Canopy Structural Properties Research Articles

Improving tropical forest mapping using multi-date Landsat TM data and pre-classification image smoothing

The present study explores the possibility of using Landsat imagery for mapping tropical forest types with relevance to forest ecosystem services. The central part in the classification process is the use of multi-date image data and pre-classification image smoothing. The study argues that multi-date imagery contains information on phenological and canopy structural properties, and shows how the use of multi-date imagery has a significant impact on classification accuracy. Furthermore, the study shows the value of applying small kernel smoothing filters to reduce in-class spectral variability and enhance between-class spectral separability. Making use of these approaches and a maximum likelihood algorithm, six tropical forest types were classified with an overall accuracy of 90.94%, and with individual forest classes mapped with accuracies above 75.19% (user's accuracy) and above 74.17% (producer's accuracy).

Radiation measurement for plant ecophysiology.

The principles of radiation physics for plant ecophysiological studies are outlined with an emphasis on choosing appropriate sensors for specific purposes such as for studies of photosynthesis, UV-B damage or canopy energy balance. Remote sensing, both from the ground and from aircraft or satellites, is increasingly being used as a tool for the study of plant canopies. Therefore, relevant terminology and applications are discussed, including the use of remote sensing for the determination of canopy structural properties and the use of thermal remote sensing for the measurement of canopy temperature, for example, in energy balance studies.

Remote sensing of nitrogen and lignin in Mediterranean vegetation from AVIRIS data: Decomposing biochemical from structural signals

Remote sensing estimates of vegetation nitrogen (N) and lignin concentration are central to assess ecosystem processes such as growth and decomposition. Although remote sensing techniques have been proven useful to assess N and lignin contents in continuous green canopies, more studies are needed to address their capabilities, particularly in low and sparsely vegetated ecosystems. We investigated the possibility of estimating canopy N and lignin concentrations in chaparral vegetation using Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) reflectance acquired over an area around Point Dume in the Santa Monica Mountains (Los Angeles, CA, USA). Two approaches were tested: multiple stepwise regression based on first difference reflectance (FDR) and reflectance ( R) indices. Multiple stepwise regressions (of three or fewer wavelengths) accounted for a large variance in canopy biochemical concentration ( r 2∼0.9, P<0.01). Log transformed R indices [log (1/ R)] formulated on the basis of previously known N and lignin absorption wavelengths also showed significant correlations ( P<0.01) with canopy biochemical concentration ( r 2 ranging from 0.39 to 0.48). In addition, the contribution of structural and biochemical signals and background effects on the performance of these indices was evaluated. These indices accounted for a increased variance when adding information on canopy structural attributes (e.g., relative contribution of each species and biomass amount) to foliar biochemical concentration. The relative contributions of foliar biochemical concentration and canopy structure (biomass amount) on the spectral signal were further evaluated by analyzing the residuals from linear regressions: foliar N concentration accounted for 42% of the variance for a normalized difference index based on the 1510-nm N absorption feature, while the foliar lignin concentration accounted for 44% of the variance for a normalized difference index based on the 1754 nm lignin absorption feature. These percentages increased to 58% when stands with senescing vegetation were disregarded. We propose the two indices, Normalized Difference Nitrogen Index (NDNI=[log (1/ R 1510)−log (1/ R 1680)]/[log (1/ R 1510)+log (1/ R 1680)]) and Normalized Difference Lignin Index (NDLI=[log (1/ R 1754)−log (1/ R 1680)]/[log (1/ R 1754)+log (1/ R 1680)]) as indices to assess N and lignin in native shrub vegetation.

Mapping glider habitat in dry eucalypt forests for Montreal Process indicator 1.1e: Fragmentation of forest types

Summary Accurate habitat mapping is critical to landscape ecological studies such as required for developing and testing Montreal Process indicator 1.1e, fragmentation of forest types. This task poses a major challenge to remote sensing, especially in mixed—species, variable-age forests such as dry eucalypt forests of subtropical eastern Australia. In this paper, we apply an innovative approach that uses a small section of one-metre resolution airborne data to calibrate a moderate spatial resolution model (30 m resolution; scale 1:50 000) based on Landsat Thematic Mapper data to estimate canopy structural properties in St Marys State Forest, near Maryborough, south-eastern Queensland. The approach applies an image-processing model that assumes each image pixel is significantly larger than individual tree crowns and gaps to estimate crown-cover percentage, stem density and mean crown diameter. These parameters were classified into three discrete habitat classes to match the ecology of four exudivorous arboreal species (yellow—bellied glider Petaurus australis, sugar glider P. breviceps, squirrel glider P. norfolcensis, and feathertail glider Acrobates pygmaeus), and one folivorous arboreal marsupial, the greater glider Petauroides volons. These species were targeted due to the known ecological preference for old trees with hollows, and differences in their home range requirements. The overall mapping accuracy, visually assessed against transects (n = 93) interpreted from a digital orthophoto and validated in the field, was 79% (KHAT statistic = 0.72). The KHAT statistic serves as an indicator of the extent that the percentage correct values of the error matrix are due to ‘true’ agreement verses ‘chance’ agreement. This means that we are able to reliably report on the effect of habitat loss on target species, especially those with a large home range size (e.g. yellow-bellied glider). However, the classified habitat map failed to accurately capture the spatial patterning (e.g. patch size and shape) of stands with a trace or sub-dominance of senescent trees. This outcome makes the reporting of the effects of habitat fragmentation more problematic, especially for species with a small home range size (e.g. feathertail glider). With further model refinement and validation, however, this moderate-resolution approach offers an important, cost effective advancement in mapping the age of dry eucalypt forests in the region.

HETEROGENEITY OF SAVANNA CANOPY STRUCTURE AND FUNCTION FROM IMAGING SPECTROMETRY AND INVERSE MODELING

Arid and semiarid ecosystems cover >40% of the earth’s terrestrial surface. Woody plants and herbaceous species, each with distinct allocation and phenological characteristics, respond to and constrain ecosystem function and biogeochemistry in these regions. High-resolution estimates of vegetation extent, aboveground allocation, and phenology are needed to improve regional links between ecosystem structure and function, especially under conditions of changing land use. We used a combination of imaging spectrometry and radiative transfer inverse modeling techniques to quantify the structural and biophysical attributes of plant canopies and land-cover types in a South Texas savanna. Estimated canopy structural attributes were used to calculate the fraction of photosynthetically active radiation (PAR; 400–700 nm) absorbed by plant canopies and the live foliage and nonphotosynthetic components of the canopies. A landscape analysis of these variables revealed the complexity of the biotic, abiotic, and anthropogenic factors involved in land-cover change in the region. The accuracy in estimating these plant canopy structural and functional attributes has significant implications for range management, fire forecasting, physiological ecology, biogeochemistry, and analyses of landscape structure and diversity.

Heterogeneity of Savanna Canopy Structure and Function from Imaging Spectrometry and Inverse Modeling

Arid and semiarid ecosystems cover >40% of the earth’s terrestrial surface. Woody plants and herbaceous species, each with distinct allocation and phenological characteristics, respond to and constrain ecosystem function and biogeochemistry in these regions. High-resolution estimates of vegetation extent, aboveground allocation, and phenology are needed to improve regional links between ecosystem structure and function, especially under conditions of changing land use. We used a combination of imaging spectrometry and radiative transfer inverse modeling techniques to quantify the structural and biophysical attributes of plant canopies and land-cover types in a South Texas savanna. Estimated canopy structural attributes were used to calculate the fraction of photosynthetically active radiation (PAR; 400–700 nm) absorbed by plant canopies and the live foliage and nonphotosynthetic components of the canopies. A landscape analysis of these variables revealed the complexity of the biotic, abiotic, and anthropogenic factors involved in land-cover change in the region. The accuracy in estimating these plant canopy structural and functional attributes has significant implications for range management, fire forecasting, physiological ecology, biogeochemistry, and analyses of landscape structure and diversity.

Biophysical and Biochemical Sources of Variability in Canopy Reflectance

Analyses of various biophysical and biochemical factors affecting plant canopy reflectance have been carried out over the past few decades, yet the relative importance of these factors has not been adequately addressed. A combination of field and modeling techniques were used to quantify the relative contribution of leaf, stem, and litter optical properties (incorporating known variation in foliar biochemical properties) and canopy structural attributes to nadir-viewed vegetation reflectance data. Variability in tissue optical properties was wavelength-dependent. For green foliage, the lowest variation was in the visible (VIS) spectral region and the highest in the near-infrared (NIR). For standing litter material, minimum variation occurred in the VIS/NIR, while the largest differences were observed in the shortwave-IR (SWIR). Woody stem material showed opposite trends, with lowest variation in the SWIR and highest in the NIR. Leaf area index (LAI) and leaf angle distribution (LAD) were the dominant controls on canopy reflectance data with the exception of soil reflectance and vegetation cover in sparse canopies. Leaf optical properties (and thus foliar chemistry) were expressed most directly at the canopy level in the NIR, but LAI and LAD strongly controlled the relationship between leaf and canopy spectral characteristics. Stem material played a small but significant role in determining canopy reflectance in woody plant canopies, especially those with LAI<5.0. Standing litter significantly affected the reflectance characteristics of grassland canopies; small increases in the percentage of standing litter had a disproportionately large affect on canopy reflectance. The structural attributes of ecosystems determine the relative contribution of tissue, canopy, and landscape factors that drive variation in a reflectance signal. Deconvolution of these factors requires an understanding of the sources of variance at each scale (which is ecosystem dependent) as well as an adequate sampling (spectral, angular, and temporal) of the shortwave (400–2500 nm) spectrum.