scholarly journals Evaluación del uso de LiDAR discreto, full-waveform y TLS en la clasificación por composición de especies en bosques mediterráneos

2018 ◽  
pp. 27 ◽  
Author(s):  
J. Torralba ◽  
P. Crespo-Peremarch ◽  
L. A. Ruiz
Keyword(s):  
Species Composition ◽  
Vegetation Cover ◽  
Forest Ecosystems ◽  
Pinus Halepensis ◽  
Laser Scanner ◽  
Understory Vegetation ◽  
Data Sets ◽  
Full Waveform ◽  
Understory Vegetation Cover

<p>LiDAR technology –airborne and terrestrial- is becoming more relevant in the development of forest inventories, which are crucial to better understand and manage forest ecosystems. In this study, we assessed a classification of species composition in a Mediterranean forest following the C4.5 decision tree. Different data sets from airborne laser scanner full-waveform (ALS<sub>FW</sub>), discrete (ALS<sub>D</sub>) and terrestrial laser scanner (TLS) were combined as input data for the classification. Species composition were divided into five classes: pure Quercus ilex plots (QUI); pure Pinus halepensis dense regenerated (HALr); pure P. halepensis (HAL); pure P. pinaster (PIN); and mixed P. pinaster and Q. suber (mPIN). Furthermore, the class HAL was subdivided in low and dense understory vegetation cover. As a result, combination of ALS<sub>FW</sub> and TLS reached 85.2% of overall accuracy classifying classes HAL, PIN and mPIN. Combining ALS<sub>FW</sub> and ALS<sub>D</sub>, the overall accuracy was 77.0% to discriminate among the five classes. Finally, classification of understory vegetation cover using ALS<sub>FW</sub> reached an overall accuracy of 90.9%. In general, combination of ALS<sub>FW</sub> and TLS improved the overall accuracy of classifying among HAL, PIN and mPIN by 7.4% compared to the use of the data sets separately, and by 33.3% with respect to the use of ALS<sub>D</sub> only. ALS<sub>FW</sub> metrics, in particular those specifically designed for detection of understory vegetation, increased the overall accuracy 9.1% with respect to ALS<sub>D</sub> metrics. These analyses show that classification in forest ecosystems with presence of understory vegetation and intermediate canopy strata is improved when ALS<sub>FW</sub> and/or TLS are used instead of ALS<sub>D</sub>.</p>

Prediction of understory vegetation cover with airborne lidar in an interior ponderosa pine forest

2012 ◽  
Vol 124 ◽  
pp. 730-741 ◽  
Author(s):  
Brian M. Wing ◽  
Martin W. Ritchie ◽  
Kevin Boston ◽  
Warren B. Cohen ◽  
Alix Gitelman ◽  
...  
Keyword(s):  
Ponderosa Pine ◽  
Vegetation Cover ◽  
Pine Forest ◽  
Understory Vegetation ◽  
Airborne Lidar ◽  
Understory Vegetation Cover ◽  
Ponderosa Pine Forest

scholarly journals CONCEPTUAL ISSUES OF IMPROVING THE FORESTRY SYSTEM OF REFORESTATION AND ITS USE

ÈKOBIOTEH ◽  
2020 ◽  
Vol 3 (3) ◽  
pp. 457-471
Author(s):  
V.I. Zheldak ◽  
◽  
A.A. Kulagin ◽  
E.V. Doroschenkova ◽  
T.V. Lipkina ◽  
...  
Keyword(s):  
Species Composition ◽  
Natural Regeneration ◽  
Forest Ecosystems ◽  
Forest Vegetation ◽  
Target Species ◽  
Rational Use ◽  
Limited Availability ◽  
Detailed Classification ◽  
Negative Factors

Discusses the principle issues of the maximum rational use of the potential for natural regeneration of target species in operational and protective forests, including in conditions of limited availability for carrying out intensive reforestation and forest care activities. Herewith, in order to improve the species composition, the value of restored forest ecosystems in view to logging, as well as their loss during fires, the spread of pathology and other negative factors, it is planned to use a detailed classification of methods and types of renewal measures, caring for renewed target species plants, expanding the possibilities of using these most important forestry activities. In these purposes formed based on existing advanced classification of activities of renewal on the types and kinds of objects cutting change forest generations, plots covered by forest vegetation to varying degrees as well as complex - with considering methods of renewal forests, species specificity, typology and diversity of the target purpose of forests. A conceptual (principle) composition scheme and content methods and types of renewal forests have been formed based on which provides the ability to choose options the most acceptable, available in specific conditions these forestry activities.

scholarly journals Composition and diversity of psocid (Insecta: Psocoptera) taxocoenoses in forest ecosystems of the Abieti-fageta s. lat. zone in the Western Carpathian Mts.

2009 ◽  
Vol 55 (No. 4) ◽  
pp. 184-192 ◽  
Author(s):  
O. Holuša
Keyword(s):  
Cluster Analysis ◽  
Species Composition ◽  
Correspondence Analysis ◽  
Dominant Species ◽  
Forest Ecosystems ◽  
Forest Type ◽  
Detrended Correspondence Analysis ◽  
Vegetation Zones ◽  
Characteristic Species

Psocid (Psocoptera) taxocoenoses were studied in forest ecosystems of the Western Carpathian Mts. in 1997–2001. As a study frame, vegetation tiers (VT = altitudinal vegetation zones) of geobiocoenological or forest-typological system were used. Lower units of forest typological system (forest type complexes) were used for the classification of ecological conditions and the material found in forest ecosystems of <I>Abieti-fageta</I> s. lat. communities (5<sup>th</sup> fir-beech VT) was evaluated in detail. This VT is the most widespread in the regions under study (the Moravskoslezské Beskydy Mts., the Vsetínské vrchy Hills and Javorníky). 2,023 adults comprising 28 species were found in the 5<sup>th</sup> VT. <I>Caecilius burmeisteri</I> was found as eudominant species; <I>Philotarsus picicornis, Caecilius flavidus</I> and <I>Peripsocus subfasciatus</I> were found as dominant species. In natural geobiocoenoses with the level of naturalness 1 or 2, the following species were found: <I>Mesopsocus unipunctatus, Caecilius flavidus</I>, and <I>Caecilius burmeisteri</I> as eudominant and <I>Caecilius despaxi</I> as dominant. Taxocoenoses of psocids were evaluated by Detrended Correspondence Analysis (DCA) and Divisive Cluster Analysis (DvClA). The axes were interpreted in DCA-analysis as follows: the <I>x</I>-axis denotes the influence of VTs and the <I>q</I>-axis refers to the influence of hydricity. This material was compared with other material obtained from various vegetation tiers in the Western Carpathians Mts. The characteristic species composition of psocids in the 5<sup>th</sup> VT was as follows: <I>Caecilius flavidus – C. burmeisteri – C. despaxi – Metylophorus nebulosus – Philotarsus picicornis</I>.

scholarly journals Coastal Sand Dunes Monitoring by Low Vegetation Cover Classification and Digital Elevation Model Improvement Using Synchronized Hyperspectral and Full-Waveform LiDAR Remote Sensing

2020 ◽  
Vol 13 (1) ◽  
pp. 29
Author(s):  
Giovanni Frati ◽  
Patrick Launeau ◽  
Marc Robin ◽  
Manuel Giraud ◽  
Martin Juigner ◽  
...  
Keyword(s):  
Vegetation Cover ◽  
Large Scale ◽  
Sand Dunes ◽  
Physiological State ◽  
Hierarchical Classification ◽  
Field Measurements ◽  
Range Resolution ◽  
Full Waveform ◽  
Waveform Lidar

Due to the coastal morphodynamic being impacted by climate change there is a need for systematic and large-scale monitoring. The monitoring of sandy dunes in Pays-de-la-Loire (France) requires a simultaneous mapping of (i) its morphology, allowing to assess the sedimentary stocks and (ii) its low vegetation cover, which constitutes a significant proxy of the dune dynamics. The synchronization of hyperspectral imaging (HSI) with full-waveform (FWF) LiDAR is possible with an airborne platform. For a more intimate combination, we aligned the 1064 nm laser beam of a bi-spectral Titan FWF LiDAR with 401 bands and the 15 cm range resolution on the Hyspex VNIR camera with 160 bands and a 4.2 nm spectral resolution, making both types of data follow the same emergence angle. A ray tracing procedure permits to associate the data while keeping the acquisition angles. Stacking multiple shifted FWFs, which are linked to the same pixel, enables reaching a 5 cm range resolution grid. The objectives are (i) to improve the accuracy of the digital terrain models (DTM) obtained from an FWF analysis by calibrating it on dGPS field measurements and correcting it from local deviations induced by vegetation and (ii) in combination with airborne reflectances obtained with PARGE and ATCOR-4 corrections, to implement a supervised hierarchic classification of the main foredune vegetation proxies independently of the acquisition year and the physiological state. The normalization of the FWF LiDAR range to a dry sand reference waveform and the centering on their top canopy echoes allows to isolate Ammophilia arenaria from other vegetation types using two FWF indices, without confusion with slope effects. Fourteen HSI reflectance indices and 19 HSI Spectral Angle Mapping (SAM) indices based on 2017 spectral field measurements performed with the same Hyspex VNIR camera were stacked with both FWF indices into a single co-image for each acquisition year. A simple straightforward hierarchical classification of all 35 pre-classified co-image bands was successfully applied along 20 km, out of the 250 km of coastline acquired from 2017 to 2019, prefiguring its systematic application to the whole 250 km every year.

scholarly journals Classification of homogeneous regions of vegetation cover in the State of Rio Grande do Sul, Brazil and its temporal dynamics, using AVHRR GIMMS and MODIS data sets

2021 ◽  
Vol 93 (3) ◽  
Author(s):  
ANA PAULA A. CORDEIRO ◽  
RITA DE CÁSSIA M. ALVES ◽  
ANA PAULA L.W. STEFFLER ◽  
VAGNER P. MENGUE ◽  
DENISE C. FONTANA ◽  
...  
Keyword(s):  
Vegetation Cover ◽  
Temporal Dynamics ◽  
Rio Grande ◽  
The State ◽  
Data Sets ◽  
Rio Grande Do Sul ◽  
Modis Data ◽  
Homogeneous Regions

scholarly journals Psocid taxocenoses structure and diversity (Insecta: Psocoptera) in the forest ecosystems of the Piceeti–fageta s. lat. zone in the Western Carpathian Mts.

2011 ◽  
Vol 59 (5) ◽  
pp. 95-104 ◽  
Author(s):  
Otakar Holuša
Keyword(s):  
Cluster Analysis ◽  
Species Composition ◽  
Correspondence Analysis ◽  
Dominant Species ◽  
Forest Ecosystems ◽  
Forest Type ◽  
Detrended Correspondence Analysis ◽  
Vegetation Zones ◽  
Characteristic Species

Psocid taxocenoses (Psocoptera) were studied in forest ecosystems of the Western Carpathian Mts. during 1997–2001. As a study frame, vegetation tiers (= altitudinal vegetation zones) were used. Lower units of forest typological system (forest type complexes) were used for a classification of ecological conditions as well. Only a part of material, i.e. individuals that was found in the forest ecosystems of Piceeti-fageta s. lat. communities (= the 6th spruce-beech vegetation tier) was evaluated for purpose of this work. This vegetation tier is widespread in higher parts of mountains (the Moravskoslezské Beskydy Mts. and partly in the Oravské Beskydy Mts.). 554 adults comprising 17 species were found in total in the 6th vegetation tier. As eudominant species, the following ones were found: Caecilius despaxi, Caecilius burmeisteri, Mesopsocus unipunctatus, and Stenopsocus lachlani; as dominant species, the following ones were found: Caecilius flavidus and Reuterella helvimacula. In natural geobiocenoses with the level of naturalness of 1 or 2, the following species were found: as eudominant species: Mesopsocus unipunctatus, Stenopsocus lachlani, Caecilius despaxi, Amphigerontia bifasciata and Reuterella helvimacula. Dominant species was Caecilius burmeisteri and Caecilius flavidus. Taxocenoses of psocids were evaluated by Detrended Correspondence analysis (DCA) and Divisive Cluster analysis (DvClA). This material was compared to another material gained from various vegetation tiers in the Western Carpathians Mts. The characteristic species composition of psocids in the 6th vegetation tier was as follows – Cecilius despaxi – Stenopsocus lachlani – Mesopsocus unipunctatus – Reuterella helvimacula.

Classification of vegetation in an open landscape using full-waveform airborne laser scanner data

2015 ◽  
Vol 41 ◽  
pp. 76-87 ◽  
Author(s):  
Cici Alexander ◽  
Balázs Deák ◽  
Adam Kania ◽  
Werner Mücke ◽  
Hermann Heilmeier
Keyword(s):  
Laser Scanner ◽  
Scanner Data ◽  
Airborne Laser Scanner ◽  
Full Waveform ◽  
Airborne Laser

scholarly journals Classification of jujube defects in small data sets based on transfer learning

2021 ◽  
Author(s):  
Jianping Ju ◽  
Hong Zheng ◽  
Xiaohang Xu ◽  
Zhongyuan Guo ◽  
Zhaohui Zheng ◽  
...  
Keyword(s):  
Transfer Learning ◽  
Loss Function ◽  
Training Model ◽  
Parameter Distribution ◽  
Test Accuracy ◽  
Small Data ◽  
Data Sets ◽  
Data Set ◽  
Small Data Sets

AbstractAlthough convolutional neural networks have achieved success in the field of image classification, there are still challenges in the field of agricultural product quality sorting such as machine vision-based jujube defects detection. The performance of jujube defect detection mainly depends on the feature extraction and the classifier used. Due to the diversity of the jujube materials and the variability of the testing environment, the traditional method of manually extracting the features often fails to meet the requirements of practical application. In this paper, a jujube sorting model in small data sets based on convolutional neural network and transfer learning is proposed to meet the actual demand of jujube defects detection. Firstly, the original images collected from the actual jujube sorting production line were pre-processed, and the data were augmented to establish a data set of five categories of jujube defects. The original CNN model is then improved by embedding the SE module and using the triplet loss function and the center loss function to replace the softmax loss function. Finally, the depth pre-training model on the ImageNet image data set was used to conduct training on the jujube defects data set, so that the parameters of the pre-training model could fit the parameter distribution of the jujube defects image, and the parameter distribution was transferred to the jujube defects data set to complete the transfer of the model and realize the detection and classification of the jujube defects. The classification results are visualized by heatmap through the analysis of classification accuracy and confusion matrix compared with the comparison models. The experimental results show that the SE-ResNet50-CL model optimizes the fine-grained classification problem of jujube defect recognition, and the test accuracy reaches 94.15%. The model has good stability and high recognition accuracy in complex environments.

Sign in / Sign up

Export Citation Format

Share Document