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High Spatial Resolution Remote Sensing: Data, Analysis, and Applications

High Spatial Resolution Remote Sensing: Data, Analysis, and Applications

Autorzy
Wydawnictwo Taylor & Francis Inc
Data wydania 10/07/2018
Liczba stron 381
Forma publikacji książka w twardej oprawie
Poziom zaawansowania Dla szkół wyższych i kształcenia podyplomowego
Język angielski
ISBN 9781498767682
Kategorie Geografia człowieka, Nauki środowiskowe, inżynieria i technologia
1 199.10 PLN (z VAT)
$269.73 / €257.09 / £223.18 /
Produkt na zamówienie
Dostawa 5-6 tygodni
Ilość
Do schowka

Opis książki

High spatial resolution remote sensing is an area of considerable current interest and builds on developments in object-based image analysis, commercial high-resolution satellite sensors, and UAVs. It captures more details through high and very high resolution images (10 to 100 cm/pixel). This unprecedented level of detail offers the potential extraction of a range of multi-resource management information, such as precision farming, invasive and endangered vegetative species delineation, forest gap sizes and distribution, locations of highly valued habitats, or sub-canopy topographic information. Information extracted in high spatial remote sensing data right after a devastating earthquake can help assess the damage to roads and buildings and aid in emergency planning for contact and evacuation.





To effectively utilize information contained in high spatial resolution imagery, High Spatial Resolution Remote Sensing: Data, Analysis, and Applications addresses some key questions:








What are the challenges of using new sensors and new platforms?







What are the cutting-edge methods for fine-level information extraction from high spatial resolution images?







How can high spatial resolution data improve the quantification and characterization of physical-environmental or human patterns and processes?








The answers are built in three separate parts: (1) data acquisition and preprocessing, (2) algorithms and techniques, and (3) case studies and applications. They discuss the opportunities and challenges of using new sensors and platforms and high spatial resolution remote sensing data and recent developments with a focus on UAVs. This work addresses the issues related to high spatial image processing and introduces cutting-edge methods, summarizes state-of-the-art high spatial resolution applications, and demonstrates how high spatial resolution remote sensing can support the extraction of detailed information needed in different systems. Using various high spatial resolution data, the third part of this book covers a range of unique applications, from grasslands to wetlands, karst areas, and cherry orchard trees.

High Spatial Resolution Remote Sensing: Data, Analysis, and Applications

Spis treści

Section I: Data Acquisition and Preprocessing





1. High-Resolution UAS Imagery in Agricultural Research: Concepts, Issues, and Research Directions





[Michael P. Bishop, Muthukumar V. Bagavathiannan, Dale A. Cope, Da Huo, Seth C. Murray, Jeffrey A. Olsenholler, William L. Rooney, J. Alex Thomasson, John Valasek, Brennan W. Young, Anthony M. Filippi, Dirk B. Hays, Lonesome Malambo, Sorin C. Popescu, Nithya Rajan, Vijay P. Singh, Bill McCutchen, Bob Avant, and Misty Vidrine]





2. Building a UAV-Hyperspectral System I: UAV and Sensor Considerations





[Cameron Proctor]





3. Building a UAV-Hyperspectral System II: Hyperspectral Sensor Considerations and Data Preprocessing





[Cameron Proctor]





4. LiDAR and Spectral Data Integration for Coastal Wetland Assessment





[Kunwar K. Singh, Lindsey Smart, and Gang Chen]





5. Multiview Image Matching for 3D Earth Surface Reconstruction





[Chuiqing Zeng and Jinfei Wang]





6. High-Resolution Radar Data Processing and Applications





[Joseph R. Buckley]





Section II: Algorithms and Techniques





7. Structure from Motion Techniques for Estimating the Volume of Wood Chips





[Travis L. Howell, Kunwar K. Singh, and Lindsey Smart]





8. A Workflow to Quantify the Carbon Storage in Urban Trees Using Multispectral ALS Data





[Xinqu Chen and Jonathan Li]





9. Suitable Spectral Mixing Space Selection for Linear Spectral Unmixing of Fine-Scale Urban Imagery





[Jian Yang]





10. Segmentation Scale Selection in Geographic Object-Based Image Analysis





[Xiuyuan Zhang, Shihong Du, and Dongping Ming]





11. Computer Vision Methodologies for Automated Processing of Camera Trap Data: A Technological Review





[Joshua Seltzer, Michael Guerzhoy, and Monika Havelka]





Section III: Case Studies and Applications





12. UAV-Based Multispectral Images for Investigating Grassland Biophysical and Biochemical Properties





[Bing Lu and Yuhong He]





13. Inversion of a Radiative Transfer Model Using Hyperspectral Data for Deriving Grassland Leaf Chlorophyll





[Alexander Tong, Bing Lu, and Yuhong He]





14. Wetland Detection Using High Spatial Resolution Optical Remote Sensing Imagery





[Amy B. Mui]





15. Geomorphic and Biophysical Characterization of Wetland Ecosystems with Airborne LiDAR: Concepts, Methods, and a Case Study





[Murray Richardson and Koreen Millard]





16. Fraction Vegetation Cover Extraction Using High Spatial Resolution Imagery in Karst Areas





[Xiangkun Qi, Chunhua Zhang, Yuhong He, and Kelin Wang]





17. Using High Spatial Resolution Imagery to Estimate Cherry Orchard Acreage in Michigan





[Kin M. Ma]

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