Compound Topographic Index Research Articles

Delineation of site-specific management zones by fuzzy clustering of soil and topographic attributes: A case study of East Nile Delta, Egypt

The objective of this study was to define site-specific management zones of 67.2 ha of a wheat pivot field at East of Nile Delta, Egypt for use in precision agriculture based on spatial variability of soil and topographic attributes. The field salinity was analysed by reading the apparent soil electrical conductivity (ECa) with the EM38 sensor horizontally and vertically at 432 locations. The field was sampled for soil attributes systematically with a total of 80 sampling location points. All samples were located using GPS hand held unit. Soil sampling for management zones included soil reaction pH, soil saturation percentage, organic matter, calcium carbonates content, available nitrogen, available phosphorus and available potassium. The field topographic attributes were digital elevation model (DEM), slope, profile curvature, plane curvature, compound topographic index (CTI) and power stream index (PSI). The maps of spatial variability of soil and field topographic attributes were generated using ordinary kriging geostatistical method. Principal component analysis (PCA) was used to determine the most important soil and topographic attributes for representing within-field variability. Principal component analysis of input variables indicated that EM38 horizontal readings (EM38h), soil saturation percentage and digital elevation model were more important attributes for defining field management zones. The fuzzy c-means clustering method was used to divide the field into potential management zones, fuzzy performance index (FPI) and normalized classification entropy (NCE) were used to determine the optimal cluster numbers. Measures of cluster performance indicated no advantage of dividing these fields into more than five management zones. The defined management zones not only provided a better description of the soil properties, but also can direct soil sampling design and provide valuable information for site-specific management in precision agriculture.

Shallow landslide initiation susceptibility mapping by GIS-based weights-of-evidence analysis of multi-class spatial data-sets: a case study from the natural sloping terrain of Western Ghats, India

The study aims to prepare a landslide susceptibility zonation (LSZ) map and quantitative validation of the result at a scale of 1:50,000 for the upland catchment of river Meenachil, Kottayam district, Kerala, India by recognizing and mapping the palaeoslide locations and the associated terrain attributes. A bivariate statistical method, known as the weights of evidence modeling, which offers a flexible way of testing the importance of input factors used for landslide susceptibility assessment, were used for the analysis. Fourteen terrain parameters namely, geomorphology, drainage density, soil type, soil thickness, land use, Normalized Difference Vegetation Index (NDVI), slope, aspect, relative relief, slope length, profile curvature, plan curvature, flowpath length and topographic wetness index were generated and its weights were determined. The conditional independence of the evidential themes was assessed by ‘Omnibus test’ and those themes which found to be conditionally independent were integrated to produce the final LSZ map. The results shows that areas having slope less than 16° is classified as stable areas since the active geomorphic processes are absent there, areas covered by side-slope plateau and denudational hills constitutes the very high and high susceptible region and is contributed by the slope between 16° and 45°, facing southwest, west and northwest directions, with extended influence from relative relief, slope length, flowpath length and compound topographic index. The reliability and accuracy of the LSZ map was assessed by the receiver operating characteristic curve analysis and the model shows a prediction accuracy of 89.2% (area under the curve = 0.892). In order to enhance the utility of the results of the present study, a panchayath-wise (the lowest administrative body) susceptibility map was produced and it shows that, among these panchayaths, highly susceptible areas are found in Munnilavu, Talanad, Tikoy and Poonjar Thekkekara. Suggestions are also drawn to optimize the hazard assessment for the region having high landslide susceptibility.

Predicting Ephemeral Gully Location and Length Using Topographic Index Models

Abstract. Ephemeral gullies (EGs) are incised channels resulting from concentrated overland flow that often form in a similar location every year. These erosional features add to producers’ management efforts and costs. Locating EGs and predicting their length is crucial for estimating sediment load and planning conservation strategies. Since topography plays an important role in the formation of EGs, this study investigated the prediction of EG location and length in two agricultural areas (S1 and S2) in two different physiographic regions using four topographic index models: compound topographic index (CTI), slope area (SA), wetness topographic index (WTI), and slope area power (SAP). The impacts of digital elevation model (DEM) resolution, agricultural land use mask data source, and topographic model critical thresholds were also evaluated. Automated geospatial models were developed to locate and derive EG length. Results show that the SA model predicted EG occurrence and length better than other models tested. The SA and CTI model predictions had similar patterns in terms of locating EG trajectory; however, the CTI model had greater discontinuity along the trajectory. The method developed to derive length in this study was sensitive to discontinuity, so the performance of the CTI model was poor. Finer-resolution DEMs (2 m) predicted EG location and lengths better than coarser-resolution DEMs (10 m or greater). Use of actual field-level reconnaissance data instead of NASS data for agricultural land use masking decreased false negative classification by 16% or more for all models. Detailed calibration of the SA model yielded different optimal thresholds for the two study regions: TSA = 30 for S1 and TSA = 50 for S2. Topographic index models were found to be useful in locating EGs and estimating expected lengths, but site-specific calibration of the topographic index model threshold was required, which might limit the general utility of these methods.

Predicting Ephemeral Gully Location and Length Using Topographic Index Models

<abstract><title><italic>Abstract.</italic></title> Ephemeral gullies (EGs) are incised channels resulting from concentrated overland flow that often form in a similar location every year. These erosional features add to producers’ management efforts and costs. Locating EGs and predicting their length is crucial for estimating sediment load and planning conservation strategies. Since topography plays an important role in the formation of EGs, this study investigated the prediction of EG location and length in two agricultural areas (S1 and S2) in two different physiographic regions using four topographic index models: compound topographic index (CTI), slope area (SA), wetness topographic index (WTI), and slope area power (SAP). The impacts of digital elevation model (DEM) resolution, agricultural land use mask data source, and topographic model critical thresholds were also evaluated. Automated geospatial models were developed to locate and derive EG length. Results show that the SA model predicted EG occurrence and length better than other models tested. The SA and CTI model predictions had similar patterns in terms of locating EG trajectory; however, the CTI model had greater discontinuity along the trajectory. The method developed to derive length in this study was sensitive to discontinuity, so the performance of the CTI model was poor. Finer-resolution DEMs (2 m) predicted EG location and lengths better than coarser-resolution DEMs (10 m or greater). Use of actual field-level reconnaissance data instead of NASS data for agricultural land use masking decreased false negative classification by 16% or more for all models. Detailed calibration of the SA model yielded different optimal thresholds for the two study regions: T_SA = 30 for S1 and T_SA = 50 for S2. Topographic index models were found to be useful in locating EGs and estimating expected lengths, but site-specific calibration of the topographic index model threshold was required, which might limit the general utility of these methods.

Terrain-Shape Indices for Modeling Soil Moisture Dynamics

This study examined spatial and temporal relationships between measured soil moisture and terrain-based proxies for soil moisture dynamics. Two catenas were intensively sampled reflecting a mosaic of differences in degree of soil development in California's Sierra Foothill Region. A catena containing weakly developed soils (Haploxerepts + Haploxeralfs) formed from granitic parent materials was compared to a catena of well-developed soils (Haploxeralfs and Palexeralfs) formed from metavolcanic parent materials. Soil moisture was monitored at 10-, 30-, and 50-cm depths in 15 profiles in the granitic catena and 100 profiles in the metavolcanic catena. Seven post-rainfall periods during the 2008-2009 water year were selected to compare terrain shape indices and measured soil moisture. No single terrain index (slope, tangential curvature, profile curvature, mean curvature, topographic prominence, terrain characterization index, and compound topographic index [CTI]) consistently described variability in mean water content or dry-down rates, across depth or space. However, within the granitic catena, a combination of CTI and modeled beam radiance consistently accounted for 30 to 70% of the total variance in mean water content at 10 cm, and 10 to 40% at 30- and 50-cm depths. The predictive capacity of digital elevation model (DEM)-derived terrain shape indices for soil moisture dynamics varied widely in time and space, and was influenced by spatial patterns in the degree of soil development. Efforts to describe soil moisture variability are an important attribute of digital soil mapping (DSM). Moreover, soil variability influences soil moisture dynamics, thus synergistic activities are needed to integrate landscape scale variability with digital soil mapping. © Soil Science Society of America, All rights reserved.

Delineating hydrologic response units in large upland catchments and its evaluation using soil moisture simulations

We present here a basis for delineating Hydrologic Response Units (HRUs) to capture heterogeneity in the catchment's topography, landforms and geomorphologic attributes. To delineate topologically connected HRUs, the catchment is divided into four landforms and sub-basins. These four major landforms represent macroscopic changes in the catchment landscapes, using thresholds derived from a range of terrain analysis techniques – the Cumulative Area Distribution (CAD) curve, average local slope, curvature, Compound Topographic Index (CTI) and the MultiResolution Valley Bottom Flatness (MRVBF) index. The adequacy of the HRUs delineation approach is ascertained by soil moisture movement modelling in the unsaturated zone based on a two-dimensional solution of Richards' equation, across multiple cross-sections of the catchment. The modelling results of the four landform delineated cross-sections are compared with those from the simplest case of a single landform delineated cross-section and with the most complex case of cross-sections divided on a pixel basis. The modelling results indicate gain in accuracy when using the four landform formulation compared to the use of a single landform, and little loss of accuracy compared to simulations on a pixel basis. This study investigates the stability of this HRUs delineation methodology using the data for the Maclaughlin, Bombala and Delegate catchments of the Snowy River at Burnt Hut, New South Wales, Australia.

Comparison between artificial neural networks and maximum likelihood classification in digital soil mapping

Soil surveys are the main source of spatial information on soils and have a range of different applications, mainly in agriculture. The continuity of this activity has however been severely compromised, mainly due to a lack of governmental funding. The purpose of this study was to evaluate the feasibility of two different classifiers (artificial neural networks and a maximum likelihood algorithm) in the prediction of soil classes in the northwest of the state of Rio de Janeiro. Terrain attributes such as elevation, slope, aspect, plan curvature and compound topographic index (CTI) and indices of clay minerals, iron oxide and Normalized Difference Vegetation Index (NDVI), derived from Landsat 7 ETM+ sensor imagery, were used as discriminating variables. The two classifiers were trained and validated for each soil class using 300 and 150 samples respectively, representing the characteristics of these classes in terms of the discriminating variables. According to the statistical tests, the accuracy of the classifier based on artificial neural networks (ANNs) was greater than of the classic Maximum Likelihood Classifier (MLC). Comparing the results with 126 points of reference showed that the resulting ANN map (73.81 %) was superior to the MLC map (57.94 %). The main errors when using the two classifiers were caused by: a) the geological heterogeneity of the area coupled with problems related to the geological map; b) the depth of lithic contact and/or rock exposure, and c) problems with the environmental correlation model used due to the polygenetic nature of the soils. This study confirms that the use of terrain attributes together with remote sensing data by an ANN approach can be a tool to facilitate soil mapping in Brazil, primarily due to the availability of low-cost remote sensing data and the ease by which terrain attributes can be obtained.

Relação entre atributos do terreno, material de origem e solos em uma área no noroeste do estado do Rio de Janeiro

O presente estudo teve como objetivo identificar as relações existentes entre atributos do terreno, material de origem e os solos em uma área no Noroeste do Estado do Rio de Janeiro. Os atributos do terreno: elevação, declividade, aspecto, plano de curvatura, radiação solar e índice CTI, e as litologias dominantes na área tiveram influência direta na distribuição dos solos da área, com os solos derivados de migmatitos apresentando mudança textural abrupta, diferentemente dos solos originados dos granulitos. A elevação e a declividade ajudaram a explicar a distribuição dos Neossolos Litólicos e Cambissolos na área, enquanto a declividade e o índice CTI explicam a ocorrência dos Argissolos Amarelos, Vermelho-Amarelos e dos Gleissolos em relevo suavizado. Diferenças na profundidade são condicionadas pela curvatura do terreno, com áreas côncavas apresentando solos menos profundos (saprolíticos) do que nas áreas convexas. Os Argissolos derivados dos granulitos, que ocorrem em relevo forte ondulado ou montanhoso, em encostas voltadas para norte, com maior incidência de radiação solar, apresentam coloração vermelha e são eutróficos, enquanto os Argissolos das encostas voltadas para sul (&lt; radiação solar) são vermelho-amarelados, distróficos e com horizonte B latossólico em profundidade.

Effect of Topographic Characteristics on Compound Topographic Index for Identification of Gully Channel Initiation Locations

<abstract><title><italic>Abstract.</italic></title> Sediment loads from gully erosion contribute to water quality problems, reduction in crop productivity by removal of nutrient-rich topsoil, and damage to downstream ecosystems. The identification of areas with high potential for gully channel development is often performed using spatially derived stream power estimates from second-order topographic indices, such as the compound topographic index (CTI). The utilization of CTI to identify where gullies develop is affected by field and local topographic characteristics and DEM resolution. In this study, the effect of overall terrain slope, local relief variance, and raster grid cell size on CTI cumulative distribution values was investigated using theoretical and observed catchment methodology. In the theoretical analysis, stochastic methods were used to generate simulated catchments to quantify the influence of overall terrain slope, local relief variance, and raster grid cell size (each considered individually). The observed methodology used three sites with distinct topographic characteristics, measured gully channels, and high-resolution topographic information. Raster grids for the three observed study sites were generated at varying raster grid cell sizes. Critical CTI values were determined through comparison of measured gully thalwegs with threshold CTI raster grids of the observed watersheds at different resolutions. Results from the theoretical investigation indicate that CTI values were linearly influenced by changes in relief variance and overall slope, while variations in raster grid cell size caused an inverse power variation in CTI values. In addition, variations in raster grid cell size, produced changes in cumulative distributions of the top 0.1% CTI values. The use of normalized CTI values (CTI_n) produced merged cumulative distribution curves when varying overall slope, terrain relief variance, and to a lesser degree DEM resolution. Similar findings were obtained from the analysis of observed catchments. When DEM resolution varied, the differences in critical CTI_n values in the same field were significantly reduced when compared to original critical CTI values, although differences were not fully eliminated. Normalization of the CTI cumulative distributions improved comparisons between different sites with distinct drainage area sizes and topographic characteristics, providing a possible alternative for investigations of large watersheds with more than one topographic characteristic. Results suggest that a normalized critical CTI between 1 and 2 could be used for the identification of areas with high potential for gully development. Knowing where gullies develop is important in understanding the effect of conservation practices on soil erosion through the use of field-scale and watershed-scale simulation models. Effective watershed management plans depend on this information to target the placement of conservation practices for the efficient use of available resources.

Impact of terrain attributes, parent material and soil types on gully erosion

Gully erosion is a worldwide matter of concern because of the irreversible losses of fertile land, which often have severe environmental, economic and social consequences. While most of the studies on the gullying process have investigated the involved mechanisms (either overland flow incision, seepage or piping erosion), only few have been conducted on the controlling factors of gully wall retreat, an important, if not the dominant, land degradation process and sediment source in river systems. In a representative 4.4km2 degraded area of the Drakensberg foothills (South Africa) the main objective of this study was to evaluate the relationship between the rate of gully bank retreat (GBR) and parent material, soil types and selected terrain attributes (elevation, specific drainage area, mean slope gradient, slope length factor, stream power index, compound topographic index and slope curvatures). The survey of gully bank retreat was performed during an entire hydrological year, from September 2007 to September 2008, using a network of pins (n=440 from 110 pits). Both the gully contours and pin coordinates were determined, using a GPS with a 0.5m horizontal accuracy (n=20,120). The information on the parent material and the soil types was obtained from field observations complemented by laboratory analysis, while terrain attributes were extracted from a 20m DEM generated from 5m interval contour lines. The average GBR value for the 6512m of gully banks found in the area was 0.049±0.0013my−1, which, considering bank height and soil bulk density, corresponded to an erosion rate of 2.30tonha−1y−1. There was no significant difference in GBR between sandstone and dolerite and between Acrisols and Luvisols. Despite a weak one-to-one correlation with the selected terrain attributes (r<0.2), a principal component analysis (PCA), the first two axes of which explained 68% of the data variability, pointed out that GBR was the highest at hillslope inflexion points (profile and plan slope curvatures close to zero), in the vicinity of the head cuts and for drainage areas up to 500m2, as both situations experience a high removal rate of the soil material produced from the gully bank collapse and protecting gullies from laterally retreating. These results could be used to digitally map the more active gully banks for the improved implementation of preventive measures of gully growth, if high resolution DEMs are available. There remained, however, a certain amount of unexplained variability in the data, that further research studies on the mechanisms and associated factors of control of GBR could help to address.

Favorability values of geological, vegetation and relief factors for the development of soil creep landforms

Soil creep can produce small landforms on slope surfaces, such as lobes, terraces or steps, stripes and superficial irregularities or hummock topographies. The aim of the present study was to analyze the relationship between the cartographic distribution of these small landforms and some geological, geomorphologic, vegetation and relief variables. A total of 1,324 landforms produced by soil creep were mapped within a study area of 250 km2, located in an Atlantic mountainous zone in the north of the Iberian Peninsula. The variables considered in the analysis were: 1) geological-geomorphologic variables: bedrock geology, surficial formations (lithologic and genetic classification), vegetation and presence of rock area, and 2) relief variables: elevation, slope, aspect, curvature, relief index, flow accumulation and Compound Topographic Index (CTI). The influence of each variable in the development of the creep landforms was studied by favorability analysis. The results obtained show that presence of creep landforms is more frequent in slopes with calcareous lithologies and surficial deposits, and in those slopes with higher values of elevation, slope angles of approximately 25–35°, high curvature value and relief index higher than 5 an lower than 9. Nevertheless, some relief variables, such as CTI or flow accumulation (which describes water movement and accumulation over the surface of the slope), do not appear to have a strong influence on the distribution of creep landforms.

A wavelet-based approach for monitoring plantation crops (tea: Camellia sinensis) in North East India

This study analysed the monitoring of tea replantation using Linear Imaging Self‐Scanning Sensor (LISS-III) and Cartosat-1 images and identified patterns based on wavelet approaches. Monitoring identifies four phases of replantation and rejuvenation, starting at the time of uprooting and finishing when new plants are planted. The study was conducted within the Dooars region of North East India. The perpendicular vegetation index and perpendicular soil index were derived to measure changes from bare soil reflectances caused by vegetation, whereas the soil index was designed to enhance brightness. Being a multi-resolution study, wavelets such as Haar, Daubechies and Symlet were compared at different levels of decomposition, and information was extracted at different scales. Using topographic and hydrological parameters, informative patterns for each stage of replantation were selected at individual sections within the estate on the basis of spatial correlation. The study showed that levels 3 and 4 gave superior information compared with the other levels. Anisotropic autocorrelation gave constant spatial variation at different scales and in different directions. The selected patterns were weakly correlated with slope, flow accumulation and the compound topographic index, whereas management activities and a small variation in elevation proved less efficient in explaining the extracted patterns. It also showed that hydrological processes could be evaluated using cross-correlations. From the study, it was observed that the asymmetric Daubechies-4 wavelet gave the best results for extraction of fine features, whereas the symmetric Symlet-8 wavelet best represented the extraction of smooth features. Although a strong quantitative linear relationship between the extracted patterns and topographic parameters could not be established, we conclude that wavelets are useful to extract patterns and interpret spatial variations observed at different phases of tea replantation.

AGNPS GIS-Based Tool for Watershed-Scale Identification and Mapping of Cropland Potential Ephemeral Gullies

The formation of ephemeral gullies in agricultural fields has been recognized as an important source of sediment contributing to environmental degradation and compromising crop productivity. Methodologies are being developed for assessing gully formation and gully sediment yield. The Annualized Agricultural Non-Point Source (AnnAGNPS) pollution model is an important tool for multi-temporal watershed-scale simulations because it contains the necessary components for ephemeral gully investigation, making AnnAGNPS a commonly used tool for evaluations of agricultural conservation and operation practices. AnnAGNPS requires the user to define the location of ephemeral gullies throughout the watershed, what often constitutes a time consuming task where users may not accurately locate and describe all ephemeral gully locations. Alternatively, herein a GIS-based graphical user interface is described for the automated identification of areas with high probability of forming ephemeral gullies, referred to as potential ephemeral gullies (PEGs), based on the modified Compound Topographic Index (CTI). Through the aid of a study case, PEG mouth locations along with their attributes are generated through an iterative procedure by varying different CTI threshold values (99.9%, 99.5%, 99.0%, 98.5%, and 98%). Three sets of yielded PEG mouth locations and corresponding attributes were then integrated with AnnAGNPS for assessment of the impact of potential ephemeral gullies in the watershed sediment erosion. Analysis of the spatial distribution of estimates of annual average of gully erosion identifies the sub-watersheds prone to ephemeral gully erosion, thus enhancing the applicability of AnnAGNPS to evaluate conservation practices and/or targeted interventions designed to address ephemeral gully erosion at the watershed-scale.

Quantifying the ability of environmental parameters to predict soil texture fractions using regression-tree model with GIS and LIDAR data: The case study of Denmark

Soil texture is an important soil characteristic that drives crop production and field management, and is the basis for environmental monitoring (including soil quality and sustainability, hydrological and ecological processes, and climate change simulations). The combination of coarse sand, fine sand, silt, and clay in soil determines its textural classification. This study used Geographic Information Systems (GIS) and regression-tree modeling to precisely quantify the relationships between the soil texture fractions and different environmental parameters on a national scale, and to detect the most important parameters that can be used as weighted input data in soil environmental prediction models. Seven primary terrain parameters (elevation, slope gradient, slope aspect, plan curvature, profile curvature, flow direction, flow accumulation) and one compound topographic index (CTI) were generated from a Digital Elevation Model (DEM) acquired using airborne LIDAR (Light Detection and Ranging) systems. They were used along with digital data collected from other sources (existing maps and available pluviometric stations), i.e. parent materials, landscape types, geographic regions, yearly precipitation, seasonal precipitation to statistically explain soil texture fractions field/laboratory measurements (45,224 sampling sites) in the area of interest (Denmark). The developed strongest relationships were associated with clay and silt, variance being equal to 60%, followed by coarse sand (54.5%) and fine sand (52%) as the weakest relationship. This study also showed that parent materials (with a relative importance varying between 47% and 100%), geographic regions (31–100%) and landscape types (68–100%) considerably influenced all soil texture fractions, which is not the case for climate and DEM parameters. Yearly and seasonal precipitation had a significant impact on clay and silt; elevation had higher influence on coarse sand (13%), fine sand (12%) and clay (10%) where; slope gradient influenced silt (11.5%); slope aspect (14%) and CTI (9%) influenced fine sand; and profile/plan curvatures and flow direction/accumulation did not interfere in the building of the soil texture regression trees and associated relationships. The latter can be extrapolated to other areas sharing similar geo-environmental conditions.

Catchment‐Scale Soil Water Dynamics in a Mediterranean‐Type Oak Woodland

Water availability is one of the most serious natural resource issues facing California, especially given projections for future climate change. The 3 million ha of oak (Quercus spp.) woodlands play a critical role in California's water supply system, providing runoff primarily from winter rainfall events and hosting two‐thirds of the state's drinking water reservoirs. Thus, understanding water storage and streamflow regulation by soils in these watersheds is essential for water resource planning under future climate change scenarios. The primary objectives of this research were to identify the drivers of soil moisture dynamics and the hydrologic budget at the catchment scale and their relationships with streamflow generation. A 33‐ha headwater catchment in the Sierra Nevada foothills of northern California was instrumented to monitor climate, subsurface lateral flow, streamflow, and soil moisture within 100 pedons distributed throughout the catchment. A catchment‐scale water balance was used to examine factors regulating spatial and temporal soil water distribution within the catchment. Relationships between soil, topographic, and vegetation characteristics and soil water content at each pedon were analyzed using a linear mixed‐effects model with four fixed effects: vegetation type or cover, presence or absence of a claypan, compound topographic index, and solar radiation. Streamflow was limited to periods when lower soil horizons were at or near saturation. Following saturation, each rainfall event generated subsurface lateral flow above the clay‐rich argillic horizon, resulting in sharp hydrograph responses with little base flow between rainfall events. For the two water years examined, 9.6 and 11.5% of rainfall left the catchment as streamflow. Vegetation (tree vs. grassland), presence or absence of a claypan, aspect, and compound topographic index (index of wetness) were all significantly correlated to soil moisture status and thus streamflow generation at various times throughout the year. Findings indicated that watershed‐scale hydrologic models based solely on surface topography will not fully explain dynamic temporal and spatial variability in hydrologic flow paths and streamflow generation in these oak woodland catchments. In particular, watershed‐scale knowledge of soil stratigraphy (e.g., claypan distribution) was important for understanding catchment hydrology, especially the occurrence of subsurface lateral flow dynamics.

Integração de dados do quickbird e atributos do terreno no mapeamento digital de solos por redes neurais artificiais

No presente estudo, foi realizada uma avaliação de diferentes variáveis ambientais no mapeamento digital de solos em uma região no norte do Estado de Minas Gerais, utilizando redes neurais artificiais (RNA). Os atributos do terreno declividade e índice topográfico combinado (CTI), derivados de um modelo digital de elevação, três bandas do sensor Quickbird e um mapa de litologia foram combinados, e a importância de cada variável para discriminação das unidades de mapeamento foi avaliada. O simulador de redes neurais utilizado foi o "Java Neural Network Simulator", e o algoritmo de aprendizado, o "backpropagation". Para cada conjunto testado, foi selecionada uma RNA para a predição das unidades de mapeamento; os mapas gerados por esses conjuntos foram comparados com um mapa de solos produzido com o método convencional, para determinação da concordância entre as classificações. Essa comparação mostrou que o mapa produzido com o uso de todas as variáveis ambientais (declividade, índice CTI, bandas 1, 2 e 3 do Quickbird e litologia) obteve desempenho superior (67,4 % de concordância) ao dos mapas produzidos pelos demais conjuntos de variáveis. Das variáveis utilizadas, a declividade foi a que contribuiu com maior peso, pois, quando suprimida da análise, os resultados da concordância foram os mais baixos (33,7 %). Os resultados demonstraram que a abordagem utilizada pode contribuir para superar alguns dos problemas do mapeamento de solos no Brasil, especialmente em escalas maiores que 1:25.000, tornando sua execução mais rápida e mais barata, sobretudo se houver disponibilidade de dados de sensores remotos de alta resolução espacial a custos mais baixos e facilidade de obtenção dos atributos do terreno nos sistemas de informação geográfica (SIG).

Landscape‐scale controls on the spatial distribution of caesium 137: A study based on an airborne geophysical survey across Northern Ireland

AbstractThe spatial distribution of 137Cs across the landscape and the processes controlling its redistribution are of interest because (i) 137Cs has been widely used to quantify the movement of soil and sediments and (ii) substantial fallout of 137Cs after the Chernobyl accident has led to contamination of foodstuffs in some places. A high‐resolution airborne geophysical radiometric survey of Northern Ireland has provided an opportunity to study the distribution and possible redistribution of 137Cs.The 137Cs activity (recorded at 1·2 million points) is distributed in a series of bands oriented approximately 160° and 115° clockwise from north. Geostatistical analysis of the data shows a strong, short‐range structure (correlation ranges between 0·6 and 8 km) in 137Cs activity across the vast majority of the region; the spatial distribution shows association with a published, coarse‐scale depositional pattern of 137Cs from Chernobyl. Two indices of land form derived from a digital elevation model, namely compound topographic index and the length–slope factor of the Revised Universal Soil Loss Equation, account for only 3% of the variance in 137Cs activity. In contrast, soil type and land cover in combination (including their interaction) account for 20% of the variance. In areas that received moderate fallout from Chernobyl, soil type alone accounts for a substantial proportion of the spatially correlated 137Cs activity. We attribute this to each soil type having a fairly uniform radiocaesium interception potential that differs from those of other soil types and that this potential controls the vertical migration of 137Cs. Over the granitic Mourne Mountains there is a strong spatial cross‐correlation between 137Cs activity and airborne estimates of soil potassium, suggesting that the latter provides a measure of the soil's radiocaesium interception potential; this is probably dominated by the quantity of the mineral illite. Copyright © 2010 John Wiley &amp; Sons, Ltd. and British Geological Survey

Prediction of Soil Depth from Digital Terrain Data by Integrating Statistical and Visual Approaches

Information about the spatial distribution of soil attributes is indispensable for many land resource management applications; however, the ability of soil maps to supply such information for modern modeling tools is questionable. The objectives of this study were to investigate the possibility of predicting soil depth using some terrain attributes derived from digital elevation models (DEMs) with geographic information systems (GIS) and to suggest an approach to predict other soil attributes. Soil depth was determined at 652 field observations over the Al-Muwaqqar Watershed (70 km 2)in Jordan. Terrain attributes derived from 30-m resolution DEMs were utilized to predict soil depth. The results indicated that the use of multiple linear regression models within small watershed subdivisions enabled the prediction of soil depth with a difference of 50 cm for 77% of the field observations. The spatial distribution of the predicted soil depth was visually coincided and had good correlations with the spatial distribution of the classes amalgamating three terrain attributes, slope steepness, slope shape, and compound topographic index. These suggested that the modeling of soil-landscape relationships within small watershed subdivisions using the three terrain attributes was a promising approach to predict other soil attributes.

Estimating carbon stocks at a regional level using soil information and easily accessible auxiliary variables

One of the most important challenges of digital soil mapping is the development of methods that allow the characterisation of large areas with a high-resolution. Surface soils, forming the largest pool of terrestrial organic carbon, may be able to sequester atmospheric carbon and thus mitigate climate change. However, this remains controversial, largely due to insufficient information on SOC stocks worldwide. One reason for this is the generally limited number of available data points, especially when large areas are considered, while another reason lies on the accuracy of interpolation techniques used for SOC mapping. The study was performed in Laos, a 230,566 km 2 area mostly forested and with steep slopes, and where soil data from 2806 pits is available. Our objective was to estimate SOC stocks to a depth of 1 m over the whole country while improving regional digital soil mapping (RDSM). SOC mapping by using purely spatial approaches of ordinary kriging (OK), inverse distance weighting (IDW) and regularized spline with tension (RST) was compared with the use of additional information on relief, climate and soils through co-kriging (OCK). The generation and validation data sets were composed of 2665 and 141 data points respectively. Overall, OCK using a multiple correlation with elevation above sea level, compound topographic index, mean slope gradient, average annual rainfall, and soil clay content ( R 2 = 0.42; P level < 0.001) as covariate, yielded the most accurate predictions (19.7 kg C m − 2 with standard error of ± 3.2 kg C m − 2 ; and 4.54 ± 0.74 billion tons of SOC for Laos). The pure interpolation techniques were less accurate with 4.51 ± 1.02 billion tons of SOC for OK and 4.88 ± 0.94 billion tons of SOC for RST. Besides providing nationwide estimates of SOC stocks these results indicate that using collectively soil punctual information on SOC stocks and their inter-relationships with controlling factors which are easy to gather might be an efficient way to improve RDSM.

Terrain characteristics and Tai toponyms: a GIS analysis of Muang, Chiang and Viang

Tai is a family of related languages and dialects, a subgroup of the Tai-Kadai languages, spoken by more than 85 million speakers in southern China and Southeast Asia. This paper uses GIS to map the spatial distribution of Tai toponyms (Muang, Chiang and Viang) and analyzes their relationship with terrain characteristics. In Tai, Muang means flat “basin”, Chiang means “town”, and Viang provides defense for a Chiang. These Tai toponyms are found at places with a significantly higher compound topographic index (or wetness index) than others. Watershed basins with more Muang toponyms are characterized by lower elevation, gentler slope, near zero concavity. All of these are consistent with physical conditions favorable for wet rice agriculture, culture, and commerce. The transnational spatial distribution of these toponyms and associated terrain characteristics reveal a significant regional pattern that reflects not only the geomorphology of the places where Tai expanded and settled, but also a common history and culture of naming places influenced by their wet rice agriculture and associated cultural practices and commerce.