Soil mapping based on vegetation spectral sensing

• Grant number: 14/04005-0
• Support Opportunities: Scholarships in Brazil - Scientific Initiation
• Start date: June 01, 2014
• End date: May 31, 2015
• Field of knowledge: Agronomical Sciences - Agronomy - Soil Science
• Principal Investigator: José Alexandre Melo Demattê
• Grantee: Veridiana Maria Sayão
• Host Institution: Escola Superior de Agricultura Luiz de Queiroz (ESALQ). Universidade de São Paulo (USP). Piracicaba , SP, Brazil

Abstract

Soil digital mapping is the most important goal for land use planning looking forward a better sustainable environment. In this aspect, Africa is one of the most important countries where there is new space for agriculture and food demand. On the other hand, several Brazilian states such as Mato Grosso and others where the Amazon Rainforest occurs have the same situation. The objective of this research is to achieve a soil pedological and attributes map using natural vegetation information, such as vegetation indexes. The area of interest occurs in Angola, city of Pungo Andongo, where we have a soils and image databank. We will use two Landsat images. One from a dry and another from a moist season. The images will be atmospheric corrected and transformed into reflectance. Augers of soils using topossequences methodswere performed, in a total of 450 points. Each one was collected at four depths (0-20, 40-60, 80-100, 100-120cm). The samples were analyzed in laboratory for chemical and granulometric information. Afterwards, the Normalized Differentiated Vegetation Index (NDVI) and the Perpendicular Vegetation Index (PVI) will be calculated into the two seasons. The images with the indexes will be classified with an unsupervised spectral segmentation. Thematic maps of soil attributes, in different depths and with relief elements to be determined, will be made aswell. Soil thematic maps will be superposed overthe vegetation maps and kappa index will indicatethe accuracy between them.

Scientific publications

(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)

DEMATTE, JOSE A. M.; SAYAO, VERIDIANA MARIA; RIZZO, RODNEI; FONGARO, CAIO T.. Soil class and attribute dynamics and their relationship with natural vegetation based on satellite remote sensing. Geoderma, v. 302, p. 39-51, SEP 15 2017. (14/04005-0)