scholarly journals Automated Mapping of Woody Debris over Harvested Forest Plantations Using UAVs, High-Resolution Imagery, and Machine Learning

2019 ◽  
Vol 11 (6) ◽  
pp. 733 ◽  
Author(s):  
Lloyd Windrim ◽  
Mitch Bryson ◽  
Micheal McLean ◽  
Jeremy Randle ◽  
Christine Stone
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
High Resolution ◽  
Coarse Woody Debris ◽  
Woody Debris ◽  
Cost Effective ◽  
Small Sample ◽  
Site Quality ◽  
Survey Method ◽  
Monitoring Site

Surveying of woody debris left over from harvesting operations on managed forests is an important step in monitoring site quality, managing the extraction of residues and reconciling differences in pre-harvest inventories and actual timber yields. Traditional methods for post-harvest survey involving manual assessment of debris on the ground over small sample plots are labor-intensive, time-consuming, and do not scale well to heterogeneous landscapes. In this paper, we propose and evaluate new automated methods for the collection and interpretation of high-resolution, Unmanned Aerial Vehicle (UAV)-borne imagery over post-harvested forests for estimating quantities of fine and coarse woody debris. Using high-resolution, geo-registered color mosaics generated from UAV-borne images, we develop manual and automated processing methods for detecting, segmenting and counting both fine and coarse woody debris, including tree stumps, exploiting state-of-the-art machine learning and image processing techniques. Results are presented using imagery over a post-harvested compartment in a Pinus radiata plantation and demonstrate the capacity for both manual image annotations and automated image processing to accurately detect and quantify coarse woody debris and stumps left over after harvest, providing a cost-effective and scalable survey method for forest managers.

scholarly journals Automobile tire life prediction based on image processing and machine learning technology

2021 ◽  
Vol 13 (3) ◽  
pp. 168781402110027
Author(s):  
Jianchen Zhu ◽  
Kaixin Han ◽  
Shenlong Wang
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Life Prediction ◽  
Traffic Accidents ◽  
Confusion Matrix ◽  
Small Sample ◽  
Image Feature ◽  
Engineering Practice ◽  
Learning Technology ◽  
K Nearest Neighbor

With economic growth, automobiles have become an irreplaceable means of transportation and travel. Tires are important parts of automobiles, and their wear causes a large number of traffic accidents. Therefore, predicting tire life has become one of the key factors determining vehicle safety. This paper presents a tire life prediction method based on image processing and machine learning. We first build an original image database as the initial sample. Since there are usually only a few sample image libraries in engineering practice, we propose a new image feature extraction and expression method that shows excellent performance for a small sample database. We extract the texture features of the tire image by using the gray-gradient co-occurrence matrix (GGCM) and the Gauss-Markov random field (GMRF), and classify the extracted features by using the K-nearest neighbor (KNN) classifier. We then conduct experiments and predict the wear life of automobile tires. The experimental results are estimated by using the mean average precision (MAP) and confusion matrix as evaluation criteria. Finally, we verify the effectiveness and accuracy of the proposed method for predicting tire life. The obtained results are expected to be used for real-time prediction of tire life, thereby reducing tire-related traffic accidents.

scholarly journals Leveraging machine learning to produce a cost-effective national building height map of Ireland

2021 ◽  
Author(s):  
Eoghan Keany ◽  
Geoffrey Bessardon ◽  
Emily Gleeson
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
High Resolution ◽  
Land Cover ◽  
Urban Areas ◽  
Cost Effective ◽  
Land Cover Classification ◽  
National Scale ◽  
Building Height ◽  
Height Data

<p>To represent surface thermal, turbulent and humidity exchanges, Numerical Weather Prediction (NWP) systems require a land-cover classification map to calculate sur-face parameters used in surface flux estimation. The latest land-cover classification map used in the HARMONIE-AROME configuration of the shared ALADIN-HIRLAMNWP system for operational weather forecasting is ECOCLIMAP-SG (ECO-SG). The first evaluation of ECO-SG over Ireland suggested that sparse urban areas are underestimated and instead appear as vegetation areas (1). While the work of (2) on land-cover classification helps to correct the horizontal extent of urban areas, the method does not provide information on the vertical characteristics of urban areas. ECO-SG urban classification implicitly includes building heights (3), and any improvement to ECO-SG urban area extent requires a complementary building height dataset.</p><p>Openly accessible building height data at a national scale does not exist for the island of Ireland. This work seeks to address this gap in availability by extrapolating the preexisting localised building height data across the entire island. The study utilises information from both the temporal and spatial dimensions by creating band-wise temporal aggregation statistics from morphological operations, for both the Sentinel-1A/B and Sentinel-2A/B constellations (4). The extrapolation uses building height information from the Copernicus Urban Atlas, which contains regional coverage for Dublin at 10 m x10 m resolution (5). Various regression models were then trained on these aggregated statistics to make pixel-wise building height estimates. These model estimates were then evaluated with an adjusted RMSE metric, with the most accurate model chosen to map the entire country. This method relies solely on freely available satellite imagery and open-source software, providing a cost-effective mapping service at a national scale that can be updated more frequently, unlike expensive once-off private mapping services. Furthermore, this process could be applied by these services to reduce costs by taking a small representative sample and extrapolating the rest of the area. This method can be applied beyond national borders providing a uniform map that does not depends on the different private service practices facilitating the updates of global or continental land-cover information used in NWP.</p><p> </p><p>(1) G. Bessardon and E. Gleeson, “Using the best available physiography to improve weather forecasts for Ireland,” in Challenges in High-Resolution Short Range NWP at European level including forecaster-developer cooperation, European Meteorological Society, 2019.</p><p>(2) E. Walsh, et al., “Using machine learning to produce a very high-resolution land-cover map for Ireland, ” Advances in Science and Research,  (accepted for publication).</p><p>(3) CNRM, "Wiki - ECOCLIMAP-SG" https://opensource.umr-cnrm.fr/projects/ecoclimap-sg/wiki</p><p>(4) D. Frantz, et al., “National-scale mapping of building height using sentinel-1 and sentinel-2 time series,” Remote Sensing of Environment, vol. 252, 2021.</p><p>(5) M. Fitrzyk, et al., “Esa Copernicus sentinel-1 exploitation activities,” in IGARSS 2019-2019 IEEE International Geoscience and Remote Sensing Symposium, IEEE, 2019.</p>

scholarly journals Assessment of coarse woody debris following selective logging in Caspian forests: implications for conservation and management

2013 ◽  
Vol 59 (No. 3) ◽  
pp. 117-124
Author(s):  
F.K. Behjou ◽  
O.G. Mollabashi
Keyword(s):  
Coarse Woody Debris ◽  
Forest Ecosystems ◽  
High Efficiency ◽  
Sampling Methods ◽  
Woody Debris ◽  
Forest Health ◽  
Selective Logging ◽  
Survey Method ◽  
Fuel Loading ◽  
Fixed Area

Information on the amount, distribution, and characteristics of coarse woody debris (CWD) in forest ecosystems is highly demanded by wildlife biologists, fire specialists and ecologists. Owing to its important role in wildlife habitats, fuel loading, forest productivity, and carbon sequestration, coarse woody debris is an indicator of forest health. Two sampling methods including fixed-area plot and line intersect sampling were compared for accuracy and efficiency in measuring CWD. Data were selected from mature beech stands following selective logging in Caspian forests. Line intersect sampling consistently provided estimates similar to the results of a 100% survey (high accuracy). This method also took the least amount of time and effort to map the layout and field line location (high efficiency). Finally, line intersect sampling as an easy and fast survey method is suggested to monitor coarse woody debris (CWD) in Caspian forests.  

Comparison of Conventional and Electron Spectroscopic Imaging in the Fixed Beam Electron Microscope of Ordered Protein Arrays

1985 ◽  
Vol 43 ◽  
pp. 74-75
Author(s):  
E. L. Buhle ◽  
U. Aebi
Keyword(s):  
Image Processing ◽  
Electron Microscope ◽  
High Resolution ◽  
Image Plane ◽  
Electron Spectroscopic Imaging ◽  
Protein Arrays ◽  
Beam Electron ◽  
Average Unit ◽  
Fixed Beam ◽  
Energy Components

CTEM brightfield images are formed by a combination of relatively high resolution elastically scattered electrons and unscattered and inelastically scattered electrons. In the case of electron spectroscopic images (ESI), the inelastically scattered electrons cause a loss of both contrast and spatial resolution in the image. In the case of ESI imaging on the Zeiss EM902, the transmited electrons are dispersed into their various energy components by passing them through a magnetic prism spectrometer; a slit is then placed in the image plane of the prism to select the electrons of a given energy loss for image formation. The purpose of this study was to compare CTEM with ESI images recorded on a Zeiss EM902 of ordered protein arrays. Digital image processing was employed to analyze the average unit cell morphologies of the two types of images.

Accurate Detection and Quantization of Leaf- Diseases through Soft Computing

2018 ◽  
Vol 1 (1) ◽  
pp. 236-247
Author(s):  
Divya Srivastava ◽  
Rajitha B. ◽  
Suneeta Agarwal
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Agricultural Production ◽  
Bacterial Blight ◽  
Early Stage ◽  
Second Phase ◽  
Computationally Efficient ◽  
Stage Of Disease ◽  
Accurate Detection ◽  
Two Phases

Diseases in leaves can cause the significant reduction in both quality and quantity of agricultural production. If early and accurate detection of disease/diseases in leaves can be automated, then the proper remedy can be taken timely. A simple and computationally efficient approach is presented in this paper for disease/diseases detection on leaves. Only detecting the disease is not beneficial without knowing the stage of disease thus the paper also determine the stage of disease/diseases by quantizing the affected of the leaves by using digital image processing and machine learning. Though there exists a variety of diseases on leaves, but the bacterial and fungal spots (Early Scorch, Late Scorch, and Leaf Spot) are the most prominent diseases found on leaves. Keeping this in mind the paper deals with the detection of Bacterial Blight and Fungal Spot both at an early stage (Early Scorch) and late stage (Late Scorch) on the variety of leaves. The proposed approach is divided into two phases, in the first phase, it identifies one or more disease/diseases existing on leaves. In the second phase, amount of area affected by the disease/diseases is calculated. The experimental results obtained showed 97% accuracy using the proposed approach.

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