http://hdl.handle.net/10204/5169 2013-05-24T14:16:50Z 2013-05-24T14:16:50Z Mngenge, NA Nelufule, NN Nelwamondo, FV Msimang, M http://hdl.handle.net/10204/6693 2013-04-18T21:55:14Z 2012-11-01T00:00:00Z

Title: Fingerprint pores extractor Authors: Mngenge, NA; Nelufule, NN; Nelwamondo, FV; Msimang, M Abstract: Automatic Fingerprint Recognition Systems (AFRSs) rely on minutiae position and orientation within the fingerprint image for matching. Minutiae information is highly accurate provided that the fingerprint image matched is of high quality. However, this is not always the case because of diseases and hash working conditions that affect fingerprints. In order to maintain high level of security independent of varying fingerprint image quality research suggests the use of other fingerprint features to compliment minutiae. These are things like ridge contours, sweat pores, dots, and incipient ridges. Sweat pores have been proven as one of the most distinctive among these feature. Thus in order to improve accuracy of AFRSs pores can be fused with minutiae or used alone. Sweat pores have been less utilized in the past due to constraints imposed by fingerprint scanning devices and resolution standards. Recently, progress has been made on both scanning devices and resolution standards to support the use of pores in AFRSs. However, very few techniques exist for extracting, matching and fusing them with minutiae. Matching and fusion can only be possible if pores are available. Some techniques have been proposed to reliable extract pores. However, existing techniques can only work on one resolution i.e. an algorithm proposed and tested on 500dpi cannot work on 1000dpi without minor modifications because pores size change if resolution changes. In addition, existing pore extraction techniques are computationally expensive. In this paper an algorithm to extract feature level 3 (pores) is proposed. The algorithm uses Laplacian of Gaussian (LoG) in Fourier domain in order to reduce computation. The performance of the proposed algorithm is tested on two distinct databases with different resolutions in order to validate its accuracy. The accuracy of the proposed algorithm is further measured using false detection rate (FDR) and true detection rate (TDR). Results show that FDR ranges from 10-35% while TDR ranges from 65-90%. Description: 2012 National Conference on Computing and Communication Systems, Durgapur, West Bengal, India, 21- 22 November 2012. To be published in IEEE Xplore

2012-11-01T00:00:00Z Sjöström, M Zhao, M Archibald, S Arneth, A Cappelaere, B Falk, U De Grandcourt, A Hanan, N Kergoat, L Kutsch, W Merbold, L Mougin, E Nickless, A Nouvellon, A Scholes, RJ Veenendaal, EM Ardö, J http://hdl.handle.net/10204/6669 2013-04-17T21:55:17Z 2013-04-01T00:00:00Z

Title: Evaluation of MODIS gross primary productivity for Africa using eddy covariance data Authors: Sjöström, M; Zhao, M; Archibald, S; Arneth, A; Cappelaere, B; Falk, U; De Grandcourt, A; Hanan, N; Kergoat, L; Kutsch, W; Merbold, L; Mougin, E; Nickless, A; Nouvellon, A; Scholes, RJ; Veenendaal, EM; Ardö, J Abstract: MOD17A2 provides operational gross primary production (GPP) data globally at 1 km spatial resolution and 8-day temporal resolution. MOD17A2 estimates GPP according to the light use efficiency (LUE) concept assuming a fixed maximum rate of carbon assimilation per unit photosynthetically active radiation absorbed by the vegetation (emax). Minimum temperature and vapor pressure deficit derived from meteorological data down-regulate emax and constrain carbon assimilation. This data is useful for regional to global studies of the terrestrial carbon budget, climate change and natural resources. In this study we evaluated the MOD17A2 product and its driver data by using in situ measurements of meteorology and eddy covariance GPP for 12 African sites. MOD17A2 agreed well with eddy covariance GPP for wet sites. Overall, seasonality was well captured but MOD17A2 GPP was underestimated for the dry sites located in the Sahel region. Replacing the meteorological driver data derived from coarse resolution reanalysis data with tower measurements reduced MOD17A2 GPP uncertainties, however, the underestimations at the dry sites persisted. Inferred emax calculated from tower data was higher than the emax prescribed in MOD17A2. This, in addition to uncertainties in fraction of absorbed photosynthetically active radiation (FAPAR) explains some of the underestimations. The results suggest that improved quality of driver data, but primarily a readjustment of the parameters in the biome parameter look-up table (BPLUT) may be needed to better estimate GPP for African ecosystems in MOD17A2. Description: Copyright: 2012 Elsevier. This is the Pre/post print version of the work. The definitive version is published in Fuel, vol. 131, pp 275-286

2013-04-01T00:00:00Z Ramoelo, A Skidmore, A Schlerf, M Heitkönig, I Mathieu, R Cho, M http://hdl.handle.net/10204/6631 2013-03-26T21:55:16Z 2013-08-01T00:00:00Z

Title: Savanna grass nitrogen to phosphorus ratio estimation using field spectroscopy and the potential for estimation with imaging spectroscopy Authors: Ramoelo, A; Skidmore, A; Schlerf, M; Heitkönig, I; Mathieu, R; Cho, M Abstract: Determining the foliar N: P ratio provides a tool for understanding nutrient limitation on plant production and consequently for the feeding patterns of herbivores. In order to understand the nutrient limitation at landscape scale, remote sensing techniques offer that opportunity. The objective of this study is to investigate the utility of in situ hyperspectral remote sensing to estimate foliar N: P ratio. Field spectral measurements were undertaken, and grass samples were collected for foliar N and P extraction. The foliar N: P ratio prediction models were developed using partial least square regression (PLSR) with original spectra and transformed spectra. Spectral transformations included the continuum removal (CR), water removal (WR), first difference derivative (FD) and log transformation (Log(1/R)). The results showed that CR and WR spectra in combination with PLSR predicted foliar N: P ratio with higher accuracy as compared to FD and R spectra. The performance of CR and WR spectra were attributed to their ability to minimize sensor and water effects on the fresh leaf spectra, respectively. The study demonstrated a potential to predict foliar N: P ratio using field and HyMap simulated spectra and shortwave infrared (SWIR) found to be highly sensitive to foliar N: P ratio. The study recommends the prediction of foliar N: P ratio at landscape level using airborne hyperspectral data and could be used by the resource managers, park managers, farmers and ecologists to understand the feeding patterns, resource selection and distribution of herbivores (i.e. wild and livestock). Description: Copyright: 2012 Elsevier. This is the preprint version of the work. The definitive version is published in International Journal of Applied Earth Observation and Geoinformation, vol. 23, pp. 334-343

2013-08-01T00:00:00Z Van Aardt, JAN Wu, J McGlinchy, J Wessels, K Mathieu, R Kennedy Bowdoin, T Knapp, DE Asner, GP http://hdl.handle.net/10204/6565 2013-04-09T09:03:45Z 2012-07-01T00:00:00Z

Title: On using discrete return LIDAR distributions as a proxy for waveform LIDAR signals when modeling vegetation structure Authors: Van Aardt, JAN; Wu, J; McGlinchy, J; Wessels, K; Mathieu, R; Kennedy Bowdoin, T; Knapp, DE; Asner, GP Abstract: The goals of the study were to (i) determine if there is a direct relationship between waveform LiDAR intensity-by-height and discrete return frequency-by-height (do the distributions match?) and (ii) assess the impact of scale (does this relationship vary by the area used for signal integration?). Results have significant implications in terms of a cost-benefit analysis: The use of a discrete return instead of a waveform system leads to a reduction in cost, data volume, signal complexity, and processing requirements. Description: IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Munich, Germany, 22-27 July 2012. Published in IGARSS 2012.

2012-07-01T00:00:00Z