scholarly journals Potential of P-Band SAR Tomography in Forest Type Classification

2021 ◽  
Vol 13 (4) ◽  
pp. 696
Author(s):  
Dinh Ho Tong Minh ◽  
Yen-Nhi Ngo ◽  
Thu Trang Lê
Keyword(s):  
Forest Type ◽  
Three Dimensional ◽  
Strong Support ◽  
Low Frequency ◽  
High Sensitivity ◽  
Forest Site ◽  
Forest Types ◽  
Forest Classification ◽  
Sar Data ◽  
Type Classification

Forest type classification using spaceborne remote sensing is a challenge. Low-frequency Synthetic Aperture Radar (SAR) signals (i.e., P-band, ∼0.69 m wavelength) are needed to penetrate a thick vegetation layer. However, this measurement alone does not guarantee a good performance in forest classification tasks. SAR tomography, a technique employing multiple acquisitions over the same areas to form a three-dimensional image, has been demonstrated to improve SAR’s capability in many applications. Our study shows the potential value of SAR tomography acquisitions to improve forest classification. By using P-band tomographic SAR data from the German Aerospace Center F-SAR sensor during the AfriSAR campaign in February 2016, the vertical profiles of five different forest types at a tropical forest site in Mondah, Gabon (South Africa) were analyzed and exploited for the classification task. We demonstrated that the high sensitivity of SAR tomography to forest vertical structure enables the improvement of classification performance by up to 33%. Interestingly, by using the standard Random Forest technique, we found that the ground (i.e., at 5–10 m) and volume layers (i.e., 20–40 m) play an important role in identifying the forest type. Together, these results suggested the promise of the TomoSAR technique for mapping forest types with high accuracy in tropical areas and could provide strong support for the next Earth Explorer BIOMASS spaceborne mission which will collect P-band tomographic SAR data.

scholarly journals Mapping Forest Types in China with 10 m Resolution Based on Spectral–Spatial–Temporal Features

2021 ◽  
Vol 13 (5) ◽  
pp. 973
Author(s):  
Kai Cheng ◽  
Juanle Wang ◽  
Xinrong Yan
Keyword(s):  
Remote Sensing ◽  
Forest Type ◽  
Google Earth ◽  
Growth Patterns ◽  
Forest Types ◽  
Forest Classification ◽  
Classification Framework ◽  
Temporal Features ◽  
Occurrence Matrix ◽  
Type Classification

The comprehensive application of spectral, spatial, and temporal (SST) features derived from remote sensing images is a significant technique for classifying and mapping forest types. Facing limitations in the availability of detailed forest type identification processes for large regions, a forest type classification framework based on SST features was developed in this study. The advantages of Sentinel-2 and Landsat series imagery were used to extract SST forest type classification features, using red-edge bands, a gray-level co-occurrence matrix, and harmonic analysis, with the assistance of the Google Earth Engine platform. Considering four representative Chinese geographic regions—middle and high latitudes, complex mountainous areas, cloudy and rainy areas, and the N–S climate transition zone—our method was proven to be effective, with overall classification accuracies > 85%. The scheme to assess the importance of SST features for forest classification in various regions was designed using the Gini criterion in the random forest algorithm and revealed that spectral features were more effective in classifying forest types with complex compositions. Temporal features were found to be favorable for identifying forest types with obvious evergreen and deciduous growth patterns, while spatial features produced better classification results for forest types with different spatial structures, such as needle- or broad-leaved forests. The findings of this study can provide a reference for feature selection in remote sensing forest type classification processes, and identifying forest types in this way could provide support for the accurate and sustainable management of forest resources.

scholarly journals Forest Site and Type Variability in ALS-Based Forest Resource Inventory Attribute Predictions over Three Ontario Forest Sites

Forests ◽  
2019 ◽  
Vol 10 (3) ◽  
pp. 226 ◽  
Author(s):  
Karin van Ewijk ◽  
Paul Treitz ◽  
Murray Woods ◽  
Trevor Jones ◽  
John Caspersen
Keyword(s):  
Prediction Accuracy ◽  
Forest Type ◽  
Stem Density ◽  
Forest Site ◽  
Forest Resource ◽  
Forest Types ◽  
Resource Inventory ◽  
Forest Resource Inventory ◽  
Forest Attributes ◽  
The Impact

Over the last decade, spatially-explicit modeling of landscape-scale forest attributes for forest inventories has greatly benefitted from airborne laser scanning (ALS) and the area-based approach (ABA) to derive wall-to-wall maps of these forest attributes. Which ALS-derived metrics to include when modeling forest inventory attributes, and how prediction accuracies vary over forest types depends largely on the structural complexity of the forest(s) being studied. Hence, the purpose of this study was to (i) examine the usefulness of adding texture and intensity metrics to height-based ALS metrics for the prediction of several forest resource inventory (FRI) attributes in one boreal and two Great Lakes, St. Lawrence (GLSL) forest region sites in Ontario and (ii) quantify and compare the site and forest type variability within the context of the FRI prediction accuracies. Basal area (BA), quadratic mean diameter-at-breast height (QMD), and stem density (S) were predicted using the ABA and a nonparametric Random Forests (RF) regression model. At the site level, prediction accuracies (i.e., expressed as RMSE (Root Mean Square Error), bias, and R2) improved at the three sites when texture and intensity metrics were included in the predictor set, even though no significant differences (p > 0.05) could be detected using the nonparametric RMANOVA test. Stem density benefitted the most from the inclusion of texture and intensity, particularly in the GLSL sites (% RMSE improved up to 6%). Combining site and forest type results indicated that improvements in site level predictions, due to the addition of texture and intensity metrics to the ALS predictor set, were the result of changes in prediction accuracy in some but not all forest types present at a site and that these changes in prediction accuracy were site and FRI attribute specific. The nonparametric Kruskal–Wallis test indicated that prediction errors between the different forest types were significantly different (p ≤ 0.01). In the boreal site, prediction accuracies for conifer forest types were higher than for deciduous and mixedwoods. Such patterns in prediction accuracy among forest types and FRI attributes could not be observed in the GLSL sites. In the Petawawa Research Forest (PRF), we did detect the impact of silvicultural treatments especially on QMD and S predictions.

scholarly journals Russian Approaches to the Forest Type Classification

2021 ◽  
Vol 906 (1) ◽  
pp. 012023
Author(s):  
Valery Fomin ◽  
Anna Mikhailovich
Keyword(s):  
Geographical Distribution ◽  
Russian Federation ◽  
Forest Type ◽  
Anthropogenic Factors ◽  
Genetic Approach ◽  
Habitat Conditions ◽  
Forest Types ◽  
The Russian Federation ◽  
Forest Typology ◽  
Type Classification

Abstract The results of researches characterizing the geographical distribution of forest-ecological, phytocoenotic, and genetic classifications of forest types in the Russian Federation nowadays are presented in the thesis. A comparative analysis was carried out for the following items: the inclusive concept of a classification unit (a type of habitat conditions; a type of forest); features of distinguishing the border of the classification units; classification features used to determine the type of habitat conditions; features of the classification of phytocoenoses used to determine the forest type; the degree to which the successional dynamics of forest stands are taken into consideration; the degree to which the influence of anthropogenic factors are taken into consideration; the level of implementation in forest management and forestry practice; regions of implementation. In the process of development of forest typologies, the concept of a forest type changed from understanding it as a forest area homogeneous by composition, structure, and appearance (homogeneity in space) in natural classifications to the concepts of a forest type, in which priority is given to homogeneity in origin (genesis), as well as developmental processes and dynamics (homogeneity in time) in genetic and dynamic typologies. Currently, there is the following forest type classification in the Russian Federation: forest-ecological, phytocoenotic, genetic, and dynamic. When classifying forest areas within the forest-ecological direction provided by E.V. Alekseev – P.S. Pogrebnyak, the priority is given to the characteristics of the habitat conditions. Within the phytocoenotic direction provided by V.N. Sukachev, the priority is given to the phytocoenosis characteristics. Within the genetic approach provided by B.A. Ivashkevich – B.P. Kolesnikov, a forest type is considered as a series of alternating phases – types of phytocoenosis within the same type of habitat conditions. In this case, phytocoenotic classifications can be a part of the genetic classifications for the climax forest phytocoenosis. And the dynamic approach provided by I.S. Melekhov is very close to the genetic one and is a superstructure over the classical phytocoenotic forest typology provided by V.N. Sukachev. The current use of forest typological classifications by forest inventory management enterprises in the Russian Federation was studied. A map of the geographical distribution of forest typologies of the above-listed directions of forest typology researches was created. Forest-ecological classifications are used mainly in the southern regions of the European part of Russia and the North Caucasus. Forest typologies created based on a genetic approach to the forest type classification are used in Western Siberia, in the south of the Far East and Eastern Siberia, and in some regions of the Urals. Phytocoenotic classifications of forest types are used in other regions of the Russian Federation.

scholarly journals Лісівничо-екологічний аналіз насаджень Populus alba та Populus nigra на ріках лівобережного лісостепу України

2017 ◽  
Vol 7 (1) ◽  
pp. 84-91
Author(s):  
O. B. Bondar ◽  
L. I. Tkach ◽  
I. S. Lisina ◽  
M. S. Kolienkina ◽  
S. I. Musiyenko
Keyword(s):  
Forest Type ◽  
Site Quality ◽  
Forest Steppe ◽  
Forest Site ◽  
Left Bank ◽  
Forest Types ◽  
Pine Tree ◽  
Lime Tree ◽  
Black Poplar

<p>Here the sylvicultural and ecological analysis of typological structure of plantings silver and black poplar are presented for the riverine habitats of the Psel, Sula and Vorskla (the middle reaches of Seversky Donets river). Our analysis was based on forestry management electronic databases of Ukrainian National Forest Project Enterprise.</p><p>More than 38 forest types on the area of 4.9 thousand hectares were examined. The biological features of silver and black poplar were described briefly. The silver and black poplar reproduction pattern of the Left-bank Forest-steppe of Ukraine was also examined. There was carried out the area allocation of tree species according the following points: forest type and origin, forest site quality, closure degree and age groups. By the tree stratum origin silver and black poplar are mostly artificially propagated, what is equivalent to 77.3 and 88.3 percent.</p><p>The silver and black poplar area around the rivers’ watershed of the Left-bank Forest-steppe of Ukraine occupies 2813 and 2173 ha consequently.</p><p>Among forest types on research subject there are some forest types which dominate:  fresh quercetum fluvialis (25.0 %), wet quercetum fluvialis (17.4 %), wet quercetum-birchbark-maple fluvialis (16.3 %), wet lime tree-oak-pine tree sudubrava (11.4 %), fresh lime tree, oak, pine tree sudubrava (5.2 %), the rest of tree types represents less than 4.0 % of the total land area, covered with sylva. The silver and black poplar plantings’ distribution according to the site quality of forest on the rivers’ columbine of the Left-bank Forest-steppe of Ukraine can be described in the following way: II and IV classes of the site quality of forest prevail, and the medium stocked tree stratum fluctuates from 52.9 to 87.8 per cent according to the normality.</p>

A pattern of forest types on ribbed moraines in eastern Newfoundland

1978 ◽  
Vol 8 (1) ◽  
pp. 116-120 ◽  
Author(s):  
B. B. Delaney ◽  
M. J. Cahill
Keyword(s):  
Forest Type ◽  
Black Spruce ◽  
Vegetation Succession ◽  
Balsam Fir ◽  
North Slope ◽  
Forest Site ◽  
Distinctive Pattern ◽  
Forest Types ◽  
White Birch ◽  
Avalon Peninsula

A distinctive pattern of forest types has been observed on ribbed moraines of the Avalon Peninsula, Newfoundland. This previously unreported pattern is interesting in that the best forests occur on the exposed tops and the theoretically less favourable north slopes. On each moraine, the south slopes characteristically had an uncommercial forest of balsam fir (Abiesbalsamea (L.) (Mill.)) and black spruce (Piceamariana (Mill.) BSP.), the top and upper north slopes had a forest of white birch (Betulapapyrifera Marsh.) and balsam fir, and the lower north slope had a pure balsam fir forest. Site descriptions are provided for each forest type and the vegetation succession following fire is proposed.

A review of forest site classification activities in Newfoundland and Labrador

1992 ◽  
Vol 68 (1) ◽  
pp. 25-33 ◽  
Author(s):  
W. J. Meades ◽  
B. A. Roberts
Keyword(s):  
Newfoundland And Labrador ◽  
Forest Ecology ◽  
Site Classification ◽  
Forest Site ◽  
Forest Types ◽  
Pilot Projects ◽  
Stand Productivity ◽  
Forest Classification ◽  
Intensive Forest Management ◽  
Forest Site Classification

This paper provides a review of past and present forest site classification activities in Newfoundland and Labrador over the last thirty years. Initially, research concentrated on the development of a classification system using floristic and edaphic criteria to define forest types. This was followed by a period in which the relationships between forest types and stand productivity were assessed. Subsequently, pilot projects were undertaken in which the forest site classification was incorporated into the biophysical land classification approach and applied to forest capability mapping. In recent years the trend towards more intensive forest management has rekindled interest in forest site classification: emphasis is being placed on technology transfer of site classification to operational foresters in industry and government. Key words: Forest classification, site classification, soils, vegetation, Newfoundland, Labrador, forest ecology

scholarly journals MAPPING SPATIAL ACCURACY OF FOREST TYPE CLASSIFICATION IN JAXA’s HIGH-RESOLUTION LAND USE AND LAND COVER MAP

2019 ◽  
Vol IV-3/W1 ◽  
pp. 57-63
Author(s):  
N. Tsutsumida ◽  
S. Nagai ◽  
P. Rodríguez-Veiga ◽  
J. Katagi ◽  
K. Nasahara ◽  
...  
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Forest Type ◽  
Accuracy Assessment ◽  
Spatial Accuracy ◽  
Forest Types ◽  
Broadleaf Forest ◽  
Northern Edge ◽  
Accuracy Measures ◽  
Type Classification

<p><strong>Abstract.</strong> Accuracy assessment of forest type maps is essential to evaluate the classification of forest ecosystems quantitatively. However, map users do not understand in which regions those forest types are well classified from conventional static accuracy measures. Hence, the objective of this study is to unveil spatial heterogeneities of accuracies of forest type classification in a map. Four forest types (deciduous broadleaf forest (DBF), deciduous needleleaf forest (DNF), evergreen broadleaf forest (EBF), and evergreen needleleaf forest (ENF)) found in the JAXA’s land use / cover map of Japan were assessed by a volunteered Site-based dataset for Assessment of Changing LAnd cover by JAXA (SACLAJ). A geographically weighted (GW) correspondence matrix was applied to them to calculate the degree of overall agreements of forest type classes (forest overall accuracy), and the degree of accuracy for each forest class (forest user’s and producer’s accuracies) in a spatially varying way. This study compared spatial surfaces of these measures with static ones of them. The results show that the forest overall accuracy of the forest map tends to be relatively more accurate in the central Japan, while less in the Kansai and Chubu regions and the northern edge of Hokkaido. Static forest user’s accuracy measures for DBF, DNF, and ENF are better than forest producer’s accuracy ones, while the GW approach tells us such characteristics vary spatially and some areas have opposite trends. This kind of spatial accuracy assessment provides a more informative description of the accuracy than the simple use of conventional accuracy measures.</p>

scholarly journals Moroccan Video Intelligent Transport System: Vehicle Type Classification Based on Three-Dimensional and Two-Dimensional Features

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 72528-72537 ◽  
Author(s):  
Hatim Derrouz ◽  
Abderrahim Elbouziady ◽  
Hamd Ait Abdelali ◽  
Rachid Oulad Haj Thami ◽  
Sanaa El Fkihi ◽  
...  
Keyword(s):  
Transport System ◽  
Three Dimensional ◽  
Intelligent Transport System ◽  
Two Dimensional ◽  
Intelligent Transport ◽  
Vehicle Type ◽  
Vehicle Type Classification ◽  
Type Classification

scholarly journals Magnetoelastic Coupling and Delta-E Effect in Magnetoelectric Torsion Mode Resonators

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2022
Author(s):  
Benjamin Spetzler ◽  
Elizaveta V. Golubeva ◽  
Ron-Marco Friedrich ◽  
Sebastian Zabel ◽  
Christine Kirchhof ◽  
...  
Keyword(s):  
Low Frequency ◽  
High Sensitivity ◽  
Element Model ◽  
Mode Number ◽  
Frequency Change ◽  
General Description ◽  
Resonance Modes ◽  
Field Sensor ◽  
Magnetic Sensitivity ◽  
Bending Modes

Magnetoelectric resonators have been studied for the detection of small amplitude and low frequency magnetic fields via the delta-E effect, mainly in fundamental bending or bulk resonance modes. Here, we present an experimental and theoretical investigation of magnetoelectric thin-film cantilevers that can be operated in bending modes (BMs) and torsion modes (TMs) as a magnetic field sensor. A magnetoelastic macrospin model is combined with an electromechanical finite element model and a general description of the delta-E effect of all stiffness tensor components Cij is derived. Simulations confirm quantitatively that the delta-E effect of the C66 component has the promising potential of significantly increasing the magnetic sensitivity and the maximum normalized frequency change ∆fr. However, the electrical excitation of TMs remains challenging and is found to significantly diminish the gain in sensitivity. Experiments reveal the dependency of the sensitivity and ∆fr of TMs on the mode number, which differs fundamentally from BMs and is well explained by our model. Because the contribution of C11 to the TMs increases with the mode number, the first-order TM yields the highest magnetic sensitivity. Overall, general insights are gained for the design of high-sensitivity delta-E effect sensors, as well as for frequency tunable devices based on the delta-E effect.

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