Search results for: conifer

Authors: Kelli G. Thorup, Kristopher A. Blee

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Climate change enhances the occurrence of extreme weather: wildfires, drought, rising summer temperatures, all of which dramatically decline forest growth and increase tree mortality in the mixed-conifer forests of Sierra Nevada, California. However, microbiota living in mutualistic relations with plant rhizospheres have been found to mitigate the effects of suboptimal environmental conditions. The goal of this research is to isolate native beneficial bacteria, plant-growth promoting rhizobacteria (PGPR), that can alleviate heat stress in Pinus ponderosa seedlings. Bacteria were isolated from the rhizosphere of Pinus ponderosa juveniles located in mixed-conifer stand and further characterized for PGP potential based on their ability to produce key growth regulatory phytohormones including auxin, cytokinin, and gibberellic acid. Out of ten soil samples taken, sixteen colonies were isolated and qualitatively confirmed to produce indole-3-acetic acid (auxin) using Salkowski’s reagent. Future testing will be conducted to quantitatively assess phytohormone production in bacterial isolates. Furthermore, bioassays will be performed to determine isolates abilities to increase tolerance in heat-stressed Pinus ponderosa seedlings. Upon completion of this research, a PGPR could be utilized to support the growth and transplantation of conifer seedlings as summer temperatures continue to rise due to the effects of climate change.

Keywords: conifer, heat-stressed, phytohormones, Pinus ponderosa, plant-growth promoting rhizobacteria

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Authors: Bilel Chalghaf, Mathieu Varin

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Forest characterization in Quebec, Canada, is usually assessed based on photo-interpretation at the stand level. For species identification, this often results in a lack of precision. Very high spatial resolution imagery, such as DigitalGlobe, and Light Detection and Ranging (LiDAR), have the potential to overcome the limitations of aerial imagery. To date, few studies have used that data to map a large number of species at the tree level using machine learning techniques. The main objective of this study is to map 11 individual high tree species ( > 17m) at the tree level using an object-based approach in the broadleaf forest of Kenauk Nature, Quebec. For the individual tree crown segmentation, three canopy-height models (CHMs) from LiDAR data were assessed: 1) the original, 2) a filtered, and 3) a corrected model. The corrected CHM gave the best accuracy and was then coupled with imagery to refine tree species crown identification. When compared with photo-interpretation, 90% of the objects represented a single species. For modeling, 313 variables were derived from 16-band WorldView-3 imagery and LiDAR data, using radiance, reflectance, pixel, and object-based calculation techniques. Variable selection procedures were employed to reduce their number from 313 to 16, using only 11 bands to aid reproducibility. For classification, a global approach using all 11 species was compared to a semi-hierarchical hybrid classification approach at two levels: (1) tree type (broadleaf/conifer) and (2) individual broadleaf (five) and conifer (six) species. Five different model techniques were used: (1) support vector machine (SVM), (2) classification and regression tree (CART), (3) random forest (RF), (4) k-nearest neighbors (k-NN), and (5) linear discriminant analysis (LDA). Each model was tuned separately for all approaches and levels. For the global approach, the best model was the SVM using eight variables (overall accuracy (OA): 80%, Kappa: 0.77). With the semi-hierarchical hybrid approach, at the tree type level, the best model was the k-NN using six variables (OA: 100% and Kappa: 1.00). At the level of identifying broadleaf and conifer species, the best model was the SVM, with OA of 80% and 97% and Kappa values of 0.74 and 0.97, respectively, using seven variables for both models. This paper demonstrates that a hybrid classification approach gives better results and that using 16-band WorldView-3 with LiDAR data leads to more precise predictions for tree segmentation and classification, especially when the number of tree species is large.

Keywords: tree species, object-based, classification, multispectral, machine learning, WorldView-3, LiDAR

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Authors: M. Varin, A. M. Dubois, R. Gadbois-Langevin, B. Chalghaf

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Good knowledge of the physical environment is essential for an integrated forest planning. This information enables better forecasting of operating costs, determination of cutting volumes, and preservation of ecologically sensitive areas. The use of satellite images in stereoscopic pairs gives the capacity to generate high precision 3D models, which are scale-adapted for harvesting operations. These models could represent an alternative to 3D LiDAR data, thanks to their advantageous cost of acquisition. The objective of the study was to assess the quality of stereo-derived canopy height models (CHM) in comparison to a traditional LiDAR CHM and ground tree-height samples. Two study sites harboring two different forest stand types (broadleaf and conifer) were analyzed using stereo pairs and tri-stereo images from the WorldView-3 satellite to calculate CHM. Acquisition of multispectral images from an Unmanned Aerial Vehicle (UAV) was also realized on a smaller part of the broadleaf study site. Different algorithms using two softwares (PCI Geomatica and Correlator3D) with various spatial resolutions and band selections were tested to select the 3D modeling technique, which offered the best performance when compared with LiDAR. In the conifer study site, the CHM produced with Corelator3D using only the 50-cm resolution panchromatic band was the one with the smallest Root-mean-square deviation (RMSE: 1.31 m). In the broadleaf study site, the tri-stereo model provided slightly better performance, with an RMSE of 1.2 m. The tri-stereo model was also compared to the UAV, which resulted in an RMSE of 1.3 m. At individual tree level, when ground samples were compared to satellite, lidar, and UAV CHM, RMSE were 2.8, 2.0, and 2.0 m, respectively. Advanced analysis was done for all of these cases, and it has been noted that RMSE is reduced when the canopy cover is higher when shadow and slopes are lower and when clouds are distant from the analyzed site.

Keywords: very high spatial resolution, satellite imagery, WorlView-3, canopy height models, CHM, LiDAR, unmanned aerial vehicle, UAV

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Authors: Pawan Kumar, Pitamber Singh Negi

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Many of the regeneration failures in conifers are often being attributed to heavy insect attack and pathogens during the period of seed formation and under storage conditions. Conifer berries and seed insects occur throughout the known range of the hosts and also limit the production of seed for nursery stock. On occasion, even entire seed crops are lost due to insect attacks. The berry and seeds of both the species have been found to be infected with insects. Recently, heavy damage to the berry and seeds of Juniper and Chilgoza Pine was observed in the field as well as in stored conditions, leading to reduction in the viability of seeds to germinate. Both the species are under great threat and regeneration of the species is very low. Due to lack of adequate literature, the study on the damage potential of seed insects was urgently required to know the exact status of the insect-pests attacking seeds/berries of both the pine species so as to develop pest management practices against the insect pests attack. As both the species are also under threat and are fighting for survival, so the study is important to develop management practices for the insect-pests of seeds/berries of Juniper and Chilgoza pine so as to evaluate in the nursery, as these species form major vegetation of their distribution zones. A six-year study on the management of insect pests of seeds of Chilgoza revealed that seeds of this species are prone to insect pests mainly borers. During present investigations, it was recorded that cones of are heavily attacked only by Dioryctria abietella (Lepidoptera: Pyralidae) in natural conditions, but seeds which are economically important are heavily infected, (sometimes up to 100% damage was also recorded) by insect borer, Plodia interpunctella (Lepidoptera: Pyralidae) and is recorded for the first time ‘to author’s best knowledge’ infesting the stored Chilgoza seeds. Similarly, Juniper berries and seeds were heavily attacked only by a single borer, Homaloxestis cholopis (Lepidoptera: Lecithoceridae) recorded as a new report in natural habitat as well as in stored conditions. During the present investigation details of insect pest attack on Juniper and Chilgoza pine seeds and berries was observed and suitable management practices were also developed to contain the insect-pests attack.

Keywords: borer, chilgozapine, cones, conifer, Lepidoptera, juniper, management, seed

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Authors: Ablo Paul Igor Hounzandji, Benoit Lafleur, Annie DesRochers

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Introduction: Populus tremuloides [Michx] regenerates rapidly and abundantly by root suckering after harvest, creating stands with interconnected stems. Pre-commercial thinning can be used to concentrate growth on fewer stems to reach merchantability faster than un-thinned stands. However, conventional thinning methods are typically designed to reach even spacing between residual stems (1,100 stem ha⁻¹, evenly distributed), which can lead to treated stands consisting of weaker/smaller stems compared to the original stands. Considering the nature of P. tremuloides's regeneration, with large underground biomass of interconnected roots, aiming to keep the most vigorous and largest stems, regardless of their spatial distribution, inverted diameter-limit thinning could be more beneficial to post-thinning stand productivity because it would reduce the imbalance between roots and leaf area caused by thinning. Aims: This study aimed to compare stand and stem productivity of P. tremuloides stands thinned with a conventional thinning treatment (CT; 1,100 stem ha⁻¹, evenly distributed), two levels of inverted diameter-limit thinning (DL1 and DL2, keeping the largest 1100 or 2200 stems ha⁻¹, respectively, regardless of their spatial distribution) and a control unthinned treatment. Because DL treatments can create substantial or frequent gaps in the thinned stands, we also aimed to evaluate the potential of this treatment to recreate mixed conifer-broadleaf stands by fill-planting Picea glauca seedlings. Methods: Three replicate 21 year-old sucker-regenerated aspen stands were thinned in 2010 according to four treatments: CT, DL1, DL2, and un-thinned control. Picea glauca seedlings were underplanted in gaps created by the DL1 and DL2 treatments. Stand productivity per hectare, stem quality (diameter and height, volume stem⁻¹) and survival and height growth of fill-planted P. glauca seedlings were measured 8 year post-treatments. Results: Productivity, volume, diameter, and height were better in the treated stands (CT, DL1, and DL2) than in the un-thinned control. Productivity of CT and DL1 stands was similar 4.8 m³ ha⁻¹ year⁻¹. At the tree level, diameter and height of the trees in the DL1 treatment were 5% greater than those in the CT treatment. The average volume of trees in the DL1 treatment was 11% higher than the CT treatment. Survival after 8 years of fill planted P. glauca seedlings was 2% greater in the DL1 than in the DL2 treatment. DL1 treatment also produced taller seedlings (+20 cm). Discussion: Results showed that DL treatments were effective in producing post-thinned stands with larger stems without affecting stand productivity. In addition, we showed that these treatments were suitable to introduce slower growing conifer seedlings such as Picea glauca in order to re-create or maintain mixed stands despite the aggressive nature of P. tremuloides sucker regeneration.

Keywords: Aspen, inverted diameter-limit, mixed forest, populus tremuloides, silviculture, thinning

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Authors: Dubravka Štajner, Boris M. Popović, Saša Orlović, Ružica Ždero, Milan Popović, Aleksandra Popović

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Many plants possess antioxidant ingredients that provides efficacy by additive or synergistic activities. Present article highlights an antioxidant capacity of Serbian conifer plants. Antioxidant activities of the crude extracts were assessed using different assays. In this study, quantities of phenolic compounds (total phenols, flavonoids, tannins and proanthocyanidins), contents of photosynthetic pigments (chlorophyll a and b and carotenoids), soluble proteins and proline were examined. MDA quantities and ability of extracts to remove reactive nitrogen and oxygen species (RNOS) were also investigated. Furthermore, antioxidant activities of extracts against DPPH∙, ferric reducing antioxidant power, permanganate reducing antioxidant capacity were also determined. According to almost all used assays, antioxidant and scavenging capacities of silver fir (Abies alba Mill.), and Douglas fir (Pseudotsuga menziesii) were superior compared to spruce. Presented results implicated that leaves of Douglas fir and silver fir possessed outstanding antioxidant characteristics that could diminish damage caused by oxygen radicals which are responsible for many of the bodily changes and susceptibility to different diseases.

Keywords: conifers, antioxidant activity, reducing power, lipid peroxidation

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Authors: Yunwei Tang, Linhai Jing, Hui Li, Qingjie Liu, Xiuxia Li, Qi Yan, Haifeng Ding

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The giant panda (Ailuropoda melanoleuca) is an endangered species, mainly live in central China, where bamboos act as the main food source of wild giant pandas. Knowledge of spatial distribution of bamboos therefore becomes important for identifying the habitat of giant pandas. There have been ongoing studies for mapping bamboos and other tree species using remote sensing. WorldView-2 (WV-2) is the first high resolution commercial satellite with eight Multi-Spectral (MS) bands. Recent studies demonstrated that WV-2 imagery has a high potential in classification of tree species. The advanced classification techniques are important for utilising high spatial resolution imagery. It is generally agreed that object-based image analysis is a more desirable method than pixel-based analysis in processing high spatial resolution remotely sensed data. Classifiers that use spatial information combined with spectral information are known as contextual classifiers. It is suggested that contextual classifiers can achieve greater accuracy than non-contextual classifiers. Thus, spatial correlation can be incorporated into classifiers to improve classification results. The study area is located at Wuyipeng area in Wolong, Sichuan Province. The complex environment makes it difficult for information extraction since bamboos are sparsely distributed, mixed with brushes, and covered by other trees. Extensive fieldworks in Wuyingpeng were carried out twice. The first one was on 11th June, 2014, aiming at sampling feature locations for geometric correction and collecting training samples for classification. The second fieldwork was on 11th September, 2014, for the purposes of testing the classification results. In this study, spectral separability analysis was first performed to select appropriate MS bands for classification. Also, the reflectance analysis provided information for expanding sample points under the circumstance of knowing only a few. Then, a spatially weighted object-based k-nearest neighbour (k-NN) classifier was applied to the selected MS bands to identify seven land cover types (bamboo, conifer, broadleaf, mixed forest, brush, bare land, and shadow), accounting for spatial correlation within classes using geostatistical modelling. The spatially weighted k-NN method was compared with three alternatives: the traditional k-NN classifier, the Support Vector Machine (SVM) method and the Classification and Regression Tree (CART). Through field validation, it was proved that the classification result obtained using the spatially weighted k-NN method has the highest overall classification accuracy (77.61%) and Kappa coefficient (0.729); the producer’s accuracy and user’s accuracy achieve 81.25% and 95.12% for the bamboo class, respectively, also higher than the other methods. Photos of tree crowns were taken at sample locations using a fisheye camera, so the canopy density could be estimated. It is found that it is difficult to identify bamboo in the areas with a large canopy density (over 0.70); it is possible to extract bamboos in the areas with a median canopy density (from 0.2 to 0.7) and in a sparse forest (canopy density is less than 0.2). In summary, this study explores the ability of WV-2 imagery for bamboo extraction in a mountainous region in Sichuan. The study successfully identified the bamboo distribution, providing supporting knowledge for assessing the habitats of giant pandas.

Keywords: bamboo mapping, classification, geostatistics, k-NN, worldview-2

Procedia PDF Downloads 285