scholarly journals How Much Can We See from a UAV-Mounted Regular Camera? Remote Sensing-Based Estimation of Forest Attributes in South American Native Forests

2021 ◽  
Vol 13 (11) ◽  
pp. 2151
Author(s):  
Alejandro Miranda ◽  
Germán Catalán ◽  
Adison Altamirano ◽  
Carlos Zamorano-Elgueta ◽  
Manuel Cavieres ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Mixed Models ◽  
Forest Canopy ◽  
Complex Structure ◽  
Boosted Regression Trees ◽  
Tree Level ◽  
Old Growth ◽  
Individual Tree ◽  
Native Forests ◽  
Old Growth Forests

Data collection from large areas of native forests poses a challenge. The present study aims at assessing the use of UAV for forest inventory on native forests in Southern Chile, and seeks to retrieve both stand and tree level attributes from forest canopy data. Data were collected from 14 plots (45 × 45 m) established at four locations representing unmanaged Chilean temperate forests: seven plots on secondary forests and seven plots on old-growth forests, including a total of 17 different native species. The imagery was captured using a fixed-wing airframe equipped with a regular RGB camera. We used the structure from motion and digital aerial photogrammetry techniques for data processing and combined machine learning methods based on boosted regression trees and mixed models. In total, 2136 trees were measured on the ground, from which 858 trees were visualized from the UAV imagery of the canopy, ranging from 26% to 88% of the measured trees in the field (mean = 45.7%, SD = 17.3), which represented between 70.6% and 96% of the total basal area of the plots (mean = 80.28%, SD = 7.7). Individual-tree diameter models based on remote sensing data were constructed with R2 = 0.85 and R2 = 0.66 based on BRT and mixed models, respectively. We found a strong relationship between canopy and ground data; however, we suggest that the best alternative was combining the use of both field-based and remotely sensed methods to achieve high accuracy estimations, particularly in complex structure forests (e.g., old-growth forests). Field inventories and UAV surveys provide accurate information at local scales and allow validation of large-scale applications of satellite imagery. Finally, in the future, increasing the accuracy of aerial surveys and monitoring is necessary to advance the development of local and regional allometric crown and DBH equations at the species level.

scholarly journals Forests Growth Monitoring Based on Tree Canopy 3D Reconstruction Using UAV Aerial Photogrammetry

Forests ◽  
2019 ◽  
Vol 10 (12) ◽  
pp. 1052 ◽  
Author(s):  
Zhang ◽  
Wu ◽  
Yang
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Point Cloud ◽  
Forest Canopy ◽  
Forest Growth ◽  
Tree Level ◽  
Ground Vegetation ◽  
Growth Monitoring ◽  
Group Level ◽  
Individual Tree

Land cover monitoring is a major task for remote sensing. Comparing to traditional methods for forests monitoring which mostly use orthoimages from satellite or aircraft, there are very few researches use forest 3D canopy structure to monitor the forest growth. UAV aerial can be a novel and feasible platform to provide high resolution and more timely images that can be used to generate high resolution forest 3D canopy. In spring, the small forest is supposed to experience rapid growth. In this research, we used a small UAV to monitor campus forest growth in spring at 2days interval. Each time 140 images were acquired and the ground surface dense point cloud was reconstructed at high precision. Color indexes ExG (Excess Green) was used to extract the green canopy point. The segmented point cloud was triangulated using greedy projection triangulation method into a mesh and its area was calculated. Forest canopy growth was analyzed at 3 level: forest level, selected group level and individual tree level. Logistic curve was used to fit the time series canopy growth. Strong correlation was found R2 = 0.8517 at forest level, R2=0.9652 at selected group level and R2 = 0.9606 at individual tree level. Moreover, high correlation was found between canopy by observing these results, we can conclude that the ground 3D model can act as a useful data type as orthography to monitor the forest growth. Moreover the UAV aerial remote sensing has advantages at monitoring forest in periods when the ground vegetation is growing and changing fast.

scholarly journals Estimating the Height and Basal Area at Individual Tree and Plot Levels in Canadian Subarctic Lichen Woodlands Using Stereo WorldView-3 Images

2019 ◽  
Vol 11 (3) ◽  
pp. 248 ◽  
Author(s):  
Benoît St-Onge ◽  
Simon Grandin
Keyword(s):  
Remote Sensing ◽  
Basal Area ◽  
Ground Cover ◽  
Structural Data ◽  
Tree Level ◽  
Individual Tree ◽  
Visual Contrast ◽  
The Relationship ◽  
Height Data

Lichen woodlands (LW) are sparse forests that cover extensive areas in remote subarctic regions where warming due to climate change is fastest. They are difficult to study in situ or with airborne remote sensing due to their remoteness. We have tested a method for measuring individual tree heights and predicting basal area at tree and plot levels using WorldView-3 stereo images. Manual stereo measurements of tree heights were performed on short trees (2–12 m) of a LW region of Canada with a residual standard error of ≈0.9 m compared to accurate field or UAV height data. The number of detected trees significantly underestimated field counts, especially in peatlands in which the visual contrast between trees and ground cover was low. The heights measured from the WorldView-3 images were used to predict the basal area at individual tree level and summed up at plot level. In the best conditions (high contrast between trees and ground cover), the relationship to field basal area had a R2 of 0.79. Accurate estimates of above ground biomass should therefore also be possible. This method could be used to calibrate an extensive remote sensing approach without in-situ measurements, e.g., by linking precise structural data to ICESAT-2 footprints.

scholarly journals High-Resolution Multisensor Remote Sensing to Support Date Palm Farm Management

Agriculture ◽  
2019 ◽  
Vol 9 (2) ◽  
pp. 26
Author(s):  
Maggie Mulley ◽  
Lammert Kooistra ◽  
Laurens Bierens
Keyword(s):  
Remote Sensing ◽  
Precision Agriculture ◽  
Vegetation Index ◽  
Date Palm ◽  
Sub Saharan Africa ◽  
Added Value ◽  
Tree Level ◽  
Individual Tree ◽  
Red Edge ◽  
Height Data

Date palms are a valuable crop in areas with limited water availability such as the Middle East and sub-Saharan Africa, due to their hardiness in tough conditions. Increasing soil salinity and the spread of pests including the red palm weevil (RPW) are two examples of growing threats to date palm plantations. Separate studies have shown that thermal, multispectral, and hyperspectral remote sensing imagery can provide insight into the health of date palm plantations, but the added value of combining these datasets has not been investigated. The current study used available thermal, hyperspectral, Light Detection and Ranging (LiDAR) and visual Red-Green-Blue (RGB) images to investigate the possibilities of assessing date palm health at two “levels”; block level and individual tree level. Test blocks were defined into assumed healthy and unhealthy classes, and thermal and height data were extracted and compared. Due to distortions in the hyperspectral imagery, this data was only used for individual tree analysis; methods for identifying individual tree points using Normalized Difference Vegetation Index (NDVI) maps proved accurate. A total of 100 random test trees in one block were selected, and comparisons between hyperspectral, thermal and height data were made. For the vegetation index red-edge position (REP), the R-squared value in correlation with temperature was 0.313 and with height was 0.253. The vegetation index—the Vogelmann Red Edge Index (VOGI)—also has a relatively strong correlation value with both temperature (R2 = 0.227) and height (R2 = 0.213). Despite limited field data, the results of this study suggest that remote sensing data has added value in analyzing date palm plantations and could provide insight for precision agriculture techniques.

scholarly journals Identifying European Old-Growth Forests using Remote Sensing: A Study in the Ukrainian Carpathians

Forests ◽  
2019 ◽  
Vol 10 (2) ◽  
pp. 127 ◽  
Author(s):  
Benedict D. Spracklen ◽  
Dominick V. Spracklen
Keyword(s):  
Remote Sensing ◽  
Random Forest ◽  
Norway Spruce ◽  
Satellite Images ◽  
Textural Features ◽  
Old Growth ◽  
Random Forest Classification ◽  
Forest Classification ◽  
Old Growth Forests ◽  
Sentinel 2

Old-growth forests are an important, rare and endangered habitat in Europe. The ability to identify old-growth forests through remote sensing would be helpful for both conservation and forest management. We used data on beech, Norway spruce and mountain pine old-growth forests in the Ukrainian Carpathians to test whether Sentinel-2 satellite images could be used to correctly identify these forests. We used summer and autumn 2017 Sentinel-2 satellite images comprising 10 and 20 m resolution bands to create 6 vegetation indices and 9 textural features. We used a Random Forest classification model to discriminate between dominant tree species within old-growth forests and between old-growth and other forest types. Beech and Norway spruce were identified with an overall accuracy of around 90%, with a lower performance for mountain pine (70%) and mixed forest (40%). Old-growth forests were identified with an overall classification accuracy of 85%. Adding textural features, band standard deviations and elevation data improved accuracies by 3.3%, 2.1% and 1.8% respectively, while using combined summer and autumn images increased accuracy by 1.2%. We conclude that Random Forest classification combined with Sentinel-2 images can provide an effective option for identifying old-growth forests in Europe.

scholarly journals LiDAR Applications to Estimate Forest Biomass at Individual Tree Scale: Opportunities, Challenges and Future Perspectives

Forests ◽  
2021 ◽  
Vol 12 (5) ◽  
pp. 550
Author(s):  
Dandan Xu ◽  
Haobin Wang ◽  
Weixin Xu ◽  
Zhaoqing Luan ◽  
Xia Xu
Keyword(s):  
Remote Sensing ◽  
Laser Scanning ◽  
Forest Biomass ◽  
Remotely Sensed ◽  
Tree Level ◽  
Biomass Estimation ◽  
Future Perspectives ◽  
Individual Tree ◽  
The Individual ◽  
Individual Trees

Accurate forest biomass estimation at the individual tree scale is the foundation of timber industry and forest management. It plays an important role in explaining ecological issues and small-scale processes. Remotely sensed images, across a range of spatial and temporal resolutions, with their advantages of non-destructive monitoring, are widely applied in forest biomass monitoring at global, ecoregion or community scales. However, the development of remote sensing applications for forest biomass at the individual tree scale has been relatively slow due to the constraints of spatial resolution and evaluation accuracy of remotely sensed data. With the improvements in platforms and spatial resolutions, as well as the development of remote sensing techniques, the potential for forest biomass estimation at the single tree level has been demonstrated. However, a comprehensive review of remote sensing of forest biomass scaled at individual trees has not been done. This review highlights the theoretical bases, challenges and future perspectives for Light Detection and Ranging (LiDAR) applications of individual trees scaled to whole forests. We summarize research on estimating individual tree volume and aboveground biomass (AGB) using Terrestrial Laser Scanning (TLS), Airborne Laser Scanning (ALS), Unmanned Aerial Vehicle Laser Scanning (UAV-LS) and Mobile Laser Scanning (MLS, including Vehicle-borne Laser Scanning (VLS) and Backpack Laser Scanning (BLS)) data.

How Often Should Native Forests Be Logged? A Case Study on the Eden Management Area

1995 ◽  
Vol 6 (2) ◽  
pp. 196-215 ◽  
Author(s):  
Stephen Hartley
Keyword(s):  
Rotation Period ◽  
The Other ◽  
Old Growth ◽  
Native Forests ◽  
Optimal Rotation ◽  
Old Growth Forests ◽  
Management Area ◽  
Over Time

The question of how often to log is examined applying two different methods –one optimizes over time the value of its timber harvested and the other considers the forest as a multi-use resource. The former, applied to data for the Eden area given an optimal rotation period of 25 years, the latter suggests that it is optimal never to harvest pristine native old growth forests.

Remote sensing can locate and assess the changing abundance of hollow-bearing trees for wildlife in Australian native forests

2014 ◽  
Vol 41 (8) ◽  
pp. 703 ◽  
Author(s):  
Christopher J. Owers ◽  
Rodney P. Kavanagh ◽  
Eleanor Bruce
Keyword(s):  
Remote Sensing ◽  
Early Stage ◽  
Structural Parameters ◽  
Low Cost ◽  
Species Abundance ◽  
Aerial Imagery ◽  
Individual Tree ◽  
Persistent Problem ◽  
Native Forests ◽  
Remote Sensing Techniques

Context Hollow-bearing trees are an important breeding and shelter resource for wildlife in Australian native forests and hollow availability can influence species abundance and diversity in forest ecosystems. A persistent problem for forest managers is the ability to locate and survey hollow-bearing trees with a high level of accuracy at low cost over large areas of forest. Aims The aim of this study was to determine whether remote-sensing techniques could identify key variables useful in classifying the likelihood of a tree to contain hollows suitable for wildlife. Methods The data were high-resolution, multispectral aerial imagery and light detection and ranging (Lidar). A ground-based survey of 194 trees, 96 Eucalyptus crebra and 98 E. chloroclada and E. blakelyi, were used to train and validate tree-senescence classification models. Key results We found that trees in the youngest stage of tree senescence, which had a very low probability of hollow occurrence, could be distinguished using multispectral aerial imagery from trees in the later stages of tree senescence, which had a high probability of hollow occurrence. Independently, the canopy-height model used to estimate crown foliage density demonstrated the potential of Lidar-derived structural parameters as predictors of senescence and the hollow-bearing status of individual trees. Conclusions This study demonstrated a ‘proof of concept’ that remotely sensed tree parameters are suitable predictor variables for the hollow-bearing status of an individual tree. Implications Distinguishing early stage senescence trees from later-stage senescence trees using remote sensing offers potential as an efficient, repeatable and cost-effective way to map the distribution and abundance of hollow-bearing trees across the landscape. Further development is required to automate this process across the landscape, particularly the delineation of tree crowns. Further improvements may be obtained using a combination of these remote-sensing techniques. This information has important applications in commercial forest inventory and in biodiversity monitoring programs.

Forest structure and regeneration of the Tertiary relict Taiwania cryptomerioides in the Gaoligong Mountains, Yunnan, southwestern China

2015 ◽  
Vol 45 (1) ◽  
pp. 135-155 ◽  
Author(s):  
Long-Yuan He ◽  
Cindy Q. Tang ◽  
Zhao-Lu Wu ◽  
Huan-Chong Wang ◽  
Masahiko Ohsawa ◽  
...  
Keyword(s):  
Forest Structure ◽  
Forest Floor ◽  
Forest Canopy ◽  
Age Classes ◽  
Old Growth ◽  
Old Growth Forests ◽  
Competition For Light ◽  
Taiwania Cryptomerioides ◽  
Steep Slopes ◽  
Survival Mechanisms

We studied forests containing Taiwania cryptomerioides of various ages and habitats on the eastern slopes of the Gaoligong Mountains in terms of forest structure and composition, population structure (size, age), regeneration patterns, and persistence of the species in relation to their favored habitats. Taiwania thrives in unstable habitats on riverbanks in deep valleys, on steep slopes, on cliffs, on roadsides and by mountain paths at the altitudes of 1175-2500 m a.s.l. All these locations were subject to frequent landslides, whereas Taiwania was very rare at similar altitudes on stable gentle slopes or on mountain ridges free of major disturbances. The maximum age of Taiwania was calculated to be c. 1,872 yr, with 358 cm DBH (diameter at a height of 1.3 m) and 70 m high. The size and age classes of Taiwania in old-growth forests were multimodal, indicating that the regeneration varied by chance, depending on disturbances. In the old-growth forests where above-ground competition for light was intense, shade-intolerant and long-lived coniferous Taiwania became emergent (40–70 m), rising above a forest canopy comprised of more shade-tolerant evergreen broad-leaved trees. The reproduction of the species was mainly by means of minute wind-dispersed seeds falling into rock crevices on cliffs or a rocky forest floor, or on disturbed sites. These populations depended on disturbances or gap regeneration to survive. Taiwania gave way to evergreen broad-leaved tree species of Lithocarpus, Cyclobalanopsis, and Manglietia, and to other conifers such as Tsuga dumosa, where landslides were infrequent. Our results provide insights into the ecological characteristics and survival mechanisms of this East Asian paleoendemic conifer, and contribute to our understanding of the differentiation of forests.

scholarly journals Remote sensing of bark beetle damage in urban forests at individual tree level using a novel hyperspectral camera from UAV and aircraft

2018 ◽  
Vol 30 ◽  
pp. 72-83 ◽  
Author(s):  
Roope Näsi ◽  
Eija Honkavaara ◽  
Minna Blomqvist ◽  
Päivi Lyytikäinen-Saarenmaa ◽  
Teemu Hakala ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Bark Beetle ◽  
Urban Forests ◽  
Tree Level ◽  
Individual Tree ◽  
Beetle Damage ◽  
Hyperspectral Camera

Base-cation Cycling by Individual Tree Species in Old-growth Forests of Upper Michigan, USA

2005 ◽  
Vol 74 (3) ◽  
pp. 357-376 ◽  
Author(s):  
Ryo Fujinuma ◽  
James Bockheim ◽  
Nick Balster
Keyword(s):  
Tree Species ◽  
Base Cation ◽  
Old Growth ◽  
Individual Tree ◽  
Old Growth Forests
Sign in / Sign up

Export Citation Format

Share Document