Geomorphology and land development in the Maraca area of northern Roraima, Brazil.

The geomorphological materials and forms of the Maraca area of Roraima, Brazil are described, an their sgnificance for land development examined. Significant contrasts are noted in areas presently under rainforest and savanna vegetation. Lateritic gravels and extensive shetwash accumulations in savanna areas constrast with incipient or absent plinthite development, few gravels and limited evidence of colluvium under rainforest. Terrain is in general relatively highly-dissected. Slope profiles are characterised, particularly within the savanna zone, by a relatively steep lower concavity. These contrasts are sharply-demarcated by the present savanna/rainforest bondary, unexpectedly in view of the generally accepted hypothesis of repeated contraction an expansion of Amazonian rainforest throughout the Pleistocene. It is concluded that geomorphological conditions in the Maraca area are not favorable for land develoment.

Land of the rising soy an internationalization plan for nutre to the Japanese Soy milk market

The present work project studies the next step in the internationalization process of Shoyce, the soy milk products brand of Nutre. In order to select the best target market in the Asia-Pacific for Nutre to export, a sequential screening process was developed using two complementary approaches: preliminary country screening and country ranking, followed by an in-depth analysis of the country ranking first. The analysis revealed Japan as the most attractive country for Shoyce’s international expansion. Potential entry modes in the Japanese soy milk market were then evaluated, whereby direct exporting via a local distributor was found to be the most appropriate.

Assessment of NDVI, land surface temperature and precipitation anomalies for drought monitoring in Bayankhongor province, Mongolia

During the last decade Mongolia’s region was characterized by a rapid increase of both severity and frequency of drought events, leading to pasture reduction. Drought monitoring and assessment plays an important role in the region’s early warning systems as a way to mitigate the negative impacts in social, economic and environmental sectors. Nowadays it is possible to access information related to the hydrologic cycle through remote sensing, which provides a continuous monitoring of variables over very large areas where the weather stations are sparse. The present thesis aimed to explore the possibility of using NDVI as a potential drought indicator by studying anomaly patterns and correlations with other two climate variables, LST and precipitation. The study covered the growing season (March to September) of a fifteen year period, between 2000 and 2014, for Bayankhongor province in southwest Mongolia. The datasets used were MODIS NDVI, LST and TRMM Precipitation, which processing and analysis was supported by QGIS software and Python programming language. Monthly anomaly correlations between NDVI-LST and NDVI-Precipitation were generated as well as temporal correlations for the growing season for known drought years (2001, 2002 and 2009). The results show that the three variables follow a seasonal pattern expected for a northern hemisphere region, with occurrence of the rainy season in the summer months. The values of both NDVI and precipitation are remarkably low while LST values are high, which is explained by the region’s climate and ecosystems. The NDVI average, generally, reached higher values with high precipitation values and low LST values. The year of 2001 was the driest year of the time-series, while 2003 was the wet year with healthier vegetation. Monthly correlations registered weak results with low significance, with exception of NDVI-LST and NDVI-Precipitation correlations for June, July and August of 2002. The temporal correlations for the growing season also revealed weak results. The overall relationship between the variables anomalies showed weak correlation results with low significance, which suggests that an accurate answer for predicting drought using the relation between NDVI, LST and Precipitation cannot be given. Additional research should take place in order to achieve more conclusive results. However the NDVI anomaly images show that NDVI is a suitable drought index for Bayankhongor province.

Hand gesture recognition for human computer interaction : a comparative study of different image features

Hand gesture recognition for human computer interaction, being a natural way of human computer interaction, is an area of active research in computer vision and machine learning. This is an area with many different possible applications, giving users a simpler and more natural way to communicate with robots/systems interfaces, without the need for extra devices. So, the primary goal of gesture recognition research is to create systems, which can identify specific human gestures and use them to convey information or for device control. For that, vision-based hand gesture interfaces require fast and extremely robust hand detection, and gesture recognition in real time. In this study we try to identify hand features that, isolated, respond better in various situations in human-computer interaction. The extracted features are used to train a set of classifiers with the help of RapidMiner in order to find the best learner. A dataset with our own gesture vocabulary consisted of 10 gestures, recorded from 20 users was created for later processing. Experimental results show that the radial signature and the centroid distance are the features that when used separately obtain better results, with an accuracy of 91% and 90,1% respectively obtained with a Neural Network classifier. These to methods have also the advantage of being simple in terms of computational complexity, which make them good candidates for real-time hand gesture recognition.

Hand gesture recognition system based in computer vision and machine learning

"Lecture notes in computational vision and biomechanics series, ISSN 2212-9391, vol. 19"

Generic system for human-computer gesture interaction: applications on sign language recognition and robotic soccer refereeing

Hand gestures are a powerful way for human communication, with lots of potential applications in the area of human computer interaction. Vision-based hand gesture recognition techniques have many proven advantages compared with traditional devices, giving users a simpler and more natural way to communicate with electronic devices. This work proposes a generic system architecture based in computer vision and machine learning, able to be used with any interface for human-computer interaction. The proposed solution is mainly composed of three modules: a pre-processing and hand segmentation module, a static gesture interface module and a dynamic gesture interface module. The experiments showed that the core of visionbased interaction systems could be the same for all applications and thus facilitate the implementation. For hand posture recognition, a SVM (Support Vector Machine) model was trained and used, able to achieve a final accuracy of 99.4%. For dynamic gestures, an HMM (Hidden Markov Model) model was trained for each gesture that the system could recognize with a final average accuracy of 93.7%. The proposed solution as the advantage of being generic enough with the trained models able to work in real-time, allowing its application in a wide range of human-machine applications. To validate the proposed framework two applications were implemented. The first one is a real-time system able to interpret the Portuguese Sign Language. The second one is an online system able to help a robotic soccer game referee judge a game in real time.

Vision-based portuguese sign language recognition system

Vision-based hand gesture recognition is an area of active current research in computer vision and machine learning. Being a natural way of human interaction, it is an area where many researchers are working on, with the goal of making human computer interaction (HCI) easier and natural, without the need for any extra devices. So, the primary goal of gesture recognition research is to create systems, which can identify specific human gestures and use them, for example, to convey information. For that, vision-based hand gesture interfaces require fast and extremely robust hand detection, and gesture recognition in real time. Hand gestures are a powerful human communication modality with lots of potential applications and in this context we have sign language recognition, the communication method of deaf people. Sign lan- guages are not standard and universal and the grammars differ from country to coun- try. In this paper, a real-time system able to interpret the Portuguese Sign Language is presented and described. Experiments showed that the system was able to reliably recognize the vowels in real-time, with an accuracy of 99.4% with one dataset of fea- tures and an accuracy of 99.6% with a second dataset of features. Although the im- plemented solution was only trained to recognize the vowels, it is easily extended to recognize the rest of the alphabet, being a solid foundation for the development of any vision-based sign language recognition user interface system.

Leaf micronutrient concentrations and potential photosynthesis in Ochroma pyramidale established in a degraded land

In the Brazilian Amazon, large areas of abandoned lands may revert to secondary forest. In the process, pioneer tree species have an important role to restore productivity in old fields and improve environmental conditions. To determine potential photosynthesis (Apot), stomatal conductance (g), transpiration (E), and leaf micronutrient concentrations in Ochroma pyramidale (Cav. ex Lam.) Urban a study was carried out in the Brazilian Amazon (01o 51' S; 60o 04' W). Photosynthetic parameters were measured at increasing [CO2], saturating light intensity (1 mmol (photons) m-2 s-1), and ambient temperature. The rate of electron-transport (J), Apot,and water-use efficiency (WUE) increased consistently at increasing internal CO2 concentration (Ci). Conversely, increasing [CO2] decreased gs, E, and photorespiration (Pr). At the CO2-saturated region of the CO2 response curve (1.1 mmol (CO2) mol-1(air), J was 120 μmol (e-) m-2s-1 and Apot reached up to 24 μmol (CO2) m-2s-1. Likewise, at saturating C1 g and E were 30 and 1.4 mmol (H2O) m-2s-1, respectively, and P 2 r about 1.5 μmol (CO2) m-2s-1. Foliar nutrients were 185, 134, 50, and 10 μmol (element) m-2 (leaf area) for Fe, Mn, Zn, and Cu, respectively. It was concluded that [CO ] probably limits light saturated photosynthesis in this site. Furthermore, from a nutritional point of view, the low Fe to Cu ratio (15:1) may reflect nutritional imbalance in O. pyramidale at this site.

An integrated system for human action recognition from video using hidden Markov model

In this paper, we present an integrated system for real-time automatic detection of human actions from video. The proposed approach uses the boundary of humans as the main feature for recognizing actions. Background subtraction is performed using Gaussian mixture model. Then, features are extracted from silhouettes and Vector Quantization is used to map features into symbols (bag of words approach). Finally, actions are detected using the Hidden Markov Model. The proposed system was validated using a newly collected real- world dataset. The obtained results show that the system is capable of achieving robust human detection, in both indoor and outdoor environments. Moreover, promising classification results were achieved when detecting two basic human actions: walking and sitting.

Enrollment time as a requirement for biometric hand recognition systems

Biometric systems are increasingly being used as a means for authentication to provide system security in modern technologies. The performance of a biometric system depends on the accuracy, the processing speed, the template size, and the time necessary for enrollment. While much research has focused on the first three factors, enrollment time has not received as much attention. In this work, we present the findings of our research focused upon studying user’s behavior when enrolling in a biometric system. Specifically, we collected information about the user’s availability for enrollment in respect to the hand recognition systems (e.g., hand geometry, palm geometry or any other requiring positioning the hand on an optical scanner). A sample of 19 participants, chosen randomly apart their age, gender, profession and nationality, were used as test subjects in an experiment to study the patience of users enrolling in a biometric hand recognition system.

Changes in population and land use over time in the Ecuadorian Amazon

This paper draws upon a detailed longitudinal survey of households living on agricultural plots in the northern three provinces of the Ecuadorian Amazon, the principal region of colonization by migrants in Ecuador since the 1970s. Following the discovery of petroleum in 1967 near what has subsequently come to be the provincial capital and largest Amazonian city of Lago Agrio, oil companies built roads to lay pipelines to extract and pump oil across the Andes for export. As a result, for the past 30 years over half of both Ecuador's export earnings and government revenues have come from petroleum extracted from this region. But the roads also facilitated massive spontaneous in-migration of families from origin areas in the Ecuadorian Sierra, characterized by minifundia and rural poverty. This paper is about those migrants and their effects on the Amazonian landscape. We discuss the data collection methodology and summarize key results on settler characteristics and changes in population, land use, land ownership, technology, labor allocation, and living conditions, as well as the relationships between changes in population and changes in land use over time. The population in the study region has been growing rapidly due to both natural population growth (high fertility) and in-migration. This has led to a dramatic process of subdivision and fragmentation of plots in the 1990's, which contrasts with the consolidation of plots that has occurred in most of the mature frontier areas of the Brazilian Amazon. This fragmentation has led to important changes in land tenure and land use, deforestation, cattle raising, labor allocation, and settler welfare.

Diversity of the land resources in the Amazonian State of Rondônia, Brazil

Conflicting opinions are recorded in the literature concerning the suitability of Amazon lands for sustainable agriculture following deforestation. This article has been written to shed light on this question by summarizing climate, landform, soil and vegetation features from the findings of a land resource study of the Brazilian state of Rondônia in south-west Amazonia. The work, which followed the World Soils and Terrain Digital Database (SOTER) methodology, was financed by the World Bank. During the course of the survey special emphasis was given to studying soils; 2914 profiles were analyzed and recorded. The study identified a complex pattern of land units with clear differences in climate, landform, soils and native vegetation. Forested areas mosaic with lesser areas of natural savannas. The latter occur on both poorly-drained and well-drained, albeit nutrient deficient sandy soils. The tallest and most vigorous forests or their remnants were seen growing on well-drained soils formed from nutrient-rich parent materials. Many of these soils could, or are being used for productive agriculture. Soils developed on nutrient-poor parent materials support forests that are significantly lower in height, and would require large lime and fertilizer inputs for agriculture. Low forests with high palm populations and minor areas of wet land savannas cover the poorly drained soils. It is evident that forest clearing in the past was indiscriminant; this cannot be condoned. The diversity of land conditions found throughout Rondônia would suggest that many past studies in the Amazon have simply been too broad to identify significant soil differences.

Land cover classification of Lago Grande de Curuai floodplain (Amazon, Brazil) using multi-sensor and image fusion techniques

Given the limitations of different types of remote sensing images, automated land-cover classifications of the Amazon várzea may yield poor accuracy indexes. One way to improve accuracy is through the combination of images from different sensors, by either image fusion or multi-sensor classifications. Therefore, the objective of this study was to determine which classification method is more efficient in improving land cover classification accuracies for the Amazon várzea and similar wetland environments - (a) synthetically fused optical and SAR images or (b) multi-sensor classification of paired SAR and optical images. Land cover classifications based on images from a single sensor (Landsat TM or Radarsat-2) are compared with multi-sensor and image fusion classifications. Object-based image analyses (OBIA) and the J.48 data-mining algorithm were used for automated classification, and classification accuracies were assessed using the kappa index of agreement and the recently proposed allocation and quantity disagreement measures. Overall, optical-based classifications had better accuracy than SAR-based classifications. Once both datasets were combined using the multi-sensor approach, there was a 2% decrease in allocation disagreement, as the method was able to overcome part of the limitations present in both images. Accuracy decreased when image fusion methods were used, however. We therefore concluded that the multi-sensor classification method is more appropriate for classifying land cover in the Amazon várzea.

Spatial and temporal evolution of family-farming land use in the Tapajós region of the Brazilian Amazon

Pressures on the Brazilian Amazon forest have been accentuated by agricultural activities practiced by families encouraged to settle in this region in the 1970s by the colonization program of the government. The aims of this study were to analyze the temporal and spatial evolution of land cover and land use (LCLU) in the lower Tapajós region, in the state of Pará. We contrast 11 watersheds that are generally representative of the colonization dynamics in the region. For this purpose, Landsat satellite images from three different years, 1986, 2001, and 2009, were analyzed with Geographic Information Systems. Individual images were subject to an unsupervised classification using the Maximum Likelihood Classification algorithm available on GRASS. The classes retained for the representation of LCLU in this study were: (1) slightly altered old-growth forest, (2) succession forest, (3) crop land and pasture, and (4) bare soil. The analysis and observation of general trends in eleven watersheds shows that LCLU is changing very rapidly. The average deforestation of old-growth forest in all the watersheds was estimated at more than 30% for the period of 1986 to 2009. The local-scale analysis of watersheds reveals the complexity of LCLU, notably in relation to large changes in the temporal and spatial evolution of watersheds. Proximity to the sprawling city of Itaituba is related to the highest rate of deforestation in two watersheds. The opening of roads such as the Transamazonian highway is associated to the second highest rate of deforestation in three watersheds.