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Estimating Control Total Acres for Desired Geographies Using Cropland Data Layer

Abstract Number:

2391 

Submission Type:

Contributed Abstract 

Contributed Abstract Type:

Paper 

Participants:

Mingyue Hu (1)

Institutions:

(1) N/A, N/A

First Author:

Mingyue Hu  
N/A

Presenting Author:

Mingyue Hu  
N/A

Abstract Text:

This project aims to accurately estimate the total acreage of pastureland in any specified geographic region. Image photo interpretation data from the National Resource Inventory (NRI), which is a well-established longitudinal survey to assess conditions and trends of soil, water, and related resources on non-federal lands of the U.S., is used to achieve this goal. However, the weights of NRI data are developed to represent non-federal land of states only, and the number of points falling into the target region might be small. In this paper, we develop a model-assisted approach utilizing satellite-based cropland data layer (CDL) data as auxiliary information via machine learning methods to accurately estimate the total acreage of pastureland in any arbitrary geographic region. This procedure encompasses three key steps: firstly, estimating the relationship between pastureland indicators in survey data and numerous CDL variables; secondly, applying this relationship to project pastureland probabilities across the entire U.S. map; and finally, extracting specific regions from this imputed map to calculate the total acreage of pastureland.

Keywords:

Sample surveys|Spatial data analysis|Machine learning| | |

Sponsors:

Survey Research Methods Section

Tracks:

Survey Methodology/Modes

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