Note-type based species classification by black-capped and mountain chickadees

The forest of Halle (560 ha), situated 20 km south of Brussels is covered by a beech (Fagus sylvatica) forest, locally mixed with secundary species (Tilia, Fraxinus, Acer, Quercus,... ). In almost all stands, herbal vegetation is dominated by bluebell (Hyacinthoides non-scripta). The research intended to classify 36 plots of different tree species composition according to their site quality. Three classification methods were compared: the first one based on the indicator value of the understorey vegetation, a second one on the humus morphology and a last one on some quantitative soil characteristics. According to the plant sociological site classification, the plots have the same site quality. However, humus forms differ apparently and significant differences were found in pH value and base cation saturation of the soil, abundance and biomass of earthworms and biomass of the ectorganic horizon. Tree species proved to be the main cause of these differences. The results illustrate that the herbal vegetation is not always a reliable indicator of site quality. In the case of a homogeneous vegetation dominated by one or more indifferent species, classification on humus morphology or soil analysis are more appropriate. In the forest of Halle, the tree species is probably the main cause of the observed differences in site quality.

Download Full-text

A combined CNN and LSH for fast plant species classification

Proceedings of the 4th International Conference on Big Data and Internet of Things ◽

10.1145/3372938.3372974 ◽

2019 ◽

Author(s):

Abdelkhalak Bahri ◽

Karim El Moutaoikil ◽

Imad Badi

Keyword(s):

Plant Species ◽

Species Classification

Download Full-text

Achromatic plumage variation between and within hybridizing Black‐capped and Mountain chickadees

Journal of Field Ornithology ◽

10.1111/jofo.12368 ◽

2021 ◽

Author(s):

Katherine B. Feldmann ◽

Kathryn C. Grabenstein ◽

Scott A. Taylor

Keyword(s):

Mountain Chickadees

Download Full-text

Is High Whitefly Abundance on Cassava in Sub-Saharan Africa Driven by Biological Traits of a Specific, Cryptic Bemisia tabaci Species?

Insects ◽

10.3390/insects12030260 ◽

2021 ◽

Vol 12 (3) ◽

pp. 260

Author(s):

Habibu Mugerwa ◽

Peter Sseruwagi ◽

John Colvin ◽

Susan Seal

Keyword(s):

Food Security ◽

Bemisia Tabaci ◽

Development Time ◽

Adult Emergence ◽

Sub Saharan Africa ◽

Biological Traits ◽

Population Development ◽

Species Classification ◽

First Instar ◽

Sub Saharan

In East Africa, the prevalent Bemisia tabaci whiteflies on the food security crop cassava are classified as sub-Saharan Africa (SSA) species. Economically damaging cassava whitefly populations were associated with the SSA2 species in the 1990s, but more recently, it has been to SSA1 species. To investigate whether biological traits (number of first instar nymphs, emerged adults, proportion of females in progeny and development time) of the cassava whitefly species are significant drivers of the observed field abundance, our study determined the development of SSA1 sub-group (SG) 1 (5 populations), SG2 (5 populations), SG3 (1 population) and SSA2 (1 population) on cassava and eggplant under laboratory conditions. SSA1-(SG1-SG2) and SSA2 populations’ development traits were similar. Regardless of the host plant, SSA1-SG2 populations had the highest number of first instar nymphs (60.6 ± 3.4) and emerged adults (50.9 ± 3.6), followed by SSA1-SG1 (55.5 ± 3.2 and 44.6 ± 3.3), SSA2 (45.8 ± 5.7 and 32.6 ± 5.1) and the lowest were SSA1-SG3 (34.2 ± 6.1 and 32.0 ± 7.1) populations. SSA1-SG3 population had the shortest egg–adult emergence development time (26.7 days), followed by SSA1-SG1 (29.1 days), SSA1-SG2 (29.6 days) and SSA2 (32.2 days). Regardless of the whitefly population, development time was significantly shorter on eggplant (25.1 ± 0.9 days) than cassava (34.6 ± 1.0 days). These results support that SSA1-(SG1-SG2) and SSA2 B. tabaci can become highly abundant on cassava, with their species classification alone not correlating with observed abundance and prevalence.

Download Full-text

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

Remote Sensing ◽

10.3390/rs13101868 ◽

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.

Download Full-text