Spatial analysis of multispectral and thermal imagery from multiple platforms

Gregory Rouze; Haly Neely; Cristine Morgan; Chenghai Yang

doi:10.1117/12.2305896

21 May 2018 Spatial analysis of multispectral and thermal imagery from multiple platforms

Gregory Rouze, Haly Neely, Cristine Morgan, Chenghai Yang

Proceedings Volume 10664, Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping III; 106640T (2018) https://doi.org/10.1117/12.2305896
Event: SPIE Commercial + Scientific Sensing and Imaging, 2018, Orlando, FL, United States

Abstract

Airborne and satellite remote sensing can potentially be used to model crop characteristics. However, satellite imagery usually exhibit low spatial and temporal resolutions, and manned aircraft imagery, despite improved resolutions, is not cost-effective. Recent developments in UAV remote sensing have allowed for imagery at improved spatial resolutions relative to satellites and at a fraction of the cost relative to manned aircraft. Furthermore, UAVs offer potential advantages over proximal soil sensors (i.e. EM-38) in terms of in-season decision making. However, it is unclear at this point whether these benefits translate to higher quality information. This question has relevance within fields that exhibit contrasting environments, such as soil spatial variability. Therefore, the objectives of this paper were twofold: 1) to quantify improvements in UAV-based plant (cotton) modelling relative to proximal sensing (i.e. EM-38), manned aircraft, and satellites (Landsat 8); and 2) to determine how such modeling can be affected by soil spatial variability. Results indicate that UAVs show higher nugget/sill ratios and larger ranges than manned aircraft and satellites. These results have implications for predicting agronomic variables (i.e. yield, plant height), as well as soil/plant sampling.

Conference Presentation

Citation Download Citation

Gregory Rouze, Haly Neely, Cristine Morgan, and Chenghai Yang "Spatial analysis of multispectral and thermal imagery from multiple platforms", Proc. SPIE 10664, Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping III, 106640T (21 May 2018); https://doi.org/10.1117/12.2305896

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available