The relationship between effective plant area index and Landsat spectral response across elevation, solar insolation, and spatial scales in a northern Idaho forest

2004 ◽  
Vol 34 (2) ◽  
pp. 465-480 ◽  
Author(s):  
Amy L Pocewicz ◽  
Paul Gessler ◽  
Andrew P Robinson
Keyword(s):  
Spatial Scale ◽  
Complex Terrain ◽  
Vegetation Index ◽  
Remotely Sensed ◽  
Solar Insolation ◽  
Area Index ◽  
Plant Area Index ◽  
Plant Area ◽  
Northern Idaho ◽  
The Relationship

Leaf area index (LAI) is an important forest characteristic related to photosynthesis and carbon sequestration, and gains in efficiency for LAI measurements are possible using remotely sensed imagery. However, the potential effects of complex topography on this measurement system are not well understood. Our objective was to understand how complex terrain and measurement aggregation influence the relationship between LAI and remotely sensed vegetation indices across a mountainous conifer forest. We identified NDVIc, a middle-infrared (MIR) correction to NDVI (Normalized Difference Vegetation Index), as the vegetation index providing the best prediction of effective plant area index (PAIe), used to approximate LAI. We tested formal hypotheses to identify how elevation and solar insolation gradients and spatial scale of measurement aggregation affected the PAIe–NDVIc relationship and found that it changed across elevation at one spatial scale. Comparisons of NDVIc with NDVI revealed that vegetation index choice is important in complex terrain, and we concluded that the MIR correction improves the PAIe–NDVI relationship by explaining variation related to solar insolation. Our results suggest that NDVIc calculated from Landsat ETM+ provides a practical estimate of PAIe across our northern Idaho study area and potentially other conifer forests in complex terrain.

scholarly journals Phytomass mapping of the "seridó caatinga" vegetation by the plant area and the normalized difference vegetation indeces

2002 ◽  
Vol 59 (4) ◽  
pp. 707-715 ◽  
Author(s):  
Thomaz Corrêa e Castro da Costa ◽  
Luciano José de Oliveira Accioly ◽  
Maria Ap. José de Oliveira ◽  
Nivaldo Burgos ◽  
Flávio Hugo Barreto Batista da Silva
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Linear Models ◽  
Dry Weight ◽  
Empirical Method ◽  
Orbital Parameter ◽  
Landsat Images ◽  
Area Index ◽  
Plant Area Index ◽  
Plant Area

Phytomass is a critical information for economic and environmental activities like the establishment of policies for timber resources, forest management, studies of plant nutrient cycling, CO2 sink, among other. The phytomass of a Caatinga area was obtained by an empirical method using normalized difference vegetation index (NDVI) of Landsat images, the plant area index (PAI) and the phytomass inventory. At a first stage, linear, logarithmic and non-linear models were developed and tested. Bush and tree specimens were considered in the study, so that most of the individuals that contribute to the spectral answer detected by satellite images were included. At a second stage, the orbital parameter NDVI was used to map the PAI, which was used to map the phytomass, based on the relationship of this phytomass as a function of PAI. The residues between measurements and estimates based on NDVI varied from 0 to 84%, while the residues of total dry weight of phytomass per ha obtained by mapping and by dendrometrical equations varied from 5 to 104%, with a large trend of 166 and 448% in open Caatinga areas, due to the contribution of the herbaceous stratum to NDVI.

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 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

2013 ◽  
Vol 59 (1) ◽  
pp. 13-27 ◽  
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.

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

Forests ◽  
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.

Modelling understory light availability in a heterogeneous landscape using drone-derived structural parameters and a 3D radiative transfer model

2020 ◽  
Author(s):  
Dominic Fawcett ◽  
Jonathan Bennie ◽  
Karen Anderson
Keyword(s):  
Radiative Transfer ◽  
Vegetation Index ◽  
Structural Parameters ◽  
Point Clouds ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Area Index ◽  
Vegetation Height ◽  
Plant Area Index ◽  
The Impact

<p>The light environment within vegetated landscapes is a key driver of microclimate, creating varied habitats over small spatial extents and controls the distribution of understory plant species. Modelling spatial variations of light at these scales requires finely resolved (< 1 m) information on topography and canopy properties. We demonstrate an approach to modelling spatial distributions and temporal progression of understory photosynthetically active radiation (PAR) utilising a three dimensional radiative transfer model (discrete anisotropic radiative transfer model: DART) where the scene is parameterised by drone-based data.</p><p>The study site, located in west Cornwall, UK, includes a small mixed woodland as well as isolated free-standing trees. Data were acquired from March to August 2019. Vegetation height and distribution were derived from point clouds generated from drone image data using structure-from-motion (SfM) photogrammetry. These data were supplemented by multi-temporal multispectral imagery (Parrot Sequoia camera) which were used to generate an empirical model by relating a vegetation index to plant area index derived from hemispherical photography taken over the same time period. Simulations of the 3D radiative budget were performed for the PAR wavelength interval (400 – 700 nm) using DART.</p><p>Besides maps of instantaneous above and below canopy irradiance, we provide models of daily light integrals (DLI) which are assessed against field validation measurements with PAR quantum sensors. We find relatively good agreement for simulated PAR in the woodland. The impact of simplifying assumptions regarding leaf angular distributions and optical properties are discussed. Finally, further opportunities which fine-grained drone data can provide in a radiative transfer context are highlighted.</p>

Plant area index inEucalyptus globulusplantations determined indirectly by a light interception method

1990 ◽  
Vol 7 (1-2-3-4) ◽  
pp. 107-113 ◽  
Author(s):  
L. C. Gazarini ◽  
M. C. C. Araújo ◽  
N. Borralho ◽  
J. S. Pereira
Keyword(s):  
Light Interception ◽  
Area Index ◽  
Plant Area Index ◽  
Plant Area

scholarly journals Local Illumination Influence on Vegetation Indices and Plant Area Index (PAI) Relationships

2014 ◽  
Vol 6 (7) ◽  
pp. 6266-6282 ◽  
Author(s):  
Flávio Ponzoni ◽  
Clayton Silva ◽  
Sandra Santos ◽  
Otávio Montanher ◽  
Thiago Santos
Keyword(s):  
Vegetation Indices ◽  
Area Index ◽  
Plant Area Index ◽  
Plant Area ◽  
Local Illumination
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