Geographic information systems in the pay of alternative tourism – methods with landscape evaluation and target group preference weighting

There is a large variety of types of rural areas and many of them are rich in landscape beauty. However, their preserved culture and traditions are revalued in today’s rapid transformation of lifestyles. Alternative tourism is thus an emerging potential to economically support these areas, at the same time helps to preserve natural and cultural heritage. The methods provide sophisticated means to analyse the characteristics and the potential attractiveness of landscape and cultural attractions from the viewpoint of alternative tourism development. Homogenous tourism sub-regions can be defined and the most suitable development scenarios can be found to the certain areas

Landscape Classification using Principal Component Analysis and Fuzzy Classification: Archaeological Sites and their Natural Surroundings in Central Mongolia

The middle and upper Orkhon Valley in Central Mongolia (47.5°N, 102.5°E) hosts a multitude of diverse archaeological features. Most of them – including the well-known ancient cities of Karakorum and Karabalgasun – have only rarely been described in their geographical setups. The aim of this study is to describe, classify and analyse their surrounding landscapes and consequently characterise these sites geographically. This analysis is based on freely available raster datasets that offer information about topography, surface reflectance and derivatives. Principal component analysis is applied as a dimensional reduction technique. Subsequently, a fuzzy-logic approach leads to a classification scheme in which archaeological features are embedded and therefore distinguishable. A distinct difference in preferences regarding to choose a site location can be made and confirmed by semiautomatic analysis, comparing burial and ritual places and settlements. Walled enclosures and settlements are connected to planar steppe regions, whereas burial and ritual places are embedded in mountainous and hilly environments

Comparison between spectral angle mapper and euclidean distance in landform mapping

A classificação supervisionada por assinaturas geomorfométricas é um procedimento que pode auxiliar no mapeamento de formas de terreno a partir da utilização de medidas de similaridade ou distância. Este trabalho tem como objetivo comparar os métodos supervisionados de classifi cação a partir de medidas de similaridade e distância para o mapeamento do relevo. A comparação foi realizada no Campo de Instrução Militar de Formosa (GO), seguindo os seguintes passos: aquisição de dados HydroSHEDS, geração de imagens de curvatura, seleção de assinaturas geomorfométricas (AG); classifi cação formas de terreno usando o método de classifi cação por ângulo espectral (SAM) e a distância euclidiana (DE); comparação da classifi cação através da matriz de tabulação cruzada, análise de modelo de elevação em 3D, avaliação da média e do desvio padrão das curvaturas para cada classe mapeada e observação em campo. A seleção de assinaturas geomorfométrica considerou as seguintes etapas: (a) redução dos atributos geomorfométricos pela transformação de Fração Mínima de Ruído (MNFTipo 1 - quando a curvatura longitudinal tem um valor maior que a curvatura transversal; e Tipo 2 - quando ocorre o inverso. O processo foi simplifi cado para seis classes de formas de terreno (FT): Convexo/Convexo (Cx/Cx); Côncavo/Convexo (Cc/Cx); Côncavo/Côncavo (Cc/Cc); Côncavo/Retilíneo (Cc/Rt); Convexo/Retilíneo (Cx/Rt); Retilíneo/Retilíneo (Rt/Rt). No mapeamento utilizando o SAM, as formas de relevo predominantes são Cc/Rt, Cx/Rt e Cc/Cx, indicando uma heterogeneidade com muitas áreas de transição e côncavas. A classifi cação a partir da DE mostrou prevalência de feições retilíneas (Rt/Rt). Apesar disso, essa FT apresentou os menores desvios e valores médios próximos de zero para todas as curvaturas, indicando que a DE foi o método mais efi ciente para o mapeamento das áreas retilíneas.); (b) redução espacial pelo Índice de Pureza de Pixel (PPI); e (c) a seleção manual peloThe supervised classifi cation from geomorphometric signatures (AG) is a proceeding that can help landform mapping using similarity or distance measures. This study aims to compare the supervised classifi cation of similarity and distance methods to the landform mapping. The comparison was made in Campo de Instrução de Formosa (GO), divided into the following steps: acquiring HydroSHEDS data, generation of bending images, selection of geomorphometric signatures; landforms classifi cation using the spectral angle mapper (SAM) and Euclidean Distance (DE) methods; comparing the classifi cation using the cross-tabulation matrix, elevation model analysis in 3D, evaluation of the mean and standard deviation of the curvatures for each mapped class, and fi eld observation. Selecting geomorphometric signatures considered the following steps: (a) reduction of geomorphometric attributes the transformation Minimum Noise Fraction (MNF); (B) spatial reduction by the Pixel Purity Index (PPI); and (c) the manual selection by the n-Dimensional Viewer. The classifi cation process took 14 AG describing two behaviors: Type 1 - when the longitudinal curvature has a value greater than the transverse curvature; and Type 2 - when the opposite occurs. The process has been simplifi ed to six landforms classes: Convex/Convex (Cx/Cx); Concave/Convex (Cc/Cx); Concave/Concave (Cc/cc); Concave/Rectilinear (Cc/ Rt); Convex/Rectilinear (Cx/Rt); Rectilinear/Rectilinear (Rt/Rt). In SAM mapping, the predominant landforms are Cc/Rt, Cx/Rt and Cc/Cx, indicating heterogeneity in many transition and concave areas. The classifi cation from DE showed prevalence of rectilinear features (Rt/Rt). However, this FT showed the smallest standard deviations and average values close to zero for all curvatures, indicating that was the most effi cient method for mapping the rectilinear areas

Comparação dos métodos de classificação por ângulo espectral e distância euclidiana no mapeamento das formas de terreno

Dissertação (mestrado)—Universidade de Brasília, Pós-Graduação em Geografia, 2015.O Bioma Cerrado apresenta a maior biodiversidade e heterogeneidade de paisagens entre as savanas do mundo. Essa abrangência evidencia a importância de estudos sistemáticos sobre os diversos aspectos desse domínio. Na região central desse bioma encontra-se a ecorregião do Planalto Central, onde o relevo norteia a dinâmica evolutiva da paisagem. Está associado à distribuição espacial dos tipos de solo e dos organismos vivos, incluindo as atividades humanas. Esse elemento da paisagem pode ser estudado de forma qualitativa ou quantitativa. A geomorfometria é o campo da ciência que estuda de forma quantitativa o relevo, principalmente as formas de terreno. Dentre os métodos de classificação das formas do terreno destaca-se a classificação supervisionada pelo emprego de assinaturas geomorfométricas de referência. A classificação por assinaturas geomorfométricas pode utilizar métricas de similaridade e distância. Nesse contexto, o objetivo do presente trabalho é comparar os métodos de classificação utilizando métricas de similaridade e de distância no mapeamento das formas de terreno, aplicados na área do Campo de Instrução Militar de Formosa (CIF) localizado na bacia do Rio Preto. A metodologia possui as seguintes etapas: aquisição dos dados HydroSHEDS, geração das imagens de curvaturas, seleção das assinaturas geomorfométricas de referência; classificação pelo método SAM e Distância Euclidiana; comparação dos resultados usando tabulação cruzada. Para a definição das assinaturas geomorfométricas foi elaborada uma imagem unindo os conjuntos de curvaturas Longitudinal e Transversal, Mínima e Máxima. A detecção das assinaturas de referência adotou as seguintes etapas: (a) redução da quantidade de informações espectrais pelo algoritmo Minimun Noise Fraction (MNF) que separa a fração sinal do ruído; (b) redução da quantidade de informação espacial selecionando os pixels mais puros pelo Índice de Pureza do Pixel (PPI); e (c) seleção das assinaturas geomorfométricas em um visualizador n-dimensional. A classificação das assinaturas foi simplificada nas seis formas de terreno: Convexo/Convexo (Cx/Cx); Côncavo/Côncavo (Cc/Cc); Convexo/Côncavo (Cx/Cc); Côncavo/Retilíneo (Cc/Rt); Convexo/Retilíneo (Cx/Rt); Retilíneo/Retilíneo (Rt/Rt). A classificação foi realizada a partir de 14 assinaturas geomorfométricas. O método SAM evidenciou formas de terreno de transição e côncavas. Por outro lado, o resultado da classificação por distância euclidiana contemplou principalmente os padrões retilíneos. Os resultados mostraram que a distância euclidiana foi mais adequada por mapear principalmente as assinaturas que se aproximam dos valores de curvaturas da área de estudo. Considerando apenas as seis formas de terreno, a comparação pela tabulação cruzada mostrou um aumento na precisão entre os dois métodos, mas os resultados continuaram diferentes. O SAM apresentou um mapeamento com predomínio de formas Cc/Rt, Cx/Rt e Cc/Cx, indicando um terreno heterogêneo com muitas áreas de transição. A distância euclidiana mostrou um terreno com o predomínio das formas Rt/Rt. Esta análise confirmou que a classificação de assinaturas geomorfométricas de referência com o método SAM é mais adequada em relevo fragmentado. Assim, a distância euclidiana foi a classificação mais adequada para o mapeamento das formas de terreno do CIF, que tem um relevo com predomínio de áreas planas e suaves.The Cerrado has the highest biodiversity and landscapes variety between the savannahs of the world. This range highlights the importance of systematic studies on the various aspects of this field. In the central region of this biome, where the Central Plateau eco-region is located, the relief guides the evolutionary dynamics of the landscape. Is associated with the spatial distribution of soil types and living organisms, including human activities. This landscape element can be studied qualitatively or quantitatively. The geomorphometry is a field of science that studies quantitatively the topographic surface. Among the landform classification methods highlight the supervised classification by use of reference geomorphometric signatures. Geomorphometric signatures classification can use similarity metrics and distance metrics. In this context, the objective of this study is to compare the classification methods using similarity metrics and distance in the landforms mapping, applied in the Campo de Instrução Militar de Formosa (CIF), Rio Preto basin. The methodology has the following steps: acquisition of HydroSHEDS data, generation of curvature images, reference geomorphometric signatures selection; SAM and Euclidean distance classification method; results comparison using cross tabulation. For the definition of geomorphometric signatures was drawn a picture joining the sets of Longitudinal and Cross-sectional curvatures, Minimum and Maximum. The reference signatures detection adopted the following steps: (a) reducing the amount of spectral information by Minimun Noise Fraction algorithm (MNF) separating the fraction noise/ signal; (b) reducing the amount of spatial information by selecting the most pure pixels by the Pixel Purity Index (PPI); and (c) selection of geomorphometric signatures in a n-dimensional viewer. The signatures classification has been simplified in the six landforms: Convex/Convex (Cx/Cx); Convex/Concave; Concave/Concave; Concave/Rectilinear; Convex/Rectilinear; Rectilinear/Rectilinear. Classification was carried out from 14 geomorphometric signatures. The SAM method showed the transition and concave landform. On the other hand, the result of the mainly included the rectilinear patterns. The Euclidean distance classification results showed that the Euclidean distance is more particularly suitable for mapping the signatures that are close to the values of study area curvature. Considering only the six landforms, compared to the cross tabulation showed an increase in accuracy between the two methods, but the results remain different. SAM presented a mapping with predominant forms Cc/Rt, Cx/Rt and Cc/Cx, indicating a heterogeneous terrain with many transition areas. The Euclidean distance showed a plot with the predominance of forms Rt/Rt. This analysis confirmed that the geomorphometric signatures classification to the SAM method is more appropriate in fragmented relief. Thus, the Euclidean distance was the most appropriate classification for the landforms mapping of the the CIF, which has a predominantly flat and smooth relief

Estimating Soil Organic Carbon in Cultivated Soils Using Soil Test Data, Remote Sensing Imagery from Satellites (Landsat 8 and PlantScope), and Web Soil Survey Data

Soil organic carbon (SOC) is an important soil parameter of cultivated soils that needs to be monitored and mapped regularly to enhance soil health and productivity. SOC levels in cultivated areas is difficult to monitor for farmers and is costly to analyze using traditional methods. The objective of this study was to estimate surface SOC distribution in selected soils of Major Land Resource Areas (MLRA) 102A (Rolling Till Plain, Brookings County, SD) and 103 (Central Iowa and Minnesota Till Prairies, Lac qui Parle County, MN) using soil sample data, Web Soil Survey (WSS) data, and satellite imagery (Landsat 8 and PlanetScope). Different satellite imagery bands and band combinations were used to reach more accurate results. The dominant soils in the area are Haplustolls, Calciustolls, and Endoaquolls formed in silty sediments, local silty alluvium, and till. Sites were selected and soil samples were collected in May 2018 after planting. SOC and soil properties were measured at the 0-15 cm depth. SOC was mainly affected by soil texture in the studied selected soils. Multiple-linear regression was used to build SOC prediction models from soil test data. The final SOC model (using stepwise regression) is SOCp = 3.98 + (-0.210 pH) + (-0.220 Sand [g kg-1]) + (0.040 Sum of Extractable Cation, SOEC [cmolc kg-1]). The Ridge Regression (RR) (CV = 0.066, MSE = 0.063) and Principal Component Regression (PCR) (CV = 0.071, MSE = 0.068) were used to deal with multicollinearity and RR was determined to be as the best model, with 82.7% of variation in SOC explained by the RR model. Landsat 8 and PlanetScope spectral bands and different indices were also used to develop SOC prediction models. The stepwise regression analyses revealed that the Landsat 8 prediction model had multicollinearity problem. Ridge regression and PCR were applied, and RR was chosen as the best model with SOCp = -26.7 + (0.310 BSIL) + (-23.2 Band 5L) + (75.8 Band 2L) + (-51.1 Band 3L) + ( -3.05 Band 7L). The RR model (CV = 0.24, MSE = 0.22) explained 37.0% of the variation in SOC for Landsat 8. The reduced PlanetScope model was SOCp = -25.1 + (2980 Band4P) + (0.327 BSIP). Approximately 60.0% of the variation in SOC was explanined by the Ordinary Least Square (OLS) (CV = 0.15, MSE = 0.14) model and was free of multicollinearity. WSS data showed similar patterns as soil test data for SOC predictions. The best model for WSS data was a linear regression, SOCp = 3.37 + (-0.0200 Sand WSS [g kg-1]) and 49.0% of the variation in SOC was explained by this model. WSS data were then added as variables into the spatial (satellite) estimation models. The Landsat 8 and WSS data explained 53.3%, PlanetScope and WSS data explained 68.8% of the SOC variation. Based on these results, deciding on the number of soil sampling points, and the use of specific variables in the model is very crucial for the model development. Estimating SOC by minimizing the number of needed soil sampling points, using satellite imagery, and public free sources provides an easy, efficient and cost-effective way to monitor SOC levels and identify the best management systems for producers and natural resource managers. This project produced accurate SOC prediction models using soil test data, satellite imagery and Web Soil Survey data. This SOC estimation model helps farmers, resource managers, and researchers to monitor SOC concentration on the soil surface using remote sensing alone, or with WSS data, or with a minimal amount of soil test data

Modelo educativo exponencial

The Interamerican University for Development SAC (UNID) arises through Resolution No. 199-2010-CONAFU of the National Council for the authorization of the operation of the universities that grants the provisional authorization of operation dated April 8, 2010, with the objective of providing educational services at the university level, in the city, province and department of Lima, in the careers of Nursing, Pharmacy and Biochemistry. In view of this, academic activities began on May 7, 2010, the day after the publication in the official newspaper of Peru with Resolution No. 199-2010-CONAFU.La Universidad Interamericana para el Desarrollo SAC (UNID) surge mediante la Resolución N°199-2010-CONAFU del Consejo Nacional para la autorización del funcionamiento de las universidades que otorga la autorización provisional de funcionamiento con fecha 08 de abril del año 2010, con el objetivo de brindar servicios educativos de nivel universitario, en la ciudad, provincia y departamento de Lima, en las carreras de Enfermería, Farmacia y Bioquímica. Visto ello, inicia actividades académicas el 07 de mayo de 2010, día siguiente de la publicación en el diario oficial el peruano con Resolución N° 199-2010-CONAFU

Modelo educativo exponencial

The Interamerican University for Development SAC (UNID) arises through Resolution No. 199-2010-CONAFU of the National Council for the authorization of the operation of the universities that grants the provisional authorization of operation dated April 8, 2010, with the objective of providing educational services at the university level, in the city, province and department of Lima, in the careers of Nursing, Pharmacy and Biochemistry. In view of this, academic activities began on May 7, 2010, the day after the publication in the official newspaper of Peru with Resolution No. 199-2010-CONAFU.La Universidad Interamericana para el Desarrollo SAC (UNID) surge mediante la Resolución N°199-2010-CONAFU del Consejo Nacional para la autorización del funcionamiento de las universidades que otorga la autorización provisional de funcionamiento con fecha 08 de abril del año 2010, con el objetivo de brindar servicios educativos de nivel universitario, en la ciudad, provincia y departamento de Lima, en las carreras de Enfermería, Farmacia y Bioquímica. Visto ello, inicia actividades académicas el 07 de mayo de 2010, día siguiente de la publicación en el diario oficial el peruano con Resolución N° 199-2010-CONAFU

Spectroscopy-supported digital soil mapping

Global environmental changes have resulted in changes in key ecosystem services that soils provide. It is necessary to have up to date soil information on regional and global scales to ensure that these services continue to be provided. As a result, Digital Soil Mapping (DSM) research priorities are among others, advancing methods for data collection and analyses tailored towards large-scale mapping of soil properties. Scientifically, this thesis contributed to the development of methodologies, which aim to optimally use remote and proximal sensing (RS and PS) for DSM to facilitate regional soil mapping. The main contributions of this work with respect to the latter are (I) the critical evaluation of recent research achievements and identification of knowledge gaps for large-scale DSM using RS and PS data, (II) the development of a sparse RS-based sampling approach to represent major soil variability at regional scale, (III) the evaluation and development of different state-of-the-art methods to retrieve soil mineral information from PS, (IV) the improvement of spatially explicit soil prediction models and (V) the integration of RS and PS methods with geostatistical and DSM methods. A review on existing literature about the use of RS and PS for soil and terrain mapping was presented in Chapter 2. Recent work indicated the large potential of using RS and PS methods for DSM. However, for large-scale mapping, current methods will need to be extended beyond the plot. Improvements may be expected in the fields of developing more quantitative methods, enhanced geostatistical analysis and improved transferability to other areas. From these findings, three major research interests were selected: (I) soil sampling strategies, (II) retrieval of soil information from PS and (III) spatially continuous mapping of soil properties at larger scales using RS. Budgetary constraints, limited time and available soil legacy data restricted the soil data acquisition, presented in Chapter 3. A 15.000 km2 area located in Northern Morocco served as test case. Here, a sample was collected using constrained Latin Hypercube Sampling (cLHS) of RS and elevation data. The RS data served as proxy for soil variability, as alternative for the required soil legacy data supporting the sampling strategy. The sampling aim was to optimally sample the variability in the RS data while minimizing the acquisition efforts. This sample resulted in a dataset representing major soil variability. The cLHS sample failed to express spatial correlation; constraining the LHS by a distance criterion favoured large spatial variability over short distances. The absence of spatial correlation in the sampled soil variability precludes the use of additional geostatistical analyses to spatially predict soil properties. Predicting soil properties using the cLHS sample is thus restricted to a modelled statistical relation between the sample and exhaustive predictor variables. For this, the RS data provided the necessary spatial information because of the strong spatial correlation while the spectral information provided the variability of the environment (Chapter 3 and 6). Concluding, the RS-based cLHS approach is considered a time and cost efficient method for acquiring information on soil resources over extended areas. This sample was further used for developing methods to derive soil mineral information from PS, and to characterize regional soil mineralogy using RS. In Chapter 4, the influences of complex scattering within the mixture and overlapping absorption features were investigated. This was done by comparing the success of PRISM’s MICA in determining mineralogy of natural samples and modelled spectra. The modelled spectra were developed by a linearly forward model of reflectance spectra, using the fraction of known constituents within the sample. The modelled spectra accounted for the co-occurrence of absorption features but eluded the complex interaction between the components. It was found that more minerals could be determined with higher accuracy using modelled reflectance. The absorption features in the natural samples were less distinct or even absent, which hampered the classification routine. Nevertheless, grouping the individual minerals into mineral categories significantly improved the classification accuracy. These mineral categories are particularly useful for regional scale studies, as key soil property for parent material characterization and soil formation. Characterizing regional soil mineralogy by mineral categories was further described in Chapter 6. Retrieval of refined information from natural samples, such as mineral abundances, is more complex; estimating abundances requires a method that accounts for the interaction between minerals within the intimate mixture. This can be done by addressing the interaction with a non-linear model (Chapter 5). Chapter 5 showed that mineral abundances in complex mixtures could be estimated using absorption features in the 2.1–2.4 µm wavelength region. First, the absorption behaviour of mineral mixtures was parameterized by exponential Gaussian optimization (EGO). Next, mineral abundances were successfully predicted by regression tree analysis, using these parameters as inputs. Estimating mineral abundances using prepared mixes of calcite, kaolinite, montmorillonite and dioctahedral mica or field samples proved the validity of the proposed method. Estimating mineral abundances of field samples showed the necessity to deconvolve spectra by EGO. Due to the nature of the field samples, the simple representation of the complex scattering behaviour by a few Gaussian bands required the parameters asymmetry and saturation to accurately deconvolve the spectra. Also, asymmetry of the EGO profiles showed to be an important parameter for estimating the abundances of the field samples. The robustness of the method in handling the omission of minerals during the training phase was tested by replacing part of the quartz with chlorite. It was found that the accuracy of the predicted mineral content was hardly affected. Concluding, the proposed method allowed for estimating more than two minerals within a mixture. This approach advances existing PS methods and has the potential to quantify a wider set of soil properties. With this method the soil science community was provided an improved inference method to derive and quantify soil properties The final challenge of this thesis was to spatially explicit model regional soil mineralogy using the sparse sample from Chapter 3. Prediction models have especially difficulties relating predictor variables to sampled properties having high spatial correlation. Chapter 6 presented a methodology that improved prediction models by using scale-dependent spatial variability observed in RS data. Mineral predictions were made using the abundances from X-ray diffraction analysis and mineral categories determined by PRISM. The models indicated that using the original RS data resulted in lower model performance than those models using scaled RS data. Key to the improved predictions was representing the variability of the RS data at the same scale as the sampled soil variability. This was realized by considering the medium and long-range spatial variability in the RS data. Using Fixed Rank Kriging allowed smoothing the massive RS datasets to these ranges. The resulting images resembled more closely the regional spatial variability of soil and environmental properties. Further improvements resulted from using multi-scale soil-landscape relationships to predict mineralogy. The maps of predicted mineralogy showed agreement between the mineral categories and abundances. Using a geostatistical approach in combination with a small sample, substantially improves the feasibility to quantitatively map regional mineralogy. Moreover, the spectroscopic method appeared sufficiently detailed to map major mineral variability. Finally, this approach has the potential for modelling various natural resources and thereby enhances the perspective of a global system for inventorying and monitoring the earth’s soil resources. With this thesis it is demonstrated that RS and PS methods are an important but also an essential source for regional-scale DSM. Following the main findings from this thesis, it can be concluded that: Improvements in regional-scale DSM result from the integrated use of RS and PS with geostatistical methods. In every step of the soil mapping process, spectroscopy can play a key role and can deliver data in a time and cost efficient manner. Nevertheless, there are issues that need to be resolved in the near future. Research priorities involve the development of operational tools to quantify soil properties, sensor integration, spatiotemporal modelling and the use of geostatistical methods that allow working with massive RS datasets. This will allow us in the near future to deliver more accurate and comprehensive information about soils, soil resources and ecosystem services provided by soils at regional and, ultimately, global scale.</p