Evaluation of geostatistical techniques to estimate the spatial distribution of aboveground biomass in the Amazon rainforest using high-resolution remote sensing data

ABSTRACT The spatial distribution of forest biomass in the Amazon is heterogeneous with a temporal and spatial variation, especially in relation to the different vegetation types of this biome. Biomass estimated in this region varies significantly depending on the applied approach and the data set used for modeling it. In this context, this study aimed to evaluate three different geostatistical techniques to estimate the spatial distribution of aboveground biomass (AGB). The selected techniques were: 1) ordinary least-squares regression (OLS), 2) geographically weighted regression (GWR) and, 3) geographically weighted regression - kriging (GWR-K). These techniques were applied to the same field dataset, using the same environmental variables derived from cartographic information and high-resolution remote sensing data (RapidEye). This study was developed in the Amazon rainforest from Sucumbíos - Ecuador. The results of this study showed that the GWR-K, a hybrid technique, provided statistically satisfactory estimates with the lowest prediction error compared to the other two techniques. Furthermore, we observed that 75% of the AGB was explained by the combination of remote sensing data and environmental variables, where the forest types are the most important variable for estimating AGB. It should be noted that while the use of high-resolution images significantly improves the estimation of the spatial distribution of AGB, the processing of this information requires high computational demand.

Evolución de las poblaciones humanas originarias en la región central de Argentina: Nuevas evidencias bioantropológicas y arqueológicas. Evolution of the native human populations in the central territory of Argentina: New bioanthropological and archaeological evidences.

El presente proyecto retoma los interrogantes acerca de los movimientos de población humana que se sucedieron en el area central de Argentina (actual territorio de Córdoba y parte de San Luis), desde los primeros asentamientos hasta la Conquista, valiéndose de la información proporcionada por la arqueología, la antropología física y la genética molecular, de manera interdisciplinaria. Con base en investigaciones previas realizadas por nuestro grupo y otros autores, se aplicarán nuevas metodologías y enfoques teóricos para echar luz sobre interrogantes acerca de las probables vías de poblamiento de la región y la evolución local de esas poblaciones. Se someterán a prueba hipótesis migratorias y de colonización, incluyendo estos eventos locales en un contexto más general sobre los procesos ocurridos a nivel regional y continental. Para los datos biológicos moleculares y morfológicos se emplearán técnicas de filogeografía (distribución espacial de linajes mitocondriales y del cromosoma Y) y genética del paisaje (autocorrelación espacial, kriging, barreras genéticas). La perspectiva arqueológica del proyecto intenta desde los análisis de diseño y función en instrumentos líticos discutir expectativas en cuanto a la permanencia o no de ciertas formas de diseño a través del tiempo, comparando conjuntos tempranos (asociados a tecnología "Fell 1") con otros de épocas posteriores. Esta línea se llevará a cabo utilizando la comparación entre los materiales provenientes de excavaciones estratigráficas para realizar análisis tecno-morfológicos sensu Aschero (1975-1983) y análisis de microhuellas de uso que nos permiten hablar de la función en los filos líticos. Esta línea se complementa con el desarrollo de programas experimentales de estudio sobre las diversas materias primas líticas utilizadas en el pasado en ambas áreas (Chert, vulcanita, cuarzo y calcedonia, entre las principales). Ambos enfoques nos permitirán evaluar la posible existencia de variaciones tecnológicas locales producto de procesos adaptativos o modos de producción o uso diferenciales. Una segunda línea propone el estudio del paisaje y los recursos líticos en la región utilizando SIG. Con respecto a esta perspectiva de investigación se postula analizar la forma en la cual los cazadores-recolectores utilizaron el espacio desde fines del Pleistoceno/Holoceno Temprano hasta el Holoceno Tardío partiendo de un conocimiento profundo de la distribución de los recursos líticos. En particular, conocer y discutir distintos aspectos de la disponibilidad, tipo, calidad y accesibilidad a las rocas. Este enfoque es fundamental para entender los procesos de elección y uso de estos recursos en el pasado logrando entender las diversas formas de organización de la tecnología.

Estudio de los modos de vida e historia biológica de las poblaciones humanas de la región central de argentina desde el registro bioarqueológico. Lifestyles and biological history of human populations of the central region of argentina from bioarchaeological record.

Se pretende aportar al estudio de la estructura, historia biológica y estilos de vida de las poblaciones que habitaron la región central de Argentina durante el Holoceno, desde una perspectiva que combina los aportes teóricos y metodológicos de la Genética del paisaje y la Bioarqueología. Interesa a) identificar barreras de diferenciación morfológica entre poblaciones, b) poner a prueba modelos poblacionales para explicar la variación observada e identificar las variables que contribuyan a dicha diferenciación, c) evaluar la congruencia de los resultados obtenidos, d) reconstruir los patrones de movilidad residencial de las poblaciones, e) estudiar sus patrones dietarios considerando diferencias temporales y espaciales, f) identificar indicadores de diversos tipos de estrés (nutricional, funcional), así como traumas, g) estudiar las historias tafonómicas del registro bioarqueológico regional, y h) proponer un modelo para explicar el poblamiento y la evolución local de las poblaciones que habitaron esta región, a partir de la información arqueológica y bioantropológica. Para el análisis de los patrones espaciales de variación biológica se trabajará a partir del registro de rasgos epigenéticos craneales, medidas lineales y datos obtenidos a partir de morfometría geométrica sobre fotografías en 2D sobre muestras arqueológicas procedentes de esta región y de otras regiones geográficas de la Argentina. Para el análisis de la estructura de la población se trabajará a partir del cálculo de la matriz R para datos morfológicos y sus estimaciones derivadas (distancia D², Fst, coordenadas principales) y la aplicación del modelo de Harpending y Ward. Desde la genética del paisaje, se realizarán análisis de autocorrelación espacial, barreras genéticas y análisis geoestadísticos (kriging). Para el estudio de los modos de vida a partir del registro bioarqueológico se relevarán patologías dento-alveolares y alteraciones vinculadas con la salud bucal tales como desgaste dental –a nivel micro y macroscópico- caries, abscesos, pérdidas dentales antemortem, cálculos, hipoplasias, marcadores esqueletales de salud y lesiones traumáticas. Se analizarán isótopos estables (δ13C, δ15N, 86Sr y 87Sr) en restos óseos humanos de diversos sitios arqueológicos con el objetivo de reconstruir patrones dietarios y analizar la movilidad residencial y migración de las poblaciones. Paralelamente, se establecerán procedimientos de control tafonómico de los restos óseos, y se harán análisis específicos para estudiar las historias tafonómicas y evaluar el grado de integridad de los contextos de depositación y de las colecciones en general. Estimamos que el análisis de los patrones espaciales y temporales de variabilidad morfológica craneofacial, así como el estudio de las dietas a partir de información isotópica y bioarqueológica, de las migraciones y la movilidad residencial de las poblaciones a partir de isótopos de estroncio, la reconstrucción de comportamientos y actividades cotidianas a partir de marcadores de estrés músculo-esqueletal, en un marco cronológico y espacial constituye un aporte novedoso y eficaz que permitirá incrementar de manera substancial la información sobre la evolución de las poblaciones originarias del centro del territorio argentino. The aim of this project is to study the structure, biological history and lifestyles of the people that inhabitated the central region of Argentina during the Holocene, from a perspective that combines theoretical and methodological contributions of Landscape Genetics and Bioarchaeology. To analyze the spatial patterns of biological variation we consider epigenetic cranial traits, linear measurements and data obtained from geometric morphometric on 2D photographs. Morphological variation will be focused on landscape genetics (autocorrelation, genetic barriers and geostatistical analysis –kriging-) and population structure (matrix R, D², Fst, principal coordinates, Harpendig and Ward model). For the study of lifestyles from bioarchaeological record we consider alveolar pathologies and disorders related to oral health such as tooth wear, micro and macroscopic level, caries, abscesses, antemortem tooth loss, hypoplasia, markers skeletal health and traumatic injuries, as well as taphonomic processes. Stable isotopes will be analyzed (δ13C, d15N, 86Sr and 87Sr) in human skeletal remains from various archaeological sites in order to reconstruct and analyze dietary patterns of residential mobility and migration of populations. It will be established procedures of taphonomic control on skeletal remains, analysis to study taphonomic histories and assess the degree of completeness of depositional context and collection, in general terms. We consider that analysis of spatial and temporal patterns of variability in craniofacial morphology and the study of health and diets from isotopic and bioarchaeological data, migration and residential mobility patterns from strontium isotopes, as well as activity patterns from stress markers is a novel and effective contribution that will substantially increase the information about the local evolution of populations that inhabitated the center of Argentina.

Extreme Precipitation Modelling Using Geostatistics and Machine Learning Algorithms

The paper presents an approach for mapping of precipitation data. The main goal is to perform spatial predictions and simulations of precipitation fields using geostatistical methods (ordinary kriging, kriging with external drift) as well as machine learning algorithms (neural networks). More practically, the objective is to reproduce simultaneously both the spatial patterns and the extreme values. This objective is best reached by models integrating geostatistics and machine learning algorithms. To demonstrate how such models work, two case studies have been considered: first, a 2-day accumulation of heavy precipitation and second, a 6-day accumulation of extreme orographic precipitation. The first example is used to compare the performance of two optimization algorithms (conjugate gradients and Levenberg-Marquardt) of a neural network for the reproduction of extreme values. Hybrid models, which combine geostatistical and machine learning algorithms, are also treated in this context. The second dataset is used to analyze the contribution of radar Doppler imagery when used as external drift or as input in the models (kriging with external drift and neural networks). Model assessment is carried out by comparing independent validation errors as well as analyzing data patterns.

BME-based uncertainty assessment of the Chernobyl fallout

The vast territories that have been radioactively contaminated during the 1986 Chernobyl accident provide a substantial data set of radioactive monitoring data, which can be used for the verification and testing of the different spatial estimation (prediction) methods involved in risk assessment studies. Using the Chernobyl data set for such a purpose is motivated by its heterogeneous spatial structure (the data are characterized by large-scale correlations, short-scale variability, spotty features, etc.). The present work is concerned with the application of the Bayesian Maximum Entropy (BME) method to estimate the extent and the magnitude of the radioactive soil contamination by 137Cs due to the Chernobyl fallout. The powerful BME method allows rigorous incorporation of a wide variety of knowledge bases into the spatial estimation procedure leading to informative contamination maps. Exact measurements (?hard? data) are combined with secondary information on local uncertainties (treated as ?soft? data) to generate science-based uncertainty assessment of soil contamination estimates at unsampled locations. BME describes uncertainty in terms of the posterior probability distributions generated across space, whereas no assumption about the underlying distribution is made and non-linear estimators are automatically incorporated. Traditional estimation variances based on the assumption of an underlying Gaussian distribution (analogous, e.g., to the kriging variance) can be derived as a special case of the BME uncertainty analysis. The BME estimates obtained using hard and soft data are compared with the BME estimates obtained using only hard data. The comparison involves both the accuracy of the estimation maps using the exact data and the assessment of the associated uncertainty using repeated measurements. Furthermore, a comparison of the spatial estimation accuracy obtained by the two methods was carried out using a validation data set of hard data. Finally, a separate uncertainty analysis was conducted that evaluated the ability of the posterior probabilities to reproduce the distribution of the raw repeated measurements available in certain populated sites. The analysis provides an illustration of the improvement in mapping accuracy obtained by adding soft data to the existing hard data and, in general, demonstrates that the BME method performs well both in terms of estimation accuracy as well as in terms estimation error assessment, which are both useful features for the Chernobyl fallout study.

Heterogeneous 40Ar* distributions in naturally deformed muscovite: in situ UV-laser ablation evidence for micro structurally controlled intragrain diffusion

Micas are commonly used in Ar-40/Ar-39 thermochronological studies of variably deformed rocks yet the physical basis by which deformation may affect radiogenic argon retention in mica is poorly constrained. This study examines the relationship between deformation and deformation-induced microstructures on radiogenic argon retention in muscovite, A combination of furnace step-heating and high-spatial resolution in situ UV-laser ablation Ar-40/Ar-39 analyses are reported for deformed muscovites sampled from a granitic pegmatite vein within the Siviez-Mischabel Nappe, western Swiss Alps (Penninic domain, Brianconnais unit). The pegmatite forms part of the Variscan (similar to 350 Ma) Alpine basement and exhibits a prominent Alpine S-C fabric including numerous mica `fish' that developed under greenschist facies metamorphic conditions, during the dominant Tertiary Alpine tectonic phase of nappe emplacement. Furnace step-heating of milligram quantities of separated muscovite grains yields an Ar-40/Ar-39 age spectrum with two distinct staircase segments but without any statistical plateau, consistent with a previous study from the same area. A single (3 X 5 mm) muscovite porphyroclast (fish) was investigated by in situ UV-laser ablation. A histogram plot of 170 individual Ar-40/Ar-39 UV-laser ablation ages exhibit a range from 115 to 387 Ma with modes at approximately 340 and 260 Ma. A variogram statistical treatment of the (40)Ad/Ar-39 results reveals ages correlated with two directions; a highly correlated direction at 310 degrees and a lesser correlation at 0 degrees relative to the sense of shearing. Using the highly correlated direction a statistically generated (Kriging method) age contour map of the Ar-40/Ar-39 data reveals a series of elongated contours subparallel to the C-surfaces which where formed during Tertiary nappe emplacement. Similar data distributions and slightly younger apparent ages are recognized in a smaller mica fish. The observed intragrain age variations are interpreted to reflect the partial loss of radiogenic argon during Alpine (similar to 35 Ma) greenschist facies metamorphism. One-dirnensional diffusion modelling results are consistent with the idea that the zones of youngest apparent age represent incipient shear band development within the mica porphyroclasts, thus providing a network of fast diffusion pathways. During Alpine greenschist facies metamorphism the incipient shear bands enhanced the intragrain loss of radiogenic argon. The structurally controlled intragrain age variations observed in this investigation imply that deformation has a direct control on the effective length scale for argon diffusion, which is consistent with the heterogeneous nature of deformation. (C) 2001 Elsevier Science B.V. All rights reserved.

Spatial evaluation of larvae of Culicidae (Diptera) from different breeding sites: application of a geospatial method and implications for vector control

Spatial evaluation of Culicidae (Diptera) larvae from different breeding sites: application of a geospatial method and implications for vector control. This study investigates the spatial distribution of urban Culicidae and informs entomological monitoring of species that use artificial containers as larval habitats. Collections of mosquito larvae were conducted in the São Paulo State municipality of Santa Bárbara d' Oeste between 2004 and 2006 during house-to-house visits. A total of 1,891 samples and nine different species were sampled. Species distribution was assessed using the kriging statistical method by extrapolating municipal administrative divisions. The sampling method followed the norms of the municipal health services of the Ministry of Health and can thus be adopted by public health authorities in disease control and delimitation of risk areas. Moreover, this type of survey and analysis can be employed for entomological surveillance of urban vectors that use artificial containers as larval habitat.

Soil water dynamics related to the degree of compaction of two brazilian oxisols under no-tillage

Soil water properties are related to crop growth and environmental aspects and are influenced by the degree of soil compaction. The objective of this study was to determine the water infiltration and hydraulic conductivity of saturated soil under field conditions in terms of the compaction degree of two Oxisols under a no-tillage (NT). Two commercial fields were studied in the state of Rio Grande do Sul, Brazil: one a Haplortox after 14 years under NT; the other a Hapludox after seven years under NT. Maps (50 x 30 m) of the levels of mechanical penetration resistance (PR) were drawn based on the kriging method, differentiating three compaction degrees (CD): high, intermediate and low. In each CD area, the infiltration rate (initial and steady-state) and cumulative water infiltration were measured using concentric rings, with six replications, and the saturated hydraulic conductivity (K(θs)) was determined using the Guelph permeameter. Statistical evaluation was performed based on a randomized design, using the least significant difference (LSD) test and regression analysis. The steady-state infiltration rate was not influenced by the compaction degree, with mean values of 3 and 0.39 cm h-1 in the Haplortox and the Hapludox, respectively. In the Haplortox, saturated soil hydraulic conductivity was 26.76 cm h-1 at a low CD and 9.18 cm h-1 at a high CD, whereas in the Hapludox, this value was 5.16 cm h-1 and 1.19 cm h-1 for the low and high CD, respectively. The compaction degree did not affect the initial and steady-state water infiltration rate, nor the cumulative water infiltration for either soil type, although the values were higher for the Haplortox than the Hapludox.

Spatial variability of soil chemical properties after coffee tree removal

Assessing the spatial variability of soil chemical properties has become an important aspect of soil management strategies with a view to higher crop yields with minimal environmental degradation. This study was carried out at the Centro Experimental of the Instituto Agronomico, in Campinas, São Paulo, Brazil. The aim was to characterize the spatial variability of chemical properties of a Rhodic Hapludox on a recently bulldozer-cleaned area after over 30 years of coffee cultivation. Soil samples were collected in a 20 x 20 m grid with 36 sampling points across a 1 ha area in the layers 0.0-0.2 and 0.2-0.4 m to measure the following chemical properties: pH, organic matter, K+, P, Ca2+, Mg2+, potential acidity, NH4-N, and NO3-N. Descriptive statistics were applied to assess the central tendency and dispersion moments. Geostatistical methods were applied to evaluate and to model the spatial variability of variables by calculating semivariograms and kriging interpolation. Spatial dependence patterns defined by spherical model adjusted semivariograms were made for all cited soil properties. Moderate to strong degrees of spatial dependence were found between 31 and 60 m. It was still possible to map soil spatial variability properties in the layers 0-20 cm and 20-40 cm after plant removal with bulldozers.

Diagnostic techniques applied in geostatistics for agricultural data analysis

The structural modeling of spatial dependence, using a geostatistical approach, is an indispensable tool to determine parameters that define this structure, applied on interpolation of values at unsampled points by kriging techniques. However, the estimation of parameters can be greatly affected by the presence of atypical observations in sampled data. The purpose of this study was to use diagnostic techniques in Gaussian spatial linear models in geostatistics to evaluate the sensitivity of maximum likelihood and restrict maximum likelihood estimators to small perturbations in these data. For this purpose, studies with simulated and experimental data were conducted. Results with simulated data showed that the diagnostic techniques were efficient to identify the perturbation in data. The results with real data indicated that atypical values among the sampled data may have a strong influence on thematic maps, thus changing the spatial dependence structure. The application of diagnostic techniques should be part of any geostatistical analysis, to ensure a better quality of the information from thematic maps.

Spatial and temporal variability of crop yield and some Rhodic Hapludox properties under no-tillage

Soil properties are closely related with crop production and spite of the measures implemented, spatial variation has been repeatedly observed and described. Identifying and describing spatial variations of soil properties and their effects on crop yield can be a powerful decision-making tool in specific land management systems. The objective of this research was to characterize the spatial and temporal variations in crop yield and chemical and physical properties of a Rhodic Hapludox soil under no-tillage. The studied area of 3.42 ha had been cultivated since 1985 under no-tillage crop rotation in summer and winter. Yield and soil property were sampled in a regular 10 x 10 m grid, with 302 sample points. Yields of several crops were analyzed (soybean, maize, triticale, hyacinth bean and castor bean) as well as soil chemical (pH, Soil Organic Matter (SOM), P, Ca2+, Mg2+, H + Al, B, Fe, Mn, Zn, CEC, sum of bases (SB), and base saturation (V %)) and soil physical properties (saturated hydraulic conductivity, texture, density, total porosity, and mechanical penetration resistance). Data were analyzed using geostatistical analysis procedures and maps based on interpolation by kriging. Great variation in crop yields was observed in the years evaluated. The yield values in the Northern region of the study area were high in some years. Crop yields and some physical and soil chemical properties were spatially correlated.

Local influence for spatial analysis of soil physical properties and soybean yield using student's t-distribution

The modeling and estimation of the parameters that define the spatial dependence structure of a regionalized variable by geostatistical methods are fundamental, since these parameters, underlying the kriging of unsampled points, allow the construction of thematic maps. One or more atypical observations in the sample data can affect the estimation of these parameters. Thus, the assessment of the combined influence of these observations by the analysis of Local Influence is essential. The purpose of this paper was to propose local influence analysis methods for the regionalized variable, given that it has n-variate Student's t-distribution, and compare it with the analysis of local influence when the same regionalized variable has n-variate normal distribution. These local influence analysis methods were applied to soil physical properties and soybean yield data of an experiment carried out in a 56.68 ha commercial field in western Paraná, Brazil. Results showed that influential values are efficiently determined with n-variate Student's t-distribution.

Uncertainties in the prediction of spatial variability of soil CO2 emissions and related properties

The soil CO2 emission has high spatial variability because it depends strongly on soil properties. The purpose of this study was to (i) characterize the spatial variability of soil respiration and related properties, (ii) evaluate the accuracy of results of the ordinary kriging method and sequential Gaussian simulation, and (iii) evaluate the uncertainty in predicting the spatial variability of soil CO2 emission and other properties using sequential Gaussian simulations. The study was conducted in a sugarcane area, using a regular sampling grid with 141 points, where soil CO2 emission, soil temperature, air-filled pore space, soil organic matter and soil bulk density were evaluated. All variables showed spatial dependence structure. The soil CO2 emission was positively correlated with organic matter (r = 0.25, p < 0.05) and air-filled pore space (r = 0.27, p < 0.01) and negatively with soil bulk density (r = -0.41, p < 0.01). However, when the estimated spatial values were considered, the air-filled pore space was the variable mainly responsible for the spatial characteristics of soil respiration, with a correlation of 0.26 (p < 0.01). For all variables, individual simulations represented the cumulative distribution functions and variograms better than ordinary kriging and E-type estimates. The greatest uncertainties in predicting soil CO2 emission were associated with areas with the highest estimated values, which produced estimates from 0.18 to 1.85 t CO2 ha-1, according to the different scenarios considered. The knowledge of the uncertainties generated by the different scenarios can be used in inventories of greenhouse gases, to provide conservative estimates of the potential emission of these gases.

Sugarcane trash management assessed by the interaction of yield with soil properties

Currently, sugarcane plays an important global role, particularly with a view to alternative energy sources. Thus, in a sugarcane field of the mill Vale do Paraná S/A Álcool e Açúcar, Rubineia, São Paulo State, managed under two green cane harvest systems (cane trash left on and cane trash removed from the soil), Pearson and spatial correlations between the sugarcane yield (variety RB855035 in the third cut) and soil physical and chemical properties were studied to identify the property best correlated with stalk yield and the best harvest method. For this purpose, two geostatistical grids (121 sampling points on 1.30 ha) were installed on a eutrophic Red Argisol (homogeneous slope of 0.065 m m-1), in 2011, to determine the properties: stalk yield and sugarcane plant population, and soil resistance to penetration, gravimetric moisture, bulk density, and carbon stock, in the layers 0-0.20 and 0.20-0.40 m. The data were analyzed by descriptive, linear correlation and geostatistical analysis. In both treatments, the property stand density was best correlated with sugarcane yield (r = 0.725 in the trash mulching treatment - TM and r = 0.769 in the trash removal treatment - TR). However, in relation to the soil properties, bulk density (0-0.20 m) was best correlated (r = 0.305 in TM, r = 0.211 in TR). Similarly, from the spatial point of view, stand density was the property that best explained the sugarcane yield. However, in the TM treatment the density (0.20-0.40 m) was the only soil property spatially correlated with stalk yield. The carbon stock in the soil of the TM was 11.5 % higher than in the TR treatment. Results of the TM treatment were best, also with regard to soil management and conservation.