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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]

This research explores the potential benefits of fusing active and passive medium-resolution satellite-borne sensor data for forest structural assessment. Image fusion was applied as a means of retaining disparate data features relevant to modelling and mapping of forest structural attributes in even-aged (4-11 years) Eucalyptus plantations, located in the southern KwaZulu-Natal midlands of South Africa. Remote-sensing data used in this research included the visible and near-infrared bands of the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), as well as a fine-beam (6.25 m resolution) Radarsat-1 image. Both datasets were collected during the spring of 2006 and fused using a modified discrete wavelet transformation. Spatially referenced forest-inventory data were also collected during this time, with 122 plots enumerated in 38 plantation compartments. Empirical relationships (ordinary and multiple regression) were used to test whether fused data sources produced superior statistical models. Secondary objectives of the article included exploring the roles of texture, derived from grey-level co-occurrence matrices, and scale in terms of forest modelling at the plot and extended plot levels (Voroni diagrams). Results indicate that single bands from both the optical and Synthetic Aperture Radar (SAR) datasets were not adept at modelling basal area and merchantable timber volume with adjusted R2 (coefficient of determination) values < 0.3. An optimized multiple-regression approach (adjusted R2) improved results based on mean, range and standard deviation statistics when compared to single bands, but were still not suitable for operational forest applications (basal area: R2 = 0.55, volume: R2 = 0.59). No significant difference was found between fused and non-fused datasets; however, optical and fused datasets produced superior models when compared to SAR results. Investigations into potential benefits of using textural indices and varied scales also returned inconclusive results. Findings indicate that the spatial resolutions of both sensors are inappropriate for plantation forest assessment. The frequency of the C-band Radarsat-1 data is, for instance, unable to penetrate the canopy and interact with the woody structures below canopy, leading to weak statistical models. The lack of variability in both the optical and SAR data lead to unconvincing results in the fused imagery, where, in some cases, the adjusted R2 results were worse than the single-dataset approach. It was concluded that future research should focus on high-spatial-resolution optical and Light Detection and Ranging (LiDAR) data and the development of automated and semi-automated forest-inventory procedures. [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]

Remote sensing techniques provide timely, up-to-date and relatively accurate information for the management of sugarcane crop. This article reviews the literature on the application of remote sensing to sugarcane agriculture and highlights the challenges and opportunities pertinent to the success of this application. The aim of the review was to provide accurate and fundamental information relating the spectral properties of sugarcane to its agronomic, health and nutritional status characteristics that would be of importance to cane farmers and farm managers. The applications of the remote sensing techniques in sugarcane agriculture have been undertaken with particular emphasis on sugarcane classification and areal extent mapping, thermal age group identification, varietal discrimination, yield prediction and crop health and nutritional status monitoring. It can be concluded that by selecting appropriate spatial and spectral resolution as well as suitable processing techniques for extracting sugarcane spectral information, remotely sensed data should find use in sugarcane agriculture in all areas of application with satisfactory results. [ABSTRACT FROM AUTHOR]