The influence of stemflow from standing dead trees on the fluxes of some ions in a mixed deciduous forest

1988 ◽  
Vol 18 (11) ◽  
pp. 1490-1493 ◽  
Author(s):  
Robert J. Waiters ◽  
Anthony G. Price
Keyword(s):  
Nutrient Cycling ◽  
Tree Species ◽  
Deciduous Forest ◽  
Rain Event ◽  
Nitrogen And Phosphorus ◽  
Dead Tree ◽  
Dead Trees ◽  
Mixed Deciduous Forest ◽  
Standing Dead ◽  
Standing Dead Trees

Stemflow was collected from live and dead trees of trembling aspen, largetooth aspen, and maple from a mixed deciduous forest in Chalk River, Ontario, for each rain event occurring between May and August, 1984. The data showed that the chemistry of dead-tree stemflow is qualitatively different from that of live trees, with dead-tree stemflow contributing very large proportions of the total amounts of nitrate and phosphate available within the system. Given the increasing mortality of these tree species in the Chalk River area, dead-tree stemflows may assume major importance in influencing nutrient cycling of nitrogen and phosphorus within the forest.

scholarly journals Classification of Tree Species as Well as Standing Dead Trees Using Triple Wavelength ALS in a Temperate Forest

2019 ◽  
Vol 11 (22) ◽  
pp. 2614 ◽  
Author(s):  
Nina Amiri ◽  
Peter Krzystek ◽  
Marco Heurich ◽  
Andrew Skidmore
Keyword(s):  
Tree Species ◽  
Feature Selection Method ◽  
Silver Fir ◽  
Maximum Point ◽  
Individual Tree ◽  
Dead Trees ◽  
Multi Wavelength ◽  
Standing Dead ◽  
Standing Dead Trees ◽  
532 Nm

Knowledge about forest structures, particularly of deadwood, is fundamental for understanding, protecting, and conserving forest biodiversity. While individual tree-based approaches using single wavelength airborne laserscanning (ALS) can successfully distinguish broadleaf and coniferous trees, they still perform multiple tree species classifications with limited accuracy. Moreover, the mapping of standing dead trees is becoming increasingly important for damage calculation after pest infestation or biodiversity assessment. Recent advances in sensor technology have led to the development of new ALS systems that provide up to three different wavelengths. In this study, we present a novel method which classifies three tree species (Norway spruce, European beech, Silver fir), and dead spruce trees with crowns using full waveform ALS data acquired from three different sensors (wavelengths 532 nm, 1064 nm, 1550 nm). The ALS data were acquired in the Bavarian Forest National Park (Germany) under leaf-on conditions with a maximum point density of 200 points/m 2 . To avoid overfitting of the classifier and to find the most prominent features, we embed a forward feature selection method. We tested our classification procedure using 20 sample plots with 586 measured reference trees. Using single wavelength datasets, the highest accuracy achieved was 74% (wavelength = 1064 nm), followed by 69% (wavelength = 1550 nm) and 65% (wavelength = 532 nm). An improvement of 8–17% over single wavelength datasets was achieved when the multi wavelength data were used. Overall, the contribution of the waveform-based features to the classification accuracy was higher than that of the geometric features by approximately 10%. Our results show that the features derived from a multi wavelength ALS point cloud significantly improve the detailed mapping of tree species and standing dead trees.

scholarly journals CLASSIFICATION OF TREE SPECIES AND STANDING DEAD TREES BY FUSING UAV-BASED LIDAR DATA AND MULTISPECTRAL IMAGERY IN THE 3D DEEP NEURAL NETWORK POINTNET++

2020 ◽  
Vol V-2-2020 ◽  
pp. 203-210
Author(s):  
S. Briechle ◽  
P. Krzystek ◽  
G. Vosselman
Keyword(s):  
Neural Network ◽  
Tree Species ◽  
Deep Neural Network ◽  
Lidar Data ◽  
Individual Tree ◽  
Dead Trees ◽  
Standing Dead ◽  
Standing Dead Trees ◽  
Multiple Tree

Abstract. Knowledge of tree species mapping and of dead wood in particular is fundamental to managing our forests. Although individual tree-based approaches using lidar can successfully distinguish between deciduous and coniferous trees, the classification of multiple tree species is still limited in accuracy. Moreover, the combined mapping of standing dead trees after pest infestation is becoming increasingly important. New deep learning methods outperform baseline machine learning approaches and promise a significant accuracy gain for tree mapping. In this study, we performed a classification of multiple tree species (pine, birch, alder) and standing dead trees with crowns using the 3D deep neural network (DNN) PointNet++ along with UAV-based lidar data and multispectral (MS) imagery. Aside from 3D geometry, we also integrated laser echo pulse width values and MS features into the classification process. In a preprocessing step, we generated the 3D segments of single trees using a 3D detection method. Our approach achieved an overall accuracy (OA) of 90.2% and was clearly superior to a baseline method using a random forest classifier and handcrafted features (OA = 85.3%). All in all, we demonstrate that the performance of the 3D DNN is highly promising for the classification of multiple tree species and standing dead trees in practice.

scholarly journals Large-Scale Mapping of Tree Species and Dead Trees in Šumava National Park and Bavarian Forest National Park Using Lidar and Multispectral Imagery

2020 ◽  
Vol 12 (4) ◽  
pp. 661 ◽  
Author(s):  
Peter Krzystek ◽  
Alla Serebryanyk ◽  
Claudius Schnörr ◽  
Jaroslav Červenka ◽  
Marco Heurich
Keyword(s):  
Tree Species ◽  
National Park ◽  
Large Scale ◽  
Dead Wood ◽  
Multispectral Imagery ◽  
Normalized Cut ◽  
Dead Trees ◽  
Standing Dead ◽  
Standing Dead Trees ◽  
Bavarian Forest

Knowledge of forest structures—and of dead wood in particular—is fundamental to understanding, managing, and preserving the biodiversity of our forests. Lidar is a valuable technology for the area-wide mapping of trees in 3D because of its capability to penetrate vegetation. In essence, this technique enables the detection of single trees and their properties in all forest layers. This paper highlights a successful mapping of tree species—subdivided into conifers and broadleaf trees—and standing dead wood in a large forest 924 km2 in size. As a novelty, we calibrate the critical stopping criterion of the tree segmentation based on a normalized cut with regard to coniferous and broadleaf trees. The experiments were conducted in Šumava National Park and Bavarian Forest National Park. For both parks, lidar data were acquired at a point density of 55 points/m2. Aerial multispectral imagery was captured for Šumava National Park at a ground sample distance (GSD) of 17 cm and for Bavarian Forest National Park at 9.5 cm GSD. Classification of the two tree groups and standing dead wood—located in areas of pest infestation—is based on a diverse set of features (geometric, intensity-based, 3D shape contexts, multispectral-based) and well-known classifiers (Random forest and logistic regression). We show that the effect of under- and oversegmentation can be reduced by the modified normalized cut segmentation, thereby improving the precision by 13%. Conifers, broadleaf trees, and standing dead trees are classified with overall accuracies better than 90%. All in all, this experiment demonstrates the feasibility of large-scale and high-accuracy mapping of single conifers, broadleaf trees, and standing dead trees using lidar and aerial imagery.

scholarly journals Occurrence of termites (Isoptera) on living and standing dead trees in a tropical dry forest in Mexico

PeerJ ◽  
2018 ◽  
Vol 6 ◽  
pp. e4731 ◽  
Author(s):  
Nancy Calderón-Cortés ◽  
Luis H. Escalera-Vázquez ◽  
Ken Oyama
Keyword(s):  
Forest Canopy ◽  
Dead Wood ◽  
Tropical Dry Forest ◽  
Dry Forest ◽  
Dead Tree ◽  
Ecological Processes ◽  
Dead Trees ◽  
Standing Dead ◽  
Standing Dead Trees ◽  
Main Factor

Termites play a key role as ecosystem engineers in numerous ecological processes though their role in the dynamics of wood degradation in tropical dry forests, particularly at the level of the crown canopy, has been little studied. In this study, we analysed the occurrence of termites in the forest canopy by evaluating the density and proportion of living and standing dead trees associated with termites in deciduous and riparian habitats of the tropical dry forest in Chamela, Mexico. The results indicated that 60–98% of standing dead trees and 23–59% of living trees in Chamela were associated with termites. In particular, we found that the density of standing dead trees was higher in deciduous forests (0.057–0.066 trees/m2) than in riparian forests (0.022 and 0.027 trees/m2), even though the proportion of trees was not significantly different among habitats. Additionally, we found a higher density of trees associated with termites in trees of smaller size classes (0.01–0.09 trees/m2) than in larger class sizes (0–0.02 trees/m2). Interestingly, 72% of variation in the density of trees associated with termites is explained by the density of standing dead trees. Overall, these results indicate that standing dead tree availability might be the main factor regulating termite populations in Chamela forest and suggest that termites could play a key role in the decomposition of above-ground dead wood, mediating the incorporation of suspended and standing dead wood into the soil.

scholarly journals DETECTION OF SINGLE STANDING DEAD TREES FROM AERIAL COLOR INFRARED IMAGERY BY SEGMENTATION WITH SHAPE AND INTENSITY PRIORS

2015 ◽  
Vol II-3/W4 ◽  
pp. 181-188 ◽  
Author(s):  
P. Polewski ◽  
W. Yao ◽  
M. Heurich ◽  
P. Krzystek ◽  
U. Stilla
Keyword(s):  
Gaussian Mixture ◽  
Statistical Information ◽  
Shape Variation ◽  
Dead Tree ◽  
Detection Scheme ◽  
Pixel Intensity ◽  
Dead Trees ◽  
Standing Dead ◽  
Standing Dead Trees ◽  
The Dead

Standing dead trees, known as snags, are an essential factor in maintaining biodiversity in forest ecosystems. Combined with their role as carbon sinks, this makes for a compelling reason to study their spatial distribution. This paper presents an integrated method to detect and delineate individual dead tree crowns from color infrared aerial imagery. Our approach consists of two steps which incorporate statistical information about prior distributions of both the image intensities and the shapes of the target objects. In the first step, we perform a Gaussian Mixture Model clustering in the pixel color space with priors on the cluster means, obtaining up to 3 components corresponding to dead trees, living trees, and shadows. We then refine the dead tree regions using a level set segmentation method enriched with a generative model of the dead trees’ shape distribution as well as a discriminative model of their pixel intensity distribution. The iterative application of the statistical shape template yields the set of delineated dead crowns. The prior information enforces the consistency of the template’s shape variation with the shape manifold defined by manually labeled training examples, which makes it possible to separate crowns located in close proximity and prevents the formation of large crown clusters. Also, the statistical information built into the segmentation gives rise to an implicit detection scheme, because the shape template evolves towards an empty contour if not enough evidence for the object is present in the image. We test our method on 3 sample plots from the Bavarian Forest National Park with reference data obtained by manually marking individual dead tree polygons in the images. Our results are scenario-dependent and range from a correctness/completeness of 0.71/0.81 up to 0.77/1, with an average center-of-gravity displacement of 3-5 pixels between the detected and reference polygons.

scholarly journals Standing Dead Trees are a Conduit for the Atmospheric Flux of CH4 and CO2 from Wetlands

Wetlands ◽  
2017 ◽  
Vol 38 (1) ◽  
pp. 133-143 ◽  
Author(s):  
Mary Jane Carmichael ◽  
Ashley M. Helton ◽  
Joseph C. White ◽  
William K. Smith
Keyword(s):  
Atmospheric Flux ◽  
Dead Trees ◽  
Standing Dead ◽  
Standing Dead Trees

scholarly journals MENAKAR SIFAT INVASIF SPESIES AKASIA MANGIUM (Acacia mangium Willd.) DI HUTAN PENELITIAN DAN PENDIDIKAN BUKIT SOEHARTO

2017 ◽  
Vol 1 (1) ◽  
Author(s):  
Sutedjo Sutedjo ◽  
Warsudi Warsudi
Keyword(s):  
Acacia Mangium ◽  
Dead Trees ◽  
Standing Dead ◽  
Standing Dead Trees

 Akasia mangium (Acacia mangium Willd) bukan tumbuhan asli Kalimantan namun sejak puluhan tahun tumbuh berkembang pesat di berbagai wilayah Kalimantan termasuk Kalimantan Timur. Dikenal sebagai tumbuhan yang mampu tumbuh di lahan kritis sehingga pada awal tahun 1990-an dijadikan tanaman  reboisasi sekaligus pengendali alang-alang di wilayah kritis hutan penelitian dan pendidikan Universitas Mulawarman di Bukit Soeharto. Mengherankan, bahwa beberapa tahun taerkhir sebagian praktisi kehutanan dan reklamasi pascatambang merasa gamang menggunakan A. mangium, khawatir jika jenis tersebut akan benar benar menjadi spesies invasif.  Gejala untuk menolak bahkan menghindari  A. mangium sebagai komoditas kehutanan terutama sebagai jenis pengendali lahan kritis mulai meluas. Untuk mengetahui seberapa benar anggapan Acacia mangium sebagai jenis invasif maka dilakukan evaluasi dengan melakukan analisis vegetasi terhadap 3 ha tegakan hutan A. mangium yang ditanam di Bukit Soeharto sebagai uji petik yang saat sekarang telah berumur sekitar 25 tahun. Hasil evaluasi membuktikan bahwa jumlah tanaman per ha (kerapatan) pohon A. mangium menurun (kurang dari jumlah saat ditanam atau sekitar 800 individu/ha). Jumlah yang menurun itupun cenderung mengelompok. Sebagian pohon bahkan ditemukan dalam kondisi mati generasi (standing dead trees). Sementara itu jumlah spesies pohon setempat (local trees species) juga mulai muncul di antara tegakan A.mangium. Dengan demikian terbukti  bahwa A. mngium bukanlah tipe invasif  yang sesungguhnya dan tidak ada alasan utuk menolak penggunaannya sebagai tanaman pengendali lahan kritis selama potensi ancaman terjadinya kebakaran lahan hutan dapat dicegah.

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