Disease and weeds are two main risks that threaten crop yields. In this study, we used an unmanned aerial system (UAS)-based light detection and ranging (LiDAR) to observe experimental snap bean fields and generate accurate 3D maps for detection of white mold and weeds. First, we collected dense 3D point cloud data for two snap bean fields at different growth stages. We then preprocessed these clouds to obtain the digital elevation model (DEM) from bare ground returns and then normalized the z-coordinates to extract height-above-ground. White mold detection was implemented by performing a multiscale model-to-model cloud comparison (M3C2) map between two datasets that were collected before and after white mold appearance. We also attempted weed detection by finding the LiDAR returns above a certain height threshold and relative to their neighboring points. Compared with the ground truth, i.e., manually-labeled weed points, we achieved a precision of 99.1% and a recall of 95.7%. Our results proved that the UAS-LiDAR system was highly effective in detecting the white mold and weeds in snap bean fields. This represents a novel contribution of using structural (LiDAR) sensing, as opposed to more traditional spectral approaches.

Timely assessment of crop maturity contributes to optimized harvesting schedules while limiting food loss/waste at the farm level. Maturity assessments are typically performed via costly and time-consumingin situmethods. This study aimed to evaluate pod size crop maturity using imaging spectroscopy via unmanned aerial systems (UASs), as well as identifying discriminating wavelengths, using snap bean as a proxy crop. The research utilized a UAS-mounted hyperspectral imager in the visible-to-near-infrared region. Two years’ worth of data were collected at two different geographical locations for six different snap bean cultivars. Our approach consisted of calibration to reflectance, vegetation detection, noise reduction, creating classification bins, and feature selection. We used our previously published feature selection library, Jostar, and utilized ant colony optimization and simulated annealing to detect five spectral features and Plus-L Minus-R to identify one to ten features. We utilized decision trees and random forest classifiers for the classification task. Our findings show that, given the proper wavelengths, accurate pod maturity assessment is feasible for large-sieve cultivars (F1 score = 0.79–0.91), separating sieve sizes between ready-to-harvest and not ready-to-harvest pods. These spectral features were in the ~450, ~530, ~660, 700–720, ~740, and ~760 nm regions. This bodes well for the potential extension of results to an operational, multispectral sensor, tuned with the identified bands, thereby negating the need for a costly hyperspectral system. However, this proposition mandates further investigation, including data acquisition from geographical locations with variable climates, and quantifying noise for the hyperspectral imager to compare results with noisier datasets.

Remote sensing increasingly has become an important tool for forest management. In the development of forest metrics from remote-sensing data, currently many models omit the individual leaf bidirectional scattering distribution function (BSDF). Past studies, and the currently available data, often do not adequately incorporate transmission, cover the broader reflective domain, and/or incorporate models to extend to any illumination and view angle combination. We estimated broadleaf bidirectional transmittance distribution functions (BTDFs) in this study using the goniometer of the Rochester Institute of Technology-Two (GRIT-T), which records spectral data in the UV-A through shortwave infrared (SWIR) spectral regions (350–2500 nm). We measured three species of large tree leaves, Norway maple (Acer platanoides), American sweetgum (Liquidambar styraciflua), and northern red oak (Quercus rubra). We accurately modeled leaf BTDF with extension to any illumination angle, viewing zenith, and azimuthal angle through nonlinear regression to a physically-based microfacet BTDF. The model fit showed a mean of less than 7% normalized root-mean-squared error (NRMSE) spectrally from 450 to 2300 nm (lower and upper wavelength range omitted due to detector noise). The microfacet models provide highly useful physical quantities such as a relative roughness, index of refraction, and absorption, which are all directly related to leaf optical properties. These physical quantities have implications for plant physiology, vegetation remote sensing, and physics-based image generation. Specifically, the accuracy of radiative transfer modeling in forest canopies depends on rigorous representations of leaves, and this increase in accuracy can lead to the development of higher fidelity data processing algorithms for remote sensing. Data and programing scripts are available at-http://dx.doi.org/10.21227/yjek-2059

Yield assessment in broadacre crops is often base on time-consuming and labor-intensive approximations. However, the emergence of unmanned aerial systems (UAS) has allowed for rapid and cost-effective data acquisition. We evaluated red beet plant counts using multispectral UAS data via computer vision and regression analysis. Flight data were captured twice during summer 2019. Our preprocessing steps included i) vegetation detection, ii) feature generation, and iii) feature selection. Partial least squares regression was used as a statistical predictor. Results showed that plant count could be predicted with an acceptable coefficient of determination ($R^{2}=0.76$for calibration;$R^{2}=0.54$for cross-validation) and a low root-mean-square-error ($\text{RMSE} =12.27$plants/plot for calibration,$\text{RMSE} =17.45$plants/plot for cross-validation). These results are promising, since the error margin, relative to the average density (175 plants/plot), was below 10%. Future efforts should include different geographical locations, higher resolution imagery, and more advanced approaches such as deep learning algorithms with potential for improved accuracy and precision.

Precision agriculture applications, which include spatially-explicit irrigation scheduling, nutrient application, and disease management, could greatly benefit from accurate assessment of crop physiological stages. Such an identification strategy could contribute to improved timing of crop management interventions and improved yields. Snap bean as a crop, valued at over $400 million dollars annually in the USA, is an example crop that require accurate maturity stage classification, especially in context of disease management and harvest scheduling. This study aimed to assess growth classification of snap bean crop via machine learning approaches over four major maturity stages, namely vegetative growth, budding, flowering, and pod formation using hyperspectral data, in a greenhouse setting. Our classification results show high discrimination (mean-per-class accuracy = 0.69-0.82) in a one-vs-rest fashion with both parametric and non-parametric classifiers, such as logistic regression, naïve Bayes, random forest, and perceptron. These results show promise for potential extension to remote sensing solutions that would allow growers to better manage their crops.

Ducks are monitored extensively since they are an important game species and, as migratory birds, they are protected under the Migratory Bird Treaty Act. The prairies of North Dakota are part of the Prairie Pothole Region where more than 50% of ducks are produced in North American annually. To improve our ability to monitor annual populations of ducks, we assessed the feasibility of utilizing thermal imaging systems aboard Unmanned Aerial Systems (UASs) to automatically detect duck nests. With 24 flights across five days collecting 134 images of nests, we demonstrated a nest detection of 38% and 17% from flight altitudes of 40m and 80m, respectively. With higher resolution, via a different sensor or lower altitudes, this method of duck nest detection is feasible.

The determination of structural description of crops could contribute to precision agriculture applications, such as yield assessments. However, an efficient and reliable automatic system for evaluating key structural descriptors is still lacking. Unmanned aerial systems (UAS)-based light detection and ranging (LiDAR) offers relatively affordable high spatial and temporal resolution 3D data. In this study, we used the UAS-LiDAR system to collect 3D point clouds for 24 plots of snap bean across different seasonal growth stages. We extracted a digital elevation model (DEM) from ground return points, and then introduced a parameter to calibrate data from different flights. Based on the segmentation results, key structural descriptors, including canopy height, width, and leaf area index (LAI) were calculated and compared with in situ measurements. While canopy width showed the most uncertainty, the height evaluation was fairly accurate with a RMSE = 0.04m and R2=0.72. LAI assessments also showed promise with a RMSE = 0.45 and R2=0.43. These results bode well for extension to yield modeling and within-season management interventions.

Consistent and scalable estimation of vegetation structural parameters-essential to understanding forest ecosystems-is widely investigated through remote sensing imaging spectroscopy. NASA's proposed spaceborne mission, the Hyperspectral Infrared Imager (HyspIRI), will measure spectral radiance from 380 to 2500 nm in 10-nm contiguous bands with a 60-m ground sample distance (GSD) and provide a global benchmark from which future changes can be assessed. The historic foci of spectrometers have been foliar/canopy biochemistry and species classification; however, given the relatively large GSD of a spaceborne instrument, there is uncertainty as to the effects of subpixel vegetation structure on observed radiance. This paper, therefore, evaluates the linkages between the within-pixel vegetation structure and imaging spectroscopy signals at the pixel level. We constructed a realistic virtual forest scene representing the National Ecological Observatory Network (NEON) Pacific Southwest domain site. Anticipated HyspIRI data (60-m GSD) for this site were then simulated using the physics-driven Digital Imaging and Remote Sensing Image Generation (DIRSIG) model. Both the models were first validated via comparison to overflow classic Airborne Visible/Infrared Imaging Spectrometer and NEON's imaging spectrometer (NIS). Then, to assess the impact of within-pixel: 1) tree canopy cover (CC); 2) tree positioning; and 3) distribution on large-footprint HyspIRI signals, we generated the variations of the baseline virtual forest scene and measured the anticipated spectral radiance using DIRSIG. Results indicate that HyspIRI is sensitive to subpixel vegetation structural variation in the visible to a short-wavelength infrared spectrum due to vegetation structural changes. This has implications for improving the system's suitability for consistent global vegetation structural assessments by adapting calibration strategies to account for this subpixel variation.

Reference data (“ground truth”) maps are commonly used to quantitatively assess the performance of imaging spectrometer classification algorithms. However, standard reference data scenes typically are not sufficiently detailed to support assessment of spectral unmixing algorithms. Furthermore, commonly used reference data often lack validation reports that estimate error in the reference data itself, and new reference data are prohibitively expensive to generate using traditional methods. This paper presents a summary of our work, which is focused on introducing new methodologies to efficiently generate and validate abundance map reference data (AMRD), which can then be applied to assess the performance of spectral unmixing on real remotely sensed imagery. AMRD, generated using our methodology, had a validated mean and standard deviation error of 3.0% and 6.3%, respectively, which rivaled the accuracy the best traditional methods. A separate experiment designed to replicate our methodology, using different scenes and imagery, confirmed the relative accuracy of our techniques.

Terrestrial laser scanning has demonstrated increasing potential for rapid comprehensive measurement of forest structure, especially when multiple scans are spatially registered in order to reduce the limitations of occlusion. Although marker-based registration techniques (based on retro-reflective spherical targets) are commonly used in practice, a blind marker-free approach is preferable, insofar as it supports rapid operational data acquisition. To support these efforts, we extend the pairwise registration approach of our earlier work, and develop a graph-theoretical framework to perform blind marker-free global registration of multiple point cloud data sets. Pairwise pose estimates are weighted based on their estimated error, in order to overcome pose conflict while exploiting redundant information and improving precision. The proposed approach was tested for eight diverse New England forest sites, with 25 scans collected at each site. Quantitative assessment was provided via a novel embedded confidence metric, with a mean estimated root-mean-square error of 7.2 cm and 89% of scans connected to the reference node. This paper assesses the validity of the embedded multiview registration confidence metric and evaluates the performance of the proposed registration algorithm.

Tree crown structural parameters are key inputs to studies spanning forest fire propagation, invasive species dynamics, avian habitat provision, and so on, but these parameters consistently are difficult to measure. While airborne laser scanning (ALS) provides uniform data and a consistent nadir perspective necessary for crown segmentation, the data characteristics of terrestrial laser scanning (TLS) make such crown segmentation efforts much more challenging. We present a data fusion approach to extract crown structure from TLS, by exploiting the complementary perspective of ALS. Multiple TLS point clouds are automatically registered to a single ALS point cloud by maximizing the normalized cross correlation between the global ALS canopy height model (CHM) and each of the local TLS CHMs through parameter optimization of a planar Euclidean transform. Per-tree canopy segmentation boundaries, which are reliably obtained from ALS, can then be adapted onto the more irregular TLS data. This is repeated for each TLS scan; the combined segmentation results from each registered TLS scan and the ALS data are fused into a single per-tree point cloud, from which canopy-level structural parameters readily can be extracted.

Terrestrial laser scanning (TLS) has emerged as an effective tool for rapid comprehensive measurement of object structure. Registration of TLS data is an important prerequisite to overcome the limitations of occlusion. However, due to the high dissimilarity of point cloud data collected from disparate viewpoints in the forest environment, adequate marker-free registration approaches have not been developed. The majority of studies instead rely on the utilization of artificial tie points (e.g., reflective tooling balls) placed within a scene to aid in coordinate transformation. We present a technique for generating view-invariant feature descriptors that are intrinsic to the point cloud data and, thus, enable blind marker-free registration in forest environments. To overcome the limitation of initial pose estimation, we employ a voting method to blindly determine the optimal pairwise transformation parameters, without an a priori estimate of the initial sensor pose. To provide embedded error metrics, we developed a set theory framework in which a circular transformation is traversed between disjoint tie point subsets. This provides an upper estimate of the Root Mean Square Error (RMSE) confidence associated with each pairwise transformation. Output RMSE errors are commensurate with the RMSE of input tie points locations. Thus, while the mean output RMSE=16.3cm, improved results could be achieved with a more precise laser scanning system. This study 1) quantifies the RMSE of the proposed marker-free registration approach, 2) assesses the validity of embedded confidence metrics using receiver operator characteristic (ROC) curves, and 3) informs optimal sample spacing considerations for TLS data collection in New England forests. While the implications for rapid, accurate, and precise forest inventory are obvious, the conceptual framework outlined here could potentially be extended to built environments.

Modern forest inventory is based on the accurate and precise characterization of the 3D structure of the forest. Although LiDAR (Light Detection and Ranging) is an effective tool to estimate forest parameters, when acquired from single view point it is not able to represent accurately the entire scene. To solve this problem, in this paper we present a method that integrates the terrestrial and airborne LiDAR data. The proposed method first performs an automatic co-registration of the data sources based on the spatial pattern of the structure of the stand plot. Second, it integrates the LiDAR point clouds to accurately represent the structure of the crown. The resulting fused LiDAR point cloud can be used for an accurate estimation of the crown parameters, thus making it possible a more comprehensive representation of the 3D structure of the forest stand. Experimental results carried out in a oakland savanna in Fresno (California) confirm the effectiveness of the proposed method.

Few studies have evaluated how per-pixel structural configurations could impact spectral response. This has an impact on how we assess especially large area/global ecosystems. In an effort to understand this impact of sub-pixel structural variation on large-footprint imaging spectroscopy, a simulation approach was used, which provides precise knowledge of target geometry and radiometry. We demonstrated the validity of the proposed simulation in terms of one such structural metric of interest, namely leaf area index (LAI). LAI is a key vegetation structural parameter, which has implications for predicting ecosystems' foliar spatial distribution, health, photosynthesis, transpiration, and energy transfer. Simulated LAI measurements were validated with field data obtained from AccuPAR measurements (R2= 0.76) and by comparison to NDVI data obtained from simulated AVIRIS imagery (R2= 0.92−0.65, depending on sampling interval). These data were used to propose an appropriate sampling protocol for LAI data collection, thus providing for efficient data collection, while minimizing variability of individual measurements. These efforts will support preparatory science experiments towards understanding the phenomenology of NASA's next-generation imaging spectrometer, HyspIRI.

Description and quantification of a landscape or scene can be achieved by assessing its spectral and structural properties. Fusion of spectral information from aerial imagery and 3-D structural information from LiDAR point clouds allows us to integrate these two complementary characteristics. However, in any fusion method, alignment of data sets is crucial. We registered aerial color (RGB) imagery with LiDAR data by computing a homography matrix(H), using the Levenberg-Marquardt nonlinear optimization method. The root mean square error (RMSE) of registration was less than 0.5 m. The overall classification accuracy of our fusion based object extraction algorithm was also increased from 85% to 90%, when applied to a pre and post registered data set, respectively. In this paper, two different regions were selected to demonstrate the registration method and improved classification results.

Forest monitoring for environmental policy requires accurate and efficient ground-truthing techniques in the field. In this paper, a portable terrestrial laser scanner (TLS) is utilized to estimate leaf area index (LAI) of mixed forest stands in Christchurch, New Zealand. Our method converted laser XYZ coordinates to orthographic coordinates to create fish-eye images, from which LAI was estimated. The results were highly correlated with LAI estimates from three traditional techniques: radiation obtained by AccuPAR (R2= 0.81), Landsat TM (R2= 0.79), and fish-eye lens photography (R2= 0.91). This novel technique is a simple and efficient way to collect and analyze LAI and provides good ground truth data for satellite remote sensing.

This study investigated the biomass patterns and sustainability of fuelwood extraction in the Lowveld of South Africa, where rural households are highly dependent on fuelwood from savannas. The objectives of this study were (i) to compare LiDAR-derived biomass between communal areas and references sites in conservation areas, and (ii) to investigate the sustainability of various future scenarios of fuelwood consumption, using a village-specific, supply-and-demand model based on biomass maps and socio-economic data. On granitic substrates the communal rangelands had an average of 12 ton/ha, which is less than half the biomass of the conservation sites. Under the current rate fuelwood consumption, i.e. 67% of households using fuelwood exclusively at an average of 3.5 ton per household per year, all biomass in the investigated site would be depleted within twelve years. Therefore, policies and interventions that promote the diversification of affordable energy alternatives and rural economic development are desperately needed.

The extraction of structural object metrics from a next-generation remote sensing modality, namely waveform Light Detection and Ranging (LiDAR), has garnered increasing interest from the remote sensing research community. However, the raw incoming (received) LiDAR waveform typically exhibits a stretched, misaligned, and relatively distorted character. In other words, the LiDAR signal is smeared and the effective temporal (vertical) resolution decreases, which is attributed to a fixed time span allocated for detection, the sensor's variable outgoing pulse signal, off-nadir scanning, the receiver impulse response impacts, and system noise. Theoretically, such a loss of resolution and increased data ambiguity can be remediated by using proven signal preprocessing approaches. In this paper, we present a robust signal preprocessing chain for waveform LiDAR calibration, which includes noise reduction, deconvolution, waveform registration, and angular rectification. This preprocessing chain was initially validated using simulated waveform data, which were derived via the digital imaging and remote sensing image generation modeling environment. We also verified the approach using real small-footprint waveform LiDAR data collected by the Carnegie Airborne Observatory in a savanna region of South Africa and specifically in terms of modeling woody biomass in this region. Metrics, including the spectral angle for cross-section recovery assessment and goodness-of-fit (R2) statistics, along with the root-mean-squared error for woody biomass estimation, were used to provide a comprehensive quantitative evaluation of the performance of this preprocessing chain. Results showed that our approach significantly increased our ability to recover the temporal signal resolution, improved geometric rectification of raw waveform LiDAR, and resulted in improved waveform-based woody biomass estimation. This preprocessing chain has the potential to be applied across the board for high fidelity processing of small-footprint waveform LiDAR data, thereby facilitating the extraction of valid and useful structural metrics from ground objects.

A raw incoming (received) Light Detection And Ranging (LiDAR) waveform typically exhibits a stretched and relatively featureless character, e.g., the LiDAR signal is smeared and the effective spatial resolution decreases. This is attributed to a fixed time span allocated for detection, the sensor's variable outgoing pulse signal, receiver impulse response, and system noise. Theoretically, such a loss of resolution can be recovered by deconvolving the system response from the measured signal. In this paper, we present a comparative controlled study of three deconvolution techniques, namely, Richardson-Lucy, Wiener filter, and nonnegative least squares, in order to verify which method is quantitatively superior to others. These deconvolution methods were compared in terms of two use cases: 1) ability to recover the true cross-sectional profile of an illuminated object based on the waveform simulation of a virtual 3-D tree model and 2) ability to differentiate herbaceous biomass based on the waveform simulation of virtual grass patches. All the simulated waveform data for this study were derived via the “Digital Imaging and Remote Sensing Image Generation” radiative transfer modeling environment. Results show the superior performance for the Richardson-Lucy algorithm in terms of small root mean square error for recovering the true cross section, low false discovery rate for detecting the unobservable local peaks in the stretched raw waveforms, and high classification accuracy for differentiating herbaceous biomass levels.

Previous work has shown the ability of waveform LiDAR sensors to accurately describe various land cover types [1] and biomass estimates made in the field [2]. What is lacking, however, is a way to describe the different structural components that are embedded in the digitized backscattered energy from the LiDAR pulse. This study aims to extract structural components from waveform LiDAR data in terms of woody, herbaceous, and bare ground components from data collected over a savanna environment in and around Kruger National Park (KNP), South Africa. These components are comprised of metrics extracted from the waveforms and validated using biomass measurements made in field plots. Different size windows around plot centers, 3 × 3 pixels and 9 × 9 pixels (resulting in 1.5m and 4.5 m footprint, respectively), were used to examine scale effects of larger footprints. It was found that composite waveforms resembling plot sizes (9 × 9) most often are able to describe more than 80% of the woody biomass variability across the entire study site, and individually for two of the three land uses within the area. However, the herbaceous component of the waveform did not correlate well with the field measurements, while the bare ground component was verified visually in a side-by-side comparison with optical imagery.

Differences in within-species phenology and structure are controlled by genetic variation, as well as topography, edaphic properties, and climatic variables across the landscape, and present important challenges to species differentiation with remote sensing. The objectives of this paper are as follows: 1) to evaluate the classification performance of a multiple-endmember spectral angle mapper (SAM) classification approach in discriminating ten common African savanna tree species and 2) to compare the results with the traditional SAM classifier based on a single endmember per species. The canopy spectral reflectance of the tree species (Acacia nigrescens, Combretum apiculatum, Combretum imberbe, Dichrostachys cinerea, Euclea natalensis, Gymnosporia buxifolia, Lonchocarpus capassa, Pterocarpus rotundifolius, Sclerocarya birrea, and Terminalia sericea) was extracted from airborne hyperspectral imagery that was acquired using the Carnegie Airborne Observatory system over Kruger National Park, South Africa, in May 2008. This study highlights three important phenomena: 1) Intraspecies spectral variability affected the discrimination of savanna tree species with the SAM classifier; 2) the effect of intraspecies spectral variability was minimized by adopting the multiple-endmember approach, e. g., the multiple- endmember approach produced a higher overall accuracy (mean of 54.5% for 20 bootstrapped replicates) when compared to the traditional SAM (mean overall accuracy = 20.5%); and 3) targeted band selection improved the classification of savanna tree species (the mean overall percent accuracy is 57% for 20 bootstrapped replicates). Higher overall classification accuracies were observed for evergreen trees than for deciduous trees.

The ability of various ecosystems to perform vital functions such as biodiversity production, and water, energy and nutrient cycling depends on the ecosystem state, i.e. health. Ecosystem state assessment has been a topic of intense research, but has reached a point at which accurate large scale (e.g. regional to global scale) modelling and monitoring are hindered by limitations in conventional assessment methods such as direct field sampling, modelling from environmental drivers such as temperature, precipitation and available nutrients, and modelling from remote sensing data. The Ecosystem-Earth Observation (Eco-EO) research group at the Council for Scientific and Industrial Research (CSIR), South Africa has highlighted the need in remote sensing research for an integrated sensing approach at the systems level. This perspective is based on the assumption that a modelling approach that exploits the strength of the various techniques (in situ environmental variables, direct field observation and remote sensing data) could potentially improve the assessment of ecosystem state at various geographic scales. In this light, the Eco-EO research group has embarked on an agro-system state assessment project since 2007 as a first step towards the implementation of the integrated modelling approach for various ecosystems. The agro-system consists of a monoculture forest plantation of Eucalyptus grandis situated in KwaZulu-Natal, South Africa. This paper presents preliminary results from the KwaZulu-Natal E. grandis experimental study.

Differences in within-species phenology and structure driven by factors including topography, edaphic properties, and climatic variables present important challenges for species differentiation with remote sensing in the Kruger National Park, South Africa. The objective of this study was to examine probable factors including intraspecies spectral variability and the spectral sample size that could affect remote sensing of Savanna tree species across a land-use gradient in the Kruger National park. Eighteen species were examined: Acacia gerradii, Acacia nigrescens, Combretum apiculatum, Combretum collinum, Combretum hereroense, Combretum imberbe, Combretum zeyheri, Dichrostachys cinerea, Euclea sp (E. divinurum and E. natalensis, Gymnosporia sp (G. buxifolia and G. senegalensis), Lonchocarpus capassa, Peltoforum africanum, Piliostigma thonningii, Pterocarpus rotundifolia, Sclerocarya birrea, Strychnos sp (S. madagascariensis, S. usambarensis), Terminalia sericea and Ziziphus mucronata. Discriminating species using the K-nearest neighbour (K = 1) classifier with spectral angle mapper (SAM) yielded a higher classification accuracy (48% overall accuracy) compared to 16% for the classification involving the mean spectra for each species as the training spectral set. Within-species spectral variability and the training sample size were identified as important factors affecting classification accuracy of the tree species. We recommend a non-parametric classifier such as K-nearest neighbour classifier for classifying and mapping tree species in a highly complex environment such as the savanna system of the Kruger National Park.

Factors influencing woody savanna vegetation structure across a land-use gradient of intensity (highly and lightly utilized communal rangeland) and type (national protected area, private game reserve and communal rangelands) were investigated. Small-footprint discrete return LiDAR data (1.12 m point spacing) from the Carnegie Airborne Observatory (CAO) `Alpha system' were used to measure three-dimensional vegetation structure across the different treatments. A volumetric pixel (voxel) approach was used to characterise the vertical distribution of LiDAR returns, i.e., vegetation density, in one metre increments for comparison using descriptive statistics across the land-use type and intensity gradient. Vegetation structure in the national protected area was most similar to the lightly utilized rangelands, and the private game reserve was most similar to the highly utilized rangelands with low levels of structural diversity present. Current trends in structural diversity can be related to harvesting, regeneration, herbivory and fire.

High resolution airborne hyperspectral and discrete return LiDAR data were used to assess bare soil and tree cover differences along a land use transect consisting of state-owned, privately-owned conservation areas, and communal areas in South African savannas. The results show that tree cover is higher in conservation areas as compared to communal areas where local people use fuel wood for personal consumption. Low impact communal sites (limited use) tend to have higher tree cover than higher impacted communal sites. Generally communal areas have altered tree height distribution but in diverse way depending on the geology or the level of human utilization. Bare soil cover was generally found to be quite low (< 10%) in all different land uses, suggesting that the degradation level in communal areas might not be as high as generally perceived.

Measurement and management of vegetation biomass accumulation in ecosystems typically involves extensive field data collection, which can be expensive and time consuming, while leaving the user with relatively crude inputs to intricate biomass models. Light detection and ranging (LiDAR) remote sensing, which provides extensive height measurements of terrain and vegetation, has become an effective alternative to characterize vegetation structure. In this paper, we report on ongoing efforts at developing signal processing approaches to model herbaceous biomass using a new generation of airborne laser scanners, namely full-waveform LiDAR systems. Structural and statistic-based feature metrics are directly derived from LiDAR waveforms at the pixel level and related to plot-level field data. Initial results reveal a definite correlation between the LiDAR waveform and herbaceous biomass. Ongoing research focuses on the links between fractional cover estimated from imaging spectroscopy and woody biomass.

The stability of remote sensing indicators of leaf water, chlorophyll and nutrients to discriminate betweenEucalyptusgrandisgrowing on different site qualities in KwaZulu-Natal, South Africa was evaluated for two growing seasons (winter and early summer). Site quality was defined by the total available soil water (TAW). Canopy reflectance spectra for 68 trees (winter) and 45 trees (summer) from good, medium, and poor sites were collected on clear sky days using an ASD spectroradiometer. Two-way analysis of variance showed that the discriminatory capabilities of leaf water, chlorophyll, and nutrient concentrations forE.grandisgrowing on different site qualities, are seasonal by nature. Leaf water and chlorophyll indices were better indicators of site quality in winter than foliar nutrient concentrations of N, P, and K, which in turn performed better in summer. These results will help to define the sensing timeframe for monitoringE.grandisgrowth in KwaZulu-Natal.

The repeated occurrence of severe wildfires has highlighted the need for development of effective vegetation monitoring tools. We compared the performance of indices derived from satellite and climate data as a first step toward an operational tool for fire risk assessment in savanna ecosystems. Field collected fire activity data were used to evaluate the potential of the normalized difference vegetation index (NDVI), normalized difference water index (NDWI), and the meteorological Keetch-Byram drought idex (KBDI) to assess fire risk. Performance measures extracted from the binary logistic regression model fit were used to quantitatively rank indices in terms of their effectiveness as fire risk indicators. NDWI performed better when compared to NDVI and KBDI based on the results from the ranking method. The c-index, a measure of predictive ability, indicated that the NDWI can be used to predict seasonal fire activity (c=0.78). The time lag at the start of the fire season between time-series of fire activity data and the selected indices also was studied to evaluate the ability to predict the start of the fire season. The results showed that NDVI, NDWI, and KBDI can be used to predict the start of the fire season. NDWI consequently had the highest capacity to monitor fire activity and was able to detect the start of the fire season in savanna ecosystems. It is shown that the evaluation of satellite- and meteorological fire risk indices is essential before the indices are used for operational purposes to obtain more accurate maps of fire risk for the temporal and spatial allocation of fire prevention or fire management.