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.

scholarly journals An Evaluation of Pixel- and Object-Based Tree Species Classification in Mixed Deciduous Forests Using Pansharpened Very High Spatial Resolution Satellite Imagery

2021 ◽  
Vol 13 (10) ◽  
pp. 1868
Author(s):  
Martina Deur ◽  
Mateo Gašparović ◽  
Ivan Balenović
Keyword(s):  
Spatial Resolution ◽  
Satellite Imagery ◽  
Tree Species ◽  
Classification Accuracy ◽  
New Technologies ◽  
Deciduous Forest ◽  
Species Classification ◽  
Tree Species Classification ◽  
Object Based ◽  
Very High

Quality tree species information gathering is the basis for making proper decisions in forest management. By applying new technologies and remote sensing methods, very high resolution (VHR) satellite imagery can give sufficient spatial detail to achieve accurate species-level classification. In this study, the influence of pansharpening of the WorldView-3 (WV-3) satellite imagery on classification results of three main tree species (Quercus robur L., Carpinus betulus L., and Alnus glutinosa (L.) Geartn.) has been evaluated. In order to increase tree species classification accuracy, three different pansharpening algorithms (Bayes, RCS, and LMVM) have been conducted. The LMVM algorithm proved the most effective pansharpening technique. The pixel- and object-based classification were applied to three pansharpened imageries using a random forest (RF) algorithm. The results showed a very high overall accuracy (OA) for LMVM pansharpened imagery: 92% and 96% for tree species classification based on pixel- and object-based approach, respectively. As expected, the object-based exceeded the pixel-based approach (OA increased by 4%). The influence of fusion on classification results was analyzed as well. Overall classification accuracy was improved by the spatial resolution of pansharpened images (OA increased by 7% for pixel-based approach). Also, regardless of pixel- or object-based classification approaches, the influence of the use of pansharpening is highly beneficial to classifying complex, natural, and mixed deciduous forest areas.

scholarly journals An Operational Workflow of Deciduous-Dominated Forest Species Classification: Crown Delineation, Gap Elimination, and Object-Based Classification

2019 ◽  
Vol 11 (18) ◽  
pp. 2078 ◽  
Author(s):  
Yuhong He ◽  
Jian Yang ◽  
John Caspersen ◽  
Trevor Jones
Keyword(s):  
Tree Species ◽  
Near Infrared ◽  
Deciduous Forest ◽  
Structural Diversity ◽  
Species Classification ◽  
Individual Tree ◽  
Spectral Overlap ◽  
Object Based ◽  
Early Fall ◽  
Crown Delineation

Recent advances in remote sensing technology provide sufficient spatial detail to achieve species-level classification over large vegetative ecosystems. In deciduous-dominated forests, however, as tree species diversity and forest structural diversity increase, the frequency of spectral overlap between species also increases and our ability to classify tree species significantly decreases. This study proposes an operational workflow of individual tree-based species classification for a temperate, mixed deciduous forest using three-seasonal WorldView images, involving three steps of individual tree crown (ITC) delineation, non-forest gap elimination, and object-based classification. The process of species classification started with ITC delineation using the spectral angle segmentation algorithm, followed by object-based random forest classifications. A total of 672 trees was located along three triangular transects for training and validation. For single-season images, the late-spring, mid-summer, and early-fall images achieve the overall accuracies of 0.46, 0.42, and 0.35, respectively. Combining the spectral information of the early-spring, mid-summer, and early-fall images increases the overall accuracy of classification to 0.79. However, further adding the late-fall image to separate deciduous and coniferous trees as an extra step was not successful. Compared to traditional four-band (Blue, Green, Red, Near-Infrared) images, the four additional bands of WorldView images (i.e., Coastal, Yellow, Red Edge, and Near-Infrared2) contribute to the species classification greatly (OA: 0.79 vs. 0.53). This study gains insights into the contribution of the additional spectral bands and multi-seasonal images to distinguishing species with seemingly high degrees of spectral overlap.

scholarly journals Stand und Perspektiven einer landesweiten Baumartenklassifikation mit digitalen Luftbildern

2013 ◽  
Vol 164 (4) ◽  
pp. 95-103 ◽  
Author(s):  
Lars T. Waser
Keyword(s):  
Tree Species ◽  
Forest Type ◽  
National Forest Inventory ◽  
Small Sample ◽  
Aerial Images ◽  
Deciduous Tree ◽  
Forest Composition ◽  
Silver Fir ◽  
Species Classification ◽  
Data Set

Status and perspectives of country-wide tree species classification based on digital aerial images There is an increasing interest on area-wide and high-resolution data of forest composition. In Switzerland, tree species distribution will be considered periodically by the Swiss National Forest Inventory (NFI), but the claims will be only partly fulfilled by the existing forest type maps since they are relatively poor regarding spatial accuracy, updating, and reproducibility. Providing consistent, reproducible and up-to-date information on various forest parameters is the main advantage of using the latest remote sensing data and methods. New possibilities are given by the airborne digital sensor ADS80, which records the entire country during the vegetation season every six years. This paper presents a robust methodology of classifying tree species in different study areas. The obtained accuracies for beech, ash, Norway spruce, Scots pine, larch, willow and silver fir are in average 71–85%, but lower for other deciduous tree species. These are mainly less dominant tree species within a study area such as maple and birch. A small sample data set and shadows of other neighboring trees seem to be the main reasons for this. Based on the experiences made in this study, a country-wide classification of tree species has become more feasible. The usage of airborne digital sensor ADS80 data in combination with a high degree of automation from the developed methods will enable the generation of country-wide products on the distinction of coniferous and deciduous tree species until 2015.

scholarly journals Structure and tree diversity patterns at the landscape level in Mexican tropical deciduos forest

2017 ◽  
pp. 43
Author(s):  
Elvira Durán ◽  
Jorge A. Meave ◽  
Emily J. Lott ◽  
Gerardo Segura
Keyword(s):  
Species Diversity ◽  
Tree Species ◽  
Dominant Species ◽  
Deciduous Forest ◽  
Principal Component ◽  
Species Group ◽  
Average Density ◽  
Tropical Deciduous Forest ◽  
Tree Species Diversity ◽  
Landscape Level

Landscape level variability of structure and tree species diversity was analyzed in a tropical deciduous forest at Chamela, Mexico. Trees with DBH ≥5 cm were sampled in 21 0.24 ha plots (5.04 ha in total) distributed among six different morpho-pedological land units. Average density was 1,385 individuals ha-1, basal area 15.9 m2 ha-1, and canopy height 6.8 m. Trunks with DBH ≤14 cm accounted for 90% of the entire set. A total of 148 species, 102 genera, and 43 families were recorded. Seventy percent of all species were poorly represented (< 10 individuals ha-1). A Principal Component Analysis (PCA) based on structure and diversity variables showed that plots from the same morpho-pedological land unit were not always located close to each other along the two first axes, but a further PCA based on dominant species clearly divided two groups of plots. Although canopy structure and tree species diversity varied continuously across the landscape, -diversity (evaluated through species similarity between plot pairs) and the identities of dominant species exhibited the clearest distinction. The dichotomy between granitic vs. non-granitic lithology was the condition most clearly related with a lower similarity in species composition and the strongest contrast in the dominant species group.

scholarly journals Phytosociological observations on tree species diversity of an urban tropical dry deciduous forest of central India

2020 ◽  
Vol 7 (3) ◽  
pp. 1029-1138
Author(s):  
Dharmendra Dugaya ◽  
PV Kiran ◽  
Rajnish Kumar Singh ◽  
Manmeet Kaur ◽  
Pradeep Chaudhry
Keyword(s):  
Tree Species ◽  
Deciduous Forest ◽  
Forest Type ◽  
Basal Area ◽  
Central India ◽  
Capital City ◽  
Stem Density ◽  
Importance Value Index ◽  
Tree Community ◽  
Dry Deciduous Forest

We analyzed phytosociological characteristics of a tropical dry deciduous forest located in an urban environment of Indian Institute of Forest Management (IIFM) Campus in the capital city of Bhopal of Madhya Pradesh state, Central India. A Comparison has been made among the tree community characteristics during the years 1988, 2002 and 2020 in terms of tree species composition, stem density, basal area and Importance Value Index (IVI). At the time of establishment of the institute in 1988, the forest area resembleda degraded dry scrubland. Due to continuous care/protection, plantation activities, degraded forest recovered remarkably, ecological processes evolved favorably with canopy cover reaching over 60% in some patches and about 50% in general over most part of the campus. During last two decades, tree density increased from 319 to 525 stem ha-1 indicating an increase of 64% whereas basal area increased from 18470.79 cm2 ha-1 to 29782.31 cm2 ha-1,an increase of about 61%. Leguminaceae family represented 26.4% of the tree community followed by Combretaceae (11.76%). Shannon-Weiner index (1.31), Simpson index (0.93) and evenness index (0.85) are within the reported ranges for similar forest type of dry deciduous nature in India. Theresults of the presentstudy will help forest managers in conservation planning of urban tropical forest ecosystem of central India.

scholarly journals Chlorophyll fluorescence of tropical tree species in a semi-deciduous forest gap

2004 ◽  
Vol 28 (1) ◽  
pp. 21-27 ◽  
Author(s):  
Rafael Vasconcelos Ribeiro ◽  
Gustavo Maia Souza ◽  
Angelo Gilberto Manzatto ◽  
Eduardo Caruso Machado ◽  
Ricardo Ferraz de Oliveira
Keyword(s):  
Chlorophyll Fluorescence ◽  
Tree Species ◽  
Deciduous Forest ◽  
Fluorescence Emission ◽  
Principal Component ◽  
Tropical Tree ◽  
Ecological Groups ◽  
Tropical Tree Species ◽  
Potential Tool

The characterization of different ecological groups in a forest formation/succession is unclear. To better define the different successional classes, we have to consider ecophysiological aspects, such as the capacity to use or dissipate the light energy available. The main objective of this work was to assess the chlorophyll fluorescence emission of tropical tree species growing in a gap of a semi-deciduous forest. Three species of different ecological groups were selected: Croton floribundus Spreng. (pioneer, P), Astronium graveolens Jacq. (early secondary, Si), and Esenbeckia febrifuga A. Juss. (late secondary, St). The potential (Fv/Fm) and effective (deltaF/Fm') quantum efficiency of photosystem II, apparent electron transport rate (ETR), non-photochemical (qN) and photochemical (qP) quenching of fluorescence were evaluated, using a modulated fluorometer, between 7:30 and 11:00 h. Values of Fv/Fm remained constant in St, decreasing in P and Si after 9:30 h, indicating the occurrence of photoinhibition. Concerning the measurements taken under light conditions (deltaF/Fm', ETR, qP and qN), P and Si showed better photochemical performance, i.e., values of deltaF/Fm', ETR and qP were higher than St when light intensity was increased. Values of qN indicated that P and Si had an increasing tendency of dissipating the excess of energy absorbed by the leaf, whereas the opposite was found for St. The principal component analysis (PCA), considering all evaluated parameters, showed a clear distinction between St, P and Si, with P and Si being closer. The PCA results suggest that chlorophyll fluorescence may be a potential tool to differentiate tree species from distinct successional groups.

scholarly journals Ecological exploration of Kuwana forest: A tropical moist deciduous forest of eastern Terai, India

2017 ◽  
Vol 6 (12) ◽  
pp. 1811 ◽  
Author(s):  
Omesh Bajpai ◽  
Shraddha Suman ◽  
Nirmala Upadhyay
Keyword(s):  
Tree Species ◽  
Deciduous Forest ◽  
Uttar Pradesh ◽  
Principal Component ◽  
Diversity Indices ◽  
Tree Diversity ◽  
Forest Community ◽  
Importance Value Index ◽  
Importance Value ◽  
Lowland Forest

The present study was conducted in the Kuwana forest of Gonda forest division in Uttar Pradesh to explore its ecological inventories. Random stratified sampling was adopted to collect the basic information like frequency, density and abundance for the calculation of importance value index (IVI). On the basis of principal component analysis (PCA) plot, three forest communities were identified and named as, Syzygium Lowland Forest (SLF), Shorea Miscellaneous Forest (SMF) and Mallotus Miscellaneous Forest (MMF). MMF community allowed the maximum 39 while SLF minimum 18 tree species growing in it. Conversely, SMF community showed higher heterogeneous tree diversity validated by lower Dominance index (0.088) and higher Simpson index (0.912). The values of these two indices were found very low in comparison with their range for tropical forests of India. On the otherhand the diversity indices (Shannon & Fisher alpha) was calculated as maximum (2.797 & 11.960 respectively) for MMF community, which indicates the existence of better tree diversity in this forest community. The higher values of Evenness & Equitability indices (0.646 & 0.859 respectively) for SMF community showed the more evenly distribution of tree species in this community.

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 How Spatial Resolution Affects Forest Phenology and Tree-Species Classification Based on Satellite and Up-Scaled Time-Series Images

2021 ◽  
Vol 13 (14) ◽  
pp. 2716
Author(s):  
Kaijian Xu ◽  
Zhaoying Zhang ◽  
Wanwan Yu ◽  
Ping Zhao ◽  
Jibo Yue ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Spatial Resolution ◽  
Tree Species ◽  
Deciduous Forest ◽  
Forest Tree ◽  
Image Resolution ◽  
Species Classification ◽  
Forest Tree Species ◽  
Time Series Images

The distribution of forest tree species provides crucial data for regional forest management and ecological research. Although medium-high spatial resolution remote sensing images are widely used for dynamic monitoring of forest vegetation phenology and species identification, the use of multiresolution images for similar applications remains highly uncertain. Moreover, it is necessary to explore to what extent spectral variation is responsible for the discrepancies in the estimation of forest phenology and classification of various tree species when using up-scaled images. To clarify this situation, we studied the forest area in Harqin Banner in northeast China by using year-round multiple-resolution time-series images (at four spatial resolutions: 4, 10, 16, and 30 m) and eight phenological metrics of four deciduous forest tree species in 2018, to explore potential impacts of relevant results caused by various resolutions. We also investigated the effect of using up-scaled time-series images by comparing the corresponding results that use pixel-aggregation algorithms with the four spatial resolutions. The results indicate that both phenology and classification accuracy of the dominant forest tree species are markedly affected by the spatial resolution of time-series remote sensing data (p < 0.05): the spring phenology of four deciduous forest tree species first rises and then falls as the image resolution varies from 4 to 30 m; similarly, the accuracy of tree species classification increases as the image resolution varies from 4 to 10 m, and then decreases as the image resolution gradually falls to 30 m (p < 0.05). Therefore, there remains a profound discrepancy between the results obtained by up-scaled and actual remote sensing data at the given spatial resolutions (p < 0.05). The results also suggest that combining phenological metrics and time-series NDVI data can be applied to identify the regional dominant tree species across different spatial resolutions, which would help advance the use of multiscale time-series satellite data for forest resource management.

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.

Sign in / Sign up

Export Citation Format

Share Document