Assessing Post-Fire Regeneration in a Mediterranean Mixed Forest Using Lidar Data and Artificial Neural Networks

Urban extraction mapping has become increasingly important in recent years and particularity extraction urban features based on remotely sensed data such as highresolution imagery and LiDAR data. Though the researchers used the high spatial resolution image to extract urban area but he methods are still complex and still there are challenges associated with combining data that were acquired over differing time periods using inconsistent standards. So, this study will focus on the extraction of urban area based on an object-based classification method with integration of Quickbird satellite image and digital surface elevation (DSM) extracted from LiDAR data for the Rusafa city of Baghdad, Iraq. All the processes were done in eCognition and ArcGIS software for feature extraction and mapping, respectively. The overall methodological steps proposed in this research for the extraction of urban area using object-based method. In addition of that both the image data and LiDAR-derived DSM were integrated based on the eCognition software for extraction urban map of Rusafa city, Baghdad. Finally, the results indicated that the Artificial Neural Networks (ANN) model achieved the highest training and testing accuracies and performed the best compared to RF and Support Vector Machines (SVM) methods. And also, the results showed that the Artificial Neural Networks (ANN) had capability to extract the boundaries of the buildings and other urban features more accurately than the other two methods. This could be interpreted as the Artificial Neural Networks (ANN) model can learn complex features by the optimization process of the model and its multi-level feature extraction property

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A Comprehensive Comparison of Artificial Neural Networks and Some Regression Modeling Techniques on Dominant Height Predictions of Oriental Spruce in a Mixed Forest, the Northeast Turkey

10.21203/rs.3.rs-1032663/v1 ◽

2021 ◽

Author(s):

İlker ERCANLI ◽

Ferhat Bolat ◽

Hakkı Yavuz

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Mixed Forest ◽

Random Parameter ◽

Dominant Height ◽

Modeling Techniques ◽

Artificial Neural ◽

Dominant Height Growth ◽

Height Predictions ◽

Oriental Spruce

Abstract Background: Dominant height is needed for assessing silvicultural practices in sustainable wood production management. Also, dominant height is used as an important explanatory variable in forest growth and yield models. This paper introduces the evaluation for Artificial Neural Networks and Some Regression Modeling Techniques on Dominant Height Predictions of Oriental Spruce in a Mixed Forest, the Northeast Turkey. Methods: In this study, 873 height-age pairs were obtained from oriental spruce trees in a mixed forest stand. Nonlinear mixed-effects models (NLMEs), autoregressive models (ARM), dummy variable method (DVM), and artificial neural networks (ANNs) were compared to predict dominant height growth. Results: The best predictive model was NLME with single random parameter (root mean square error, RMSE: 0.68 m). The results showed that NLMEs outperformed ARM (RMSE: 1.09 m), DVM in conjunction with ARM (RMSE: 1.09 m), and ANNs (RMSE: from 1.11 to 2.40 m) in majority of the cases. Whereas considering variations among observations by random parameter(s) significantly improved predictions of dominant height, taking into account correlated error terms by autoregressive correlation parameter(s) enhanced slightly the predictions. ANNs generally underperformed compared to NLMEs, ARM, and DVM with ARM. Conclusion: All regression techniques fulfilled the desirable characteristics such as sigmoidal pattern, polymorphism, multiple asymptote, base-age invariance, and inflection point. However, ANNs could not most of these features excluding sigmoidal pattern. Accordingly, ANNs seem to insufficient to assure biological growth assumptions regarding dominant height growth.

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