Multiple Geotechnological Tools Applied to Digital Mapping of Tropical Soils

Abstract

in recent years, geotechnologies as remote and proximal sensing and attributes derived from digital terrain elevation models indicated to be very useful for the description of soil variability. however, these information sources are rarely used together. therefore, a methodology for assessing and specialize soil classes using the information obtained from remote/proximal sensing, Gis and technical knowledge has been applied and evaluated. two areas of study, in the state of são Paulo, Brazil, totaling approximately 28.000 ha were used for this work. first, in an area (area 1), conventional pedological mapping was done and from the soil classes found patterns were obtained with the following information: a) spectral information (forms of features and absorption intensity of spectral curves with 350 wavelengths -2,500 nm) of soil samples collected at specific points in the area (according to each soil type); b) obtaining equations for determining chemical and physical properties of the soil from the relationship between the results obtained in the laboratory by the conventional method, the levels of chemical and physical attributes with the spectral data; c) supervised classification of Landsat TM 5 images, in order to detect changes in the size of the soil particles (soil texture); d) relationship between classes relief soils and attributes. subsequently, the obtained patterns were applied in area 2 obtain pedological classification of soils, but in GIS (ArcGIS). finally, we developed a conventional pedological mapping in area 2 to which was compared with a digital map, ie the one obtained only with pre certain standards. the proposed methodology had a 79 % accuracy in the first categorical level of Soil Classification System, 60 % accuracy in the second category level and became less useful in the categorical level 3 (37 % accuracy).

Vegetation Characterization through the Use of Precipitation-Affected SAR Signals

“…Both soil types consist of mineral material, highly weathered by high temperatures and precipitation, and are typical for tropical regions [30]. The main difference is that the oxisols generally have higher clay content and are uniform in clay content, approximately 380 g/kg in the top one-meter layer, whereas for ultisols, a gradual increase in clay content in deeper soil layers is observed (158 g/kg from 0-0.2 m, 201 g/kg from 0.4-0.6 m and 334 g/kg from 0.8-1 m) [32].…”

“…Specifically regarding the dry state of these specific soils types, the oxisols are characterized by the presence of deep, well-drained soils [32]. This leads to the additional assumption that the moisture content in soils that are assumed dry under the criteria reported in Section 2.2.2 is lesser for oxisols (i.e., resulting in lower backscatter) than for ultisols.…”

Soil erosion risk zoning in the Ecuadorian coastal region using geo-technological tools

“…It is as these in recent years, there have witnessed a significant increase in the use of geotechnological tools (remote sensing [RS] and GIS) to study soil erosive processes. The principle objectives of many of these contributions are: (a) the multi-temporal and spatial modeling of erosive processes (Zeng et al, 2013); (b) the analysis of the effects of the changes in vegetal cover and land use (Lech-hab et al, 2015); (c) the quantification of soil loss (Parveen and Kumar, 2012;Farhan et al, 2013;Amsalu and Mengaw, 2014;Lahlaoi et al, 2015;Ganasri and Ramesh, 2016;Tahiri et al, 2016); (d) the zoning of the hazard and risk control (Prasannakumar et al, 2012;Gaatib and Larabi, 2014;Uddin et al, 2014;Chirala et al, 2015); and (e) soil mapping and classification (Alves et al, 2015;Rizzo et al, 2015;Moura-Bueno et al, 2016).…”

Índice de área foliar residual como estratégia para manejo de pasto: estrutura do capim Tanzânia

“…No entanto, essa abordagem exige a existência de mapas legados para calibração dos modelos de predição, sendo inviável para regiões onde não há disponibilidade dessas informações. Uma alternativa para a ausência de mapas legados é a predição da ocorrência dos solos a partir de informações coletadas em perfis de solos georreferenciados (Hengl et al, 2007;Alves et al, 2015;Arruda et al, 2016). Dessa forma, pode-se realizar o mapeamento de áreas que já foram amostradas, além da confecção de bancos de dados estruturados e a extrapolação para áreas fisiograficamente semelhantes.…”