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A hyper-temporal remote sensing protocol for high-resolution mapping of ecological sites.

Jonathan J Maynard¹, Jason W Karl¹

¹USDA-ARS, Jornada Experimental Range, MSC 3JER, New Mexico State University, Las Cruces, NM, United States of America.

|April 18, 2017

Summary

Hyper-temporal remote sensing effectively maps ecological sites with 62% accuracy, offering a cost-effective alternative to traditional methods for land management. Improved sampling and data integration can further enhance predictions of these vital soil-vegetation-climate units.

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Area of Science:

Ecological mapping
Remote sensing applications
Rangeland management

Background:

Ecological site classification is crucial for land management but limited by spatial data availability.
Hyper-temporal remote sensing offers a potential solution for high-resolution ecological site mapping.

Purpose of the Study:

To evaluate hyper-temporal remote sensing for mapping ecological sites in semi-arid rangelands.
To predict the spatial distribution of ecological sites using a 28-year Landsat NDVI time series and support vector machine classification.

Main Methods:

Utilized a 28-year time series of Landsat TM 5 Normalized Difference Vegetation Index (NDVI) data.
Employed support vector machine (SVM) classification for modeling ecological site distribution.
Compared results with Gridded Soil Survey Geographic database and expert maps.

Main Results:

Support vector machine classification achieved 62% accuracy in mapping ecological site classes.
Performance was compared to soil maps (51% accuracy) and expert maps (89% accuracy).
Degraded ecological states showed higher misclassification rates due to spectral similarity.

Conclusions:

Hyper-temporal remote sensing is effective for high-resolution ecological site mapping, offering reduced cost and time.
Future improvements require enhanced sampling designs, accurate soil property characterization, and integration of additional environmental covariates.
The framework provides a standardized approach to test ecological concepts and monitor vegetation dynamics.