scholarly journals Evaluating Two Optical Methods of Woody-to-Total Area Ratio with Destructive Measurements at Five Larix gmelinii Rupr. Forest Plots in China

Forests ◽  
2018 ◽  
Vol 9 (12) ◽  
pp. 746 ◽  
Author(s):  
Jie Zou ◽  
Peng Leng ◽  
Wei Hou ◽  
Peihong Zhong ◽  
Ling Chen ◽  
...  
Keyword(s):  
Forest Canopy ◽  
Stand Density ◽  
Tree Height ◽  
Area Ratio ◽  
Optical Methods ◽  
Larix Gmelinii ◽  
Tree Age ◽  
Site Conditions ◽  
Area Index ◽  
Plant Area Index

Accurate in situ leaf area index (LAI) estimates of forest plots are required to validate currently-used LAI map products. Woody-to-total area ratio ( α ) is a crucial parameter in converting the plant area index estimates of forest plots obtained by optical methods into LAI. Although optical methods for estimating the α of forest canopy have been proposed, their performance has never been assessed. In this study, five Larix gmelinii Rupr. forest plots with contrasting plot characteristics (i.e., tree age, tree height, management activities, stand density, and site conditions) were selected. The performance of two commonly used optical methods, namely, multispectral canopy imager (MCI) and digital hemispherical photography (DHP), in estimating the α of L. gmelinii forest plots was evaluated by using the reference α of the selected forest plots. The reference α of forest plots was measured via destructive method by harvesting two or three representative trees in each plot. Large variations were observed amongst the reference α of the selected forest plots (ranging from 0% to 56%). These α were also highly correlated with the site conditions and management activities in these plots. The effective α ( α e ) or α estimated using the leaf-on and leaf-off periods MCI or DHP images with or without consideration of the clumping effects of canopy element and woody components were 1.57 to 4.63 times the reference α in the five plots. The overestimation of α or α e was mainly caused by the preferential shading of woody components by the shoots in the leaf-on canopy. Accurate α estimates for the L. gmelinii forest plots with errors of less than 20% can be obtained from MCI when the clumping effects of canopy element and woody components are considered in the estimation.

scholarly journals Optimizing smartphone-based canopy hemispherical photography

2021 ◽  
Author(s):  
Gastón Mauro Díaz
Keyword(s):  
Large Scale ◽  
Forest Canopy ◽  
Accuracy Assessment ◽  
Low Cost ◽  
R Package ◽  
Coefficient Of Determination ◽  
Native Forest ◽  
Hemispherical Photography ◽  
Area Index ◽  
Plant Area Index

1) Hemispherical photography (HP) is a long-standing tool for forest canopy characterization. Currently, there are low-cost fisheye lenses to convert smartphones into high-portable HP equipment; however, they cannot be used whenever since HP is sensitive to illumination conditions. To obtain sound results outside diffuse light conditions, a deep-learning-based system needs to be developed. A ready-to-use alternative is the multiscale color-based binarization algorithm, but it can provide moderate-quality results only for open forests. To overcome this limitation, I propose coupling it with the model-based local thresholding algorithm. I call this coupling the MBCB approach. 2) Methods presented here are part of the R package CAnopy IMage ANalysis (caiman), which I am developing. The accuracy assessment of the new MBCB approach was done with data from a pine plantation and a broadleaf native forest. 3) The coefficient of determination (R^2) was greater than 0.7, and the root mean square error (RMSE) lower than 20 %, both for plant area index calculation. 4) Results suggest that the new MBCB approach allows the calculation of unbiased canopy metrics from smartphone-based HP acquired in sunlight conditions, even for closed canopies. This facilitates large-scale and opportunistic sampling with hemispherical photography.

scholarly journals Electromagnetic Induction Is a Fast and Non-Destructive Approach to Estimate the Influence of Subsurface Heterogeneity on Forest Canopy Structure

Water ◽  
2021 ◽  
Vol 13 (22) ◽  
pp. 3218
Author(s):  
Simon Damien Carrière ◽  
Nicolas K. Martin-StPaul ◽  
Claude Doussan ◽  
François Courbet ◽  
Hendrik Davi ◽  
...  
Keyword(s):  
Forest Structure ◽  
Electromagnetic Induction ◽  
Forest Canopy ◽  
Soil Depth ◽  
Soil Conditions ◽  
Mediterranean Forests ◽  
Area Index ◽  
Edaphic Conditions ◽  
Plant Area Index ◽  
Plant Area

The spatial forest structure that drives the functioning of these ecosystems and their response to global change is closely linked to edaphic conditions. However, the latter properties are particularly difficult to characterize in forest areas developed on karst, where soil is highly rocky and heterogeneous. In this work, we investigated whether geophysics, and more specifically electromagnetic induction (EMI), can provide a better understanding of forest structure. We use EMI (EM31, Geonics Limited, Ontario, Canada) to study the spatial variability of ground properties in two different Mediterranean forests. A naturally post-fire regenerated forest composed of Aleppo pines and Holm oaks and a monospecific plantation of Altlas cedar. To better interpret EMI results, we used electrical resistivity tomography (ERT), soil depth surveys, and field observations. Vegetation was also characterized using hemispherical photographs that allowed to calculate plant area index (PAI). Our results show that the variability of ground properties contribute to explaining the variability in the vegetation cover development (plant area index). Vegetation density is higher in areas where the soil is deeper. We showed a significant correlation between edaphic conditions and tree development in the naturally regenerated forest, but this relationship is clearly weaker in the cedar plantation. We hypothesized that regular planting after subsoiling, as well as sylvicultural practices (thinning and pruning) influenced the expected relationship between vegetation structure and soil conditions measured by EMI. This work opens up new research avenues to better understand the interplay between soil and subsoil variability and forest response to climate change.

scholarly journals Comparison of Seven Inversion Models for Estimating Plant and Woody Area Indices of Leaf-on and Leaf-off Forest Canopy Using Explicit 3D Forest Scenes

2018 ◽  
Vol 10 (8) ◽  
pp. 1297 ◽  
Author(s):  
Jie Zou ◽  
Yinguo Zhuang ◽  
Francesco Chianucci ◽  
Chunna Mai ◽  
Weimu Lin ◽  
...  
Keyword(s):  
Forest Canopy ◽  
Estimation Algorithm ◽  
Area Ratio ◽  
Forest Canopies ◽  
Tree Species Composition ◽  
Area Index ◽  
Inversion Model ◽  
Segment Size ◽  
Clumping Index ◽  
Index Estimation

Optical methods require model inversion to infer plant area index (PAI) and woody area index (WAI) of leaf-on and leaf-off forest canopy from gap fraction or radiation attenuation measurements. Several inversion models have been developed previously, however, a thorough comparison of those inversion models in obtaining the PAI and WAI of leaf-on and leaf-off forest canopy has not been conducted so far. In the present study, an explicit 3D forest scene series with different PAI, WAI, phenological periods, stand density, tree species composition, plant functional types, canopy element clumping index, and woody component clumping index was generated using 50 detailed 3D tree models. The explicit 3D forest scene series was then used to assess the performance of seven commonly used inversion models to estimate the PAI and WAI of the leaf-on and leaf-off forest canopy. The PAI and WAI estimated from the seven inversion models and simulated digital hemispherical photography images were compared with the true PAI and WAI of leaf-on and leaf-off forest scenes. Factors that contributed to the differences between the estimates of the seven inversion models were analyzed. Results show that both the factors of inversion model, canopy element and woody component projection functions, canopy element and woody component estimation algorithms, and segment size are contributed to the differences between the PAI and WAI estimated from the seven inversion models. There is no universally valid combination of inversion model, needle-to-shoot area ratio, canopy element and woody component clumping index estimation algorithm, and segment size that can accurately measure the PAI and WAI of all leaf-on and leaf-off forest canopies. The performance of the combinations of inversion model, needle-to-shoot area ratio, canopy element and woody component clumping index estimation algorithm, and segment size to estimate the PAI and WAI of leaf-on and leaf-off forest canopies is the function of the inversion model as well as the canopy element and woody component clumping index estimation algorithm, segment size, PAI, WAI, tree species composition, and plant functional types. The impact of canopy element and woody component projection function measurements on the PAI and WAI estimation of the leaf-on and leaf-off forest canopy can be reduced to a low level (<4%) by adopting appropriate inversion models.

Estimation of foliage area in dense Montana lodgepole pine stands

1987 ◽  
Vol 17 (4) ◽  
pp. 320-324 ◽  
Author(s):  
Roger D. Hungerford
Keyword(s):  
Lodgepole Pine ◽  
Basal Area ◽  
Stand Density ◽  
Tree Height ◽  
Area Ratio ◽  
Density Range ◽  
Sapwood Area ◽  
Crown Length ◽  
Pine Stands

Six stands of lodgepole pine, Pinuscontorta ssp. latifolia (Engelm.) Critchfield, in Montana were sampled to evaluate sapwood area (at 1.37 m and the crown base), basal area (at 1.37 m), tree height, and crown length as predictors of foliage area. Densities of the six stands ranged from 2900 to 17 800 stems/ha. This density range was picked to determine how stand density affects the ratio of foliage area to basal sapwood area. Regression estimates of foliage area using basal area and sapwood area at 1.37 m and the crown base were equally good. Within the sampled range of stand densities, differences in the foliage area to sapwood area ratio were not significant. The amount of foliage area served per unit of sapwood area (at 1.37 m) averaged 0.25 m2/cm2 for all 54 trees sampled. This value of foliage area per unit of sapwood area in dense stands was smaller than most other published values.

scholarly journals Monitoring Key Forest Structure Attributes across the Conterminous United States by Integrating GEDI LiDAR Measurements and VIIRS Data

2021 ◽  
Vol 13 (3) ◽  
pp. 442
Author(s):  
Khaldoun Rishmawi ◽  
Chengquan Huang ◽  
Xiwu Zhan
Keyword(s):  
Forest Structure ◽  
Infrared Imaging ◽  
Carbon Sink ◽  
3D Structure ◽  
Three Dimensional ◽  
Tree Height ◽  
Ecosystem Dynamics ◽  
Accurate Information ◽  
Area Index ◽  
Plant Area Index

Accurate information on the global distribution and the three-dimensional (3D) structure of Earth’s forests is needed to assess forest biomass stocks and to project the future of the terrestrial Carbon sink. In spite of its importance, the 3D structure of forests continues to be the most crucial information gap in the observational archive. The Global Ecosystem Dynamics Investigation (GEDI) Light Detection and Ranging (LiDAR) sensor is providing an unprecedented near-global sampling of tropical and temperate forest structural properties. The integration of GEDI measurements with spatially-contiguous observations from polar orbiting optical satellite data therefore provides a unique opportunity to produce wall-to-wall maps of forests’ 3D structure. Here, we utilized Visible Infrared Imaging Radiometer Suite (VIIRS) annual metrics data to extrapolate GEDI-derived forest structure attributes into 1-km resolution contiguous maps of tree height (TH), canopy fraction cover (CFC), plant area index (PAI), and foliage height diversity (FHD) for the conterminous US (CONUS). The maps were validated using an independent subset of GEDI data. Validation results for TH (r2 = 0.8; RMSE = 3.35 m), CFC (r2 = 0.79; RMSE = 0.09), PAI (r2 = 0.76; RMSE = 0.41), and FHD (r2 = 0.83; RMSE = 0.25) demonstrated the robustness of VIIRS data for extrapolating GEDI measurements across the nation or even over larger areas. The methodology developed through this study may allow multi-decadal monitoring of changes in multiple forest structural attributes using consistent satellite observations acquired by orbiting and forthcoming VIIRS instruments.

scholarly journals Tracking the long-term structure changes of a mature deciduous broadleaf forest stand using digital hemispherical photography

2019 ◽  
Vol 70 (1) ◽  
pp. 80-87
Author(s):  
Mait Lang ◽  
Jan Pisek
Keyword(s):  
Term Structure ◽  
Forest Canopy ◽  
Forest Growth ◽  
Repeated Measurements ◽  
Hemispherical Photography ◽  
Area Index ◽  
Plant Area Index ◽  
Plant Area ◽  
Understory Trees

Abstract Hemispherical photography provides permanent records of forest canopy structure. We analysed digital hemispherical images taken during the period of 2007–2018 in a mature silver birch stand located in Järvselja, Estonia. The stand was thinned in 2004. Understory trees were removed in the spring of 2018. Images were processed using the LinearRatioSC method. Effective plant area index Leff during the leafless phenophase increased as a result of tree growth from 0.92 to 1.24 and understory cutting was not detectable. During the full foliage condition Leff increased from 3.6 in 2008 to 5.8 in 2017. After removal of understory trees from the stand Leff decreased, and repeated measurements in the summer of 2018 estimated the plant area index range 4.5 < Leff < 4.8. The results are in agreement with the expected changes following forest growth and demonstrate that LinearRatioSC is a suitable method for the estimation and long-term monitoring of forest canopy properties from digital hemispherical images.

scholarly journals Comparison of Smartphone and Drone Lidar Methods for Characterizing Spatial Variation in PAI in a Tropical Forest

2020 ◽  
Vol 12 (11) ◽  
pp. 1765
Author(s):  
Tamara E. Rudic ◽  
Lindsay A. McCulloch ◽  
Katherine Cushman
Keyword(s):  
Image Processing ◽  
Tropical Forest ◽  
Spatial Variation ◽  
Forest Canopy ◽  
Explanatory Power ◽  
Leaf Angle ◽  
Secondary Forests ◽  
Area Index ◽  
Lowland Tropical Forest ◽  
Plant Area Index

Estimating leaf area index (LAI) and assessing spatial variation in LAI across a landscape is crucial to many ecological studies. Several direct and indirect methods of LAI estimation have been developed and compared; however, many of these methods are prohibitively expensive and/or time consuming. Here, we examine the feasibility of using the free image processing software CAN-EYE to estimate effective plant area index (PAIeff) from hemispherical canopy images taken with an extremely inexpensive smartphone clip-on fisheye lens. We evaluate the effectiveness of this inexpensive method by comparing CAN-EYE smartphone PAIeff estimates to those from drone lidar over a lowland tropical forest at La Selva Biological Station, Costa Rica. We estimated PAIeff from drone lidar using a method based in radiative transfer theory that has been previously validated using simulated data; we consider this a conservative test of smartphone PAIeff reliability because above-canopy lidar estimates share few assumptions with understory image methods. Smartphone PAIeff varied from 0.1 to 4.4 throughout our study area and we found a significant correlation (r = 0.62, n = 42, p < 0.001) between smartphone and lidar PAIeff, which was robust to image processing analytical options and smartphone model. When old growth and secondary forests are assumed to have different leaf angle distributions for the lidar PAIeff algorithm (spherical and planophile, respectively) this relationship is further improved (r = 0.77, n = 42, p < 0.001). However, we found deviations in the magnitude of the PAIeff estimations depending on image analytical options. Our results suggest that smartphone images can be used to characterize spatial variation in PAIeff in a complex, heterogenous tropical forest canopy, with only small reductions in explanatory power compared to true digital hemispherical photography.

scholarly journals Estimation of Forest LAI Using Discrete Airborne LiDAR: A Review

2021 ◽  
Vol 13 (12) ◽  
pp. 2408
Author(s):  
Luo Tian ◽  
Yonghua Qu ◽  
Jianbo Qi
Keyword(s):  
Forest Canopy ◽  
Input Parameter ◽  
3D Structure ◽  
Tree Height ◽  
Heterogeneous Data ◽  
Airborne Lidar ◽  
Area Index ◽  
Cloud Data ◽  
Time Space ◽  
The Impact

The leaf area index (LAI) is an essential input parameter for quantitatively studying the energy and mass balance in soil-vegetation-atmosphere transfer systems. As an active remote sensing technology, light detection and ranging (LiDAR) provides a new method to describe forest canopy LAI. This paper reviewed the primary LAI retrieval methods using point cloud data (PCD) obtained by discrete airborne LiDAR scanner (DALS), its validation scheme, and its limitations. There are two types of LAI retrieval methods based on DALS PCD, i.e., the empirical regression and the gap fraction (GF) model. In the empirical model, tree height-related variables, LiDAR penetration indexes (LPIs), and canopy cover are the most widely used proxy variables. The height-related proxies are used most frequently; however, the LPIs proved the most efficient proxy. The GF model based on the Beer-Lambert law has been proven useful to estimate LAI; however, the suitability of LPIs is site-, tree species-, and LiDAR system-dependent. In the local validation in previous studies, poor scalability of both empirical and GF models in time, space, and across different DALS systems was observed, which means that field measurements are still needed to calibrate both types of models. The method to correct the impact from the clumping effect and woody material using DALS PCD and the saturation effect for both empirical and GF models still needs further exploration. Of most importance, further work is desired to emphasize assessing the transferability of published methods to new geographic contexts, different DALS sensors, and survey characteristics, based on figuring out the influence of each factor on the LAI retrieval process using DALS PCD. In addition, from a methodological perspective, taking advantage of DALS PCD in characterizing the 3D structure of the canopy, making full use of the ability of machine learning methods in the fusion of multisource data, developing a spatiotemporal scalable model of canopy structure parameters including LAI, and using multisource and heterogeneous data are promising areas of research.

scholarly journals Spring Freeze Damage of Pecan Bloom: A Review

Horticulturae ◽  
2020 ◽  
Vol 6 (4) ◽  
pp. 82
Author(s):  
Amandeep Kaur ◽  
Louise Ferguson ◽  
Niels Maness ◽  
Becky Carroll ◽  
William Reid ◽  
...  
Keyword(s):  
Tree Height ◽  
The United States ◽  
Reproductive Organs ◽  
Tree Age ◽  
The Us ◽  
Nut Production ◽  
Freeze Damage ◽  
Terminal Buds ◽  
And Function ◽  
The Impact

Pecan is native to the United States. The US is the world’s largest pecan producer with an average yearly production of 250 to 300 million pounds; 80 percent of the world’s supply. Georgia, New Mexico, Texas, Arizona, Oklahoma, California, Louisiana, and Florida are the major US pecan producing states. Pecan trees frequently suffer from spring freeze at bud break and bloom as the buds are quite sensitive to freeze damage. This leads to poor flower and nut production. This review focuses on the impact of spring freeze during bud differentiation and flower development. Spring freeze kills the primary terminal buds, the pecan tree has a second chance for growth and flowering through secondary buds. Unfortunately, secondary buds have less bloom potential than primary buds and nut yield is reduced. Spring freeze damage depends on severity of the freeze, bud growth stage, cultivar type and tree age, tree height and tree vigor. This review discusses the impact of temperature on structure and function of male and female reproductive organs. It also summarizes carbohydrate relations as another factor that may play an important role in spring growth and transition of primary and secondary buds to flowers.

scholarly journals UAV Based Estimation of Forest Leaf Area Index (LAI) through Oblique Photogrammetry

2021 ◽  
Vol 13 (4) ◽  
pp. 803
Author(s):  
Lingchen Lin ◽  
Kunyong Yu ◽  
Xiong Yao ◽  
Yangbo Deng ◽  
Zhenbang Hao ◽  
...  
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Point Cloud ◽  
Forest Canopy ◽  
Estimation Method ◽  
Vertical Direction ◽  
Estimation Accuracy ◽  
3D Point Cloud ◽  
Area Index ◽  
Better Than

As a key canopy structure parameter, the estimation method of the Leaf Area Index (LAI) has always attracted attention. To explore a potential method to estimate forest LAI from 3D point cloud at low cost, we took photos from different angles of the drone and set five schemes (O (0°), T15 (15°), T30 (30°), OT15 (0° and 15°) and OT30 (0° and 30°)), which were used to reconstruct 3D point cloud of forest canopy based on photogrammetry. Subsequently, the LAI values and the leaf area distribution in the vertical direction derived from five schemes were calculated based on the voxelized model. Our results show that the serious lack of leaf area in the middle and lower layers determines that the LAI estimate of O is inaccurate. For oblique photogrammetry, schemes with 30° photos always provided better LAI estimates than schemes with 15° photos (T30 better than T15, OT30 better than OT15), mainly reflected in the lower part of the canopy, which is particularly obvious in low-LAI areas. The overall structure of the single-tilt angle scheme (T15, T30) was relatively complete, but the rough point cloud details could not reflect the actual situation of LAI well. Multi-angle schemes (OT15, OT30) provided excellent leaf area estimation (OT15: R2 = 0.8225, RMSE = 0.3334 m2/m2; OT30: R2 = 0.9119, RMSE = 0.1790 m2/m2). OT30 provided the best LAI estimation accuracy at a sub-voxel size of 0.09 m and the best checkpoint accuracy (OT30: RMSE [H] = 0.2917 m, RMSE [V] = 0.1797 m). The results highlight that coupling oblique photography and nadiral photography can be an effective solution to estimate forest LAI.

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