjunction with GIS to assess error and accuracy. The results indicated the samples collected in winter showed less variation than the samples collected in summer when compared to the true land area. However, statistical test results suggested the season was not a significant factor. In the second study, two different sampling approaches, random point-sampling and plot/grid were employed for estimating urban tree canopy cover within two U.S. cities, using aerial photography and Google Earth imagery. The results indicated the two different sampling approaches could produce similar estimates of urban canopy cover, although one (point-based) was more time-efficient. Mixed results were observed when considering the imagery sources. In the third study, the urban canopy cover was again assessed within same two U.S. cities yet this was a remote sensing study that incorporated LiDAR data with high resolution remotely sensed imagery, and urban canopy cover was estimated using the pixel-based supervised maximum likelihood classification method. The results suggested using LiDAR data along with high resolution remotely sensed imagery or using LiDAR data by itself can improve the process of identifying vegetated areas. These tactics increase accuracy of the vegetation class, and produce more accurate estimates of land areas when compared to using high resolution remotely sensed imagery alone. These studies demonstrate the importance in forestry and natural resources fields of continuous evaluation of advanced technologies with a variety of experimental designs. The results might assist the endeavors of resource managers, the environmental community, manufacturers, and policy makers, and contribute to further advance in the field of precision forestry. INDEX WORDS: Global positioning systems, geographic information systems, remote sensing, forestry, urban forests, urban vegetation, accuracy assessment, sampling, supervised classification
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