scholarly journals Assessing the Potential of Multi-spectral and Multi-temporal Satellite Images for Classification and Mapping of Plant Communities in a Temperate Region

2021 ◽  
Author(s):  
Ram C. Sharma ◽  
Hidetake Hirayama ◽  
Masatsugu Yasuda ◽  
Miki Asai ◽  
Keitarou Hara
Keyword(s):  
Land Cover ◽  
Plant Community ◽  
Plant Communities ◽  
Gradient Boosting ◽  
Temperate Region ◽  
Multi Temporal ◽  
Community Types ◽  
Study Sites ◽  
Sentinel 2

Classification and mapping of plant communities is an essential step for conservation and management of ecosystems and biodiversity. We adopt the Genus-Physiognomy-Ecosystem (GPE) system developed in previous study for satellite-based classification of plant communities. This paper assesses the potential of multi-spectral and multi-temporal images collected by Sentinel-2 satellites. This research was conducted in five representative study sites in a temperate region. It consists of 44 types of plant communities including a few land cover types as well. The plant community types were enumerated in the study sites and ground truth data were prepared with reference to extant vegetation surveys, visual interpretation of high-resolution images, and onsite field observations. We acquired all Sentinel-2 Level-1C product images available for the study sites between 2017-2019 and generated monthly median composite images consisting of ten spectral and twelve spectral-indices. Gradient Boosting Decision Trees (GBDT) classifier was employed as an efficient and distributed gradient boosting technique for the supervised classification of big datasets involved in the research. The cross-validation accuracy in terms of kappa coefficient varied from 87% in Oze site with 41 land cover and plant community types to 95% in Hakkoda site with 19 land cover and plant community types; with average performance of 91% across all sites. In addition, the resulting maps demonstrated a clear distribution of plant community types involved in all sites, highlighting the potential of Sentinel-2 multi-spectral and multi-temporal images with GPE classification system for operational and broad-scale mapping of land cover and plant communities.

Classification of woody-shrub vegetation of the forest-steppe complex of Privolzhskaya Upland

2009 ◽  
pp. 27-53
Author(s):  
A. Yu. Kudryavtsev
Keyword(s):  
Plant Community ◽  
Plant Communities ◽  
Dominant Species ◽  
Large Scale ◽  
Nature Reserve ◽  
Vegetation Mapping ◽  
Vegetation Classification ◽  
Forest Steppe ◽  
Mapping Data

Diversity of plant communities in the nature reserve “Privolzhskaya Forest-Steppe”, Ostrovtsovsky area, is analyzed on the basis of the large-scale vegetation mapping data from 2000. The plant community classi­fication based on the Russian ecologic-phytocoenotic approach is carried out. 12 plant formations and 21 associations are distinguished according to dominant species and a combination of ecologic-phytocoenotic groups of species. A list of vegetation classification units as well as the characteristics of theshrub and woody communities are given in this paper.

Post-processing tools for land cover classification of Sentinel-2

2019 ◽  
Author(s):  
Ewa Gromny ◽  
Stanisław Lewiński ◽  
Marcin Rybicki ◽  
Radosław Malinowski ◽  
Michał Krupiński ◽  
...  
Keyword(s):  
Land Cover ◽  
Land Cover Classification ◽  
Post Processing ◽  
Sentinel 2

scholarly journals CLASSIFICATION OF LAND COVER FOR NPP "PRIPYAT STOKHID" ACCORDING TO SENTINEL-2 DATA

2019 ◽  
Vol 0 (66) ◽  
pp. 116-127
Author(s):  
Oleksandr Melnyk ◽  
Volodymyr Voloshyn ◽  
Pavlo Manko ◽  
Mykhailo Voloshyn
Keyword(s):  
Land Cover ◽  
Sentinel 2

scholarly journals Plant Community Analysis along Environmental Gradients in Moist Afromontane Forest of Gerba Dima, South-western Ethiopia

2020 ◽  
Author(s):  
Abyot Dibaba Hundie ◽  
Teshome Soromessa Urgessa ◽  
Bikila Warkineh Dullo
Keyword(s):  
Cluster Analysis ◽  
Species Diversity ◽  
Plant Species ◽  
Plant Community ◽  
Plant Communities ◽  
Environmental Gradients ◽  
Floristic Composition ◽  
Regional Government ◽  
Community Types ◽  
Western Ethiopia

Abstract Background This study was carried out in Gerba Dima Forest, South-Western Ethiopia, to determine the floristic composition, species diversity and community types along environmental gradients. Ninety sample plots having a size of 25 × 25 m (625 m2) were laid by employing stratified random sampling. Nested plots were used to sample plants of different sizes and different environmental variables. All woody plant species with Diameter at breast height (DBH) ≥ 2.5 cm and height ≥ 1.5 m were recorded in 25 m X 25 m plots. Within the major plots, five 3 m x 3 m subplots (9 m2) was used to collect shrubs with dbh < 2.5 cm and > 1.5 m height. Within each 9 m2subplots, two 1 m2 subplots were used to collect data on the species and abundance of herbaceous plants. Hierarchical (agglomerative) cluster analysis was performed using the free statistical software R version 3.6.1 using package cluster to classify the vegetation into plant community types. Redundancy Analysis (RDA) ordination was used in describing the pattern of plant communities along an environmental gradient. Result One hundred and eighty plant species belonging to 145 genera, 69 families and comprising of 15 endemic species were recorded. Cluster analysis resulted in five different plant communities and this result was supported by the ordination result. RDA result showed altitude was the main environmental variable in determining the plant communities. The ANOVA test indicated that the five community types differ significantly from each other with regard to EC and K. Conclusions The studied forest can play a significant role in biodiversity conservation since it harbours high species diversity and richness. Thus, all Stakeholders including Oromia Forest and wildlife enterprise (OFWE) and the regional government should work to designate the forest as a biosphere reserve and being registered under UNESCO.

scholarly journals A development of the Enhanced Built-up and Bareness Index (EBBI) based on combination of multi-resolution Landsat 8 and Sentinel 2 MSI images

2021 ◽  
Vol 62 (1) ◽  
pp. 1-9
Author(s):  
Hung Le Trinh ◽  
Ha Thu Thi Le ◽  
Loc Duc Le ◽  
Long Thanh Nguyen ◽  
Keyword(s):  
Land Cover ◽  
Satellite Imagery ◽  
Near Infrared ◽  
Urban Land ◽  
Urban Land Use ◽  
Landsat 8 ◽  
Infrared Band ◽  
Bare Land ◽  
Sentinel 2

Classification of built-up land and bare land on remote sensing images is a very difficult problem due to the complexity of the urban land cover. Several urban indices have been proposed to improve the accuracy in classifying urban land use/land cover from optical satellite imagery. This paper presents an development of the EBBI (Enhanced Built-up and Bareness Index) index based on the combination of Landsat 8 and Sentinel 2 multi-resolution satellite imagery. Near infrared band (band 8a), short wave infrared band (band 11) of Sentinel 2 MSI image and thermal infrared band (band 10) Landsat 8 image were used to calculate EBBI index. The results obtained show that the combination of Landsat 8 and Sentinel 2 satellite images improves the spatial resolution of EBBI index image, thereby improving the accuracy of classification of bare land and built-up land by about 5% compared with the case using only Landsat 8 images.

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