General Technical Report Iitf
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Annual Technical Report
Author | : Aberdeen Plant Materials Center (U.S.) |
Publisher | : |
Total Pages | : 344 |
Release | : 2006 |
Genre | : Plant materials centers |
ISBN | : |
Creating Cloud-free Landsat ETM+ Data Sets in Tropical Landscapes
Author | : Sebastian Martinuzzi |
Publisher | : |
Total Pages | : 20 |
Release | : 2007 |
Genre | : Landsat satellites |
ISBN | : |
Common Woody Plants and Cacti of South Texas
Author | : Richard B. Taylor |
Publisher | : University of Texas Press |
Total Pages | : 224 |
Release | : 2014-08-15 |
Genre | : Nature |
ISBN | : 0292763069 |
Woody plants and cacti are vital staple foods for cattle, deer, and other wildlife in drought-prone South Texas. Ranchers, hunters, and land managers who need to identify these plants relied on A Field Guide to Common South Texas Shrubs (published by Texas Parks & Wildlife Press and distributed by UT Press), which is no longer in print. Responding to ongoing demand for the book, Richard B. Taylor has completely updated and expanded it with seven new species, new photographs, and a quick plant identification key. Common Woody Plants and Cacti of South Texas is an easy-to-use plant identification field guide to fifty species that comprise an estimated 90 percent of the region’s woody canopy cover north of the Rio Grande Valley. The species accounts include photographs, descriptions, values to livestock and wildlife, and nutritional information. The book also provides historical perspectives and information on brush management techniques and strategies, as well as habitat appraisal. All of these resources will enable readers to analyze stocking rates for deer and cattle, evaluate a prospective hunting lease, or buy property.
A Caribbean Forest Tapestry
Author | : Nicholas V. L. Brokaw |
Publisher | : Oxford University Press, USA |
Total Pages | : 483 |
Release | : 2012-06-28 |
Genre | : Business & Economics |
ISBN | : 0195334698 |
This book explains how to foresee and manage ecosystem changes in the Luquillo Mountains in Puerto Rico, by looking at underlying causes and effects. The lessons from the abiotic and biotic environments, populations, and ecosystems in this region apply to analogous forest biomes in Central and South America, as well as around the world.
Multisensor Data Fusion and Machine Learning for Environmental Remote Sensing
Author | : Ni-Bin Chang |
Publisher | : CRC Press |
Total Pages | : 508 |
Release | : 2018-02-21 |
Genre | : Technology & Engineering |
ISBN | : 1498774342 |
In the last few years the scientific community has realized that obtaining a better understanding of interactions between natural systems and the man-made environment across different scales demands more research efforts in remote sensing. An integrated Earth system observatory that merges surface-based, air-borne, space-borne, and even underground sensors with comprehensive and predictive capabilities indicates promise for revolutionizing the study of global water, energy, and carbon cycles as well as land use and land cover changes. The aim of this book is to present a suite of relevant concepts, tools, and methods of integrated multisensor data fusion and machine learning technologies to promote environmental sustainability. The process of machine learning for intelligent feature extraction consists of regular, deep, and fast learning algorithms. The niche for integrating data fusion and machine learning for remote sensing rests upon the creation of a new scientific architecture in remote sensing science that is designed to support numerical as well as symbolic feature extraction managed by several cognitively oriented machine learning tasks at finer scales. By grouping a suite of satellites with similar nature in platform design, data merging may come to help for cloudy pixel reconstruction over the space domain or concatenation of time series images over the time domain, or even both simultaneously. Organized in 5 parts, from Fundamental Principles of Remote Sensing; Feature Extraction for Remote Sensing; Image and Data Fusion for Remote Sensing; Integrated Data Merging, Data Reconstruction, Data Fusion, and Machine Learning; to Remote Sensing for Environmental Decision Analysis, the book will be a useful reference for graduate students, academic scholars, and working professionals who are involved in the study of Earth systems and the environment for a sustainable future. The new knowledge in this book can be applied successfully in many areas of environmental science and engineering.