scholarly journals Using UAV Imagery to Detect and Map Woody Species Encroachment in a Subalpine Grassland: Advantages and Limits

2021 ◽  
Vol 13 (7) ◽  
pp. 1239
Author(s):  
Ludovica Oddi ◽  
Edoardo Cremonese ◽  
Lorenzo Ascari ◽  
Gianluca Filippa ◽  
Marta Galvagno ◽  
...  
Keyword(s):  
Reference Data ◽  
Woody Species ◽  
Accurate Method ◽  
Machine Learning Algorithms ◽  
Italian Alps ◽  
Photo Interpretation ◽  
Tree Detection ◽  
Grassland Ecosystems ◽  
Object Based ◽  
Rgb Images

Woody species encroachment on grassland ecosystems is occurring worldwide with both negative and positive consequences for biodiversity conservation and ecosystem services. Remote sensing and image analysis represent useful tools for the monitoring of this process. In this paper, we aimed at evaluating quantitatively the potential of using high-resolution UAV imagery to monitor the encroachment process during its early development and at comparing the performance of manual and semi-automatic classification methods. The RGB images of an abandoned subalpine grassland on the Western Italian Alps were acquired by drone and then classified through manual photo-interpretation, with both pixel- and object-based semi-automatic models, using machine-learning algorithms. The classification techniques were applied at different resolution levels and tested for their accuracy against reference data including measurements of tree dimensions collected in the field. Results showed that the most accurate method was the photo-interpretation (≈99%), followed by the pixel-based approach (≈86%) that was faster than the manual technique and more accurate than the object-based one (≈78%). The dimensional threshold for juvenile tree detection was lower for the photo-interpretation but comparable to the pixel-based one. Therefore, for the encroachment mapping at its early stages, the pixel-based approach proved to be a promising and pragmatic choice.

scholarly journals Abandoned Land Classification Using Classical Theory Method

2019 ◽  
Vol 10 ◽  
pp. 60-68 ◽  
Author(s):  
Jūrate Sužiedelyte Visockiene ◽  
Egle Tumeliene
Keyword(s):  
Agricultural Land ◽  
Spatial Information ◽  
Region Growing ◽  
Cost Effective ◽  
Accurate Method ◽  
Land Classification ◽  
Information Portal ◽  
Abandoned Land ◽  
Rgb Images

According to the official statistics the areas of abandoned agricultural land in Lithuania are gradually decreasing, but very slightly. The aim of this study is to research spatial determination and abandoned land classification in the territory of Vilnius District Municipality. Vilnius District Municipality was chosen for the research because it, although located near the capital of the country and has a high population density, it is still the district having the largest percent of abandoned land plots. A fast, cost-effective and sufficiently accurate method for determination of abandoned land plots would allow to constantly monitor, to fix changes and foresee the abandoned land plots reduction possibilities. In the study there was used the multispectral RGB and NIR color Sentinel-2 satellite images, the layer of the administrative boundary of Vilnius County and layer of abandoned agriculture land, which is available in Lithuanian Spatial Information Portal (www.geoportal.lt). The data was processed by Geographic Information System (GIS) techniques using classical classification Region Growing Algorithm. The research shows that NIR image classification result is more reliable than the result from RGB images.

scholarly journals Delineation of Tree Patches in a Mangrove-Marsh Transition Zone by Watershed Segmentation of Aerial Photographs

2020 ◽  
Vol 12 (13) ◽  
pp. 2086
Author(s):  
Himadri Biswas ◽  
Keqi Zhang ◽  
Michael S. Ross ◽  
Daniel Gann
Keyword(s):  
Vegetation Index ◽  
Vegetation Indices ◽  
Aerial Photographs ◽  
Watershed Segmentation ◽  
Marsh Vegetation ◽  
Tree Detection ◽  
Object Based ◽  
Segmented Images ◽  
Individual Trees ◽  
True Color

Mangrove migration, or transgression in response to global climatic changes or sea-level rise, is a slow process; to capture it, understanding both the present distribution of mangroves at individual patch (single- or clumped trees) scale, and their rates of change are essential. In this study, a new method was developed to delineate individual patches and to estimate mangrove cover from very high-resolution (0.08 m spatial resolution) true color (Red (R), Green (G), and Blue (B) spectral channels) aerial photography. The method utilizes marker-based watershed segmentation, where markers are detected using a vegetation index and Otsu’s automatic thresholding. Fourteen commonly used vegetation indices were tested, and shadows were removed from the segmented images to determine their effect on the accuracy of tree detection, cover estimation, and patch delineation. According to point-based accuracy analysis, we obtained adjusted overall accuracies >90% in tree detection using seven vegetation indices. Likewise, using an object-based approach, the highest overlap accuracy between predicted and reference data was 95%. The vegetation index Excess Green (ExG) without shadow removal produced the most accurate mangrove maps by separating tree patches from shadows and background marsh vegetation and detecting more individual trees. The method provides high precision delineation of mangrove trees and patches, and the opportunity to analyze mangrove migration patterns at the scale of isolated individuals and patches.

scholarly journals Deep learning and taphonomy: high accuracy in the classification of cut marks made on fleshed and defleshed bones using convolutional neural networks

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Gabriel Cifuentes-Alcobendas ◽  
Manuel Domínguez-Rodrigo
Keyword(s):  
Machine Learning ◽  
Accurate Method ◽  
High Accuracy ◽  
Machine Learning Algorithms ◽  
Accurate Identification ◽  
Cut Marks ◽  
Eating Meat ◽  
Microscopic Features ◽  
Immense Potential

AbstractAccurate identification of bone surface modifications (BSM) is crucial for the taphonomic understanding of archaeological and paleontological sites. Critical interpretations of when humans started eating meat and animal fat or when they started using stone tools, or when they occupied new continents or interacted with predatory guilds impinge on accurate identifications of BSM. Until now, interpretations of Plio-Pleistocene BSM have been contentious because of the high uncertainty in discriminating among taphonomic agents. Recently, the use of machine learning algorithms has yielded high accuracy in the identification of BSM. A branch of machine learning methods based on imaging, computer vision (CV), has opened the door to a more objective and accurate method of BSM identification. The present work has selected two extremely similar types of BSM (cut marks made on fleshed an defleshed bones) to test the immense potential of artificial intelligence methods. This CV approach not only produced the highest accuracy in the classification of these types of BSM until present (95% on complete images of BSM and 88.89% of images of only internal mark features), but it also has enabled a method for determining which inconspicuous microscopic features determine successful BSM discrimination. The potential of this method in other areas of taphonomy and paleobiology is enormous.

A lightweight vehicle detection and tracking technique for advanced driving assistance systems

2020 ◽  
Vol 39 (3) ◽  
pp. 2693-2710 ◽  
Author(s):  
Wael Farag
Keyword(s):  
Real Time ◽  
Vehicle Detection ◽  
Machine Learning Algorithms ◽  
Tracking Technique ◽  
Detection And Tracking ◽  
Vehicle Detection And Tracking ◽  
Driving Assistance ◽  
Rgb Images ◽  
Assistance Systems ◽  
Self Driving Cars

In this paper, an advanced-and-reliable vehicle detection-and-tracking technique is proposed and implemented. The Real-Time Vehicle Detection-and-Tracking (RT_VDT) technique is well suited for Advanced Driving Assistance Systems (ADAS) applications or Self-Driving Cars (SDC). The RT_VDT is mainly a pipeline of reliable computer vision and machine learning algorithms that augment each other and take in raw RGB images to produce the required boundary boxes of the vehicles that appear in the front driving space of the car. The main contribution of this paper is the careful fusion of the employed algorithms where some of them work in parallel to strengthen each other in order to produce a precise and sophisticated real-time output. In addition, the RT_VDT provides fast enough computation to be embedded in CPUs that are currently employed by ADAS systems. The particulars of the employed algorithms together with their implementation are described in detail. Additionally, these algorithms and their various integration combinations are tested and their performance is evaluated using actual road images, and videos captured by the front-mounted camera of the car as well as on the KITTI benchmark with 87% average precision achieved. The evaluation of the RT_VDT shows that it reliably detects and tracks vehicle boundaries under various conditions.

Assessment of CNN-Based Methods for Single Tree Detection on High-Resolution RGB Images in Urban Areas

2021 ◽  
Author(s):  
Pedro Alberto Pereira ZamboniThgeThe ◽  
Jose Marcato ◽  
Gabriela Takahashi Miyoshi ◽  
Jonathan de Andrade Silva ◽  
Jose Martins ◽  
...  
Keyword(s):  
High Resolution ◽  
Urban Areas ◽  
Tree Detection ◽  
Single Tree ◽  
Rgb Images

Object-Based Building Damage Assessment Methodology Using Only Post Event ALOS-2/PALSAR-2 Dual Polarimetric SAR Intensity Images

2017 ◽  
Vol 12 (2) ◽  
pp. 259-271 ◽  
Author(s):  
Yanbing Bai ◽  
◽  
Bruno Adriano ◽  
Erick Mas ◽  
Hideomi Gokon ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Damage Assessment ◽  
Building Damage ◽  
Machine Learning Algorithms ◽  
Assessment Methodology ◽  
K Nearest Neighbor ◽  
Polarimetric Sar ◽  
Object Based ◽  
Sar Imagery

Earthquake-induced building damage assessment is an indispensable prerequisite for disaster impact assessment, and the increasing availability of high-resolution Synthetic Aperture Radar (SAR) imagery has made it possible to construct damaged building inventories soon after earthquakes strike. However, the shortage of pre-seismic SAR datasets and the lack of available building footprint data pose challenges for rapid building damage assessment. Taking advantage of recent advances in machine learning algorithms, this study proposes an object-based building damage assessment methodology that uses only post-event SAR imagery. A Random Forest machine learning-based object classification, a simplified approach to the extraction of built-up areas, was developed and tested on two ALOS2/PALSAR-2 dual polarimetric SAR images acquired in affected areas soon after the 2015 Nepal earthquake. In addition, a series of texture metrics as well as the random scattering metric and reflection symmetry metric were found to significantly enhance classification accuracy. The feature selection was found to have a positive effect on overall performance. Moreover, the proposed Random Forest framework resulted in overall accuracies of 93% with a kappa coefficient of 0.885 when the object scale of 60 × 60 pixels and 15 features were adopted. A comparative experiment with the k-nearest neighbor framework demonstrated that the Random Forest framework is a significant step toward the achievement of a balanced, two-class classification.

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