Mapping dead forest cover using a deep convolutional neural network and digital aerial photography

Sylvain, Jean‐Daniel; Drolet, Guillaume; Brown, Nicolas

doi:10.1016/j.isprsjprs.2019.07.010

Cited by 85 publications

(32 citation statements)

References 27 publications

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“…The data in this study were limited in the sample size of this study is 2024, of which 1720 were used for training, and so the effectiveness and success of artificial intelligence models in modeling big data may not have been obtained sufficiently, or a limited number of data may have negative effects on iteration success. However, while data pools in the forest growth and yield modeling studies such as this study remain limited the sample size, data analysis which may consist of millions or even millions of data, also called as big data, may be involved in applications such as forestry image processing such as Hamdi et al (2019), Fricker et al (2019) and Sylvain et al (2019). In the analysis of forestry image processing data based on big data, the effectiveness of deep learning techniques will be even more apparent.…”

Section: Discussionmentioning

confidence: 99%

Innovative deep learning artificial intelligence applications for predicting relationships between individual tree height and diameter at breast height

Ercanlı

2020

For. Ecosyst.

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Background: Deep Learning Algorithms (DLA) have become prominent as an application of Artificial Intelligence (AI) Techniques since 2010. This paper introduces the DLA to predict the relationships between individual tree height (ITH) and the diameter at breast height (DBH). Methods: A set of 2024 pairs of individual height and diameter at breast height measurements, originating from 150 sample plots located in stands of even aged and pure Anatolian Crimean Pine (Pinus nigra J.F. Arnold ssp. pallasiana (Lamb.) Holmboe) in Konya Forest Enterprise. The present study primarily investigated the capability and usability of DLA models for predicting the relationships between the ITH and the DBH sampled from some stands with different growth structures. The 80 different DLA models, which involve different the alternatives for the numbers of hidden layers and neuron, have been trained and compared to determine optimum and best predictive DLAs network structure. Results: It was determined that the DLA model with 9 layers and 100 neurons has been the best predictive network model compared as those by other different DLA, Artificial Neural Network, Nonlinear Regression and Nonlinear Mixed Effect models. The alternative of 100 # neurons and 9 # hidden layers in deep learning algorithms resulted in best predictive ITH values with root mean squared error (RMSE, 0.5575), percent of the root mean squared error (RMSE%, 4.9504%), Akaike information criterion (AIC, − 998.9540), Bayesian information criterion (BIC, 884.6591), fit index (FI, 0.9436), average absolute error (AAE, 0.4077), maximum absolute error (max. AE, 2.5106), Bias (0.0057) and percent Bias (Bias%, 0.0502%). In addition, these predictive results with DLAs were further validated by the Equivalence tests that showed the DLA models successfully predicted the tree height in the independent dataset. Conclusion: This study has emphasized the capability of the DLA models, novel artificial intelligence technique, for predicting the relationships between individual tree height and the diameter at breast height that can be required information for the management of forests.

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Section: Discussionmentioning

confidence: 99%

Innovative deep learning artificial intelligence applications for predicting relationships between individual tree height and diameter at breast height

Ercanlı

2020

For. Ecosyst.

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“…Deep learning approaches often require a large amount of training data, and there are benchmark datasets publicly available for training and testing of deep learning approaches in the abovementioned remote sensing fields. Compared with the studies mentioned above, very few studies using deep learning have focused on trees or forest classification [34]. Flood et al [35] used a U-net convolutional neural network to extract woody vegetation extent from high-resolution three-band Earth-I imagery.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Approaches for the Mapping of Tree Species Diversity in a Tropical Wetland Using Airborne LiDAR and High-Spatial-Resolution Remote Sensing Images

Sun

Huang

et al. 2019

Forests

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The monitoring of tree species diversity is important for forest or wetland ecosystem service maintenance or resource management. Remote sensing is an efficient alternative to traditional field work to map tree species diversity over large areas. Previous studies have used light detection and ranging (LiDAR) and imaging spectroscopy (hyperspectral or multispectral remote sensing) for species richness prediction. The recent development of very high spatial resolution (VHR) RGB images has enabled detailed characterization of canopies and forest structures. In this study, we developed a three-step workflow for mapping tree species diversity, the aim of which was to increase knowledge of tree species diversity assessment using deep learning in a tropical wetland (Haizhu Wetland) in South China based on VHR-RGB images and LiDAR points. Firstly, individual trees were detected based on a canopy height model (CHM, derived from LiDAR points) by the local-maxima-based method in the FUSION software (Version 3.70, Seattle, USA). Then, tree species at the individual tree level were identified via a patch-based image input method, which cropped the RGB images into small patches (the individually detected trees) based on the tree apexes detected. Three different deep learning methods (i.e., AlexNet, VGG16, and ResNet50) were modified to classify the tree species, as they can make good use of the spatial context information. Finally, four diversity indices, namely, the Margalef richness index, the Shannon–Wiener diversity index, the Simpson diversity index, and the Pielou evenness index, were calculated from the fixed subset with a size of 30 × 30 m for assessment. In the classification phase, VGG16 had the best performance, with an overall accuracy of 73.25% for 18 tree species. Based on the classification results, mapping of tree species diversity showed reasonable agreement with field survey data (R2Margalef = 0.4562, root-mean-square error RMSEMargalef = 0.5629; R2Shannon–Wiener = 0.7948, RMSEShannon–Wiener = 0.7202; R2Simpson = 0.7907, RMSESimpson = 0.1038; and R2Pielou = 0.5875, RMSEPielou = 0.3053). While challenges remain for individual tree detection and species classification, the deep-learning-based solution shows potential for mapping tree species diversity.

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“…A recently published research tested the performance of object detection using deep networks like YOLOv3 (55), RetinaNet (56), and Faster-RCNN (57) to detect tree canopy in RGB imagery covering an urban area (9). Another study modified the VGG16 (58) to monitor the health conditions of vegetation (59). A combination of LiDAR and RGB images was also used with the RetinaNet to identify tree-crowns in UAV images (19).…”

Section: P R E P R I N Tmentioning

confidence: 99%

Mapping Single Palm-Trees Species in Forest Environments with a Deep Convolutional Neural Network

Lsd¹,

MdSd²,

Deg³

et al. 2021

Preprint

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Accurately mapping individual tree species in densely forested environments is crucial to forest inventory. When considering only RGB images, this is a challenging task for many automatic photogrammetry processes. The main reason for that is the spectral similarity between species in RGB scenes, which can be a hindrance for most automatic methods. State-of-the-art deep learning methods could be capable of identifying tree species with an attractive cost, accuracy, and computational load in RGB images. This paper presents a deep learning-based approach to detect an important multi-use species of palm trees (Mauritia flexuosa; i.e., Buriti) on aerial RGB imagery. In South-America, this palm tree is essential for many indigenous and local communities because of its characteristics. The species is also a valuable indicator of water resources, which comes as a benefit for mapping its location. The method is based on a Convolutional Neural Network (CNN) to identify and geolocate singular tree species in a high-complexity forest environment, and considers the likelihood of every pixel in the image to be recognized as a possible tree by implementing a confidence map feature extraction. This study compares the performance of the proposed method against state-of-the-art object detection networks. For this, images from a dataset composed of 1,394 airborne scenes, where 5,334 palm-trees were manually labeled, were used. The results returned a mean absolute error (MAE) of 0.75 trees and an F1-measure of 86.9%. These results are better than both Faster R-CNN and RetinaNet considering equal experiment conditions. The proposed network provided fast solutions to detect the palm trees, with a delivered image detection of 0.073 seconds and a standard deviation of 0.002 using the GPU. In conclusion, the method presented is efficient to deal with a high-density forest scenario and can accurately map the location of single species like the M flexuosa palm tree and may be useful for future frameworks.

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Mapping dead forest cover using a deep convolutional neural network and digital aerial photography

Cited by 85 publications

References 27 publications

Innovative deep learning artificial intelligence applications for predicting relationships between individual tree height and diameter at breast height

Innovative deep learning artificial intelligence applications for predicting relationships between individual tree height and diameter at breast height

Deep Learning Approaches for the Mapping of Tree Species Diversity in a Tropical Wetland Using Airborne LiDAR and High-Spatial-Resolution Remote Sensing Images

Mapping Single Palm-Trees Species in Forest Environments with a Deep Convolutional Neural Network

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