Discrimination of irrigated and rainfed rice in a tropical agricultural system using SPOT VEGETATION NDVI and rainfall data

The classification of irrigated crops by remote sensing requires the use of time series data, since the timing, cropping intensity and duration of cropping is quite variable over the course of a year. Rice is the dominant irrigated crop in tropical and sub‐tropical Asia, where rainfall is high, but is seasonal and often uni‐modal. Existing crop classification methods for rice are not able to distinguish between rainfed and irrigated crops, leading to errors in classification and estimated irrigated area. This paper describes a technique, a ‘peak detector algorithm’, to successfully discriminate between rainfed and irrigated rice crops in Suphanburi province, Thailand. The methodology uses a three‐year time series of Satellite pour l'Observation de la Terre (SPOT) VEGETATION S10 Normalized Difference Vegetation Index (NDVI) data (10 day composites) to identify cropping intensity (number, timing and peak values). Peak NDVI is then lag‐correlated with long term average rainfall data. There is a high correlation at a 40–50 day lag, between a peak rainfall and a ‘single’ peak NDVI of rainfed rice. In irrigated areas, there are multiple peaks, and multiple correlations with low values for at least 90 days after peak rainfall. The methodology currently uses a mask to remove un‐cropped and non‐rice areas, which is derived from existing Geographical Information Systems (GIS). The method achieves a classification accuracy of 89% or better against independent groundtruth data. The procedure is designed as a second level of analysis to refine classifications using other techniques of mapping irrigated area at global and regional scales.     

Effect of red-edge and texture features for object-based paddy rice crop classification using RapidEye multi-spectral satellite image data

Recent satellite missions have provided new perspectives by offering high spatial resolution, a variety of spectral properties, and fast revisit rates to the same regions. In this study, we examined the utility of both broadband red-edge spectral information and texture features for classifying paddy rice crops in South Korea into three different growth stages. The rice grown in South Korea can be grouped into early-maturing, medium-maturing, and medium-late-maturing cultivars, and each cultivar is known to have a minimum and maximum productivity. Therefore, the accurate classification of paddy rice crops into a certain time line enables pre-estimation of the expected rice yields. For the analysis, two seasons of RapidEye satellite image data were used. The results showed that the broadband red-edge information slightly improved the classification accuracy of the paddy rice crops, particularly when single-season image data were used. In contrast, texture information resulted in only minor improvement or even a slight decline in accuracy, although it is known to be advantageous for object-based classification. This was due to the homogeneous nature of paddy rice fields, as different rice cultivars are similar in terms of their morphology. Based on these results, we conclude that the additional spectral information such as the red-edge band is more useful than the texture features to detect different crop conditions in relatively homogeneous rice paddy environments. We therefore confirm the potential of broadband red-edge information to improve the classification of paddy rice crops. However, there is still a need to examine the relationship between textural properties and paddy rice crop parameters in greater depth.     

Mapping population density distribution at multiple scales in Zhejiang Province using Landsat Thematic Mapper and census data

Population density is usually calculated from the census data, but it is dynamic over time and updating population data is often challenging because it is time-consuming and costly. Another problem is that population data for public use are often too coarse, such as at the county scale in China. Previous research on population estimation mainly focused on megacities due to their importance in socio-economic conditions, but has not paid much attention to the township or village scale because of the sparse population density and less importance in economic conditions. In reality, population density in townships and villages plays an important role in land-use/cover change and environmental conditions. It is an urgent task to timely update population density at the township and cell-size scales. Therefore, this article aims to develop an approach to estimate population density at the township scale and at a cell size of 1 km by 1 km through downscaling the population density from county to township and then to cell size. We estimated population density using Landsat Thematic Mapper (TM) and census data in Zhejiang Province, China. Landsat TM images in 2010 were used to map impervious surface area (ISA) distribution using a hybrid approach, in which a decision tree classifier was used to extract ISA data and cluster analysis was used to further modify the ISA results. A population density estimation model was developed at the county scale, and this model was then transferred to the township scale. The population density was finally redistributed to cell-size scale based on the assumption that population only occupied the sites having ISA. This research indicates that most townships have residuals within ±50 persons/km² with a root mean squared error (RMSE) of 71.56 persons/km², and a relative RMSE of 27.6%. The spatial patterns of population density distribution at the 1 km² cell size are much improved compared to the township and county scales. This research indicates the importance of using the ISA for population density estimation, where ISA can be accurately extracted from remotely sensed data.     

Assessing the potential of VEGETATION sensor data for mapping snow and ice cover: A Normalized Difference Snow and Ice Index

The VEGETATION (VGT) sensor in SPOT 4 has four spectral bands that are equivalent to Landsat Thematic Mapper (TM) bands (blue, red, near-infrared and mid-infrared spectral bands) and provides daily images of the global land surface at a 1-km spatial resolution. We propose a new index for identifying and mapping of snow/ice cover, namely the Normalized Difference Snow/Ice Index (NDSII), which uses reflectance values of red and mid-infrared spectral bands of Landsat TM and VGT. For Landsat TM data, NDSII is calculated as NDSII_TM=(TM3-TM5)/(TM3+TM5); for VGT data, NDSII is calculated as NDSII_VGT=(B2-MIR)/(B2+MIR). As a case study we used a Landsat TM image that covers the eastern part of the Qilian mountain range in the Qinghai-Xizang (Tibetan) plateau of China. NDSIITM gave similar estimates of the area and spatial distribution of snow/ice cover to the Normalized Difference Snow Index (NDSI=(TM2-TM5)/(TM2+TM5)) which has been proposed by Hall et al. The results indicated that the VGT sensor might have the potential for operational monitoring and mapping of snow/ice cover from regional to global scales, when using NDSII_VGT.     

Assessing the extent of agriculture/pasture and secondary succession forest in the Brazilian Legal Amazon using SPOT VEGETATION data

There has been growing concern about land use/land cover change in tropical regions, as there is evidence of its influence on the observed increase in atmospheric carbon dioxide concentration and consequent climatic changes. Mapping of deforestation by the Brazil's National Space Research Institute (INPE) in areas of primary tropical forest using satellite data indicates a value of 587,727 km² up to the year 2000. Although most of the efforts have been concentrated in mapping primary tropical forest deforestation, there is also evidence of large-scale deforestation in the cerrado savanna, the second most important biome in the region.The main purpose of this work was to assess the extent of agriculture/pasture and secondary succession forest in the Brazilian Legal Amazon (BLA) in 2000, using a set of multitemporal images from the 1-km SPOT-4 VEGETATION (VGT) sensor. Additionally, we discriminated primary tropical forest, cerrado savanna, and natural/artificial waterbodies. Four classification algorithms were tested: quadratic discriminant analysis (QDA), simple classification trees (SCT), probability-bagging classification trees (PBCT), and k-nearest neighbors (K-NN). The agriculture/pasture class is a surrogate for those areas cleared of its original vegetation cover in the past, acting as a source of carbon. On the contrary, the secondary succession forest class behaves as a sink of carbon.We used a time series of 12 monthly composite images of the year 2000, derived from the SPOT-4 VGT sensor. A set of 19 Landsat scenes was used to select training and testing data. A 10-fold cross validation procedure rated PBCT as the best classification algorithm, with an overall sample accuracy of 0.92. High omission and commission errors occurred in the secondary succession forest class, due to confusion with agriculture/pasture and primary tropical forest classes. However, the PBCT algorithm generated the lower misclassification error in this class. Besides, this algorithm yields information about class membership probability, with ∼80% of the pixels with class membership probability greater or equal than 0.8. The estimated total area of agriculture/pasture and secondary succession forest in 2000 in the BLA was 966 × 10³ and 140 × 10³ km², respectively. Comparison with an existing land cover map indicates that agriculture/pasture occurred primarily in areas previously occupied by primary tropical forest (46%) and cerrado savanna (33%), and also in transition forest (19%), and other vegetation types (2%). This further confirms the existing evidence of extensive cerrado savanna conversion.This study also concludes that SPOT-4 VGT data are adequate for discriminating several major land cover types in tropical regions. Agriculture/pasture was mapped with errors of about 5%. Very high classification errors were associated with secondary succession forest, suggesting that a different methodology/sensor has to be used to address this difficult land cover class (namely with the inclusion of ancillary data). For the other classes, we consider that accurate maps can be derived from SPOT-4 VGT data with errors lower than 20% for the cerrado savanna, and errors lower than 10% for the other land cover classes. These estimates may be useful to evaluate impacts of land use/land cover change on the carbon and water cycles, biotic diversity, and soil degradation.     

Assessing the landscape context and conversion risk of protected areas using satellite data products

Since the establishment of the first national park (Yellowstone National Park in 1872) and the first wildlife refuge (Pelican Island in 1903), dramatic changes have occurred in both ecological and cultural landscapes across the U.S. The ability of these protected areas to maintain current levels of biodiversity depend, at least in part, on the integrity of the surrounding landscape. Our objective was to quantify and compare the extent and pattern of natural land cover, risk of conversion, and relationships with demographic and economic variables in counties near National Park Service units and U.S. Fish and Wildlife Service refuges with those counties distant from either type of protected area in the coterminous United States. Our results indicate that landscapes in counties within 10 km of both parks and refuges and those within 10 km of just parks were more natural, more intact, and more protected than those in counties within 10 km of just refuges and counties greater than 10 km from either protected area system. However, they also had greater human population density and change in population, indicating potential conversion risk since the percent of landscape protected averaged < 5% in both groups and human population dynamics are primary drivers of change in many landscapes. Conversion outweighed protection by at least two times (Conservation Risk Index > 2) in 76% of counties near both parks and refuges, 81% of counties near just parks, 91% of counties near just refuges, and 93% of distant counties. Thirteen percent of counties in the coterminous U.S. had moderate to high amounts of natural land cover (> 60%), low protection (< 20%), and the greatest change in population (> 20%). Although these areas are not the most critically endangered, they represent the greatest conservation opportunity, need, and urgency. Our approach is based on national level metrics that are simple, general, informative, and can be understood by broad audiences and by policy makers and managers to assess the health of lands surrounding parks and refuges. Regular monitoring of these metrics with satellite data products in counties surrounding protected areas provides a consistent, national level assessment of management opportunities and potentially adverse changes on adjacent lands.     

Use of ENVISAT/ASAR wide-swath data for timely rice fields mapping in the Mekong River Delta

Because of the importance of rice for the global food security and because of the role of inundated paddy fields in greenhouse gases emissions, monitoring the rice production world-wide has become a challenging issue for the coming years. Local rice mapping methods have been developed previously in many studies by using the temporal change of the backscatter from C-band synthetic aperture radar (SAR) co-polarized data. The studies indicated in particular the need of a high observation frequency. In the past, the operational use of these methods has been limited by the small coverage and the poor acquisition frequency of the available data (ERS-1/2, Radarsat-1). In this paper, the method is adapted for the first time to map rice at large scale, by using wide-swath images of the Advanced SAR (ASAR) instrument onboard ENVISAT. To increase the observation frequency, data from different satellite tracks are combined. The detection of rice fields is achieved by exploiting the high backscatter increase at the beginning of the growing cycle, which allows the production of rice maps early in the season (in the first 50 days). The method is tested in the Mekong delta in Vietnam. The mapping results are compared to existing rice maps in the An Giang province, with a good agreement (higher than 81%). The rice planted areas are retrieved from the maps and successfully validated with the official statistics available at each province (R² = 0.92). These results show that the method is useful for large scale early mapping of rice areas, using current and future C band wide-swath SAR data.     

Characterization of a sulfur-rich Arctic spring site and field analog to Europa using hyperspectral data

A unique glacial spring system exists at Borup Fiord pass, in the Canadian High Arctic, emerging from a glacial surface and depositing elemental sulfur, gypsum and calcite across a portion of the glacier. The presence of sulfur springs associated with glacial ice is extremely rare in a terrestrial context, and the resulting deposits may provide a field analog to non-ice materials on the surface of Europa. Spectral characterization of the supraglacial deposits in the visible-near infrared (VIS-NIR) range, 0.4-2.5 µm, was carried out using reflectance spectra collected in situ using a field spectrometer during the 2006 field season. These spectra, while dominated by melting snow, ice, and sulfur, show that some absorption features of the sulfates are shifted in wavelength with respect to library spectra due to the effects of mixing or temperature. Absorption features of calcite are largely absent, potentially due to mineral partitioning effects within the deposits. Investigations into changes in mineralogy within the deposits over the course of the active season using data collected by the Hyperion hyperspectral visible/infrared spectrometer aboard the Earth Observing 1 spacecraft (EO-1) were limited by low signal-to-noise (SNR) ratios in the data, although they indicate that sulfur is remaining stable. This is confirmed by seasonal data on the extent of the deposits, obtained using a classification algorithm running onboard the satellite, which continued to detect the presence of sulfur until snow obscured the site. Ground truth for the observations is provided by mineralogical analyses obtained by X-ray diffraction (XRD) measurements and laboratory reflectance spectra from 0.2-25 µm obtained of samples returned from the site in 2006. We show that while sulfur, the main constituent of the deposits, is well represented in Hyperion data, minor constituents such as calcite and gypsum can be partially or entirely masked in the data. In spite of these effects, autonomous detection methods can be utilized to monitor the generation and extent of the deposits, whose spectral properties show similarities with those of Europa's non-ice materials.     

Comparison of MODIS broadband albedo over an agricultural site with ground measurements and values derived from Earth observation data at a range of spatial scales

Land surface albedo is dependent on atmospheric state and hence is difficult to validate. Over the UK persistent cloud cover and land cover heterogeneity at moderate (km-scale) spatial resolution can also complicate comparison of field-measured albedo with that derived from instruments such as the Moderate Resolution Imaging Spectrometer (MODIS). A practical method of comparing moderate resolution satellite-derived albedo with ground-based measurements over an agricultural site in the UK is presented. Point measurements of albedo made on the ground are scaled up to the MODIS resolution (1 km) through reflectance data obtained at a range of spatial scales. The point measurements of albedo agreed in magnitude with MODIS values over the test site to within a few per cent, despite problems such as persistent cloud cover and the difficulties of comparing measurements made during different years. Albedo values derived from airborne and field-measured data were generally lower than the corresponding satellite-derived values. This is thought to be due to assumptions made regarding the ratio of direct to diffuse illumination used when calculating albedo from reflectance. Measurements of albedo calculated for specific times fitted closely to the trajectories of temporal albedo derived from both Systeme pour l'Observation de la Terre (SPOT) Vegetation (VGT) and MODIS instruments.     

Observation of the Kuroshio intrusion region in the South China Sea from AVHRR data

The variability of sea surface temperature in the region of the Kuroshio intrusion into the South China Sea (SCS) through the Luzon Strait was studied using sea surface temperature (SST) derived from Advanced Very High Resolution Radiometer (AVHRR) from 1985 to 2002. The covariance empirical orthogonal function (CEOF) method was applied for analysing the temporal and spatial variability in the study area. The results show that the Kuroshio intrusion during El Niño periods is weaker than that in La Niña periods. The calculation of surface layer heat of the Kuroshio intrusion region also shows response to the El Niño-La Niña events. The variation is attributed to the changes in wind fields during those events.     

Observation of an agricultural biomass burning in central and east China using merged aerosol optical depth data from multiple satellite missions

Agricultural biomass burning (ABB) in central and east China occurs every year from May to October and peaks in June. During the period from 26 May to 16 June 2007, one strong ABB procedure happened mainly in Anhui, Henan, Jiangsu and Shandong provinces. This article focuses on analysis of this ABB procedure using a comprehensive set of aerosol optical depth (AOD) data merged by using the optimal interpolation method from the Moderate Resolution Imaging Spectroradiometer, the Multi-angle Imaging Spectroradiometer (MIRS) as well as Sea-viewing Wide Field-of-view Sensor (SeaWiFS)-derived AOD products. In addition, the following additional data are used: fire data from the National Satellite Meteorological Centre of China Meteorological Administration, the mass trajectory analyses from hybrid single-particle Lagrangian integrated trajectory (HYSPLIT) model and ground-based AOD and Ångström data derived from the Aerosol Robotic Network and China Aerosol Remote Sensing Network. The results show that merged satellite AOD values can expand the spatial coverage of agricultural biomass aerosol distributions with good accuracy (R = 0.93, root mean square error = 0.37). Based on the merged AOD images, the highest AOD values were found concentrated in central China as well as in eastern China before 6 June and further extended to northeast China after 12 June. AODs from ground measurement show that eastern China always keeps high AOD values (>1.0), with a maximum exceeding 3.0 and extending as high as nearly 5.0 during this ABB event. With the help of the HYSPLIT model, we analysed the ABB sources and examined how transport paths affect the concentrations of air pollutants in some sites. The results show that Henan, Jiangsu and Anhui provinces are the three main sources in this ABB.     

Estimation of rice-planted area in the tropical zone using a combination of optical and microwave satellite sensor data

The area planted with rice in Indramayu Province, Indonesia was estimated using satellite sensor data. We determined the arable land area using Landsat Thematic Mapper (TM) data acquired in the dry season and identified newly rice-planted fields using JERS-1 Synthetic Aperture Radar (SAR) data acquired in the rainy season (planting season). Two rice crops per year are common and rice is not planted simultaneously in this region. Since the planted area changes monthly, some asumptions were necessary for estimating the planted area. Our estimates suggested that the actual rice-planted area in Indonesia was 12-14% smaller than that reported by the Indonesian government.     

Species stratification and upscaling of forest carbon estimates to landscape scale using GeoEye-1 image and lidar data in sub-tropical forests of Nepal

Aboveground forest biomass and carbon estimation at landscape scale is crucial for implementation of REDD+ programmes. This study aims to upscale the forest carbon estimates using GeoEye-1 image and small footprint lidar data from small areas to a landscape level using RapidEye image. Species stratification was carried out based on the spectral separability curve of GeoEye-1 image, and comparison of mean intensity and mean plot height of the trees from lidar data. GeoEye-1 image and lidar data were segmented using region growing approach to delineate individual tree crowns; and the segmented crowns (CPA) of tree were further used to establish a relationship with field measured carbon and total trees’ height. Carbon stock measured from field, individual tree crown (ITC) segmentation approach and area-based approach (ABA) was compared at plot level using one-way ANOVA and post hoc Tukey comparison test. ITC-based carbon estimates was used to establish a relationship with spectral reflectance of RapidEye image variables (NDVI, RedEdge NDVI, PC1, single band of RedEdge, and NIR) to upscale the carbon at landscape level. One-way ANOVA resulted in a highly significant difference (p-value < 0.005) between the mean plot height and lidar intensity to stratify Shorea robusta and Other species successfully. ITC carbon stock estimation models of two major tree species explained about 88% and 79% of the variances, respectively, at 95% confidence level. The ABA estimated carbon was highly correlated (R² = 0.83, RMSE = 20.04) to field measured carbon with higher accuracy than the ITC estimated carbon. A weak relationship was observed between the carbon stock and the RapidEye image variables. However, upscaling of carbon estimates from ABA is likely to improve the relationship of the RapidEye variables rather than upscaling the carbon estimates from ITC approach.     

Retrieving photometric properties of desert surfaces in China using the Hapke model and MISR data

The retrieval of photometric properties of desert surfaces is an important first step in the parameterization of land surface components of regional dust emission and global radiation models and in Earth system modeling. In this study, the values of Hapke's photometric parameters (ω, h, b, c, B0, and θ?) were retrieved from the Multi-angle Imaging SpectroRadiometer (MISR) instrument at locations in China's deserts. Four pixels represented the typical surface characteristics of the Taklimakan Desert, sand dunes of Kumtag Desert, relatively smooth areas of the Kumtag Desert and the aeolian sandy soil of Loulan. In contrast to earlier studies, we found that the retrieved parameter values were largely affected by the initial value. To combat this problem we used a Monte Carlo method with physical constraints and a conformity indicator to ensure physically meaningful inversion.The results showed that the angular domain of MISR observations was sufficiently large to determine confidently the values of Hapke's photometric parameters with the exception of the opposition effect width (h). Retrieved values for the single scattering albedo (ω) and macroscopic roughness (θ?) were consistent with qualitative observations about the structure and composition of the surface material and the nature of the dune forms, respectively. At Loulan, where the surface was smoother than other sites, retrieved values exhibited the strongest backward scattering. These results indicated that at the sensor scale, a rough surface (e.g., dunes) does not necessarily mean more backward scattering than a smooth surface. This finding has significant implications for empirical methods (e.g., using the normalized index of backward-scattered radiance minus forward-scattered radiance as an indicator to indicate surface roughness) which should be used carefully for analyzing surface roughness from remote sensing data. Future research is needed to 1) understand how surface roughness at the sub-pixel scale modifies the angular characteristics of reflectance and to 2) find practical methods for rapid whole image processing for mapping the photometric parameters.     

Establishing snowmelt runoff simulating model using remote sensing data and GIS in the west of China

Supported by Geographical Information Systems (GIS), snowmelt runoff simulation models have been built for the large-scale basin in the west of China. Digital Terrain Factors (DTF) were employed for dividing the basin. Using image-processing software, the problems of image correction and match between the satellite image and the images transformed from vector files have been resolved. Meanwhile, the thresholds of snow-pixel in images were achieved by means of a trial-and-error iterative process. Supervising classification techniques were utilized to determine the percentage of snow cover area in different zones and in the whole basin. The experimental watershed of the Qushenan (5286 km 2 ), in the upper reaches of the Yellow River was selected as a test basin. Snowmelt runoff models were developed for each sub-basin from which three vertical zones were simulated separately. The snowmelt runoff was computed by weighting coefficients. The results show that this approach is significant and practical.     

The use of time-integrated NOAA NDVI data and rainfall to assess landscape degradation in the arid shrubland of Western Australia

Ground-based data on total phytomass were collected in 1998 and 1999 from four sampling domains, each nearly 1000 km², within the arid shrubland of Western Australia. These data were used in models relating rainfall and landscape characteristics to total phytomass to provide landscape-scale estimates of total phytomass and rainfall-use efficiency for 1992-1999 (referred to as RUE_P). These modelled estimates were compared with remotely sensed estimates of total phytomass (I-NDVI) and rainfall-use efficiency (I-NDVI/annual rainfall; referred to as RUE_N) using data from NOAA satellites.There was good agreement between ground-based and remotely sensed estimates of total phytomass but less agreement between estimates of rainfall-use efficiency. Partitioning the landscape on the basis of landscape resilience did not improve the amount of variance accounted for in RUE_P or RUE_N and there were conflicting relationships between landscape-scale indicators of landscape function and RUE_N. There was, however, evidence to suggest that temporal change in RUE_N may provide a useful broad-scale indicator of landscape degradation or recovery over decadal time intervals. Recommendations are made for broad-scale application of this indicator based on temporal trends in RUE_N.     

Tracking bamboo dynamics in Zhejiang,China, using time-series of Landsat data from 1990 to 2014

Bamboo is an important vegetation type and provides a number of critical ecosystem services. Reliable and consistent information on bamboo distribution is required to better estimate its effect on climate change mitigation and socio-economic development. However, such information is rare over a large spatial area. In this study, we evaluate the contribution of different features in the identification of bamboo stands and determine a more discriminative set of features. We propose a bamboo mapping system including feature extraction and feature selection and derive the long-term trends of bamboo distribution in Zhejiang Province, China, using time-series of Landsat data from 1990 to 2014, with an increment of 5 years (1990, 1995, 2000, 2005, 2010, and 2014). The resultant maps of bamboo in the six epochs were evaluated using independent validation samples. The overall accuracies (OAs) of all six epochs range from 85.9% to 90.7%. We found that bamboo distribution in Zhejiang substantially increased from 1990 to 2014, particularly during the 2000s. Based on the produced maps, the area of bamboo in this region increased from 5363 ± 490 km² in 1990 to 11671 ± 653 km² in 2014, which is consistent with the National Forest Resource Inventory (NFRI) data. Our study demonstrates the capability of time-series of Landsat data for continuous monitoring of bamboo at a large spatial scale.     

Exploring approaches to improve the performance of autonomous monitoring with imperfect data in location-aware wireless sensor networks

In recent years, information and sensing technologies have been applied to the construction industry to collect and provide rich information to facilitate decision making processes. One of the applications is using location data to support autonomous crane safety monitoring (e.g., collision avoidance and dangerous areas control). Several location-aware wireless technologies such as GPS (Global Positioning System), RFID (Radio-frequency identification), and Ultra-Wide Band sensors, have been proposed to provide location information for autonomous safety monitoring. However, previous studies indicated that imperfections (errors, uncertainty, and inconsistency) exist in the data collected from those sensors and the data imperfections have great impacts on autonomous safety monitoring system performance. This paper explores five computationally light-weight approaches to deal with the data imperfections, aiming to improve the system performance. The authors built a scaled autonomous crane safety monitoring testbed with a mounted localization system to collect location data and developed five representative test cases based on a live construction jobsite. Seven hundred and sixty location readings were collected at thirty-eight test points from the sensors. Those location data was fed into the reasoning mechanisms with five approaches to generate the safety decisions at those thirty-eight test points and evaluate system performance in terms of precision, recall and accuracy. The results indicate that system performance can be improved if at least ten position readings from sensors can be collected at small intervals at any location along the moving path. However, by including additional data such as velocity and acceleration that may be read from devices mounted on workers, localization error may be significantly reduced. These findings represent a path forward to improve localization accuracy by mixing imperfect data from the sensed environment with supplemental input.     

Comparison between back propagation neural network and regression models for the estimation of pigment content in rice leaves and panicles using hyperspectral data

Two nitrogen experiments on rice were conducted in 2002, and the reflectances (350 to 2500 nm) and pigment contents (chlorophylls a and b, total chlorophylls and carotenoids) for leaf and panicle samples at different growth stages were measured in the laboratory. After performing an outlier analysis, the number of samples were 843 for leaves and 188 for panicles. Absorption features at 430, 460, 470, 640 and 660 nm for different pigments, and the relative reflectance of the green peak around 550 nm calculated by the continuum‐removed method, as well as the red edge position (REP) of rice leaves and panicles were selected as the independent variables, and measured pigment contents were selected as the dependent variables. Then, back propagation neural network (BPN) models, a kind of artificial neuron network (ANN), and multivariate linear regression models (MLR) were trained and tested. The main objective of this study was to compare the predictive ability of the ANN models to that of the MLR models in estimating the content of pigments in rice leaves and panicles. Results showed that all BPN models gave higher coefficients of determination (R²) and lower absolute errors (ABSEs) and root mean squared errors (RMSEs) than the corresponding MLR models, in both calibration and validation tests. Further significance tests by paired t tests and bootstrapping algorithms indicated that most of the BPN models outperformed the MLR models. When trained by combination data that did not meet the assumption of normal distribution, the BPN models appeared to not only have a better learning ability, but also had a more accurate predictive power than the MLR models. The estimation of leaf pigments was more accurate than that of panicle pigments, independent of which model was used.     

Investigation of structural requirements of anticancer activity at the paclitaxel/tubulin binding site using CoMFA and CoMSIA

CoMFA and CoMSIA analysis were utilized in this investigation to define the important interacting regions in paclitaxel/tubulin binding site and to develop selective paclitaxel-like active compounds. The starting geometry of paclitaxel analogs was taken from the crystal structure of docetaxel. A total of 28 derivatives of paclitaxel were divided into two groups-a training set comprising of 19 compounds and a test set comprising of nine compounds. They were constructed and geometrically optimized using SYBYL v6.6. CoMFA studies provided a good predictability (q(2)=0.699, r(2)=0.991, PC=6, S.E.E.=0.343 and F=185.910). They showed the steric and electrostatic properties as the major interacting forces whilst the lipophilic property contribution was a minor factor for recognition forces of the binding site. These results were in agreement with the experimental data of the binding activities of these compounds. Five fields in CoMSIA analysis (steric, electrostatic, hydrophobic, hydrogen-bond acceptor and donor properties) were considered contributors in the ligand-receptor interactions. The results obtained from the CoMSIA studies were: q(2)=0.535, r(2)=0.983, PC=5, S.E.E.=0.452 and F=127.884. The data obtained from both CoMFA and CoMSIA studies were interpreted with respect to the paclitaxel/tubulin binding site. This intuitively suggested where the most significant anchoring points for binding affinity are located. This information could be used for the development of new compounds having paclitaxel-like activity with new chemical entities to overcome the existing pharmaceutical barriers and the economical problem associated with the synthesis of the paclitaxel analogs. These will boost the wide use of this useful class of compounds, i.e. in brain tumors as the most of the present active compounds have poor blood-brain barrier crossing ratios and also, various tubulin isotypes has shown resistance to taxanes and other antimitotic agents.