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\begin{document}

\title{Characterization of land disturbances based on Landsat time series}



\author[1]{Shi Qiu}%
\author[1]{Zhe Zhu}%
\author[1]{Xiucheng Yang}%
\affil[1]{University of Connecticut}%


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  \date{May 17, 2024}


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\begin{abstract}
We developed a new Object-based Disturbance Agent Classification
Approach (ODACA) to characterize land disturbance agents based on
Landsat time series. Seven major disturbance agents were characterized,
including harvest, mechanical, stress, debris, hydrology, and fire. We
first created the land disturbance map by using a modified COntinuous
monitoring of Land Disturbance (COLD) algorithm (Zhu et al., 2020), and
then established a semi-automated disturbance agent training dataset
extraction framework based on existing open-source datasets, with very
limited human intervention. The modified COLD algorithm was implemented
based on Landsat time series from a single Landsat path to reduce the
bidirectional reflectance distribution function effect and issues caused
by data density disparity, and the model updating frequency was reduced
from every new observation to every three percent of the number of
observations used in the previous model updating to improve
computational efficiency. Finally, disturbance agents were classified
based on ODACA using a Random Forest model with a total of 175 predictor
variables that contain rich information in the spectral, temporal, and
spatial domains. Accurate land disturbance agent maps were created for
five sites in the United States, with an overall accuracy of
approximately 99\%, and producer's and user's accuracies range from 57
to 100\%, depending on specific disturbance agents.%
\end{abstract}%



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