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

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.

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

Forestry Studies / Metsanduslikud Uurimused ◽

10.2478/fsmu-2019-0007 ◽

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.

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Maximizing the value of forest restoration for tropical mammals by detecting three-dimensional habitat associations

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.2001823117 ◽

2020 ◽

Vol 117 (42) ◽

pp. 26254-26262

Author(s):

Nicolas J. Deere ◽

Gurutzeta Guillera-Arroita ◽

Tom Swinfield ◽

David T. Milodowski ◽

David A. Coomes ◽

...

Keyword(s):

Tropical Forests ◽

Forest Structure ◽

Forest Restoration ◽

Three Dimensional ◽

Habitat Associations ◽

Vertical Column ◽

Area Index ◽

Maximum Benefit ◽

Plant Area Index ◽

Plant Area

Tropical forest ecosystems are facing unprecedented levels of degradation, severely compromising habitat suitability for wildlife. Despite the fundamental role biodiversity plays in forest regeneration, identifying and prioritizing degraded forests for restoration or conservation, based on their wildlife value, remains a significant challenge. Efforts to characterize habitat selection are also weakened by simple classifications of human-modified tropical forests as intact vs. degraded, which ignore the influence that three-dimensional (3D) forest structure may have on species distributions. Here, we develop a framework to identify conservation and restoration opportunities across logged forests in Borneo. We couple high-resolution airborne light detection and ranging (LiDAR) and camera trap data to characterize the response of a tropical mammal community to changes in 3D forest structure across a degradation gradient. Mammals were most responsive to covariates that accounted explicitly for the vertical and horizontal characteristics of the forest and actively selected structurally complex environments comprising tall canopies, increased plant area index throughout the vertical column, and the availability of a greater diversity of niches. We show that mammals are sensitive to structural simplification through disturbance, emphasizing the importance of maintaining and enhancing structurally intact forests. By calculating occurrence thresholds of species in response to forest structural change, we identify areas of degraded forest that would provide maximum benefit for multiple high-conservation value species if restored. The study demonstrates the advantages of using LiDAR to map forest structure, rather than relying on overly simplistic classifications of human-modified tropical forests, for prioritizing regions for restoration.

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Optimizing smartphone-based canopy hemispherical photography

10.1101/2021.03.17.435793 ◽

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.

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Restoration of above canopy reference hemispherical image from below canopy measurements for plant area index estimation in forests/ Metsa võrastiku läbipaistvuse mõõtmine digitaalsete poolsfäärikaamerate abil

Forestry Studies / Metsanduslikud Uurimused ◽

10.2478/fsmu-2013-0008 ◽

2013 ◽

Vol 59 (1) ◽

pp. 13-27 ◽

Cited By ~ 2

Author(s):

Mait Lang ◽

Ave Kodar ◽

Tauri Arumäe

Keyword(s):

Field Measurements ◽

Incident Radiation ◽

Canopy Gap ◽

Raw Data ◽

Area Index ◽

Gap Fraction ◽

Cmos Sensor ◽

Plant Area Index ◽

Plant Area ◽

Thresholding Algorithm

Abstract Canopy gap fraction has been estimated from hemispherical images using a thresholding method to separate sky and canopy pixels. The optimal objective thresholding rule has been searched by many authors without satisfactory results due to long list of reasons. Some recent studies have shown that unprocessed readings of camera CCD or CMOS sensor (raw data) have linear relationship with incident radiation. This allows a pair of cameras used in similar to a pair of plant canopy analyzers and canopy gap fraction can be calculated as the ratio of below canopy image and above canopy image. We tested new freeware program HemiSpherical Project Manager (HSP) for the restoration of the above canopy image from below canopy image which allows making field measurements with single below canopy operated camera. Results of perforated panel image analysis and comparison of plant area index (PAI) estimated independently by three operators from real canopy hemispherical images showed high degree of reliability of the new approach. Determination coefficients of linear regression of the PAI estimations of the three operators were 0.9962, 0.9875 and 0.9825. The canopy gap fraction data obtained from HSP were used to validate Nobis-Hunziker automatic thresholding algorithm. The results indicated that the Nobis-Hunziker algorithm underestimated PAI from out of camera JPEG images and overestimated PAI from raw data.

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Applying LiDAR to Quantify the Plant Area Index Along a Successional Gradient in a Tropical Forest of Thailand

Forests ◽

10.3390/f11050520 ◽

2020 ◽

Vol 11 (5) ◽

pp. 520

Author(s):

Siriruk Pimmasarn ◽

Nitin Kumar Tripathi ◽

Sarawut Ninsawat ◽

Nophea Sasaki

Keyword(s):

National Park ◽

Forest Succession ◽

Secondary Forests ◽

Area Index ◽

Khao Yai National Park ◽

Plant Area Index ◽

Plant Area ◽

Successional Process ◽

Term Monitoring

Long-term monitoring of vegetation is critical for understanding the dynamics of forest ecosystems, especially in Southeast Asia’s tropical forests, which play a significant role in the global carbon cycle and have continually been converted into various stages of secondary forests. In Thailand, long-term monitoring of forest dynamics during the successional process is limited to plot scales assuming from the distinct structure of successional stages. Our study highlights the potential of coupling airborne light detection and ranging (LiDAR) technology and stand age data derived from Landsat time-series to track back forest succession, and infer patterns in the plant area index (PAI) recovery. Here, using LIDAR data, we estimated the PAI of the 510 sample plots of a seasonal evergreen forest dispersed over the study area in Khao Yai National Park, Thailand, capturing a successional gradient of tropical secondary forests. The sample plots age was derived from the available Landsat time-series dataset (1972–2017). We developed a PAI recovery model during the first 42 years of the succession process. We investigated the relationship between the model residuals and PAI values with topographic factors, such as elevation, slope, and topographic wetness index. The results show that the PAI increased non-linearly (pseudo-R2 of 0.56) during the first 42 years of forest succession, and all three topographic factors have less influence on PAI variability. These results provide valuable information of the spatio-temporal PAI patterns during the successional process and help understand the dynamics of tropical secondary forests in Khao Yai National Park, Thailand. Such information is essential for forest management and local, regional, and global PAI synthesis. Moreover, our results provide significant information for ground-based spatial sampling strategies to enable more accurate PAI measurements.

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