An Examination of an Enhanced Remote Sensing Method for Agent Attribution of Forest Disturbance

Abstract

Patterns of disturbance in Sierra Nevada forests are shifting as a result of changing climate and land uses. These changes have underscored the need for a monitoring system that both detects disturbances and attributes them to different agents. Addressing this need will aid forest management and conservation decision-making, potentially enhancing forests resilience to changing climatic conditions. In addition, it will advance understanding of the patterns, drivers, and consequences of forest disturbance in space and time. This study proposed and evaluated an enhanced method for disturbance agent attribution. Specifically, it tested the extent to which textural information could improve the performance of an ensemble learning method in predicting the agents of disturbance from remote sensing observations. Random Forest (RF) models were developed to attribute disturbance to three primary agents (fire, harvest, and drought) in Stanislaus National Forest, California, U.S.A., between 1999 and 2015. To account for spectral behavior and topographical characteristics that regulate vegetation and disturbance dynamics, the models were trained on predictors derived from both the Landsat record and from a digital elevation model. The predictors included measurements of spectral change acquired through temporal segmentation of Landsat data; measurements of patch geometry; and a series of landscape texture metrics. The texture metrics were generated using the Grey-Level Co-Occurrence Matrix (GLCM). Two models were produced: one with GLCM texture metrics and one without. The per-class and overall accuracies of each model were evaluated with out-of-bag (OOB) observations and compared statistically to quantify the contribution of texture metrics to classification skill. Overall OOB accuracy was 72.0% for the texture-free model and 72.2% for the texture-dependent model, with no significant accuracy difference between them. Spatial patterns in prediction maps cohered with expectations, with most harvest concentrated in mid-elevation forests and fire and stress co-occurring at lower elevations. Altogether, the method yielded adequate identification of disturbance and moderate attribution accuracy for multiple disturbance agents. While textures did not contribute meaningfully to model skill, the study offers a strong foundation for future development, which should focus on improving the efficacy of the model and generalizing it for systems beyond the Central Sierra Nevada.