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Detection of individual trees remains a challenge for forest inventory efforts especially in homogeneous, even-aged plantation scenarios. Airborne imagery has mainly been used for detection of individual trees using local maxima filtering, as point spread function and signal-to-noise ratio are smaller than with satellite-borne imagery. This led to the development of a novel approach to local maxima filtering for tree detection in plantation forests in KwaZulu-Natal, South Africa, using satellite remote sensing imagery. Our approach is based on Gaussian smoothing for noise elimination and image classification, that is, natural break classification to determine the threshold for removing pixels of extremely bright and dark areas in the imagery. These pixels are assumed to belong to the background and hinder the search for tree peaks. A semivariogram technique was applied to determine variable window sizes for local maxima filtering within a plantation stand. A fixed window size for local maxima filtering was also applied using pre-determined tree spacing. Evaluation of the various approaches was based on aggregated assessment methods. The overall accuracy using a variable window size was 85%, root mean square error (RMSE) = 189 trees, whereas a fixed window size resulted in an accuracy of 80%, RMSE = 258 trees. The approach worked remarkably well in mature forest stands as compared to young forest stands. These results are encouraging for temperate-warm climate plantation forest companies, who deal with even-aged, broadleaf plantations and forest inventory practices that require assessment 1 year before harvesting. [ABSTRACT FROM AUTHOR]

The accuracy of lidar remote sensing in characterizing three-dimensional forest structural attributes has encouraged foresters to integrate lidar approaches in routine inventories. However, lidar point density is an important consideration when assessing forest biophysical parameters, given the direct relationship between higher spatial resolution and lidar acquisition and processing costs. The aim of this study was to investigate the effect of point density on mean and dominant tree height estimates at plot level. The study was conducted in an intensively managed Eucalyptus grandis plantation. High point density (eight points/m2) discrete-return, small-footprint lidar data were used to generate point density simulations averaging 0.25, one, two, three, four, five, and six points/m2. Field surveyed plot-level mean and dominant heights were regressed against metrics derived from lidar data at each simulated point density. Stepwise regression was used to identify which lidar metrics produced the best models. Mean height was estimated at accuracy of R2 ranging between 0.93 and 0.94 while dominant height was estimated with an R2 of 0.95. Root mean square error (RMSE) was also similar at all densities for mean height (∼1.0 m) and dominant height (∼1.2 m); the relative RMSE compared to field-measured mean was constant at approximately 5%. Analysis of bias showed that the estimation of both variables did not vary with density. The results indicated that all lidar point densities resulted in reliable models. It was concluded that plot-level height can be estimated with reliable accuracy using relatively low density lidar point spacing. Additional research is required to investigate the effect of low point density on estimation of other forest biophysical attributes. [ABSTRACT FROM AUTHOR]

Reflectance-converted imagery is a requirement for establishing temporally robust remote sensing algorithms, given the reduction of time-specific atmospheric effects. Thus, in this study image-based atmospheric correction methods for ASTER and IKONOS imagery for retrieving surface reflectance of plantation forests in KwaZulu-Natal, South Africa were evaluated. This effort formed part of a larger initiative that focused on retrieval of forest structural attributes from resultant reflectance imagery. Atmospheric correction methods in this study included the apparent reflectance model (AR), dark object subtraction model (DOS), and the cosine approximation model (COST). Spectral signatures derived from different image-based models for ASTER and IKONOS were inspected visually as first departure. This was followed by comparison of the total accuracy and Kappa index computed from supervised classification of images that were derived from different image-based atmospheric correction of ASTER and IKONOS imagery. The classification accuracy of DOS images derived from ASTER and IKONOS imagery exhibited percentages of 93.3% and 94.7%, respectively. Classification accuracies for images from AR and COST, on the other hand, resulted in lower accuracy values of 87.9% and 83.6% for ASTER and 90.5% and 92.8% for IKONOS, respectively. We concluded that the image-based DOS model was better suited to atmospheric correction for ASTER and IKONOS imagery in this study area and for the purpose of forest structural assessment. This has important implications for the operational use of similar imagery types for forest inventory approaches. [ABSTRACT FROM AUTHOR]

This study assessed the suitability of both visible and shortwave infrared of ASTER reflectance bands and various vegetation indices for estimating forest structural attributes of Eucalyptus species. The study was conducted in even-aged monoculture plantations of E. grandis and E. nitens in the southern KwaZulu-Natal Midlands of South Africa. Empirical relationships between forest structural attributes, i.e. stems per hectare (SPHA), diameter at breast height (DBH), mean tree height (MTH), basal area and volume, and ASTER data were derived using correlation and canonical correlation analysis (CCA). The results indicated weak relationships between the studied forest structural attributes and ASTER data. In the younger plantation stands (4-6 years) the adjusted R² values from CCA regression for SPHA, DBH, MTH, basal area and volume were 54.2, 63.5, 33.8, 25.4 and 30.3, respectively. The adjusted R² values in the mature stands (7-9 years) were distinctly weaker with values of 50.7, 55.8, 25.1, 20.2 and 27.3 for SPHA, DBH, MTH, basal area and volume, respectively. The results imply that ASTER satellite data are not applicable to forest structural attribute estimation in commercially managed forest stands. [ABSTRACT FROM AUTHOR]