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Your search keyword '"John B. Kilbride"' showing total 2 results
Start Over Author "John B. Kilbride" Publisher elsevier
2 results on '"John B. Kilbride"'

1. Not-so-random forests: Comparing voting and decision tree ensembles for characterizing partial harvest events

Author

Valerie J. Pasquarella, Luca L. Morreale, Christopher F. Brown, John B. Kilbride, and Jonathan R. Thompson

Subjects
Change detection, Forest harvest, Temporal segmentation, LandTrendr, Forest Inventory and Analysis, Ensemble methods, Physical geography, GB3-5030, Environmental sciences, GE1-350
Abstract

Ensemble-based change detection can improve map accuracies by combining information from multiple datasets. There is a growing literature investigating ensemble inputs and applications for forest disturbance detection and mapping. However, few studies have evaluated ensemble methods other than Random Forest classifiers, which rely on uninterpretable “black box” algorithms with hundreds of parameters. Additionally, most ensemble-based disturbance maps do not utilize independently and systematically collected field-based forest inventory measurements. Here, we compared three approaches for combining change detection results generated from multi-spectral Landsat time series with forest inventory measurements to map forest harvest events at an annual time step. We found that seven-parameter degenerate decision tree ensembles performed at least as well as 500-tree Random Forest ensembles trained and tested on the same LandTrendr segmentation results and both supervised decision tree methods consistently outperformed the top-performing voting approach (majority). Comparisons with an existing national forest disturbance dataset indicated notable improvements in accuracy that demonstrate the value of developing locally calibrated, process-specific disturbance datasets like the harvest event maps developed in this study. Furthermore, by using multi-date forest inventory measurements, we are able to establish a lower bound of 30% basal area removal on detectable harvests, providing biophysical context for our harvest event maps. Our results suggest that simple interpretable decision trees applied to multi-spectral temporal segmentation outputs can be as effective as more complex machine learning approaches for characterizing forest harvest events ranging from partial clearing to clear cuts, with important implications for locally accurate mapping of forest harvests and other types of disturbances.

Published
2023
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2. Deep learning approach for Sentinel-1 surface water mapping leveraging Google Earth Engine

Author

Arjen Haag, Kel Markert, Farrukh Chishtie, David Saah, Biplov Bhandari, Ate Poortinga, Amanda Markert, T. Mayer, Nicholas Clinton, Andrea Puzzi Nicolau, Amit Wadhwa, John B. Kilbride, and Nyein Soe Thwal

Subjects
Synthetic aperture radar, Image segmentation, Geography (General), TA501-625, business.industry, Computer science, Deep learning, Surface water mapping, Surveying, Cloud computing, computer.software_genre, Google earth engine, Convolutional neural network, U-net, Data set, Cross entropy, G1-922, Pairwise comparison, Artificial intelligence, Data mining, Scale (map), business, computer
Abstract

Satellite remote sensing plays an important role in mapping the location and extent of surface water. A variety of approaches are available for mapping surface water, but deep learning approaches are not commonplace as they are ‘data hungry’ and require large amounts of computational resources. However, with the availability of various satellite sensors and rapid development in cloud computing, the remote sensing scientific community is adapting modern deep learning approaches. The new integration of cloud-based Google AI platform and Google Earth Engine enables users to deploy calculations at scale. In this paper, we investigate two methods of automatic data labeling: 1. the Joint Research Centre (JRC) surface water maps; 2. an Edge-Otsu dynamic threshold approach. We deployed a U-Net convolutional neural network to map surface water from Sentinel-1 Synthetic Aperture Radar (SAR) data and tested the model performance using different hyperparameter tuning combinations to identify the optimal learning rate and loss function. The performance was then evaluated using an independent validation data set. We tested 12 models overall and found that the models utilizing the JRC data labels showed a better model performance, with F1-scores ranging from 0.972 to 0.986 for the training test and validation efforts. Additionally, an independently sampled high-resolution data set was used to further evaluate model performance. From this independent validation effort we observed models leveraging JRC data labels produced F1-Scores ranging from 0.9130.922. A pairwise comparison of models, through varying input data, learning rates, and loss functions constituents, revealed the JRC Adjusted Binary Cross Entropy Dice model to be statistically different than the 66 other model combinations and displayed the highest relative evaluations metrics including accuracy, precision score, Cohen Kappa coefficient, and F1-score. These results are in the same range as many of the conventional methods. We observed that the integration of Google AI Platform into Google Earth Engine can be a powerful tool to deploy deep-learning algorithms at scale and that automatic data labeling can be an effective strategy in the development of deep-learning models, however independent data validation remains an important step in model evaluation.

Published
2021

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