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Leaf area index (LAI) is an important forest structural parameter that can be used to characterize various biophysical processes such as photosynthesis, evapotranspiration, and carbon flux. Accurate monitoring of LAI therefore is crucial for efficient management of managed and natural vegetation ecosystems. Remote sensing techniques have proved useful in the quantification and monitoring of LAI in different vegetation types; however, most of the focus has been on vegetation with relatively large LAI ranges. This study aimed to investigate the utility of airborne light detection and ranging (lidar) data to estimate narrow-range LAI (min = 0.71, max = 1.56, mean = 1.08 ± 0.18) of intensively-managed Eucalyptus grandis plantations. The secondary aim of the study was to assess the effect of lidar point density on LAI retrieval. Reference LAI was quantified in 15 m radius sample plots (n = 46) using hemispherical photography. Akaike Information Criterion (AIC) regression was used to build candidate models that estimate LAI from lidar-derived height and density metrics. The correlations were investigated at different point densities, including the original (>6 points/m2) and reduced density levels (0.25–5 points/m2). Candidate models returned adjusted coefficient of determination (adj. R2) ranging between 0.65–0.83 (RMSE 7.0-10.0% of observed mean) depending on the number of predicting metrics included in the models. A model that had two non-collinear metrics was selected as a compromise model (adj. R2 = 0.67; RMSE = 9.7%); this model was comparable to the best model, which had many collinear metrics. Estimation accuracies were similar for lidar densities of the original, 2–5 points/m2 and less accurate for 0.25–1 point/m2. These findings demonstrated the capability of lidar in estimating observed LAI with low range and variation. The study also suggests the efficacy of moderate lidar point densities acquired at relatively low-cost surveys in attaining acceptable LAI estimation accuracy.

Woody vegetation structure affects wildlife habitat selection and species diversity for a wide range of taxa, and at a variety of scales. Indicators of woody structure can indicate the spatio-temporal variability of biodiversity, species occurrence and assemblages. Woody cover is the simplest and most widely used structural metric. Combined with the vegetation height, it provides a volumetric indicator, which is more informative, and is simple to calculate. We therefore assessed the utility of multitemporal polarimetric RADARSAT-2 C-band imagery to map measures of woody volumetric indices in Lowveld savannahs, in the vicinity of the Kruger National Park, South Africa. RADARSAT-2 Quad-Pol fine beam images were acquired at three key phenological stages of the seasonal savannah cycle: i) wet (summer), ii) dry (winter), and iii) end of wet (autumn). Multi-polarized band intensities (C-HH, C-HV, and C-VV, with V = vertical and H = horizontal) and polarimetric decomposition variables (Freeman–Durden, Cloude–Pottier, and Van Zyl) were derived from the SAR images and used to predict structural metrics (woody canopy cover, cylindrical woody volume, and woody canopy volume, see definition in Section 3.3 ) derived from 1.1 m LiDAR strips acquired across the study area, and coinciding with 12% of the SAR dataset. The best single relationship (R2 = 0.66) was obtained between the cross-polarized HV intensity band and the total canopy volume (TCV). In terms of the seasonality, the best results were obtained using the SAR imagery from the dry season when most woody plants have lost their leaves and the grass-soil layer was dry. Validation outputs of best predictive models for TCV, at the individual season level, yielded an R2 of 0.67, a Standard Error of Prediction (SEP) of 39%, and consisted of the SAR parameters: C-HH, C-HV, C-VV, and Freeman–Durden decomposition parameters. At the multi-seasonal level, the best predictive models for TCV yielded an R2 of 0.75, a SEP of 35%, and comprised of the same variables but for all three seasons. The C-band SAR data thus provided encouraging results in open, semi-arid savannahs and hint at larger area structural assessments than is possible with LiDAR sensors alone. The combined use of C-band and L-band (ALOS-Palsar 2) should also be investigated.

Mapping savanna tree species is of broad interest for savanna ecology and rural resource inventory. We investigated the utility of (i) the Carnegie Airborne Observatory (CAO) hyperspectral data, and WorldView-2 and Quickbird multispectral spectral data and (ii) a combined spectral + tree height dataset (derived from the CAO LiDAR system) for mapping seven common savanna tree species or genera in the Sabi Sands Reserve and communal lands adjacent to Kruger National Park, South Africa. We convolved the 72 spectral bands of the CAO imagery to eight and four multispectral channels available in the WorldView-2 and Quickbird satellite sensors, respectively. A combination of the simulated WorldView-2 data and LiDAR tree height imagery was also assessed for species classification. First, the simulated WorldView-2 imagery provided a higher classification accuracy (77% ± 3.1 (mean ± standard deviation)) when compared to the simulated Quickbird (65% ± 1.9) and CAO (65% ± 1.2) data. Secondly, the combined spectral + height dataset provided a slightly higher overall classification accuracy (79% ± 1.8) when compared to the WorldView-2 spectral only dataset. The difference was however, statistically significant (p

This study assessed the suitability of both visible and shortwave infrared ASTER data and vegetation indices for estimating forest structural attributes of Eucalyptus species in the southern KwaZulu Natal, South Africa. The empirical relationships between forest structural attributes and ASTER data were derived using stepwise multiple regression analysis; Modified Soil Adjusted Vegetation Index (MSVI) and band 3 were selected for analysis as it showed best relationships with forest structural attributes. The ancillary data such as age and site index were also included in the analysis. Although the results of this study have indicated statistically significant relationships between the forest structural attributes and the ASTER data in the plantation forests stands with adjusted R 2 -values for volume, basal area (BA), stem per hectare (SPHA), and tree height of 0.51, 0.67, 0.65, and 0.52, respectively, but these results are not suitable for operational purpose in a forest company. However, the structural forest attribute predictions were markedly improved after incorporating age and site index as predictor variable. R 2 -values for the stands increased by 42%, 20.2%, 16.8%, and 42.2% for volume, basal area, SPHA, and tree height, respectively. These results imply that ASTER satellite data alone are not applicable to forest structural attribute estimation; however, ASTER data can provide useful information if it is used in conjunction with age and site index data for forest structural attribute estimation in plantation forests.

Although a variety of hierarchical image segmentation procedures for remote sensing imagery have been published, none of them specifically integrates remote sensing time series in spatial or hierarchical segmentation concepts. However, this integration is important for the analysis of ecosystems which are hierarchical in nature, with different ecological processes occurring at different spatial and temporal scales. Therefore, the objective of this paper is to introduce a multi-temporal hierarchical image segmentation (MTHIS) methodology to generate a hierarchical set of segments based on spatial similarity of remote sensing time series. MTHIS employs the similarity of the fast Fourier transform (FFT) components of multi-seasonal time series to group pixels with similar temporal behavior into hierarchical segments at different scales. Use of the FFT allows the distinction between noise and vegetation related signals and increases the computational efficiency. The MTHIS methodology is demonstrated on the area of South Africa in an MTHIS protocol for Normalized Difference Vegetation Index (NDVI) time series. Firstly, the FFT components that express the major spatio-temporal variation in the NDVI time series, the average and annual term, are selected and the segmentation is performed based on these components. Secondly, the results are visualized by means of a boundary stability image that confirms the accuracy of the algorithm to spatially group pixels at different scale levels. Finally, the segmentation optimum is determined based on discrepancy measures which illustrate the correspondence of the applied MTHIS output with landcover–landuse maps describing the actual vegetation. In future research, MTHIS can be used to analyze the spatial and hierarchical structure of any type of remote sensing time series and their relation to ecosystem processes.

The use of hyperspectral approaches for early detection of plant stress caused by Venturia inaequalis (apple scab) was investigated to move towards more efficient and reduced application of pesticides, fertilizers or other crop management treatments in apple orchards. Apple leaves of the resistant cultivar, Rewena and the susceptible cultivar, Braeburn, were artificially inoculated with conidia of V. inaequalis in a controlled greenhouse environment. The research focused on (i) determining if leaves infected with V. inaequalis could be differentiated from non-infected leaves, (ii) investigating at which developmental stage V. inaequalis infection could be detected, and (iii) selecting wavelengths that best differentiated between infected and non-infected leaves. Hyperspectral data were used because of their contiguous nature and the abundance of narrow wavelength bands in the electromagnetic reflectance spectrum, thereby providing the spectral sensitivity needed to detect subtle variations in reflectance. Processing the data, however, presented challenges, given the need to avoid data redundancy, identification of data extraction techniques, and maintaining modeling accuracy. Statistical techniques therefore had to be robust. Logistic regression, partial least squares logistic discriminant analysis, and tree-based modeling were used to select the hyperspectral bands that best defined differences among infected and non-infected leaves. Results suggested that good predictability (c-values > 0.8) could be achieved when classifying infected plants based on these supervised classification techniques. It was concluded that the spectral domains between 1350–1750 nm and 2200–2500 nm were the most important regions for separating stressed from healthy leaves immediately after infection. The visible wavelengths, especially around 650–700 nm, increased in importance three weeks after infection at a well-developed infection stage. Identification of such critical spectral regions constitutes the logical first step towards development of robust stress indicators based on hyperspectral imagery. © 2007 Elsevier B.V. All rights reserved.

The objectives of this research were to investigate (1) the influence of a geometric laser measurement pattern and (2) the shadow effect on the accuracy of a quantitative mathematical description of individual tree structure using a terrestrial laser system. The commercially available SICK Laser-Measurement-System-200 (LMS200, Sick, A.G.) was used in this study to obtain 3D structural datasets with high point densities. An artificial tree was constructed in an experimental set-up, thereby creating the possibility to arrange the structural key elements (leaves and branches) according to predetermined patterns. The multidirectional accuracy and quality of the laser measurement set-ups (lateral sideway, lateral bottom-up, hemispherical bottom-up) subsequently were compared based on the development of a mathematical description of each case. Vertical plant profiles were computed for the different experimental set-ups showing the direct influence of the vertical distribution pattern of the laser beams on the estimation of plant material in a certain height bin. The main measurement direction proved to have the biggest impact on the quality of the dataset, since the derived vertical profile followed the main trend of this laser beam distribution pattern. The use of probability-based statistics was necessary to correct the vertical profiles in order to obtain an acceptable estimation of the amount of plant material per height bin of 0.05 m.