scholarly journals Improving Tree Species Classification Using UAS Multispectral Images and Texture Measures

2018 ◽  
Vol 7 (8) ◽  
pp. 315 ◽  
Author(s):  
Rossana Gini ◽  
Giovanna Sona ◽  
Giulia Ronchetti ◽  
Daniele Passoni ◽  
Livio Pinto
Keyword(s):  
High Resolution ◽  
Tree Species ◽  
Principal Component ◽  
Texture Features ◽  
Unmanned Aircraft ◽  
Multispectral Images ◽  
Species Classification ◽  
Ground Sample ◽  
Plant Nursery ◽  
Multi Temporal

This paper focuses on the use of ultra-high resolution Unmanned Aircraft Systems (UAS) imagery to classify tree species. Multispectral surveys were performed on a plant nursery to produce Digital Surface Models and orthophotos with ground sample distance equal to 0.01 m. Different combinations of multispectral images, multi-temporal data, and texture measures were employed to improve classification. The Grey Level Co-occurrence Matrix was used to generate texture images with different window sizes and procedures for optimal texture features and window size selection were investigated. The study evaluates how methods used in Remote Sensing could be applied on ultra-high resolution UAS images. Combinations of original and derived bands were classified with the Maximum Likelihood algorithm, and Principal Component Analysis was conducted in order to understand the correlation between bands. The study proves that the use of texture features produces a significant increase of the Overall Accuracy, whose values change from 58% to 78% or 87%, depending on components reduction. The improvement given by the introduction of texture measures is highlighted even in terms of User’s and Producer’s Accuracy. For classification purposes, the inclusion of texture can compensate for difficulties of performing multi-temporal surveys.

scholarly journals Assessing Forest Type and Tree Species Classification Using Sentinel-1 C-Band SAR Data in Southern Sweden

2021 ◽  
Vol 13 (16) ◽  
pp. 3237
Author(s):  
Alberto Udali ◽  
Emanuele Lingua ◽  
Henrik J. Persson
Keyword(s):  
Tree Species ◽  
Deciduous Forest ◽  
Forest Type ◽  
Temporal Stability ◽  
Winter Season ◽  
Principal Component ◽  
Species Classification ◽  
Explanatory Variables ◽  
Random Forest Models ◽  
Sar Data

The multitemporal acquisition of images from the Sentinel-1 satellites allows continuous monitoring of a forest. This study focuses on the use of multitemporal C-band synthetic aperture radar (SAR) data to assess the results for forest type (FTY), between coniferous and deciduous forest, and tree species (SPP) classification. We also investigated the temporal stability through the use of backscatter from multiple seasons and years of acquisition. SAR acquisitions were pre-processed, histogram-matched, smoothed, and temperature-corrected. The normalized average backscatter was extracted for interpreted plots and used to train Random Forest models. The classification results were then validated with field plots. A principal component analysis was tested to reduce the dimensionality of the explanatory variables, which generally improved the results. Overall, the FTY classifications were promising, with higher accuracies (OA of 0.94 and K = 0.86) than the SPP classification (OA of 0.66 and K = 0.54). The use of merely winter images (OA = 0.89) reached, on average, results that were almost as good as those using of images from the entire year. The use of images from a single winter season reached a similar result (OA = 0.87). We conclude that multiple Sentinel-1 images acquired in winter conditions are feasible to classify forest types in a hemi-boreal Swedish forest.

Detection and monitoring of hydrothermal alteration by Principal Component Analysis applied on UAS derived optical data, Vulcano Island - Italy

2021 ◽  
Author(s):  
Daniel Müller ◽  
Stefan Bredemeyer ◽  
Edgar Zorn ◽  
Erica De Paolo ◽  
Thomas Walter
Keyword(s):  
Principal Component Analysis ◽  
High Resolution ◽  
Hydrothermal Alteration ◽  
High Efficiency ◽  
Principal Component ◽  
Component Analysis ◽  
Hydrothermal Systems ◽  
Unmanned Aircraft ◽  
Optical Data ◽  
Vulcano Island

<p>Modern UAS (unmanned aircraft system), light weight sensor systems and new processing routines allow us to gather optical data of volcanoes at a high resolution. However, due to the typically poor colorization, our ability to investigate and interpret such data is limited. Further, the information stored in the red, green and blue channel (RGB) is correlated. This makes any analysis a 3 dimensional task. Principal Component Analysis (PCA) helps us to overcome these problems by decorrelating the original band information and generating a variance representation of the original data. Therefore PCA is a suitable tool to detect optical anomalies, as might be caused by volcanic degassing and associated processes.</p><p>Applied in a case study at La Fossa Cone (Vulcano Island - Italy), the PCA showed a high efficiency for the detection and pixel based extraction of areas subject to hydrothermal alteration and sulfur deposition. We observed a broad alteration zone surrounding the active fumarole field, but also heterogeneities within, indicating a segmentation. Systematic variations in color and density distribution of sulfur deposits have implications for structural controls on the degassing system.</p><p>Combining the efficiency of PCA with the high resolution of UAS derived data, this methodology has a high potential to be employed in the spatio-temporal monitoring of volcanic hydrothermal systems and processes at surface.</p><p> </p>

Progressive landslide activity analysis and monitoring from Multi-temporal high-resolution geoinfomatic data sets

2020 ◽  
Author(s):  
Kuo-Jen Chang ◽  
Chih-Ming Tseng ◽  
Ho-Hsuan Chang ◽  
Mei-Jen Huang
Keyword(s):  
High Resolution ◽  
Unmanned Aircraft ◽  
Airborne Lidar ◽  
Landslide Monitoring ◽  
Data Sets ◽  
Data Set ◽  
Wide Range ◽  
Hazardous Area ◽  
Multi Temporal ◽  
Rtk Gps

<p>Due to the high seismicity and high annual precipitation, numerous landslides have occurred and caused severe impact in Taiwan. In recent years, the remote sensing technology improves rapidly, providing a wide range of image, essential and precise geoinformation. The Small unmanned aircraft system (sUAS) has been widely used in landslide monitoring and geomorphic change detection. To access potential hazards we combine sUAS, field survey, terrestrial laser scanner (ground LiDAR) and UAS LiDAR for data acquisition. Based on the methods we construct multi-temporal high-resolution DTMs so as to access the activity and to monitoring the creeping landslides in Paolai village, southern Taiwan. The data set are qualified from 21 ground control points (GCPs) and 11 check points (CPs) based on real-time kinematic-global positioning system (RTK-GPS) and VBS RTK-GPS (e-GNSS). Since 2015, more than 10 geospatial datasets have been produced for an area between 5-80 Km<sup>2</sup> with 8-12 cm spatial resolution. These datasets were then compared with the airborne LiDAR data to access the quality and interpretability of the data sets. Since 2017, we integrate UAS LiDAR to monitoring landslide area, and re-evaluate the data accuracy. Since 2018 we have integrate UAS LiDAR, terrestrial LiDAR, and photogrammetric point cloud for landslide study, to ensure no shadow effect of the dataset. The geomorphologic changes and landslide activities were quantified in Paolai area. The results of this study provide not only geoinfomatic datasets of the hazardous area, but also for essential geomorphologic information for other study, and for hazard mitigation and planning, as well.</p>

scholarly journals Comparison of Independent Component Analysis, Principal Component Analysis, and Minimum Noise Fraction Transformation for Tree Species Classification Using APEX Hyperspectral Imagery

2018 ◽  
Vol 7 (12) ◽  
pp. 488 ◽  
Author(s):  
Zahra Dabiri ◽  
Stefan Lang
Keyword(s):  
Tree Species ◽  
Hyperspectral Imagery ◽  
Principal Component ◽  
Component Analysis ◽  
Species Classification ◽  
Data Cubes ◽  
Tree Species Classification ◽  
Reduction Techniques ◽  
Noise Fraction ◽  
Minimum Noise

Hyperspectral imagery provides detailed spectral information that can be used for tree species discrimination. The aim of this study is to assess spectral–spatial complexity reduction techniques for tree species classification using an airborne prism experiment (APEX) hyperspectral image. The methodology comprised the following main steps: (1) preprocessing (removing noisy bands) and masking out non-forested areas; (2) applying dimensionality reduction techniques, namely, independent component analysis (ICA), principal component analysis (PCA), and minimum noise fraction transformation (MNF), and stacking the selected dimensionality-reduced (DR) components to create new data cubes; (3) super-pixel segmentation on the original image and on each of the dimensionality-reduced data cubes; (4) tree species classification using a random forest (RF) classifier; and (5) accuracy assessment. The results revealed that tree species classification using the APEX hyperspectral imagery and DR data cubes yielded good results (with an overall accuracy of 80% for the APEX imagery and an overall accuracy of more than 90% for the DR data cubes). Among the classification results of the DR data cubes, the ICA-transformed components performed best, followed by the MNF-transformed components and the PCA-transformed components. The best class performance (according to producer’s and user’s accuracy) belonged to Picea abies and Salix alba. The other classes (Populus x (hybrid), Alnus incana, Fraxinus excelsior, and Quercus robur) performed differently depending on the different DR data cubes used as the input to the RF classifier.

scholarly journals Tree Species Classification Based on Sentinel-2 Imagery and Random Forest Classifier in the Eastern Regions of the Qilian Mountains

Forests ◽  
2021 ◽  
Vol 12 (12) ◽  
pp. 1736
Author(s):  
Minfei Ma ◽  
Jianhong Liu ◽  
Mingxing Liu ◽  
Jingchao Zeng ◽  
Yuanhui Li
Keyword(s):  
Tree Species ◽  
Texture Features ◽  
Qilian Mountains ◽  
Airborne Lidar ◽  
Tree Level ◽  
Species Classification ◽  
Sabina Przewalskii ◽  
Tree Species Classification ◽  
The Qilian Mountains ◽  
Sentinel 2

Obtaining accurate forest coverage of tree species is an important basis for the rational use and protection of existing forest resources. However, most current studies have mainly focused on broad tree classification, such as coniferous vs. broadleaf tree species, and a refined tree classification with tree species information is urgently needed. Although airborne LiDAR data or unmanned aerial vehicle (UAV) images can be used to acquire tree information even at the single tree level, this method will encounter great difficulties when applied to a large area. Therefore, this study takes the eastern regions of the Qilian Mountains as an example to explore the possibility of tree species classification with satellite-derived images. We used Sentinel-2 images to classify the study area’s major vegetation types, particularly four tree species, i.e., Sabina przewalskii (S.P.), Picea crassifolia (P.C.), Betula spp. (Betula), and Populus spp. (Populus). In addition to the spectral features, we also considered terrain and texture features in this classification. The results show that adding texture features can significantly increase the separation between tree species. The final classification result of all categories achieved an accuracy of 86.49% and a Kappa coefficient of 0.83. For trees, the classification accuracy was 90.31%, and their producer’s accuracy (PA) and user’s (UA) were all higher than 84.97%. We found that altitude, slope, and aspect all affected the spatial distribution of these four tree species in our study area. This study confirms the potential of Sentinel-2 images for the fine classification of tree species. Moreover, this can help monitor ecosystem biological diversity and provide references for inventory estimation.

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