NASA Logo

NTRS

NTRS - NASA Technical Reports Server

Collections About News Help Login
Back to Results
Very High Resolution Tree Cover Mapping for Continental United States using Deep Convolutional Neural NetworksUncertainties in input land cover estimates contribute to a significant bias in modeled above ground biomass (AGB) and carbon estimates from satellite-derived data. The resolution of most currently used passive remote sensing products is not sufficient to capture tree canopy cover of less than ca. 10-20 percent, limiting their utility to estimate canopy cover and AGB for trees outside of forest land. In our study, we created a first of its kind Continental United States (CONUS) tree cover map at a spatial resolution of 1-m for the 2010-2012 epoch using the USDA NAIP imagery to address the present uncertainties in AGB estimates. The process involves different tasks including data acquisition ingestion to pre-processing and running a state-of-art encoder-decoder based deep convolutional neural network (CNN) algorithm for automatically generating a tree non-tree map for almost a quarter million scenes. The entire processing chain including generation of the largest open source existing aerial satellite image training database was performed at the NEX supercomputing and storage facility. We believe the resulting forest cover product will substantially contribute to filling the gaps in ongoing carbon and ecological monitoring research and help quantifying the errors and uncertainties in derived products.
Document ID
20180000915
Acquisition Source
Ames Research Center
Document Type
Presentation
Authors
Ganguly, Sangram (Bay Area Environmental Research Inst. Moffett Field, CA, United States)
Kalia, Subodh (Bay Area Environmental Research Inst. Moffett Field, CA, United States)
Li, Shuang (Bay Area Environmental Research Inst. Moffett Field, CA, United States)
Michaelis, Andrew (California State Univ. at Monterey Bay Seaside, CA, United States)
Nemani, Ramakrishna R. (NASA Ames Research Center Moffett Field, CA, United States)
Saatchi, Sassan A (Jet Propulsion Lab., California Inst. of Tech. Pasadena, CA, United States)
Date Acquired
February 5, 2018
Publication Date
December 11, 2017
Subject Category
Earth Resources And Remote Sensing
Report/Patent Number
ARC-E-DAA-TN48007
Meeting Information
Meeting: 2017 AGU Fall Meeting
Location: New Orleans, LA
Country: United States
Start Date: December 11, 2017
End Date: December 15, 2017
Sponsors: American Geophysical Union
Funding Number(s)
CONTRACT_GRANT: NNX12AD05A
Distribution Limits
Public
Copyright
Public Use Permitted.
Keywords
High Resolution
Tree Cover
Mappin

Related Records

There are no records associated with this record.
No Preview Available