Browsing by discipline "Forest Resource Science and Technology (Forest inventory)"

As an alternative to complex 3D-modelling of structure, the canopy spectral invariants are a novel concept to describe the average behavior of photons in a vegetated canopy. The probabilities of canopy absorption and scattering can be summarized with only three parameters (?L, p and R): The green leaf single scattering albedo (?L) describes the wavelength-dependent probabilities of absorption for each time a photon interacts with a leaf. In the event of scattering, a photon’s probabilities of reinteraction (photon recollision probability p) and exiting the canopy in a given direction (directional escape factor R) can be described as independent of wavelength; as the size of the scattering elements is considerably larger than wavelengths in the shortwave radiation budget, p and R depend only upon the structural arrangement of the scattering elements. In this work, a recently published (2011) approach to infer remotely sensed (spaceborne) hyperspectral imagery (also referred to as imaging spectroscopy data) based on the canopy spectral invariants was tested in a case study on southern boreal forests at full leaf development. An atmospherically corrected image taken with the Hyperion imaging spectrometer aboard the National Aeronautics and Space Administration’s (NASA) Earth Observing-1 (EO-1) spacecraft was interpreted with a single reference transformed green leaf scattering albedo. Transforming of a traditionally defined leaf albedo means correcting the measurements for the effect of surface reflectance, resulting in probabilities of leaf scattering and absorption given a photon interacts with the leaf internal constituents. Utilizing such transformed albedo as reference results in reference (canopy) spectral invariants describing the relative difference between the reference and the scattering properties of (theoretical) mean leaves at the scale of inference (pixel). The results of the study are parallel to those of previously published and ongoing research: In essence, even while the individual parameters p and R depend on the reference, the ratio R/(1–p) (directional escape factor to total escape probability) was found practically independent of the selection of the reference, thus implicating a possibility to develop a physically-based algorithm to infer hyperspectral imagery in vegetated areas. Moreover, the reference (canopy) spectral invariants were found as highly applicable in retrieval of forest structural properties such as dominant forest type (broadleaved, coniferous, mixed) and a quantitative estimate of the broadleaf fraction of a forest area.

Leaf and needle biomasses are key factors in forest health. Insects that feed on needles cause growth losses and tree mortality. Insect outbreaks in Finnish forests have increased rapidly during the last decade and due to climate change the damages are expected to become more serious. There is a need for cost-efficient methods for inventorying these outbreaks. Remote sensing is a promising means for estimating forests and damages. The purpose of this study is to investigate the usability of airborne laser scanning in estimating Scots pine defoliation caused by the common pine sawfly (Diprion pini L.). The study area is situated in Ilomantsi district, eastern Finland. Study materials included high-pulse airborne laser scannings from July and October 2008. Reference data consisted of 90 circular field plots measured in May-June 2009. Defoliation percentage on these field plots was estimated visually. The study was made on plot-level and methods used were linear regression, unsupervised classification, Maximum likelihood method, and stepwise linear regression. Field plots were divided in defoliation classes in two different ways: When divided in two classes the defoliation percentages used were 0–20 % and 20–100 % and when divided in four classes 0–10 %, 10–20 %, 20–30 % and 30–100 %. The results varied depending on method and laser scanning. In the first laser scanning the best results were obtained with stepwise linear regression. The kappa value was 0,47 when using two classes and 0,37 when divided in four classes. In the second laser scanning the best results were obtained with Maximum likelihood. The kappa values were 0,42 and 0,37, correspondingly. The feature that explained defoliation best was vegetation index (pulses reflected from height > 2m / all pulses). There was no significant difference in the results between the two laser scannings so the seasonal change in defoliation could not be detected in this study.

The purpose of this study was to investigate the usability of aerial images and Landsat TM in estimating Scots pine defoliation. Estimation methods tested were unsupervised classification, maximum likelihood method, mixed model and linear regression model. Image features for needle loss detection were selected with stepwise linear regression and mixed model technique. As a part of the study the relationship between needle loss and leaf area index (LAI) was examined. The relationship between image features, needle loss and leaf area index was also examined. Numerical aerial images and Landsat TM satellite images were used. Textural features were calculated from aerial images and spectral vegetation indices from the satellite image. The study site was located in Ilomantsi, Finland. 71 field sample plots were measured and located with GPS. Field plots were circular plots. Trees with diameter brest height (dbh) over 13,9 cm were measured from 13 meter radius and trees with dbh 5,0 - 13,8 cm were measured from 7 meter radius. Needle loss of all pines was estimated. Needle loss for the plot was calculated as an average weighted by tree height. Four different class combinations were tested in classification. Plots were divided in 2, 3, 4 and 9 classes depending on their needle loss. Different image feature combinations and classification methods were tested. Classification was done by cross validation. Classification results were compared with original classes. The reliability of the classification was tested using accuracy matrix and kappa value. A mixed model was also used for aerial image features. The best image feature combination with all classification methods was the aerial image feature combination selected with stepwise selection method. Both spectral and textural features were included in the stepwise selection result. Classification accuracy varied between 38,0 % (9 classes) and 88,7 % (2 classes). The best explanatory variable for needle loss was the aerial image NIR channel maximum radiation (r2=0,69). However, unsupervised and supervised classification might have produced too positive results because of correlation in the data. Mixed model technique was used to select the variables for the linear model. Mixed model was used to reduce the effects of the correlation. The model classification accuracy varied between 35,2 % (9classes) and 87,3 % (2classes). According to mixed model selection result no textural features were significant predictors for needle loss. Classification results with Landsat image features were slightly poorer than with the best aerial image feature set (accuracy between 25,4 % and 88,7 %). The relationship between needle loss and LAI was poor (r2=0,27). Needle loss and LAI also correlated with different image features. LAI correlated slightly better with textural features than needle loss. Spectral vegetation indices calculated from Landsat TM correlated moderately with both needle loss and LAI. Indices VI3 (r2=0,56), MIR/NIR (r2=0,51) and RSR (r2=0,44) had the strongest connection to needle loss. Spectral vegetation indices could be a potential way for large area needle loss detection.