Mr. Daniel Lapidus Profile

Daniel Lapidus

at RTI International

SPIE Involvement:

Author

Publications (1)

This will count as one of your downloads.

You will have access to both the presentation and article (if available).

DOWNLOAD NOW

This content is available for download via your institution's subscription. To access this item, please sign in to your personal account.

Email or Username Forgot your username?

Password Forgot your password?

Show

Keep me signed in

No SPIE account? Create an account

Proceedings Article | 21 October 2019 Paper

The use of Sentinel-1 and -2 data for monitoring maize production in Rwanda

Jason Polly, Meghan Hegarty-Craver, James Rineer, Margaret O'Neil, Daniel Lapidus, Robert Beach, Dorota Temple

Proceedings Volume 11149, 111491Y (2019) https://doi.org/10.1117/12.2533221

KEYWORDS: Agriculture, Unmanned aerial vehicles, Data modeling, Vegetation, Satellites, Satellite imaging, Synthetic aperture radar, Clouds

Read Abstract +

Although Rwanda has accomplished significant improvements in food production in recent years, one fifth of its population remains food insecure. Agricultural information is currently collected through seasonal agricultural surveys, but more frequent and timely data collection is needed to adequately inform public and private decision-makers about the status of crops during the growing season. Sentinel-1 and -2 data are freely available with new images provided every 4-5 days. While analysis of these multispectral images has been used for agricultural applications, there are few applications to smallholder agriculture. Major challenges for satellite image analysis in the context of Rwanda include heavily clouded scenes and small plot sizes that are often intercropped. Sentinel-2 scenes corresponding to mid-season were analyzed, and spectral signatures of maize could be distinguished from those of other crops. Seasonal mean filtering was applied to Sentinel-1 scenes, and there was significant overlap in the spectral signatures across different types of vegetation. Random Forest models for classification of Sentinel scenes were developed using a training dataset that was constructed from high-resolution multispectral images acquired by unmanned aerial vehicles (UAVs) in several different locations in Rwanda and labeled as to the crop type by trained observers. The models were applied to satellite images of the whole country of Rwanda and validated using a test dataset from the UAV images. The Sentinel-2 model had the user’s accuracy for maize classification of 75%, while the Sentinel-1 model overestimated the maize area resulting in a user’s accuracy of <50%.

My Library

You currently do not have any folders to save your paper to! Create a new folder below.

Folder Name

Folder Description

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks. You are receiving this notice because your organization may not have SPIE eBooks access.*

*Shibboleth/Open Athens users─please sign in to access your institution's subscriptions.

To obtain this item, you may purchase the complete book in print or electronic format on SPIE.org.

ORGANIZATIONAL
Sign in with credentials provided by your organization.

Organizational Username

Organizational Password

Show/Hide Password

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

Members:

Non-members: ADD TO CART

Keywords/Phrases

Search In:

Publication Years