Multi-year simulation of Mediterranean forest transpiration by the integration of NOAA-AVHRR and ancillary data

The estimation of transpiration fluxes through wide vegetated land surfaces is of great importance for the proper planning and management of environmental resources, particularly in areas where water is a main limiting factor during at least part of the growing cycle. While remotely sensed techniques cannot directly measure these fluxes, they can provide useful information on vegetation variables such as Leaf Area Index (LAI), which are functionally related to the mentioned processes. The aims of the present work were: (a) to illustrate the use of multi-temporal LAI profiles derived from National Oceanic and Atmospheric Administration Advanced Very High Resolution Radiometer (NOAA-AVHRR) Normalized Difference Vegetation Index (NDVI) data as input for a biogeochemical model (Forest-BGC) which simulates the main processes of forest vegetation (transpiration and photosynthesis); and (b) to analyse the sensitivity of the calibrated model to its main driving variables (meteorological data and NDVI-derived LAI profiles) in order to assess their relative importance for operational transpiration monitoring. In particular, the model was applied to two oak stands in the Tuscany Region (central Italy), which are representative of Mediterranean forests and for which a calibration phase had already been performed. Simulations were carried out for a 15-year period (1986–2000) using as inputs daily meteorological data and NDVI-derived monthly LAI profiles. The sensitivity of the model to both input types was then assessed through other model runs with fixed values of the two variables. The results of these experiments indicated that the remotely sensed LAI estimates are the main determinant of simulated transpirations, especially during the Mediterranean arid season (summer) when water resources are the primary limiting factor for vegetation development.     

Soil loss predictions with three erosion simulation models

Quantification of soil loss is one of the greatest challenges in natural resources and environmental planning. Computer simulation models are becoming increasingly popular in predicting soil loss for various land use and management practices. In this study, three soil erosion prediction models — the Water Erosion Prediction Project (WEPP), the Erosion Productivity Impact Calculator (EPIC), and the Areal Nonpoint Source Watershed Environment Response Simulation (ANSWERS) were used for simulating soil loss and testing the capability of the models in predicting soil losses for three different tillage systems (ridge-till, chisel-plow, and no-till). For each model, the most sensitive model parameters were calibrated using measured soil erosion data. After calibration, models were run and predicted soil loss values were compared with the measured soil loss values. The measured soil erosion data were collected from an erosion experiment field of Kansas State University at Ottawa (Kansas), USA. Field experiments were conducted from 1995 to 1997 on small plots to measure runoff and soil losses under all three tillage systems. All three models were evaluated on the basis of individual event, total yearly, and mean event-based soil loss predictions. Results showed that all the three models performed reasonably well and the predicted soil looses were within the range of measured values. For ridge-till and chisel-plow systems, WEPP and ANSWERS gave better predictions than those by EPIC model. For no-till system, WEPP and EPIC predictions were better than those by ANSWERS. The overall results indicate that WEPP predictions were better than those by the other two models in most of the cases, and it can be used with reasonable degree of confidence for soil loss quantification for all the three tillage systems.     

Comparison of four line-based positional assessment methods by means of synthetic data

Positional accuracy of spatial data can be assessed by means of line-based methods. In this work we develop an analysis of the following four methods: Hausdorff Distance, Mean Distance, Single Buffer Overlay and Double Buffer Overlay, using a set of 12 synthetic cases. The synthetic cases incorporate specific shape features for bias, random errors and outliers which correspond to simplified versions of real world possibilities. The use of synthetic cases helps us to understand the basic behavioral differences between the methods. Numerical results for the positional accuracy estimations are different between methods and cases due to the different concepts of distance involved and the specific configurations of each case. When the method results in a function, patterns related to different types of errors can be detected in this function. The length-inclusion level of each method is revealed as the base criterion for comparison. The Single Buffer Overlay Method offers the more general solution because it includes the others’ results.     

Improving satellite images classification using remote and ground data integration by means of stochastic simulation

A methodology is proposed, to assess land surface cover classification using a geostatistical methodology of stochastic simulation, direct sequential cosimulation, to combine field observations with remotely sensed data classified with the classical algorithm of maximum likelihood classification. This procedure has two main advantages: (1) incorporation of a spatial continuity statistics; and (2) integration of different scales of information, contained in polygons (training areas) and point information (field observations), which also involves different qualities of information that is less reliable and more reliable, respectively. Moreover, this methodology allows production not only of a classified map, but also of maps of occupation proportions and of uncertainty for each thematic class. Local co‐regionalization models are applied to account for local differences in both field data availability and distribution, and the correlation between these hard data and the classified satellite images as soft data. The methodology is based on two criteria: the influence of the hard data dependent on their availability and proportional to their proximity; and the influence of the soft data dependent on their local correlation to the hard data. The method is applied to a study of four economically important forest tree species on the Setúbal Peninsula (south of Lisbon, Portugal). The results show more contiguous forest covers, i.e. more spatial contiguity, than the classical classification. In comparison to a contemporary field inventory, the proposed method improved forest cover estimations, showing a difference of only 3%.     

On the use of ancillary data by applying the concepts of the theory of evidence to remote sensing digital image classification

In this study we investigate a new approach to implement concepts developed by the ‘theory of evidence’ to remote sensing digital image classification. In the proposed approach, auxiliary variables are structured as layers in a Geographical Information System (GIS)-like format to produce layers of belief and plausibility. Thresholds are applied to the layers of belief and plausibility to detect errors of commission and omission, respectively on the thematic image. The thresholds are estimated as functions of the user's and producer's accuracy. Preliminary tests were performed over an area covered by natural forest with Araucaria, showing some promising results.     

Iterative sound synthesis by means of cross-coupled digital oscillators

Abstract

Musicians have long been interested in using iterative processes to aid the composition of musical forms (macrostructure) and to synthesize sounds (microstructure). This paper introduces a new sound synthesis method exploring the non-linear behaviour of two iterative cross-coupled digital oscillators. It begins with a brief introduction to iterative systems followed by background information on previous attempts at using them for synthesizing sounds (e.g. feedback frequency and amplitude modulations). Next, it introduces our synthesis method and briefly explains how it has been implemented in a system for real-time composition and performance. The paper concludes with a discussion on how the system has been put into practice to compose and perform a number of works.     

Classification of Mediterranean vegetation by TM and ancillary data for the evaluation of fire risk

Mediterranean vegetation is strongly subjected to the risk of wildfires, which can become a major cause of land degradation. The knowledge of the spatial variations of this risk is essential, therefore, for forest resource management. Relying on the fact that different vegetation types can be associated with different risk levels, a classification approach based on the use of Landsat Thematic Mapper (TM) scenes is currently proposed for the generation of maps related to fire risk. Hard and fuzzy classifications were tested for this purpose on Elba island (central Italy), taking into account the effects of the use of scenes from different periods (spring and summer) and of ancillary data. The fire risk images obtained were evaluated by comparison with the fire events that occurred on the island during the last decade. The results show that, while the acquisition period has only minor effects, classification accuracy is strongly dependent on the inclusion of ancillary data. Moreover, the fuzzy approach better exploits the information of the integrated datasets, producing maps which are temporally stable and highly indicative of the fire risk in the study area.     

氮磷配合对土壤氮素径流流失的影响

大田试验研究结果表明 :增施N、P均能增加作物的产量和减少水土流失 ;当N、P用量分别达到 5 5 .2kgN/hm2 和 90kgP2 O5/hm2 时 ,泥沙有机质和全氮流失最少 ,流失量分别为 2 0 89和 1 75kg/km2 ;当N、P用量分别为 5 5 .2kgN/hm2 和 4 5kgP2 O5/hm2 时 ,土壤矿质氮流失最小 ,其流失量仅为 2 7.9kg/km2 ;作物对土壤氮素的吸收 ,可减少土壤氮素的流失 .     

Dynamic simulation and visualisation of coastal erosion

A key requirement for effective coastal zone management is good knowledge and prediction of land erosion rates due to encroachment of the sea. However, in addition to demarcation of the hazard through modelling and mapping, a policy of risk mitigation necessitates significant attention should also be addressed to communicating the transient behaviour of the predictions and associated uncertainty. With climate change and sea level rise implying that historical rates of change may not be a reliable guide for the future, enhanced visualisation of the evolving coastline has the potential to improve awareness of this changing risk. This visual content is developed by linking scientific modelling with the transformation of digital elevation models, and then using GIS to integrate other spatiotemporal content. The resulting high-resolution visualisations may meet demands from decision-makers for tools to communicate scientific results more effectively, due to their realism and apparent authenticity. Nevertheless they can also produces a tension with the underlying scientific content because of the necessary extrapolation of extra detail, and the lack of established procedures to communicate the resulting uncertainty in the visualisation. Coastal managers also have concerns about releasing the visualisations to the general public. These issues are explored through analysis of future cliff erosion in Norfolk on the eastern coast of Great Britain.     

Improving digital image watermarking by means of optimal channel selection

Supporting safe and resilient authentication and integrity of digital images is of critical importance in a time of enormous creation and sharing of these contents. This paper presents an improved digital image watermarking model based on a coefficient quantization technique that intelligently encodes the owner’s information for each color channel to improve imperceptibility and robustness of the hidden information. Concretely, a novel color channel selection mechanism automatically selects the optimal HL4 and LH4 wavelet coefficient blocks for embedding binary bits by adjusting block differences, calculated between LH and HL coefficients of the host image. The channel selection aims to minimize the visual difference between the original image and the embedded image. On the other hand, the strength of the watermark is controlled by a factor to achieve an acceptable tradeoff between robustness and imperceptibility. The arrangement of the watermark pixels before shuffling and the channel into which each pixel is embedded is ciphered in an associated key. This key is utterly required to recover the original watermark, which is extracted through an adaptive clustering thresholding mechanism based on the Otsu’s algorithm. Benchmark results prove the model to support imperceptible watermarking as well as high robustness against common attacks in image processing, including geometric, non-geometric transformations, and lossy JPEG compression. The proposed method enhances more than 4 dB in the watermarked image quality and significantly reduces Bit Error Rate in the comparison of state-of-the-art approaches.     

An assessment of landsat MSS and TM data for urban and near-urban land-cover digital classification

The information content of Landsat TM and MSS data was examined to assess the ability to digitally differentiate urban and near-urban land covers around Miami, FL. This examination included comparisons of unsupervised signature extractions for various cover types, training site statistics for intraclass and interclass separability, and band and band combination selection from an 11-band multisensor data set. The principal analytical tool used in this study was transformed divergence calculations. The TM digital data are typically more useful than the MSS data in the homogeneous near-urban land-covers and less useful in the heterogeneous urban areas.     

An assessment of the realism of digital human manikins used for simulation in ergonomics

In this study, the accuracy of the joint centres of the manikins generated by RAMSIS and Human Builder (HB), two digital human modelling (DHM) systems widely used in industry for virtual ergonomics simulation, was investigated. Eighteen variously sized females and males were generated from external anthropometric dimensions and six joint centres (knee, hip and four spine joints) were compared with their anatomic locations obtained from the three-dimensional reconstructed bones from a low-dose X-ray system. Both RAMSIS and HB could correctly reproduce external anthropometric dimensions, while the estimation of internal joint centres location presented an average error of 27.6 mm for HB and 38.3 mm for RAMSIS. Differences between both manikins showed that a more realistic kinematic linkage led to better accuracy in joint location. This study opens the way to further research on the relationship between the external body geometry and internal skeleton in order to improve the realism of the internal skeleton of DHMs, especially for a biomechanical analysis requiring information of joint load and muscle force estimation.

Practitioner summary: This study assessed two digital human modelling (DHM) systems widely used in industry for virtual ergonomics. Results support the need of a more realistic human modelling, especially for a biomechanical analysis and a standardisation of DHMs.     

Land degradation and erosion risk mapping by fusion of spectrally-based information and digital geomorphometric attributes

The Guadalentin basin, located in the SE of Spain, has a semiarid climate and presents typical characteristics of Mediterranean landscapes vulnerable to land degradation processes and desertification risks. In such an environment, when the vegetation cover is low, the signal received by satellites is dominated by the spectral properties of soils. Changes in these properties can be interpreted in terms of varying soil surface conditions. These optical changes underline the major modifications affecting soil surface under land degradation processes. The present research uses remote sensing techniques to characterise land degradation based on two approaches: spectral mixture analysis and a set of indices describing the spectrum shape. It also presents an integrated approach for evaluating ecosystem vulnerability to land degradation, through the combined analysis of spectrally-derived land units and geomorphometric units. Specific objectives consist of evaluating the potential of extending the indices describing the spectrum shape to the short-wave infrared region, and of identifying landscape units according to their sensitivity to land degradation. Our results demonstrate that the spatial distribution of regional patterns of land degradation can be reliably mapped by using both indices describing the spectrum shape and spectral unmixing. The latter holds great potential for operational mapping of soil conditions and erosion features from optical images. Moreover, landscape-unit analysis shows that DEM (Digital Elevation Model) variables combined with spectral information are very useful for land degradation assessment. This approach allowed us to segment the landscape into different units according to their lithology and vegetation characteristics, as well as their susceptibility to water erosion.     

Simulation and error search in teaching digital electronics by means of a microcomputer

This paper deals with the introduction of the microcomputer in digital electronics courses as a demonstration tool for simulating the working principles of basic digital electronic circuits. Another usage of the microcomputer is made in laboratory work where it is introduced as a circuit debugger.     

Three-parametrical harmonization model in project management by means of simulation

A PERT-COST type project with random activity duration is considered. The project comprises several essential parameters which practically define the quality of the project as a whole:

• •the budget assigned to the project;
• •the project’s due date;
• •the project’s reliability, i.e. the probability of meeting the project’s due date on time.

To establish the quality of the project, the concept of the project’s utility is introduced. In order to maximize the project’s utility, a three-parametrical harmonization model is developed.     

Estimating crop yields and production by integrating the FAO Crop Specific Water Balance model with real-time satellite data and ground-based ancillary data

An operational crop yield model was developed by introducing realtime satellite imagery into a Geographical Information System (GIS) and the Crop Specific Water Balance (CSWB) model of the Food and Agriculture Organization (FAO). Input databases were developed with three different resolutions; agro-ecological zone (AEZ) polygons, 7.6 km and 1.1 km pixels; from archived satellite data commonly used by Early Warning Systems (EWS) to simulate maize yield and production in Kenya from 1989 to 1997. Simulated production results from the GIS-based CSWB model were compared to historical maize production reports from two Government of Kenya (GoK) agencies. The coefficients of determination (r ²) between the model and GoK district reports ranged from 0.86 to 0.89. The results indicated the 7.6 km pixel-by-pixel analysis was the most favorable method due to the Rainfall Estimate (RFE) input data having the same resolution. The GIS-based CSWB model developed by this study could also be easily expanded for use in other countries, extended for other crops, and improved in the future as satellite technologies improve.     

Ergonomic assessment of handle design by means of electromyography and subjective rating

Stress and strain during manual tool handling not only depend on factors such as weight to be handled, but are also determined by the design of the man-machine interface. In this study, three different handles of electric hedge-clippers were analysed; the results of a comparative investigation into the physiological cost demanded by the use of the different handles are discussed. Muscular strain was measured via surface electromyography in laboratory experiments with nine male subjects. The results showed significant differences in physiological cost depending on both work height and the handles' shape. Systematic differences in muscular strain between the utilized tools were found, despite the fact that all clippers were compensated with respect to weight and location of the centre of gravity. One of the handle designs enabled working under varying conditions (work height and direction) at a reduced level of muscular strain of the right arm. Results from the physiological evaluation were partly supported by the working persons' own subjective experience. The results of this investigation show that further ergonomic tool and handle design is necessary.     

Accuracy assessment of automatically derived digital elevation models from aster data in mountainous terrain

The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) aboard the Terra satellite was designed to generate along‐track stereo images. The data are available at low cost, providing a feasible opportunity for generating digital elevation models (DEMs) in areas where little or no elevation data are yet available. This study evaluates the accuracy of DEMs extracted from ASTER data covering mountainous terrain. For an assessment of the achieved accuracies in the Andean study site, comparisons were made to similar topographical conditions in Switzerland, where reference data were available. All raw DEMs were filtered and interpolated by the post‐processing tools included with PCI Geomatica, the software package used. After carefully checking the DEM quality, further post‐processing was undertaken to eliminate obvious artefacts such as peaks and sinks. Accuracy was tested by comparing the DEMs in the Swiss Alps to three reference models. The achieved results of the generated DEMs are promising, considering the extreme terrain. Given accurate and well‐distributed ground control points (GCPs), it is possible to generate DEMs with a root mean square (RMS) error between 15?m and 20?m in hilly terrain and about 30?m in mountainous terrain. The DEMs are very accurate in nearly flat regions and on smooth slopes with southern expositions: errors are generally within ±10?m in those cases. Larger errors do appear in forested, snow covered or shady areas and at steep cliffs and deep valleys with extreme errors of a few hundred metres. The evaluation showed that the quality of the DEMs is sufficient for enabling atmospheric, topographic and geometric correction to various satellite datasets and for deriving additional products.